Sampling and Hypothesis Testing in R
Arif Y.
07 April 2022
Libraries
library(tidyverse)
library(openintro)
library(assertive)
library(fst)
library(broom)
library(coin) # chiq_test
library(infer)
library(infer)
library(anytime)
library(lubridate)
library(random)Data
spotify_population <- read.fst("spotify_2000_2020.fst")
attrition_pop <- read.fst("attrition.fst")
late_shipments <- read.fst("late_shipments.fst")
dem_votes_potus_12_16 <- read.fst("dem_county_pres_joined.fst")
bootstrap_distribution <- read.fst("late_ship_boot_distn.fst")0. Course Description
This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions.
Hypothesis testing lets you ask questions about your datasets and answer them in a statistically rigorous way. In this course you’ll learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. You’ll gain a deep understanding of how they work, and the assumptions that underlie them. You’ll also learn how different hypothesis tests are related using the “There is only one test” framework, and use non-parametric tests that let you side-step the requirements of traditional hypothesis tests. Throughout the course, you’ll explore a Stack Overflow user survey, and a dataset of late shipments of medical supplies.
1. Bias Any Stretch of the Imagination
Learn what sampling is and why it is useful, understand the problems caused by convenience sampling, and learn about the differences between true randomness and pseudo-randomness.
1.1 Simple sampling with dplyr
Throughout this chapter you’ll be exploring song data from Spotify. Each row of the dataset represents a song, and there are 41656 rows. Columns include the name of the song, the artists who performed it, the release year, and attributes of the song like its duration, tempo, and danceability. We’ll start by looking at the durations.
Your first task is to sample the song dataset and compare a calculation on the whole population and on a sample.
# View the whole population dataset
head(spotify_population)## acousticness artists danceability duration_ms duration_minutes
## 1 0.97200 ['David Bauer'] 0.567 313293 5.221550
## 2 0.32100 ['Etta James'] 0.821 360240 6.004000
## 3 0.00659 ['Quasimoto'] 0.706 202507 3.375117
## 4 0.00390 ['Millencolin'] 0.368 173360 2.889333
## 5 0.12200 ['Steve Chou'] 0.501 344200 5.736667
## 6 0.06660 ['Prodigy', 'Nas'] 0.829 195293 3.254883
## energy explicit id instrumentalness key liveness loudness
## 1 0.227 0 0w0D8H1ubRerCXHWYJkinO 0.601000 10 0.1100 -13.441
## 2 0.418 0 4JVeqfE2tpi7Pv63LJZtPh 0.000372 9 0.2220 -9.841
## 3 0.602 1 5pxtdhLAi0RTh1gNqhGMNA 0.000138 11 0.4000 -8.306
## 4 0.977 0 3jRsoe4Vkxa4BMYqGHX8L0 0.000000 11 0.3500 -2.757
## 5 0.511 0 4mronxcllhfyhBRqyZi8kU 0.000000 7 0.2790 -9.836
## 6 0.614 1 0SlljMy4uEgoVPCyavtcHH 0.000850 1 0.0975 -8.546
## mode name popularity release_date speechiness
## 1 1 Shout to the Lord 47 2000 0.0290
## 2 0 Miss You 51 2000-12-12 0.0407
## 3 0 Real Eyes 44 2000-06-13 0.3420
## 4 0 Penguins & Polarbears 52 2000-02-22 0.1270
## 5 0 黃昏 53 2000-12-25 0.0291
## 6 1 Self Conscience - Dirty Version 46 2000-11-06 0.2670
## tempo valence year
## 1 136.123 0.0396 2000
## 2 117.382 0.8030 2000
## 3 89.692 0.4790 2000
## 4 165.889 0.5480 2000
## 5 78.045 0.1130 2000
## 6 90.924 0.4960 2000# Sample 1000 rows from spotify_population
spotify_sample <- spotify_population %>% slice_sample(n = 1000)
# See the result
spotify_sample## acousticness
## 1 4.00e-01
## 2 2.57e-04
## 3 1.96e-01
## 4 5.83e-03
## 5 4.50e-01
## 6 6.48e-02
## 7 1.23e-02
## 8 2.90e-02
## 9 8.78e-03
## 10 1.26e-01
## 11 6.85e-01
## 12 3.61e-01
## 13 2.39e-01
## 14 2.55e-01
## 15 1.83e-02
## 16 8.38e-03
## 17 9.72e-01
## 18 5.79e-04
## 19 3.44e-03
## 20 2.72e-02
## 21 1.27e-01
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## 688 9.20e-01
## 689 6.91e-01
## 690 2.93e-02
## 691 8.82e-05
## 692 9.04e-02
## 693 7.96e-03
## 694 3.76e-01
## 695 9.54e-01
## 696 6.48e-03
## 697 2.86e-02
## 698 2.89e-01
## 699 1.02e-02
## 700 1.17e-06
## 701 9.38e-01
## 702 8.01e-01
## 703 1.35e-02
## 704 2.19e-01
## 705 2.58e-01
## 706 5.29e-01
## 707 7.35e-01
## 708 2.14e-02
## 709 2.14e-04
## 710 4.59e-02
## 711 2.20e-05
## 712 5.60e-03
## 713 8.87e-01
## 714 4.36e-01
## 715 7.58e-03
## 716 4.89e-02
## 717 3.37e-01
## 718 9.44e-01
## 719 2.16e-01
## 720 1.31e-02
## 721 2.91e-04
## 722 4.00e-02
## 723 3.31e-02
## 724 2.39e-01
## 725 8.19e-01
## 726 7.42e-01
## 727 5.71e-01
## 728 4.87e-01
## 729 6.67e-01
## 730 8.71e-02
## 731 4.66e-01
## 732 6.94e-01
## 733 4.24e-05
## 734 7.04e-01
## 735 5.59e-04
## 736 1.74e-02
## 737 1.10e-01
## 738 3.90e-04
## 739 2.60e-01
## 740 3.24e-01
## 741 3.00e-01
## 742 4.93e-01
## 743 7.75e-01
## 744 2.99e-02
## 745 7.30e-06
## 746 1.14e-01
## 747 1.47e-01
## 748 1.86e-01
## 749 1.58e-01
## 750 4.48e-01
## 751 3.76e-03
## 752 9.65e-01
## 753 9.49e-01
## 754 1.53e-01
## 755 1.62e-02
## 756 3.99e-03
## 757 2.16e-01
## 758 7.24e-01
## 759 2.35e-01
## 760 7.95e-01
## 761 2.29e-01
## 762 3.38e-01
## 763 8.10e-01
## 764 2.48e-01
## 765 2.18e-03
## 766 1.13e-01
## 767 4.26e-02
## 768 8.74e-01
## 769 4.99e-01
## 770 3.18e-02
## 771 8.84e-01
## 772 4.90e-02
## 773 2.91e-06
## 774 9.31e-06
## 775 1.68e-02
## 776 2.53e-04
## 777 6.23e-02
## 778 8.21e-04
## 779 2.52e-01
## 780 3.80e-04
## 781 1.50e-02
## 782 1.76e-01
## 783 1.85e-01
## 784 1.02e-01
## 785 1.76e-01
## 786 2.84e-01
## 787 3.05e-02
## 788 4.82e-01
## 789 1.02e-04
## 790 8.26e-02
## 791 4.44e-01
## 792 6.26e-04
## 793 9.37e-03
## 794 1.56e-01
## 795 9.80e-01
## 796 9.90e-01
## 797 2.29e-01
## 798 2.99e-02
## 799 5.63e-01
## 800 2.74e-01
## 801 1.12e-01
## 802 1.38e-02
## 803 5.57e-02
## 804 1.12e-04
## 805 9.59e-04
## 806 1.55e-01
## 807 3.00e-01
## 808 3.81e-01
## 809 5.95e-02
## 810 8.92e-01
## 811 5.89e-02
## 812 2.63e-01
## 813 6.47e-01
## 814 9.92e-01
## 815 2.32e-02
## 816 4.60e-02
## 817 9.20e-02
## 818 7.96e-01
## 819 5.43e-02
## 820 5.07e-02
## 821 1.51e-02
## 822 1.32e-02
## 823 4.51e-01
## 824 8.42e-02
## 825 2.13e-01
## 826 6.44e-01
## 827 6.09e-01
## 828 3.63e-01
## 829 1.18e-01
## 830 4.44e-02
## 831 8.72e-02
## 832 1.31e-01
## 833 2.55e-01
## 834 8.19e-01
## 835 4.09e-02
## 836 7.14e-02
## 837 5.75e-02
## 838 6.70e-05
## 839 4.48e-01
## 840 8.71e-01
## 841 6.35e-02
## 842 4.40e-01
## 843 8.61e-02
## 844 1.10e-01
## 845 3.72e-02
## 846 5.40e-01
## 847 8.54e-01
## 848 3.23e-01
## 849 3.07e-02
## 850 3.86e-03
## 851 3.70e-02
## 852 1.53e-01
## 853 6.37e-01
## 854 9.12e-03
## 855 2.24e-01
## 856 7.05e-01
## 857 1.30e-01
## 858 2.56e-01
## 859 9.57e-02
## 860 2.42e-04
## 861 4.45e-01
## 862 5.77e-03
## 863 5.05e-02
## 864 1.42e-02
## 865 2.93e-01
## 866 2.53e-01
## 867 7.50e-02
## 868 8.84e-02
## 869 2.69e-04
## 870 2.64e-03
## 871 7.88e-02
## 872 5.41e-02
## 873 2.82e-04
## 874 1.43e-01
## 875 3.04e-01
## 876 2.99e-01
## 877 2.13e-01
## 878 2.04e-01
## 879 7.04e-03
## 880 8.63e-01
## 881 5.74e-01
## 882 4.16e-03
## 883 4.72e-01
## 884 3.03e-02
## 885 3.10e-01
## 886 1.35e-01
## 887 4.30e-02
## 888 8.13e-03
## 889 1.35e-01
## 890 6.52e-01
## 891 3.51e-01
## 892 1.20e-03
## 893 5.41e-03
## 894 1.55e-01
## 895 5.73e-02
## 896 8.52e-03
## 897 5.90e-02
## 898 6.32e-01
## 899 3.52e-03
## 900 5.74e-01
## 901 1.10e-03
## 902 7.37e-01
## 903 8.68e-01
## 904 1.06e-01
## 905 2.38e-02
## 906 1.39e-01
## 907 2.61e-01
## 908 3.90e-02
## 909 2.19e-01
## 910 7.10e-02
## 911 5.65e-02
## 912 5.21e-01
## 913 4.07e-02
## 914 6.25e-02
## 915 8.71e-01
## 916 2.46e-01
## 917 7.22e-01
## 918 2.99e-02
## 919 1.87e-02
## 920 1.15e-04
## 921 4.80e-04
## 922 4.19e-03
## 923 4.80e-05
## 924 1.23e-03
## 925 4.72e-01
## 926 6.60e-01
## 927 7.28e-01
## 928 2.95e-01
## 929 7.34e-04
## 930 6.58e-02
## 931 7.54e-02
## 932 2.01e-02
## 933 6.08e-02
## 934 2.90e-01
## 935 5.91e-03
## 936 1.72e-01
## 937 8.26e-01
## 938 6.19e-02
## 939 3.48e-02
## 940 1.04e-03
## 941 3.15e-02
## 942 1.50e-05
## 943 9.82e-01
## 944 2.76e-01
## 945 1.27e-02
## 946 9.52e-01
## 947 9.33e-02
## 948 1.62e-01
## 949 7.93e-03
## 950 8.42e-03
## 951 1.44e-01
## 952 1.30e-01
## 953 5.79e-01
## 954 5.19e-01
## 955 9.87e-01
## 956 9.87e-03
## 957 1.46e-03
## 958 7.37e-01
## 959 8.11e-02
## 960 1.30e-01
## 961 8.45e-02
## 962 2.45e-01
## 963 7.40e-02
## 964 5.87e-01
## 965 2.92e-01
## 966 8.87e-02
## 967 5.88e-01
## 968 1.81e-01
## 969 1.01e-01
## 970 7.90e-01
## 971 9.98e-02
## 972 7.95e-01
## 973 2.05e-01
## 974 2.28e-03
## 975 6.45e-01
## 976 1.74e-01
## 977 1.31e-01
## 978 1.91e-03
## 979 2.20e-02
## 980 1.01e-02
## 981 3.57e-01
## 982 1.41e-02
## 983 8.67e-03
## 984 5.89e-02
## 985 9.57e-03
## 986 7.87e-02
## 987 8.02e-02
## 988 1.93e-03
## 989 7.99e-01
## 990 2.37e-01
## 991 6.56e-01
## 992 4.33e-01
## 993 2.15e-02
## 994 3.38e-01
## 995 1.66e-02
## 996 2.15e-01
## 997 3.34e-02
## 998 2.35e-01
## 999 1.27e-03
## 1000 1.53e-03
## artists
## 1 ['Kenny Elrod']
## 2 ['Animal Collective']
## 3 ['Lil Wayne', 'Bobby V.', 'Kidd Kidd']
## 4 ['Long Beach Dub Allstars']
## 5 ['Ben Howard']
## 6 ['El Dipy']
## 7 ['Chiddy Bang']
## 8 ['Lupe Fiasco', 'Jill Scott']
## 9 ['Asking Alexandria']
## 10 ['Joe Nichols']
## 11 ['Lady Gaga']
## 12 ['Shlohmo']
## 13 ['Flo Milli']
## 14 ['La Ley', 'Ely Guerra']
## 15 ['Chayanne']
## 16 ['Escape the Fate']
## 17 ['Bon Iver']
## 18 ['Sum 41']
## 19 ['The Olivia Tremor Control']
## 20 ['Maná']
## 21 ['Jack & Jack']
## 22 ['Harry Styles']
## 23 ['Leona Lewis']
## 24 ['Audioslave']
## 25 ['Stevie Nicks']
## 26 ['RÜFÜS DU SOL']
## 27 ['Taylor Swift']
## 28 ['Brielle Von Hugel']
## 29 ['The Weeknd']
## 30 ['The Microphones']
## 31 ['Chris Young']
## 32 ['Green Day']
## 33 ['WNX']
## 34 ['Lil Nas X', 'Cardi B']
## 35 ['Jim Sturgess']
## 36 ['Eraldo Bernocchi', 'Harold Budd', 'Robin Guthrie']
## 37 ['Jason Aldean']
## 38 ['Jason Aldean']
## 39 ['Kendrick Lamar']
## 40 ['Hozier']
## 41 ['Phantogram']
## 42 ['YoungBoy Never Broke Again']
## 43 ['Collective Soul']
## 44 ['Luke Combs']
## 45 ['Two Door Cinema Club']
## 46 ['Polo G']
## 47 ['Lil Skies']
## 48 ['Trey Songz', 'Twista']
## 49 ['The Game', '50 Cent']
## 50 ['The Avett Brothers']
## 51 ['Post Malone']
## 52 ['Jimmy Eat World']
## 53 ['Tory Lanez', 'Lil Tjay']
## 54 ['Tom Kenny', 'Bill Fagerbakke', 'The Monsters', 'Clancy Brown', 'Dee Bradley Baker', 'Mr Lawrence', 'Rodger Bumpass', 'Tom Wilson']
## 55 ['Logic']
## 56 ['Sister Sledge']
## 57 ['Taylor Swift']
## 58 ['Jeremy Camp']
## 59 ['Jimmy Eat World']
## 60 ['Daniel Calveti']
## 61 ['Lil Scrappy']
## 62 ["Da Vinci's Notebook"]
## 63 ['Freddy.b']
## 64 ['Alabama']
## 65 ['Dayvi', 'Victor Cardenas', 'Kelly Ruíz']
## 66 ['Aretha Franklin']
## 67 ['Incubus']
## 68 ['Future']
## 69 ['Slipknot']
## 70 ['Kevin Gates']
## 71 ['Band Aid']
## 72 ['Journey']
## 73 ['Ginuwine']
## 74 ['Glee Cast']
## 75 ['Rockabye Baby!']
## 76 ['Kip Moore']
## 77 ['Yelawolf', 'Kid Rock']
## 78 ['Breaking Benjamin']
## 79 ['The White Stripes']
## 80 ['24kGoldn']
## 81 ['Christian French']
## 82 ['Barbara Mandrell']
## 83 ['Yoskar Sarante']
## 84 ['The Band']
## 85 ['Wisin & Yandel', 'Héctor "El Father"']
## 86 ['Manchester Orchestra']
## 87 ['Banda Rancho Viejo De Julio Aramburo La Bandononona']
## 88 ['Kygo', 'Conrad Sewell']
## 89 ['Alesso', 'Matthew Koma']
## 90 ['Squeeze']
## 91 ['Kygo', 'Zak Abel']
## 92 ['Sublime With Rome']
## 93 ['Tourist', 'Ardyn']
## 94 ['NxWorries', 'Anderson .Paak', 'Knxwledge']
## 95 ['Petit Biscuit']
## 96 ['Craig Morgan']
## 97 ['Tigers Jaw']
## 98 ['T-Pain', 'Lily Allen', 'Wiz Khalifa']
## 99 ['SZA', 'Calvin Harris', 'Funk Wav']
## 100 ['Twenty One Pilots']
## 101 ['Slipknot']
## 102 ['Key Glock']
## 103 ['Tmsoft’s White Noise Sleep Sounds']
## 104 ['Chairlift']
## 105 ['Seal']
## 106 ['Kesha']
## 107 ['King Critical']
## 108 ['Madeleine Peyroux']
## 109 ['Chris Brown']
## 110 ['Cobra Starship', 'Sabi']
## 111 ['Khalid']
## 112 ['Enigma']
## 113 ['Bastille']
## 114 ['Petula Clark']
## 115 ['Meaghan Martin']
## 116 ['Ant Saunders', 'Audrey Mika']
## 117 ['Sandy Cheeks with Junior Brown']
## 118 ['Hilary Duff']
## 119 ['Luis Coronel']
## 120 ['Nine Inch Nails']
## 121 ['Turnpike Troubadours']
## 122 ['Trivium']
## 123 ['Alabama Shakes']
## 124 ['Ed Sheeran']
## 125 ['Belle & Sebastian']
## 126 ['Zion & Lennox']
## 127 ['Common']
## 128 ['Tally Hall']
## 129 ['Angus & Julia Stone']
## 130 ['Trevor Hall']
## 131 ['Mother Goose Club']
## 132 ['The Killers']
## 133 ['Héctor Acosta "El Torito"']
## 134 ['Halestorm']
## 135 ['PAW Patrol']
## 136 ['Rob Thomas']
## 137 ['Moloko']
## 138 ['The Lacs', 'Colt Ford', 'JJ Lawhorn']
## 139 ['Artist Vs Poet']
## 140 ['Cold War Kids']
## 141 ['Forest FX']
## 142 ['Sean Paul']
## 143 ['The Piano Guys']
## 144 ['Stevie Ray Vaughan']
## 145 ['Spare Lead']
## 146 ['Blueface', 'Offset']
## 147 ['blink-182']
## 148 ['YoungBoy Never Broke Again']
## 149 ['Linkin Park']
## 150 ['Daniel Calveti']
## 151 ['Lil Wayne']
## 152 ['Belle & Sebastian']
## 153 ['Ross Lynch', 'Maia Mitchell', 'Teen Beach Movie Cast']
## 154 ['AFI']
## 155 ['2Pac']
## 156 ['MARINA']
## 157 ["Los Player's"]
## 158 ['Ed Sheeran', 'Justin Bieber']
## 159 ['Samsa']
## 160 ['John Legend', 'Jason Agel', 'Tiësto']
## 161 ['Powerwolf']
## 162 ['Playa Limbo']
## 163 ['The Frights']
## 164 ['Belanova']
## 165 ['The All-American Rejects']
## 166 ['Remy Ma']
## 167 ['The Dig']
## 168 ['Modest Mouse']
## 169 ['Michael Bublé']
## 170 ['Drake']
## 171 ['Slightly Stoopid']
## 172 ['Michael Bublé']
## 173 ['Jack Johnson']
## 174 ['38 Special']
## 175 ['Miike Snow']
## 176 ['Klangkarussell', 'Jaymes Young']
## 177 ['Billie Eilish']
## 178 ['Vino Nuevo']
## 179 ['Los Traileros Del Norte']
## 180 ['Death Cab for Cutie']
## 181 ['Wisin & Yandel', 'Luny Tunes']
## 182 ['Cody Jinks']
## 183 ['Dario Marianelli', 'Jack Liebeck', 'Benjamin Wallfisch']
## 184 ['Katy Perry']
## 185 ['Cake']
## 186 ['The Killers']
## 187 ['XXXTENTACION', 'Trippie Redd']
## 188 ['The Moldy Peaches']
## 189 ['La Sonora Dinamita', 'Lucho Argain', 'La India Meliyara']
## 190 ['RY X']
## 191 ['John Williams', 'London Symphony Orchestra']
## 192 ['Parkway Drive']
## 193 ['The Derek Trucks Band']
## 194 ['Frank Sinatra']
## 195 ['Zella Day']
## 196 ['Foo Fighters']
## 197 ['Victor Wong']
## 198 ['The Veer Union']
## 199 ['Eazy-E']
## 200 ['Pepe Aguilar']
## 201 ['C418']
## 202 ['Valentín Elizalde']
## 203 ['Ed Sheeran']
## 204 ['New Medicine']
## 205 ['Bunbury']
## 206 ['Babik Reinhardt']
## 207 ['Unclenathannn', 'Shiloh Dynasty']
## 208 ['Stephen Speaks']
## 209 ['Zedd', 'Matthew Koma']
## 210 ['JAY-Z']
## 211 ['Billy Currington']
## 212 ['Blake Shelton']
## 213 ['Hannah Montana']
## 214 ['Jurassic 5']
## 215 ['Soulja Boy', 'I-15']
## 216 ['Trevor Daniel']
## 217 ['Lil Uzi Vert']
## 218 ['Xavier Rudd']
## 219 ['Owl City']
## 220 ['Lifehouse']
## 221 ['Slipknot']
## 222 ['Jurassic 5']
## 223 ['Tyrese']
## 224 ['Taylor Swift']
## 225 ['Liz Callaway', 'Gene Miller', 'Disney Studio Chorus']
## 226 ['Mild High Club']
## 227 ['PARTYNEXTDOOR']
## 228 ['Coast Modern']
## 229 ['Ralph Stanley']
## 230 ['blackbear']
## 231 ['Anoushka Shankar']
## 232 ['JAY-Z', 'Nas']
## 233 ['The Ballroom Thieves']
## 234 ['Jonas Brothers']
## 235 ['Enya']
## 236 ['Dance Gavin Dance']
## 237 ['Iron & Wine']
## 238 ['Wolfmother']
## 239 ['John Mayer']
## 240 ["Martin O'Donnell", 'Michael Salvatori']
## 241 ['Shakira', 'Anuel AA']
## 242 ['One Direction']
## 243 ['Dierks Bentley']
## 244 ['Lil Wayne']
## 245 ['Bubba Sparxxx', 'Colt Ford', 'Danny Boone']
## 246 ['HARBOUR']
## 247 ['Switchfoot']
## 248 ['Blest']
## 249 ['Bullet For My Valentine']
## 250 ['Nelly', 'Murphy Lee', 'Ali', 'Kyjuan']
## 251 ['Wilderado']
## 252 ['Los Lonely Boys']
## 253 ['Cold War Kids']
## 254 ['Bow Wow', 'Ciara']
## 255 ['Tamar Braxton', 'Future']
## 256 ['Eminem']
## 257 ['Bad Bunny']
## 258 ['Russ', 'Rick Ross']
## 259 ['Limp Bizkit']
## 260 ['Toby Keith']
## 261 ['Beyoncé']
## 262 ['Coldplay']
## 263 ['Ennio Morricone', 'Yo-Yo Ma', 'Roma Sinfonietta']
## 264 ['Ski Mask The Slump God']
## 265 ['Berner', 'B-Real', 'Snoop Dogg', 'Vital']
## 266 ['Natalie Cole', 'Juan Luis Guerra 4.40']
## 267 ['Elisa Mosel']
## 268 ['Rockabye Lullaby']
## 269 ['Metallica']
## 270 ['Further Seems Forever']
## 271 ['Marc E. Bassy']
## 272 ['YoungBoy Never Broke Again']
## 273 ['Jason Aldean']
## 274 ['Gorillaz']
## 275 ['R. Kelly']
## 276 ['Tenacious D']
## 277 ['Pop Smoke', 'A Boogie Wit da Hoodie']
## 278 ['Kutless']
## 279 ['Danny Schmidt']
## 280 ['Boosie Badazz', 'Webbie', 'Big Head', 'Foxx']
## 281 ['CHAPPO']
## 282 ['Big Daddy Weave']
## 283 ['The Cinematic Orchestra']
## 284 ['Toby Keith']
## 285 ['Pantera']
## 286 ['Stephen Schwartz', 'Kristin Chenoweth', 'Idina Menzel', 'Stephen Oremus', 'Alex Lacamoire']
## 287 ['Trace Adkins']
## 288 ['Boney James']
## 289 ['Twenty One Pilots']
## 290 ['Smokie Norful']
## 291 ['Bring It On: The Musical - Original Broadway Cast']
## 292 ['Los Inquietos Del Norte']
## 293 ['Eva Cassidy']
## 294 ['Snow Patrol']
## 295 ['The Lacs']
## 296 ['Phoenix']
## 297 ['NOFX']
## 298 ['Red Hot Chili Peppers']
## 299 ['Otis Redding']
## 300 ["D'Angelo"]
## 301 ['Rezz']
## 302 ['A$AP Ferg', 'MadeinTYO']
## 303 ['Wale']
## 304 ['Howard Shore']
## 305 ['Jónsi', 'Alex Somers']
## 306 ['100 gecs', 'Laura Les', 'Dylan Brady']
## 307 ['Rauw Alejandro', 'Myke Towers', 'Sky Rompiendo']
## 308 ['Lucy Dacus']
## 309 ['NB Ridaz', 'Gemini', 'Ladi Bug']
## 310 ['Drake']
## 311 ['This Will Destroy You']
## 312 ['Maluma']
## 313 ['Tash Sultana']
## 314 ['Jack Johnson']
## 315 ['Khalid']
## 316 ['Cartel De Santa']
## 317 ['Katy Perry', 'Juicy J']
## 318 ['Red Hot Chili Peppers']
## 319 ['Huncho Jack', 'Travis Scott', 'Quavo', 'Takeoff']
## 320 ['Gunna']
## 321 ['John Paul Young']
## 322 ['Lauv']
## 323 ['6ix9ine']
## 324 ['Ed Sheeran', 'Camila Cabello', 'Cardi B']
## 325 ['Daft Punk']
## 326 ['Ayumi Hamasaki']
## 327 ['Los Angeles Negros']
## 328 ['Britney Spears']
## 329 ['Avril Lavigne']
## 330 ['Maelo Ruiz']
## 331 ['La Numero 1 Banda Jerez De Marco A. Flores']
## 332 ['Rod Stewart']
## 333 ['Theory of a Deadman']
## 334 ['The Beatles']
## 335 ['LANCO']
## 336 ['6ix9ine', 'Anuel AA']
## 337 ['Eagles']
## 338 ['The Walkmen']
## 339 ['Tribal Seeds']
## 340 ['Novo Amor', 'Ed Tullett']
## 341 ['Joan Sebastian']
## 342 ['Miniature Tigers']
## 343 ['alt-J']
## 344 ['Brantley Gilbert']
## 345 ['Sabaton']
## 346 ['Alacranes Musical']
## 347 ['The Strokes']
## 348 ['Nelly Furtado']
## 349 ['6 Dogs']
## 350 ['Nature Sounds']
## 351 ['Tamela Mann', 'Kirk Franklin']
## 352 ['Daft Punk']
## 353 ['The Spill Canvas']
## 354 ['Halestorm']
## 355 ['Río Roma']
## 356 ['mansionz']
## 357 ['J. Cole']
## 358 ['Avicii']
## 359 ['Blake Shelton']
## 360 ['Chris Malinchak']
## 361 ['Kygo', 'Sandro Cavazza']
## 362 ['Udit Narayan']
## 363 ['All Time Low']
## 364 ['Jason Aldean']
## 365 ['Anna Kendrick', 'Justin Timberlake']
## 366 ['Meek Mill']
## 367 ['SWV']
## 368 ['Héctor Acosta "El Torito"']
## 369 ['Eric Church']
## 370 ['Stromae']
## 371 ['Glee Cast']
## 372 ['Los Caminantes']
## 373 ['A.B. Quintanilla III Y Los Kumbia Kings', 'Fito Olivares']
## 374 ['Mt. Joy']
## 375 ['Lil Wayne']
## 376 ['Twenty One Pilots']
## 377 ['Future', 'Juice WRLD', 'Young Thug']
## 378 ['Gucci Mane', 'Megan Thee Stallion']
## 379 ['JJ Grey & Mofro']
## 380 ['The Shins']
## 381 ['Alexandre Desplat']
## 382 ['Trina']
## 383 ['Casting Crowns']
## 384 ['Rehab', 'Denny aka "Steakknife"']
## 385 ['Juvenile']
## 386 ['Lil Tjay']
## 387 ['Pimpinela']
## 388 ['JayDaYoungan', 'Kevin Gates']
## 389 ['R.E.M.']
## 390 ['Gerardo Ortiz']
## 391 ['No Doubt', 'Lady Saw']
## 392 ["Adolescent's Orquesta"]
## 393 ['Los Extranos']
## 394 ['Bob Marley & The Wailers']
## 395 ['Eagles Of Death Metal']
## 396 ['Alicia Keys']
## 397 ['Hawthorne Heights']
## 398 ['Kings of Leon']
## 399 ['Josh A', 'Iamjakehill']
## 400 ['Lil Baby', 'Rylo Rodriguez']
## 401 ['Vicente Fernández', 'Alejandro Fernández']
## 402 ['Luke Bryan']
## 403 ['Tina Turner']
## 404 ['Sukima Switch']
## 405 ['DJ Khaled', 'Kanye West', 'Rick Ross']
## 406 ["Israel Kamakawiwo'ole"]
## 407 ['Conjunto Primavera']
## 408 ['Archie Eversole']
## 409 ['Dixie Chicks']
## 410 ['Anita Baker']
## 411 ['La Quinta Estacion', 'Marc Anthony']
## 412 ['Train']
## 413 ['Nas', 'Bravehearts']
## 414 ['Lin-Manuel Miranda', 'Olga Merediz']
## 415 ['Los Inquietos Del Norte']
## 416 ['Taylor Swift']
## 417 ['Alter Bridge']
## 418 ['Tyler Childers']
## 419 ['Eminem']
## 420 ['Gesaffelstein']
## 421 ['Wisin & Yandel']
## 422 ['Orquesta Internacional Hermanos Flores']
## 423 ['Chance the Rapper']
## 424 ['Rihanna', 'Calvin Harris']
## 425 ['Los Lonely Boys']
## 426 ['Pink Guy']
## 427 ['Big Sean', 'Wiz Khalifa', 'Chiddy Bang']
## 428 ['Maoli']
## 429 ['Stacey Kent']
## 430 ['Lil Uzi Vert']
## 431 ['Alta Consigna']
## 432 ['Mariah Carey']
## 433 ['Jay Chou']
## 434 ['ayokay', 'Nightly']
## 435 ['Hobo Johnson']
## 436 ['Bazzi']
## 437 ['Billie Eilish']
## 438 ['Avenged Sevenfold']
## 439 ['Wilco']
## 440 ['Stone Temple Pilots']
## 441 ['Michael Card']
## 442 ['Carbon Leaf']
## 443 ['Dirt Poor Robins']
## 444 ['Justin Moore']
## 445 ['Evans Blue']
## 446 ['BTS']
## 447 ['Ice Cube']
## 448 ['Lokey']
## 449 ['Led Zeppelin']
## 450 ['John Powell']
## 451 ['Remy Ma', 'Ne-Yo']
## 452 ['KT Tunstall']
## 453 ['The Union Underground']
## 454 ['Eminem', 'Nate Dogg']
## 455 ['Melanie Martinez']
## 456 ['Breaking Point']
## 457 ['Ben Folds']
## 458 ['Florida Georgia Line']
## 459 ['James Bay']
## 460 ['Oasis']
## 461 ['Lost Kings', 'Wiz Khalifa', 'Social House']
## 462 ['Victorious Cast', 'Victoria Justice']
## 463 ['Five Finger Death Punch']
## 464 ['Home Free', 'Avi Kaplan']
## 465 ['Halsey', 'Big Sean', 'Stefflon Don']
## 466 ['Slipknot']
## 467 ['Beenie Man']
## 468 ['Skillet']
## 469 ['Control Machete']
## 470 ['Dyland & Lenny', 'Zion', 'Arcangel', 'Luny Tunes']
## 471 ['The 1975']
## 472 ['Fish Leong']
## 473 ['Kesha']
## 474 ['Linkin Park']
## 475 ['Snow Patrol']
## 476 ['Tito Nieves']
## 477 ['Drake']
## 478 ['Journey']
## 479 ['Ariel Camacho y Los Plebes Del Rancho']
## 480 ['Rod Wave']
## 481 ['Declan McKenna']
## 482 ['El Coyote Y Su Banda Tierra Santa']
## 483 ['blackbear']
## 484 ['Zedd', 'Alessia Cara']
## 485 ['Lindsey Stirling']
## 486 ['Banda El Recodo']
## 487 ['Angels & Airwaves']
## 488 ['Trippie Redd']
## 489 ['Hoodie Allen', 'Jhameel']
## 490 ['La Numero 1 Banda Jerez De Marco A. Flores']
## 491 ['Lil Skies']
## 492 ['The Disco Biscuits']
## 493 ['Bill Burr']
## 494 ['Frou Frou']
## 495 ['The 1975']
## 496 ['JAY-Z', 'Beanie Sigel', 'Memphis Bleek']
## 497 ['Scarlxrd']
## 498 ['Darude']
## 499 ['Jenny Lewis']
## 500 ['BTS']
## 501 ['Three 6 Mafia', 'Lil Jon']
## 502 ['John Williams']
## 503 ['Rex Orange County']
## 504 ['Kimya Dawson']
## 505 ['Soulfly']
## 506 ['DaBaby']
## 507 ['Sasha Sloan']
## 508 ['The Band']
## 509 ['Bill Burr']
## 510 ['Saga Rosen']
## 511 ['Robert Plant', 'Alison Krauss']
## 512 ['Matthew Mole']
## 513 ['Cultura Profética']
## 514 ['Mac Miller', 'Ab-Soul']
## 515 ['Hubert Clifford', 'BBC Philharmonic', 'Martyn Brabbins']
## 516 ['Keane']
## 517 ['Lil Durk', 'Teyana Taylor', 'Melii']
## 518 ['Yella Beezy']
## 519 ['Dave Matthews Band']
## 520 ['James Bay']
## 521 ['Jordan Fisher', 'Lin-Manuel Miranda']
## 522 ['Immortal Technique']
## 523 ['McFly']
## 524 ['21 Savage', 'Offset', 'Metro Boomin', 'Quavo']
## 525 ['KeKe Wyatt']
## 526 ['Russ']
## 527 ['Wilco']
## 528 ['Daughtry']
## 529 ['Imagine Dragons']
## 530 ['Linkin Park']
## 531 ['Uncle Kracker']
## 532 ['Killswitch Engage']
## 533 ['Leeland']
## 534 ['Lake Street Dive']
## 535 ['Brad Paisley']
## 536 ['Paul Simon']
## 537 ['Fuel']
## 538 ['Los Rieleros Del Norte']
## 539 ['iann dior', 'Bernard Jabs']
## 540 ['Lana Del Rey']
## 541 ['Glee Cast']
## 542 ['The Lumineers']
## 543 ['Seal']
## 544 ['NF']
## 545 ['Max Richter']
## 546 ['Drake']
## 547 ['Underoath']
## 548 ['Fizzonaut']
## 549 ['Velvet Revolver']
## 550 ['Gary Allan']
## 551 ['Kodaline']
## 552 ['Jann Arden']
## 553 ['Sick Puppies']
## 554 ['Maoli']
## 555 ['Converge']
## 556 ['21 Savage']
## 557 ['Nelly']
## 558 ['The Kooks']
## 559 ['Pink Noise']
## 560 ['MARINA']
## 561 ['Imagine Dragons']
## 562 ['Emery']
## 563 ['Walker Hayes']
## 564 ['Alexandre Desplat']
## 565 ['Daddy Yankee']
## 566 ['Elvis Presley']
## 567 ['Jadon Lavik']
## 568 ['Khruangbin']
## 569 ['Jarabe De Palo']
## 570 ['Fabrizio Paterlini']
## 571 ['G-Unit']
## 572 ['George Strait']
## 573 ['Lana Del Rey', 'Stevie Nicks']
## 574 ['Toby Keith']
## 575 ['Boosie Badazz']
## 576 ['Taylor Swift']
## 577 ['Three Days Grace']
## 578 ['Turnpike Troubadours']
## 579 ['Weezer']
## 580 ['Taylor Swift']
## 581 ['Grupo Laberinto']
## 582 ['Newsboys']
## 583 ['Skillet']
## 584 ['Los Originales De San Juan']
## 585 ['Bring Me The Horizon']
## 586 ['Rammstein']
## 587 ['NEEDTOBREATHE']
## 588 ['Piso 21', 'Christian Nodal']
## 589 ['Lil Baby']
## 590 ['Tyler, The Creator']
## 591 ['Ben Howard']
## 592 ['Rex Orange County']
## 593 ['Gojira']
## 594 ['Mrs. GREEN APPLE']
## 595 ['Miracle Musical']
## 596 ['Fat Joe', 'Remy Ma', 'French Montana', 'InfaRed']
## 597 ['2 Chainz', 'Drake']
## 598 ['The Ghost Inside']
## 599 ['Alina Baraz', 'Galimatias']
## 600 ['Maroon 5']
## 601 ['Luther Vandross']
## 602 ['Young Maylay']
## 603 ['A Flock Of Seagulls']
## 604 ['MC Magic', 'Zig-Zag']
## 605 ['Goodnight, Texas']
## 606 ['Banda MS de Sergio Lizárraga']
## 607 ['Thomas Rhett']
## 608 ['Eagles']
## 609 ['JAY-Z']
## 610 ['Deltron 3030', 'Del The Funky Homosapien', 'Dan The Automator', 'Kid Koala']
## 611 ['Benjy Wertheimer', 'John De Kadt']
## 612 ['Rascal Flatts']
## 613 ['Tash Sultana']
## 614 ['Savannah Outen']
## 615 ['Missy Elliott', 'Pharrell Williams']
## 616 ['Poison']
## 617 ['Vance Joy']
## 618 ['Denzel Curry']
## 619 ['Bad Bunny', 'J Balvin', 'Ozuna', 'Arcangel']
## 620 ['Dan + Shay']
## 621 ['Cavetown']
## 622 ['Bankrol Hayden']
## 623 ['DragonForce']
## 624 ['Dream']
## 625 ['Otto Wahl']
## 626 ['Newton Faulkner']
## 627 ['Nipsey Hussle', 'Drake']
## 628 ['K.A.A.N.']
## 629 ['Rascal Flatts']
## 630 ['Jason Mraz']
## 631 ["Dustin O'Halloran"]
## 632 ['Justin Timberlake']
## 633 ['Jana Kramer']
## 634 ['Vampire Weekend']
## 635 ['Banda MS de Sergio Lizárraga']
## 636 ['keshi']
## 637 ['Mac Miller']
## 638 ['Tercer Cielo']
## 639 ['Linkin Park']
## 640 ['Sponge']
## 641 ['Lil Baby']
## 642 ['Godspeed You! Black Emperor']
## 643 ['Metallica']
## 644 ['The Black Keys']
## 645 ['The Lumineers']
## 646 ['Train']
## 647 ['Frank Ocean', 'John Mayer']
## 648 ['Cody Johnson']
## 649 ['Disturbed']
## 650 ['mike.']
## 651 ['Three Legged Fox']
## 652 ["Olivia O'Brien"]
## 653 ['JJ Hairston']
## 654 ['Bebe Rexha', 'G-Eazy']
## 655 ['Cuco']
## 656 ['Neck Deep']
## 657 ['Peach Pit']
## 658 ['Jon Bellion']
## 659 ['The Cyrkle']
## 660 ['Beto Y Sus Canarios']
## 661 ['Conjunto Primavera']
## 662 ['Creed']
## 663 ['Mariah Carey']
## 664 ['Chris Smither']
## 665 ['Mr. Cheeks']
## 666 ['Alabama']
## 667 ["Why Don't We"]
## 668 ['Aaron Shust']
## 669 ['Matt Corby']
## 670 ['MercyMe']
## 671 ['La Roux', 'Sonny Moore']
## 672 ['Michael Jackson']
## 673 ['The Black Dahlia Murder']
## 674 ['Grupo Firme']
## 675 ['Flex']
## 676 ['The Maine']
## 677 ['Speaker Knockerz']
## 678 ['Frank Sinatra', 'Count Basie']
## 679 ['Chris Brown', 'Young Thug']
## 680 ['mysticphonk', 'Lil Peep']
## 681 ['Mariah Carey']
## 682 ['Leon Bridges', 'Terrace Martin']
## 683 ['Shannon']
## 684 ['Stick Figure']
## 685 ['Grupo Laberinto']
## 686 ['Weezer']
## 687 ['Future']
## 688 ['David Hazeltine']
## 689 ['Tinlicker', 'Helsloot']
## 690 ['Incubus']
## 691 ['Mushroomhead']
## 692 ['The Neighbourhood']
## 693 ['Sara Bareilles']
## 694 ['León Larregui']
## 695 ['Daniel Lanois']
## 696 ['August Alsina']
## 697 ['Rob Thomas']
## 698 ['Lorde']
## 699 ['Queens of the Stone Age']
## 700 ['Rain Sounds Factory STHLM']
## 701 ['Parijat']
## 702 ['Ström']
## 703 ['Kid Rock']
## 704 ['Deerhunter']
## 705 ['The Sandals']
## 706 ['El Fantasma']
## 707 ['múm']
## 708 ['Puscifer']
## 709 ['Busted']
## 710 ['Freddie Mercury']
## 711 ['Killswitch Engage']
## 712 ['Andy Black']
## 713 ['Chelsea Cutler', 'Jeremy Zucker']
## 714 ['Los Razos']
## 715 ['Arcangel']
## 716 ['Fanny Lu']
## 717 ['Drake']
## 718 ['Pyotr Ilyich Tchaikovsky', 'Sir Simon Rattle', 'Berliner Philharmoniker']
## 719 ['Kool & The Gang']
## 720 ['Santigold']
## 721 ['Kittie']
## 722 ['BTS']
## 723 ['Aloe Blacc']
## 724 ['Sharpay Evans', 'Ryan']
## 725 ['Lana Del Rey', 'Sean Ono Lennon']
## 726 ['Ariel Camacho y Los Plebes Del Rancho']
## 727 ['Petit Biscuit', 'Lido']
## 728 ['Kanye West']
## 729 ['Sean Paul']
## 730 ['blink-182']
## 731 ['YoungBoy Never Broke Again', 'Sherhonda Gaulden']
## 732 ["Olivia O'Brien"]
## 733 ['311']
## 734 ['Enrique Iglesias', 'Marco Antonio Solís']
## 735 ['5 Seconds of Summer']
## 736 ['Keith Urban']
## 737 ['Kid Cudi', 'Pharrell Williams']
## 738 ['CKY']
## 739 ['Joe']
## 740 ['Rascal Flatts']
## 741 ['The Gourds']
## 742 ['George Strait']
## 743 ['Jim Croce']
## 744 ['Real Estate']
## 745 ['In Flames']
## 746 ['Iration']
## 747 ['Calibre 50']
## 748 ['Yelawolf']
## 749 ['YoungBoy Never Broke Again']
## 750 ['Daniel Powter']
## 751 ['Motörhead']
## 752 ['Portico Quartet']
## 753 ['Sung Si-kyung']
## 754 ['Godsmack']
## 755 ['Gorilla Zoe']
## 756 ['The Pussycat Dolls']
## 757 ['Fergie']
## 758 ['Billie Eilish']
## 759 ['Morgan Wallen']
## 760 ['Nick Mulvey']
## 761 ['Alan Jackson']
## 762 ['Flatland Cavalry']
## 763 ['Anna Kendrick', 'Justin Timberlake']
## 764 ['SNBRN', 'Autograf', 'KOLE']
## 765 ['Dorrough Music']
## 766 ['Shoreline Mafia']
## 767 ['Rare Earth']
## 768 ['Donnie Elbert']
## 769 ['Thurston Harris', 'The Sharps']
## 770 ['Futuristic']
## 771 ['David Crosby', 'Graham Nash']
## 772 ['King Princess']
## 773 ['Gojira']
## 774 ['Saliva']
## 775 ['Avenged Sevenfold']
## 776 ['Disturbed']
## 777 ['Bad Suns']
## 778 ['Arctic Monkeys']
## 779 ['WILLOW']
## 780 ['AFI']
## 781 ['The Goo Goo Dolls']
## 782 ['Garbage']
## 783 ['Madison Beer', 'Offset']
## 784 ['Hedley']
## 785 ['Erick Sermon', 'Redman']
## 786 ['Mischief Brew']
## 787 ['Styles P', 'Pharoahe Monch']
## 788 ['Sade']
## 789 ['Dry Kill Logic']
## 790 ['MGMT']
## 791 ['Fabolous']
## 792 ['Nature Sounds']
## 793 ['Ariana Grande', 'The Weeknd']
## 794 ['311']
## 795 ['Johann Nepomuk Hummel', 'Tine Thing Helseth']
## 796 ['Frédéric Chopin', 'Luis Fernando Pérez']
## 797 ['Samantha Mumba']
## 798 ['The Chainsmokers', 'ROZES']
## 799 ['Jakey']
## 800 ['Shakira']
## 801 ['BTS']
## 802 ['Playboi Carti', 'Lil Uzi Vert']
## 803 ['DJ Omidia']
## 804 ['Drake', 'Lil Wayne']
## 805 ['The Strokes']
## 806 ['Yoskar Sarante']
## 807 ['George Clinton']
## 808 ['Peter Tosh']
## 809 ['Carrie Underwood']
## 810 ['Tyler Childers']
## 811 ['Fergie', 'will.i.am']
## 812 ['Little Big Town']
## 813 ['Taylor Swift']
## 814 ['Helios']
## 815 ['Jazmine Sullivan']
## 816 ['Omega', 'Gocho', 'Jo-Well']
## 817 ['Bad Meets Evil']
## 818 ['Renée Elise Goldsberry', 'Lin-Manuel Miranda', 'Phillipa Soo', 'Original Broadway Cast of Hamilton']
## 819 ['Ruff Endz']
## 820 ['Afro Celt Sound System']
## 821 ['Los Brios']
## 822 ['Tyler Farr', 'Jason Aldean']
## 823 ['Los Acosta']
## 824 ['Alesso', 'Nico & Vinz']
## 825 ['Sublime']
## 826 ['Slightly Stoopid', 'G. Love & Special Sauce']
## 827 ['Led Zeppelin']
## 828 ['Johnny Cash']
## 829 ['The Beatles']
## 830 ['Eric Church']
## 831 ['DJ Drama', 'Skeme', 'Lyquin', 'Chris Brown']
## 832 ['NCT DREAM']
## 833 ['El Alfa', 'Lil Pump', 'Sech', 'Myke Towers', 'Vin Diesel']
## 834 ['Zac Brown Band']
## 835 ['Wiz Khalifa']
## 836 ['Santana', 'Michelle Branch']
## 837 ['Beats Antique']
## 838 ['The Strokes']
## 839 ['People Under The Stairs']
## 840 ['Harry McClintock']
## 841 ['BTS']
## 842 ['Nico & Vinz', 'Kid Ink', 'Bebe Rexha']
## 843 ['Mr. Criminal', 'Nate Dogg', 'Mr. Capone-E']
## 844 ['Gucci Mane']
## 845 ['Echosmith']
## 846 ['Idealism']
## 847 ['A Heart Beats']
## 848 ['Lykke Li']
## 849 ['JAY-Z', 'Memphis Bleek', 'Snoop Dogg']
## 850 ['Fobia']
## 851 ['Calvin Harris', 'Dua Lipa']
## 852 ['Factorial FX']
## 853 ['Jack Johnson', 'Kawika Kahiapo']
## 854 ['Hey Monday']
## 855 ['Vance Joy']
## 856 ['The Coasters']
## 857 ['Mac DeMarco']
## 858 ['John K']
## 859 ['Bingo Players']
## 860 ['Guru Josh Project', 'Klaas']
## 861 ['Randy Houser']
## 862 ['Natasha Bedingfield']
## 863 ['Cody Johnson']
## 864 ['Interpol']
## 865 ['Leslie Odom Jr.', 'Lin-Manuel Miranda', 'Original Broadway Cast of Hamilton']
## 866 ['Ten Feet']
## 867 ['Mika Singh']
## 868 ['Jadakiss']
## 869 ['Backstreet Boys']
## 870 ['Fall Out Boy']
## 871 ['PSY', 'CL']
## 872 ['The Avalanches']
## 873 ['CeeLo Green']
## 874 ['Maná']
## 875 ['A$AP Rocky']
## 876 ['Reel Big Fish']
## 877 ['Forrest.', 'Biskwiq', 'Ryce']
## 878 ['Hillsong UNITED']
## 879 ['Fall Out Boy']
## 880 ['Katharine McPhee']
## 881 ['Porte Diferente']
## 882 ['Passafire']
## 883 ['Rayito Colombiano']
## 884 ['Ariana Grande']
## 885 ['Bethel Music', 'Paul McClure']
## 886 ['The Outfield']
## 887 ['Kevin Rudolf', 'Birdman', 'Jay Sean', 'Lil Wayne']
## 888 ['Marilyn Manson']
## 889 ['Outside HD Samples']
## 890 ['Guardianes Del Amor']
## 891 ['The Youngbloods']
## 892 ['TOOL']
## 893 ['Wiz Khalifa']
## 894 ['Arctic Monkeys']
## 895 ['Meghan Trainor']
## 896 ['BTS']
## 897 ['2 Chainz']
## 898 ['Beyoncé']
## 899 ['FLOW']
## 900 ['Dave Matthews Band']
## 901 ['A Change of Pace']
## 902 ['Stephen Schwartz', 'Kristin Chenoweth', 'Idina Menzel', 'Stephen Oremus', 'Alex Lacamoire']
## 903 ['Billie Eilish']
## 904 ['Skillet']
## 905 ['Minus the Bear']
## 906 ['Kanye West']
## 907 ['Blue Swede', 'Björn Skifs']
## 908 ['ILLENIUM', "Liam O'Donnell", 'T-Mass', 'LZRD']
## 909 ['Moonstar88']
## 910 ['Fall Out Boy']
## 911 ['Reggie P']
## 912 ['Toby Keith']
## 913 ['Lauv', 'LANY']
## 914 ['Jai Paul']
## 915 ['Steam Powered Giraffe']
## 916 ['Intocable']
## 917 ['Ducktails']
## 918 ['Joe Bonamassa']
## 919 ['Matchbox Twenty']
## 920 ['A Day To Remember']
## 921 ['A Perfect Circle']
## 922 ['The Strokes']
## 923 ['Shadows Fall']
## 924 ['Creed']
## 925 ['Seckond Chaynce']
## 926 ['Lily Allen']
## 927 ['Pink Martini']
## 928 ['Eminem']
## 929 ['The Used']
## 930 ['Dua Lipa', 'BLACKPINK']
## 931 ['Craig Morgan']
## 932 ['Tritonal', 'Ross Lynch', 'R5']
## 933 ['Fitz and The Tantrums']
## 934 ['Giorgio Moroder', 'Joe Esposito']
## 935 ['Charles Bradley', 'The Budos Band']
## 936 ['David Olivarez']
## 937 ['Bo Burnham']
## 938 ['Beyoncé']
## 939 ['Daft Punk']
## 940 ['Lady Gaga']
## 941 ['Playboi Carti', 'Travis Scott']
## 942 ['Suicide Silence']
## 943 ['Frank Ocean']
## 944 ['Donavon Frankenreiter']
## 945 ['Big Boss Man']
## 946 ['Lotte Kestner']
## 947 ['Gesaffelstein', 'The Weeknd']
## 948 ['MIKA', 'Ariana Grande', 'Jason Nevins']
## 949 ['Modjo']
## 950 ['Kavinsky']
## 951 ['G. Love & Special Sauce']
## 952 ['Born Without Bones']
## 953 ['Drake']
## 954 ['Sofia Karlberg']
## 955 ['Nursery Rhymes 123']
## 956 ['Bloodhound Gang']
## 957 ['Kings of Leon']
## 958 ['Esteban Gabriel']
## 959 ['Sean Paul']
## 960 ['Eric Church']
## 961 ['T.O.K']
## 962 ['RADWIMPS']
## 963 ['XXXTENTACION']
## 964 ['Prince Royce']
## 965 ['Voz De Mando']
## 966 ['Red Hot Chili Peppers']
## 967 ['Willie Nelson']
## 968 ['The Ronettes']
## 969 ['alt-J']
## 970 ['Banda Cuisillos']
## 971 ['Anderson .Paak']
## 972 ['George Strait']
## 973 ['Fergie']
## 974 ['Parkway Drive']
## 975 ['Liza Anne']
## 976 ['Selective Sounds PTA']
## 977 ['Hampton The Hampster']
## 978 ['Future']
## 979 ['Brantley Gilbert']
## 980 ['All Time Low']
## 981 ['Jack Johnson']
## 982 ['Cake']
## 983 ['Yung Berg']
## 984 ['Deltron 3030', 'Del The Funky Homosapien', 'Dan The Automator', 'Kid Koala']
## 985 ['Taylor Swift']
## 986 ['Lorde']
## 987 ['Lucinda Williams']
## 988 ['The Protomen']
## 989 ['Johann Sebastian Bach', 'Robert Hill', 'Kolner Kammerorchester', 'Helmut Muller-Bruhl']
## 990 ['G.E.M.']
## 991 ['Ripe']
## 992 ['Ellie Goulding']
## 993 ['Stephen Marley', 'Damian Marley', 'Buju Banton']
## 994 ['Tony Rebel']
## 995 ['88GLAM']
## 996 ['Big Gigantic', 'Kasbo', 'Angela McCluskey']
## 997 ['Black Eyed Peas', 'J Balvin']
## 998 ['Johnnyswim']
## 999 ['Cannibal Corpse']
## 1000 ['BØRNS']
## danceability duration_ms duration_minutes energy explicit
## 1 0.6170 238524 3.9754000 6.40e-01 0
## 2 0.2420 313560 5.2260000 8.27e-01 0
## 3 0.9150 286907 4.7817833 5.56e-01 1
## 4 0.6420 194867 3.2477833 9.31e-01 0
## 5 0.4010 328507 5.4751167 3.64e-01 0
## 6 0.9030 217013 3.6168833 5.21e-01 0
## 7 0.7460 240147 4.0024500 8.82e-01 1
## 8 0.5330 234307 3.9051167 7.39e-01 1
## 9 0.2330 213640 3.5606667 9.43e-01 1
## 10 0.6860 230360 3.8393333 6.31e-01 0
## 11 0.4060 328893 5.4815500 3.59e-01 0
## 12 0.3560 302211 5.0368500 4.69e-01 0
## 13 0.8570 147692 2.4615333 5.75e-01 1
## 14 0.3400 328907 5.4817833 4.45e-01 0
## 15 0.6090 283893 4.7315500 6.07e-01 0
## 16 0.3990 266733 4.4455500 9.55e-01 0
## 17 0.2980 168000 2.8000000 1.12e-01 0
## 18 0.4020 178267 2.9711167 9.14e-01 1
## 19 0.5360 112067 1.8677833 8.21e-01 0
## 20 0.5420 276213 4.6035500 8.95e-01 0
## 21 0.7000 182920 3.0486667 7.48e-01 0
## 22 0.3890 253387 4.2231167 5.37e-01 0
## 23 0.5840 234173 3.9028833 7.00e-01 0
## 24 0.2140 206560 3.4426667 9.45e-01 0
## 25 0.6430 250027 4.1671167 7.12e-01 0
## 26 0.6860 275493 4.5915500 7.12e-01 0
## 27 0.6080 225320 3.7553333 8.21e-01 0
## 28 0.2810 247696 4.1282667 1.39e-01 0
## 29 0.6370 306773 5.1128833 6.52e-01 0
## 30 0.6530 114627 1.9104500 3.25e-01 0
## 31 0.6860 158293 2.6382167 8.16e-01 0
## 32 0.8630 213107 3.5517833 7.28e-01 0
## 33 0.1490 200000 3.3333333 2.54e-01 0
## 34 0.7060 158707 2.6451167 6.79e-01 1
## 35 0.4590 146573 2.4428833 4.98e-01 0
## 36 0.0821 293627 4.8937833 1.04e-01 0
## 37 0.4810 231667 3.8611167 7.50e-01 0
## 38 0.5120 178147 2.9691167 5.80e-01 0
## 39 0.6700 160293 2.6715500 7.00e-01 1
## 40 0.5630 223120 3.7186667 7.27e-01 0
## 41 0.3370 223227 3.7204500 8.59e-01 0
## 42 0.7770 155350 2.5891667 6.52e-01 1
## 43 0.5350 177213 2.9535500 8.54e-01 0
## 44 0.5220 191987 3.1997833 7.62e-01 0
## 45 0.5300 236440 3.9406667 7.51e-01 0
## 46 0.6220 174968 2.9161333 6.79e-01 1
## 47 0.6150 146184 2.4364000 7.04e-01 1
## 48 0.7530 243280 4.0546667 8.81e-01 0
## 49 0.8020 206400 3.4400000 7.85e-01 1
## 50 0.6380 147600 2.4600000 5.56e-01 0
## 51 0.6800 231267 3.8544500 5.78e-01 1
## 52 0.4910 220733 3.6788833 9.78e-01 0
## 53 0.6970 204117 3.4019500 6.75e-01 1
## 54 0.3950 110080 1.8346667 9.47e-01 0
## 55 0.8630 200800 3.3466667 8.15e-01 1
## 56 0.8080 500867 8.3477833 7.89e-01 0
## 57 0.5620 233627 3.8937833 6.89e-01 0
## 58 0.4960 202760 3.3793333 6.99e-01 0
## 59 0.5470 321307 5.3551167 7.28e-01 0
## 60 0.6620 282107 4.7017833 4.29e-01 0
## 61 0.8190 236565 3.9427500 8.45e-01 1
## 62 0.9250 109120 1.8186667 4.43e-01 0
## 63 0.7600 233160 3.8860000 4.76e-01 1
## 64 0.4660 217987 3.6331167 5.50e-01 0
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## 784 0.4280 251427 4.1904500 8.54e-01 0
## 785 0.5840 217840 3.6306667 8.94e-01 1
## 786 0.5320 200213 3.3368833 4.70e-01 0
## 787 0.4270 189427 3.1571167 7.06e-01 1
## 788 0.7280 221293 3.6882167 4.92e-01 0
## 789 0.3990 272267 4.5377833 9.37e-01 1
## 790 0.5750 255907 4.2651167 8.04e-01 0
## 791 0.6640 209693 3.4948833 9.09e-01 1
## 792 0.0735 570203 9.5033833 9.97e-01 0
## 793 0.4720 236133 3.9355500 7.14e-01 0
## 794 0.6630 206973 3.4495500 6.97e-01 0
## 795 0.4960 224640 3.7440000 2.49e-01 0
## 796 0.3870 268427 4.4737833 4.51e-02 0
## 797 0.7290 201067 3.3511167 7.52e-01 0
## 798 0.7180 225813 3.7635500 8.08e-01 0
## 799 0.5150 226411 3.7735167 6.05e-01 1
## 800 0.7650 287333 4.7888833 5.10e-01 0
## 801 0.6250 292773 4.8795500 6.63e-01 0
## 802 0.7850 235535 3.9255833 6.20e-01 1
## 803 0.7440 249313 4.1552167 6.68e-01 0
## 804 0.7790 181587 3.0264500 4.55e-01 0
## 805 0.4300 196067 3.2677833 9.13e-01 0
## 806 0.9190 268707 4.4784500 6.83e-01 0
## 807 0.8950 286160 4.7693333 6.65e-01 0
## 808 0.7950 268213 4.4702167 7.31e-01 0
## 809 0.4560 204093 3.4015500 8.74e-01 0
## 810 0.4490 194813 3.2468833 2.96e-01 0
## 811 0.9070 292373 4.8728833 5.85e-01 0
## 812 0.4820 228373 3.8062167 7.07e-01 0
## 813 0.6660 245333 4.0888833 4.34e-01 0
## 814 0.2410 154733 2.5788833 1.52e-02 0
## 815 0.5290 257427 4.2904500 6.59e-01 0
## 816 0.8110 241680 4.0280000 7.73e-01 0
## 817 0.4430 209613 3.4935500 8.95e-01 1
## 818 0.5190 270172 4.5028667 1.87e-01 0
## 819 0.6370 297360 4.9560000 6.78e-01 0
## 820 0.6380 314160 5.2360000 8.22e-01 0
## 821 0.6540 213000 3.5500000 5.91e-01 0
## 822 0.4810 185520 3.0920000 7.48e-01 0
## 823 0.7220 183120 3.0520000 7.27e-01 0
## 824 0.6550 240413 4.0068833 8.18e-01 0
## 825 0.6660 125880 2.0980000 7.06e-01 0
## 826 0.7790 280627 4.6771167 5.19e-01 0
## 827 0.3380 485013 8.0835500 3.32e-01 0
## 828 0.3780 249867 4.1644500 5.36e-01 0
## 829 0.4260 131213 2.1868833 7.60e-01 0
## 830 0.5880 265680 4.4280000 6.44e-01 0
## 831 0.4770 252307 4.2051167 6.47e-01 1
## 832 0.9140 195080 3.2513333 5.16e-01 0
## 833 0.8830 250169 4.1694833 8.53e-01 0
## 834 0.6390 227222 3.7870333 1.96e-01 0
## 835 0.7870 219027 3.6504500 6.66e-01 1
## 836 0.5900 254467 4.2411167 9.29e-01 0
## 837 0.6010 225000 3.7500000 7.55e-01 0
## 838 0.4260 176160 2.9360000 9.40e-01 0
## 839 0.8590 264152 4.4025333 6.84e-01 0
## 840 0.6680 135147 2.2524500 1.72e-01 0
## 841 0.6430 226425 3.7737500 9.03e-01 0
## 842 0.7690 186443 3.1073833 4.37e-01 1
## 843 0.8260 235013 3.9168833 6.84e-01 1
## 844 0.8120 311160 5.1860000 5.86e-01 1
## 845 0.7190 237627 3.9604500 6.71e-01 0
## 846 0.7880 108000 1.8000000 4.46e-01 0
## 847 0.5070 146173 2.4362167 2.55e-01 0
## 848 0.7890 139840 2.3306667 5.34e-01 1
## 849 0.8800 262133 4.3688833 5.69e-01 1
## 850 0.6980 270533 4.5088833 5.43e-01 0
## 851 0.7910 214847 3.5807833 8.62e-01 0
## 852 0.1320 172083 2.8680500 1.52e-01 0
## 853 0.9020 202360 3.3726667 4.35e-01 0
## 854 0.3930 197760 3.2960000 9.65e-01 0
## 855 0.6450 217156 3.6192667 5.57e-01 0
## 856 0.7150 113040 1.8840000 6.69e-01 0
## 857 0.5680 212746 3.5457667 6.74e-01 0
## 858 0.6600 216938 3.6156333 5.81e-01 0
## 859 0.7440 167845 2.7974167 4.83e-01 0
## 860 0.5560 226480 3.7746667 8.83e-01 0
## 861 0.6800 174067 2.9011167 7.86e-01 0
## 862 0.7060 259333 4.3222167 8.01e-01 0
## 863 0.5880 186813 3.1135500 8.73e-01 0
## 864 0.4170 388067 6.4677833 8.45e-01 0
## 865 0.7360 150064 2.5010667 3.70e-01 0
## 866 0.6600 257613 4.2935500 4.79e-01 0
## 867 0.7380 245100 4.0850000 8.73e-01 0
## 868 0.8220 212907 3.5484500 5.75e-01 1
## 869 0.7090 231187 3.8531167 5.55e-01 0
## 870 0.6060 188514 3.1419000 8.99e-01 0
## 871 0.7620 230635 3.8439167 8.70e-01 0
## 872 0.5950 261960 4.3660000 9.79e-01 0
## 873 0.6320 218280 3.6380000 7.49e-01 0
## 874 0.7910 264720 4.4120000 7.82e-01 0
## 875 0.8970 187105 3.1184167 7.43e-01 1
## 876 0.7480 184840 3.0806667 9.13e-01 0
## 877 0.7180 207000 3.4500000 6.32e-01 0
## 878 0.4950 249640 4.1606667 6.08e-01 0
## 879 0.5380 169027 2.8171167 8.89e-01 0
## 880 0.4300 210333 3.5055500 2.03e-01 0
## 881 0.8110 228466 3.8077667 5.80e-01 0
## 882 0.7240 267133 4.4522167 6.39e-01 0
## 883 0.5880 256493 4.2748833 4.87e-01 0
## 884 0.5360 260000 4.3333333 6.94e-01 0
## 885 0.2530 415079 6.9179833 3.59e-01 0
## 886 0.5840 216707 3.6117833 6.72e-01 0
## 887 0.5920 249547 4.1591167 9.44e-01 1
## 888 0.3100 245467 4.0911167 8.74e-01 1
## 889 0.1400 151715 2.5285833 1.87e-01 0
## 890 0.6940 217293 3.6215500 9.14e-01 0
## 891 0.5330 276467 4.6077833 5.80e-01 0
## 892 0.5670 403533 6.7255500 7.14e-01 0
## 893 0.4200 207478 3.4579667 6.69e-01 1
## 894 0.4810 197320 3.2886667 5.24e-01 0
## 895 0.8070 187920 3.1320000 8.87e-01 1
## 896 0.5100 240339 4.0056500 8.42e-01 0
## 897 0.9390 188067 3.1344500 6.89e-01 1
## 898 0.2500 195987 3.2664500 2.15e-01 0
## 899 0.4060 199560 3.3260000 9.02e-01 0
## 900 0.7020 338067 5.6344500 6.30e-01 0
## 901 0.5280 183893 3.0648833 9.46e-01 0
## 902 0.4560 183853 3.0642167 5.84e-01 0
## 903 0.4270 229426 3.8237667 1.23e-01 0
## 904 0.4820 220080 3.6680000 8.73e-01 0
## 905 0.6030 310733 5.1788833 6.75e-01 0
## 906 0.7440 278893 4.6482167 5.24e-01 1
## 907 0.5470 172867 2.8811167 8.20e-01 0
## 908 0.5360 204778 3.4129667 7.82e-01 0
## 909 0.3580 264202 4.4033667 4.00e-01 0
## 910 0.4870 206507 3.4417833 9.37e-01 0
## 911 0.8540 214120 3.5686667 6.74e-01 0
## 912 0.4240 195533 3.2588833 6.61e-01 0
## 913 0.7460 232853 3.8808833 4.50e-01 0
## 914 0.7160 162480 2.7080000 7.40e-01 0
## 915 0.3520 309000 5.1500000 1.86e-01 0
## 916 0.4260 218776 3.6462667 5.84e-01 0
## 917 0.5710 259318 4.3219667 9.01e-01 0
## 918 0.3930 492067 8.2011167 6.04e-01 0
## 919 0.4950 249390 4.1565000 8.08e-01 0
## 920 0.2960 200031 3.3338500 9.93e-01 1
## 921 0.4560 288360 4.8060000 8.47e-01 0
## 922 0.3570 235547 3.9257833 7.75e-01 0
## 923 0.4270 298333 4.9722167 9.38e-01 0
## 924 0.3280 226827 3.7804500 9.26e-01 0
## 925 0.7080 216973 3.6162167 7.16e-01 0
## 926 0.2830 187413 3.1235500 7.01e-01 0
## 927 0.5710 168187 2.8031167 3.52e-01 0
## 928 0.8120 319867 5.3311167 8.80e-01 1
## 929 0.2790 190813 3.1802167 8.78e-01 1
## 930 0.6780 189173 3.1528833 7.29e-01 0
## 931 0.5910 263587 4.3931167 8.80e-01 0
## 932 0.5550 213120 3.5520000 8.13e-01 0
## 933 0.6960 189893 3.1648833 8.86e-01 0
## 934 0.5330 255013 4.2502167 5.96e-01 0
## 935 0.3730 360829 6.0138167 4.69e-01 0
## 936 0.8260 201400 3.3566667 7.12e-01 0
## 937 0.4440 238587 3.9764500 8.29e-01 1
## 938 0.2300 226400 3.7733333 6.94e-01 0
## 939 0.6480 235467 3.9244500 6.71e-01 0
## 940 0.6140 264520 4.4086667 8.79e-01 0
## 941 0.7240 180560 3.0093333 4.50e-01 1
## 942 0.2980 244533 4.0755500 9.68e-01 0
## 943 0.3500 202200 3.3700000 1.20e-01 0
## 944 0.8540 194421 3.2403500 7.32e-01 0
## 945 0.8030 268173 4.4695500 7.59e-01 0
## 946 0.5760 269920 4.4986667 1.21e-01 0
## 947 0.6580 202093 3.3682167 6.71e-01 1
## 948 0.6440 200613 3.3435500 8.40e-01 0
## 949 0.7200 307154 5.1192333 8.08e-01 0
## 950 0.5320 258413 4.3068833 8.74e-01 0
## 951 0.4530 261867 4.3644500 7.69e-01 0
## 952 0.4930 213831 3.5638500 3.43e-01 0
## 953 0.7490 188189 3.1364833 4.76e-01 1
## 954 0.5400 190250 3.1708333 4.99e-01 0
## 955 0.6380 84494 1.4082333 7.89e-02 0
## 956 0.8000 260160 4.3360000 9.23e-01 1
## 957 0.3470 128507 2.1417833 8.08e-01 0
## 958 0.6650 159739 2.6623167 5.12e-01 0
## 959 0.7570 232507 3.8751167 7.80e-01 0
## 960 0.7090 209787 3.4964500 6.24e-01 0
## 961 0.5190 212667 3.5444500 5.21e-01 0
## 962 0.4960 344053 5.7342167 4.62e-01 0
## 963 0.8170 104960 1.7493333 6.97e-01 1
## 964 0.7470 238707 3.9784500 6.86e-01 0
## 965 0.8260 203573 3.3928833 5.13e-01 0
## 966 0.5580 256960 4.2826667 9.24e-01 0
## 967 0.7090 152707 2.5451167 7.29e-01 0
## 968 0.5110 160907 2.6817833 7.69e-01 0
## 969 0.5750 239013 3.9835500 6.03e-01 0
## 970 0.3800 181650 3.0275000 4.95e-01 0
## 971 0.3870 328813 5.4802167 7.26e-01 1
## 972 0.2930 199960 3.3326667 2.82e-01 0
## 973 0.7080 268120 4.4686667 6.41e-01 0
## 974 0.4040 276223 4.6037167 9.64e-01 0
## 975 0.4370 219000 3.6500000 3.01e-01 0
## 976 0.1300 234197 3.9032833 1.67e-01 0
## 977 0.7250 212881 3.5480167 9.24e-01 0
## 978 0.7180 173347 2.8891167 6.79e-01 1
## 979 0.4900 286520 4.7753333 7.16e-01 0
## 980 0.4960 189418 3.1569667 9.13e-01 0
## 981 0.8560 208267 3.4711167 4.18e-01 0
## 982 0.6860 275853 4.5975500 5.81e-01 0
## 983 0.6300 253840 4.2306667 7.26e-01 1
## 984 0.6970 299093 4.9848833 7.56e-01 0
## 985 0.6280 191880 3.1980000 6.76e-01 0
## 986 0.5120 234286 3.9047667 4.74e-01 0
## 987 0.6810 278267 4.6377833 4.84e-01 0
## 988 0.6020 271133 4.5188833 9.02e-01 0
## 989 0.4030 167533 2.7922167 2.63e-01 0
## 990 0.5510 258866 4.3144333 4.03e-01 0
## 991 0.6820 343405 5.7234167 4.66e-01 0
## 992 0.7020 286322 4.7720333 7.81e-01 0
## 993 0.6590 283013 4.7168833 8.83e-01 0
## 994 0.8460 229000 3.8166667 7.06e-01 0
## 995 0.7640 180813 3.0135500 6.72e-01 1
## 996 0.4900 183893 3.0648833 5.80e-01 0
## 997 0.7210 221714 3.6952333 7.16e-01 1
## 998 0.6470 211107 3.5184500 6.27e-01 0
## 999 0.2910 270920 4.5153333 9.80e-01 0
## 1000 0.5910 256653 4.2775500 8.74e-01 0
## id instrumentalness key liveness loudness mode
## 1 1W5StOzPxW32oXTlHspq2Q 1.07e-06 7 0.1610 -7.280 1
## 2 0qWfR0C4ul3S1f1HWIp5O0 2.71e-02 11 0.1200 -6.338 0
## 3 2sQuds8s3EVbhXJpgVWpO4 0.00e+00 6 0.0739 -5.026 1
## 4 6K8hK9VfSqlOWby6v4O0a8 2.32e-05 9 0.0942 -2.236 1
## 5 3CAX47TnPqTujLIQTw8nwI 5.03e-02 5 0.1620 -10.836 1
## 6 09bBwB9wctmnYtxMOdNGRd 0.00e+00 11 0.1060 -4.372 0
## 7 0YQLJDhHVQfZJZmHlml15F 0.00e+00 11 0.3480 -2.902 0
## 8 50M2QjfSM82wkJ1d0iV4mh 0.00e+00 5 0.2660 -3.573 0
## 9 0Fk2NcRQpungRyAx9Y6KTk 0.00e+00 2 0.3590 -3.443 0
## 10 0WB48shl2wAH4KqyQkQ8nJ 1.83e-04 10 0.1070 -5.182 1
## 11 2fbJ5Msx8KoDKpyufH5YsU 3.67e-06 7 0.1220 -6.145 1
## 12 3oGqaG8lbdMzw4kgRb0CYz 8.82e-01 6 0.3960 -8.403 0
## 13 4J2NxCNqehE0MiZhbu97Jx 0.00e+00 7 0.2930 -8.360 1
## 14 1BH4LMGVMLBGc9uEJdm8D8 3.25e-04 4 0.6940 -7.926 0
## 15 7zuwr7YqqpveNtxlKe1vn6 1.53e-05 1 0.2050 -7.947 0
## 16 1z33QOn2Hcq9SfI5pES25L 0.00e+00 4 0.0759 -5.593 0
## 17 5oK98mpTJSU0iqLHN1hZ3y 8.23e-02 1 0.0935 -15.765 1
## 18 4KacUpvbA3Mfo05gttTjhN 0.00e+00 9 0.0650 -5.215 1
## 19 6Y9WuYN6OfMIzKeEgx6El5 8.97e-03 10 0.3070 -8.950 1
## 20 3FyqYODt6oFxjmFiZs9Jlz 0.00e+00 0 0.1110 -3.635 0
## 21 4cZ3UsiKd1kUQIaq4BFIj2 0.00e+00 1 0.0941 -6.044 1
## 22 5LABCxgmP7DATATIJXOh6n 3.13e-06 10 0.1250 -6.761 1
## 23 2iXBZ32Fz5VDCLeE0JIdX5 2.43e-05 6 0.1300 -4.251 1
## 24 5M8EfAPEPzTQcr4tsjcimR 7.01e-05 4 0.3810 -6.022 0
## 25 65ILbAZRAwZQ3omWKE0OIW 4.67e-03 5 0.2870 -6.385 1
## 26 0PHWXLXOQXGlyUGq7woVFZ 4.20e-03 5 0.3680 -7.580 0
## 27 47HtKpfzpAt8rQjjXWotFj 0.00e+00 10 0.1100 -4.985 1
## 28 1tg28JiXBuaUj5bZ8Ppuum 0.00e+00 4 0.0990 -8.970 1
## 29 6V9TlCdwLeQes4FX5zxz3f 1.75e-01 1 0.0714 -5.237 0
## 30 1HTR2cbs7Nor722yzYQi63 3.40e-02 2 0.1170 -12.423 1
## 31 4elsQHzndSMtjrsbcwnZgf 0.00e+00 3 0.2520 -5.953 1
## 32 3zca0nsKOLnN28ftZEXAQn 0.00e+00 11 0.1040 -4.693 1
## 33 4WIupOFPnMTMYf3DVBbQ62 9.65e-01 1 0.1110 -38.619 1
## 34 4ak7xjvBeBOcJGWFDX9w5n 6.98e-05 9 0.4650 -5.614 1
## 35 7H5Z1do4yZfAO9AL8TAx9j 1.90e-06 1 0.3550 -8.361 0
## 36 6RtdBwIiYavQENjkE6JCba 7.94e-01 11 0.0907 -21.088 1
## 37 1pm6AoOYO81llP3gwkTAbI 1.20e-06 6 0.0958 -5.593 0
## 38 4TCc369aRPRubv1m8R1TBG 1.72e-05 3 0.5130 -6.658 0
## 39 4BHSjbYylfOH5WAGusDyni 5.34e-06 1 0.2260 -7.893 1
## 40 0PywzrV955BnXDC8FHAf0n 7.59e-06 6 0.0937 -4.202 1
## 41 5UKj2UGT4AMc1GMLk5S5sw 0.00e+00 2 0.2030 -3.189 0
## 42 5a5uR7dPCLyiwLdefyHmGQ 0.00e+00 7 0.0853 -5.616 0
## 43 3vqt0fEt6I52ANHu7mAlIP 1.49e-03 1 0.0676 -6.519 1
## 44 6guWjUuNYziyNXgjFo1IpF 6.21e-06 9 0.3950 -5.059 1
## 45 5ZLCyAR6Ti5ueOiPGl41XH 1.75e-03 0 0.0926 -5.165 1
## 46 3TciLI5wo7RddPtAFhiU9V 0.00e+00 10 0.1120 -6.183 0
## 47 4n6TxUxKF7VBXZSRLGivAC 0.00e+00 10 0.1320 -6.955 0
## 48 4xONUhRpKk1UIBsCSdviQo 0.00e+00 7 0.1730 -5.063 1
## 49 2wGSgTmgSF3xjRrHkTc25R 0.00e+00 9 0.1230 -4.781 1
## 50 37knHIXTllop4sngQ75MSC 0.00e+00 4 0.2740 -6.745 1
## 51 7dt6x5M1jzdTEt8oCbisTK 0.00e+00 10 0.1350 -5.804 1
## 52 463wBlCYhDPuOVvAU8bS1h 0.00e+00 2 0.3730 -3.654 1
## 53 26nWw7LWlId9myIxHMHXmm 0.00e+00 10 0.1920 -7.332 0
## 54 65c0WFZ8C6RWxUosvkdfzX 0.00e+00 8 0.6670 -5.102 1
## 55 4iq3zHwgHSxstFvYw4yIsQ 0.00e+00 11 0.0887 -6.242 0
## 56 04Qr1mzADw8sHGsTEtf8Pn 1.28e-04 2 0.0406 -6.805 1
## 57 0VE4kBnHJUgtMf0dy6DRmW 2.23e-06 2 0.0888 -6.745 1
## 58 0lJH7CAlpvdPgxUgGMY0ow 0.00e+00 6 0.1170 -3.663 1
## 59 7pyt9J4sp3cDgI0xkGrRiN 3.05e-01 9 0.1460 -6.771 1
## 60 3cqQX5zJVW1wv6vzhz9qzP 1.78e-06 7 0.4560 -8.562 1
## 61 3vR6ivBSe0SWntHyEzh4X1 0.00e+00 4 0.0492 -5.659 1
## 62 5q3U3cXNtPvbBZAL2bKmbO 2.88e-06 9 0.0747 -8.058 1
## 63 2UXhfe9tPeLtBmTOznG5mr 0.00e+00 0 0.0681 -9.928 1
## 64 0mGat0NgAb9PoZMc978Y8O 0.00e+00 9 0.1490 -9.517 1
## 65 4Clmg1g8n2yBYG13Z1oSdG 4.65e-01 7 0.2970 -3.506 1
## 66 0jkBzn7J5jc889TYxwvXy6 0.00e+00 6 0.6030 -6.449 1
## 67 5JTgqOeHWg4bxMZYMRTE4H 2.51e-06 4 0.1750 -4.063 0
## 68 66z5CuZkqkb18VhFTRwbZk 0.00e+00 10 0.1010 -9.922 1
## 69 3Lkm8fyUksuC8hr9I35nio 7.35e-02 6 0.2520 -5.612 1
## 70 2HYvkdq6lpFXkVz9Tfqi8r 8.76e-06 10 0.1440 -5.602 0
## 71 0247StOpd3AkeBQzANX4Zf 3.33e-05 0 0.0992 -6.936 1
## 72 11Uatd4av1y0YD2x0mQvao 0.00e+00 9 0.1170 -7.430 0
## 73 2KIxyCvBR4u1f9B2IRdlXX 2.39e-06 8 0.1640 -6.918 0
## 74 0UrheRSNBXBiZgudI7tVQ2 0.00e+00 9 0.0801 -6.284 1
## 75 55yXC8emtySeXTjyn7DsjZ 8.07e-01 9 0.0686 -13.097 0
## 76 6Ymvlzom4TQeoKqAWsZRD8 0.00e+00 11 0.6300 -5.417 1
## 77 61Qah5aEk9024AEaXCgXtN 0.00e+00 5 0.0828 -6.028 0
## 78 6VGKfZmYkkMsd2pij0jNiF 8.60e-03 5 0.0835 -4.893 0
## 79 3oJRqdtk5Xc6mX5AdfUXgx 0.00e+00 0 0.1130 -8.232 1
## 80 2aV5ZEAvHwvL332EsJ1gWc 1.53e-06 0 0.1500 -7.822 1
## 81 3bjLCKsBNSFyx6Gfsb7X4h 1.02e-06 4 0.1190 -7.687 0
## 82 2Ln5JJOBoVDIoA8MJ2vG0C 0.00e+00 0 0.0736 -9.408 1
## 83 7eVVnUD9Zcr7IlwZnCDH1E 0.00e+00 5 0.1020 -6.872 1
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## 980 1SeefzwSDiFCjRWaBslRIj 0.00e+00 4 0.1180 -3.286 1
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## 987 4qCzHeR0X0zGK0oT2AOlhW 3.69e-03 7 0.2100 -9.775 1
## 988 4GwNeCsNlcnxtss75iWnDY 7.45e-02 1 0.0726 -4.094 0
## 989 61RhvwAkvVnPLTv0nzUK0w 3.83e-03 2 0.2180 -19.718 1
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## name
## 1 Sunrise
## 2 Bluish
## 3 Mrs. Officer
## 4 Rolled Up
## 5 Old Pine
## 6 Soy Soltero
## 7 Mind Your Manners (feat. Travie McCoy & Icona Pop)
## 8 Daydreamin' (feat. Jill Scott)
## 9 A Prophecy
## 10 Brokenheartsville
## 11 I'll Never Love Again - Extended Version
## 12 Places
## 13 Beef FloMix
## 14 El Duelo (feat. Ely Guerra)
## 15 Y Tú Te Vas
## 16 This War Is Ours (The Guillotine II)
## 17 22 (OVER S∞∞N)
## 18 Fat Lip
## 19 Jumping Fences
## 20 Labios compartidos - Radio Edit
## 21 No One Compares To You
## 22 Ever Since New York
## 23 Better in Time
## 24 Hypnotize
## 25 Talk to Me
## 26 Sundream
## 27 Forever & Always
## 28 The One That Got Away
## 29 Wanderlust
## 30 I Felt Your Shape
## 31 I Can Take It From There
## 32 Macy's Day Parade
## 33 Lo-Air
## 34 Rodeo
## 35 All My Loving - From "Across The Universe" Soundtrack
## 36 Don't Go Where I Can't Find You
## 37 Tonight Looks Good on You
## 38 Got What I Got
## 39 YAH.
## 40 Jackie and Wilson
## 41 Fall In Love
## 42 On The Rest
## 43 Heavy
## 44 Houston, We Got a Problem
## 45 Sleep Alone
## 46 Don't Believe The Hype
## 47 The Clique
## 48 Gotta Make It (feat. Twista)
## 49 Hate It Or Love It
## 50 Distraction #74
## 51 Better Now
## 52 Big Casino
## 53 Accidents Happen (with Lil Tjay)
## 54 Now That We're Men
## 55 Run It
## 56 We Are Family - 2006 Remaster
## 57 Getaway Car
## 58 Here I Am To Worship
## 59 Cautioners
## 60 Jesús, Has Mi Carácter
## 61 Head Bussa (feat. Lil Jon)
## 62 The Gates
## 63 She Only Bitches When She Breathes
## 64 Mountain Music
## 65 Baila Conmigo (feat. Kelly Ruiz)
## 66 Think
## 67 Love Hurts
## 68 Hard To Choose One
## 69 Purity
## 70 Big Gangsta
## 71 Do They Know It's Christmas? - 1984 Version
## 72 Who's Crying Now
## 73 Love You More
## 74 My Life Would Suck Without You (Glee Cast Version)
## 75 Homesick
## 76 Somethin' 'Bout A Truck
## 77 Let's Roll
## 78 Sooner Or Later
## 79 I Just Don't Know What To Do With Myself
## 80 DROPPED OUTTA COLLEGE
## 81 love ride
## 82 Sleeping Single In A Double Bed - Single Version
## 83 Te Perdi
## 84 Up on Cripple Creek - Concert Version
## 85 La Barría
## 86 Shake It Out
## 87 Una Entre Un Millón
## 88 Firestone (feat. Conrad Sewell)
## 89 Years
## 90 Black Coffee in Bed
## 91 Freedom
## 92 Can You Feel It (feat. Wiz Khalifa)
## 93 We Stayed Up All Night
## 94 What More Can I Say
## 95 Sunset Lover
## 96 Bonfire
## 97 Never Saw It Coming
## 98 5 O'Clock (feat. Lily Allen & Wiz Khalifa)
## 99 The Weekend - Funk Wav Remix
## 100 Jumpsuit
## 101 Danger - Keep Away
## 102 Gang Shit No Lame Shit
## 103 Air Conditioner
## 104 I Belong in Your Arms
## 105 Stand by Me
## 106 Blow
## 107 Why Is Everything Chrome (Lean Swag Rock Wit It)
## 108 La Javanaise
## 109 Winner
## 110 You Make Me Feel... (feat. Sabi)
## 111 Eleven
## 112 Modern Crusaders
## 113 What Would You Do
## 114 Don't Sleep in the Subway
## 115 2 Stars
## 116 Yellow Hearts (feat. Audrey Mika)
## 117 Texas Song
## 118 Someone's Watching Over Me
## 119 Mi Niña Traviesa
## 120 Discipline
## 121 Good Lord Lorrie
## 122 Throes of Perdition
## 123 You Ain't Alone
## 124 Bloodstream
## 125 Another Sunny Day
## 126 Yo Voy (feat. Daddy Yankee)
## 127 The 6th Sense
## 128 Turn the Lights Off
## 129 Mango Tree
## 130 Green Mountain State
## 131 If You're Happy and You Know It
## 132 Sweet Talk
## 133 Perdóname la Vida
## 134 Innocence
## 135 PAW Patrol on a Roll
## 136 Someday
## 137 Time Is Now
## 138 Field Party (Remix) [feat. Colt Ford & JJ Lawhorn]
## 139 Kids Again
## 140 Hospital Beds
## 141 Campfire Rain
## 142 Temperature
## 143 Beethoven's 5 Secrets
## 144 Rude Mood
## 145 Round Like An 8
## 146 Bussdown (feat. Offset)
## 147 Anthem Part Two
## 148 Genie
## 149 New Divide
## 150 La Ultima Palabra
## 151 I Miss My Dawgs
## 152 Your Cover's Blown
## 153 Surf’s Up
## 154 Death Of Seasons
## 155 Hit 'em Up
## 156 Oh No!
## 157 Ya Para Qué (Para Qué)
## 158 I Don't Care (with Justin Bieber)
## 159 Haunt Me
## 160 All of Me (Tiësto's Birthday Treatment Remix) - Radio Edit
## 161 Raise Your Fist, Evangelist
## 162 El Eco de Tu Voz
## 163 Tongues
## 164 Cada Que...
## 165 Gives You Hell
## 166 Conceited (There's Something About Remy)
## 167 I Already Forgot Everything You Said
## 168 Black Cadillacs
## 169 Fever
## 170 Losses
## 171 Cool Down
## 172 Moondance
## 173 Ones And Zeros
## 174 Back Where You Belong - Edited Version
## 175 Song For No One
## 176 Sun Don't Shine
## 177 bellyache
## 178 Solo Me Imagino
## 179 Amnesia
## 180 Soul Meets Body
## 181 Entregate
## 182 Last Call for the Blues
## 183 My Edward and I (feat. Jack Liebeck)
## 184 The One That Got Away
## 185 War Pigs
## 186 A Dustland Fairytale
## 187 Fuck Love (feat. Trippie Redd)
## 188 Anyone Else But You
## 189 Mi Cucu
## 190 Berlin
## 191 Anakin's Betrayal
## 192 Vice Grip
## 193 Down In the Flood
## 194 Witchcraft - Remastered 2000
## 195 Hypnotic
## 196 Come Alive
## 197 我以為
## 198 Seasons
## 199 Real Muthaphuckkin' G's
## 200 Cielo Rojo
## 201 Door
## 202 Mi Amante
## 203 Photograph
## 204 Rich Kids - [Middle Cla$$ MIX]
## 205 ...Y al final
## 206 Swing 42
## 207 Pure Imagination
## 208 Out of My League
## 209 Spectrum
## 210 Public Service Announcement (Interlude)
## 211 I Got A Feelin'
## 212 A Guy with a Girl
## 213 I Wanna Know You
## 214 What's Golden
## 215 Soulja Girl
## 216 Mess
## 217 Bust Me
## 218 Spirit Bird
## 219 Fireflies
## 220 Hanging By A Moment
## 221 Left Behind
## 222 The Influence
## 223 On Top Of Me
## 224 Wonderland
## 225 Love Will Find A Way - From "Simba's Pride"
## 226 Windowpane
## 227 Right Now
## 228 Hollow Life
## 229 O Death
## 230 Idfc
## 231 Naked
## 232 Success
## 233 Bees
## 234 Fly With Me
## 235 Wild Child
## 236 Inspire The Liars
## 237 Resurrection Fern
## 238 Vagabond
## 239 Free Fallin' - Live at the Nokia Theatre, Los Angeles, CA - December 2007
## 240 A Walk in the Woods
## 241 Me Gusta
## 242 A.M.
## 243 Home
## 244 I Am Not A Human Being
## 245 Country Folks (feat. Colt Ford & Danny Boone)
## 246 Get You High
## 247 Dare You To Move
## 248 Tu Eres Mi Respirar
## 249 Waking the Demon
## 250 Air Force Ones
## 251 Morning Light
## 252 Señorita
## 253 First
## 254 Like You (feat. Ciara)
## 255 Let Me Know (feat. Future)
## 256 Love You More
## 257 Tenemos Que Hablar
## 258 GUESS WHAT (feat. Rick Ross)
## 259 Build A Bridge
## 260 American Ride
## 261 Before I Let Go - Homecoming Live Bonus Track
## 262 A Rush of Blood to the Head
## 263 Giuseppe Tornatore Suite: Playing Love from "The Legend of 1900"
## 264 So High
## 265 Faded
## 266 Bachata Rosa
## 267 Fields Of Gold
## 268 Reflection - Mulan
## 269 The Day That Never Comes
## 270 The Moon Is Down
## 271 Morning
## 272 Genie
## 273 This I Gotta See
## 274 Starshine
## 275 Strip For You
## 276 POD
## 277 Foreigner (feat. A Boogie Wit da Hoodie)
## 278 Better Is One Day
## 279 This Too Shall Pass
## 280 Say Round (feat. Lil Boosie, Big Head, Webbie & Foxx)
## 281 Come Home
## 282 The Only Name (Yours Will Be)
## 283 That Home
## 284 Good To Go To Mexico
## 285 Yesterday Don't Mean Shit
## 286 What Is This Feeling? - From "Wicked" Original Broadway Cast Recording/2003
## 287 Til The Last Shot's Fired
## 288 Stop, Look, Listen (To Your Heart)
## 289 Air Catcher
## 290 I Understand
## 291 What I Was Born To Do
## 292 2 Besos (Un Beso Donde Ella Quiera)
## 293 Imagine
## 294 Make This Go On Forever
## 295 Country Boy's Paradise
## 296 Lasso
## 297 Leaving Jesusland
## 298 The Longest Wave
## 299 (Sittin' On) the Dock of the Bay
## 300 I Found My Smile Again (Radio Edit)
## 301 Edge
## 302 WAM
## 303 90210
## 304 The Shire
## 305 Boy 1904
## 306 money machine
## 307 Ponte Pa' Mi
## 308 I Don't Wanna Be Funny Anymore
## 309 Until I Die
## 310 Furthest Thing
## 311 Quiet
## 312 Qué Chimba
## 313 Notion
## 314 Do You Remember
## 315 Location
## 316 Vato Sencillo
## 317 Dark Horse
## 318 Universally Speaking
## 319 Eye 2 Eye
## 320 I'M ON SOME
## 321 Love Is In The Air
## 322 Sad Forever
## 323 BILLY
## 324 South of the Border (feat. Camila Cabello & Cardi B)
## 325 Nightvision
## 326 Dearest
## 327 Debut Y Despedida
## 328 I'm a Slave 4 U
## 329 Nobody's Fool
## 330 Te Va a Doler
## 331 Tamarindo
## 332 Handbags & Gladrags
## 333 Hallelujah
## 334 Day Tripper - Remastered 2015
## 335 Greatest Love Story
## 336 BEBE
## 337 Hollywood Waltz - 2013 Remaster
## 338 The Rat
## 339 Dawn of Time
## 340 Alps
## 341 Amor Sin Maquillaje
## 342 Hot Venom
## 343 Every Other Freckle
## 344 One Hell Of An Amen
## 345 Wolfpack
## 346 Zapateado Encabronado #2
## 347 Last Nite
## 348 Say It Right
## 349 Faygo Dreams
## 350 Soothing Ocean Waves
## 351 Take Me to the King (feat. Kirk Franklin)
## 352 The Grid
## 353 The Tide
## 354 You Call Me a Bitch Like It's a Bad Thing
## 355 Me Cambiaste la Vida
## 356 Gorgeous
## 357 Villuminati
## 358 Wake Me Up - Radio Edit
## 359 All About Tonight
## 360 So Good to Me - Radio Edit
## 361 Beautiful
## 362 Main Yahaan Hoon
## 363 Stella
## 364 Amarillo Sky
## 365 True Colors
## 366 Blue Notes
## 367 I'm So Into You
## 368 Me Voy - Bachata
## 369 Can't Take It With You
## 370 Papaoutai
## 371 If I Die Young (Glee Cast Version)
## 372 Maria Elena
## 373 Azúcar
## 374 Strangers
## 375 DontGetIt
## 376 Tear in My Heart
## 377 Red Bentley (feat. Young Thug)
## 378 Big Booty (feat. Megan Thee Stallion)
## 379 Tupelo Honey
## 380 Gone for Good
## 381 Harry and Ginny
## 382 Da Baddest B***h
## 383 Oh My Soul
## 384 Drinkin' Problem (feat. Denny aka "Steaknife")
## 385 Rodeo
## 386 Laneswitch
## 387 A Esa
## 388 Peer Pressure (feat. Kevin Gates)
## 389 Bad Day
## 390 El Trokero Lokochon
## 391 Underneath It All
## 392 Virgen
## 393 Hasta En El Aire
## 394 Buffalo Soldier
## 395 Miss Alissa
## 396 If I Ain't Got You - Piano & Vocal Version
## 397 Niki Fm
## 398 California Waiting
## 399 Endless Nightmare
## 400 Forget That (feat. Rylo Rodriguez)
## 401 Perdón (with Alejandro Fernández) - Remasterizado
## 402 She Get Me High
## 403 The Best
## 404 奏(かなで)
## 405 I Wish You Would
## 406 Over the Rainbow
## 407 Morir De Amor
## 408 We Ready
## 409 Tortured, Tangled Hearts
## 410 Angel
## 411 Recuérdame (with Marc Anthony)
## 412 Mermaid
## 413 Oochie Wally - Remix
## 414 Hundreds of Stories
## 415 Locos Desde Ayer
## 416 Teardrops on My Guitar - Pop Version
## 417 Open Your Eyes
## 418 Whitehouse Road
## 419 The Way I Am
## 420 Opr
## 421 Llamé Pa' Verte (Bailando Sexy)
## 422 La Bala
## 423 Same Drugs
## 424 We Found Love
## 425 Heaven
## 426 Stfu
## 427 High
## 428 No One
## 429 Ces petits riens
## 430 New Patek
## 431 No Te Pido Mucho
## 432 I Stay In Love
## 433 藉口
## 434 Sleepless Nights (feat. Nightly)
## 435 Romeo & Juliet
## 436 Star
## 437 watch
## 438 Gunslinger
## 439 Heavy Metal Drummer
## 440 Big Empty
## 441 Sleep Sound In Jesus
## 442 What About Everything?
## 443 Great Vacation
## 444 I Could Kick Your Ass
## 445 Erase My Scars
## 446 I Need U
## 447 Gangsta Rap Made Me Do It
## 448 I Got A Thang 4 Ya!
## 449 Houses of the Holy - 2007 Remaster
## 450 Coming Back Around
## 451 Feels So Good
## 452 Black Horse And The Cherry Tree
## 453 Turn Me On "Mr. Deadman"
## 454 'Till I Collapse
## 455 Class Fight
## 456 One Of A Kind
## 457 Rockin' the Suburbs
## 458 Simple
## 459 Us
## 460 Champagne Supernova
## 461 Don't Kill My High
## 462 You're The Reason (feat. Victoria Justice)
## 463 Meet My Maker
## 464 Ring of Fire (feat. Avi Kaplan)
## 465 Alone (Feat. Big Sean & Stefflon Don)
## 466 My Plague
## 467 King Of The Dancehall
## 468 Better Than Drugs
## 469 Si Senor
## 470 Nadie Te Amará Como Yo (Remix) (feat. Zion & Arcángel)
## 471 Paris
## 472 會呼吸的痛
## 473 We R Who We R
## 474 Forgotten
## 475 You Could Be Happy
## 476 De Mí Enamórate
## 477 Behind Barz - Bonus
## 478 Lovin', Touchin', Squeezin'
## 479 Hablemos
## 480 How Would You Feel
## 481 Why Do You Feel So Down
## 482 Treinta Cartas
## 483 Idfc
## 484 Stay
## 485 Roundtable Rival
## 486 Y Llegaste Tú
## 487 Rite Of Spring
## 488 Real Feel
## 489 No Faith in Brooklyn (feat. Jhameel)
## 490 El Polvorete
## 491 Big Money
## 492 Portal to an Empty Head
## 493 Toddlers
## 494 Must Be Dreaming
## 495 You
## 496 Change The Game
## 497 Heart Attack
## 498 Sandstorm
## 499 Acid Tongue
## 500 No More Dream
## 501 Act A Fool
## 502 Leaving Hogwarts
## 503 4 Seasons
## 504 Loose Lips
## 505 Jumpdafuckup
## 506 INTRO
## 507 Dancing With Your Ghost
## 508 The Weight - Remastered
## 509 Being a Mother
## 510 Gentle Waltz
## 511 Rich Woman
## 512 Take Yours, I'll Take Mine
## 513 Para Estar
## 514 Two Matches (feat. Ab-Soul)
## 515 5 English Nursery Tunes: IV. Curly Locks: Andantino
## 516 Bedshaped
## 517 Home Body (feat. Teyana Taylor & Melii) - Remix
## 518 Going Through Some Thangs
## 519 Everyday
## 520 Let It Go - James Bay Spotify Session 2015
## 521 You're Welcome - Jordan Fisher/Lin-Manuel Miranda Version
## 522 Dance With the Devil
## 523 Five Colours In Her Hair
## 524 Rap Saved Me
## 525 If Only You Knew
## 526 Losin Control
## 527 I'm the Man Who Loves You
## 528 What About Now
## 529 I’m So Sorry
## 530 Don't Stay
## 531 Follow Me
## 532 My Curse
## 533 Where You Are (Live)
## 534 Good Kisser
## 535 Water
## 536 50 Ways to Leave Your Lover
## 537 Bad Day
## 538 Dime Quién Es
## 539 molly
## 540 Old Money
## 541 Cough Syrup (Glee Cast Version)
## 542 Ophelia
## 543 Fly Like an Eagle
## 544 When I Grow Up
## 545 Dream 1 (before the wind blows it all away) - Pt. 1
## 546 Do Not Disturb
## 547 In Regards To Myself
## 548 Tepid Rainscape
## 549 She Builds Quick Machines
## 550 Nothing On But The Radio
## 551 Brother
## 552 Insensitive
## 553 My World
## 554 Write Me A Letter
## 555 Concubine
## 556 Dip Dip
## 557 Hot In Herre
## 558 Sweet Emotion
## 559 Pink Noise for Sleeping
## 560 Bubblegum Bitch
## 561 Gold
## 562 Studying Politics
## 563 90's Country
## 564 Statues
## 565 La Nueva Y La Ex
## 566 A Little Less Conversation
## 567 Blessed Assurance
## 568 White Gloves
## 569 La flaca
## 570 Soffia la notte
## 571 Beg For Mercy
## 572 I Cross My Heart - Pure Country Soundtrack Version
## 573 Beautiful People Beautiful Problems (feat. Stevie Nicks)
## 574 As Good As I Once Was
## 575 Bank Roll
## 576 I Think He Knows
## 577 The Mountain
## 578 The Bird Hunters
## 579 Lost in the Woods - Weezer Version
## 580 All You Had To Do Was Stay
## 581 El Amo
## 582 The King Is Coming
## 583 Forgiven
## 584 Flor Hermosa (Eres Flor, Eres Hermosa)
## 585 Fuck
## 586 MANN GEGEN MANN
## 587 Difference Maker
## 588 Pa' Olvidarme De Ella
## 589 How
## 590 Answer
## 591 I Forget Where We Were
## 592 Pluto Projector
## 593 L'enfant sauvage
## 594 インフェルノ
## 595 Isle Unto Thyself
## 596 All The Way Up
## 597 Big Amount
## 598 Engine 45
## 599 Make You Feel
## 600 One More Night
## 601 Superstar
## 602 San Andreas Theme Song
## 603 Space Age Love Song
## 604 Be With You
## 605 The Railroad
## 606 Piénsalo - Inédita
## 607 Remember You Young
## 608 Heartache Tonight - 2013 Remaster
## 609 Interlude
## 610 Madness
## 611 Tabla Breath
## 612 These Days
## 613 Jungle (Radio Edit)
## 614 All Time Low
## 615 WTF (Where They From) [feat. Pharrell Williams]
## 616 Talk Dirty To Me
## 617 I'm with You
## 618 This Life
## 619 Soy Peor - Remix
## 620 Somewhere Only We Know
## 621 Fool
## 622 Costa Rica
## 623 Soldiers Of The Wastelands
## 624 This Is Me
## 625 Awakening
## 626 Dream Catch Me
## 627 Killer (feat. Drake)
## 628 Concealed the Outro
## 629 Life is a Highway - From "Cars"
## 630 Look For The Good - Single Version
## 631 Opus 23
## 632 Take Back the Night
## 633 Why Ya Wanna
## 634 Bryn
## 635 Increíble
## 636 just friends
## 637 Everybody
## 638 Yo Te Extrañare
## 639 I'LL BE GONE
## 640 Plowed
## 641 Emotionally Scarred
## 642 Storm
## 643 The End Of The Line
## 644 Lies
## 645 Ophelia
## 646 When I Look to the Sky
## 647 White
## 648 Wild as You
## 649 Intoxication
## 650 2 birds
## 651 Maybe I'm Sorry
## 652 Trust Issues
## 653 Incredible God, Incredible Praise
## 654 F.F.F. (feat. G-Eazy)
## 655 Lava Lamp
## 656 December
## 657 Peach Pit
## 658 Overwhelming
## 659 Red Rubber Ball
## 660 Palomita De Alas Blancas
## 661 Vuelve Conmigo
## 662 Stand Here With Me
## 663 Circles
## 664 Cave Man
## 665 Lights, Camera, Action!
## 666 Love in the First Degree
## 667 Chills
## 668 My Savior My God
## 669 Resolution
## 670 Dear Younger Me
## 671 In For The Kill - Skrillex Remix
## 672 Rock with You - Single Version
## 673 What a Horrible Night to Have a Curse
## 674 La Estoy Pasando Mal
## 675 Te Amo Tanto
## 676 Everything I Ask For
## 677 Rico Story
## 678 Fly Me To The Moon - Remastered
## 679 Go Crazy
## 680 Haunt U
## 681 Bye Bye
## 682 Sweeter (feat. Terrace Martin)
## 683 Let The Music Play
## 684 Winds From the West
## 685 El Tapete Y La Rubí
## 686 (If You're Wondering If I Want You To) I Want You To
## 687 Sh!t
## 688 I'll Let You Know
## 689 Because You Move Me
## 690 Dig
## 691 Sun Doesn't Rise
## 692 Everybody's Watching Me (Uh Oh)
## 693 Many the Miles
## 694 Brillas
## 695 Desert Rose
## 696 Kissin' On My Tattoos
## 697 Little Wonders
## 698 A World Alone
## 699 The Way You Used to Do
## 700 Rain Sound : Sunday Dreaming
## 701 Reiki Healing Waves
## 702 Ankomst
## 703 Forever
## 704 Helicopter
## 705 Theme From "The Endless Summer"
## 706 Mi 45
## 707 Green Grass of Tunnel
## 708 Rev 22-20
## 709 Year 3000
## 710 The Great Pretender
## 711 A Bid Farewell
## 712 We Don't Have To Dance
## 713 please
## 714 El Tarasco
## 715 Pa' Que La Pases Bien
## 716 Celos
## 717 U With Me?
## 718 Tchaikovsky: The Nutcracker, Op. 71, Act 2: No. 13 Waltz of the Flowers
## 719 Ladies Night
## 720 Disparate Youth
## 721 Brackish
## 722 Dionysus
## 723 The Man
## 724 HUMUHUMUNUKUNUKUAPUA'A
## 725 Tomorrow Never Came (feat. Sean Ono Lennon)
## 726 El Señor de los Cielos
## 727 Problems
## 728 On Sight
## 729 Infiltrate
## 730 Blow Job - Live/1999
## 731 Bout My Business (feat. Sherhonda Gaulden)
## 732 Complicated
## 733 Don't Tread On Me
## 734 El Perdedor
## 735 Out Of My Limit
## 736 Put You In A Song
## 737 Surfin'
## 738 Flesh Into Gear
## 739 I Wanna Know
## 740 Here Comes Goodbye - AC Mix
## 741 Gin and Juice
## 742 Cold Beer Conversation
## 743 I Got A Name
## 744 Talking Backwards
## 745 Trigger
## 746 Back Around
## 747 Préstamela a Mí
## 748 American You
## 749 Seeming Like It
## 750 Bad Day
## 751 The Game
## 752 Prickly Pear
## 753 Two People
## 754 Hollow
## 755 Lost
## 756 Don't Cha - Radio Edit
## 757 London Bridge
## 758 ilomilo
## 759 Cover Me Up
## 760 Fever To The Form
## 761 Hard Hat And A Hammer
## 762 No Shade of Green
## 763 True Colors - Film Version
## 764 Move All Night
## 765 Ice Cream Paint Job
## 766 Bottle Service
## 767 Get Ready - Single Version
## 768 What Can I Do
## 769 Little Bitty Pretty One
## 770 Too Easy
## 771 Lay me down
## 772 Pussy Is God
## 773 The Heaviest Matter Of The Universe
## 774 Your Disease
## 775 Seize the Day
## 776 Believe
## 777 Daft Pretty Boys
## 778 Black Treacle
## 779 Marceline
## 780 Bleed Black
## 781 Let Love In
## 782 Cherry Lips (Go Baby Go)
## 783 Hurts Like Hell (feat. Offset)
## 784 Perfect
## 785 React (feat. Redman)
## 786 Thanks, Bastards!
## 787 The Life - Album Version Edit (Explicit)
## 788 Your Love Is King - Remastered
## 789 Rot
## 790 Flash Delirium
## 791 You Be Killin Em
## 792 Relaxing Constant Rain Storm with Distant Thunder Sfx
## 793 Love Me Harder
## 794 Amber
## 795 Hummel: Trumpet Concerto In E Flat - Iii Rondo
## 796 Nocturne en mi bémol majeur opus 9 n°2: Ballade en Sol Mineur No.1
## 797 Gotta Tell You
## 798 Roses (feat. ROZES)
## 799 Saintlike
## 800 Que Me Quedes Tu
## 801 Lights
## 802 wokeuplikethis*
## 803 I Gotta Feeling
## 804 The Motto
## 805 Take It Or Leave It
## 806 Si Te Llego A Perder
## 807 Atomic Dog
## 808 Wanted Dread and Alive - 2002 Remaster
## 809 Dirty Laundry
## 810 Rock Salt and Nails (Live)
## 811 Fergalicious - Album Version (Edited)
## 812 When Someone Stops Loving You
## 813 The Best Day
## 814 Emancipation
## 815 Need U Bad
## 816 Dandole Remix
## 817 Above The Law
## 818 It's Quiet Uptown
## 819 Someone To Love You
## 820 When You're Falling
## 821 Ya Se Que Te Acordaras...(De Mi)
## 822 Damn Good Friends (with Jason Aldean) - Duet with Jason Aldean
## 823 No Tuve Amor
## 824 I Wanna Know
## 825 One Cup Of Coffee / Judge Not - Rarities Version
## 826 Mellow Mood - feat. G. Love
## 827 Stairway to Heaven - 2007 Remaster
## 828 Sunday Morning Coming Down - Live
## 829 Can't Buy Me Love - Remastered 2015
## 830 Pledge Allegiance To The Hag
## 831 Wishing (feat. Chris Brown, Skeme & Lyquin)
## 832 BOOM
## 833 Coronao Now (Remix)
## 834 My Old Man
## 835 Work Hard, Play Hard
## 836 The Game of Love (feat. Michelle Branch) - Main / Radio Mix
## 837 Beauty Beats
## 838 Meet Me in the Bathroom
## 839 Acid Raindrops
## 840 The Big Rock Candy Mountain
## 841 24/7=Heaven
## 842 That's How You Know (feat. Kid Ink & Bebe Rexha)
## 843 Mami Mira
## 844 Pillz
## 845 Cool Kids
## 846 controlla
## 847 A Thousand Miles Away
## 848 sex money feelings die
## 849 Get Your Mind Right Mami
## 850 Hoy Tengo Miedo
## 851 One Kiss (with Dua Lipa)
## 852 Soft Rain Pouring
## 853 Talk Of The Town
## 854 How You Love Me Now
## 855 Straight into Your Arms - Bonus Track Version
## 856 Yakety Yak
## 857 Brother
## 858 6 months
## 859 Cry (Just a Little)
## 860 Infinity - Klaas Vocal Mix
## 861 My Kinda Country
## 862 Unwritten
## 863 Lucky
## 864 Stella Was a Diver and She Was Always Down
## 865 Your Obedient Servant
## 866 Islands in the Stream
## 867 Mauja Hi Mauja
## 868 Knock Yourself Out
## 869 Just Want You to Know
## 870 Homesick at Space Camp
## 871 DADDY
## 872 Radio
## 873 Bright Lights Bigger City
## 874 Bendita Tu Luz
## 875 Babushka Boi
## 876 Brown Eyed Girl
## 877 Lately
## 878 Oceans (Where Feet May Fail) - Radio Version
## 879 A Little Less Sixteen Candles, A Little More "Touch Me"
## 880 Somewhere Over The Rainbow
## 881 Por Tu Culpa
## 882 Submersible
## 883 Te Extraño Tanto
## 884 Touch It
## 885 Jesus We Love You (Live)
## 886 Your Love
## 887 I Made It (Cash Money Heroes)
## 888 The Golden Age Of Grotesque
## 889 Calm Rain pt. 1
## 890 El Perro, el Gato y Yo
## 891 Get Together
## 892 Schism
## 893 Never Been - Remix
## 894 No Buses
## 895 All About That Bass
## 896 I'm Fine
## 897 El Chapo Jr
## 898 Pray You Catch Me
## 899 Re:member
## 900 If Only
## 901 Weekend Warriors
## 902 One Short Day - From "Wicked" Original Broadway Cast Recording/2003
## 903 hostage
## 904 One Day Too Late
## 905 Into the Mirror
## 906 Family Business
## 907 Hooked on a Feeling
## 908 It's All on U - T-Mass & LZRD Remix
## 909 Torete
## 910 Irresistible
## 911 Why Me?
## 912 Courtesy Of The Red, White And Blue (The Angry American)
## 913 Mean It
## 914 Str8 Outta Mumbai
## 915 Honeybee
## 916 El Amigo Que Se Fué
## 917 Killin' the Vibe
## 918 Sloe Gin
## 919 The Difference
## 920 Right Back At It Again
## 921 Orestes
## 922 Under Cover of Darkness
## 923 The Light That Blinds
## 924 Overcome
## 925 My World
## 926 Littlest Things
## 927 Sympathique
## 928 My Mom
## 929 Let It Bleed
## 930 Kiss and Make Up
## 931 This Ole Boy
## 932 I Feel The Love
## 933 Moneygrabber
## 934 Lady, Lady - Remastered
## 935 Changes
## 936 Y Te Lo Pido
## 937 My Whole Family... (live)
## 938 Crazy In Love - Remix
## 939 One More Time - Radio Edit [Short Radio Edit]
## 940 Marry The Night
## 941 Love Hurts (feat. Travis Scott)
## 942 Disengage
## 943 Cayendo (Side A - Acoustic)
## 944 Shine
## 945 Sea Groove
## 946 Halo
## 947 Lost in the Fire (feat. The Weeknd)
## 948 Popular Song
## 949 Lady - Hear Me Tonight
## 950 Nightcall
## 951 50 Ways To Leave Your Lover
## 952 Stone
## 953 November 18th
## 954 Crazy in Love
## 955 Rock a Bye Baby
## 956 Uhn Tiss Uhn Tiss Uhn Tiss
## 957 Four Kicks
## 958 Noche de Desmadre
## 959 Like Glue
## 960 Broke Record
## 961 Money 2 Burn
## 962 Nandemonaiya - movie ver.
## 963 A GHETTO CHRISTMAS CAROL
## 964 La Carretera
## 965 Y Ahora Resulta
## 966 Universally Speaking
## 967 On the Road Again - Live
## 968 Be My Baby
## 969 Dissolve Me
## 970 Cartas Marcadas
## 971 The Season | Carry Me
## 972 Famous Last Words Of A Fool - Edit
## 973 Big Girls Don't Cry (Personal)
## 974 Crushed
## 975 1,000 Years
## 976 Spring Rain
## 977 The HampsterDance Song
## 978 Wicked
## 979 You Don't Know Her Like I Do
## 980 Something's Gotta Give
## 981 Better Together
## 982 Long Time
## 983 The Business (featuring Casha)
## 984 Time Keeps on Slipping
## 985 We Are Never Ever Getting Back Together
## 986 400 Lux
## 987 Righteously
## 988 The Hounds
## 989 Sinfonia in D Major, BWV 1045: III. Allegro
## 990 泡沫
## 991 Pretty Dirty (In the Fading Light)
## 992 Anything Could Happen
## 993 Jah Army
## 994 Fresh Vegetable
## 995 Lil Boat
## 996 The Little Things (feat. Angela McCluskey) - Kasbo Remix
## 997 RITMO (Bad Boys For Life)
## 998 Home
## 999 Evisceration Plague
## 1000 Holy Ghost
## popularity release_date speechiness tempo valence year
## 1 60 2020-04-17 0.1750 125.049 0.59100 2020
## 2 42 2009-01-20 0.1120 88.537 0.45200 2009
## 3 50 2008-01-01 0.1730 111.982 0.94400 2008
## 4 42 2001-01-01 0.0847 97.126 0.61900 2001
## 5 69 2011-01-01 0.0330 129.570 0.22400 2011
## 6 62 2011-08-03 0.1410 139.438 0.89100 2011
## 7 44 2012-06-19 0.1990 92.968 0.69500 2012
## 8 59 2006-06-27 0.0602 81.370 0.61700 2006
## 9 56 2009-09-15 0.1710 149.992 0.05010 2009
## 10 58 2002-01-01 0.0232 103.953 0.50900 2002
## 11 70 2018-10-05 0.0293 109.246 0.20200 2018
## 12 54 2011-08-09 0.1200 85.228 0.23100 2011
## 13 71 2019-06-13 0.2210 116.993 0.90100 2019
## 14 64 2001-08-28 0.0305 112.532 0.17000 2001
## 15 62 2002 0.0297 139.927 0.52500 2002
## 16 58 2010-04-27 0.1630 95.498 0.15200 2010
## 17 57 2016-09-30 0.0353 86.786 0.09720 2016
## 18 72 2001-01-01 0.1420 196.505 0.62600 2001
## 19 44 2004-02-03 0.0385 128.132 0.68800 2004
## 20 52 2006-07-10 0.0337 161.999 0.65800 2006
## 21 77 2019-01-25 0.0434 111.939 0.63100 2019
## 22 68 2017-05-12 0.0309 127.964 0.38300 2017
## 23 69 2007 0.0506 163.953 0.54900 2007
## 24 44 2002-11-17 0.1140 78.494 0.25200 2002
## 25 44 2007-03-27 0.0266 103.070 0.63600 2007
## 26 59 2013 0.0383 121.966 0.24700 2013
## 27 52 2008-11-11 0.0576 128.042 0.54400 2008
## 28 62 2016-11-13 0.0421 63.473 0.04670 2016
## 29 53 2013-01-01 0.0435 115.924 0.07250 2013
## 30 44 2001-09-11 0.0693 114.021 0.54600 2001
## 31 51 2011-07-11 0.0318 113.939 0.87100 2011
## 32 54 2000-10-03 0.0327 108.958 0.87400 2000
## 33 67 2019-04-26 0.0728 137.110 0.06780 2019
## 34 76 2019-06-21 0.0324 140.081 0.65700 2019
## 35 45 2007-01-01 0.0806 116.485 0.43500 2007
## 36 57 2011-11-21 0.0379 74.773 0.03540 2011
## 37 57 2014-10-07 0.0537 169.958 0.52300 2014
## 38 76 2019-11-22 0.0290 159.847 0.30300 2019
## 39 65 2017-04-14 0.1960 69.986 0.64800 2017
## 40 58 2014-10-07 0.0360 82.311 0.74500 2014
## 41 48 2013-01-01 0.1060 188.049 0.26500 2013
## 42 61 2020-04-24 0.3500 161.904 0.57500 2020
## 43 54 2006-02-07 0.0368 106.929 0.61800 2006
## 44 70 2018-06-01 0.0362 143.868 0.48600 2018
## 45 55 2012-08-31 0.0432 148.063 0.52200 2012
## 46 73 2020-05-15 0.2900 160.059 0.41800 2020
## 47 59 2017-12-12 0.3450 158.509 0.51700 2017
## 48 45 2005-07-26 0.0688 140.007 0.80800 2005
## 49 72 2005 0.2070 99.998 0.43500 2005
## 50 42 2006 0.0895 115.272 0.88300 2006
## 51 85 2018-04-27 0.0400 145.038 0.34100 2018
## 52 45 2007-01-01 0.0493 136.007 0.52400 2007
## 53 65 2020-04-10 0.2270 144.948 0.18300 2020
## 54 42 2004-11-09 0.4490 64.594 0.30200 2004
## 55 56 2015-11-13 0.2320 108.972 0.63600 2015
## 56 41 2007-10-30 0.0715 116.006 0.84200 2007
## 57 69 2017-11-10 0.1270 172.054 0.35100 2017
## 58 40 2004-01-01 0.0252 77.034 0.28300 2004
## 59 38 2001-07-17 0.0283 114.189 0.39100 2001
## 60 45 2003-01-14 0.0314 105.024 0.18400 2003
## 61 45 2003-09-02 0.0890 78.020 0.61100 2003
## 62 45 2002 0.2730 127.834 0.93800 2002
## 63 44 2011-09-07 0.0383 101.293 0.53200 2011
## 64 49 2005-05-17 0.0437 109.530 0.74700 2005
## 65 82 2018-12-28 0.0774 128.031 0.55600 2018
## 66 58 2007-10-29 0.0442 110.102 0.90500 2007
## 67 57 2006-11-28 0.0266 78.102 0.40900 2006
## 68 72 2020-05-15 0.2550 119.013 0.25400 2020
## 69 55 2009-09-09 0.1800 166.178 0.39900 2009
## 70 60 2019-05-31 0.3910 144.911 0.34600 2019
## 71 48 2004-01-01 0.0363 115.412 0.34900 2004
## 72 40 2001-09-19 0.0286 122.041 0.40300 2001
## 73 44 2003-04-08 0.0282 117.007 0.42800 2003
## 74 52 2009-12-08 0.0348 145.078 0.44900 2009
## 75 44 2006-10-10 0.0318 140.037 0.19700 2006
## 76 70 2012-01-01 0.0350 176.010 0.79000 2012
## 77 55 2011-01-01 0.0318 142.995 0.20400 2011
## 78 52 2004-01-01 0.0482 92.827 0.41600 2004
## 79 48 2003-04-01 0.1000 94.677 0.35900 2003
## 80 66 2019-11-22 0.4110 120.044 0.19000 2019
## 81 65 2017-11-16 0.0784 82.270 0.48400 2017
## 82 36 2001-01-01 0.0311 139.375 0.97400 2001
## 83 37 2000 0.0367 134.970 0.81600 2000
## 84 40 2002-12-16 0.0790 132.922 0.85500 2002
## 85 44 2005-01-01 0.2050 91.962 0.78200 2005
## 86 50 2009-04-21 0.0704 148.298 0.26200 2009
## 87 57 2013-01-01 0.0310 136.079 0.91500 2013
## 88 77 2016-05-13 0.0520 113.055 0.39100 2016
## 89 48 2013-01-01 0.0804 128.004 0.32300 2013
## 90 47 2010-08-03 0.0283 108.530 0.68400 2010
## 91 71 2020-05-29 0.1160 111.329 0.38100 2020
## 92 47 2011-07-08 0.0478 161.894 0.86400 2011
## 93 60 2017-06-28 0.0431 84.997 0.59200 2017
## 94 55 2016-10-21 0.1470 74.885 0.58400 2016
## 95 77 2015 0.0503 90.838 0.23600 2015
## 96 52 2008 0.1030 175.716 0.50600 2008
## 97 48 2010-05-11 0.0261 109.988 0.39100 2010
## 98 52 2011-12-02 0.2200 83.850 0.35200 2011
## 99 78 2017-12-15 0.0585 101.925 0.66700 2017
## 100 72 2018-10-05 0.0373 127.052 0.23500 2018
## 101 47 2004 0.0282 69.081 0.04240 2004
## 102 59 2018-11-23 0.3010 115.071 0.38000 2018
## 103 50 2014-09-05 0.0976 81.254 0.01580 2014
## 104 47 2012-01-24 0.0444 172.564 0.76200 2012
## 105 38 2008-10-31 0.0280 107.453 0.77900 2008
## 106 64 2010-11-19 0.0392 120.013 0.81200 2010
## 107 68 2020-04-30 0.4750 139.940 0.67500 2020
## 108 39 2006-09-12 0.0342 107.799 0.16600 2006
## 109 45 2005 0.2010 150.103 0.59400 2005
## 110 68 2011-08-29 0.0535 131.959 0.74800 2011
## 111 77 2020-01-09 0.1190 129.004 0.11500 2020
## 112 44 2001-01-01 0.0704 114.988 0.32700 2001
## 113 55 2013-01-01 0.0417 89.994 0.36000 2013
## 114 44 2003-04-11 0.0366 133.685 0.42700 2003
## 115 45 2008-01-01 0.0515 120.006 0.94000 2008
## 116 67 2020-01-31 0.2200 129.041 0.67800 2020
## 117 38 2001-09-09 0.0498 122.718 0.43900 2001
## 118 50 2004-09-29 0.0344 152.010 0.47400 2004
## 119 56 2013-09-17 0.0285 138.571 0.59600 2013
## 120 50 2008-06-12 0.0275 122.010 0.82400 2008
## 121 61 2012-05-08 0.0284 78.994 0.58100 2012
## 122 46 2008-09-24 0.0980 125.044 0.25500 2008
## 123 43 2012-04-10 0.0343 118.440 0.34300 2012
## 124 67 2014-06-21 0.0364 91.207 0.54300 2014
## 125 46 2006-02-07 0.0419 156.108 0.83800 2006
## 126 67 2004-05-04 0.2410 95.060 0.55400 2004
## 127 44 2000-01-01 0.2750 94.444 0.89900 2000
## 128 62 2011-06-21 0.0848 188.606 0.77100 2011
## 129 48 2007-09-08 0.0305 107.747 0.28500 2007
## 130 64 2014-06-17 0.0323 129.879 0.28600 2014
## 131 51 2013-05-21 0.0438 129.989 0.96600 2013
## 132 50 2007-01-01 0.0509 179.844 0.38800 2007
## 133 53 2010 0.0334 125.006 0.62800 2010
## 134 43 2009-02-25 0.0904 167.964 0.67000 2009
## 135 59 2013 0.0751 200.107 0.63600 2013
## 136 54 2009-06-22 0.0343 135.943 0.68300 2009
## 137 59 2006-07-17 0.0878 127.988 0.73500 2006
## 138 54 2013-08-20 0.1150 150.063 0.69800 2013
## 139 56 2014-12-16 0.0277 113.029 0.55600 2014
## 140 44 2007-05-11 0.0292 95.835 0.13000 2007
## 141 61 2017-05-05 0.0487 78.961 0.00001 2017
## 142 77 2005-09-26 0.0685 125.040 0.82200 2005
## 143 58 2013-01-18 0.0414 150.285 0.11100 2013
## 144 42 2006 0.0506 131.096 0.76800 2006
## 145 43 2007-06-12 0.0791 120.052 0.59800 2007
## 146 66 2019-08-09 0.4820 121.082 0.87300 2019
## 147 62 2001 0.0426 103.108 0.51800 2001
## 148 62 2018-06-08 0.4520 159.517 0.51300 2018
## 149 69 2009-06-12 0.0362 117.971 0.38000 2009
## 150 54 2005-05-06 0.0318 119.800 0.21600 2005
## 151 44 2004-01-01 0.2870 78.031 0.62800 2004
## 152 44 2004 0.0667 109.111 0.39400 2004
## 153 51 2013-01-01 0.1210 103.008 0.50000 2013
## 154 42 2003 0.0632 159.076 0.07980 2003
## 155 60 2003-10-07 0.3260 95.155 0.80900 2003
## 156 58 2010-02-15 0.0382 127.051 0.96300 2010
## 157 44 2004-01-01 0.0743 134.423 0.88300 2004
## 158 85 2019-05-10 0.0442 101.956 0.84200 2019
## 159 58 2017-05-16 0.2760 85.016 0.83600 2017
## 160 63 2014-02-25 0.0283 127.986 0.17900 2014
## 161 53 2009 0.0511 112.045 0.27000 2009
## 162 58 2007-06-01 0.0374 124.017 0.84400 2007
## 163 58 2014-04-15 0.0731 97.236 0.47900 2014
## 164 57 2007 0.0456 134.954 0.51300 2007
## 165 72 2008-01-01 0.0387 100.008 0.55200 2008
## 166 53 2006-01-01 0.5080 123.855 0.58200 2006
## 167 47 2012-05-29 0.0378 116.444 0.34300 2012
## 168 42 2004-04-05 0.0879 120.097 0.80600 2004
## 169 48 2003 0.0619 127.992 0.36000 2003
## 170 73 2020-05-01 0.3510 85.687 0.50500 2020
## 171 41 2001-03-16 0.0471 146.563 0.78300 2001
## 172 47 2003 0.0737 140.736 0.48200 2003
## 173 50 2013-01-01 0.0400 180.020 0.26300 2013
## 174 47 2000-01-01 0.0318 124.820 0.88000 2000
## 175 47 2009 0.0342 113.956 0.59200 2009
## 176 48 2014-07-28 0.0644 119.940 0.21300 2014
## 177 78 2017-02-24 0.1060 99.939 0.40800 2017
## 178 49 2004-10-16 0.0353 145.079 0.21500 2004
## 179 49 2005-01-01 0.0475 98.785 0.96400 2005
## 180 59 2005-08-29 0.0253 128.142 0.71400 2005
## 181 47 2006-09-26 0.1350 93.998 0.69100 2006
## 182 47 2010-06-28 0.0261 75.018 0.57600 2010
## 183 52 2011-03-08 0.0476 67.598 0.03750 2011
## 184 72 2012-03-12 0.0353 133.962 0.86400 2012
## 185 56 2007-11-13 0.0357 83.904 0.44900 2007
## 186 56 2008-11-18 0.0553 134.370 0.33000 2008
## 187 84 2017-08-25 0.0412 131.036 0.32900 2017
## 188 57 2001-09-11 0.0510 145.856 0.58000 2001
## 189 52 2015-07-01 0.0369 104.815 0.91700 2015
## 190 60 2016-05-06 0.0404 136.938 0.03840 2016
## 191 55 2005-05-03 0.0354 138.947 0.03850 2005
## 192 59 2015-09-25 0.0838 124.041 0.25200 2015
## 193 49 2009-01-13 0.0323 114.136 0.20700 2009
## 194 43 2000-01-01 0.0342 116.124 0.43400 2000
## 195 62 2015-01-01 0.0403 80.982 0.41400 2015
## 196 53 2007-09-25 0.0355 138.351 0.16000 2007
## 197 57 2006-11-10 0.0320 76.966 0.23800 2006
## 198 45 2009-01-01 0.0550 171.962 0.34100 2009
## 199 69 2007-01-01 0.2830 170.510 0.77500 2007
## 200 57 2003-10-27 0.0315 181.949 0.39500 2003
## 201 55 2011-03-04 0.0388 65.272 0.07890 2011
## 202 55 2008-01-01 0.0339 95.864 0.96400 2008
## 203 65 2013 0.0476 107.989 0.20100 2013
## 204 51 2010-09-28 0.0739 108.011 0.73900 2010
## 205 61 2002-03-18 0.0354 121.959 0.18100 2002
## 206 36 2003-03-07 0.0493 108.043 0.69200 2003
## 207 62 2018-03-19 0.0921 72.727 0.31400 2018
## 208 65 2001-09-04 0.0285 92.642 0.40000 2001
## 209 61 2012-01-01 0.0340 129.990 0.40700 2012
## 210 46 2003-11-14 0.6340 177.547 0.42500 2003
## 211 47 2011-01-01 0.0275 112.098 0.63100 2011
## 212 65 2016-05-20 0.0383 163.948 0.77700 2016
## 213 57 2009-01-01 0.0276 83.999 0.82100 2009
## 214 57 2002-01-01 0.1640 94.008 0.78600 2002
## 215 45 2007-01-01 0.0349 73.007 0.14500 2007
## 216 69 2018-10-11 0.0389 154.957 0.09170 2018
## 217 62 2020-03-13 0.3550 151.923 0.60800 2020
## 218 62 2012-01-01 0.0383 125.874 0.27600 2012
## 219 79 2009-01-01 0.0439 180.114 0.47200 2009
## 220 65 2000 0.0357 124.557 0.43500 2000
## 221 61 2001 0.2200 156.623 0.11500 2001
## 222 35 2000-01-01 0.2880 98.987 0.64000 2000
## 223 42 2002-12-01 0.0527 114.823 0.31600 2002
## 224 53 2014-10-27 0.0549 184.014 0.19700 2014
## 225 40 2004-01-01 0.0381 124.838 0.11600 2004
## 226 58 2015-09-18 0.0239 88.996 0.30400 2015
## 227 53 2013-07-01 0.1360 83.496 0.05250 2013
## 228 58 2017-07-28 0.0392 118.937 0.24300 2017
## 229 46 2000-12-05 0.1180 111.405 0.50200 2000
## 230 80 2015-02-14 0.0611 144.871 0.31500 2015
## 231 48 2005-09-26 0.0398 80.869 0.03840 2005
## 232 44 2007-11-06 0.3330 88.964 0.84200 2007
## 233 59 2016-10-21 0.0352 101.580 0.42800 2016
## 234 56 2009-01-01 0.0576 149.957 0.52300 2009
## 235 62 2000-11-11 0.0256 79.705 0.55300 2000
## 236 55 2016-10-07 0.2600 178.947 0.36500 2016
## 237 46 2007-09-25 0.0287 75.040 0.21800 2007
## 238 47 2006-01-01 0.0678 100.983 0.33700 2006
## 239 74 2008 0.0317 172.638 0.43600 2008
## 240 40 2002-06-11 0.0417 129.925 0.19700 2002
## 241 83 2020-01-13 0.0681 92.007 0.77500 2020
## 242 69 2015-11-13 0.0309 141.839 0.50600 2015
## 243 51 2012-01-01 0.0378 118.038 0.46200 2012
## 244 44 2010-01-01 0.3760 72.709 0.64900 2010
## 245 55 2013-10-15 0.0533 96.009 0.74700 2013
## 246 55 2017-08-04 0.0396 169.930 0.42500 2017
## 247 58 2000-01-01 0.0404 139.986 0.25900 2000
## 248 48 2007-07-26 0.0268 135.922 0.17300 2007
## 249 52 2008-01-28 0.1000 127.908 0.13500 2008
## 250 61 2002-06-25 0.3170 164.062 0.61800 2002
## 251 56 2017-07-28 0.0285 76.049 0.33500 2017
## 252 40 2004-11-16 0.0541 96.329 0.77400 2004
## 253 66 2014-10-31 0.0295 78.009 0.56100 2014
## 254 43 2000 0.1550 81.900 0.50100 2000
## 255 48 2014-10-07 0.0346 78.019 0.43300 2014
## 256 49 2004-11-12 0.0897 93.238 0.30200 2004
## 257 66 2018-12-23 0.0534 146.075 0.14400 2018
## 258 66 2020-01-31 0.2720 92.008 0.35700 2020
## 259 47 2003-01-01 0.0332 75.203 0.21600 2003
## 260 61 2009-10-06 0.0665 174.085 0.79400 2009
## 261 62 2019-04-17 0.0984 102.968 0.43500 2019
## 262 57 2002-08-08 0.0256 138.390 0.11600 2002
## 263 43 2004 0.0381 80.402 0.07220 2004
## 264 59 2018-11-30 0.0428 141.914 0.21000 2018
## 265 53 2014-10-14 0.3000 89.106 0.16000 2014
## 266 60 2013-01-01 0.0305 111.914 0.48600 2013
## 267 65 2017-10-26 0.0426 72.953 0.20700 2017
## 268 60 2011-04-26 0.0372 77.861 0.14700 2011
## 269 56 2008-09-12 0.0576 124.668 0.48000 2008
## 270 38 2001-01-01 0.0691 143.864 0.26500 2001
## 271 71 2016-08-05 0.0953 107.364 0.34200 2016
## 272 57 2018-06-29 0.4520 159.517 0.51300 2018
## 273 47 2009-04-07 0.0242 82.019 0.46000 2009
## 274 46 2001 0.0409 107.770 0.76000 2001
## 275 42 2000-11-07 0.0487 138.134 0.64200 2000
## 276 44 2006-11-02 0.0334 130.080 0.91300 2006
## 277 66 2020-02-07 0.3370 105.904 0.58600 2020
## 278 41 2005-01-01 0.0283 150.008 0.36700 2005
## 279 41 2005-07-12 0.0457 127.957 0.35000 2005
## 280 40 2007-05-15 0.2440 162.347 0.37100 2007
## 281 46 2012-01-01 0.0305 141.965 0.67800 2012
## 282 47 2012-04-17 0.0301 115.981 0.52000 2012
## 283 45 2007-06-05 0.0268 85.508 0.03860 2007
## 284 37 2002-01-01 0.0765 109.071 0.88100 2002
## 285 48 2000-01-01 0.1600 146.478 0.44900 2000
## 286 47 2003-01-01 0.0953 152.311 0.51600 2003
## 287 42 2008-01-01 0.0376 73.415 0.21800 2008
## 288 48 2009-01-01 0.0346 84.909 0.39500 2009
## 289 55 2009-12-29 0.0285 109.962 0.13300 2009
## 290 44 2004-01-01 0.0721 121.840 0.41800 2004
## 291 47 2012-10-16 0.1740 125.185 0.49000 2012
## 292 46 2012-03-26 0.0739 108.162 0.63500 2012
## 293 50 2002 0.0490 129.029 0.28800 2002
## 294 48 2006-01-01 0.0295 177.942 0.06380 2006
## 295 42 2010-09-28 0.0267 122.039 0.80400 2010
## 296 54 2009-05-25 0.0349 150.948 0.94400 2009
## 297 44 2006-04-18 0.0508 156.047 0.67400 2006
## 298 62 2016-06-17 0.0324 148.158 0.33900 2016
## 299 65 2007-10-30 0.0533 205.917 0.59800 2007
## 300 50 2008-12-01 0.0800 94.959 0.68800 2008
## 301 59 2016-01-22 0.0453 99.990 0.35200 2016
## 302 62 2019-08-16 0.2280 95.936 0.11700 2019
## 303 46 2009-01-01 0.3920 133.418 0.39800 2009
## 304 55 2001 0.0371 92.137 0.15800 2001
## 305 42 2009-07-20 0.0390 123.139 0.03760 2009
## 306 65 2019-05-31 0.1510 98.877 0.75000 2019
## 307 79 2020-04-16 0.1550 85.011 0.62900 2020
## 308 55 2016-06-22 0.0367 134.900 0.34500 2016
## 309 39 2004 0.3160 74.799 0.71800 2004
## 310 57 2013-01-01 0.1350 137.101 0.27100 2013
## 311 46 2006-06-06 0.0573 115.011 0.04270 2006
## 312 74 2020-03-13 0.0814 128.001 0.58100 2020
## 313 58 2016-12-19 0.0254 60.042 0.06880 2016
## 314 61 2005-01-01 0.0334 92.035 0.71400 2005
## 315 81 2017-04-27 0.4250 80.126 0.32600 2017
## 316 61 2008-12-06 0.1260 93.031 0.57800 2008
## 317 75 2013-10-18 0.0513 131.931 0.35300 2013
## 318 47 2002-07-09 0.0367 117.111 0.34400 2002
## 319 61 2017-12-21 0.1480 146.015 0.15600 2017
## 320 70 2020-05-22 0.4100 114.036 0.56700 2020
## 321 63 2009-09-18 0.0389 122.340 0.89500 2009
## 322 71 2020-03-06 0.1040 117.319 0.38900 2020
## 323 77 2018-04-10 0.2880 90.076 0.77400 2018
## 324 83 2019-07-12 0.0824 97.986 0.66800 2019
## 325 53 2001-03-07 0.0321 119.881 0.45700 2001
## 326 52 2002-01-01 0.0263 126.947 0.38500 2002
## 327 58 2003-01-01 0.0405 141.522 0.22800 2003
## 328 66 2001-10-31 0.1060 110.027 0.96300 2001
## 329 54 2002-06-04 0.0590 89.980 0.45900 2002
## 330 68 2003-10-27 0.0461 177.634 0.74200 2003
## 331 51 2006-01-01 0.0470 156.049 0.97200 2006
## 332 35 2002-11-19 0.0357 78.607 0.27900 2002
## 333 60 2016-11-11 0.0339 103.653 0.15600 2016
## 334 63 2000-11-13 0.0307 137.453 0.73100 2000
## 335 74 2018-01-19 0.0561 147.986 0.62000 2018
## 336 78 2018-11-27 0.1090 102.063 0.28600 2018
## 337 45 2000 0.0269 110.051 0.44400 2000
## 338 56 2004-02-03 0.0635 168.197 0.32500 2004
## 339 56 2008-01-01 0.0553 153.942 0.89900 2008
## 340 62 2016-04-15 0.0321 146.696 0.13500 2016
## 341 51 2008-09-01 0.0525 176.356 0.39700 2008
## 342 40 2009-09-22 0.0367 85.004 0.79500 2009
## 343 55 2014-09-22 0.0700 143.915 0.51500 2014
## 344 59 2015-01-01 0.0281 138.055 0.54900 2015
## 345 53 2010-09-24 0.0393 79.033 0.16600 2010
## 346 46 2011-01-01 0.2510 137.898 0.70100 2011
## 347 64 2001-07-30 0.0295 104.055 0.79700 2001
## 348 59 2006-01-01 0.1390 116.950 0.83400 2006
## 349 68 2017-07-03 0.0487 137.033 0.32800 2017
## 350 53 2010-09-17 0.0632 86.656 0.00001 2010
## 351 53 2012-08-12 0.0327 115.568 0.35100 2012
## 352 47 2010-01-01 0.0355 102.062 0.09730 2010
## 353 36 2003 0.0406 80.184 0.43400 2003
## 354 48 2012-04-09 0.0488 124.939 0.56100 2012
## 355 52 2011-11-08 0.0301 139.973 0.30800 2011
## 356 67 2017-03-24 0.4620 77.035 0.54100 2017
## 357 52 2013-06-14 0.1960 107.352 0.04900 2013
## 358 1 2020-06-12 0.0616 124.014 0.55700 2020
## 359 52 2010-08-06 0.0408 118.060 0.85600 2010
## 360 49 2013-11-19 0.0394 121.028 0.36900 2013
## 361 72 2020-05-29 0.0428 123.843 0.26900 2020
## 362 54 2004-09-18 0.0565 171.973 0.74100 2004
## 363 47 2009-07-07 0.0372 81.508 0.55900 2009
## 364 60 2005-07-26 0.0393 154.988 0.42800 2005
## 365 59 2016-09-23 0.0277 143.598 0.09990 2016
## 366 64 2016-10-28 0.2570 141.392 0.18400 2016
## 367 42 2001-11-06 0.0498 99.018 0.59600 2001
## 368 51 2014-04-03 0.0392 131.987 0.86300 2014
## 369 46 2006-01-01 0.0264 122.015 0.76200 2006
## 370 55 2013-01-01 0.0839 116.022 0.25400 2013
## 371 62 2013-10-15 0.0360 130.843 0.35800 2013
## 372 53 2002-08-20 0.0337 104.769 0.96900 2002
## 373 49 2007-01-01 0.0537 195.969 0.93200 2007
## 374 64 2020-02-19 0.0418 101.913 0.21800 2020
## 375 48 2008-06-10 0.2750 76.577 0.22500 2008
## 376 73 2015-05-15 0.0489 120.113 0.44700 2015
## 377 60 2018-10-19 0.2370 149.991 0.26800 2018
## 378 66 2019-10-18 0.2150 159.973 0.43600 2019
## 379 49 2009-11-04 0.0301 62.030 0.35300 2009
## 380 46 2003-10-21 0.0261 114.060 0.48800 2003
## 381 42 2010-11-23 0.0426 66.960 0.03860 2010
## 382 43 2000-01-01 0.1140 114.184 0.63500 2000
## 383 59 2016-09-30 0.0302 157.938 0.14100 2016
## 384 44 2000 0.2820 179.857 0.77200 2000
## 385 47 2006-02-27 0.1560 157.137 0.67000 2006
## 386 64 2019-10-11 0.0863 128.002 0.34600 2019
## 387 60 2002-09-03 0.0892 171.644 0.57100 2002
## 388 52 2020-06-05 0.1480 119.918 0.50300 2020
## 389 51 2003-01-01 0.0385 98.198 0.54100 2003
## 390 55 2010 0.1220 175.887 0.92400 2010
## 391 59 2001-12-11 0.0682 138.204 0.83800 2001
## 392 63 2008-05-01 0.0437 91.306 0.80700 2008
## 393 43 2000-01-01 0.0420 78.019 0.59600 2000
## 394 58 2002-01-01 0.2770 124.065 0.77700 2002
## 395 47 2004 0.1200 172.389 0.88200 2004
## 396 55 2004-02-17 0.0430 118.363 0.46400 2004
## 397 52 2004 0.0344 111.987 0.25100 2004
## 398 55 2003-08-19 0.0563 159.550 0.61600 2003
## 399 64 2018-02-18 0.0347 139.947 0.31500 2018
## 400 61 2020-02-28 0.4060 150.085 0.76900 2020
## 401 55 2005-11-15 0.0280 97.576 0.21700 2005
## 402 51 2015-03-10 0.0380 173.932 0.78400 2015
## 403 63 2004-11-01 0.0318 103.857 0.72900 2004
## 404 68 2004-06-23 0.0262 149.839 0.46100 2004
## 405 49 2012-01-01 0.0504 141.973 0.61900 2012
## 406 71 2001-09-25 0.0404 85.600 0.65800 2001
## 407 48 2000-01-01 0.0272 122.051 0.63900 2000
## 408 51 2002-01-01 0.0934 149.975 0.86900 2002
## 409 44 2002-07-26 0.0284 123.996 0.71300 2002
## 410 53 2016-11-30 0.0315 81.034 0.48600 2016
## 411 45 2012-08-07 0.0331 94.061 0.25600 2012
## 412 53 2012-04-17 0.0398 104.008 0.92100 2012
## 413 36 2001-04-24 0.3220 93.533 0.72200 2001
## 414 43 2008-06-03 0.0928 92.163 0.55800 2008
## 415 56 2009-09-16 0.0494 124.369 0.74900 2009
## 416 48 2006-10-24 0.0537 199.997 0.48300 2006
## 417 56 2004-08-10 0.0624 162.397 0.38000 2004
## 418 69 2017-08-04 0.0274 84.467 0.87500 2017
## 419 52 2005-12-06 0.2720 87.027 0.32500 2005
## 420 54 2011-07-18 0.0654 101.033 0.19300 2011
## 421 58 2005-01-01 0.0790 95.017 0.63500 2005
## 422 38 2001 0.0403 107.341 0.96500 2001
## 423 67 2016-05-27 0.0555 180.297 0.53100 2016
## 424 60 2012-07-18 0.0383 127.986 0.60000 2012
## 425 57 2004-11-16 0.0638 91.896 0.79800 2004
## 426 61 2017-01-04 0.0536 136.785 0.35600 2017
## 427 48 2011-01-01 0.2960 131.401 0.41800 2011
## 428 49 2008-11-11 0.0418 79.922 0.84900 2008
## 429 56 2007-09-03 0.0511 149.183 0.62700 2007
## 430 71 2018-09-18 0.3450 145.063 0.71800 2018
## 431 61 2017-12-01 0.0354 139.885 0.66600 2017
## 432 55 2008-01-01 0.0393 129.979 0.19300 2008
## 433 51 2004-08-03 0.0273 130.927 0.16900 2004
## 434 64 2018-08-31 0.0349 139.980 0.44900 2018
## 435 59 2017-11-07 0.7540 61.939 0.64200 2017
## 436 65 2018-04-12 0.0706 90.984 0.40900 2018
## 437 75 2017-12-22 0.2250 165.212 0.35000 2017
## 438 59 2007-10-26 0.0356 163.127 0.36700 2007
## 439 54 2002-04-16 0.0373 120.617 0.81400 2002
## 440 49 2003-11-10 0.0404 121.761 0.30300 2003
## 441 38 2002-01-01 0.0303 107.311 0.29300 2002
## 442 37 2004 0.0492 96.182 0.57300 2004
## 443 43 2007-03-12 0.0463 160.683 0.51700 2007
## 444 56 2009-01-01 0.0386 128.665 0.55500 2009
## 445 54 2010-07-27 0.1010 147.935 0.37300 2010
## 446 70 2016-05-02 0.0736 78.918 0.64200 2016
## 447 56 2008-01-01 0.0970 87.019 0.38800 2008
## 448 44 2002-01-01 0.0433 132.996 0.86400 2002
## 449 45 2007 0.0347 123.086 0.94400 2007
## 450 49 2010-01-01 0.0699 112.991 0.12500 2010
## 451 47 2006-01-01 0.1740 80.477 0.43800 2006
## 452 64 2005-01-01 0.0641 104.833 0.91700 2005
## 453 48 2000-06-20 0.0545 142.366 0.65300 2000
## 454 83 2002-05-26 0.1860 171.447 0.10000 2002
## 455 72 2019-09-06 0.0489 92.015 0.77200 2019
## 456 45 2001-01-01 0.1230 140.075 0.35100 2001
## 457 47 2001-09-11 0.0666 94.865 0.63600 2001
## 458 75 2019-02-15 0.0973 199.811 0.86400 2019
## 459 74 2018-05-18 0.0364 69.973 0.34100 2018
## 460 44 2006-11-20 0.0406 150.518 0.11600 2006
## 461 66 2019-01-18 0.0718 144.039 0.82600 2019
## 462 53 2011-08-01 0.1820 98.066 0.53800 2011
## 463 50 2015-09-04 0.1440 127.972 0.36900 2015
## 464 52 2014-01-14 0.0390 104.882 0.33900 2014
## 465 61 2018-03-15 0.1810 200.040 0.47900 2018
## 466 40 2001-08-27 0.2090 88.553 0.10700 2001
## 467 46 2005-01-01 0.1020 104.741 0.80400 2005
## 468 46 2006-10-03 0.0696 176.173 0.59200 2006
## 469 47 2000-01-01 0.2310 94.009 0.69800 2000
## 470 56 2010-03-02 0.0381 87.961 0.72900 2010
## 471 56 2016-02-26 0.0356 117.012 0.86300 2016
## 472 54 2007-11-09 0.0278 112.006 0.19500 2007
## 473 72 2010-11-19 0.0407 119.950 0.65300 2010
## 474 40 2000-10-24 0.1100 108.193 0.49800 2000
## 475 51 2006-01-01 0.0273 110.928 0.25300 2006
## 476 56 2006-01-01 0.0382 94.241 0.66900 2006
## 477 68 2019-09-13 0.1720 142.069 0.27200 2019
## 478 43 2006-07-31 0.0481 211.397 0.71000 2006
## 479 62 2015-10-07 0.0295 85.012 0.57600 2015
## 480 61 2019-06-14 0.4290 157.978 0.73900 2019
## 481 70 2017-07-21 0.0416 114.523 0.44000 2017
## 482 41 2002-01-01 0.0870 201.542 0.97200 2002
## 483 62 2015-06-02 0.0621 144.678 0.45500 2015
## 484 78 2017-02-23 0.0622 102.040 0.54400 2017
## 485 53 2014-04-29 0.0597 128.069 0.61900 2014
## 486 62 2001-01-01 0.0425 123.273 0.97600 2001
## 487 40 2007-01-01 0.0637 148.105 0.41400 2007
## 488 62 2019-11-22 0.0767 74.514 0.42500 2019
## 489 57 2012-04-10 0.0346 91.947 0.40000 2012
## 490 46 2006-01-01 0.0409 109.432 0.98000 2006
## 491 60 2017-12-12 0.0661 160.946 0.54600 2017
## 492 42 2011-07-12 0.0428 175.022 0.38000 2011
## 493 42 2010-10-05 0.9120 88.694 0.51700 2010
## 494 39 2002-01-01 0.0476 147.976 0.53400 2002
## 495 49 2013 0.0566 138.006 0.96600 2013
## 496 46 2000-10-31 0.2800 94.582 0.48200 2000
## 497 66 2017-04-01 0.1360 119.904 0.18100 2017
## 498 51 2007-04-18 0.0434 136.033 0.49800 2007
## 499 40 2008-09-23 0.0336 130.960 0.38300 2008
## 500 67 2013-06-12 0.4720 167.766 0.61200 2013
## 501 48 2006-11-07 0.2850 149.956 0.81400 2006
## 502 54 2001-10-30 0.0375 73.398 0.03650 2001
## 503 62 2017-04-26 0.0392 123.922 0.17000 2017
## 504 47 2006-05-09 0.2500 144.700 0.79900 2006
## 505 57 2000-01-01 0.0801 100.980 0.23500 2000
## 506 71 2019-09-27 0.2420 100.024 0.67200 2019
## 507 77 2019-06-28 0.0290 74.953 0.38100 2019
## 508 39 2000-01-01 0.0526 144.137 0.48400 2000
## 509 45 2010-10-05 0.9290 144.240 0.28800 2010
## 510 59 2016-12-11 0.0397 71.996 0.38600 2016
## 511 40 2007-10-23 0.0579 127.673 0.64200 2007
## 512 56 2013-07-29 0.0437 138.096 0.39800 2013
## 513 57 2010-08-23 0.0390 120.993 0.63100 2010
## 514 51 2015-09-18 0.2980 79.715 0.49200 2015
## 515 49 2003-01-01 0.0415 79.285 0.05740 2003
## 516 46 2004-01-01 0.0344 75.042 0.07150 2004
## 517 62 2019-03-29 0.4880 84.898 0.21000 2019
## 518 52 2016-10-07 0.0997 130.987 0.62500 2016
## 519 48 2001-02-27 0.0387 92.980 0.52700 2001
## 520 66 2015-08-07 0.0420 140.230 0.39000 2015
## 521 60 2016-11-18 0.1000 101.097 0.39300 2016
## 522 58 2001-09-18 0.4150 89.186 0.23200 2001
## 523 56 2004-01-01 0.0462 97.978 0.63600 2004
## 524 66 2017-10-30 0.3090 143.049 0.14800 2017
## 525 43 2001-01-01 0.0274 132.278 0.29600 2001
## 526 77 2017-05-05 0.1380 96.992 0.20900 2017
## 527 45 2002-04-16 0.0396 94.014 0.89000 2002
## 528 48 2006-11-20 0.0436 161.972 0.27500 2006
## 529 61 2014-09-18 0.0565 142.588 0.37000 2014
## 530 52 2003-03-24 0.1080 90.058 0.67900 2003
## 531 73 2000-06-13 0.0301 105.014 0.91600 2000
## 532 68 2006-01-21 0.0663 177.018 0.20900 2006
## 533 50 2014-06-10 0.0271 143.869 0.09500 2014
## 534 64 2018-05-04 0.0453 160.336 0.63600 2018
## 535 56 2009-06-30 0.0269 97.540 0.69400 2009
## 536 52 2007 0.0640 101.631 0.34100 2007
## 537 41 2005 0.0434 175.917 0.20000 2005
## 538 43 2006-01-01 0.0378 106.951 0.80800 2006
## 539 74 2019-05-22 0.1240 80.029 0.38800 2019
## 540 63 2014-01-01 0.0332 108.115 0.15400 2014
## 541 58 2012-08-28 0.0276 129.009 0.34300 2012
## 542 54 2016-04-08 0.0277 76.023 0.56600 2016
## 543 53 2009-11-27 0.0595 97.140 0.63100 2009
## 544 69 2019-06-27 0.1850 109.994 0.24700 2019
## 545 64 2015 0.0487 72.046 0.08660 2015
## 546 71 2017-03-18 0.4500 170.982 0.45400 2017
## 547 44 2006-01-01 0.1210 159.811 0.21700 2006
## 548 68 2019-11-29 0.0000 0.000 0.00000 2019
## 549 48 2007-07-03 0.0403 148.818 0.67300 2007
## 550 57 2003-01-01 0.0260 109.444 0.66400 2003
## 551 69 2018-09-28 0.0482 120.103 0.21000 2018
## 552 38 2001 0.0260 178.624 0.31100 2001
## 553 44 2007-01-01 0.0533 158.661 0.27500 2007
## 554 44 2008-11-11 0.0550 139.949 0.95000 2008
## 555 43 2001 0.2010 168.116 0.18700 2001
## 556 56 2016-05-16 0.1450 83.021 0.36700 2016
## 557 72 2002-06-25 0.1200 107.075 0.91200 2002
## 558 58 2014-01-01 0.0711 98.020 0.60600 2014
## 559 64 2016-05-24 0.0748 78.386 0.01400 2016
## 560 62 2012-04-30 0.0311 158.024 0.60900 2012
## 561 56 2014-09-18 0.0317 164.929 0.28600 2014
## 562 41 2005-01-01 0.1690 164.009 0.30800 2005
## 563 61 2018-08-30 0.0414 125.047 0.68500 2018
## 564 46 2011-07-12 0.0373 128.980 0.07950 2011
## 565 64 2013-01-01 0.1370 198.062 0.63700 2013
## 566 53 2007 0.0784 114.172 0.83500 2007
## 567 43 2008-01-01 0.0285 140.004 0.20700 2008
## 568 66 2015-11-06 0.0285 91.452 0.77200 2015
## 569 55 2002-10-22 0.0383 103.063 0.66400 2002
## 570 57 2007-01-01 0.0388 110.259 0.21300 2007
## 571 50 2003-01-01 0.3750 93.063 0.82600 2003
## 572 44 2004-01-01 0.0258 131.000 0.26400 2004
## 573 62 2017-07-21 0.0325 122.919 0.12800 2017
## 574 44 2008-01-01 0.0410 118.994 0.65600 2008
## 575 44 2009-06-30 0.2190 83.977 0.40300 2009
## 576 71 2019-08-23 0.0569 100.003 0.41600 2019
## 577 64 2018-03-09 0.0520 92.497 0.33000 2018
## 578 52 2015-09-18 0.0318 147.515 0.54300 2015
## 579 67 2019-11-15 0.0241 75.592 0.29400 2019
## 580 51 2014-10-27 0.0323 96.970 0.53900 2014
## 581 37 2000-08-23 0.0486 104.204 0.93800 2000
## 582 47 2011-01-01 0.0346 127.024 0.38500 2011
## 583 47 2009-08-24 0.0384 154.094 0.39000 2009
## 584 45 2011-01-01 0.1890 207.105 0.86500 2011
## 585 44 2010-10-05 0.1840 123.582 0.03950 2010
## 586 49 2005-10-27 0.0727 166.105 0.57900 2005
## 587 50 2014-04-17 0.0304 114.038 0.07530 2014
## 588 82 2019-09-06 0.0514 90.016 0.63700 2019
## 589 62 2020-05-01 0.4550 80.325 0.30800 2020
## 590 64 2013-04-01 0.0487 72.190 0.47700 2013
## 591 62 2014-10-20 0.0338 113.468 0.18100 2014
## 592 70 2019-10-25 0.0285 80.978 0.19100 2019
## 593 56 2012-06-26 0.1850 87.803 0.07900 2012
## 594 67 2019-07-18 0.0581 92.466 0.69500 2019
## 595 46 2012 0.1370 148.010 0.37700 2012
## 596 67 2016-03-02 0.3860 117.139 0.39300 2016
## 597 56 2017-06-16 0.2600 133.854 0.15400 2017
## 598 56 2012-06-19 0.0964 146.526 0.48200 2012
## 599 57 2015-05-19 0.0399 100.008 0.10300 2015
## 600 66 2012-01-01 0.0323 92.998 0.61300 2012
## 601 43 2003 0.0294 115.682 0.12900 2003
## 602 57 2005-11-07 0.2130 96.855 0.48600 2005
## 603 48 2008-08-19 0.0311 139.959 0.55200 2008
## 604 42 2006-05-09 0.2560 69.967 0.56900 2006
## 605 52 2012-10-02 0.0253 91.044 0.43900 2012
## 606 63 2015-08-28 0.0653 157.358 0.95600 2015
## 607 69 2019-05-31 0.0327 113.994 0.29900 2019
## 608 40 2003-10-21 0.0409 113.153 0.85800 2003
## 609 36 2003-11-14 0.1900 84.008 0.34400 2003
## 610 41 2000 0.4190 174.935 0.74100 2000
## 611 51 2012-06-19 0.0339 86.004 0.03620 2012
## 612 54 2002-01-01 0.0343 81.943 0.31900 2002
## 613 52 2016-12-19 0.0308 146.035 0.56400 2016
## 614 45 2000 0.0810 178.435 0.35000 2000
## 615 56 2015-11-12 0.2030 119.941 0.55600 2015
## 616 54 2011-01-01 0.0966 158.075 0.62800 2011
## 617 66 2018-02-23 0.0293 110.003 0.11500 2018
## 618 60 2016-10-14 0.0617 152.043 0.81100 2016
## 619 65 2017-06-22 0.1190 115.991 0.26800 2017
## 620 48 2014-04-01 0.0503 133.919 0.79500 2014
## 621 70 2018-01-01 0.1490 79.509 0.13100 2018
## 622 80 2020-01-10 0.4100 127.803 0.60600 2020
## 623 43 2004 0.1290 199.947 0.14200 2004
## 624 37 2001 0.1560 78.053 0.91800 2001
## 625 62 2016-07-20 0.0411 122.311 0.05690 2016
## 626 51 2007-07-30 0.0277 118.003 0.58000 2007
## 627 58 2013-10-02 0.2870 75.682 0.38000 2013
## 628 57 2015-10-11 0.0530 120.068 0.14600 2015
## 629 57 2009-01-01 0.0580 103.057 0.60600 2009
## 630 70 2020-04-17 0.1330 90.263 0.74600 2020
## 631 44 2006-10-10 0.0364 71.187 0.19700 2006
## 632 53 2013-09-27 0.1590 106.502 0.33300 2013
## 633 54 2012-06-01 0.0510 167.498 0.29800 2012
## 634 44 2008-01-29 0.0481 179.752 0.83300 2008
## 635 49 2009-01-01 0.0515 165.097 0.96200 2009
## 636 66 2017-12-19 0.1010 119.993 0.19900 2017
## 637 65 2020-01-17 0.1380 152.061 0.11200 2020
## 638 64 2008-09-01 0.0409 81.875 0.41500 2008
## 639 54 2012-06-20 0.0292 139.961 0.61100 2012
## 640 47 2011-11-07 0.0329 105.365 0.34900 2011
## 641 67 2020-05-01 0.1330 88.284 0.26600 2020
## 642 46 2000-01-01 0.0649 182.349 0.03790 2000
## 643 41 2008-09-12 0.0921 106.552 0.20200 2008
## 644 39 2008-04-01 0.0325 138.782 0.38200 2008
## 645 58 2016-02-05 0.0277 76.023 0.56600 2016
## 646 49 2003-06-03 0.0259 80.299 0.49800 2003
## 647 57 2012-07-10 0.0596 139.232 0.29400 2012
## 648 61 2016-08-05 0.0278 74.979 0.49000 2016
## 649 41 2002-09-16 0.0729 100.076 0.30200 2002
## 650 63 2020-05-29 0.0710 100.949 0.31600 2020
## 651 41 2009-03-17 0.0411 89.819 0.86800 2009
## 652 61 2016-08-12 0.0555 92.921 0.14700 2016
## 653 39 2005-08-23 0.0851 137.848 0.32600 2005
## 654 61 2017-02-16 0.1480 92.003 0.30700 2017
## 655 59 2017-01-28 0.0353 119.809 0.09470 2017
## 656 58 2015-08-14 0.0272 144.949 0.46600 2015
## 657 68 2018-06-27 0.0398 123.969 0.29800 2018
## 658 62 2016-06-10 0.2250 91.770 0.66700 2016
## 659 42 2002 0.0278 92.150 0.79200 2002
## 660 44 2002-01-01 0.0542 119.102 0.96200 2002
## 661 55 2003-01-01 0.0245 138.091 0.35700 2003
## 662 48 2001-01-01 0.0813 158.681 0.39500 2001
## 663 39 2005-01-01 0.1300 81.894 0.65200 2005
## 664 39 2000-07-18 0.0315 84.526 0.31800 2000
## 665 45 2001-01-01 0.0693 105.505 0.81200 2001
## 666 38 2004-10-08 0.0334 103.606 0.57100 2004
## 667 73 2019-12-30 0.0658 162.919 0.78300 2019
## 668 54 2005-08-23 0.0241 77.982 0.24100 2005
## 669 48 2013-06-18 0.0378 126.819 0.17500 2013
## 670 52 2014-04-08 0.0862 99.014 0.63100 2014
## 671 47 2009 0.0854 144.955 0.49300 2009
## 672 50 2005-07-19 0.0393 114.526 0.84500 2005
## 673 50 2007 0.1390 108.012 0.10400 2007
## 674 70 2020-01-11 0.0467 123.884 0.59200 2020
## 675 49 2009-01-01 0.1040 182.021 0.50700 2009
## 676 47 2008-01-01 0.0410 105.006 0.59200 2008
## 677 47 2013-05-17 0.0343 139.955 0.40500 2013
## 678 66 2015-04-21 0.0564 119.344 0.45000 2015
## 679 84 2020-05-08 0.1450 94.148 0.58100 2020
## 680 68 2018-02-24 0.0525 163.710 0.08940 2018
## 681 59 2008-01-01 0.0559 136.002 0.84000 2008
## 682 64 2020-06-08 0.0403 128.000 0.27000 2020
## 683 55 2000-12-01 0.1190 118.074 0.90100 2000
## 684 45 2006-01-01 0.0874 77.663 0.52200 2006
## 685 50 2010-04-29 0.0529 122.326 0.96700 2010
## 686 50 2009-11-03 0.0392 110.021 0.79900 2009
## 687 50 2014-04-22 0.1130 105.990 0.15900 2014
## 688 52 2012-09-18 0.0388 65.003 0.20000 2012
## 689 65 2017-07-31 0.1490 122.995 0.16600 2017
## 690 61 2006-11-28 0.0386 77.577 0.46300 2006
## 691 57 2003-01-01 0.2160 192.153 0.50700 2003
## 692 53 2013-04-19 0.0558 150.105 0.49300 2013
## 693 50 2007-07-03 0.0457 158.050 0.46200 2007
## 694 73 2012-01-01 0.0366 93.041 0.27400 2012
## 695 44 2005-07-12 0.0348 138.599 0.12900 2005
## 696 60 2014-01-01 0.0423 120.133 0.18400 2014
## 697 57 2007-01-01 0.0440 81.993 0.33000 2007
## 698 57 2013-01-01 0.0362 118.017 0.06630 2013
## 699 63 2017-08-25 0.2720 97.691 0.43300 2017
## 700 63 2017-04-21 0.0000 0.000 0.00000 2017
## 701 50 2008-11-11 0.0350 164.010 0.14300 2008
## 702 61 2019-11-22 0.0399 68.584 0.03880 2019
## 703 45 2001-11-19 0.0984 91.693 0.68500 2001
## 704 50 2010-09-27 0.0390 73.984 0.41800 2010
## 705 46 2011-01-01 0.0355 84.230 0.52700 2011
## 706 67 2016-01-11 0.0398 100.989 0.96900 2016
## 707 39 2002-05-20 0.0409 180.077 0.21400 2002
## 708 47 2007-10-19 0.0522 116.005 0.54100 2007
## 709 43 2004-01-01 0.0365 107.025 0.83200 2004
## 710 45 2006-09-04 0.0297 104.100 0.25600 2006
## 711 46 2004-05-03 0.1460 119.944 0.29900 2004
## 712 61 2016-03-18 0.0958 146.068 0.64900 2016
## 713 67 2019-05-03 0.0344 97.947 0.23700 2019
## 714 41 2000-01-01 0.0452 114.164 0.96400 2000
## 715 65 2008-12-09 0.1440 110.008 0.56700 2008
## 716 52 2008-01-01 0.0766 132.017 0.97200 2008
## 717 60 2016-05-06 0.1650 174.984 0.12500 2016
## 718 58 2010-10-11 0.0459 46.771 0.11300 2010
## 719 49 2007-10-29 0.1800 111.393 0.92100 2007
## 720 58 2012-04-21 0.0417 152.005 0.54800 2012
## 721 44 2000-01-11 0.0863 142.956 0.84300 2000
## 722 71 2019-04-12 0.1070 176.084 0.58800 2019
## 723 59 2014-01-01 0.0650 81.853 0.48800 2014
## 724 54 2007-01-01 0.2240 170.014 0.78000 2007
## 725 62 2017-07-21 0.0346 138.019 0.17700 2017
## 726 53 2014-09-23 0.0420 109.787 0.91800 2014
## 727 62 2017-11-10 0.1310 113.951 0.26300 2017
## 728 56 2013-01-01 0.3250 102.528 0.51500 2013
## 729 44 2000 0.3820 88.868 0.59300 2000
## 730 39 2000 0.0528 153.414 0.46400 2000
## 731 63 2020-04-24 0.4180 83.973 0.63100 2020
## 732 77 2015-08-09 0.0714 74.990 0.08080 2015
## 733 47 2005-08-16 0.0729 176.538 0.67500 2005
## 734 61 2014-01-01 0.0536 137.799 0.28800 2014
## 735 54 2014-06-27 0.1100 144.009 0.68000 2014
## 736 44 2010-01-01 0.0333 134.022 0.82200 2010
## 737 58 2016-09-30 0.0450 130.008 0.82700 2016
## 738 50 2002-09-24 0.0897 94.464 0.36800 2002
## 739 64 2000-04-18 0.0368 136.086 0.59900 2000
## 740 55 2009-01-01 0.0342 145.977 0.19600 2009
## 741 52 2001-01-01 0.0445 120.334 0.81700 2001
## 742 50 2015-09-25 0.0325 200.007 0.39400 2015
## 743 44 2012-01-01 0.0398 87.321 0.60600 2012
## 744 54 2014-03-03 0.0301 150.782 0.56000 2014
## 745 54 2002-09-02 0.0897 92.413 0.24600 2002
## 746 53 2013-07-02 0.0370 125.016 0.64800 2013
## 747 57 2015-11-06 0.0463 144.149 0.85800 2015
## 748 57 2015-04-21 0.1330 82.986 0.35300 2015
## 749 61 2019-10-11 0.4350 79.824 0.64900 2019
## 750 76 2005-02-22 0.0309 140.046 0.52000 2005
## 751 39 2002-04-09 0.1300 169.942 0.25300 2002
## 752 59 2010 0.0338 164.165 0.26800 2010
## 753 53 2005-04-07 0.0360 142.417 0.45900 2005
## 754 41 2006-01-01 0.0263 89.396 0.18100 2006
## 755 56 2009-03-16 0.0668 136.039 0.55300 2009
## 756 40 2005-01-01 0.0526 120.180 0.53800 2005
## 757 62 2006-01-01 0.3570 90.951 0.63300 2006
## 758 79 2019-03-29 0.0585 120.020 0.57200 2019
## 759 75 2019-04-05 0.0284 128.132 0.19400 2019
## 760 64 2014-05-12 0.0296 106.489 0.26400 2014
## 761 50 2010-03-30 0.0294 124.942 0.66600 2010
## 762 48 2015-05-19 0.0354 109.011 0.49000 2015
## 763 62 2016-09-23 0.0370 71.276 0.36900 2016
## 764 62 2018-10-12 0.0370 124.000 0.18400 2018
## 765 47 2009-04-07 0.4630 180.079 0.85400 2009
## 766 62 2018-05-04 0.2140 100.049 0.51600 2018
## 767 38 2005-01-07 0.0311 136.828 0.95900 2005
## 768 45 2005 0.0321 101.246 0.54500 2005
## 769 51 2011-05-24 0.2110 88.275 0.87900 2011
## 770 50 2015-05-12 0.1540 130.054 0.49200 2015
## 771 48 2004-08-10 0.0248 93.056 0.24900 2004
## 772 64 2018-11-02 0.2380 85.969 0.46200 2018
## 773 45 2005 0.0907 103.102 0.23900 2005
## 774 53 2001-03-27 0.0782 159.887 0.50500 2001
## 775 61 2005-06-06 0.0367 140.101 0.33800 2005
## 776 44 2002-09-16 0.1410 198.073 0.39700 2002
## 777 62 2016-09-16 0.0582 136.998 0.61300 2016
## 778 46 2011-06-07 0.0655 100.061 0.52500 2011
## 779 62 2015-01-11 0.1020 129.761 0.23000 2015
## 780 41 2003 0.0414 116.959 0.22000 2003
## 781 41 2006 0.0408 118.935 0.17400 2006
## 782 40 2001-01-01 0.0424 116.179 0.96400 2001
## 783 75 2018-11-09 0.1790 75.003 0.60900 2018
## 784 49 2009 0.0564 153.240 0.32800 2009
## 785 49 2002 0.4290 94.383 0.78600 2002
## 786 45 2006-03-26 0.0477 128.990 0.76800 2006
## 787 46 2002-01-01 0.3550 173.913 0.50800 2002
## 788 62 2011-04-29 0.0269 134.616 0.61600 2011
## 789 50 2001-06-18 0.0969 136.460 0.29000 2001
## 790 49 2010-04-09 0.0626 122.080 0.39900 2010
## 791 55 2010-01-01 0.2200 93.977 0.55700 2010
## 792 70 2012-09-25 0.0995 81.214 0.01190 2012
## 793 74 2014-08-25 0.0334 98.992 0.24000 2014
## 794 61 2001-06-19 0.0473 166.001 0.74400 2001
## 795 50 2007-11-12 0.0411 144.239 0.71900 2007
## 796 66 2010-05-18 0.0364 61.494 0.07100 2010
## 797 43 2000 0.0280 110.007 0.79200 2000
## 798 62 2015-10-23 0.0473 100.033 0.30900 2015
## 799 58 2017-07-13 0.0709 76.318 0.35400 2017
## 800 52 2001-11-13 0.0271 113.023 0.41700 2001
## 801 71 2019-07-03 0.0302 98.998 0.39800 2019
## 802 75 2017-04-14 0.2540 78.476 0.47800 2017
## 803 51 2011-06-10 0.0288 127.959 0.64300 2011
## 804 42 2011-11-15 0.3470 201.876 0.35800 2011
## 805 49 2001-07-30 0.0389 154.066 0.48100 2001
## 806 40 2000 0.0388 121.869 0.80900 2000
## 807 41 2000-01-01 0.2240 107.654 0.73400 2000
## 808 56 2002-07-08 0.0529 96.161 0.88400 2002
## 809 57 2015-10-23 0.1140 200.179 0.50000 2015
## 810 59 2018-06-29 0.0361 139.828 0.13900 2018
## 811 41 2006-01-01 0.3190 129.058 0.83700 2006
## 812 57 2017-02-24 0.0347 125.294 0.23800 2017
## 813 50 2008-11-11 0.0289 126.084 0.39600 2008
## 814 47 2009-01-16 0.0401 73.218 0.08390 2009
## 815 60 2008-09-23 0.0629 138.196 0.36000 2008
## 816 55 2011-04-28 0.0519 117.026 0.87400 2011
## 817 58 2011-01-01 0.4690 183.602 0.52000 2011
## 818 61 2015-09-25 0.0421 129.442 0.22600 2015
## 819 49 2002 0.0345 77.928 0.42600 2002
## 820 42 2001 0.0380 99.012 0.80000 2001
## 821 46 2007-06-19 0.0239 97.887 0.75000 2007
## 822 59 2015-04-27 0.0296 157.989 0.63100 2015
## 823 43 2001-01-01 0.0246 102.068 0.93900 2001
## 824 63 2016-04-01 0.0330 115.971 0.47400 2016
## 825 48 2006-11-14 0.0830 148.751 0.92500 2006
## 826 53 2003-03-18 0.0920 88.984 0.55100 2003
## 827 49 2007 0.0352 81.957 0.20400 2007
## 828 42 2002-02-12 0.0275 78.212 0.80500 2002
## 829 57 2000-11-13 0.0414 171.838 0.95500 2000
## 830 51 2006-01-01 0.0515 92.924 0.27000 2006
## 831 57 2016-05-09 0.3430 109.879 0.54900 2016
## 832 70 2019-07-26 0.0918 119.969 0.70200 2019
## 833 73 2020-02-12 0.1680 118.003 0.69500 2020
## 834 58 2017-05-12 0.0267 143.907 0.30000 2017
## 835 62 2012-12-04 0.0485 140.008 0.48500 2012
## 836 61 2002-09-30 0.0478 119.818 0.67400 2002
## 837 44 2008-08-26 0.0405 180.020 0.50800 2008
## 838 51 2003-10-28 0.0337 139.952 0.59500 2003
## 839 56 2002-12-10 0.2800 91.601 0.61300 2002
## 840 52 2002-04-23 0.2000 94.510 0.57300 2002
## 841 56 2014-08-20 0.1170 96.068 0.57300 2014
## 842 60 2015-07-17 0.0363 103.921 0.73700 2015
## 843 44 2007-11-20 0.1210 101.002 0.82500 2007
## 844 41 2006-10-10 0.3030 156.029 0.77700 2006
## 845 70 2013-10-04 0.0336 130.027 0.78600 2013
## 846 70 2017-08-30 0.0693 80.033 0.54500 2017
## 847 37 2003-12-16 0.0651 97.231 0.43800 2003
## 848 70 2018-06-08 0.0709 134.055 0.63100 2018
## 849 38 2000-10-31 0.3060 86.445 0.77200 2000
## 850 51 2005-08-23 0.0359 117.982 0.20900 2005
## 851 84 2018-04-06 0.1100 123.994 0.59200 2018
## 852 69 2019-09-30 0.0559 142.521 0.03490 2019
## 853 50 2006 0.0661 130.044 0.71100 2006
## 854 49 2008-10-07 0.0734 134.919 0.37000 2008
## 855 56 2014-09-09 0.0329 100.026 0.46100 2014
## 856 58 2005-12-20 0.1280 120.789 0.97600 2005
## 857 55 2014-04-01 0.0301 93.194 0.51200 2014
## 858 68 2020-02-21 0.0408 145.912 0.32900 2020
## 859 47 2011-10-04 0.0408 125.953 0.29800 2011
## 860 42 2009-06-30 0.0718 128.109 0.49000 2009
## 861 43 2008-01-01 0.0763 80.996 0.69400 2008
## 862 77 2004-08-30 0.0399 100.013 0.63000 2004
## 863 51 2014-01-14 0.0365 112.961 0.75200 2014
## 864 47 2002 0.0310 114.467 0.45400 2002
## 865 60 2015-09-25 0.5020 165.908 0.62000 2015
## 866 34 2000-01-01 0.0450 181.894 0.65200 2000
## 867 52 2007-09-21 0.1070 104.983 0.48400 2007
## 868 44 2001-01-01 0.1550 92.022 0.80900 2001
## 869 54 2005-06-14 0.0348 106.000 0.63800 2005
## 870 44 2003-05-06 0.0524 104.663 0.37900 2003
## 871 61 2015-12-01 0.1280 132.049 0.75300 2015
## 872 36 2000-01-01 0.1480 105.190 0.50100 2000
## 873 47 2010-11-09 0.0294 108.994 0.70200 2010
## 874 69 2006-05-09 0.0289 115.992 0.63500 2006
## 875 74 2019-08-28 0.2220 134.979 0.90500 2019
## 876 48 2009-01-19 0.0954 93.508 0.97000 2009
## 877 64 2018-11-01 0.1450 160.039 0.85900 2018
## 878 52 2013-09-10 0.0305 131.962 0.22700 2013
## 879 59 2005-05-03 0.0596 133.044 0.69600 2005
## 880 39 2006-06-27 0.0369 119.884 0.10500 2006
## 881 64 2020-02-26 0.0650 79.952 0.65900 2020
## 882 54 2007 0.0488 135.084 0.73000 2007
## 883 47 2001-01-01 0.0505 178.666 0.68800 2001
## 884 65 2016-05-20 0.0332 100.906 0.29100 2016
## 885 54 2015-01-26 0.0317 115.348 0.11500 2015
## 886 40 2001-08-08 0.0563 129.530 0.61400 2001
## 887 54 2010-01-01 0.1110 124.420 0.57400 2010
## 888 40 2003-01-01 0.1370 104.994 0.18200 2003
## 889 67 2019-09-30 0.0554 84.077 0.03330 2019
## 890 48 2001-01-26 0.2080 157.924 0.95600 2001
## 891 46 2002-05-06 0.0256 103.127 0.65600 2002
## 892 68 2001-05-15 0.0302 107.438 0.19100 2001
## 893 44 2010-04-14 0.2290 72.597 0.10000 2010
## 894 51 2006-05-09 0.0369 101.883 0.42500 2006
## 895 71 2015-01-09 0.0503 134.052 0.96100 2015
## 896 70 2018-08-24 0.1030 87.605 0.30200 2018
## 897 52 2015-08-14 0.0877 137.050 0.50200 2015
## 898 62 2016-04-23 0.0370 174.824 0.07460 2016
## 899 54 2003 0.0635 171.945 0.69300 2003
## 900 46 2012-09-11 0.0294 99.023 0.53600 2012
## 901 43 2011-02-22 0.0704 133.074 0.71800 2011
## 902 36 2003-01-01 0.1160 118.174 0.52300 2003
## 903 76 2017-12-22 0.0497 129.565 0.03940 2017
## 904 51 2009-08-21 0.0348 143.941 0.44100 2009
## 905 44 2010-05-04 0.0294 118.180 0.49100 2010
## 906 59 2004-02-10 0.2100 93.691 0.60600 2004
## 907 64 2014-09-30 0.0805 118.208 0.93300 2014
## 908 59 2016-12-16 0.0485 154.930 0.40500 2016
## 909 57 2001 0.0269 88.239 0.31100 2001
## 910 64 2015-01-20 0.1100 164.996 0.44500 2015
## 911 38 2005-03-01 0.1430 113.031 0.65000 2005
## 912 69 2002-01-01 0.0425 111.762 0.38400 2002
## 913 28 2020-06-03 0.0873 95.998 0.33600 2020
## 914 54 2013-04-13 0.2620 114.916 0.59700 2013
## 915 53 2012-05-23 0.0346 87.919 0.21600 2012
## 916 54 2000-01-01 0.2450 187.573 0.87700 2000
## 917 46 2011-01-18 0.0529 125.581 0.60800 2011
## 918 50 2007-08-21 0.0395 137.997 0.30400 2007
## 919 40 2002-11-01 0.0290 101.818 0.35100 2002
## 920 57 2013-10-08 0.3330 169.813 0.30600 2013
## 921 50 2000-01-01 0.0340 147.911 0.29900 2000
## 922 66 2011-03-18 0.0540 199.853 0.58900 2011
## 923 47 2004-07-13 0.0706 181.910 0.50200 2004
## 924 48 2009-01-01 0.0756 194.877 0.39200 2009
## 925 58 2018-03-01 0.0444 130.103 0.30900 2018
## 926 40 2006-05-08 0.1350 165.752 0.56400 2006
## 927 53 2006 0.0531 129.443 0.77500 2006
## 928 54 2009-05-15 0.2590 90.098 0.77800 2009
## 929 45 2004-09-27 0.2610 150.406 0.52300 2004
## 930 77 2018-10-19 0.1370 99.895 0.64300 2018
## 931 45 2012-02-28 0.0532 114.110 0.66100 2012
## 932 58 2016-09-09 0.0330 125.034 0.43500 2016
## 933 53 2010-08-24 0.0291 112.520 0.80100 2010
## 934 50 2011 0.0263 83.565 0.49600 2011
## 935 61 2016-04-01 0.0415 150.740 0.22000 2016
## 936 36 2000-02-28 0.0631 160.071 0.95700 2000
## 937 45 2009-03-09 0.7920 98.544 0.74700 2009
## 938 68 2015-09-04 0.1090 172.751 0.26000 2015
## 939 62 2006-03-31 0.1040 122.891 0.53300 2006
## 940 55 2011-01-01 0.0560 131.092 0.40700 2011
## 941 69 2018-05-11 0.0496 140.981 0.46400 2018
## 942 48 2009-07-13 0.1230 100.004 0.22100 2009
## 943 72 2020-04-03 0.0393 68.995 0.12100 2020
## 944 56 2012-05-29 0.0349 119.093 0.55000 2012
## 945 49 2001-03-12 0.0520 110.026 0.86900 2001
## 946 62 2013-02-26 0.0360 116.826 0.06300 2013
## 947 71 2019-03-08 0.0363 100.966 0.16600 2019
## 948 53 2012-01-01 0.0418 99.036 0.84100 2012
## 949 74 2001 0.0379 126.041 0.86900 2001
## 950 69 2010 0.0482 91.038 0.36100 2010
## 951 45 2011 0.1870 91.846 0.45500 2011
## 952 51 2013-07-16 0.0293 103.370 0.16300 2013
## 953 61 2009 0.5070 71.937 0.53400 2009
## 954 66 2014-08-19 0.0544 120.055 0.31600 2014
## 955 64 2016-01-26 0.0308 89.988 0.29500 2016
## 956 55 2005-01-01 0.0436 134.993 0.84900 2005
## 957 48 2005-02-22 0.0609 168.126 0.78200 2005
## 958 60 2017-12-02 0.0281 109.741 0.81800 2017
## 959 44 2002-11-12 0.3190 97.917 0.59000 2002
## 960 46 2014-01-01 0.0380 82.984 0.96400 2014
## 961 46 2001-10-30 0.3110 151.529 0.92900 2001
## 962 65 2016-08-24 0.0307 167.936 0.51400 2016
## 963 65 2017-12-19 0.2110 147.965 0.33400 2017
## 964 64 2017-02-24 0.0308 122.005 0.91300 2017
## 965 63 2012-01-01 0.0485 149.794 0.84800 2012
## 966 61 2002-07-09 0.0367 117.111 0.34400 2002
## 967 47 2008-04-01 0.0454 111.138 0.76300 2008
## 968 71 2011-02-22 0.0420 129.657 0.81800 2011
## 969 53 2012-09-18 0.0376 107.947 0.12000 2012
## 970 39 2001-04-10 0.0421 179.581 0.96400 2001
## 971 53 2016-01-15 0.4120 78.846 0.63200 2016
## 972 40 2004-01-01 0.0349 200.547 0.14000 2004
## 973 45 2006-01-01 0.0336 113.082 0.25300 2006
## 974 57 2015-09-25 0.1150 84.996 0.24000 2015
## 975 54 2014-02-04 0.0384 126.016 0.20300 2014
## 976 66 2017-05-05 0.0507 82.477 0.00719 2017
## 977 51 2000-10-24 0.0414 136.034 0.96700 2000
## 978 61 2016-04-13 0.0706 157.970 0.20500 2016
## 979 60 2010 0.0441 74.988 0.28800 2010
## 980 55 2015-04-07 0.0449 165.986 0.67500 2015
## 981 1 2020-06-12 0.0438 110.093 0.65700 2020
## 982 43 2011 0.0422 119.988 0.55500 2011
## 983 52 2008-08-08 0.1770 133.317 0.32700 2008
## 984 49 2000 0.0884 96.015 0.78500 2000
## 985 58 2012-10-22 0.0916 85.984 0.75000 2012
## 986 58 2013-09-27 0.0371 183.991 0.19400 2013
## 987 47 2003-01-01 0.0251 102.315 0.65200 2003
## 988 46 2009-09-08 0.0959 103.812 0.57900 2009
## 989 44 2000-08-20 0.0314 94.913 0.85300 2000
## 990 60 2012-06-27 0.0307 136.020 0.16200 2012
## 991 50 2013-05-04 0.0353 90.989 0.63900 2013
## 992 57 2012-01-01 0.0300 102.997 0.38200 2012
## 993 51 2011-01-01 0.2860 77.068 0.70900 2011
## 994 40 2000-07-08 0.2650 82.131 0.92300 2000
## 995 74 2018-11-16 0.0655 150.021 0.59400 2018
## 996 60 2017-08-30 0.0427 85.133 0.20700 2017
## 997 88 2019-10-12 0.0657 104.994 0.66700 2019
## 998 50 2014-04-29 0.0756 110.121 0.51700 2014
## 999 48 2009-02-03 0.1530 79.792 0.25700 2009
## 1000 56 2015-10-16 0.0562 89.998 0.77500 2015# From previous step
spotify_sample <- spotify_population %>%
slice_sample(n = 1000)
# Calculate the mean duration in mins from spotify_population
mean_dur_pop <- spotify_population %>%
summarize(mean_dur = mean(duration_minutes))
# Calculate the mean duration in mins from spotify_sample
mean_dur_samp <- spotify_sample %>%
summarize(mean_dur = mean(duration_minutes))
# See the results
mean_dur_pop## mean_dur
## 1 3.852152mean_dur_samp## mean_dur
## 1 3.935161.2 Simple sampling with base-R
While dplyr provides great tools for sampling data frames, if you want to work with vectors you can use base-R.
Let’s turn it up to eleven and look at the loudness property of each song.
# Get the loudness column of spotify_population
loudness_pop <- spotify_population$loudness
# Sample 100 values of loudness_pop
loudness_samp <- loudness_pop %>% sample(100)
# See the results
loudness_samp## [1] -8.143 -3.822 -15.465 -4.248 -7.208 -7.463 -29.881 -3.400 -9.341
## [10] -4.691 -7.916 -3.489 -10.555 -9.188 -7.294 -5.959 -7.293 -7.678
## [19] -5.060 -4.555 -2.078 -7.947 -4.274 -3.107 -5.725 -5.447 -3.458
## [28] -8.219 -3.222 -4.541 -13.288 -11.688 -3.772 -3.544 -6.981 -3.453
## [37] -2.796 -9.054 -5.608 -7.938 -4.702 -4.227 -4.269 -4.637 -8.905
## [46] -10.006 -5.664 -4.960 -4.579 -4.156 -5.147 -5.470 -15.481 -4.516
## [55] -7.872 -2.337 -7.993 -11.328 -6.811 -4.265 -12.420 -5.962 -12.411
## [64] -3.659 -6.218 -6.347 -6.139 -6.429 -4.427 -6.564 -9.815 -4.341
## [73] -6.976 -14.903 -11.763 -4.733 -5.973 -7.030 -1.946 -8.660 -5.216
## [82] -13.039 -6.861 -5.639 -7.728 -16.757 -5.493 -8.052 -6.133 -8.371
## [91] -6.336 -8.265 -10.330 -10.146 -6.857 -12.690 -26.102 -6.314 -3.878
## [100] -7.361# Calculate the standard deviation of loudness_pop
sd_loudness_pop <- sd(loudness_pop)
# Calculate the standard deviation of loudness_samp
sd_loudness_samp <- sd(loudness_samp)
# See the results
sd_loudness_pop## [1] 4.524076sd_loudness_samp## [1] 4.3301141.3 Are findings from the sample generalizable?
You just saw how convenience sampling—collecting data via the easiest method can result in samples that aren’t representative of the whole population. Equivalently, this means findings from the sample are not generalizable to the whole population. Visualizing the distributions of the population and the sample can help determine whether or not the sample is representative of the population.
# Visualize the distribution of acousticness as a histogram with a binwidth of 0.01
ggplot(spotify_population, aes(acousticness)) +geom_histogram(binwidth = 0.01)
# Update the histogram to use spotify_mysterious_sample with x-axis limits from 0 to 1
ggplot(spotify_population, aes(acousticness)) +
geom_histogram(binwidth = 0.01) +xlim(0, 1)## Warning: Removed 2 rows containing missing values (geom_bar).
1.4 Generating random numbers
You’ve seen sample() and it’s dplyr cousin, slice_sample() for generating pseudo-random numbers from a set of values. A related task is to generate random numbers that follow a statistical distribution, like the uniform distribution or the normal distribution.
n_numbers <- 5000
# Generate random numbers from ...
randoms <- data.frame(
# a uniform distribution from -3 to 3
uniform = runif(n_numbers, min = -3, max = 3),
# a normal distribution with mean 5 and sd 2
normal = rnorm(n_numbers, mean = 5, sd = 2))
# Plot a histogram of uniform values, binwidth 0.25
ggplot(randoms, aes(uniform)) + geom_histogram(binwidth = 0.25)
# Plot a histogram of normal values, binwidth 0.5
ggplot(randoms, aes(normal)) + geom_histogram(binwidth = 0.5)
2. Don’t get theory eyed
Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.
2.1 Simple random sampling
The simplest method of sampling a population is the one you’ve seen already. It is known as simple random sampling (sometimes abbreviated to “SRS”), and involves picking rows at random, one at a time, where each row has the same chance of being picked as any other. To make it easier to see which rows end up in the sample, it’s helpful to include a row ID column in the dataset before you take the sample.
# Set the seed
set.seed(123)
attrition_samp <- attrition_pop %>%
# Add a row ID column
rowid_to_column() %>%
# Get 200 rows using simple random sampling
slice_sample(n = 200)
# View the attrition_samp dataset
head(attrition_samp)## rowid Age Attrition BusinessTravel DailyRate Department
## 1 415 37 No Travel_Frequently 1278 Sales
## 2 463 29 No Travel_Rarely 144 Sales
## 3 179 23 No Travel_Rarely 1309 Research_Development
## 4 526 34 Yes Travel_Rarely 699 Research_Development
## 5 195 22 Yes Travel_Rarely 617 Research_Development
## 6 938 29 No Travel_Rarely 1107 Research_Development
## DistanceFromHome Education EducationField EnvironmentSatisfaction Gender
## 1 1 Master Medical High Male
## 2 10 Below_College Marketing Very_High Female
## 3 26 Below_College Life_Sciences High Male
## 4 6 Below_College Medical Medium Male
## 5 3 Below_College Life_Sciences Medium Female
## 6 28 Master Life_Sciences High Female
## HourlyRate JobInvolvement JobLevel JobRole JobSatisfaction
## 1 31 Low 2 Sales_Executive Very_High
## 2 39 Medium 2 Sales_Executive Medium
## 3 83 High 1 Research_Scientist Very_High
## 4 83 High 1 Research_Scientist Low
## 5 34 High 2 Manufacturing_Director High
## 6 93 High 1 Research_Scientist Very_High
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked OverTime
## 1 Divorced 9525 7677 1 No
## 2 Divorced 8268 11866 1 Yes
## 3 Divorced 2904 16092 1 No
## 4 Single 2960 17102 2 No
## 5 Married 4171 10022 0 Yes
## 6 Divorced 2514 26968 4 No
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## 1 14 Excellent High 2
## 2 14 Excellent Low 2
## 3 12 Excellent High 2
## 4 11 Excellent High 0
## 5 19 Excellent Low 1
## 6 22 Outstanding Low 1
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 6 2 Good 6
## 2 7 2 Better 7
## 3 4 2 Good 4
## 4 8 2 Better 4
## 5 4 3 Best 3
## 6 11 1 Better 7
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 3 1 3
## 2 7 1 7
## 3 2 0 2
## 4 2 1 3
## 5 2 0 2
## 6 5 1 72.2 Systematic sampling
One sampling method that avoids randomness is called systematic sampling. Here, you pick rows from the population at regular intervals.
For example, if the population dataset had one thousand rows and you wanted a sample size of five, you’d pick rows 200, 400, 600, 800, and 1000.
# Set the sample size to 200
sample_size <- 200
# Get the population size from attrition_pop
pop_size <- nrow(attrition_pop)
# Calculate the interval
interval <- pop_size %/% sample_size
# Get row indexes for the sample
row_indexes <- seq_len(sample_size) * interval
attrition_sys_samp <- attrition_pop %>%
# Add a row ID column
rowid_to_column() %>%
# Get 200 rows using systematic sampling
slice(row_indexes)
# See the result
head(attrition_sys_samp)## rowid Age Attrition BusinessTravel DailyRate Department
## 1 7 18 No Non-Travel 287 Research_Development
## 2 14 35 No Travel_Rarely 464 Research_Development
## 3 21 22 No Travel_Rarely 534 Research_Development
## 4 28 21 Yes Travel_Rarely 156 Sales
## 5 35 20 No Travel_Rarely 959 Research_Development
## 6 42 29 Yes Travel_Rarely 805 Research_Development
## DistanceFromHome Education EducationField EnvironmentSatisfaction Gender
## 1 5 College Life_Sciences Medium Male
## 2 4 College Other High Male
## 3 15 Bachelor Medical Medium Female
## 4 12 Bachelor Life_Sciences High Female
## 5 1 Bachelor Life_Sciences Very_High Female
## 6 1 College Life_Sciences Medium Female
## HourlyRate JobInvolvement JobLevel JobRole JobSatisfaction
## 1 73 High 1 Research_Scientist Very_High
## 2 75 High 1 Laboratory_Technician Very_High
## 3 59 High 1 Laboratory_Technician Very_High
## 4 90 Very_High 1 Sales_Representative Medium
## 5 83 Medium 1 Research_Scientist Medium
## 6 36 Medium 1 Laboratory_Technician Low
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked OverTime
## 1 Single 1051 13493 1 No
## 2 Divorced 1951 10910 1 No
## 3 Single 2871 23785 1 No
## 4 Single 2716 25422 1 No
## 5 Single 2836 11757 1 No
## 6 Married 2319 6689 1 Yes
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StockOptionLevel
## 1 15 Excellent Very_High 0
## 2 12 Excellent High 1
## 3 15 Excellent High 0
## 4 15 Excellent Very_High 0
## 5 13 Excellent Very_High 0
## 6 11 Excellent Very_High 1
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 0 2 Better 0
## 2 1 3 Better 1
## 3 1 5 Better 0
## 4 1 0 Better 1
## 5 1 0 Best 1
## 6 1 1 Better 1
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 02.3 Is systematic sampling OK?
Systematic sampling has a problem: if the data has been sorted, or there is some sort of pattern or meaning behind the row order, then the resulting sample may not be representative of the whole population. The problem can be solved by shuffling the rows, but then systematic sampling is equivalent to simple random sampling.
attrition_pop_id <- attrition_pop %>%
rowid_to_column()
# Using attrition_pop_id, plot YearsAtCompany vs. rowid
ggplot(attrition_pop_id, aes(rowid, YearsAtCompany)) +
# Make it a scatter plot
geom_point() +
# Add a smooth trend line
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
# Shuffle the rows of attrition_pop then add row IDs
attrition_shuffled <- attrition_pop %>% slice_sample(prop = 1) %>% rowid_to_column()
# Using attrition_shuffled, plot YearsAtCompany vs. rowid
# Add points and a smooth trend line
ggplot(attrition_shuffled, aes(rowid, YearsAtCompany)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
2.4 Proportional stratified sampling
If you are interested in subgroups within the population, then you may need to carefully control the counts of each subgroup within the population. Proportional stratified sampling results in subgroup sizes within the sample that are representative of the subgroup sizes within the population. It is equivalent to performing a simple random sample on each subgroup.
education_counts_pop <- attrition_pop %>%
# Count the employees by Education level, sorting by n
count(Education, sort = TRUE) %>%
# Add a percent column
mutate(percent = 100 * n/sum(n))
# See the results
education_counts_pop## Education n percent
## 1 Bachelor 572 38.911565
## 2 Master 398 27.074830
## 3 College 282 19.183673
## 4 Below_College 170 11.564626
## 5 Doctor 48 3.265306# Use proportional stratified sampling to get 40% of each Education group
attrition_strat <- attrition_pop %>%
group_by(Education) %>%
slice_sample(prop = 0.4) %>%
ungroup()
# See the result
attrition_strat## # A tibble: 586 × 31
## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## <int> <fct> <fct> <int> <fct> <int>
## 1 29 No Travel_Rarely 1086 Research_Develo… 7
## 2 29 No Travel_Frequently 995 Research_Develo… 2
## 3 21 No Travel_Rarely 1343 Sales 22
## 4 27 No Travel_Rarely 269 Research_Develo… 5
## 5 41 No Non-Travel 247 Research_Develo… 7
## 6 58 Yes Travel_Frequently 781 Research_Develo… 2
## 7 39 No Travel_Frequently 1218 Research_Develo… 1
## 8 40 No Travel_Rarely 989 Research_Develo… 4
## 9 27 No Travel_Frequently 1410 Sales 3
## 10 25 No Travel_Rarely 882 Research_Develo… 19
## # … with 576 more rows, and 25 more variables: Education <ord>,
## # EducationField <fct>, EnvironmentSatisfaction <ord>, Gender <fct>,
## # HourlyRate <int>, JobInvolvement <ord>, JobLevel <int>, JobRole <fct>,
## # JobSatisfaction <ord>, MaritalStatus <fct>, MonthlyIncome <int>,
## # MonthlyRate <int>, NumCompaniesWorked <int>, OverTime <fct>,
## # PercentSalaryHike <int>, PerformanceRating <ord>,
## # RelationshipSatisfaction <ord>, StockOptionLevel <int>, …# Get the counts and percents from attrition_strat
education_counts_strat <- attrition_strat %>% count(Education, sort =TRUE) %>% mutate(percent = 100 * n/sum(n))
# See the results
education_counts_strat## # A tibble: 5 × 3
## Education n percent
## <ord> <int> <dbl>
## 1 Bachelor 228 38.9
## 2 Master 159 27.1
## 3 College 112 19.1
## 4 Below_College 68 11.6
## 5 Doctor 19 3.242.5 Equal counts stratified sampling
If one subgroup is larger than another subgroup in the population, but you don’t want to reflect that difference in your analysis, then you can use equal counts stratified sampling to generate samples where each subgroup has the same amount of data.
For example, if you are analyzing blood types, O is the most common blood type worldwide, but you may wish to have equal amounts of O, A, B, and AB in your sample.
# Use equal counts stratified sampling to get 30 employees from each Education group
attrition_eq <- attrition_pop %>% group_by(Education) %>%
slice_sample(n = 30) %>% ungroup()
# See the results
attrition_eq## # A tibble: 150 × 31
## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## <int> <fct> <fct> <int> <fct> <int>
## 1 22 Yes Travel_Rarely 391 Research_Develo… 7
## 2 27 No Non-Travel 210 Sales 1
## 3 31 No Travel_Rarely 140 Research_Develo… 12
## 4 49 No Travel_Frequently 279 Research_Develo… 8
## 5 31 No Travel_Frequently 853 Research_Develo… 1
## 6 33 No Travel_Rarely 461 Research_Develo… 13
## 7 41 No Travel_Rarely 796 Sales 4
## 8 52 Yes Travel_Rarely 266 Sales 2
## 9 37 No Travel_Rarely 571 Research_Develo… 10
## 10 28 No Travel_Rarely 1083 Research_Develo… 29
## # … with 140 more rows, and 25 more variables: Education <ord>,
## # EducationField <fct>, EnvironmentSatisfaction <ord>, Gender <fct>,
## # HourlyRate <int>, JobInvolvement <ord>, JobLevel <int>, JobRole <fct>,
## # JobSatisfaction <ord>, MaritalStatus <fct>, MonthlyIncome <int>,
## # MonthlyRate <int>, NumCompaniesWorked <int>, OverTime <fct>,
## # PercentSalaryHike <int>, PerformanceRating <ord>,
## # RelationshipSatisfaction <ord>, StockOptionLevel <int>, …# Get the counts and percents from attrition_eq
education_counts_eq <- attrition_eq %>%
count(Education, sort =TRUE) %>% mutate(percent = 100 * n/sum(n))
# See the results
education_counts_eq## # A tibble: 5 × 3
## Education n percent
## <ord> <int> <dbl>
## 1 Below_College 30 20
## 2 College 30 20
## 3 Bachelor 30 20
## 4 Master 30 20
## 5 Doctor 30 202.6 Weighted sampling
Stratified sampling provides rules about the probability of picking rows from your dataset at the subgroup level. A generalization of this is weighted sampling, which lets you specify rules about the probability of picking rows at the row level. The probability of picking any given row is proportional to the weight value for that row.
# Using attrition_pop, plot YearsAtCompany as a histogram with binwidth 1
ggplot(attrition_pop, aes(YearsAtCompany)) + geom_histogram(binwidth = 1)
# Sample 400 employees weighted by YearsAtCompany
attrition_weight <- attrition_pop %>%
slice_sample(n = 400, weight_by = YearsAtCompany)
# See the results
attrition_weight## Age Attrition BusinessTravel DailyRate Department
## 1 44 No Travel_Rarely 1099 Sales
## 2 46 No Travel_Rarely 1319 Sales
## 3 50 No Travel_Rarely 1046 Research_Development
## 4 41 No Travel_Rarely 645 Sales
## 5 37 No Travel_Rarely 674 Research_Development
## 6 32 No Travel_Rarely 234 Sales
## 7 31 Yes Travel_Rarely 542 Sales
## 8 29 No Travel_Rarely 726 Research_Development
## 9 24 Yes Travel_Frequently 1287 Research_Development
## 10 53 Yes Travel_Rarely 607 Research_Development
## 11 54 No Travel_Rarely 431 Research_Development
## 12 25 No Travel_Rarely 882 Research_Development
## 13 31 No Travel_Rarely 311 Research_Development
## 14 56 No Travel_Frequently 906 Sales
## 15 35 No Travel_Rarely 528 Human_Resources
## 16 48 No Travel_Rarely 855 Research_Development
## 17 50 No Travel_Rarely 1452 Research_Development
## 18 51 Yes Travel_Rarely 1323 Research_Development
## 19 52 No Travel_Rarely 621 Sales
## 20 33 No Travel_Rarely 217 Sales
## 21 52 No Travel_Rarely 956 Research_Development
## 22 52 No Non-Travel 585 Sales
## 23 35 Yes Travel_Rarely 303 Sales
## 24 45 No Non-Travel 1050 Sales
## 25 26 No Travel_Rarely 1355 Human_Resources
## 26 43 No Travel_Rarely 244 Human_Resources
## 27 31 No Travel_Rarely 1154 Sales
## 28 42 No Travel_Frequently 288 Research_Development
## 29 46 No Travel_Rarely 430 Research_Development
## 30 48 Yes Travel_Frequently 708 Sales
## 31 30 No Travel_Rarely 1176 Research_Development
## 32 26 No Travel_Frequently 921 Research_Development
## 33 38 No Travel_Rarely 322 Sales
## 34 46 No Travel_Rarely 991 Human_Resources
## 35 31 No Travel_Rarely 1082 Research_Development
## 36 37 No Travel_Rarely 589 Sales
## 37 36 No Travel_Frequently 1195 Research_Development
## 38 39 No Travel_Rarely 117 Research_Development
## 39 45 No Travel_Frequently 1249 Research_Development
## 40 33 No Travel_Frequently 553 Research_Development
## 41 40 No Travel_Frequently 1395 Research_Development
## 42 30 No Travel_Rarely 1092 Research_Development
## 43 35 Yes Travel_Frequently 130 Research_Development
## 44 40 No Travel_Rarely 898 Human_Resources
## 45 23 No Travel_Rarely 571 Research_Development
## 46 49 No Travel_Frequently 636 Research_Development
## 47 37 No Travel_Rarely 161 Research_Development
## 48 41 No Travel_Rarely 427 Human_Resources
## 49 41 Yes Travel_Rarely 1085 Research_Development
## 50 31 No Travel_Rarely 1463 Research_Development
## 51 42 No Travel_Frequently 570 Research_Development
## 52 38 No Travel_Rarely 827 Research_Development
## 53 48 No Travel_Rarely 1108 Research_Development
## 54 49 No Travel_Rarely 722 Research_Development
## 55 29 No Travel_Frequently 1413 Sales
## 56 30 No Non-Travel 829 Research_Development
## 57 23 No Travel_Rarely 507 Research_Development
## 58 27 No Travel_Rarely 1302 Research_Development
## 59 29 Yes Travel_Rarely 341 Sales
## 60 40 No Travel_Rarely 1416 Research_Development
## 61 54 No Travel_Rarely 397 Human_Resources
## 62 45 No Non-Travel 1238 Research_Development
## 63 55 No Travel_Frequently 135 Research_Development
## 64 53 No Travel_Rarely 1376 Sales
## 65 46 No Travel_Rarely 717 Research_Development
## 66 35 No Travel_Rarely 662 Sales
## 67 54 No Non-Travel 142 Human_Resources
## 68 40 No Travel_Rarely 1124 Sales
## 69 27 No Travel_Frequently 294 Research_Development
## 70 49 No Travel_Rarely 1495 Research_Development
## 71 35 No Travel_Rarely 776 Sales
## 72 35 No Travel_Rarely 1276 Research_Development
## 73 28 No Travel_Rarely 857 Research_Development
## 74 37 No Travel_Rarely 1470 Research_Development
## 75 38 No Travel_Rarely 201 Research_Development
## 76 35 No Travel_Rarely 1029 Research_Development
## 77 40 No Travel_Rarely 989 Research_Development
## 78 51 No Travel_Rarely 1469 Research_Development
## 79 41 No Travel_Rarely 802 Sales
## 80 44 No Travel_Rarely 1199 Research_Development
## 81 26 Yes Travel_Frequently 575 Research_Development
## 82 32 No Travel_Frequently 1316 Research_Development
## 83 50 No Travel_Rarely 854 Sales
## 84 36 No Non-Travel 301 Sales
## 85 36 No Travel_Frequently 541 Sales
## 86 29 Yes Travel_Frequently 337 Research_Development
## 87 44 No Travel_Rarely 170 Research_Development
## 88 31 No Travel_Rarely 326 Sales
## 89 39 No Travel_Rarely 722 Sales
## 90 43 No Travel_Rarely 1034 Sales
## 91 35 No Travel_Rarely 1137 Research_Development
## 92 36 No Travel_Frequently 469 Research_Development
## 93 41 No Non-Travel 267 Sales
## 94 34 No Travel_Rarely 1326 Sales
## 95 32 No Travel_Rarely 1018 Research_Development
## 96 33 Yes Travel_Rarely 813 Research_Development
## 97 29 Yes Travel_Rarely 121 Sales
## 98 35 No Travel_Frequently 853 Sales
## 99 32 No Travel_Rarely 604 Sales
## 100 39 No Travel_Rarely 1462 Sales
## 101 49 No Travel_Rarely 1091 Research_Development
## 102 41 No Travel_Rarely 334 Sales
## 103 47 No Travel_Rarely 1001 Research_Development
## 104 47 No Travel_Rarely 1225 Sales
## 105 54 No Travel_Rarely 685 Research_Development
## 106 38 No Travel_Frequently 216 Research_Development
## 107 29 Yes Travel_Rarely 428 Sales
## 108 50 No Travel_Rarely 1207 Research_Development
## 109 60 No Travel_Frequently 1499 Sales
## 110 33 No Travel_Frequently 1392 Research_Development
## 111 40 Yes Travel_Rarely 299 Sales
## 112 24 No Non-Travel 1269 Research_Development
## 113 59 No Travel_Rarely 1435 Sales
## 114 43 No Travel_Rarely 982 Research_Development
## 115 47 No Travel_Rarely 571 Sales
## 116 36 No Travel_Rarely 913 Research_Development
## 117 34 No Travel_Rarely 1239 Sales
## 118 29 No Travel_Rarely 332 Human_Resources
## 119 38 No Travel_Rarely 1009 Sales
## 120 40 No Travel_Rarely 329 Research_Development
## 121 58 Yes Travel_Rarely 601 Research_Development
## 122 45 No Travel_Rarely 556 Research_Development
## 123 52 No Travel_Rarely 258 Research_Development
## 124 53 No Travel_Rarely 102 Research_Development
## 125 23 Yes Travel_Rarely 427 Sales
## 126 50 No Travel_Frequently 1246 Human_Resources
## 127 37 No Non-Travel 1063 Research_Development
## 128 39 No Travel_Frequently 945 Research_Development
## 129 31 No Travel_Rarely 480 Research_Development
## 130 30 No Travel_Rarely 853 Research_Development
## 131 40 Yes Travel_Rarely 575 Sales
## 132 28 No Travel_Rarely 866 Sales
## 133 38 No Travel_Rarely 345 Sales
## 134 52 No Non-Travel 771 Sales
## 135 23 No Travel_Rarely 160 Research_Development
## 136 29 No Travel_Rarely 592 Research_Development
## 137 38 No Non-Travel 1327 Sales
## 138 37 Yes Travel_Rarely 807 Human_Resources
## 139 35 Yes Travel_Rarely 1204 Sales
## 140 41 No Non-Travel 247 Research_Development
## 141 40 No Travel_Rarely 1398 Sales
## 142 53 No Travel_Rarely 1223 Research_Development
## 143 34 No Travel_Rarely 131 Sales
## 144 42 No Non-Travel 495 Research_Development
## 145 29 No Non-Travel 746 Sales
## 146 42 No Travel_Rarely 319 Research_Development
## 147 50 No Travel_Frequently 1234 Research_Development
## 148 34 Yes Non-Travel 1362 Sales
## 149 29 Yes Travel_Rarely 408 Research_Development
## 150 54 No Travel_Rarely 157 Research_Development
## 151 33 No Travel_Rarely 117 Research_Development
## 152 39 No Travel_Rarely 412 Research_Development
## 153 39 No Travel_Frequently 505 Research_Development
## 154 42 No Travel_Frequently 748 Research_Development
## 155 37 No Travel_Rarely 1107 Research_Development
## 156 44 Yes Travel_Rarely 1376 Human_Resources
## 157 47 No Travel_Frequently 217 Sales
## 158 49 No Travel_Rarely 1490 Research_Development
## 159 27 No Travel_Frequently 1297 Research_Development
## 160 50 No Travel_Rarely 989 Research_Development
## 161 46 No Travel_Rarely 526 Sales
## 162 58 Yes Travel_Rarely 147 Research_Development
## 163 44 No Travel_Rarely 200 Research_Development
## 164 36 No Travel_Frequently 1302 Research_Development
## 165 43 No Travel_Rarely 930 Research_Development
## 166 19 No Travel_Rarely 1181 Research_Development
## 167 45 No Travel_Rarely 1316 Research_Development
## 168 47 No Non-Travel 1162 Research_Development
## 169 33 No Travel_Frequently 1296 Research_Development
## 170 38 No Travel_Rarely 397 Research_Development
## 171 38 No Travel_Frequently 653 Research_Development
## 172 44 No Travel_Frequently 602 Human_Resources
## 173 38 No Travel_Rarely 1333 Research_Development
## 174 32 Yes Travel_Rarely 515 Research_Development
## 175 39 No Travel_Rarely 116 Research_Development
## 176 24 No Travel_Rarely 506 Research_Development
## 177 34 No Travel_Rarely 937 Sales
## 178 32 No Travel_Rarely 498 Research_Development
## 179 58 No Travel_Rarely 848 Research_Development
## 180 50 No Non-Travel 881 Research_Development
## 181 59 No Travel_Rarely 1429 Research_Development
## 182 32 No Travel_Rarely 601 Sales
## 183 37 No Travel_Rarely 977 Research_Development
## 184 30 No Travel_Rarely 1334 Sales
## 185 36 No Travel_Rarely 1351 Research_Development
## 186 47 Yes Travel_Frequently 1093 Sales
## 187 58 No Travel_Rarely 1055 Research_Development
## 188 38 No Travel_Rarely 849 Research_Development
## 189 44 No Travel_Rarely 136 Research_Development
## 190 32 No Travel_Frequently 379 Sales
## 191 56 No Travel_Rarely 1369 Research_Development
## 192 36 No Travel_Rarely 1040 Research_Development
## 193 32 No Non-Travel 953 Research_Development
## 194 39 No Travel_Rarely 466 Research_Development
## 195 42 Yes Travel_Frequently 481 Sales
## 196 37 Yes Travel_Rarely 625 Sales
## 197 43 No Travel_Frequently 957 Research_Development
## 198 35 No Travel_Rarely 538 Research_Development
## 199 39 No Travel_Rarely 1132 Research_Development
## 200 39 No Travel_Rarely 1431 Research_Development
## 201 53 No Travel_Rarely 1219 Sales
## 202 42 No Travel_Rarely 557 Research_Development
## 203 46 No Travel_Rarely 563 Sales
## 204 30 No Travel_Rarely 921 Research_Development
## 205 47 No Travel_Frequently 1379 Research_Development
## 206 43 No Travel_Frequently 1001 Research_Development
## 207 45 No Travel_Rarely 788 Human_Resources
## 208 33 No Travel_Rarely 267 Research_Development
## 209 50 No Travel_Frequently 333 Research_Development
## 210 37 No Travel_Rarely 1439 Research_Development
## 211 40 No Travel_Rarely 630 Sales
## 212 45 No Travel_Frequently 1297 Research_Development
## 213 45 No Travel_Rarely 1329 Research_Development
## 214 40 No Travel_Rarely 555 Research_Development
## 215 48 No Travel_Frequently 117 Research_Development
## 216 52 No Travel_Rarely 699 Research_Development
## 217 30 No Travel_Rarely 501 Sales
## 218 28 No Travel_Rarely 760 Sales
## 219 58 No Travel_Rarely 682 Sales
## 220 40 No Travel_Rarely 1194 Research_Development
## 221 47 No Travel_Rarely 202 Research_Development
## 222 32 No Travel_Rarely 427 Research_Development
## 223 37 No Travel_Rarely 558 Sales
## 224 55 No Travel_Rarely 692 Research_Development
## 225 25 No Travel_Rarely 309 Human_Resources
## 226 31 No Travel_Rarely 741 Research_Development
## 227 35 No Travel_Rarely 195 Sales
## 228 38 No Travel_Rarely 1321 Sales
## 229 26 No Travel_Rarely 474 Research_Development
## 230 38 No Travel_Frequently 888 Human_Resources
## 231 35 No Travel_Rarely 672 Research_Development
## 232 49 No Travel_Rarely 1261 Research_Development
## 233 25 No Travel_Rarely 1280 Research_Development
## 234 52 No Non-Travel 715 Research_Development
## 235 39 No Travel_Rarely 408 Research_Development
## 236 40 No Travel_Frequently 530 Research_Development
## 237 38 No Travel_Rarely 1153 Research_Development
## 238 34 No Travel_Rarely 628 Research_Development
## 239 46 No Travel_Rarely 1402 Sales
## 240 36 No Travel_Frequently 607 Sales
## 241 35 No Travel_Frequently 138 Research_Development
## 242 39 No Travel_Rarely 119 Sales
## 243 55 No Non-Travel 177 Research_Development
## 244 40 No Non-Travel 1151 Research_Development
## 245 45 Yes Travel_Rarely 1449 Sales
## 246 32 Yes Non-Travel 1474 Sales
## 247 41 No Travel_Rarely 582 Research_Development
## 248 33 No Non-Travel 722 Sales
## 249 53 No Travel_Rarely 238 Sales
## 250 33 Yes Travel_Rarely 603 Sales
## 251 49 No Travel_Rarely 809 Research_Development
## 252 35 No Travel_Rarely 583 Research_Development
## 253 45 No Travel_Rarely 538 Research_Development
## 254 31 Yes Travel_Frequently 1445 Research_Development
## 255 35 No Travel_Rarely 219 Research_Development
## 256 29 No Travel_Rarely 153 Research_Development
## 257 29 No Travel_Rarely 1010 Research_Development
## 258 45 No Travel_Rarely 1015 Research_Development
## 259 35 No Travel_Rarely 809 Research_Development
## 260 30 No Travel_Rarely 330 Human_Resources
## 261 49 No Travel_Frequently 279 Research_Development
## 262 47 No Travel_Rarely 1454 Sales
## 263 45 No Non-Travel 248 Research_Development
## 264 35 No Travel_Rarely 1232 Sales
## 265 46 No Travel_Frequently 638 Research_Development
## 266 49 No Travel_Rarely 174 Sales
## 267 30 Yes Travel_Rarely 1005 Research_Development
## 268 25 No Travel_Rarely 266 Research_Development
## 269 32 No Travel_Rarely 977 Research_Development
## 270 35 No Travel_Frequently 1199 Research_Development
## 271 49 No Travel_Rarely 470 Research_Development
## 272 55 No Travel_Rarely 685 Sales
## 273 35 No Travel_Rarely 1214 Research_Development
## 274 35 No Travel_Rarely 1343 Research_Development
## 275 58 Yes Travel_Rarely 286 Research_Development
## 276 36 No Travel_Rarely 429 Research_Development
## 277 29 No Travel_Rarely 657 Research_Development
## 278 36 No Travel_Rarely 1223 Research_Development
## 279 31 No Travel_Rarely 1274 Research_Development
## 280 31 No Travel_Rarely 182 Research_Development
## 281 32 No Travel_Rarely 929 Sales
## 282 39 Yes Travel_Rarely 895 Sales
## 283 24 No Travel_Rarely 1371 Sales
## 284 50 No Non-Travel 145 Sales
## 285 46 Yes Travel_Rarely 1254 Sales
## 286 29 No Travel_Rarely 1378 Research_Development
## 287 43 No Travel_Frequently 422 Research_Development
## 288 43 No Travel_Frequently 559 Research_Development
## 289 52 No Travel_Rarely 1053 Research_Development
## 290 45 No Travel_Rarely 954 Sales
## 291 33 No Travel_Rarely 589 Research_Development
## 292 54 No Travel_Rarely 1217 Research_Development
## 293 41 No Travel_Rarely 1276 Sales
## 294 50 No Travel_Rarely 1464 Research_Development
## 295 34 No Non-Travel 1065 Sales
## 296 34 Yes Travel_Rarely 790 Sales
## 297 39 Yes Non-Travel 592 Research_Development
## 298 40 No Travel_Rarely 444 Sales
## 299 44 No Travel_Rarely 1313 Research_Development
## 300 26 Yes Travel_Rarely 920 Human_Resources
## 301 48 No Travel_Rarely 492 Sales
## 302 51 No Travel_Rarely 770 Human_Resources
## 303 34 No Travel_Rarely 1130 Research_Development
## 304 31 No Travel_Rarely 329 Research_Development
## 305 50 No Travel_Rarely 813 Research_Development
## 306 44 No Travel_Rarely 477 Research_Development
## 307 38 No Travel_Frequently 594 Research_Development
## 308 40 No Non-Travel 218 Research_Development
## 309 29 No Travel_Rarely 718 Research_Development
## 310 30 No Travel_Rarely 1339 Sales
## 311 25 No Travel_Rarely 810 Sales
## 312 39 No Non-Travel 1251 Sales
## 313 34 No Travel_Frequently 829 Research_Development
## 314 39 No Travel_Frequently 443 Research_Development
## 315 48 No Travel_Rarely 1224 Research_Development
## 316 36 No Travel_Rarely 506 Research_Development
## 317 45 No Non-Travel 1052 Sales
## 318 34 No Travel_Rarely 970 Research_Development
## 319 42 No Travel_Frequently 555 Sales
## 320 36 No Travel_Rarely 1403 Research_Development
## 321 54 No Travel_Frequently 966 Research_Development
## 322 34 No Travel_Rarely 678 Research_Development
## 323 39 No Travel_Rarely 524 Research_Development
## 324 30 No Travel_Rarely 1288 Sales
## 325 37 No Travel_Rarely 1189 Sales
## 326 37 No Travel_Frequently 1231 Sales
## 327 30 No Travel_Rarely 305 Research_Development
## 328 38 No Travel_Frequently 1394 Research_Development
## 329 40 No Travel_Rarely 759 Sales
## 330 38 No Travel_Rarely 1206 Research_Development
## 331 25 No Travel_Rarely 1356 Sales
## 332 54 No Travel_Rarely 971 Research_Development
## 333 41 No Travel_Rarely 930 Sales
## 334 34 No Travel_Rarely 470 Research_Development
## 335 29 No Travel_Rarely 991 Sales
## 336 51 No Travel_Frequently 541 Sales
## 337 39 No Travel_Rarely 1329 Sales
## 338 49 No Travel_Rarely 1245 Research_Development
## 339 37 No Travel_Rarely 446 Research_Development
## 340 42 No Travel_Rarely 469 Research_Development
## 341 40 Yes Travel_Rarely 676 Research_Development
## 342 33 No Travel_Frequently 970 Sales
## 343 36 Yes Travel_Rarely 530 Sales
## 344 32 No Travel_Rarely 801 Sales
## 345 55 No Travel_Rarely 836 Research_Development
## 346 26 Yes Travel_Rarely 1449 Research_Development
## 347 30 No Travel_Frequently 721 Research_Development
## 348 27 No Travel_Frequently 994 Sales
## 349 31 No Travel_Rarely 1062 Research_Development
## 350 24 No Travel_Frequently 567 Research_Development
## 351 34 No Travel_Rarely 810 Sales
## 352 28 No Non-Travel 1103 Research_Development
## 353 28 Yes Non-Travel 1366 Research_Development
## 354 34 No Travel_Rarely 419 Research_Development
## 355 55 No Travel_Rarely 452 Research_Development
## 356 29 No Travel_Rarely 1252 Research_Development
## 357 40 No Travel_Rarely 1171 Research_Development
## 358 46 No Travel_Rarely 1277 Sales
## 359 53 No Travel_Frequently 124 Sales
## 360 36 No Travel_Rarely 884 Sales
## 361 34 No Travel_Rarely 1354 Research_Development
## 362 26 No Travel_Rarely 1066 Research_Development
## 363 35 No Travel_Rarely 607 Research_Development
## 364 58 No Travel_Rarely 1272 Research_Development
## 365 35 No Travel_Rarely 1146 Human_Resources
## 366 33 No Travel_Rarely 867 Research_Development
## 367 31 No Non-Travel 587 Sales
## 368 40 No Travel_Frequently 692 Research_Development
## 369 51 No Travel_Rarely 1302 Research_Development
## 370 28 No Travel_Rarely 1217 Research_Development
## 371 34 No Travel_Rarely 1480 Sales
## 372 36 No Travel_Rarely 164 Sales
## 373 31 No Travel_Rarely 1222 Research_Development
## 374 52 Yes Travel_Rarely 1030 Sales
## 375 49 No Travel_Rarely 527 Research_Development
## 376 34 No Travel_Frequently 618 Research_Development
## 377 38 No Travel_Rarely 243 Sales
## 378 30 No Travel_Rarely 852 Sales
## 379 45 No Travel_Rarely 252 Research_Development
## 380 38 No Travel_Frequently 1490 Research_Development
## 381 38 No Travel_Rarely 1035 Sales
## 382 51 No Travel_Rarely 1318 Sales
## 383 34 No Travel_Frequently 653 Research_Development
## 384 36 No Travel_Rarely 922 Research_Development
## 385 41 No Travel_Frequently 840 Research_Development
## 386 36 No Non-Travel 1434 Sales
## 387 50 No Travel_Frequently 1115 Research_Development
## 388 55 No Travel_Frequently 1091 Research_Development
## 389 29 Yes Travel_Rarely 318 Research_Development
## 390 43 No Travel_Frequently 394 Sales
## 391 46 No Travel_Rarely 1450 Research_Development
## 392 35 No Travel_Rarely 287 Research_Development
## 393 24 No Travel_Rarely 1206 Research_Development
## 394 47 Yes Travel_Frequently 719 Sales
## 395 52 Yes Travel_Rarely 266 Sales
## 396 36 No Travel_Rarely 928 Sales
## 397 28 Yes Travel_Rarely 1404 Research_Development
## 398 30 No Travel_Rarely 911 Research_Development
## 399 42 No Travel_Rarely 532 Research_Development
## 400 36 No Travel_Rarely 325 Research_Development
## DistanceFromHome Education EducationField EnvironmentSatisfaction
## 1 5 Bachelor Marketing Medium
## 2 3 Bachelor Technical_Degree Low
## 3 10 Bachelor Technical_Degree Very_High
## 4 1 Bachelor Marketing Medium
## 5 13 Bachelor Medical Low
## 6 1 Master Medical Medium
## 7 20 Bachelor Life_Sciences Medium
## 8 29 Below_College Life_Sciences Very_High
## 9 7 Bachelor Life_Sciences Low
## 10 2 Doctor Technical_Degree High
## 11 7 Master Medical Very_High
## 12 19 Below_College Medical Very_High
## 13 20 Bachelor Life_Sciences Medium
## 14 6 Bachelor Life_Sciences High
## 15 8 Master Technical_Degree High
## 16 4 Bachelor Life_Sciences Very_High
## 17 11 Bachelor Life_Sciences High
## 18 4 Master Life_Sciences Low
## 19 3 Master Marketing High
## 20 10 Master Marketing Medium
## 21 6 College Technical_Degree Very_High
## 22 29 Master Life_Sciences Low
## 23 27 Bachelor Life_Sciences High
## 24 9 Master Life_Sciences Medium
## 25 25 Below_College Life_Sciences High
## 26 2 Bachelor Life_Sciences Medium
## 27 2 College Life_Sciences Low
## 28 2 Bachelor Life_Sciences Very_High
## 29 1 Master Medical Very_High
## 30 7 College Medical Very_High
## 31 20 Bachelor Other High
## 32 1 Below_College Medical Low
## 33 7 College Medical Low
## 34 1 College Life_Sciences Very_High
## 35 1 Master Medical High
## 36 9 College Marketing Medium
## 37 11 Bachelor Life_Sciences Medium
## 38 10 Below_College Medical High
## 39 7 Bachelor Life_Sciences Low
## 40 5 Master Life_Sciences Very_High
## 41 26 Bachelor Medical Medium
## 42 10 Bachelor Medical Low
## 43 25 Master Life_Sciences Very_High
## 44 6 College Medical High
## 45 12 College Other Very_High
## 46 10 Master Life_Sciences High
## 47 10 Bachelor Life_Sciences High
## 48 10 Master Human_Resources Medium
## 49 2 Master Life_Sciences Medium
## 50 23 Bachelor Medical Medium
## 51 8 Bachelor Life_Sciences Medium
## 52 1 Master Life_Sciences Medium
## 53 15 Master Other High
## 54 25 Master Life_Sciences High
## 55 1 Below_College Medical Medium
## 56 1 Below_College Life_Sciences High
## 57 20 Below_College Life_Sciences Low
## 58 19 Bachelor Other Very_High
## 59 1 Bachelor Medical Medium
## 60 2 College Medical Low
## 61 19 Master Medical High
## 62 1 Below_College Life_Sciences High
## 63 18 Master Medical High
## 64 2 College Medical High
## 65 13 Master Life_Sciences High
## 66 1 Doctor Marketing High
## 67 26 Bachelor Human_Resources Very_High
## 68 1 College Medical Medium
## 69 10 College Life_Sciences Very_High
## 70 5 Master Technical_Degree Low
## 71 1 Master Marketing High
## 72 16 Bachelor Life_Sciences Very_High
## 73 10 Bachelor Other High
## 74 10 Bachelor Medical Medium
## 75 10 Bachelor Medical Medium
## 76 16 Bachelor Life_Sciences Very_High
## 77 4 Below_College Medical Very_High
## 78 8 Master Life_Sciences Medium
## 79 9 Below_College Life_Sciences High
## 80 4 College Life_Sciences High
## 81 3 Below_College Technical_Degree High
## 82 2 College Life_Sciences Very_High
## 83 1 Master Medical Very_High
## 84 15 Master Marketing Very_High
## 85 3 Master Medical Low
## 86 14 Below_College Other High
## 87 1 Master Life_Sciences Medium
## 88 8 College Life_Sciences Low
## 89 24 Below_College Marketing Medium
## 90 16 Bachelor Marketing Very_High
## 91 21 Below_College Life_Sciences Very_High
## 92 3 Bachelor Technical_Degree High
## 93 10 College Life_Sciences Very_High
## 94 3 Bachelor Other Very_High
## 95 2 Master Medical Low
## 96 14 Bachelor Medical High
## 97 27 Bachelor Marketing Medium
## 98 18 Doctor Life_Sciences Medium
## 99 8 Bachelor Medical High
## 100 6 Bachelor Medical Very_High
## 101 1 College Technical_Degree High
## 102 2 Master Life_Sciences Very_High
## 103 4 Bachelor Life_Sciences High
## 104 2 Master Life_Sciences Medium
## 105 3 Bachelor Life_Sciences Very_High
## 106 23 Bachelor Life_Sciences Very_High
## 107 9 Bachelor Marketing Medium
## 108 28 Below_College Medical Very_High
## 109 28 Bachelor Marketing High
## 110 3 Master Life_Sciences Very_High
## 111 25 Master Marketing Very_High
## 112 4 Below_College Life_Sciences Low
## 113 25 Bachelor Life_Sciences Low
## 114 12 Bachelor Life_Sciences Low
## 115 14 Bachelor Medical High
## 116 9 College Medical Medium
## 117 13 Master Medical Very_High
## 118 17 Bachelor Other Medium
## 119 2 College Life_Sciences Medium
## 120 1 Master Life_Sciences Medium
## 121 7 Master Medical High
## 122 25 College Life_Sciences Medium
## 123 8 Master Other High
## 124 23 Master Life_Sciences Very_High
## 125 7 Bachelor Life_Sciences High
## 126 3 Bachelor Medical Low
## 127 25 Doctor Medical Medium
## 128 22 Bachelor Medical Very_High
## 129 7 College Medical Medium
## 130 7 Master Life_Sciences High
## 131 22 College Marketing High
## 132 5 Bachelor Medical Very_High
## 133 10 College Life_Sciences Low
## 134 2 Master Life_Sciences Low
## 135 4 Below_College Medical High
## 136 7 Bachelor Life_Sciences Very_High
## 137 2 College Life_Sciences Very_High
## 138 6 Master Human_Resources High
## 139 4 Bachelor Technical_Degree Very_High
## 140 7 Below_College Life_Sciences Medium
## 141 2 Master Life_Sciences High
## 142 7 College Medical Very_High
## 143 2 Bachelor Marketing High
## 144 2 Below_College Life_Sciences High
## 145 2 Bachelor Life_Sciences Very_High
## 146 24 Bachelor Medical Very_High
## 147 20 Doctor Medical Medium
## 148 19 Bachelor Marketing Low
## 149 25 Doctor Technical_Degree High
## 150 10 Bachelor Medical High
## 151 9 Bachelor Medical Low
## 152 13 Master Medical High
## 153 2 Master Technical_Degree High
## 154 9 College Medical Low
## 155 14 Bachelor Life_Sciences Very_High
## 156 1 College Medical Medium
## 157 3 Bachelor Medical Very_High
## 158 7 Master Life_Sciences High
## 159 5 College Life_Sciences Very_High
## 160 7 College Medical Medium
## 161 1 College Marketing Medium
## 162 23 Master Medical Very_High
## 163 29 Master Other Very_High
## 164 6 Master Life_Sciences Low
## 165 6 Bachelor Medical Low
## 166 3 Below_College Medical Medium
## 167 29 Bachelor Medical High
## 168 1 Below_College Medical High
## 169 6 Bachelor Life_Sciences High
## 170 2 College Medical Very_High
## 171 29 Doctor Life_Sciences Very_High
## 172 1 Doctor Human_Resources Low
## 173 1 Bachelor Technical_Degree Very_High
## 174 1 Bachelor Life_Sciences Very_High
## 175 24 Below_College Life_Sciences Low
## 176 29 Below_College Medical Medium
## 177 1 Bachelor Marketing Low
## 178 3 Master Medical High
## 179 23 Master Life_Sciences Low
## 180 2 Master Life_Sciences Low
## 181 18 Master Medical Very_High
## 182 7 Doctor Marketing Very_High
## 183 1 Bachelor Life_Sciences Very_High
## 184 4 College Medical High
## 185 26 Master Life_Sciences Low
## 186 9 Bachelor Life_Sciences High
## 187 1 Bachelor Medical Very_High
## 188 25 College Life_Sciences Low
## 189 28 Bachelor Life_Sciences Very_High
## 190 5 College Life_Sciences Medium
## 191 23 Bachelor Life_Sciences Very_High
## 192 3 College Life_Sciences Very_High
## 193 5 Master Technical_Degree Medium
## 194 1 Below_College Life_Sciences Very_High
## 195 12 Bachelor Life_Sciences High
## 196 1 Master Life_Sciences Low
## 197 28 Bachelor Medical Medium
## 198 25 College Other Low
## 199 1 Bachelor Medical High
## 200 1 Master Medical High
## 201 2 Master Life_Sciences Low
## 202 18 Master Life_Sciences Very_High
## 203 1 Master Life_Sciences Very_High
## 204 1 Bachelor Life_Sciences Very_High
## 205 16 Master Medical High
## 206 9 Doctor Medical Very_High
## 207 24 Master Medical Medium
## 208 21 Bachelor Medical Medium
## 209 22 Doctor Medical High
## 210 4 Below_College Life_Sciences High
## 211 4 Master Marketing High
## 212 1 Master Medical Medium
## 213 2 College Other Very_High
## 214 2 Bachelor Medical Medium
## 215 22 Bachelor Medical Very_High
## 216 1 Master Life_Sciences High
## 217 27 Doctor Marketing High
## 218 2 Master Marketing Medium
## 219 10 Master Medical Very_High
## 220 1 Bachelor Life_Sciences High
## 221 2 College Other High
## 222 1 Bachelor Medical Low
## 223 2 Bachelor Marketing Very_High
## 224 14 Master Medical High
## 225 2 Bachelor Human_Resources High
## 226 2 Master Life_Sciences Medium
## 227 1 Bachelor Medical Low
## 228 1 Master Life_Sciences Very_High
## 229 3 Bachelor Life_Sciences Low
## 230 10 Master Human_Resources High
## 231 25 Bachelor Technical_Degree Very_High
## 232 7 Bachelor Other Medium
## 233 7 Below_College Medical Very_High
## 234 19 Master Medical Very_High
## 235 2 Master Technical_Degree Very_High
## 236 1 Master Life_Sciences High
## 237 6 College Other Very_High
## 238 8 Bachelor Medical Medium
## 239 2 Bachelor Marketing High
## 240 7 Bachelor Marketing Low
## 241 2 Bachelor Medical Medium
## 242 15 Master Marketing Medium
## 243 8 Below_College Medical Very_High
## 244 9 Doctor Life_Sciences Very_High
## 245 2 Bachelor Marketing Low
## 246 11 Master Other Very_High
## 247 28 Master Life_Sciences Low
## 248 17 Bachelor Life_Sciences Very_High
## 249 1 Below_College Medical Very_High
## 250 9 Master Marketing Low
## 251 1 Bachelor Life_Sciences High
## 252 25 Master Medical High
## 253 1 Master Technical_Degree Low
## 254 1 Doctor Life_Sciences High
## 255 16 College Other Very_High
## 256 15 College Life_Sciences Very_High
## 257 1 Bachelor Life_Sciences Low
## 258 5 Doctor Medical High
## 259 16 Bachelor Medical Low
## 260 1 Bachelor Life_Sciences High
## 261 8 Below_College Life_Sciences High
## 262 2 Master Life_Sciences Very_High
## 263 23 College Life_Sciences Very_High
## 264 16 Bachelor Marketing High
## 265 1 Bachelor Medical High
## 266 8 Master Technical_Degree Very_High
## 267 3 Bachelor Technical_Degree Very_High
## 268 1 Bachelor Medical Very_High
## 269 2 Bachelor Medical Very_High
## 270 18 Master Life_Sciences High
## 271 20 Master Medical High
## 272 26 Doctor Marketing High
## 273 1 Bachelor Medical Medium
## 274 27 Below_College Medical High
## 275 2 Master Life_Sciences Very_High
## 276 2 Master Life_Sciences High
## 277 27 Bachelor Medical Medium
## 278 8 Bachelor Technical_Degree High
## 279 9 Below_College Life_Sciences High
## 280 8 Doctor Life_Sciences Low
## 281 10 Bachelor Marketing Very_High
## 282 5 Bachelor Technical_Degree Very_High
## 283 10 Master Marketing Very_High
## 284 1 Bachelor Life_Sciences Very_High
## 285 10 Bachelor Life_Sciences High
## 286 13 College Other Very_High
## 287 1 Bachelor Life_Sciences Very_High
## 288 10 Master Life_Sciences High
## 289 1 College Life_Sciences Very_High
## 290 2 College Technical_Degree Medium
## 291 28 Master Life_Sciences Medium
## 292 2 Master Technical_Degree Low
## 293 2 Doctor Life_Sciences Medium
## 294 2 Master Medical Medium
## 295 23 Master Marketing Medium
## 296 24 Master Medical Low
## 297 2 Bachelor Life_Sciences Low
## 298 2 College Marketing Medium
## 299 7 Bachelor Medical Medium
## 300 20 College Medical Very_High
## 301 16 Master Life_Sciences High
## 302 5 Bachelor Life_Sciences High
## 303 3 Bachelor Life_Sciences Very_High
## 304 1 College Life_Sciences Very_High
## 305 17 Doctor Life_Sciences Very_High
## 306 7 Master Medical Low
## 307 2 College Medical High
## 308 8 Below_College Medical Very_High
## 309 8 Below_College Medical Medium
## 310 5 Bachelor Life_Sciences Medium
## 311 8 Bachelor Life_Sciences Very_High
## 312 21 Master Life_Sciences Low
## 313 15 Bachelor Medical Medium
## 314 8 Below_College Life_Sciences High
## 315 10 Bachelor Life_Sciences Very_High
## 316 3 Bachelor Technical_Degree High
## 317 6 Bachelor Medical Very_High
## 318 8 College Medical Medium
## 319 26 Bachelor Marketing High
## 320 6 Bachelor Life_Sciences Very_High
## 321 1 Master Life_Sciences Very_High
## 322 19 Bachelor Life_Sciences Medium
## 323 18 College Life_Sciences Low
## 324 29 Master Technical_Degree High
## 325 3 Bachelor Life_Sciences High
## 326 21 College Medical High
## 327 16 Bachelor Life_Sciences High
## 328 8 Bachelor Medical Very_High
## 329 2 College Marketing Very_High
## 330 9 College Life_Sciences Medium
## 331 10 Master Life_Sciences High
## 332 1 Bachelor Medical Very_High
## 333 3 Bachelor Life_Sciences High
## 334 2 Master Life_Sciences Very_High
## 335 5 Bachelor Medical Low
## 336 2 Bachelor Marketing Medium
## 337 4 Master Life_Sciences Very_High
## 338 18 Master Life_Sciences Very_High
## 339 1 Master Life_Sciences Medium
## 340 2 College Medical Very_High
## 341 9 Master Life_Sciences Very_High
## 342 7 Bachelor Life_Sciences Very_High
## 343 3 Below_College Life_Sciences High
## 344 1 Master Marketing High
## 345 2 Master Technical_Degree Medium
## 346 16 Master Medical Low
## 347 1 College Medical High
## 348 8 Bachelor Life_Sciences Very_High
## 349 24 Bachelor Medical High
## 350 2 Below_College Technical_Degree Low
## 351 8 College Technical_Degree Medium
## 352 16 Bachelor Medical High
## 353 24 College Technical_Degree Medium
## 354 7 Master Life_Sciences Low
## 355 1 Bachelor Medical Very_High
## 356 23 College Life_Sciences High
## 357 10 Master Life_Sciences Very_High
## 358 2 Bachelor Life_Sciences High
## 359 2 Bachelor Marketing High
## 360 1 Master Life_Sciences Medium
## 361 5 Bachelor Medical High
## 362 2 College Medical Very_High
## 363 9 Bachelor Life_Sciences Very_High
## 364 5 Bachelor Technical_Degree High
## 365 26 Master Life_Sciences High
## 366 8 Master Life_Sciences Very_High
## 367 2 Master Life_Sciences Very_High
## 368 11 Bachelor Technical_Degree Very_High
## 369 2 Bachelor Medical Very_High
## 370 1 Bachelor Medical High
## 371 4 Bachelor Life_Sciences High
## 372 2 College Medical Medium
## 373 11 Master Life_Sciences Very_High
## 374 5 Bachelor Life_Sciences Medium
## 375 8 College Other Low
## 376 3 Below_College Life_Sciences Low
## 377 7 Master Marketing Very_High
## 378 10 Bachelor Marketing High
## 379 1 Bachelor Other High
## 380 2 College Life_Sciences Very_High
## 381 3 Master Life_Sciences Medium
## 382 26 Master Marketing Low
## 383 10 Master Technical_Degree Very_High
## 384 3 College Life_Sciences Low
## 385 9 Bachelor Medical Low
## 386 8 Master Life_Sciences Low
## 387 1 Bachelor Life_Sciences Low
## 388 2 Below_College Life_Sciences Very_High
## 389 8 Master Other Medium
## 390 26 College Life_Sciences High
## 391 15 College Life_Sciences Very_High
## 392 1 Master Life_Sciences High
## 393 17 Below_College Medical Very_High
## 394 27 College Life_Sciences Medium
## 395 2 Below_College Marketing Low
## 396 1 College Life_Sciences Medium
## 397 17 Bachelor Technical_Degree High
## 398 1 College Medical Very_High
## 399 4 College Technical_Degree High
## 400 10 Master Technical_Degree Very_High
## Gender HourlyRate JobInvolvement JobLevel JobRole
## 1 Male 88 High 5 Manager
## 2 Female 45 Very_High 4 Sales_Executive
## 3 Male 100 Medium 3 Healthcare_Representative
## 4 Male 49 Very_High 3 Sales_Executive
## 5 Male 47 High 2 Research_Scientist
## 6 Male 68 Medium 1 Sales_Representative
## 7 Female 71 Low 2 Sales_Executive
## 8 Male 93 Low 2 Healthcare_Representative
## 9 Female 55 High 1 Laboratory_Technician
## 10 Female 78 Medium 3 Manufacturing_Director
## 11 Female 68 High 2 Research_Scientist
## 12 Male 67 High 1 Laboratory_Technician
## 13 Male 89 High 2 Laboratory_Technician
## 14 Female 86 Very_High 4 Sales_Executive
## 15 Male 100 High 1 Human_Resources
## 16 Male 54 High 3 Manufacturing_Director
## 17 Female 53 High 5 Manager
## 18 Male 34 High 1 Research_Scientist
## 19 Male 31 Medium 4 Manager
## 20 Male 43 High 2 Sales_Executive
## 21 Male 78 High 2 Research_Scientist
## 22 Male 40 High 1 Sales_Representative
## 23 Male 84 High 2 Sales_Executive
## 24 Female 65 Medium 2 Sales_Executive
## 25 Female 61 High 1 Human_Resources
## 26 Male 97 High 1 Human_Resources
## 27 Male 54 High 1 Sales_Representative
## 28 Male 40 High 3 Healthcare_Representative
## 29 Male 40 High 5 Research_Director
## 30 Female 95 High 1 Sales_Representative
## 31 Male 85 High 2 Manufacturing_Director
## 32 Female 66 Medium 1 Research_Scientist
## 33 Female 44 Very_High 2 Sales_Executive
## 34 Female 44 High 1 Human_Resources
## 35 Male 87 High 1 Research_Scientist
## 36 Male 46 Medium 2 Sales_Executive
## 37 Male 95 Medium 2 Manufacturing_Director
## 38 Male 99 High 4 Manager
## 39 Male 97 High 3 Laboratory_Technician
## 40 Female 74 High 3 Manager
## 41 Female 54 High 2 Research_Scientist
## 42 Female 64 High 3 Manufacturing_Director
## 43 Female 96 High 1 Research_Scientist
## 44 Male 38 High 4 Manager
## 45 Male 78 High 1 Laboratory_Technician
## 46 Female 35 High 5 Research_Director
## 47 Female 42 Very_High 3 Research_Director
## 48 Male 73 Medium 5 Manager
## 49 Female 57 Low 1 Laboratory_Technician
## 50 Male 64 Medium 2 Healthcare_Representative
## 51 Male 66 High 5 Manager
## 52 Female 33 Very_High 2 Healthcare_Representative
## 53 Female 65 High 1 Research_Scientist
## 54 Female 84 High 1 Laboratory_Technician
## 55 Female 42 High 3 Sales_Executive
## 56 Male 88 Medium 3 Manufacturing_Director
## 57 Male 97 High 2 Laboratory_Technician
## 58 Male 67 Medium 1 Laboratory_Technician
## 59 Female 48 Medium 1 Sales_Representative
## 60 Male 49 High 5 Research_Director
## 61 Male 88 High 3 Human_Resources
## 62 Male 74 Medium 3 Healthcare_Representative
## 63 Male 62 High 2 Healthcare_Representative
## 64 Male 45 High 4 Manager
## 65 Male 34 High 2 Healthcare_Representative
## 66 Male 94 High 3 Sales_Executive
## 67 Female 30 Very_High 4 Manager
## 68 Male 57 Low 2 Sales_Executive
## 69 Male 32 High 3 Manufacturing_Director
## 70 Male 96 High 2 Healthcare_Representative
## 71 Male 32 Medium 2 Sales_Executive
## 72 Male 72 High 3 Healthcare_Representative
## 73 Female 59 High 2 Research_Scientist
## 74 Female 71 High 1 Research_Scientist
## 75 Female 99 Low 3 Research_Director
## 76 Female 91 Medium 3 Healthcare_Representative
## 77 Female 46 High 5 Manager
## 78 Male 81 Medium 3 Research_Director
## 79 Male 96 High 3 Sales_Executive
## 80 Male 92 Very_High 5 Manager
## 81 Male 73 High 1 Research_Scientist
## 82 Female 38 High 2 Research_Scientist
## 83 Female 68 High 5 Manager
## 84 Male 88 Low 2 Sales_Executive
## 85 Male 48 Medium 3 Sales_Executive
## 86 Female 84 High 3 Healthcare_Representative
## 87 Male 78 Very_High 2 Healthcare_Representative
## 88 Male 31 High 3 Sales_Executive
## 89 Female 60 Medium 4 Sales_Executive
## 90 Female 80 High 4 Manager
## 91 Female 51 High 2 Healthcare_Representative
## 92 Male 46 High 1 Research_Scientist
## 93 Male 56 High 2 Sales_Executive
## 94 Male 81 Low 2 Sales_Executive
## 95 Female 74 Very_High 2 Research_Scientist
## 96 Male 58 High 1 Laboratory_Technician
## 97 Female 35 High 3 Sales_Executive
## 98 Male 71 High 3 Sales_Executive
## 99 Male 56 Very_High 2 Sales_Executive
## 100 Male 38 Very_High 3 Sales_Executive
## 101 Female 90 Medium 4 Healthcare_Representative
## 102 Male 88 High 4 Manager
## 103 Female 92 Medium 3 Manufacturing_Director
## 104 Female 47 Very_High 4 Manager
## 105 Male 85 High 4 Research_Director
## 106 Male 44 Medium 3 Manufacturing_Director
## 107 Female 52 Low 1 Sales_Representative
## 108 Male 74 Very_High 1 Laboratory_Technician
## 109 Female 80 Medium 3 Sales_Executive
## 110 Female 56 High 1 Research_Scientist
## 111 Male 57 Medium 3 Sales_Executive
## 112 Male 46 Medium 1 Laboratory_Technician
## 113 Female 99 High 3 Sales_Executive
## 114 Male 59 Medium 4 Research_Director
## 115 Female 78 High 2 Sales_Executive
## 116 Male 48 Medium 2 Manufacturing_Director
## 117 Male 39 High 3 Sales_Executive
## 118 Male 51 Medium 3 Human_Resources
## 119 Female 31 High 2 Sales_Executive
## 120 Male 88 High 1 Laboratory_Technician
## 121 Female 53 Medium 3 Manufacturing_Director
## 122 Female 93 Medium 2 Manufacturing_Director
## 123 Female 54 High 1 Laboratory_Technician
## 124 Female 72 High 4 Research_Director
## 125 Male 99 High 1 Sales_Representative
## 126 Male 99 High 5 Manager
## 127 Female 72 High 2 Research_Scientist
## 128 Female 82 High 3 Manufacturing_Director
## 129 Female 31 High 2 Manufacturing_Director
## 130 Male 49 High 2 Laboratory_Technician
## 131 Male 68 Medium 2 Sales_Executive
## 132 Male 84 High 2 Sales_Executive
## 133 Female 100 High 2 Sales_Executive
## 134 Male 79 Medium 5 Manager
## 135 Female 51 High 1 Laboratory_Technician
## 136 Male 59 High 1 Laboratory_Technician
## 137 Male 39 Medium 2 Sales_Executive
## 138 Male 63 High 1 Human_Resources
## 139 Male 86 High 3 Sales_Executive
## 140 Female 55 Low 5 Research_Director
## 141 Female 79 High 5 Manager
## 142 Female 50 High 5 Manager
## 143 Female 86 High 2 Sales_Executive
## 144 Male 37 High 4 Manager
## 145 Male 61 High 2 Sales_Executive
## 146 Male 56 High 3 Manufacturing_Director
## 147 Male 41 High 4 Healthcare_Representative
## 148 Male 67 Very_High 2 Sales_Executive
## 149 Female 71 Medium 1 Research_Scientist
## 150 Female 77 High 2 Manufacturing_Director
## 151 Male 60 High 1 Research_Scientist
## 152 Female 94 Medium 4 Manager
## 153 Female 64 High 3 Healthcare_Representative
## 154 Female 74 High 1 Laboratory_Technician
## 155 Female 95 High 1 Laboratory_Technician
## 156 Male 91 Medium 3 Human_Resources
## 157 Female 49 High 4 Sales_Executive
## 158 Male 35 High 3 Healthcare_Representative
## 159 Female 53 High 1 Laboratory_Technician
## 160 Female 43 Medium 5 Research_Director
## 161 Female 92 High 3 Sales_Executive
## 162 Female 94 High 3 Healthcare_Representative
## 163 Male 32 High 2 Research_Scientist
## 164 Male 80 Very_High 2 Laboratory_Technician
## 165 Female 73 Medium 2 Research_Scientist
## 166 Female 79 High 1 Laboratory_Technician
## 167 Male 83 High 1 Research_Scientist
## 168 Female 98 High 3 Research_Director
## 169 Male 30 High 2 Healthcare_Representative
## 170 Female 54 Medium 3 Manufacturing_Director
## 171 Female 50 High 2 Laboratory_Technician
## 172 Male 37 High 2 Human_Resources
## 173 Female 80 High 3 Research_Director
## 174 Male 62 Medium 1 Laboratory_Technician
## 175 Male 52 High 2 Research_Scientist
## 176 Male 91 High 1 Laboratory_Technician
## 177 Male 32 High 3 Sales_Executive
## 178 Female 93 High 2 Manufacturing_Director
## 179 Male 88 High 1 Research_Scientist
## 180 Male 98 High 4 Manager
## 181 Male 67 High 3 Manufacturing_Director
## 182 Male 97 High 2 Sales_Executive
## 183 Female 56 Medium 2 Manufacturing_Director
## 184 Female 63 Medium 2 Sales_Executive
## 185 Male 80 High 2 Healthcare_Representative
## 186 Male 82 Low 4 Sales_Executive
## 187 Female 76 High 5 Research_Director
## 188 Female 81 Medium 3 Research_Director
## 189 Male 32 High 4 Research_Director
## 190 Male 48 High 2 Sales_Executive
## 191 Male 68 High 4 Manufacturing_Director
## 192 Male 79 Very_High 2 Healthcare_Representative
## 193 Male 65 High 1 Research_Scientist
## 194 Female 65 Medium 4 Manufacturing_Director
## 195 Male 44 High 4 Sales_Executive
## 196 Male 46 Medium 3 Sales_Executive
## 197 Female 72 Very_High 1 Research_Scientist
## 198 Male 54 Medium 2 Laboratory_Technician
## 199 Male 48 Very_High 3 Healthcare_Representative
## 200 Female 96 High 1 Laboratory_Technician
## 201 Female 78 Medium 4 Manager
## 202 Male 35 High 2 Research_Scientist
## 203 Male 56 Very_High 4 Manager
## 204 Male 38 Low 1 Laboratory_Technician
## 205 Male 64 Very_High 2 Manufacturing_Director
## 206 Male 72 High 2 Laboratory_Technician
## 207 Male 36 High 1 Human_Resources
## 208 Male 79 Very_High 1 Laboratory_Technician
## 209 Male 88 Low 4 Research_Director
## 210 Male 54 High 1 Research_Scientist
## 211 Male 67 Medium 3 Sales_Executive
## 212 Male 44 High 2 Healthcare_Representative
## 213 Female 59 Medium 2 Manufacturing_Director
## 214 Female 78 Medium 2 Laboratory_Technician
## 215 Female 58 High 4 Manager
## 216 Male 65 Medium 5 Manager
## 217 Male 99 High 2 Sales_Executive
## 218 Female 81 High 2 Sales_Executive
## 219 Male 37 High 4 Sales_Executive
## 220 Female 52 High 2 Healthcare_Representative
## 221 Female 33 High 4 Manager
## 222 Male 33 High 2 Manufacturing_Director
## 223 Male 75 High 2 Sales_Executive
## 224 Male 61 Very_High 5 Research_Director
## 225 Female 82 High 1 Human_Resources
## 226 Male 69 High 1 Laboratory_Technician
## 227 Female 80 High 2 Sales_Executive
## 228 Male 86 High 2 Sales_Executive
## 229 Female 89 High 1 Research_Scientist
## 230 Male 71 High 2 Human_Resources
## 231 Male 78 Medium 3 Manufacturing_Director
## 232 Male 31 Medium 3 Healthcare_Representative
## 233 Male 64 Medium 1 Research_Scientist
## 234 Male 41 High 1 Research_Scientist
## 235 Female 80 Medium 2 Healthcare_Representative
## 236 Male 78 Medium 4 Healthcare_Representative
## 237 Female 40 Medium 1 Laboratory_Technician
## 238 Male 82 Very_High 2 Laboratory_Technician
## 239 Female 69 High 4 Manager
## 240 Female 83 Very_High 2 Sales_Executive
## 241 Female 37 High 2 Laboratory_Technician
## 242 Male 77 High 4 Sales_Executive
## 243 Male 37 Medium 4 Healthcare_Representative
## 244 Male 63 Medium 2 Healthcare_Representative
## 245 Female 94 Low 5 Manager
## 246 Male 60 Very_High 2 Sales_Executive
## 247 Female 60 Medium 4 Manufacturing_Director
## 248 Male 38 High 4 Manager
## 249 Female 34 High 2 Sales_Executive
## 250 Female 77 High 2 Sales_Executive
## 251 Male 36 High 4 Manager
## 252 Female 57 High 3 Healthcare_Representative
## 253 Male 66 High 3 Healthcare_Representative
## 254 Female 100 Very_High 3 Manufacturing_Director
## 255 Female 44 Medium 2 Manufacturing_Director
## 256 Female 49 Medium 2 Laboratory_Technician
## 257 Female 97 High 1 Research_Scientist
## 258 Female 50 Low 2 Laboratory_Technician
## 259 Male 84 Very_High 1 Laboratory_Technician
## 260 Male 46 High 1 Human_Resources
## 261 Male 61 Medium 2 Research_Scientist
## 262 Female 65 Medium 1 Sales_Representative
## 263 Male 42 High 2 Laboratory_Technician
## 264 Male 96 High 3 Sales_Executive
## 265 Male 40 Medium 3 Healthcare_Representative
## 266 Male 56 Medium 4 Sales_Executive
## 267 Female 88 High 1 Research_Scientist
## 268 Female 40 High 1 Research_Scientist
## 269 Male 45 High 2 Research_Scientist
## 270 Male 80 High 2 Healthcare_Representative
## 271 Female 96 High 2 Manufacturing_Director
## 272 Male 60 Medium 5 Manager
## 273 Male 30 Medium 1 Research_Scientist
## 274 Female 53 Medium 1 Research_Scientist
## 275 Male 31 High 5 Research_Director
## 276 Female 53 High 2 Manufacturing_Director
## 277 Female 66 High 2 Healthcare_Representative
## 278 Female 59 High 3 Healthcare_Representative
## 279 Male 33 High 3 Manufacturing_Director
## 280 Female 93 High 4 Research_Director
## 281 Male 55 High 2 Sales_Executive
## 282 Male 56 High 2 Sales_Representative
## 283 Female 77 High 2 Sales_Executive
## 284 Female 95 High 2 Sales_Executive
## 285 Female 64 High 3 Sales_Executive
## 286 Male 46 Medium 2 Laboratory_Technician
## 287 Female 33 High 2 Healthcare_Representative
## 288 Female 82 Medium 2 Laboratory_Technician
## 289 Male 70 High 4 Manager
## 290 Male 46 Low 2 Sales_Representative
## 291 Male 79 High 2 Laboratory_Technician
## 292 Female 60 High 3 Research_Director
## 293 Female 91 High 4 Manager
## 294 Male 62 High 5 Research_Director
## 295 Male 72 High 2 Sales_Executive
## 296 Female 40 Medium 2 Sales_Executive
## 297 Female 54 Medium 1 Laboratory_Technician
## 298 Female 92 High 2 Sales_Executive
## 299 Female 31 High 5 Research_Director
## 300 Female 69 High 1 Human_Resources
## 301 Female 96 High 2 Sales_Executive
## 302 Male 84 High 4 Manager
## 303 Female 66 High 2 Research_Scientist
## 304 Male 98 Medium 1 Laboratory_Technician
## 305 Female 50 Medium 3 Research_Director
## 306 Female 42 Medium 3 Healthcare_Representative
## 307 Female 75 Medium 1 Laboratory_Technician
## 308 Male 55 Medium 3 Research_Director
## 309 Male 79 Medium 2 Manufacturing_Director
## 310 Female 41 High 3 Sales_Executive
## 311 Male 57 Very_High 2 Sales_Executive
## 312 Female 32 Low 2 Sales_Executive
## 313 Male 71 High 4 Research_Director
## 314 Female 48 High 1 Laboratory_Technician
## 315 Male 91 Medium 5 Research_Director
## 316 Male 30 High 2 Research_Scientist
## 317 Female 57 Medium 3 Sales_Executive
## 318 Female 96 High 2 Healthcare_Representative
## 319 Female 77 High 4 Sales_Executive
## 320 Male 47 High 1 Laboratory_Technician
## 321 Female 53 High 3 Manufacturing_Director
## 322 Female 35 Medium 1 Research_Scientist
## 323 Male 32 High 2 Manufacturing_Director
## 324 Male 33 High 3 Sales_Executive
## 325 Male 87 High 3 Sales_Executive
## 326 Female 54 High 1 Sales_Representative
## 327 Male 58 Very_High 2 Healthcare_Representative
## 328 Female 58 Medium 2 Research_Scientist
## 329 Female 46 High 2 Sales_Executive
## 330 Male 71 High 1 Research_Scientist
## 331 Male 57 High 2 Sales_Executive
## 332 Female 54 High 4 Research_Director
## 333 Male 57 Medium 2 Sales_Executive
## 334 Male 84 Medium 2 Manufacturing_Director
## 335 Male 43 Medium 2 Sales_Executive
## 336 Male 52 High 3 Sales_Executive
## 337 Female 47 Medium 2 Sales_Executive
## 338 Male 58 Medium 5 Research_Director
## 339 Female 65 High 2 Manufacturing_Director
## 340 Male 35 High 4 Manager
## 341 Male 86 High 1 Laboratory_Technician
## 342 Female 30 High 2 Sales_Executive
## 343 Male 51 Medium 3 Sales_Executive
## 344 Female 48 High 3 Sales_Executive
## 345 Male 98 Medium 1 Research_Scientist
## 346 Male 45 High 1 Laboratory_Technician
## 347 Female 58 High 2 Laboratory_Technician
## 348 Male 37 High 3 Sales_Executive
## 349 Female 96 Medium 2 Healthcare_Representative
## 350 Female 32 High 1 Research_Scientist
## 351 Male 92 Very_High 2 Sales_Executive
## 352 Male 49 High 1 Research_Scientist
## 353 Male 72 Medium 3 Healthcare_Representative
## 354 Female 53 High 3 Research_Director
## 355 Male 81 High 5 Manager
## 356 Male 81 Very_High 1 Research_Scientist
## 357 Female 46 Very_High 1 Laboratory_Technician
## 358 Male 74 High 3 Sales_Executive
## 359 Female 38 Medium 3 Sales_Executive
## 360 Female 73 High 2 Sales_Executive
## 361 Female 45 Medium 3 Manager
## 362 Male 32 Very_High 2 Manufacturing_Director
## 363 Female 66 Medium 3 Manufacturing_Director
## 364 Female 37 Medium 3 Healthcare_Representative
## 365 Female 31 High 3 Human_Resources
## 366 Male 90 Very_High 1 Research_Scientist
## 367 Female 57 High 3 Sales_Executive
## 368 Female 73 High 2 Laboratory_Technician
## 369 Male 84 Low 2 Manufacturing_Director
## 370 Female 67 High 1 Research_Scientist
## 371 Male 64 High 3 Sales_Executive
## 372 Male 61 Medium 3 Sales_Executive
## 373 Male 48 High 1 Research_Scientist
## 374 Male 64 High 3 Sales_Executive
## 375 Female 51 High 3 Laboratory_Technician
## 376 Male 45 High 2 Healthcare_Representative
## 377 Female 46 Medium 2 Sales_Executive
## 378 Male 72 Medium 2 Sales_Executive
## 379 Male 70 Very_High 5 Manager
## 380 Male 42 High 1 Laboratory_Technician
## 381 Male 42 High 2 Sales_Executive
## 382 Female 66 High 4 Manager
## 383 Male 92 Medium 2 Healthcare_Representative
## 384 Female 39 High 1 Laboratory_Technician
## 385 Male 64 High 5 Research_Director
## 386 Male 76 Medium 3 Sales_Executive
## 387 Female 73 High 5 Research_Director
## 388 Male 65 High 3 Manufacturing_Director
## 389 Male 77 Low 1 Laboratory_Technician
## 390 Male 92 High 4 Manager
## 391 Male 52 High 5 Research_Director
## 392 Female 62 Low 1 Research_Scientist
## 393 Female 41 Medium 2 Manufacturing_Director
## 394 Female 77 Very_High 2 Sales_Executive
## 395 Female 57 Low 5 Manager
## 396 Male 56 High 2 Sales_Executive
## 397 Male 32 Medium 1 Laboratory_Technician
## 398 Male 76 High 1 Laboratory_Technician
## 399 Male 58 High 5 Manager
## 400 Female 63 High 3 Healthcare_Representative
## JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked
## 1 Medium Married 18213 8751 7
## 2 Low Divorced 13225 7739 2
## 3 Very_High Single 10496 2755 6
## 4 Low Married 8392 19566 1
## 5 Very_High Married 4285 3031 1
## 6 Medium Married 2269 18024 0
## 7 High Married 4559 24788 3
## 8 High Divorced 6384 21143 8
## 9 High Married 2886 14168 1
## 10 Very_High Married 10169 14618 0
## 11 Very_High Married 6854 15696 4
## 12 Very_High Married 3669 9075 3
## 13 High Divorced 4197 18624 1
## 14 Low Married 13212 18256 9
## 15 High Single 4323 7108 1
## 16 Very_High Single 7898 18706 1
## 17 Medium Single 19926 17053 3
## 18 High Married 2461 10332 9
## 19 Low Married 16856 10084 1
## 20 High Single 5487 10410 1
## 21 Low Divorced 5577 22087 3
## 22 Very_High Divorced 3482 19788 2
## 23 Very_High Single 5813 13492 1
## 24 High Married 5593 17970 1
## 25 High Married 2942 8916 1
## 26 Very_High Single 3539 5033 0
## 27 High Married 3067 6393 0
## 28 Very_High Married 10124 18611 2
## 29 Very_High Divorced 19627 21445 9
## 30 High Married 2655 11740 2
## 31 Low Married 9957 9096 0
## 32 High Divorced 2007 25265 1
## 33 Low Divorced 5605 19191 1
## 34 Low Single 3423 22957 6
## 35 Medium Single 2501 18775 1
## 36 Medium Married 4189 8800 1
## 37 Medium Single 6499 22656 1
## 38 Low Married 17068 5355 1
## 39 Low Divorced 5210 20308 1
## 40 Medium Married 11878 23364 6
## 41 Medium Divorced 5605 8504 1
## 42 High Single 9667 2739 9
## 43 Medium Divorced 2022 16612 1
## 44 Very_High Single 16437 17381 1
## 45 Very_High Single 2647 13672 1
## 46 Low Single 18665 25594 9
## 47 Very_High Married 13744 15471 1
## 48 Very_High Divorced 19141 8861 3
## 49 Very_High Divorced 2778 17725 4
## 50 Very_High Married 5582 14408 0
## 51 Very_High Divorced 18430 16225 1
## 52 Very_High Single 7625 19383 0
## 53 Low Married 2367 16530 8
## 54 Low Married 3211 22102 1
## 55 Very_High Married 7918 6599 1
## 56 High Single 8474 20925 1
## 57 High Single 2272 24812 0
## 58 Low Divorced 4066 16290 1
## 59 High Divorced 2800 23522 6
## 60 High Divorced 19436 5949 0
## 61 Medium Married 10725 6729 2
## 62 High Married 10748 3395 3
## 63 Medium Married 6385 12992 3
## 64 High Divorced 14852 13938 6
## 65 Medium Single 5562 9697 6
## 66 Medium Married 7295 11439 1
## 67 Very_High Single 17328 13871 2
## 68 Very_High Married 7457 13273 2
## 69 Low Divorced 8793 4809 1
## 70 High Married 6651 21534 2
## 71 Low Single 4312 23016 0
## 72 High Married 7632 14295 4
## 73 High Single 3660 7909 3
## 74 Medium Married 3936 9953 1
## 75 High Married 13206 3376 3
## 76 Medium Single 8606 21195 1
## 77 High Married 19033 6499 1
## 78 Medium Married 12490 15736 5
## 79 High Divorced 8189 21196 3
## 80 Low Divorced 19190 17477 1
## 81 Low Single 3102 6582 0
## 82 High Single 4998 2338 4
## 83 Very_High Divorced 19517 24118 3
## 84 Very_High Divorced 5406 10436 1
## 85 Very_High Married 9699 7246 4
## 86 Very_High Single 7553 22930 0
## 87 Low Married 5033 9364 2
## 88 Very_High Divorced 10793 8386 1
## 89 Very_High Married 12031 8828 0
## 90 Very_High Married 16064 7744 5
## 91 Very_High Married 4014 19170 1
## 92 Medium Married 3692 9256 1
## 93 Very_High Single 6230 13430 7
## 94 Low Single 4759 15891 3
## 95 Very_High Single 5055 10557 7
## 96 Very_High Married 2436 22149 5
## 97 Very_High Married 7639 24525 1
## 98 Low Married 9069 11031 1
## 99 Very_High Married 6209 11693 1
## 100 High Married 8237 4658 2
## 101 High Single 13964 17810 7
## 102 Medium Single 16015 15896 1
## 103 Medium Divorced 10333 19271 8
## 104 Medium Divorced 15972 21086 6
## 105 Very_High Married 17779 23474 3
## 106 High Single 9526 8787 0
## 107 Medium Single 2760 14630 1
## 108 High Married 3221 3297 1
## 109 Low Married 10266 2845 4
## 110 High Married 2909 23159 1
## 111 Medium Single 9094 17235 2
## 112 Very_High Married 3162 10778 0
## 113 Low Single 7637 2354 7
## 114 Medium Divorced 14336 4345 1
## 115 High Married 4591 24200 3
## 116 Medium Divorced 8847 13934 2
## 117 High Divorced 8628 22914 1
## 118 Low Single 7988 9769 1
## 119 Low Divorced 6893 19461 3
## 120 Medium Married 2387 6762 3
## 121 Low Married 10008 12023 7
## 122 Very_High Married 5906 23888 0
## 123 Low Married 2950 17363 9
## 124 Very_High Single 14275 20206 6
## 125 Very_High Divorced 2275 25103 1
## 126 Medium Married 18200 7999 1
## 127 High Married 4449 23866 3
## 128 Low Single 10880 5083 1
## 129 Low Married 4306 4156 1
## 130 High Divorced 3491 11309 1
## 131 High Married 6380 6110 2
## 132 Low Single 8463 23490 0
## 133 Very_High Married 5343 5982 1
## 134 High Single 19068 21030 1
## 135 Medium Single 3295 12862 1
## 136 Low Single 2062 19384 3
## 137 Very_High Married 5249 19682 3
## 138 Low Divorced 2073 23648 4
## 139 Low Single 9582 10333 0
## 140 High Divorced 19973 20284 1
## 141 High Married 18041 13022 0
## 142 High Divorced 18606 18640 3
## 143 Low Single 4538 6039 0
## 144 High Married 17861 26582 0
## 145 High Married 4649 16928 1
## 146 Low Married 7406 6950 1
## 147 High Married 11245 20689 2
## 148 Very_High Single 5304 4652 8
## 149 Medium Married 2546 18300 5
## 150 Low Single 4440 25198 6
## 151 Very_High Married 2781 6311 0
## 152 Medium Divorced 17123 17334 6
## 153 High Single 10938 6420 0
## 154 Very_High Single 3673 16458 1
## 155 Low Divorced 3034 26914 1
## 156 Low Married 10482 2326 9
## 157 High Divorced 13770 10225 9
## 158 Medium Divorced 10466 20948 3
## 159 Very_High Single 2379 19826 0
## 160 High Divorced 18740 16701 5
## 161 Low Divorced 10453 2137 1
## 162 Very_High Married 10312 3465 1
## 163 Very_High Single 4541 7744 1
## 164 Low Married 5562 19711 3
## 165 High Single 4081 20003 1
## 166 Medium Single 1483 16102 1
## 167 Very_High Single 3452 9752 5
## 168 Medium Married 11957 17231 0
## 169 Very_High Divorced 7725 5335 3
## 170 High Married 7756 14199 3
## 171 Very_High Single 2406 5456 1
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## 175 Very_High Single 4108 5340 7
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## 180 Low Divorced 17924 4544 1
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## 182 Very_High Married 9204 23343 4
## 183 Very_High Married 6474 9961 1
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## 186 High Married 12936 24164 7
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## 190 Medium Married 6524 8891 1
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## 194 Very_High Married 12742 7060 1
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## 198 Very_High Single 3681 14004 4
## 199 Very_High Divorced 9613 10942 0
## 200 High Divorced 2232 15417 7
## 201 Very_High Married 15427 22021 2
## 202 Low Divorced 5410 11189 6
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## 204 High Married 3833 24375 3
## 205 High Divorced 5067 6759 1
## 206 High Divorced 5679 19627 3
## 207 Medium Single 2177 8318 1
## 208 Medium Married 2028 13637 1
## 209 Very_High Single 14411 24450 1
## 210 High Married 2996 5182 7
## 211 Very_High Married 10855 8552 7
## 212 High Single 5399 14511 4
## 213 Very_High Divorced 5770 5388 1
## 214 High Married 3448 13436 6
## 215 Very_High Divorced 17174 2437 3
## 216 High Married 19999 5678 0
## 217 Very_High Divorced 5304 25275 7
## 218 Medium Married 4779 3698 1
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## 220 Very_High Divorced 6513 9060 4
## 221 Very_High Married 16752 12982 1
## 222 Very_High Married 6162 10877 1
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## 224 Medium Single 18722 13339 8
## 225 Medium Married 2187 19655 4
## 226 High Married 3477 18103 1
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## 228 Medium Married 4440 7636 0
## 229 Very_High Married 2061 11133 1
## 230 High Married 6077 14814 3
## 231 Medium Married 10903 9129 3
## 232 High Single 10965 12066 8
## 233 Very_High Married 2889 26897 1
## 234 Very_High Married 4258 26589 0
## 235 High Single 4553 20978 1
## 236 Medium Married 13503 14115 1
## 237 High Married 3702 16376 1
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## 242 Low Single 13341 25098 0
## 243 Medium Divorced 13577 25592 1
## 244 Very_High Married 4876 14242 9
## 245 Medium Single 18824 2493 2
## 246 High Married 4707 23914 8
## 247 Medium Married 13570 5640 0
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## 250 Low Single 8224 18385 0
## 251 High Single 15379 22384 4
## 252 High Divorced 10388 6975 1
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## 257 Very_High Divorced 3760 5598 1
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## 272 Very_High Married 19586 23037 1
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## 287 Very_High Married 5562 21782 4
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## 290 High Single 6632 12388 0
## 291 High Married 5207 22949 1
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## 293 Low Married 16595 5626 7
## 294 High Married 19237 12853 2
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## 298 Medium Married 5677 4258 3
## 299 Very_High Divorced 19049 3549 0
## 300 Medium Married 2148 6889 0
## 301 High Divorced 6439 13693 8
## 302 Medium Divorced 14026 17588 1
## 303 Medium Divorced 5433 19332 1
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## 305 Low Divorced 13269 21981 5
## 306 Very_High Married 10248 2094 3
## 307 Medium Married 2468 15963 4
## 308 Medium Divorced 13757 25178 2
## 309 Very_High Married 5056 17689 1
## 310 Very_High Married 9419 8053 2
## 311 Medium Married 4851 15678 0
## 312 High Married 5736 3987 6
## 313 Low Divorced 17007 11929 7
## 314 High Married 3755 17872 1
## 315 Medium Married 19665 13583 4
## 316 Medium Single 4485 26285 4
## 317 Very_High Single 8865 16840 6
## 318 High Single 6142 7360 3
## 319 Medium Married 13525 14864 5
## 320 Very_High Married 3210 20251 0
## 321 High Divorced 10502 9659 7
## 322 Very_High Married 2929 20338 1
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## 324 Medium Married 9250 17799 3
## 325 Very_High Single 7428 14506 2
## 326 Very_High Married 2973 21222 5
## 327 High Married 5294 9128 3
## 328 Medium Divorced 2133 18115 1
## 329 Medium Divorced 5715 22553 7
## 330 Very_High Divorced 4771 14293 2
## 331 Very_High Single 4950 20623 0
## 332 Very_High Single 17328 5652 6
## 333 Medium Divorced 8938 12227 2
## 334 Low Married 5957 23687 6
## 335 Medium Divorced 4187 3356 1
## 336 Medium Married 10596 15395 2
## 337 High Married 5902 14590 4
## 338 High Divorced 19502 2125 1
## 339 Medium Married 6447 15701 6
## 340 Low Married 17665 14399 0
## 341 Low Single 2018 21831 3
## 342 Medium Married 4302 13401 0
## 343 Very_High Married 10325 5518 1
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## 345 Very_High Married 2662 7975 8
## 346 Medium Divorced 2373 14180 2
## 347 Very_High Single 4011 10781 1
## 348 High Single 8726 2975 1
## 349 Low Single 6812 17198 1
## 350 Very_High Single 3760 17218 1
## 351 High Married 6799 22128 1
## 352 High Single 2144 2122 1
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## 354 Medium Single 11994 21293 0
## 355 Low Single 19045 18938 0
## 356 High Married 2700 23779 1
## 357 High Married 2213 22495 3
## 358 Very_High Divorced 10368 5596 4
## 359 Medium Married 7525 23537 2
## 360 High Single 6815 21447 6
## 361 Low Single 11631 5615 2
## 362 Very_High Married 5472 3334 1
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## 364 Medium Divorced 10552 9255 2
## 365 Very_High Single 8837 16642 1
## 366 Low Married 3143 6076 6
## 367 High Divorced 9852 8935 1
## 368 High Married 6323 26849 1
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## 370 Low Married 3591 12719 1
## 371 Very_High Married 9713 24444 2
## 372 High Married 7596 3809 1
## 373 Very_High Married 2356 14871 3
## 374 Medium Single 8446 21534 9
## 375 Medium Married 7403 22477 4
## 376 Very_High Single 7756 22266 0
## 377 Very_High Single 4028 7791 0
## 378 High Married 6578 2706 1
## 379 Very_High Married 19202 15970 0
## 380 Very_High Married 1702 12106 1
## 381 Very_High Single 6861 4981 8
## 382 High Married 16307 5594 2
## 383 High Married 5063 15332 1
## 384 Very_High Divorced 2835 2561 5
## 385 High Divorced 19419 3735 2
## 386 Low Single 7587 14229 1
## 387 Medium Married 18172 9755 3
## 388 Medium Married 10976 15813 3
## 389 Low Married 2119 4759 1
## 390 Very_High Married 16959 19494 1
## 391 Medium Married 19081 10849 5
## 392 Very_High Married 2977 8952 1
## 393 High Divorced 4377 24117 1
## 394 High Single 6397 10339 4
## 395 Very_High Married 19845 25846 1
## 396 Very_High Married 6201 2823 1
## 397 Very_High Divorced 2367 18779 5
## 398 Medium Married 3748 4077 1
## 399 Very_High Married 19232 4933 1
## 400 High Married 7094 5747 3
## OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 1 No 11 Excellent High
## 2 No 12 Excellent Very_High
## 3 No 15 Excellent Very_High
## 4 No 16 Excellent High
## 5 No 17 Excellent Low
## 6 No 14 Excellent Medium
## 7 Yes 11 Excellent High
## 8 No 17 Excellent Very_High
## 9 Yes 16 Excellent Very_High
## 10 No 16 Excellent Medium
## 11 No 15 Excellent Medium
## 12 No 11 Excellent High
## 13 No 11 Excellent Low
## 14 No 11 Excellent Very_High
## 15 No 17 Excellent Medium
## 16 No 11 Excellent High
## 17 No 15 Excellent Medium
## 18 Yes 12 Excellent High
## 19 No 11 Excellent Low
## 20 No 14 Excellent Medium
## 21 Yes 12 Excellent Medium
## 22 No 15 Excellent Medium
## 23 Yes 18 Excellent Very_High
## 24 No 13 Excellent Very_High
## 25 No 23 Outstanding Very_High
## 26 No 13 Excellent Medium
## 27 No 19 Excellent High
## 28 Yes 14 Excellent High
## 29 No 17 Excellent Very_High
## 30 Yes 11 Excellent High
## 31 No 15 Excellent High
## 32 No 13 Excellent High
## 33 Yes 24 Outstanding High
## 34 No 12 Excellent High
## 35 No 17 Excellent Medium
## 36 No 14 Excellent Low
## 37 No 13 Excellent High
## 38 Yes 14 Excellent Very_High
## 39 No 18 Excellent Low
## 40 No 11 Excellent Medium
## 41 No 11 Excellent Low
## 42 No 14 Excellent Medium
## 43 Yes 19 Excellent Low
## 44 Yes 21 Outstanding Very_High
## 45 No 13 Excellent High
## 46 Yes 11 Excellent Very_High
## 47 Yes 25 Outstanding Low
## 48 No 15 Excellent Medium
## 49 Yes 13 Excellent High
## 50 No 21 Outstanding Medium
## 51 No 13 Excellent Medium
## 52 No 13 Excellent High
## 53 No 12 Excellent Very_High
## 54 No 14 Excellent Very_High
## 55 No 14 Excellent Very_High
## 56 No 22 Outstanding High
## 57 No 14 Excellent Medium
## 58 No 11 Excellent Low
## 59 Yes 19 Excellent High
## 60 No 19 Excellent Very_High
## 61 No 15 Excellent High
## 62 No 23 Outstanding Very_High
## 63 Yes 14 Excellent Very_High
## 64 No 13 Excellent High
## 65 No 14 Excellent Very_High
## 66 No 13 Excellent Low
## 67 Yes 12 Excellent High
## 68 Yes 22 Outstanding High
## 69 No 21 Outstanding High
## 70 No 14 Excellent Medium
## 71 No 14 Excellent Medium
## 72 Yes 12 Excellent High
## 73 No 13 Excellent Very_High
## 74 No 11 Excellent Low
## 75 No 12 Excellent Low
## 76 No 19 Excellent Very_High
## 77 No 14 Excellent Medium
## 78 No 16 Excellent Very_High
## 79 Yes 13 Excellent High
## 80 No 14 Excellent Very_High
## 81 No 22 Outstanding High
## 82 Yes 14 Excellent Very_High
## 83 No 11 Excellent High
## 84 No 24 Outstanding Low
## 85 No 11 Excellent Low
## 86 Yes 12 Excellent Low
## 87 No 15 Excellent Very_High
## 88 No 18 Excellent Low
## 89 No 11 Excellent Low
## 90 Yes 22 Outstanding High
## 91 Yes 25 Outstanding Very_High
## 92 No 12 Excellent High
## 93 No 14 Excellent Very_High
## 94 No 18 Excellent Very_High
## 95 No 16 Excellent High
## 96 Yes 13 Excellent High
## 97 No 22 Outstanding Very_High
## 98 No 22 Outstanding Very_High
## 99 No 15 Excellent High
## 100 No 11 Excellent Low
## 101 Yes 12 Excellent Very_High
## 102 No 19 Excellent Medium
## 103 Yes 12 Excellent High
## 104 No 14 Excellent High
## 105 No 14 Excellent Low
## 106 No 21 Outstanding Medium
## 107 No 13 Excellent High
## 108 Yes 11 Excellent High
## 109 No 19 Excellent Very_High
## 110 Yes 11 Excellent High
## 111 Yes 12 Excellent High
## 112 No 17 Excellent Very_High
## 113 No 11 Excellent Very_High
## 114 No 11 Excellent High
## 115 Yes 17 Excellent High
## 116 Yes 11 Excellent High
## 117 No 18 Excellent High
## 118 No 13 Excellent Low
## 119 No 15 Excellent Very_High
## 120 No 22 Outstanding High
## 121 Yes 14 Excellent Very_High
## 122 No 13 Excellent Very_High
## 123 No 13 Excellent High
## 124 No 18 Excellent High
## 125 Yes 21 Outstanding Medium
## 126 No 11 Excellent High
## 127 Yes 15 Excellent Low
## 128 Yes 13 Excellent High
## 129 No 12 Excellent Medium
## 130 No 13 Excellent Low
## 131 Yes 12 Excellent Low
## 132 No 18 Excellent Very_High
## 133 No 11 Excellent High
## 134 Yes 18 Excellent Very_High
## 135 No 13 Excellent High
## 136 No 14 Excellent Medium
## 137 No 18 Excellent Very_High
## 138 Yes 22 Outstanding Very_High
## 139 Yes 22 Outstanding Low
## 140 No 22 Outstanding Medium
## 141 No 14 Excellent Very_High
## 142 No 18 Excellent Medium
## 143 Yes 12 Excellent Very_High
## 144 Yes 13 Excellent Very_High
## 145 No 14 Excellent Low
## 146 Yes 21 Outstanding Very_High
## 147 Yes 15 Excellent High
## 148 Yes 13 Excellent Medium
## 149 No 16 Excellent Medium
## 150 Yes 19 Excellent Very_High
## 151 No 13 Excellent Medium
## 152 Yes 13 Excellent Very_High
## 153 No 25 Outstanding Very_High
## 154 No 13 Excellent High
## 155 No 12 Excellent High
## 156 No 14 Excellent Very_High
## 157 Yes 12 Excellent Very_High
## 158 No 14 Excellent Medium
## 159 Yes 14 Excellent High
## 160 Yes 12 Excellent Very_High
## 161 No 25 Outstanding High
## 162 No 12 Excellent Very_High
## 163 No 25 Outstanding Medium
## 164 Yes 13 Excellent Very_High
## 165 Yes 14 Excellent Low
## 166 No 14 Excellent Very_High
## 167 No 13 Excellent Medium
## 168 No 18 Excellent Low
## 169 No 23 Outstanding High
## 170 Yes 19 Excellent Very_High
## 171 No 11 Excellent Very_High
## 172 Yes 11 Excellent High
## 173 No 13 Excellent Medium
## 174 Yes 14 Excellent Very_High
## 175 No 13 Excellent Low
## 176 No 13 Excellent Medium
## 177 No 21 Outstanding Low
## 178 No 12 Excellent High
## 179 No 12 Excellent Very_High
## 180 No 11 Excellent Very_High
## 181 No 12 Excellent Very_High
## 182 No 12 Excellent High
## 183 No 13 Excellent Medium
## 184 Yes 12 Excellent Medium
## 185 No 14 Excellent Medium
## 186 No 11 Excellent High
## 187 Yes 21 Outstanding High
## 188 No 17 Excellent High
## 189 No 13 Excellent High
## 190 No 14 Excellent Very_High
## 191 Yes 12 Excellent Low
## 192 No 20 Outstanding Low
## 193 No 14 Excellent Medium
## 194 No 16 Excellent High
## 195 Yes 12 Excellent Medium
## 196 No 11 Excellent High
## 197 No 12 Excellent Very_High
## 198 No 14 Excellent Very_High
## 199 No 17 Excellent Low
## 200 No 14 Excellent High
## 201 No 16 Excellent High
## 202 Yes 17 Excellent High
## 203 No 15 Excellent Medium
## 204 No 21 Outstanding High
## 205 Yes 19 Excellent High
## 206 Yes 13 Excellent Medium
## 207 No 16 Excellent Low
## 208 No 18 Excellent Very_High
## 209 Yes 13 Excellent Very_High
## 210 Yes 15 Excellent Very_High
## 211 No 11 Excellent Low
## 212 No 12 Excellent High
## 213 No 19 Excellent Low
## 214 No 22 Outstanding Medium
## 215 No 11 Excellent Medium
## 216 No 14 Excellent Low
## 217 No 23 Outstanding Very_High
## 218 Yes 20 Outstanding Low
## 219 No 13 Excellent High
## 220 No 17 Excellent Very_High
## 221 Yes 11 Excellent High
## 222 Yes 22 Outstanding Medium
## 223 Yes 11 Excellent High
## 224 No 11 Excellent Very_High
## 225 No 14 Excellent High
## 226 No 14 Excellent Very_High
## 227 No 16 Excellent Very_High
## 228 No 15 Excellent Low
## 229 No 21 Outstanding Low
## 230 No 11 Excellent High
## 231 No 16 Excellent Low
## 232 No 24 Outstanding High
## 233 No 11 Excellent High
## 234 No 18 Excellent Low
## 235 No 11 Excellent Low
## 236 No 22 Outstanding Very_High
## 237 No 11 Excellent Medium
## 238 No 12 Excellent Low
## 239 No 23 Outstanding Low
## 240 No 16 Excellent Very_High
## 241 No 11 Excellent Very_High
## 242 No 12 Excellent Low
## 243 Yes 15 Excellent Very_High
## 244 No 14 Excellent Very_High
## 245 Yes 16 Excellent Low
## 246 No 12 Excellent Very_High
## 247 No 23 Outstanding High
## 248 No 11 Excellent Very_High
## 249 No 20 Outstanding Very_High
## 250 Yes 17 Excellent Low
## 251 No 14 Excellent Low
## 252 Yes 11 Excellent High
## 253 No 12 Excellent Low
## 254 No 11 Excellent Low
## 255 Yes 11 Excellent Very_High
## 256 Yes 12 Excellent Very_High
## 257 No 15 Excellent Low
## 258 Yes 14 Excellent Low
## 259 No 13 Excellent High
## 260 No 21 Outstanding Low
## 261 No 23 Outstanding Very_High
## 262 Yes 18 Excellent Low
## 263 Yes 15 Excellent High
## 264 No 15 Excellent High
## 265 Yes 13 Excellent High
## 266 No 17 Excellent Medium
## 267 Yes 11 Excellent High
## 268 No 18 Excellent Very_High
## 269 No 13 Excellent High
## 270 Yes 14 Excellent Very_High
## 271 No 14 Excellent High
## 272 No 21 Outstanding High
## 273 No 18 Excellent Low
## 274 No 11 Excellent Very_High
## 275 Yes 12 Excellent Very_High
## 276 Yes 11 Excellent Very_High
## 277 No 12 Excellent Low
## 278 No 13 Excellent Medium
## 279 No 25 Outstanding Very_High
## 280 No 11 Excellent High
## 281 No 12 Excellent Very_High
## 282 No 14 Excellent High
## 283 Yes 12 Excellent Very_High
## 284 No 12 Excellent Low
## 285 No 21 Outstanding High
## 286 Yes 13 Excellent Low
## 287 No 13 Excellent Medium
## 288 No 11 Excellent Medium
## 289 No 15 Excellent Medium
## 290 No 13 Excellent Low
## 291 Yes 12 Excellent Medium
## 292 No 12 Excellent Low
## 293 No 16 Excellent Medium
## 294 Yes 11 Excellent Very_High
## 295 No 20 Outstanding High
## 296 Yes 23 Outstanding High
## 297 Yes 23 Outstanding Medium
## 298 No 14 Excellent High
## 299 Yes 14 Excellent Very_High
## 300 Yes 11 Excellent High
## 301 No 14 Excellent High
## 302 Yes 11 Excellent Medium
## 303 No 12 Excellent High
## 304 No 12 Excellent High
## 305 No 15 Excellent High
## 306 No 14 Excellent Very_High
## 307 No 14 Excellent Medium
## 308 No 11 Excellent High
## 309 Yes 15 Excellent High
## 310 No 12 Excellent High
## 311 No 22 Outstanding High
## 312 No 19 Excellent High
## 313 No 14 Excellent Very_High
## 314 No 11 Excellent Low
## 315 No 12 Excellent Very_High
## 316 No 12 Excellent Very_High
## 317 No 12 Excellent Very_High
## 318 No 11 Excellent Very_High
## 319 No 14 Excellent Very_High
## 320 No 11 Excellent High
## 321 No 17 Excellent Low
## 322 No 12 Excellent Medium
## 323 No 11 Excellent Low
## 324 No 12 Excellent Medium
## 325 No 12 Excellent Low
## 326 No 15 Excellent Medium
## 327 No 16 Excellent High
## 328 Yes 16 Excellent High
## 329 No 12 Excellent High
## 330 No 19 Excellent Very_High
## 331 No 14 Excellent Medium
## 332 No 19 Excellent Very_High
## 333 No 11 Excellent High
## 334 No 13 Excellent Medium
## 335 Yes 13 Excellent Medium
## 336 No 11 Excellent Medium
## 337 No 14 Excellent High
## 338 Yes 17 Excellent High
## 339 No 12 Excellent Medium
## 340 No 17 Excellent Very_High
## 341 No 14 Excellent Medium
## 342 No 17 Excellent High
## 343 Yes 11 Excellent Low
## 344 No 19 Excellent High
## 345 No 20 Outstanding Medium
## 346 Yes 13 Excellent Very_High
## 347 No 23 Outstanding Very_High
## 348 No 15 Excellent Very_High
## 349 No 19 Excellent Medium
## 350 Yes 13 Excellent High
## 351 No 21 Outstanding High
## 352 No 14 Excellent High
## 353 No 12 Excellent Low
## 354 No 11 Excellent High
## 355 Yes 14 Excellent High
## 356 No 24 Outstanding High
## 357 Yes 13 Excellent High
## 358 Yes 12 Excellent Medium
## 359 No 12 Excellent Low
## 360 No 13 Excellent Low
## 361 No 12 Excellent Very_High
## 362 No 12 Excellent Medium
## 363 Yes 20 Outstanding Medium
## 364 Yes 13 Excellent Very_High
## 365 Yes 16 Excellent High
## 366 No 19 Excellent Medium
## 367 Yes 19 Excellent Low
## 368 No 11 Excellent Low
## 369 No 18 Excellent Very_High
## 370 No 25 Outstanding High
## 371 Yes 13 Excellent Very_High
## 372 No 13 Excellent Medium
## 373 Yes 19 Excellent Medium
## 374 Yes 19 Excellent High
## 375 No 11 Excellent High
## 376 No 17 Excellent High
## 377 No 20 Outstanding Low
## 378 No 18 Excellent Low
## 379 No 11 Excellent High
## 380 Yes 23 Outstanding High
## 381 Yes 12 Excellent High
## 382 No 14 Excellent High
## 383 No 14 Excellent Medium
## 384 No 22 Outstanding Low
## 385 No 17 Excellent Medium
## 386 No 15 Excellent Medium
## 387 Yes 19 Excellent Low
## 388 No 18 Excellent Medium
## 389 Yes 11 Excellent Very_High
## 390 Yes 12 Excellent Very_High
## 391 No 11 Excellent Low
## 392 No 12 Excellent Very_High
## 393 No 15 Excellent Medium
## 394 Yes 12 Excellent Very_High
## 395 No 15 Excellent Very_High
## 396 Yes 14 Excellent Very_High
## 397 No 12 Excellent Low
## 398 No 13 Excellent High
## 399 No 11 Excellent Very_High
## 400 No 12 Excellent Low
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1 1 26 5 Better
## 2 1 25 5 Better
## 3 0 20 2 Better
## 4 1 10 2 Better
## 5 0 10 2 Better
## 6 1 3 2 Better
## 7 1 4 2 Better
## 8 2 11 3 Better
## 9 1 6 4 Better
## 10 1 34 4 Better
## 11 1 14 2 Good
## 12 3 7 6 Good
## 13 1 10 2 Better
## 14 3 36 0 Good
## 15 0 6 2 Bad
## 16 0 11 2 Better
## 17 0 21 5 Better
## 18 3 18 2 Best
## 19 0 34 3 Best
## 20 0 10 2 Good
## 21 2 18 3 Better
## 22 2 16 3 Good
## 23 0 10 2 Better
## 24 1 15 2 Better
## 25 1 8 3 Better
## 26 0 10 5 Better
## 27 1 3 1 Better
## 28 1 24 3 Bad
## 29 2 23 0 Better
## 30 2 19 3 Better
## 31 1 7 1 Good
## 32 2 5 5 Better
## 33 1 8 3 Better
## 34 0 10 3 Best
## 35 0 1 4 Better
## 36 2 5 2 Better
## 37 0 6 3 Better
## 38 0 21 3 Better
## 39 1 24 2 Better
## 40 2 12 2 Better
## 41 1 20 2 Better
## 42 0 9 3 Better
## 43 1 10 3 Good
## 44 0 21 2 Better
## 45 0 5 6 Best
## 46 0 22 4 Better
## 47 1 16 2 Better
## 48 3 23 2 Good
## 49 1 10 1 Good
## 50 1 10 2 Better
## 51 1 24 4 Good
## 52 0 10 4 Good
## 53 1 10 3 Good
## 54 1 10 3 Good
## 55 1 11 5 Better
## 56 0 12 2 Better
## 57 0 5 2 Better
## 58 2 7 3 Better
## 59 3 5 3 Better
## 60 1 22 5 Better
## 61 1 16 1 Best
## 62 1 25 3 Good
## 63 2 17 3 Better
## 64 1 22 3 Best
## 65 0 19 3 Better
## 66 2 10 3 Better
## 67 0 23 3 Better
## 68 3 6 2 Good
## 69 2 9 4 Good
## 70 1 20 0 Good
## 71 0 16 2 Better
## 72 0 10 2 Better
## 73 0 10 4 Best
## 74 1 8 2 Bad
## 75 1 20 3 Better
## 76 0 11 3 Bad
## 77 1 21 2 Better
## 78 2 16 5 Bad
## 79 1 12 2 Better
## 80 2 26 4 Good
## 81 0 7 2 Better
## 82 0 10 2 Better
## 83 1 32 3 Good
## 84 1 15 4 Good
## 85 1 16 2 Better
## 86 0 9 1 Better
## 87 1 10 5 Better
## 88 1 13 5 Better
## 89 1 21 2 Good
## 90 1 22 3 Better
## 91 1 10 2 Bad
## 92 0 12 2 Good
## 93 0 16 3 Better
## 94 0 15 2 Better
## 95 0 10 0 Good
## 96 1 8 2 Bad
## 97 3 10 3 Good
## 98 1 9 3 Good
## 99 2 10 4 Best
## 100 1 11 3 Better
## 101 0 25 2 Better
## 102 0 22 2 Better
## 103 1 28 4 Better
## 104 3 29 2 Better
## 105 0 36 2 Better
## 106 0 10 2 Better
## 107 0 2 3 Better
## 108 3 20 3 Better
## 109 0 22 5 Best
## 110 0 8 3 Better
## 111 0 9 2 Better
## 112 0 6 2 Good
## 113 0 28 3 Good
## 114 1 25 3 Better
## 115 1 11 4 Good
## 116 1 13 2 Better
## 117 1 9 2 Good
## 118 0 10 3 Good
## 119 1 11 3 Better
## 120 1 7 3 Better
## 121 0 31 0 Good
## 122 2 10 2 Good
## 123 0 12 2 Bad
## 124 0 33 0 Better
## 125 1 3 2 Better
## 126 1 32 2 Better
## 127 2 15 2 Better
## 128 0 21 2 Better
## 129 1 13 5 Bad
## 130 3 10 4 Good
## 131 2 8 6 Better
## 132 0 6 4 Better
## 133 1 10 1 Better
## 134 0 33 2 Best
## 135 0 3 3 Bad
## 136 0 11 2 Better
## 137 1 13 0 Better
## 138 0 7 3 Better
## 139 0 9 2 Better
## 140 2 21 3 Better
## 141 0 21 2 Better
## 142 1 26 6 Better
## 143 0 4 3 Better
## 144 0 21 3 Good
## 145 1 4 3 Good
## 146 1 10 5 Good
## 147 1 32 3 Better
## 148 0 9 3 Good
## 149 0 6 2 Best
## 150 0 9 3 Better
## 151 1 15 5 Better
## 152 2 21 4 Better
## 153 0 20 1 Better
## 154 0 12 3 Better
## 155 1 18 2 Good
## 156 1 24 1 Better
## 157 2 28 2 Good
## 158 2 29 3 Better
## 159 0 6 3 Good
## 160 1 29 2 Good
## 161 3 24 2 Better
## 162 1 40 3 Good
## 163 0 20 3 Better
## 164 1 9 3 Better
## 165 0 20 3 Bad
## 166 0 1 3 Better
## 167 0 9 2 Good
## 168 2 14 3 Bad
## 169 1 15 2 Bad
## 170 1 10 6 Best
## 171 0 10 2 Better
## 172 0 14 3 Better
## 173 1 15 3 Better
## 174 0 4 2 Bad
## 175 0 18 2 Better
## 176 3 6 2 Best
## 177 0 14 3 Good
## 178 1 8 2 Best
## 179 2 2 3 Better
## 180 1 31 3 Better
## 181 0 25 6 Good
## 182 1 7 3 Good
## 183 1 14 2 Good
## 184 3 11 4 Good
## 185 2 10 2 Good
## 186 0 25 3 Bad
## 187 1 32 3 Better
## 188 1 19 2 Better
## 189 1 24 1 Best
## 190 1 10 3 Better
## 191 1 33 0 Better
## 192 1 13 3 Better
## 193 0 12 3 Better
## 194 1 21 3 Better
## 195 0 22 2 Good
## 196 0 17 2 Bad
## 197 0 18 2 Better
## 198 0 9 3 Better
## 199 3 19 5 Good
## 200 3 7 1 Better
## 201 0 31 3 Better
## 202 1 9 3 Good
## 203 0 27 5 Bad
## 204 2 7 2 Better
## 205 0 20 3 Best
## 206 1 10 3 Better
## 207 0 6 3 Better
## 208 3 14 6 Better
## 209 0 32 2 Better
## 210 0 8 2 Better
## 211 1 15 2 Good
## 212 0 12 3 Better
## 213 2 10 3 Better
## 214 1 20 3 Better
## 215 1 24 3 Better
## 216 1 34 5 Better
## 217 1 10 2 Good
## 218 0 8 2 Better
## 219 0 38 1 Good
## 220 1 12 3 Better
## 221 1 26 3 Good
## 222 1 9 3 Better
## 223 1 17 3 Good
## 224 0 36 3 Better
## 225 0 6 3 Better
## 226 1 6 2 Best
## 227 0 5 3 Better
## 228 2 16 3 Better
## 229 0 1 5 Better
## 230 0 10 2 Better
## 231 0 16 2 Better
## 232 0 26 2 Better
## 233 2 2 2 Better
## 234 1 5 3 Better
## 235 0 20 4 Better
## 236 1 22 3 Good
## 237 1 5 3 Better
## 238 0 6 3 Best
## 239 0 28 2 Better
## 240 1 17 2 Good
## 241 0 10 5 Better
## 242 0 21 3 Better
## 243 1 34 3 Better
## 244 1 5 5 Bad
## 245 0 26 2 Better
## 246 0 6 2 Better
## 247 1 21 3 Better
## 248 0 10 2 Better
## 249 0 18 2 Best
## 250 0 6 3 Better
## 251 0 23 2 Better
## 252 1 16 3 Good
## 253 3 10 4 Better
## 254 0 10 2 Better
## 255 0 4 2 Better
## 256 0 10 3 Better
## 257 3 3 5 Better
## 258 0 10 3 Better
## 259 1 6 5 Better
## 260 1 6 3 Best
## 261 1 10 3 Better
## 262 0 3 3 Good
## 263 1 9 2 Better
## 264 2 12 3 Good
## 265 1 21 5 Good
## 266 1 22 3 Better
## 267 0 8 5 Better
## 268 0 2 3 Good
## 269 2 10 4 Good
## 270 2 10 2 Best
## 271 0 16 2 Good
## 272 1 36 3 Better
## 273 0 6 3 Better
## 274 0 6 3 Good
## 275 0 40 2 Better
## 276 0 18 2 Better
## 277 1 11 3 Good
## 278 3 17 2 Better
## 279 1 13 6 Best
## 280 0 9 3 Best
## 281 0 10 2 Good
## 282 1 19 6 Best
## 283 1 5 2 Best
## 284 1 19 3 Better
## 285 3 14 2 Better
## 286 1 10 2 Better
## 287 1 12 2 Good
## 288 1 9 3 Best
## 289 1 26 2 Good
## 290 0 9 3 Better
## 291 1 15 3 Better
## 292 1 16 5 Bad
## 293 1 22 2 Better
## 294 1 29 2 Good
## 295 0 10 2 Better
## 296 0 16 2 Best
## 297 0 11 2 Best
## 298 1 15 4 Better
## 299 1 23 4 Good
## 300 0 6 3 Better
## 301 1 18 2 Better
## 302 1 33 2 Better
## 303 1 11 2 Better
## 304 1 4 3 Better
## 305 3 19 3 Better
## 306 1 24 4 Better
## 307 1 9 4 Good
## 308 1 16 5 Better
## 309 1 10 2 Good
## 310 1 12 2 Better
## 311 1 4 4 Better
## 312 1 10 1 Better
## 313 2 16 3 Good
## 314 1 8 3 Better
## 315 0 29 3 Better
## 316 0 10 2 Better
## 317 0 23 2 Better
## 318 0 10 2 Better
## 319 1 23 2 Best
## 320 1 16 4 Better
## 321 1 33 2 Bad
## 322 0 10 3 Better
## 323 0 9 6 Better
## 324 1 9 3 Better
## 325 0 12 3 Better
## 326 1 10 3 Better
## 327 1 10 3 Better
## 328 1 20 3 Better
## 329 2 8 5 Better
## 330 2 10 0 Best
## 331 0 5 4 Better
## 332 0 29 3 Good
## 333 1 14 5 Better
## 334 1 13 3 Better
## 335 1 10 3 Good
## 336 0 14 5 Better
## 337 1 17 1 Best
## 338 1 31 5 Better
## 339 1 8 2 Good
## 340 1 23 3 Better
## 341 0 15 3 Bad
## 342 1 4 3 Better
## 343 1 16 6 Better
## 344 2 14 3 Better
## 345 1 19 2 Best
## 346 1 5 2 Better
## 347 0 12 2 Better
## 348 0 9 0 Better
## 349 0 10 2 Better
## 350 0 6 2 Better
## 351 2 10 5 Better
## 352 0 5 3 Good
## 353 0 10 2 Good
## 354 0 13 4 Better
## 355 0 37 2 Better
## 356 1 10 3 Better
## 357 1 10 3 Better
## 358 1 13 5 Good
## 359 1 30 2 Better
## 360 0 15 5 Better
## 361 0 14 6 Better
## 362 0 8 2 Better
## 363 1 17 2 Better
## 364 1 24 3 Better
## 365 0 9 2 Better
## 366 1 14 1 Better
## 367 1 10 5 Good
## 368 1 10 2 Best
## 369 1 13 3 Better
## 370 1 3 3 Better
## 371 3 9 3 Better
## 372 2 10 2 Better
## 373 1 8 2 Better
## 374 0 10 2 Good
## 375 1 29 3 Good
## 376 0 7 1 Good
## 377 0 8 2 Better
## 378 1 10 3 Better
## 379 1 25 2 Better
## 380 1 1 3 Better
## 381 0 19 1 Better
## 382 1 29 2 Good
## 383 1 8 3 Good
## 384 1 7 2 Better
## 385 1 21 2 Best
## 386 0 10 1 Better
## 387 0 28 1 Good
## 388 1 23 4 Better
## 389 0 7 4 Good
## 390 2 25 3 Best
## 391 1 25 2 Better
## 392 1 4 5 Better
## 393 2 5 6 Better
## 394 0 8 2 Better
## 395 1 33 3 Better
## 396 1 18 1 Good
## 397 1 6 2 Good
## 398 0 12 6 Good
## 399 0 22 3 Better
## 400 0 10 0 Better
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 1 22 9 3
## 2 19 17 2
## 3 4 3 1
## 4 10 7 0
## 5 10 8 3
## 6 2 2 2
## 7 2 2 2
## 8 7 0 1
## 9 6 3 1
## 10 33 7 1
## 11 7 1 1
## 12 3 2 1
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## 17 5 4 4
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## 19 34 6 1
## 20 10 4 0
## 21 10 9 6
## 22 9 8 0
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## 24 15 10 4
## 25 8 7 5
## 26 9 7 1
## 27 2 2 1
## 28 20 8 13
## 29 2 2 2
## 30 9 7 7
## 31 6 2 0
## 32 5 3 1
## 33 8 0 7
## 34 7 6 5
## 35 1 1 1
## 36 5 2 0
## 37 6 5 0
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## 52 9 7 1
## 53 8 2 7
## 54 9 6 1
## 55 11 10 4
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## 57 4 3 1
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## 61 9 7 7
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## 64 17 13 15
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## 73 8 7 1
## 74 8 4 7
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## 86 8 7 7
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## 94 13 9 3
## 95 7 7 0
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## 100 7 6 7
## 101 7 1 0
## 102 22 10 0
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## 104 3 2 1
## 105 10 9 0
## 106 9 7 1
## 107 2 2 2
## 108 20 8 3
## 109 18 13 13
## 110 8 7 3
## 111 5 4 1
## 112 5 2 3
## 113 21 16 7
## 114 25 10 3
## 115 5 4 1
## 116 3 2 0
## 117 8 7 1
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## 119 7 7 1
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## 121 10 9 5
## 122 9 8 3
## 123 5 4 0
## 124 12 9 3
## 125 3 2 0
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## 127 13 11 10
## 128 21 6 2
## 129 13 10 3
## 130 10 7 8
## 131 6 4 1
## 132 5 4 1
## 133 10 7 1
## 134 33 7 15
## 135 3 2 1
## 136 3 2 1
## 137 8 7 7
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## 139 8 7 4
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## 141 20 15 1
## 142 7 7 4
## 143 3 2 0
## 144 20 8 2
## 145 4 3 0
## 146 10 9 5
## 147 30 8 12
## 148 5 2 0
## 149 2 2 1
## 150 5 2 1
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## 152 19 9 15
## 153 19 6 11
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## 158 8 7 0
## 159 5 4 0
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## 162 40 10 15
## 163 20 11 13
## 164 3 2 0
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## 166 1 0 0
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## 170 5 4 0
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## 173 15 12 5
## 174 3 2 1
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## 192 5 4 0
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## 204 2 2 0
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## 212 4 2 0
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## 262 3 2 1
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## 267 5 2 0
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## 276 16 14 5
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## 300 5 1 1
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## 302 33 9 0
## 303 11 8 7
## 304 4 2 3
## 305 14 11 1
## 306 22 6 5
## 307 6 1 0
## 308 9 8 4
## 309 10 7 1
## 310 10 9 7
## 311 3 2 1
## 312 3 2 1
## 313 14 8 6
## 314 8 3 0
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## 316 8 0 7
## 317 19 7 12
## 318 5 1 4
## 319 20 4 4
## 320 15 13 10
## 321 5 4 1
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## 323 8 7 1
## 324 4 2 1
## 325 5 3 1
## 326 5 4 0
## 327 7 0 1
## 328 20 11 0
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## 331 4 3 1
## 332 20 7 12
## 333 5 4 0
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## 336 4 2 3
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## 339 6 5 4
## 340 22 6 13
## 341 5 4 1
## 342 3 2 0
## 343 16 7 3
## 344 14 10 5
## 345 5 2 0
## 346 3 2 0
## 347 12 8 3
## 348 9 8 1
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## 350 6 3 1
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## 352 5 3 1
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## 354 12 6 2
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## 356 10 7 0
## 357 7 7 1
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## 359 15 7 6
## 360 1 0 0
## 361 11 10 5
## 362 8 7 1
## 363 17 14 5
## 364 6 0 0
## 365 9 0 1
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## 367 10 8 9
## 368 10 9 9
## 369 4 1 1
## 370 3 2 1
## 371 5 3 1
## 372 10 9 9
## 373 6 4 0
## 374 8 7 7
## 375 26 9 1
## 376 6 2 0
## 377 7 7 0
## 378 10 3 1
## 379 24 0 1
## 380 1 0 0
## 381 1 0 0
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## 383 8 2 7
## 384 1 0 0
## 385 18 16 0
## 386 10 7 0
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## 391 4 2 0
## 392 4 3 1
## 393 4 2 3
## 394 5 4 1
## 395 32 14 6
## 396 18 14 4
## 397 4 1 0
## 398 12 8 1
## 399 22 17 11
## 400 7 7 1
## YearsWithCurrManager
## 1 10
## 2 8
## 3 3
## 4 7
## 5 7
## 6 2
## 7 2
## 8 6
## 9 2
## 10 9
## 11 7
## 12 2
## 13 2
## 14 7
## 15 4
## 16 8
## 17 4
## 18 7
## 19 16
## 20 9
## 21 9
## 22 0
## 23 7
## 24 12
## 25 7
## 26 8
## 27 2
## 28 9
## 29 2
## 30 7
## 31 2
## 32 3
## 33 7
## 34 7
## 35 0
## 36 3
## 37 3
## 38 10
## 39 11
## 40 8
## 41 13
## 42 2
## 43 8
## 44 7
## 45 4
## 46 2
## 47 8
## 48 6
## 49 0
## 50 8
## 51 9
## 52 8
## 53 6
## 54 4
## 55 1
## 56 8
## 57 2
## 58 7
## 59 2
## 60 9
## 61 1
## 62 4
## 63 7
## 64 2
## 65 9
## 66 6
## 67 4
## 68 2
## 69 7
## 70 2
## 71 8
## 72 0
## 73 7
## 74 7
## 75 11
## 76 3
## 77 9
## 78 7
## 79 7
## 80 13
## 81 4
## 82 7
## 83 6
## 84 11
## 85 12
## 86 7
## 87 2
## 88 9
## 89 6
## 90 9
## 91 7
## 92 7
## 93 10
## 94 12
## 95 7
## 96 4
## 97 9
## 98 8
## 99 8
## 100 6
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## 102 4
## 103 10
## 104 2
## 105 9
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## 107 2
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## 109 11
## 110 0
## 111 0
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## 114 9
## 115 2
## 116 2
## 117 1
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## 119 7
## 120 3
## 121 9
## 122 8
## 123 4
## 124 8
## 125 2
## 126 7
## 127 7
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## 129 12
## 130 9
## 131 0
## 132 3
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## 134 12
## 135 2
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## 137 5
## 138 2
## 139 7
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## 142 7
## 143 2
## 144 10
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## 149 1
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## 156 6
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## 159 2
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## 164 2
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## 166 0
## 167 3
## 168 12
## 169 7
## 170 2
## 171 9
## 172 2
## 173 11
## 174 2
## 175 7
## 176 2
## 177 1
## 178 3
## 179 2
## 180 7
## 181 4
## 182 3
## 183 11
## 184 7
## 185 2
## 186 10
## 187 5
## 188 1
## 189 17
## 190 3
## 191 9
## 192 4
## 193 7
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## 195 14
## 196 7
## 197 2
## 198 2
## 199 7
## 200 2
## 201 7
## 202 2
## 203 12
## 204 2
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## 212 3
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## 214 0
## 215 7
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## 220 3
## 221 0
## 222 8
## 223 0
## 224 15
## 225 2
## 226 3
## 227 3
## 228 8
## 229 0
## 230 2
## 231 8
## 232 0
## 233 1
## 234 2
## 235 10
## 236 11
## 237 4
## 238 2
## 239 9
## 240 13
## 241 2
## 242 10
## 243 0
## 244 2
## 245 11
## 246 2
## 247 10
## 248 0
## 249 10
## 250 3
## 251 0
## 252 1
## 253 7
## 254 7
## 255 2
## 256 8
## 257 2
## 258 4
## 259 3
## 260 3
## 261 7
## 262 2
## 263 8
## 264 9
## 265 5
## 266 3
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## 268 1
## 269 6
## 270 2
## 271 11
## 272 13
## 273 4
## 274 1
## 275 8
## 276 12
## 277 1
## 278 8
## 279 8
## 280 0
## 281 8
## 282 0
## 283 3
## 284 13
## 285 7
## 286 3
## 287 2
## 288 0
## 289 8
## 290 1
## 291 7
## 292 3
## 293 8
## 294 7
## 295 7
## 296 10
## 297 0
## 298 10
## 299 10
## 300 4
## 301 7
## 302 10
## 303 9
## 304 2
## 305 11
## 306 17
## 307 5
## 308 8
## 309 2
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## 311 2
## 312 2
## 313 9
## 314 7
## 315 9
## 316 7
## 317 8
## 318 3
## 319 8
## 320 11
## 321 4
## 322 7
## 323 7
## 324 3
## 325 3
## 326 0
## 327 7
## 328 7
## 329 3
## 330 3
## 331 1
## 332 7
## 333 4
## 334 9
## 335 9
## 336 2
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## 338 9
## 339 3
## 340 7
## 341 0
## 342 2
## 343 7
## 344 7
## 345 4
## 346 2
## 347 7
## 348 7
## 349 8
## 350 3
## 351 8
## 352 4
## 353 9
## 354 11
## 355 13
## 356 7
## 357 7
## 358 3
## 359 12
## 360 0
## 361 8
## 362 3
## 363 15
## 364 4
## 365 7
## 366 6
## 367 6
## 368 4
## 369 2
## 370 2
## 371 0
## 372 0
## 373 2
## 374 7
## 375 7
## 376 4
## 377 5
## 378 4
## 379 7
## 380 0
## 381 0
## 382 17
## 383 7
## 384 0
## 385 11
## 386 9
## 387 7
## 388 2
## 389 7
## 390 12
## 391 3
## 392 1
## 393 2
## 394 3
## 395 9
## 396 11
## 397 3
## 398 7
## 399 15
## 400 7# Using attrition_weight, plot YearsAtCompany as a histogram with binwidth 1
ggplot(attrition_weight, aes(YearsAtCompany)) + geom_histogram(binwidth = 1)
2.7 Cluster sampling
Now that you know when to use cluster sampling, it’s time to put it into action. In this exercise you’ll explore the JobRole column of the attrition dataset. You can think of each job role as a subgroup of the whole population of employees.
# Get unique JobRole values
job_roles_pop <- unique(attrition_pop$JobRole)
# Randomly sample four JobRole values
job_roles_samp <- sample(job_roles_pop, size = 4)
# See the result
job_roles_samp## [1] Laboratory_Technician Research_Director Sales_Executive
## [4] Manager
## 9 Levels: Healthcare_Representative Human_Resources ... Sales_Representative# From previous step
job_roles_pop <- unique(attrition_pop$JobRole)
job_roles_samp <- sample(job_roles_pop, size = 4)
# Filter for rows where JobRole is in job_roles_samp
attrition_filtered <- attrition_pop %>%
filter(JobRole %in% job_roles_samp)
# Randomly sample 10 employees from each sampled job role
attrition_clus <- attrition_filtered %>%
group_by(JobRole) %>%
slice_sample(n = 10)
# See the result
attrition_clus## # A tibble: 40 × 31
## # Groups: JobRole [4]
## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## <int> <fct> <fct> <int> <fct> <int>
## 1 31 No Travel_Rarely 106 Human_Resources 2
## 2 30 No Travel_Rarely 330 Human_Resources 1
## 3 27 Yes Travel_Frequently 1337 Human_Resources 22
## 4 33 No Travel_Rarely 1075 Human_Resources 3
## 5 35 No Travel_Rarely 528 Human_Resources 8
## 6 36 No Travel_Frequently 1213 Human_Resources 2
## 7 36 No Travel_Rarely 1278 Human_Resources 8
## 8 29 Yes Travel_Rarely 350 Human_Resources 13
## 9 34 Yes Travel_Rarely 1107 Human_Resources 9
## 10 30 No Travel_Rarely 1240 Human_Resources 9
## # … with 30 more rows, and 25 more variables: Education <ord>,
## # EducationField <fct>, EnvironmentSatisfaction <ord>, Gender <fct>,
## # HourlyRate <int>, JobInvolvement <ord>, JobLevel <int>, JobRole <fct>,
## # JobSatisfaction <ord>, MaritalStatus <fct>, MonthlyIncome <int>,
## # MonthlyRate <int>, NumCompaniesWorked <int>, OverTime <fct>,
## # PercentSalaryHike <int>, PerformanceRating <ord>,
## # RelationshipSatisfaction <ord>, StockOptionLevel <int>, …2.8 3 kinds of sampling
Let’s compare the performance of point estimates using simple, stratified, and cluster sampling. Before we do that, you’ll have to set up the samples.
# Perform simple random sampling to get 0.25 of the population
attrition_srs <- attrition_pop %>% slice_sample(prop = 0.25)
# Perform stratified sampling to get 0.25 of each relationship group
attrition_strat <- attrition_pop %>%
group_by(RelationshipSatisfaction) %>%
slice_sample(prop = 0.25) %>%
ungroup()
# Get unique values of RelationshipSatisfaction
satisfaction_unique <- unique(attrition_pop$RelationshipSatisfaction)
# Randomly sample for 2 of the unique satisfaction values
satisfaction_samp <- sample(satisfaction_unique, size = 2)3. The n’s justify the means
Learn how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.
3.1 Calculating relative errors
The size of the sample you take affects how accurately the point estimates reflect the corresponding population parameter. For example, when you calculate a sample mean, you want it to be close to the population mean. However, if your sample is too small, this might not be the case.
The most common metric for assessing accuracy is relative error. This is the absolute difference between the population parameter and the point estimate, all divided by the population parameter. It is sometimes expressed as a percentage.
mean_attrition_pop_srs <- 0.161
# Generate a simple random sample of 10 rows
attrition_srs10 <- attrition_pop %>% slice_sample(n= 10)
# Calculate the proportion of employee attrition in the sample
mean_attrition_srs10 <- attrition_srs10 %>%
summarize(mean_attrition = mean(Attrition == "Yes")) %>%
pull(mean_attrition)
# Calculate the relative error percentage
rel_error_pct10 <- 100*abs(mean_attrition_pop_srs-mean_attrition_srs10)/mean_attrition_pop_srs
# See the result
rel_error_pct10## [1] 86.3354# Calculate the relative error percentage again with a sample of 100 rows
attrition_srs100 <- attrition_pop %>% slice_sample(n= 100)
# Calculate the proportion of employee attrition in the sample
mean_attrition_srs100 <- attrition_srs100 %>%
summarize(mean_attrition = mean(Attrition == "Yes")) %>%
pull(mean_attrition)
# Calculate the relative error percentage
rel_error_pct100 <- 100*abs(mean_attrition_pop_srs - mean_attrition_srs100)/mean_attrition_pop_srs
# See the result
rel_error_pct100## [1] 30.434783.2 Replicating samples
When you calculate a point estimate such as a sample mean, the value you calculate depends on the rows that were included in the sample. That means that there is some randomness in the answer. In order to quantify the variation caused by this randomness, you can create many samples and calculate the sample mean (or other statistic) for each sample.
# Replicate this code 500 times
mean_attritions <- replicate(
n = 500,
attrition_pop %>%
slice_sample(n = 20) %>%
summarize(mean_attrition = mean(Attrition == "Yes")) %>%
pull(mean_attrition))
# See the result
head(mean_attritions)## [1] 0.15 0.20 0.10 0.25 0.15 0.10# Store mean_attritions in a tibble in a column named sample_mean
sample_means <- tibble(sample_mean = mean_attritions)
# Plot a histogram of the `sample_mean` column, binwidth 0.05
ggplot(sample_means, aes(sample_mean)) +
geom_histogram(binwidth = 0.05)
3.3 Exact sampling distribution
To quantify how the point estimate (sample statistic) you are interested in varies, you need to know all the possible values it can take, and how often. That is, you need to know its distribution.
The distribution of a sample statistic is called the sampling distribution. When we can calculate this exactly, rather than using an approximation, it is known as the exact sampling distribution.
# Expand a grid representing 5 8-sided dice
dice <- expand_grid(
die1 = 1:8,
die2 = 1:8,
die3 = 1:8,
die4 = 1:8,
die5 = 1:8
) %>%
# Add a column of mean rolls
mutate(mean_roll = (die1 + die2 +die3 + die4 +die5)/5)
# Using dice, draw a bar plot of mean_roll as a factor
ggplot(dice, aes(factor(mean_roll))) + geom_bar()
3.4 Approximate sampling distribution
Calculating the exact sampling distribution is only possible in very simple situations. With just five eight-sided dice, the number of possible rolls is 8 ^ 5, which is over thirty thousand. When the dataset is more complicated, for example where a variable has hundreds or thousands or categories, the number of possible outcomes becomes too difficult to compute exactly.
# Sample one to eight, five times, with replacement
five_rolls <- sample(1:8, size = 5, replace = TRUE)
# Calculate the mean of five_rolls
mean(five_rolls)## [1] 3.4# Replicate the sampling code 1000 times
sample_means_1000 <- replicate(
n = 1000,
expr = {
five_rolls <- sample(1:8, size = 5, replace = TRUE)
mean(five_rolls)})
# Wrap sample_means_1000 in the sample_mean column of a tibble
sample_means <- tibble(
sample_mean = sample_means_1000
)
# See the result
sample_means## # A tibble: 1,000 × 1
## sample_mean
## <dbl>
## 1 2.8
## 2 4
## 3 4.8
## 4 3.8
## 5 2.2
## 6 5.2
## 7 4
## 8 3.6
## 9 5
## 10 4.8
## # … with 990 more rows# Using sample_means, draw a bar plot of sample_mean as a factor
ggplot(sample_means, aes(factor(sample_mean))) + geom_bar()
4.Pull Your Data Up By Its Bootstraps
Learn how to use resampling to perform bootstrapping, used to estimate variation in an unknown population. Understand the difference between sampling distributions and bootstrap distributions.
Bootstrapping is, in some sense, the opposite of sampling from a population. Sampling treats your dataset as the population, and you generate a random subset. Bootstrapping treats your dataset as a sample and uses it to build up a theoretical population.
4.1 Generating a bootstrap distribution
The process for generating a bootstrap distribution is remarkably similar to the process for generating a sampling distribution; only the first step is different.
To make a sampling distribution, you start with the population and sample without replacement. To make a bootstrap distribution, you start with a sample and sample that with replacement. After that, the steps are the same: calculate the summary statistic that you are interested in on that sample/resample, then replicate the process many times. In each case, you can visualize the distribution with a histogram.
# Generate 1 bootstrap resample
spotify_1_resample <- spotify_sample %>% slice_sample(prop = 1, replace = TRUE)
# Calculate mean danceability of resample
mean_danceability_1 <- spotify_1_resample %>%
summarize(mean_danceability = mean(danceability)) %>%
pull(mean_danceability)
# See the result
mean_danceability_1## [1] 0.6028684# Replicate this 1000 times
mean_danceability_1000 <- replicate(
n=1000,
expr = {
spotify_1_resample <- spotify_sample %>%
slice_sample(prop = 1, replace = TRUE)
spotify_1_resample %>%
summarize(mean_danceability = mean(danceability)) %>%
pull(mean_danceability)})
# See the result
head(mean_danceability_1000)## [1] 0.6031450 0.5936968 0.6026286 0.5926567 0.6062721 0.5991313# Store the resamples in a tibble
bootstrap_distn <- tibble(
resample_mean = mean_danceability_1000)
# Draw a histogram of the resample means with binwidth 0.002
ggplot(bootstrap_distn, aes(resample_mean)) + geom_histogram(binwidth = 0.002)
4.2 Sampling distribution vs. bootstrap distribution
The sampling distribution and bootstrap distribution are closely linked. In situations where you can repeatedly sample from a population (these occasions are rare) and as you learn about both, it’s helpful to generate both the sampling distribution and the bootstrap distribution, one after the other, to see how they are related.
# Generate a sampling distribution
mean_popularity_2000_samp <- replicate(
# Use 2000 replicates
n=2000,
expr = {
# Start with the population
spotify_population %>%
# Sample 500 rows without replacement
slice_sample(n = 500) %>%
# Calculate the mean popularity as mean_popularity
summarize(mean_popularity = mean(popularity)) %>%
# Pull out the mean popularity
pull(mean_popularity)})
# See the result
head(mean_popularity_2000_samp)## [1] 55.168 53.934 54.344 55.006 54.930 55.842# Generate a bootstrap distribution
mean_popularity_2000_boot <- replicate(
# Use 2000 replicates
n = 2000,
expr = {
# Start with the sample
spotify_population %>%
# Sample same number of rows with replacement
slice_sample(prop = 1, replace = TRUE) %>%
# Calculate the mean popularity
summarize(mean_popularity = mean(popularity)) %>%
# Pull out the mean popularity
pull(mean_popularity)})
# See the result
head(mean_popularity_2000_boot)## [1] 54.95405 54.82192 54.87502 54.74143 54.85714 54.75074# Calculate the true population mean popularity
pop_mean <- spotify_population %>%
summarize(mean(popularity))
# Calculate the original sample mean popularity
sample_mean <- spotify_sample %>%
summarize(mean(popularity))5. Hypothesis Testing in R
Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you’ll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.
5.1 Calculating the sample mean, z-score, and p-value
The late_shipments dataset contains supply chain data on the delivery of medical supplies. Each row represents one delivery of a part. The late columns denotes whether or not the part was delivered late. A value of “Yes” means that the part was delivered late, and a value of “No” means the part was delivered on time.
# Calculate the proportion of late shipments
late_prop_samp <- late_shipments %>%
summarize(prop_late_shipments = mean(late == "Yes")) %>%
pull(prop_late_shipments)
# See the results
late_prop_samp## [1] 0.067late_prop_hyp <- 0.06
std_error <- 0.007929028
# Calculate the z-score of late_prop_samp
z_score <- (late_prop_samp - late_prop_hyp)/std_error
# Calculate the p-value
p_value <- pnorm(z_score, lower.tail = FALSE)
# See the result
p_value ## [1] 0.18866355.2 Visualizing the difference
Before you start running hypothesis tests, it’s a great idea to perform some exploratory data analysis. That is, calculating summary statistics and visualizing distributions.
# Calculate the differences from 2012 to 2016
sample_dem_data <- dem_votes_potus_12_16 %>% mutate(diff = dem_percent_12 - dem_percent_16)
# See the result
sample_dem_data## state county FIPS dem_cand_votes_08 dem_percent_08
## 1 Alabama Bullock 1011 4011 74.072022
## 2 Alabama Chilton 1021 3674 20.657858
## 3 Alabama Clay 1027 1760 25.810236
## 4 Alabama Cullman 1043 5864 16.609545
## 5 Alabama Escambia 1053 5188 35.355050
## 6 Alabama Fayette 1057 1994 25.059696
## 7 Alabama Franklin 1059 3469 29.667322
## 8 Alabama Hale 1065 4982 60.652544
## 9 Alabama Lamar 1075 1614 22.812721
## 10 Alabama Lauderdale 1077 13329 34.980579
## 11 Alabama Monroe 1099 5025 44.658727
## 12 Alabama Pike 1109 5879 42.128269
## 13 Alabama Shelby 1117 20625 22.754101
## 14 Alabama Walker 1127 7420 25.896971
## 15 Arizona Graham 4009 3487 29.041393
## 16 Arkansas Baxter 5005 6539 32.726090
## 17 Arkansas Benton 5007 23331 30.669226
## 18 Arkansas Bradley 5011 1680 41.573868
## 19 Arkansas Craighead 5031 11294 36.469904
## 20 Arkansas Dallas 5039 1471 44.333936
## 21 Arkansas Desha 5041 2569 54.916631
## 22 Arkansas Faulkner 5045 14955 36.317055
## 23 Arkansas Howard 5061 1746 36.029715
## 24 Arkansas Logan 5083 2286 28.911091
## 25 Arkansas Marion 5089 2384 33.286791
## 26 Arkansas Newton 5101 1182 29.848485
## 27 California Alameda 6001 489106 78.757353
## 28 California Calaveras 6009 9813 42.105037
## 29 California Humboldt 6023 39692 62.284432
## 30 California Mariposa 6043 4100 42.482644
## 31 California Merced 6047 34031 53.333438
## 32 California Modoc 6049 1313 29.705882
## 33 California Mono 6051 3093 55.519655
## 34 California Napa 6055 38849 65.136984
## 35 California Placer 6061 75112 43.390504
## 36 California Riverside 6065 325017 50.211262
## 37 California Santa Barbara 6083 105614 60.381220
## 38 California Santa Clara 6085 462241 69.448413
## 39 California Stanislaus 6099 80279 49.858088
## 40 California Tulare 6107 43634 41.474821
## 41 Colorado Alamosa 8003 3521 56.013363
## 42 Colorado Arapahoe 8005 148218 55.690067
## 43 Colorado Cheyenne 8017 198 17.821782
## 44 Colorado Elbert 8039 3819 28.918673
## 45 Colorado Las Animas 8071 3562 52.684514
## 46 Colorado Moffat 8081 1582 26.946006
## 47 Colorado Morgan 8087 3813 37.258159
## 48 Colorado Park 8093 4250 45.294682
## 49 Colorado Phillips 8095 622 27.522124
## 50 Colorado Rio Blanco 8103 655 20.813473
## 51 Connecticut Hartford 9003 268301 65.155603
## 52 Connecticut New Haven 9009 232820 60.989315
## 53 Florida Broward 12011 492640 67.126403
## 54 Florida Calhoun 12013 1821 29.163997
## 55 Florida Desoto 12027 4383 43.263251
## 56 Florida Flagler 12035 24726 50.429320
## 57 Florida Franklin 12037 2134 35.395588
## 58 Florida Hardee 12049 2568 34.646519
## 59 Florida Highlands 12055 18135 40.495277
## 60 Florida Lake 12069 62948 42.843336
## 61 Florida Madison 12079 4270 47.939823
## 62 Florida Orange 12095 273009 59.002055
## 63 Florida St. Johns 12109 35791 33.814860
## 64 Florida Walton 12131 7174 26.525179
## 65 Georgia Bacon 13005 817 20.746572
## 66 Georgia Clinch 13065 989 36.683976
## 67 Georgia Columbia 13073 15703 28.369858
## 68 Georgia Coweta 13077 15521 28.984668
## 69 Georgia Dawson 13085 1632 16.362543
## 70 Georgia Dougherty 13095 26135 67.278484
## 71 Georgia Fannin 13111 2611 24.669312
## 72 Georgia Franklin 13119 1914 23.708658
## 73 Georgia Houston 13153 22094 39.502244
## 74 Georgia Jenkins 13165 1482 43.081395
## 75 Georgia Lumpkin 13187 2586 23.349887
## 76 Georgia McDuffie 13189 3989 41.795893
## 77 Georgia Mitchell 13205 3872 47.684729
## 78 Georgia Paulding 13223 17229 30.234272
## 79 Georgia Pierce 13229 1253 18.450891
## 80 Georgia Talbot 13263 2369 64.027027
## 81 Georgia Telfair 13271 1862 42.569730
## 82 Georgia Toombs 13279 2964 30.594550
## 83 Georgia Twiggs 13289 2402 53.153353
## 84 Georgia Ware 13299 4034 32.490335
## 85 Georgia Webster 13307 515 46.354635
## 86 Georgia Wheeler 13309 794 35.927602
## 87 Hawaii Honolulu 15003 214239 69.827224
## 88 Hawaii Maui 15009 39727 76.709340
## 89 Idaho Bingham 16011 4424 25.788400
## 90 Idaho Bonneville 16019 11417 27.376927
## 91 Idaho Camas 16025 187 30.258900
## 92 Idaho Canyon 16027 20147 31.359151
## 93 Idaho Custer 16037 611 25.977891
## 94 Idaho Franklin 16041 600 11.827321
## 95 Idaho Fremont 16043 1065 18.112245
## 96 Idaho Gooding 16047 1489 27.620108
## 97 Idaho Lemhi 16059 1061 25.846529
## 98 Illinois DeKalb 17037 25784 57.530457
## 99 Illinois Franklin 17055 8880 47.636929
## 100 Illinois Grundy 17063 11063 49.898516
## 101 Illinois Kankakee 17091 24750 51.545318
## 102 Illinois McHenry 17111 72288 51.905678
## 103 Illinois Marshall 17123 3081 48.649929
## 104 Illinois Menard 17129 2706 41.862624
## 105 Illinois Moultrie 17139 2668 42.619808
## 106 Illinois Ogle 17141 11253 45.283702
## 107 Illinois Randolph 17157 7395 48.644915
## 108 Illinois Rock Island 17161 42210 61.709624
## 109 Illinois Wayne 17191 2547 31.557428
## 110 Illinois Whiteside 17195 15607 58.005649
## 111 Illinois Woodford 17203 6999 35.923626
## 112 Indiana Clark 18019 21953 45.998952
## 113 Indiana Hamilton 18057 49704 38.511126
## 114 Indiana Huntington 18069 5843 35.785154
## 115 Indiana LaGrange 18087 3663 38.594458
## 116 Indiana Newton 18111 2625 43.424318
## 117 Indiana Ohio 18115 1158 39.684716
## 118 Indiana Putnam 18133 6334 43.247303
## 119 Indiana Randolph 18135 4839 44.772391
## 120 Indiana Shelby 18145 6987 39.757596
## 121 Indiana Starke 18149 4778 50.475386
## 122 Indiana Vermillion 18165 4003 56.103714
## 123 Iowa Adair 19001 1924 47.471009
## 124 Iowa Appanoose 19007 2970 48.089378
## 125 Iowa Benton 19011 7058 51.473162
## 126 Iowa Butler 19023 3364 46.943902
## 127 Iowa Chickasaw 19037 3923 59.592891
## 128 Iowa Clinton 19045 15018 60.754885
## 129 Iowa Crawford 19047 3715 51.661799
## 130 Iowa Guthrie 19077 2625 44.894818
## 131 Iowa Harrison 19085 3555 46.912114
## 132 Iowa Howard 19089 2941 62.203892
## 133 Iowa Iowa 19095 4202 49.169202
## 134 Iowa Jackson 19097 6102 61.295831
## 135 Iowa Lee 19111 9821 57.019275
## 136 Iowa Muscatine 19139 10920 57.115958
## 137 Iowa Page 19145 2900 39.423600
## 138 Iowa Pocahontas 19151 1800 44.876589
## 139 Iowa Story 19169 26548 57.000537
## 140 Kansas Anderson 20003 1175 32.404854
## 141 Kansas Atchison 20005 3241 45.070227
## 142 Kansas Brown 20013 1317 30.095978
## 143 Kansas Clay 20027 1009 24.888999
## 144 Kansas Decatur 20039 343 22.157623
## 145 Kansas Edwards 20047 333 24.539425
## 146 Kansas Ellis 20051 4010 32.216598
## 147 Kansas Ford 20057 2991 33.743231
## 148 Kansas Franklin 20059 4433 37.763012
## 149 Kansas Hamilton 20075 233 21.259124
## 150 Kansas Lincoln 20105 347 21.878941
## 151 Kansas Miami 20121 5742 37.341484
## 152 Kansas Neosho 20133 2563 35.636819
## 153 Kansas Phillips 20147 525 19.685039
## 154 Kansas Rawlins 20153 273 17.624274
## 155 Kansas Reno 20155 9916 37.401931
## 156 Kansas Stanton 20187 188 22.732769
## 157 Kansas Wyandotte 20209 39865 69.731848
## 158 Kentucky Anderson 21005 3462 32.808946
## 159 Kentucky Barren 21009 5434 32.331767
## 160 Kentucky Bell 21013 2782 28.985205
## 161 Kentucky Boyle 21021 4769 37.744361
## 162 Kentucky Bracken 21023 1241 36.510738
## 163 Kentucky Caldwell 21033 2212 35.683175
## 164 Kentucky Cumberland 21057 697 24.919557
## 165 Kentucky Fayette 21067 66042 51.741239
## 166 Kentucky Fleming 21069 2279 39.077503
## 167 Kentucky Greenup 21089 6621 41.910368
## 168 Kentucky Knott 21119 2612 44.879725
## 169 Kentucky Madison 21151 12392 38.085871
## 170 Kentucky Mercer 21167 3159 31.401590
## 171 Kentucky Muhlenberg 21177 6221 48.265963
## 172 Kentucky Oldham 21185 10000 34.111066
## 173 Kentucky Powell 21197 2065 41.532582
## 174 Kentucky Rowan 21205 4074 49.963208
## 175 Kentucky Shelby 21211 6871 37.060410
## 176 Kentucky Todd 21219 1543 31.228496
## 177 Kentucky Trimble 21223 1484 38.929696
## 178 Louisiana Bossier 22015 12701 27.715707
## 179 Louisiana Caldwell 22021 1118 22.848968
## 180 Louisiana East Baton Rouge 22033 99652 50.495315
## 181 Louisiana Franklin 22041 2959 31.626764
## 182 Louisiana Sabine 22085 2245 23.259428
## 183 Louisiana St. Martin 22099 9419 38.838034
## 184 Maine Kennebec 23011 37238 56.433183
## 185 Maine Knox 23013 13728 59.738903
## 186 Maine Lincoln 23015 11886 55.068569
## 187 Maine Sagadahoc 23023 12152 57.048965
## 188 Maryland Garrett 24023 3736 29.024239
## 189 Maryland Montgomery 24031 314444 71.584775
## 190 Maryland Worcester 24047 11374 41.592920
## 191 Massachusetts Barnstable 25001 74264 56.145762
## 192 Massachusetts Bristol 25005 146861 60.406795
## 193 Massachusetts Essex 25009 208976 59.088686
## 194 Massachusetts Franklin 25011 27919 72.424706
## 195 Michigan Arenac 26011 4155 51.119587
## 196 Michigan Calhoun 26025 34561 53.836688
## 197 Michigan Kalamazoo 26077 77051 58.916050
## 198 Michigan Macomb 26099 223784 53.381550
## 199 Michigan Mecosta 26107 9101 48.762323
## 200 Michigan Midland 26111 20701 47.363123
## 201 Michigan Missaukee 26113 2898 38.681260
## 202 Michigan Newaygo 26123 10790 46.699849
## 203 Michigan Ottawa 26139 50828 37.300026
## 204 Michigan St. Joseph 26149 12322 47.941794
## 205 Michigan Schoolcraft 26153 2184 50.485437
## 206 Michigan Van Buren 26159 18588 53.466030
## 207 Michigan Washtenaw 26161 130578 69.784892
## 208 Minnesota Dakota 27037 116778 51.792684
## 209 Minnesota Douglas 27041 9256 44.248972
## 210 Minnesota Kanabec 27065 3743 44.040475
## 211 Minnesota Lake 27075 4174 59.893815
## 212 Minnesota Lyon 27083 6110 48.079950
## 213 Minnesota Nobles 27105 4244 48.156133
## 214 Minnesota Norman 27107 2129 61.997670
## 215 Minnesota Pennington 27113 3394 49.750806
## 216 Minnesota Pipestone 27117 2023 42.137055
## 217 Minnesota Pope 27121 3317 50.749694
## 218 Minnesota Ramsey 27123 182974 65.963675
## 219 Minnesota Renville 27129 3904 47.990166
## 220 Minnesota Roseau 27135 3097 40.220779
## 221 Minnesota Sherburne 27141 17957 39.913314
## 222 Minnesota Waseca 27161 4401 44.508495
## 223 Mississippi Bolivar 28011 10334 67.401513
## 224 Mississippi Calhoun 28013 2522 35.972044
## 225 Mississippi Claiborne 28021 4682 85.924023
## 226 Mississippi Copiah 28029 7710 53.304757
## 227 Mississippi Covington 28031 3852 40.878701
## 228 Mississippi Grenada 28043 5029 44.488677
## 229 Mississippi Jefferson Davis 28065 4454 60.598639
## 230 Mississippi Leflore 28083 8914 68.144637
## 231 Mississippi Lowndes 28087 13209 48.230912
## 232 Mississippi Pearl River 28109 4320 19.349637
## 233 Mississippi Tate 28137 5003 39.297777
## 234 Mississippi Tunica 28143 3279 75.850104
## 235 Mississippi Yalobusha 28161 3151 46.331422
## 236 Missouri Adair 29001 5735 48.311010
## 237 Missouri Barton 29011 1455 24.462004
## 238 Missouri Chariton 29041 1799 42.691030
## 239 Missouri Clark 29045 1572 45.486111
## 240 Missouri Henry 29083 4869 43.632942
## 241 Missouri Holt 29087 802 30.459552
## 242 Missouri Howell 29091 5736 33.683716
## 243 Missouri Lafayette 29107 6902 41.578313
## 244 Missouri Lincoln 29113 10234 43.456476
## 245 Missouri Madison 29123 2042 40.612570
## 246 Missouri Miller 29131 3553 30.801907
## 247 Missouri Platte 29165 21459 46.168244
## 248 Missouri Putnam 29171 695 29.713553
## 249 Missouri St. Clair 29185 1886 37.810746
## 250 Missouri St. Louis County 29189 333123 59.501763
## 251 Missouri Saline 29195 4712 47.847279
## 252 Missouri Shelby 29205 1114 33.594692
## 253 Missouri Webster 29225 5685 34.757887
## 254 Missouri St. Louis City 29510 132925 83.675358
## 255 Montana Fergus 30027 1933 31.017330
## 256 Montana Flathead 30029 16138 36.891917
## 257 Montana Glacier 30035 3423 68.859384
## 258 Montana McCone 30055 321 29.023508
## 259 Montana Meagher 30059 298 30.848861
## 260 Montana Missoula 30063 36531 61.807999
## 261 Montana Powell 30077 1021 34.727891
## 262 Montana Rosebud 30087 1919 50.367454
## 263 Montana Wheatland 30107 289 29.399797
## 264 Nebraska Antelope 31003 757 23.767661
## 265 Nebraska Banner 31007 62 14.903846
## 266 Nebraska Blaine 31009 43 13.607595
## 267 Nebraska Buffalo 31019 5867 30.409993
## 268 Nebraska Burt 31021 1413 41.718335
## 269 Nebraska Cass 31025 4753 39.209701
## 270 Nebraska Frontier 31063 349 24.857550
## 271 Nebraska Hamilton 31081 1332 27.755782
## 272 Nebraska Hooker 31091 75 17.123288
## 273 Nebraska Keya Paha 31103 115 21.575985
## 274 Nebraska Nance 31125 549 32.161687
## 275 Nebraska Perkins 31135 310 21.830986
## 276 Nebraska Sherman 31163 585 37.213740
## 277 Nebraska Sioux 31165 117 15.983607
## 278 Nebraska Thomas 31171 51 13.076923
## 279 Nebraska Valley 31175 706 29.137433
## 280 Nebraska Wayne 31179 1249 32.799370
## 281 Nevada Esmeralda 32009 104 23.690205
## 282 Nevada Eureka 32011 144 19.328859
## 283 Nevada Nye 32023 7226 41.312675
## 284 New Hampshire Grafton 33009 31446 63.030667
## 285 New Jersey Cape May 34009 22893 45.439749
## 286 New Jersey Morris 34027 112275 45.798864
## 287 New Jersey Passaic 34031 113257 60.826763
## 288 New Jersey Sussex 34037 28840 39.323698
## 289 New Mexico Chaves 35005 8197 37.070369
## 290 New Mexico De Baca 35011 359 34.386973
## 291 New Mexico Sierra 35051 2352 42.880583
## 292 New York Cattaraugus 36009 14307 43.902664
## 293 New York Chautauqua 36013 29129 49.562717
## 294 New York Clinton 36019 20216 60.683196
## 295 New York Erie 36029 256299 57.988167
## 296 New York Essex 36031 10390 55.887257
## 297 New York Franklin 36033 10571 60.419524
## 298 New York Monroe 36055 207225 58.254269
## 299 New York New York 36061 572126 85.741176
## 300 New York Oneida 36065 43506 46.129378
## 301 New York Otsego 36077 13570 51.990345
## 302 New York Richmond 36085 79311 47.639957
## 303 New York St. Lawrence 36089 23706 57.480239
## 304 New York Saratoga 36091 56645 50.914566
## 305 New York Warren 36113 16281 50.552692
## 306 North Carolina Ashe 37009 4872 37.279057
## 307 North Carolina Bertie 37015 6365 65.195124
## 308 North Carolina Catawba 37035 25656 36.941153
## 309 North Carolina Currituck 37053 3737 33.660602
## 310 North Carolina Dare 37055 8074 44.736259
## 311 North Carolina Edgecombe 37065 17403 67.115310
## 312 North Carolina Iredell 37097 27318 37.339056
## 313 North Carolina Jackson 37099 8766 51.974386
## 314 North Carolina Lincoln 37109 11713 32.719705
## 315 North Carolina Moore 37125 17624 38.881045
## 316 North Carolina Perquimans 37143 2772 42.639594
## 317 North Carolina Randolph 37151 16414 28.228455
## 318 North Carolina Rockingham 37157 17255 41.466404
## 319 North Carolina Rowan 37159 23391 37.997076
## 320 North Carolina Sampson 37163 11836 45.456640
## 321 North Carolina Stokes 37169 6875 31.619372
## 322 North Carolina Vance 37181 13166 63.082746
## 323 North Carolina Wake 37183 250891 56.731224
## 324 North Carolina Wilson 37195 19652 52.840741
## 325 North Carolina Yancey 37199 4486 46.166512
## 326 North Dakota Cavalier 38019 930 43.661972
## 327 North Dakota Foster 38031 687 41.611145
## 328 North Dakota Hettinger 38041 406 30.118694
## 329 North Dakota LaMoure 38045 868 38.732709
## 330 North Dakota McLean 38055 1867 39.421453
## 331 North Dakota Nelson 38063 907 51.769406
## 332 North Dakota Pembina 38067 1494 45.176897
## 333 North Dakota Richland 38077 3513 46.449821
## 334 Ohio Athens 39009 20722 66.634510
## 335 Ohio Carroll 39019 6423 46.033111
## 336 Ohio Delaware 39041 36653 39.660881
## 337 Ohio Erie 39043 23148 56.144947
## 338 Ohio Greene 39057 33540 40.124897
## 339 Ohio Harrison 39067 3683 47.296777
## 340 Ohio Hocking 39073 6231 48.321055
## 341 Ohio Knox 39083 11014 39.013850
## 342 Ohio Licking 39089 33896 41.194414
## 343 Ohio Marion 39101 12870 44.353310
## 344 Ohio Ottawa 39123 12049 52.230266
## 345 Ohio Paulding 39125 4165 42.634865
## 346 Ohio Vinton 39163 2463 43.623804
## 347 Ohio Washington 39167 12368 41.320326
## 348 Oklahoma Alfalfa 40003 411 16.885785
## 349 Oklahoma Carter 40019 5603 29.733602
## 350 Oklahoma Cherokee 40021 7194 43.919414
## 351 Oklahoma Cleveland 40027 39681 37.997702
## 352 Oklahoma Delaware 40041 5085 33.101159
## 353 Oklahoma Johnston 40069 1249 31.564316
## 354 Oklahoma Love 40085 1257 32.683307
## 355 Oklahoma Noble 40103 1174 23.224530
## 356 Oklahoma Pushmataha 40127 1265 28.280796
## 357 Oklahoma Rogers 40131 10772 27.968324
## 358 Oklahoma Stephens 40137 4538 23.969998
## 359 Oregon Benton 41003 29901 64.333663
## 360 Oregon Clackamas 41005 103476 53.928017
## 361 Oregon Columbia 41009 13390 54.061693
## 362 Oregon Curry 41015 5230 42.409990
## 363 Oregon Josephine 41033 17412 41.408833
## 364 Oregon Klamath 41035 9370 31.871832
## 365 Oregon Malheur 41045 2949 28.266079
## 366 Oregon Multnomah 41051 279696 76.689973
## 367 Oregon Union 41061 4613 36.628553
## 368 Pennsylvania Butler 42019 32260 35.703614
## 369 Pennsylvania Cameron 42023 879 38.035482
## 370 Pennsylvania Fayette 42051 25866 49.217947
## 371 Pennsylvania Lawrence 42073 19711 46.837278
## 372 Pennsylvania Lebanon 42075 23310 40.015793
## 373 Pennsylvania Schuylkill 42107 28300 44.882164
## 374 Pennsylvania Tioga 42117 6390 35.569162
## 375 Pennsylvania Washington 42125 46122 47.062305
## 376 Rhode Island Newport 44005 25479 60.668619
## 377 South Carolina Anderson 45007 24132 32.704948
## 378 South Carolina Beaufort 45013 30396 44.135327
## 379 South Carolina Darlington 45031 14505 49.447740
## 380 South Carolina Dillon 45033 7408 55.213535
## 381 South Carolina Lexington 45063 33303 30.410366
## 382 South Carolina Marion 45067 9608 63.318835
## 383 South Dakota Brookings 46011 7207 52.266299
## 384 South Dakota Buffalo 46017 454 74.183007
## 385 South Dakota Custer 46033 1475 33.146067
## 386 South Dakota Day 46037 1785 56.167401
## 387 South Dakota Fall River 46047 1338 35.632490
## 388 South Dakota Haakon 46055 187 16.389132
## 389 South Dakota Hughes 46065 3037 36.215120
## 390 South Dakota Jackson 46071 435 38.908766
## 391 South Dakota McPherson 46089 441 32.331378
## 392 South Dakota Potter 46107 482 33.612273
## 393 South Dakota Sully 46119 233 28.311057
## 394 South Dakota Todd 46121 2208 78.660492
## 395 South Dakota Turner 46125 1681 39.404594
## 396 South Dakota Walworth 46129 923 35.229008
## 397 Tennessee Anderson 47001 11396 36.097561
## 398 Tennessee Bedford 47003 5027 32.417618
## 399 Tennessee Carroll 47017 3980 34.174824
## 400 Tennessee Cheatham 47021 5498 33.467251
## 401 Tennessee Fayette 47047 6892 35.795159
## 402 Tennessee Grundy 47061 1971 42.551813
## 403 Tennessee Hamilton 47065 64246 43.550705
## 404 Tennessee Obion 47131 4308 32.168459
## 405 Tennessee Scott 47151 1720 25.357511
## 406 Tennessee Union 47173 1829 28.582591
## 407 Tennessee Van Buren 47175 849 38.485947
## 408 Tennessee Washington 47179 15941 32.544608
## 409 Texas Atascosa 48013 4415 44.425438
## 410 Texas Blanco 48031 1467 29.702369
## 411 Texas Briscoe 48045 205 24.698795
## 412 Texas Cameron 48061 48480 64.078671
## 413 Texas Ector 48135 9123 25.622086
## 414 Texas El Paso 48141 122021 65.874331
## 415 Texas Erath 48143 3128 22.312576
## 416 Texas Galveston 48167 41805 39.812769
## 417 Texas Gonzales 48177 2167 34.467950
## 418 Texas Hudspeth 48229 430 47.884187
## 419 Texas Jeff Davis 48243 468 37.864078
## 420 Texas Kaufman 48257 11161 31.757007
## 421 Texas Kendall 48259 3599 21.491700
## 422 Texas Lamb 48279 1156 25.546961
## 423 Texas Lampasas 48281 1903 24.927954
## 424 Texas La Salle 48283 1052 59.234234
## 425 Texas Leon 48289 1418 20.139185
## 426 Texas Lipscomb 48295 155 12.340764
## 427 Texas McLennan 48309 29998 37.650928
## 428 Texas Madison 48313 1146 28.129602
## 429 Texas Menard 48327 295 28.978389
## 430 Texas Mills 48333 398 18.282040
## 431 Texas Mitchell 48335 586 24.105306
## 432 Texas Navarro 48349 5400 33.086208
## 433 Texas Palo Pinto 48363 2499 25.267947
## 434 Texas Panola 48365 2586 25.313234
## 435 Texas Parker 48367 10502 21.901524
## 436 Texas Pecos 48371 1476 36.807980
## 437 Texas Refugio 48391 1382 42.379638
## 438 Texas Roberts 48393 41 7.915058
## 439 Texas San Saba 48411 487 19.820920
## 440 Texas Scurry 48415 1088 19.536721
## 441 Texas Shelby 48419 2548 27.635575
## 442 Texas Smith 48423 23726 29.821144
## 443 Texas Sterling 48431 97 15.670436
## 444 Texas Sutton 48435 381 24.144487
## 445 Texas Titus 48449 3145 34.018388
## 446 Texas Tom Green 48451 11158 28.712591
## 447 Texas Trinity 48455 1925 31.676814
## 448 Texas Wilson 48493 5362 32.763045
## 449 Utah Grand 49019 2067 50.699043
## 450 Utah Summit 49043 9532 56.707716
## 451 Vermont Rutland 50021 19355 61.217067
## 452 Virginia Amelia 51007 2488 38.106908
## 453 Virginia Amherst 51009 6094 41.455782
## 454 Virginia Essex 51057 2934 54.697987
## 455 Virginia Greensville 51081 3122 63.883773
## 456 Virginia Henry 51089 11118 44.092802
## 457 Virginia Highland 51091 590 37.966538
## 458 Virginia James City 51095 17352 44.949874
## 459 Virginia Mecklenburg 51117 7127 47.255006
## 460 Virginia Middlesex 51119 2391 39.810190
## 461 Virginia Stafford 51179 25716 46.372735
## 462 Virginia Surry 51181 2626 60.716763
## 463 Virginia Warren 51187 6997 43.389557
## 464 Virginia Washington 51191 8063 32.910204
## 465 Virginia Lexington 51678 1543 62.242840
## 466 Virginia Lynchburg 51680 16269 47.374858
## 467 Washington Douglas 53017 5848 38.450917
## 468 Washington Ferry 53019 1467 41.902314
## 469 Washington Mason 53045 15050 53.168939
## 470 Washington San Juan 53055 7374 70.021840
## 471 West Virginia Barbour 54001 2419 38.815789
## 472 West Virginia Braxton 54007 2704 49.990756
## 473 West Virginia Calhoun 54013 993 40.881021
## 474 West Virginia Harrison 54033 13582 42.614207
## 475 West Virginia Nicholas 54067 4357 46.544173
## 476 West Virginia Ohio 54069 8593 43.976459
## 477 West Virginia Raleigh 54081 10237 36.225627
## 478 West Virginia Tucker 54093 1288 36.726547
## 479 West Virginia Wayne 54099 6137 39.773169
## 480 Wisconsin Ashland 55003 5818 67.856310
## 481 Wisconsin Barron 55005 12078 52.774622
## 482 Wisconsin Calumet 55015 13295 50.219083
## 483 Wisconsin Chippewa 55017 16239 53.716384
## 484 Wisconsin Columbia 55021 16661 56.917874
## 485 Wisconsin Florence 55037 1134 42.234637
## 486 Wisconsin Jefferson 55055 21448 49.687254
## 487 Wisconsin Juneau 55057 6186 53.651344
## 488 Wisconsin Kewaunee 55061 5902 54.714008
## 489 Wisconsin Lafayette 55065 4732 60.426510
## 490 Wisconsin Lincoln 55069 8424 55.174221
## 491 Wisconsin Ozaukee 55089 20579 38.562728
## 492 Wisconsin Portage 55097 24817 62.952159
## 493 Wyoming Albany 56001 8644 50.252892
## 494 Wyoming Laramie 56021 16072 38.444242
## 495 Wyoming Lincoln 56023 1823 21.160766
## 496 Wyoming Uinta 56041 2317 27.524353
## 497 Wyoming Washakie 56043 1042 25.414634
## 498 Alaska District 3 2003 5657 64.526064
## 499 Alaska District 18 2018 2046 31.913898
## 500 Alaska District 24 2024 3380 43.964620
## dem_cand_votes_12 dem_percent_12 dem_cand_votes_16 dem_percent_16
## 1 4061 76.305900 3530 74.946921
## 2 3397 19.453671 2911 15.847352
## 3 1777 26.673672 1237 18.674517
## 4 5052 14.661752 3798 10.028252
## 5 5489 36.915731 4605 31.020546
## 6 1817 22.866851 1362 16.511092
## 7 3171 29.201584 2197 18.247508
## 8 5411 62.605577 4775 59.390547
## 9 1646 22.969579 1036 14.872237
## 10 12511 33.883111 9952 25.179001
## 11 4914 45.890923 4332 42.177003
## 12 6035 42.780180 5056 38.396112
## 13 20051 21.675116 22977 22.692437
## 14 6557 22.990077 4497 15.258550
## 15 3609 30.442851 3301 16.256279
## 16 5172 26.743885 4169 21.091774
## 17 22636 28.560253 28005 28.923614
## 18 1449 39.676889 1317 36.052560
## 19 10527 33.208202 10538 29.623591
## 20 1337 43.352789 1165 42.042584
## 21 2443 55.271493 2228 52.337327
## 22 13621 32.853353 14629 30.780399
## 23 1471 32.967279 1351 28.904579
## 24 2009 27.404174 1715 21.651307
## 25 2037 28.889519 1434 20.177290
## 26 993 27.123737 699 18.625100
## 27 469684 78.853405 514842 78.690013
## 28 8670 39.796199 7944 34.336100
## 29 34457 59.956499 33200 56.037538
## 30 3498 38.866667 3122 35.169539
## 31 33005 53.184975 37317 52.715817
## 32 1111 27.872554 877 23.152059
## 33 2733 52.750434 2773 52.558757
## 34 35870 62.965173 39199 63.871146
## 35 66818 39.001413 73509 40.204223
## 36 329063 49.714386 373695 49.733760
## 37 94129 57.634705 107142 60.605478
## 38 450818 70.101743 511684 72.712442
## 39 77724 50.025423 81647 47.428927
## 40 41752 41.297725 47585 42.360283
## 41 3811 56.753537 3189 45.957631
## 42 153905 53.904159 159885 52.759317
## 43 172 15.736505 132 11.978221
## 44 3603 25.412611 3134 19.614470
## 45 3445 50.204022 2650 39.010746
## 46 1330 21.562905 874 13.394636
## 47 3912 36.299527 3151 26.346154
## 48 3862 41.234252 3421 32.840549
## 49 588 25.960265 436 18.696398
## 50 568 16.859602 436 12.637681
## 51 244639 62.369723 240403 59.086820
## 52 218972 60.652695 205609 54.249537
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## diff
## 1 1.35897859
## 2 3.60631931
## 3 7.99915466
## 4 4.63350001
## 5 5.89518508
## 6 6.35575899
## 7 10.95407563
## 8 3.21502950
## 9 8.09734199
## 10 8.70411062
## 11 3.71391946
## 12 4.38406827
## 13 -1.01732036
## 14 7.73152700
## 15 14.18657219
## 16 5.65211166
## 17 -0.36336063
## 18 3.62432984
## 19 3.58461097
## 20 1.31020468
## 21 2.93416646
## 22 2.07295412
## 23 4.06270073
## 24 5.75286740
## 25 8.71222922
## 26 8.49863676
## 27 0.16339230
## 28 5.46009981
## 29 3.91896064
## 30 3.69712741
## 31 0.46915767
## 32 4.72049480
## 33 0.19167765
## 34 -0.90597302
## 35 -1.20281084
## 36 -0.01937459
## 37 -2.97077294
## 38 -2.61069923
## 39 2.59649674
## 40 -1.06255770
## 41 10.79590608
## 42 1.14484242
## 43 3.75828362
## 44 5.79814119
## 45 11.19327579
## 46 8.16826930
## 47 9.95337292
## 48 8.39370245
## 49 7.26386696
## 50 4.22192110
## 51 3.28290245
## 52 6.40315843
## 53 0.69080152
## 54 6.64028677
## 55 7.36019364
## 56 7.56408031
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## 491 -2.66522606
## 492 8.00615795
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## 494 7.93710535
## 495 2.48315514
## 496 4.87420106
## 497 6.18323546
## 498 17.21351833
## 499 8.47421995
## 500 3.50869363# Find mean and standard deviation of differences
diff_stats <- sample_dem_data %>%
summarize(xbar_diff = mean(diff),
s_diff = sd(diff)
)
# See the result
diff_stats## xbar_diff s_diff
## 1 6.829313 5.040139# Using sample_dem_data, plot diff as a histogram
ggplot(sample_dem_data, aes(diff)) + geom_histogram(binwidth = 1)
5.3 Using t.test()
Manually calculating test statistics and transforming them with a CDF to get a p-value is a lot of effort to do every time you need to compare two sample means. The comparison of two sample means is called a t-test, and R has a t.test() function to accomplish it. This function provides some flexibility in how you perform the test.
# Conduct a t-test on diff
test_results <- t.test(sample_dem_data$diff, alternative = "greater")
# See the results
test_results##
## One Sample t-test
##
## data: sample_dem_data$diff
## t = 30.298, df = 499, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0
## 95 percent confidence interval:
## 6.45787 Inf
## sample estimates:
## mean of x
## 6.829313# Conduct a paired t-test on dem_percent_12 and dem_percent_16
test_results <- t.test(
sample_dem_data$dem_percent_12,
sample_dem_data$dem_percent_16,
alternative = "greater",
paired = TRUE
)
# See the results
test_results##
## Paired t-test
##
## data: sample_dem_data$dem_percent_12 and sample_dem_data$dem_percent_16
## t = 30.298, df = 499, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 6.45787 Inf
## sample estimates:
## mean of the differences
## 6.8293135.4 Visualizing many categories
So far in this chapter, we’ve only considered the case of differences in a numeric variable between two categories. Of course, many datasets contain more categories. Before you get to conducting tests on many categories, it’s often helpful to perform exploratory data analysis. That is, calculating summary statistics for each group and visualizing the distributions of the numeric variable for each category using box plots.
# Using late_shipments, group by shipment mode, and calculate the mean and std dev of pack price
late_shipments %>% group_by(shipment_mode) %>%
summarize(
xbar_pack_price = mean(pack_price),
s_pack_price = sd(pack_price)
)## # A tibble: 4 × 3
## shipment_mode xbar_pack_price s_pack_price
## <chr> <dbl> <dbl>
## 1 Air 43.1 65.8
## 2 Air Charter 3.39 1.34
## 3 N/A 37.5 NA
## 4 Ocean 7.82 9.86# Using late_shipments, plot pack_price vs. shipment_mode
# as a box plot with flipped x and y coordinates
ggplot(late_shipments, aes(shipment_mode, pack_price)) + geom_boxplot() +
coord_flip()
5.5 ANOVA
The box plots made it look like the distribution of pack price was different for each of the three shipment modes. However, it didn’t tell us whether the mean pack price was different in each category. To determine that, we can use an ANOVA test. The null and alternative hypotheses can be written as follows.
Ho: Pack prices for every category of shipment mode are the same. Ha: Pack prices for some categories of shipment mode are different.
We’ll set a significance level of 0.1.
# Run a linear regression of pack price vs. shipment mode
mdl_pack_price_vs_shipment_mode <- lm(pack_price ~ shipment_mode, data= late_shipments)
# See the results
summary(mdl_pack_price_vs_shipment_mode)##
## Call:
## lm(formula = pack_price ~ shipment_mode, data = late_shipments)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.15 -34.55 -12.33 26.90 1199.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.145 2.086 20.678 < 2e-16 ***
## shipment_modeAir Charter -39.752 25.766 -1.543 0.123
## shipment_modeN/A -5.645 62.942 -0.090 0.929
## shipment_modeOcean -35.330 7.174 -4.925 9.88e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62.91 on 996 degrees of freedom
## Multiple R-squared: 0.02572, Adjusted R-squared: 0.02278
## F-statistic: 8.764 on 3 and 996 DF, p-value: 9.701e-06# Perform ANOVA on the regression model
anova(mdl_pack_price_vs_shipment_mode)## Analysis of Variance Table
##
## Response: pack_price
## Df Sum Sq Mean Sq F value Pr(>F)
## shipment_mode 3 104048 34683 8.7642 9.701e-06 ***
## Residuals 996 3941466 3957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.6 Pairwise t-tests
The ANOVA test didn’t tell us which categories of shipment mode had significant differences in pack prices. To pinpoint which categories had differences, we could instead use pairwise t-tests.
# Perform pairwise t-tests on pack price, grouped by shipment mode, no p-value adjustment
test_results <- pairwise.t.test(late_shipments$pack_price, late_shipments$shipment_mode, p.adjust.method = "none")
# See the results
test_results##
## Pairwise comparisons using t tests with pooled SD
##
## data: late_shipments$pack_price and late_shipments$shipment_mode
##
## Air Air Charter N/A
## Air Charter - - -
## N/A - - -
## Ocean - - -
##
## P value adjustment method: none# Modify the pairwise t-tests to use Bonferroni p-value adjustment
test_results <- pairwise.t.test(
late_shipments$pack_price,
late_shipments$shipment_mode,
p.adjust.method = "bonferroni"
)
# See the results
test_results##
## Pairwise comparisons using t tests with pooled SD
##
## data: late_shipments$pack_price and late_shipments$shipment_mode
##
## Air Air Charter N/A
## Air Charter - - -
## N/A - - -
## Ocean - - -
##
## P value adjustment method: bonferroni5.7 Test for single proportions
In Chapter 1, you calculated a p-value for a test hypothesizing that the proportion of late shipments was greater than 6%. In that chapter, you used a bootstrap distribution to estimate the standard error of the statistic. A simpler alternative is to use an equation for the standard error based on the sample proportion, hypothesized proportion, and sample size.
# Hypothesize that the proportion of late shipments is 6%
p_0 <- 0.06
# Calculate the sample proportion of late shipments
p_hat <- late_shipments %>% summarize(prop_late_shipments = mean(late == "Yes"))
# Calculate the sample size
n <- nrow(late_shipments)
# Calculate the numerator of the test statistic
numerator <- p_hat - p_0
# Calculate the denominator of the test statistic
denominator <-sqrt(p_0 * (1-p_0) / n)
# Calculate the test statistic
z_score <- numerator / denominator
# See the result
z_score## prop_late_shipments
## 1 0.9320914# Calculate the p-value from the z-score
p_value <- pnorm(0.9320914, mean = 0, sd = 1, lower.tail = FALSE)
# See the result
p_value## [1] 0.17564465.8 The chi-square distribution
Chi-square hypothesis tests rely on the chi-square distribution. Like the t-distribution, it has degrees of freedom and non-centrality parameters. The chi-square independence test compares proportions of successes of a categorical variable across categories of another categorical variable.
late_shipments_sample <- read_csv("late_shipments_sample.csv")## Rows: 1000 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): country, managed_by, fulfill_via, vendor_inco_term, shipment_mode,...
## dbl (10): id, late_delivery, unit_of_measure_per_pack, line_item_quantity, l...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Plot vendor_inco_term filled by freight_cost_group.
# Make it a proportional stacked bar plot.
late_shipments_sample_2 <- late_shipments_sample[!(late_shipments_sample$vendor_inco_term=="CIF" | late_shipments_sample$vendor_inco_term=="DAP" | late_shipments_sample$vendor_inco_term=="DDU"), ]
ggplot(late_shipments_sample_2, aes(x = vendor_inco_term, fill = freight_cost_group)) +
geom_bar(position = "fill")
# Perform a chi-square test of independence on freight_cost_group and vendor_inco_term
test_results <- late_shipments_sample_2 %>%
chisq_test(freight_cost_group ~ vendor_inco_term)
# See the results
test_results## # A tibble: 1 × 3
## statistic chisq_df p_value
## <dbl> <int> <dbl>
## 1 44.1 3 0.000000001425.9 Visualizing goodness of fit
The chi-square goodness of fit test compares proportions of each level of a categorical variable to hypothesized values. Before running such a test, it can be helpful to visually compare the distribution in the sample to the hypothesized distribution.
# Using late_shipments, count the vendor incoterms
vendor_inco_term_counts <- late_shipments_sample_2 %>% count(vendor_inco_term)
# Get the number of rows in the whole sample
n_total <- nrow(late_shipments)
hypothesized <- tribble(
~ vendor_inco_term, ~ prop,
"EXW", 0.75,
"CIP", 0.05,
"DDP", 0.1,
"FCA", 0.1
) %>%
# Add a column of hypothesized counts for the incoterms
mutate(n = prop * n_total)
# See the results
hypothesized## # A tibble: 4 × 3
## vendor_inco_term prop n
## <chr> <dbl> <dbl>
## 1 EXW 0.75 750
## 2 CIP 0.05 50
## 3 DDP 0.1 100
## 4 FCA 0.1 100# Using vendor_inco_term_counts, plot n vs. vendor_inco_term
ggplot(vendor_inco_term_counts, aes(vendor_inco_term, n)) +
# Make it a (precalculated) bar plot
geom_col() +
# Add points from hypothesized
geom_point(data = hypothesized, color ="red")
The bar plot of vendor_inco_term suggested that its distribution across the four categories was quite close to the hypothesized distribution. You’ll need to perform a chi-square goodness of fit test to see whether the differences are statistically significant.
To decide which hypothesis to choose, we’ll set a significance level of 0.1.
hypothesized_props <- c(
EXW = 0.75, CIP = 0.05, DDP = 0.1, FCA = 0.1)
# Run chi-square goodness of fit test on vendor_inco_term
test_results <- late_shipments_sample_2 %>% chisq_test(response = vendor_inco_term, p = hypothesized_props)
# See the results
test_results## # A tibble: 1 × 3
## statistic chisq_df p_value
## <dbl> <dbl> <dbl>
## 1 4861. 3 05.10 Testing sample size
In order to conduct a hypothesis test, and be sure that the result is fair, a sample must meet three requirements: it is a random sample of the population; the observations are independent; and there are enough observations. Of these, only the last condition is easily testable with code.
# Get counts by freight_cost_group
counts <- late_shipments_sample %>% count(freight_cost_group)
# See the result
counts## # A tibble: 2 × 2
## freight_cost_group n
## <chr> <int>
## 1 expensive 541
## 2 reasonable 459# Inspect whether the counts are big enough
all(counts$n >= 30)## [1] TRUE# Get counts by late
counts <- late_shipments_sample %>% count(late)
# See the result
counts## # A tibble: 2 × 2
## late n
## <chr> <int>
## 1 No 933
## 2 Yes 67# Inspect whether the counts are big enough
all(counts$n >= 10)## [1] TRUE# Count the values of vendor_inco_term and freight_cost_group
counts <- late_shipments_sample %>% count(vendor_inco_term, freight_cost_group)
# See the result
counts## # A tibble: 11 × 3
## vendor_inco_term freight_cost_group n
## <chr> <chr> <int>
## 1 CIF reasonable 1
## 2 CIP expensive 14
## 3 CIP reasonable 40
## 4 DAP reasonable 1
## 5 DDP expensive 59
## 6 DDP reasonable 27
## 7 DDU reasonable 1
## 8 EXW expensive 429
## 9 EXW reasonable 317
## 10 FCA expensive 39
## 11 FCA reasonable 72# Inspect whether the counts are big enough
all(counts$n >= 5)## [1] FALSE# Count the values of shipment_mode
counts <- late_shipments_sample %>% count(shipment_mode)
# See the result
counts## # A tibble: 4 × 2
## shipment_mode n
## <chr> <int>
## 1 Air 909
## 2 Air Charter 6
## 3 N/A 1
## 4 Ocean 84# Inspect whether the counts are big enough
all(counts$n >= 30)## [1] FALSE5.11 Specifying & hypothesizing
You ran a two sample proportion test on the proportion of late shipments across freight cost groups. Recall the hypotheses. * Ho: late_expensive - late_reasonable = * Ha: late_expensive - late_reasonable > 0
# Perform a proportion test appropriate to the hypotheses
test_results <- late_shipments_sample_2 %>%
prop_test(
late ~ freight_cost_group,
order = c("expensive", "reasonable"),
success = "Yes",
alternative = "greater",
correct = FALSE
)
# See the results
test_results## # A tibble: 1 × 6
## statistic chisq_df p_value alternative lower_ci upper_ci
## <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 15.8 1 0.0000356 greater 0.162 1## The p-value is less than or equal to the significance level, so you should reject the null hypothesis that the proportion of late shipments is the same for each freight cost group.
# Specify that we are interested in late proportions across freight_cost_groups, where "Yes" denotes success
specified <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
)
# See the result
specified## Response: late (factor)
## Explanatory: freight_cost_group (factor)
## # A tibble: 997 × 2
## late freight_cost_group
## <fct> <fct>
## 1 No reasonable
## 2 No expensive
## 3 No expensive
## 4 Yes expensive
## 5 No reasonable
## 6 No reasonable
## 7 No expensive
## 8 No expensive
## 9 No expensive
## 10 No reasonable
## # … with 987 more rows# Extend the pipeline to declare a null hypothesis that the variables are independent
hypothesized <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
hypothesize(null = "independence")
# See the result
hypothesized## Response: late (factor)
## Explanatory: freight_cost_group (factor)
## Null Hypothesis: independence
## # A tibble: 997 × 2
## late freight_cost_group
## <fct> <fct>
## 1 No reasonable
## 2 No expensive
## 3 No expensive
## 4 Yes expensive
## 5 No reasonable
## 6 No reasonable
## 7 No expensive
## 8 No expensive
## 9 No expensive
## 10 No reasonable
## # … with 987 more rows5.12 Generating & calculating
The infer pipeline for hypothesis testing requires four steps to calculate the null distribution: specify, hypothesize, generate, and calculate.
Let’s continue the pipeline you began in the previous coding exercise. We’ll get a set of differences in proportions that are distributed as though the null hypothesis, that the proportion of late shipments is the same across freight cost groups, is true.
# Extend the pipeline to generate 2000 permutations
generated <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
hypothesize(null = "independence") %>%
generate(reps = 2000, type = "permute")
# See the result
generated## Response: late (factor)
## Explanatory: freight_cost_group (factor)
## Null Hypothesis: independence
## # A tibble: 1,994,000 × 3
## # Groups: replicate [2,000]
## late freight_cost_group replicate
## <fct> <fct> <int>
## 1 No reasonable 1
## 2 No expensive 1
## 3 No expensive 1
## 4 Yes expensive 1
## 5 No reasonable 1
## 6 No reasonable 1
## 7 No expensive 1
## 8 No expensive 1
## 9 No expensive 1
## 10 No reasonable 1
## # … with 1,993,990 more rows# Extend the pipeline to calculate the difference in proportions (expensive minus reasonable)
null_distn <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
hypothesize(null = "independence") %>%
generate(reps = 2000, type = "permute") %>%
calculate(stat = "diff in props", order = c("expensive", "reasonable"))
# See the result
null_distn## Response: late (factor)
## Explanatory: freight_cost_group (factor)
## Null Hypothesis: independence
## # A tibble: 2,000 × 2
## replicate stat
## <int> <dbl>
## 1 1 -0.00144
## 2 2 0.0147
## 3 3 0.0107
## 4 4 0.0188
## 5 5 0.0147
## 6 6 0.0228
## 7 7 -0.0216
## 8 8 -0.00952
## 9 9 -0.0136
## 10 10 0.0147
## # … with 1,990 more rows# Visualize the null distribution
visualize(null_distn)
5.13 Observed statistic and p-value
You now have a null distribution. In order to get a p-value and weigh up the evidence against the null hypothesis, you need to calculate the difference in proportions that is observed in the late_shipments sample.
# Copy, paste, and modify the pipeline to get the observed statistic
obs_stat <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
calculate(
stat = "diff in props",
order = c("expensive", "reasonable")
)
# See the result
obs_stat## Response: late (factor)
## Explanatory: freight_cost_group (factor)
## # A tibble: 1 × 1
## stat
## <dbl>
## 1 0.0632# From previous steps
null_distn <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
hypothesize(null = "independence") %>%
generate(reps = 2000, type = "permute") %>%
calculate(
stat = "diff in props",
order = c("expensive", "reasonable")
)
obs_stat <- late_shipments_sample_2 %>%
specify(
late ~ freight_cost_group,
success = "Yes"
) %>%
calculate(
stat = "diff in props",
order = c("expensive", "reasonable")
)
# Visualize the null dist'n, adding a vertical line at the observed statistic
visualize(null_distn) +
geom_vline(aes(xintercept = stat), data = obs_stat)
# Get the p-value
p_value <- get_p_value(
null_distn, obs_stat,
direction = "greater"
)## Warning: Please be cautious in reporting a p-value of 0. This result is an
## approximation based on the number of `reps` chosen in the `generate()` step. See
## `?get_p_value()` for more information.# See the result
p_value## # A tibble: 1 × 1
## p_value
## <dbl>
## 1 05.14 Simulation-based t-test
# Fill out the null distribution pipeline
null_distn <- late_shipments_sample_2 %>%
# Specify weight_kilograms vs. late
specify(weight_kilograms ~ late) %>%
# Declare a null hypothesis of independence
hypothesize(null = "independence") %>%
# Generate 1000 permutation replicates
generate(reps = 1000, type = "permute") %>%
# Calculate the difference in means ("No" minus "Yes")
calculate(stat = "diff in means", order=c("No", "Yes"))
# See the results
null_distn## Response: weight_kilograms (numeric)
## Explanatory: late (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
## replicate stat
## <int> <dbl>
## 1 1 400.
## 2 2 -2561.
## 3 3 428.
## 4 4 1020.
## 5 5 -10.8
## 6 6 -182.
## 7 7 358.
## 8 8 -2.25
## 9 9 340.
## 10 10 28.8
## # … with 990 more rowsround(null_distn, 2)## Response: weight_kilograms (numeric)
## Explanatory: late (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
## replicate stat
## <dbl> <dbl>
## 1 1 400.
## 2 2 -2560.
## 3 3 428.
## 4 4 1020.
## 5 5 -10.8
## 6 6 -182.
## 7 7 358.
## 8 8 -2.25
## 9 9 340.
## 10 10 28.8
## # … with 990 more rows# Calculate the observed difference in means
obs_stat <- late_shipments_sample_2 %>%
specify(
weight_kilograms ~ late) %>%
calculate(
stat = "diff in means",
order = c("No", "Yes")
)
# See the result
obs_stat## Response: weight_kilograms (numeric)
## Explanatory: late (factor)
## # A tibble: 1 × 1
## stat
## <dbl>
## 1 -289.# Get the p-value
p_value <- get_p_value(
null_distn, obs_stat,
direction = "less"
)
# See the result
p_value## # A tibble: 1 × 1
## p_value
## <dbl>
## 1 0.2055.15 Rank sum tests
Another class of non-parametric hypothesis tests are called rank sum tests. Ranks are the positions of numeric values from smallest to largest. Think of them as positions in running events: whoever has the fastest (smallest) time is rank 1, second fastest is rank 2, and so on.
By calculating on the ranks of data instead of the actual values, you can avoid making assumptions about the distribution of the test statistic. It’s most robust in the same way that a median is more robust than a mean.
Two commonly used rank-based tests are the Wilcoxon-Mann-Whitney test, which is like a non-parametric t-test, and the Kruskal-Wallis test, which is like a non-parametric ANOVA.
# Run a Wilcoxon-Mann-Whitney test on weight_kilograms vs. late
test_results <- wilcox.test(weight_kilograms ~ late, data = late_shipments)
# See the result
test_results##
## Wilcoxon rank sum test with continuity correction
##
## data: weight_kilograms by late
## W = 21480, p-value = 1.861e-05
## alternative hypothesis: true location shift is not equal to 0# Run a Kruskal-Wallace test on weight_kilograms vs. shipment_mode
test_results <- kruskal.test(weight_kilograms ~ shipment_mode, data = late_shipments)
# See the result
test_results##
## Kruskal-Wallis rank sum test
##
## data: weight_kilograms by shipment_mode
## Kruskal-Wallis chi-squared = 159.7, df = 3, p-value < 2.2e-16The End.
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