library(readr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ purrr 1.0.1
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)
library(dplyr)
project1data <- read_csv("https://raw.githubusercontent.com/sphill12/DATA607/main/project1data.txt")## Rows: 195 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): -------------------------------------------------------------------...
##
## 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.View(project1data)# remove rows that only contain dashes
new_df = project1data %>% filter(row_number() %% 3 != 0)
new_df = new_df %>% rename(data = 1)lapply(new_df, function(x) str_extract(x, regex("|.*|")))## $data
## [1] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [26] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [51] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [76] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [101] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [126] "" "" "" "" ""string_split <- strsplit(new_df$data, "|", fixed = TRUE)
string_split## [[1]]
## [1] "Pair " " Player Name "
## [3] "Total" "Round"
## [5] "Round" "Round"
## [7] "Round" "Round"
## [9] "Round" "Round"
##
## [[2]]
## [1] "Num " " USCF ID / Rtg (Pre->Post) "
## [3] " Pts " " 1 "
## [5] " 2 " " 3 "
## [7] " 4 " " 5 "
## [9] " 6 " " 7 "
##
## [[3]]
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## [9] "D 12" "D 4"
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## [[4]]
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## [5] "B " "W "
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##
## [[5]]
## [1] "2 " " DAKSHESH DARURI "
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## [[6]]
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## [7] "W " "B "
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## [[7]]
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##
## [[9]]
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## [[11]]
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## [[13]]
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## [[15]]
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## [[17]]
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## [[18]]
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## [[19]]
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## [[21]]
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## [[23]]
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## [[24]]
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## [[25]]
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## [[26]]
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##
## [[27]]
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## [[29]]
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## [[31]]
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## [[32]]
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## [[33]]
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## [[34]]
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## [[35]]
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## [[37]]
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## [[38]]
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## [[42]]
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## [[43]]
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## [[44]]
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## [[45]]
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##
## [[46]]
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## [[47]]
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## [[48]]
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## [[49]]
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## [[51]]
## [1] "25 " " LOREN SCHWIEBERT "
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## [[55]]
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## [[59]]
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## [[60]]
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## [[61]]
## [1] "30 " " GEORGE AVERY JONES "
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## [[62]]
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## [[63]]
## [1] "31 " " RISHI SHETTY "
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## [[64]]
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## [[65]]
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## [[66]]
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## [[67]]
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## [[68]]
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## [[69]]
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## [[71]]
## [1] "35 " " JOSHUA DAVID LEE "
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## [[72]]
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## [[73]]
## [1] "36 " " SIDDHARTH JHA "
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## [[74]]
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## [[75]]
## [1] "37 " " AMIYATOSH PWNANANDAM "
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## [[77]]
## [1] "38 " " BRIAN LIU "
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## [[78]]
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## [[79]]
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## [1] "40 " " FOREST ZHANG "
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## [[85]]
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## [9] "B " "W "
##
## [[93]]
## [1] "46 " " JACOB ALEXANDER LAVALLEY "
## [3] "3.0 " "W 35"
## [5] "L 7" "L 27"
## [7] "L 50" "W 64"
## [9] "W 43" "L 23"
##
## [[94]]
## [1] "MI " " 15490981 / R: 377P3 ->1076P10 "
## [3] " " "B "
## [5] "W " "B "
## [7] "W " "B "
## [9] "W " "W "
##
## [[95]]
## [1] "47 " " ERIC WRIGHT "
## [3] "2.5 " "L 18"
## [5] "W 24" "L 21"
## [7] "W 61" "L 8"
## [9] "D 51" "L 25"
##
## [[96]]
## [1] "MI " " 12533115 / R: 1362 ->1341 "
## [3] " " "W "
## [5] "B " "W "
## [7] "B " "W "
## [9] "B " "W "
##
## [[97]]
## [1] "48 " " DANIEL KHAIN "
## [3] "2.5 " "L 17"
## [5] "W 63" "H "
## [7] "D 52" "H "
## [9] "L 29" "L 35"
##
## [[98]]
## [1] "MI " " 14369165 / R: 1382 ->1335 "
## [3] " " "B "
## [5] "W " " "
## [7] "B " " "
## [9] "W " "B "
##
## [[99]]
## [1] "49 " " MICHAEL J MARTIN "
## [3] "2.5 " "L 26"
## [5] "L 20" "D 63"
## [7] "D 64" "W 58"
## [9] "H " "U "
##
## [[100]]
## [1] "MI " " 12531685 / R: 1291P12->1259P17 "
## [3] " " "W "
## [5] "W " "B "
## [7] "W " "B "
## [9] " " " "
##
## [[101]]
## [1] "50 " " SHIVAM JHA "
## [3] "2.5 " "L 29"
## [5] "W 42" "L 33"
## [7] "W 46" "H "
## [9] "L 31" "L 30"
##
## [[102]]
## [1] "MI " " 14773178 / R: 1056 ->1111 "
## [3] " " "W "
## [5] "B " "W "
## [7] "B " " "
## [9] "B " "W "
##
## [[103]]
## [1] "51 " " TEJAS AYYAGARI "
## [3] "2.5 " "L 27"
## [5] "W 45" "L 36"
## [7] "W 57" "L 32"
## [9] "D 47" "L 33"
##
## [[104]]
## [1] "MI " " 15205474 / R: 1011 ->1097 "
## [3] " " "B "
## [5] "W " "B "
## [7] "W " "B "
## [9] "W " "W "
##
## [[105]]
## [1] "52 " " ETHAN GUO "
## [3] "2.5 " "W 30"
## [5] "D 22" "L 19"
## [7] "D 48" "L 29"
## [9] "D 35" "L 34"
##
## [[106]]
## [1] "MI " " 14918803 / R: 935 ->1092 "
## [3] "N:4 " "B "
## [5] "W " "B "
## [7] "W " "B "
## [9] "W " "B "
##
## [[107]]
## [1] "53 " " JOSE C YBARRA "
## [3] "2.0 " "H "
## [5] "L 25" "H "
## [7] "L 44" "U "
## [9] "W 57" "U "
##
## [[108]]
## [1] "MI " " 12578849 / R: 1393 ->1359 "
## [3] " " " "
## [5] "B " " "
## [7] "W " " "
## [9] "W " " "
##
## [[109]]
## [1] "54 " " LARRY HODGE "
## [3] "2.0 " "L 14"
## [5] "L 39" "L 61"
## [7] "B " "L 15"
## [9] "L 59" "W 64"
##
## [[110]]
## [1] "MI " " 12836773 / R: 1270 ->1200 "
## [3] " " "B "
## [5] "B " "W "
## [7] " " "W "
## [9] "B " "W "
##
## [[111]]
## [1] "55 " " ALEX KONG "
## [3] "2.0 " "L 62"
## [5] "D 31" "L 10"
## [7] "L 30" "B "
## [9] "D 45" "L 43"
##
## [[112]]
## [1] "MI " " 15412571 / R: 1186 ->1163 "
## [3] " " "W "
## [5] "B " "W "
## [7] "B " " "
## [9] "W " "B "
##
## [[113]]
## [1] "56 " " MARISA RICCI "
## [3] "2.0 " "H "
## [5] "L 11" "L 35"
## [7] "W 45" "H "
## [9] "L 40" "L 42"
##
## [[114]]
## [1] "MI " " 14679887 / R: 1153 ->1140 "
## [3] " " " "
## [5] "B " "W "
## [7] "W " " "
## [9] "B " "W "
##
## [[115]]
## [1] "57 " " MICHAEL LU "
## [3] "2.0 " "L 7"
## [5] "L 36" "W 42"
## [7] "L 51" "L 35"
## [9] "L 53" "B "
##
## [[116]]
## [1] "MI " " 15113330 / R: 1092 ->1079 "
## [3] " " "B "
## [5] "W " "W "
## [7] "B " "W "
## [9] "B " " "
##
## [[117]]
## [1] "58 " " VIRAJ MOHILE "
## [3] "2.0 " "W 31"
## [5] "L 2" "L 41"
## [7] "L 23" "L 49"
## [9] "B " "L 45"
##
## [[118]]
## [1] "MI " " 14700365 / R: 917 -> 941 "
## [3] " " "W "
## [5] "B " "W "
## [7] "B " "W "
## [9] " " "B "
##
## [[119]]
## [1] "59 " " SEAN M MC CORMICK "
## [3] "2.0 " "L 41"
## [5] "B " "L 9"
## [7] "L 40" "L 43"
## [9] "W 54" "L 44"
##
## [[120]]
## [1] "MI " " 12841036 / R: 853 -> 878 "
## [3] " " "W "
## [5] " " "B "
## [7] "B " "W "
## [9] "W " "B "
##
## [[121]]
## [1] "60 " " JULIA SHEN "
## [3] "1.5 " "L 33"
## [5] "L 34" "D 45"
## [7] "D 42" "L 24"
## [9] "H " "U "
##
## [[122]]
## [1] "MI " " 14579262 / R: 967 -> 984 "
## [3] " " "W "
## [5] "B " "B "
## [7] "W " "B "
## [9] " " " "
##
## [[123]]
## [1] "61 " " JEZZEL FARKAS "
## [3] "1.5 " "L 32"
## [5] "L 3" "W 54"
## [7] "L 47" "D 42"
## [9] "L 30" "L 37"
##
## [[124]]
## [1] "ON " " 15771592 / R: 955P11-> 979P18 "
## [3] " " "B "
## [5] "W " "B "
## [7] "W " "B "
## [9] "W " "B "
##
## [[125]]
## [1] "62 " " ASHWIN BALAJI "
## [3] "1.0 " "W 55"
## [5] "U " "U "
## [7] "U " "U "
## [9] "U " "U "
##
## [[126]]
## [1] "MI " " 15219542 / R: 1530 ->1535 "
## [3] " " "B "
## [5] " " " "
## [7] " " " "
## [9] " " " "
##
## [[127]]
## [1] "63 " " THOMAS JOSEPH HOSMER "
## [3] "1.0 " "L 2"
## [5] "L 48" "D 49"
## [7] "L 43" "L 45"
## [9] "H " "U "
##
## [[128]]
## [1] "MI " " 15057092 / R: 1175 ->1125 "
## [3] " " "W "
## [5] "B " "W "
## [7] "B " "B "
## [9] " " " "
##
## [[129]]
## [1] "64 " " BEN LI "
## [3] "1.0 " "L 22"
## [5] "D 30" "L 31"
## [7] "D 49" "L 46"
## [9] "L 42" "L 54"
##
## [[130]]
## [1] "MI " " 15006561 / R: 1163 ->1112 "
## [3] " " "B "
## [5] "W " "W "
## [7] "B " "W "
## [9] "B " "B "reworked_df <-data.frame(strings = unlist(string_split))
reworked_df## strings
## 1 Pair
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## 753
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## 785 W 54
## 786 W 40
## 787 L 16
## 788 W 44
## 789 L 21
## 790 L 24
## 791 MI
## 792 12923035 / R: 1436P23->1413
## 793 N:4
## 794 B
## 795 W
## 796 B
## 797 W
## 798 B
## 799 W
## 800 W
## 801 40
## 802 FOREST ZHANG
## 803 3.0
## 804 W 20
## 805 L 26
## 806 L 39
## 807 W 59
## 808 L 21
## 809 W 56
## 810 L 22
## 811 MI
## 812 14892710 / R: 1348 ->1346
## 813
## 814 B
## 815 B
## 816 W
## 817 W
## 818 B
## 819 W
## 820 W
## 821 41
## 822 KYLE WILLIAM MURPHY
## 823 3.0
## 824 W 59
## 825 L 17
## 826 W 58
## 827 L 20
## 828 X
## 829 U
## 830 U
## 831 MI
## 832 15761443 / R: 1403P5 ->1341P9
## 833
## 834 B
## 835 W
## 836 B
## 837 W
## 838
## 839
## 840
## 841 42
## 842 JARED GE
## 843 3.0
## 844 L 12
## 845 L 50
## 846 L 57
## 847 D 60
## 848 D 61
## 849 W 64
## 850 W 56
## 851 MI
## 852 14462326 / R: 1332 ->1256
## 853
## 854 B
## 855 W
## 856 B
## 857 B
## 858 W
## 859 W
## 860 B
## 861 43
## 862 ROBERT GLEN VASEY
## 863 3.0
## 864 L 21
## 865 L 23
## 866 L 24
## 867 W 63
## 868 W 59
## 869 L 46
## 870 W 55
## 871 MI
## 872 14101068 / R: 1283 ->1244
## 873
## 874 W
## 875 B
## 876 W
## 877 W
## 878 B
## 879 B
## 880 W
## 881 44
## 882 JUSTIN D SCHILLING
## 883 3.0
## 884 B
## 885 L 14
## 886 L 32
## 887 W 53
## 888 L 39
## 889 L 24
## 890 W 59
## 891 MI
## 892 15323504 / R: 1199 ->1199
## 893
## 894
## 895 W
## 896 B
## 897 B
## 898 W
## 899 B
## 900 W
## 901 45
## 902 DEREK YAN
## 903 3.0
## 904 L 5
## 905 L 51
## 906 D 60
## 907 L 56
## 908 W 63
## 909 D 55
## 910 W 58
## 911 MI
## 912 15372807 / R: 1242 ->1191
## 913
## 914 W
## 915 B
## 916 W
## 917 B
## 918 W
## 919 B
## 920 W
## 921 46
## 922 JACOB ALEXANDER LAVALLEY
## 923 3.0
## 924 W 35
## 925 L 7
## 926 L 27
## 927 L 50
## 928 W 64
## 929 W 43
## 930 L 23
## 931 MI
## 932 15490981 / R: 377P3 ->1076P10
## 933
## 934 B
## 935 W
## 936 B
## 937 W
## 938 B
## 939 W
## 940 W
## 941 47
## 942 ERIC WRIGHT
## 943 2.5
## 944 L 18
## 945 W 24
## 946 L 21
## 947 W 61
## 948 L 8
## 949 D 51
## 950 L 25
## 951 MI
## 952 12533115 / R: 1362 ->1341
## 953
## 954 W
## 955 B
## 956 W
## 957 B
## 958 W
## 959 B
## 960 W
## 961 48
## 962 DANIEL KHAIN
## 963 2.5
## 964 L 17
## 965 W 63
## 966 H
## 967 D 52
## 968 H
## 969 L 29
## 970 L 35
## 971 MI
## 972 14369165 / R: 1382 ->1335
## 973
## 974 B
## 975 W
## 976
## 977 B
## 978
## 979 W
## 980 B
## 981 49
## 982 MICHAEL J MARTIN
## 983 2.5
## 984 L 26
## 985 L 20
## 986 D 63
## 987 D 64
## 988 W 58
## 989 H
## 990 U
## 991 MI
## 992 12531685 / R: 1291P12->1259P17
## 993
## 994 W
## 995 W
## 996 B
## 997 W
## 998 B
## 999
## 1000
## 1001 50
## 1002 SHIVAM JHA
## 1003 2.5
## 1004 L 29
## 1005 W 42
## 1006 L 33
## 1007 W 46
## 1008 H
## 1009 L 31
## 1010 L 30
## 1011 MI
## 1012 14773178 / R: 1056 ->1111
## 1013
## 1014 W
## 1015 B
## 1016 W
## 1017 B
## 1018
## 1019 B
## 1020 W
## 1021 51
## 1022 TEJAS AYYAGARI
## 1023 2.5
## 1024 L 27
## 1025 W 45
## 1026 L 36
## 1027 W 57
## 1028 L 32
## 1029 D 47
## 1030 L 33
## 1031 MI
## 1032 15205474 / R: 1011 ->1097
## 1033
## 1034 B
## 1035 W
## 1036 B
## 1037 W
## 1038 B
## 1039 W
## 1040 W
## 1041 52
## 1042 ETHAN GUO
## 1043 2.5
## 1044 W 30
## 1045 D 22
## 1046 L 19
## 1047 D 48
## 1048 L 29
## 1049 D 35
## 1050 L 34
## 1051 MI
## 1052 14918803 / R: 935 ->1092
## 1053 N:4
## 1054 B
## 1055 W
## 1056 B
## 1057 W
## 1058 B
## 1059 W
## 1060 B
## 1061 53
## 1062 JOSE C YBARRA
## 1063 2.0
## 1064 H
## 1065 L 25
## 1066 H
## 1067 L 44
## 1068 U
## 1069 W 57
## 1070 U
## 1071 MI
## 1072 12578849 / R: 1393 ->1359
## 1073
## 1074
## 1075 B
## 1076
## 1077 W
## 1078
## 1079 W
## 1080
## 1081 54
## 1082 LARRY HODGE
## 1083 2.0
## 1084 L 14
## 1085 L 39
## 1086 L 61
## 1087 B
## 1088 L 15
## 1089 L 59
## 1090 W 64
## 1091 MI
## 1092 12836773 / R: 1270 ->1200
## 1093
## 1094 B
## 1095 B
## 1096 W
## 1097
## 1098 W
## 1099 B
## 1100 W
## 1101 55
## 1102 ALEX KONG
## 1103 2.0
## 1104 L 62
## 1105 D 31
## 1106 L 10
## 1107 L 30
## 1108 B
## 1109 D 45
## 1110 L 43
## 1111 MI
## 1112 15412571 / R: 1186 ->1163
## 1113
## 1114 W
## 1115 B
## 1116 W
## 1117 B
## 1118
## 1119 W
## 1120 B
## 1121 56
## 1122 MARISA RICCI
## 1123 2.0
## 1124 H
## 1125 L 11
## 1126 L 35
## 1127 W 45
## 1128 H
## 1129 L 40
## 1130 L 42
## 1131 MI
## 1132 14679887 / R: 1153 ->1140
## 1133
## 1134
## 1135 B
## 1136 W
## 1137 W
## 1138
## 1139 B
## 1140 W
## 1141 57
## 1142 MICHAEL LU
## 1143 2.0
## 1144 L 7
## 1145 L 36
## 1146 W 42
## 1147 L 51
## 1148 L 35
## 1149 L 53
## 1150 B
## 1151 MI
## 1152 15113330 / R: 1092 ->1079
## 1153
## 1154 B
## 1155 W
## 1156 W
## 1157 B
## 1158 W
## 1159 B
## 1160
## 1161 58
## 1162 VIRAJ MOHILE
## 1163 2.0
## 1164 W 31
## 1165 L 2
## 1166 L 41
## 1167 L 23
## 1168 L 49
## 1169 B
## 1170 L 45
## 1171 MI
## 1172 14700365 / R: 917 -> 941
## 1173
## 1174 W
## 1175 B
## 1176 W
## 1177 B
## 1178 W
## 1179
## 1180 B
## 1181 59
## 1182 SEAN M MC CORMICK
## 1183 2.0
## 1184 L 41
## 1185 B
## 1186 L 9
## 1187 L 40
## 1188 L 43
## 1189 W 54
## 1190 L 44
## 1191 MI
## 1192 12841036 / R: 853 -> 878
## 1193
## 1194 W
## 1195
## 1196 B
## 1197 B
## 1198 W
## 1199 W
## 1200 B
## 1201 60
## 1202 JULIA SHEN
## 1203 1.5
## 1204 L 33
## 1205 L 34
## 1206 D 45
## 1207 D 42
## 1208 L 24
## 1209 H
## 1210 U
## 1211 MI
## 1212 14579262 / R: 967 -> 984
## 1213
## 1214 W
## 1215 B
## 1216 B
## 1217 W
## 1218 B
## 1219
## 1220
## 1221 61
## 1222 JEZZEL FARKAS
## 1223 1.5
## 1224 L 32
## 1225 L 3
## 1226 W 54
## 1227 L 47
## 1228 D 42
## 1229 L 30
## 1230 L 37
## 1231 ON
## 1232 15771592 / R: 955P11-> 979P18
## 1233
## 1234 B
## 1235 W
## 1236 B
## 1237 W
## 1238 B
## 1239 W
## 1240 B
## 1241 62
## 1242 ASHWIN BALAJI
## 1243 1.0
## 1244 W 55
## 1245 U
## 1246 U
## 1247 U
## 1248 U
## 1249 U
## 1250 U
## 1251 MI
## 1252 15219542 / R: 1530 ->1535
## 1253
## 1254 B
## 1255
## 1256
## 1257
## 1258
## 1259
## 1260
## 1261 63
## 1262 THOMAS JOSEPH HOSMER
## 1263 1.0
## 1264 L 2
## 1265 L 48
## 1266 D 49
## 1267 L 43
## 1268 L 45
## 1269 H
## 1270 U
## 1271 MI
## 1272 15057092 / R: 1175 ->1125
## 1273
## 1274 W
## 1275 B
## 1276 W
## 1277 B
## 1278 B
## 1279
## 1280
## 1281 64
## 1282 BEN LI
## 1283 1.0
## 1284 L 22
## 1285 D 30
## 1286 L 31
## 1287 D 49
## 1288 L 46
## 1289 L 42
## 1290 L 54
## 1291 MI
## 1292 15006561 / R: 1163 ->1112
## 1293
## 1294 B
## 1295 W
## 1296 W
## 1297 B
## 1298 W
## 1299 B
## 1300 Bnames <- (reworked_df[seq(22,nrow(reworked_df), 20), ])names## [1] " GARY HUA " " DAKSHESH DARURI "
## [3] " ADITYA BAJAJ " " PATRICK H SCHILLING "
## [5] " HANSHI ZUO " " HANSEN SONG "
## [7] " GARY DEE SWATHELL " " EZEKIEL HOUGHTON "
## [9] " STEFANO LEE " " ANVIT RAO "
## [11] " CAMERON WILLIAM MC LEMAN " " KENNETH J TACK "
## [13] " TORRANCE HENRY JR " " BRADLEY SHAW "
## [15] " ZACHARY JAMES HOUGHTON " " MIKE NIKITIN "
## [17] " RONALD GRZEGORCZYK " " DAVID SUNDEEN "
## [19] " DIPANKAR ROY " " JASON ZHENG "
## [21] " DINH DANG BUI " " EUGENE L MCCLURE "
## [23] " ALAN BUI " " MICHAEL R ALDRICH "
## [25] " LOREN SCHWIEBERT " " MAX ZHU "
## [27] " GAURAV GIDWANI " " SOFIA ADINA STANESCU-BELLU "
## [29] " CHIEDOZIE OKORIE " " GEORGE AVERY JONES "
## [31] " RISHI SHETTY " " JOSHUA PHILIP MATHEWS "
## [33] " JADE GE " " MICHAEL JEFFERY THOMAS "
## [35] " JOSHUA DAVID LEE " " SIDDHARTH JHA "
## [37] " AMIYATOSH PWNANANDAM " " BRIAN LIU "
## [39] " JOEL R HENDON " " FOREST ZHANG "
## [41] " KYLE WILLIAM MURPHY " " JARED GE "
## [43] " ROBERT GLEN VASEY " " JUSTIN D SCHILLING "
## [45] " DEREK YAN " " JACOB ALEXANDER LAVALLEY "
## [47] " ERIC WRIGHT " " DANIEL KHAIN "
## [49] " MICHAEL J MARTIN " " SHIVAM JHA "
## [51] " TEJAS AYYAGARI " " ETHAN GUO "
## [53] " JOSE C YBARRA " " LARRY HODGE "
## [55] " ALEX KONG " " MARISA RICCI "
## [57] " MICHAEL LU " " VIRAJ MOHILE "
## [59] " SEAN M MC CORMICK " " JULIA SHEN "
## [61] " JEZZEL FARKAS " " ASHWIN BALAJI "
## [63] " THOMAS JOSEPH HOSMER " " BEN LI "state <- (reworked_df[seq(31,nrow(reworked_df), 20), ])
state## [1] "ON " "MI " "MI " "MI " "MI " "OH " "MI " "MI " "ON " "MI " "MI " "MI "
## [13] "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "ON " "MI " "ON " "MI "
## [25] "MI " "ON " "MI " "MI " "MI " "ON " "MI " "ON " "MI " "MI " "MI " "MI "
## [37] "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI "
## [49] "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI " "MI "
## [61] "ON " "MI " "MI " "MI "total_points <- (reworked_df[seq(23,nrow(reworked_df), 20), ])
total_points## [1] "6.0 " "6.0 " "6.0 " "5.5 " "5.5 " "5.0 " "5.0 " "5.0 " "5.0 "
## [10] "5.0 " "4.5 " "4.5 " "4.5 " "4.5 " "4.5 " "4.0 " "4.0 " "4.0 "
## [19] "4.0 " "4.0 " "4.0 " "4.0 " "4.0 " "4.0 " "3.5 " "3.5 " "3.5 "
## [28] "3.5 " "3.5 " "3.5 " "3.5 " "3.5 " "3.5 " "3.5 " "3.5 " "3.5 "
## [37] "3.5 " "3.0 " "3.0 " "3.0 " "3.0 " "3.0 " "3.0 " "3.0 " "3.0 "
## [46] "3.0 " "2.5 " "2.5 " "2.5 " "2.5 " "2.5 " "2.5 " "2.0 " "2.0 "
## [55] "2.0 " "2.0 " "2.0 " "2.0 " "2.0 " "1.5 " "1.5 " "1.0 " "1.0 "
## [64] "1.0 "rd1 <- (reworked_df[seq(24, nrow(reworked_df), 20), ])
rd2 <- (reworked_df[seq(25, nrow(reworked_df), 20), ])
rd3 <- (reworked_df[seq(26, nrow(reworked_df), 20), ])
rd4 <- (reworked_df[seq(27, nrow(reworked_df), 20), ])
rd5 <- (reworked_df[seq(28, nrow(reworked_df), 20), ])
rd6 <- (reworked_df[seq(29, nrow(reworked_df), 20), ])
rd7 <- (reworked_df[seq(30, nrow(reworked_df), 20), ])
all_rds <- data.frame(rd1 = rd1, rd2 = rd2, rd3 = rd3, rd4 = rd4,rd5 = rd5, rd6 = rd6, rd7 = rd7)
all_rds <- as.data.frame(lapply(all_rds, function(x) gsub("W|D|L|H|U|X|B", "", x)))
all_rds <- transform(all_rds, rd1 = as.numeric(rd1),rd2 = as.numeric(rd2),rd3 = as.numeric(rd3),rd4 = as.numeric(rd4),rd5 = as.numeric(rd5),rd6 = as.numeric(rd6),rd7 = as.numeric(rd7))
all_rds## rd1 rd2 rd3 rd4 rd5 rd6 rd7
## 1 39 21 18 14 7 12 4
## 2 63 58 4 17 16 20 7
## 3 8 61 25 21 11 13 12
## 4 23 28 2 26 5 19 1
## 5 45 37 12 13 4 14 17
## 6 34 29 11 35 10 27 21
## 7 57 46 13 11 1 9 2
## 8 3 32 14 9 47 28 19
## 9 25 18 59 8 26 7 20
## 10 16 19 55 31 6 25 18
## 11 38 56 6 7 3 34 26
## 12 42 33 5 38 NA 1 3
## 13 36 27 7 5 33 3 32
## 14 54 44 8 1 27 5 31
## 15 19 16 30 22 54 33 38
## 16 10 15 NA 39 2 36 NA
## 17 48 41 26 2 23 22 5
## 18 47 9 1 32 19 38 10
## 19 15 10 52 28 18 4 8
## 20 40 49 23 41 28 2 9
## 21 43 1 47 3 40 39 6
## 22 64 52 28 15 NA 17 40
## 23 4 43 20 58 17 37 46
## 24 28 47 43 25 60 44 39
## 25 9 53 3 24 34 10 47
## 26 49 40 17 4 9 32 11
## 27 51 13 46 37 14 6 NA
## 28 24 4 22 19 20 8 36
## 29 50 6 38 34 52 48 NA
## 30 52 64 15 55 31 61 50
## 31 58 55 64 10 30 50 14
## 32 61 8 44 18 51 26 13
## 33 60 12 50 36 13 15 51
## 34 6 60 37 29 25 11 52
## 35 46 38 56 6 57 52 48
## 36 13 57 51 33 NA 16 28
## 37 NA 5 34 27 NA 23 61
## 38 11 35 29 12 NA 18 15
## 39 1 54 40 16 44 21 24
## 40 20 26 39 59 21 56 22
## 41 59 17 58 20 NA NA NA
## 42 12 50 57 60 61 64 56
## 43 21 23 24 63 59 46 55
## 44 NA 14 32 53 39 24 59
## 45 5 51 60 56 63 55 58
## 46 35 7 27 50 64 43 23
## 47 18 24 21 61 8 51 25
## 48 17 63 NA 52 NA 29 35
## 49 26 20 63 64 58 NA NA
## 50 29 42 33 46 NA 31 30
## 51 27 45 36 57 32 47 33
## 52 30 22 19 48 29 35 34
## 53 NA 25 NA 44 NA 57 NA
## 54 14 39 61 NA 15 59 64
## 55 62 31 10 30 NA 45 43
## 56 NA 11 35 45 NA 40 42
## 57 7 36 42 51 35 53 NA
## 58 31 2 41 23 49 NA 45
## 59 41 NA 9 40 43 54 44
## 60 33 34 45 42 24 NA NA
## 61 32 3 54 47 42 30 37
## 62 55 NA NA NA NA NA NA
## 63 2 48 49 43 45 NA NA
## 64 22 30 31 49 46 42 54pre_rating <- (reworked_df[seq(32, nrow(reworked_df), 20), ])
pre_rating## [1] " 15445895 / R: 1794 ->1817 " " 14598900 / R: 1553 ->1663 "
## [3] " 14959604 / R: 1384 ->1640 " " 12616049 / R: 1716 ->1744 "
## [5] " 14601533 / R: 1655 ->1690 " " 15055204 / R: 1686 ->1687 "
## [7] " 11146376 / R: 1649 ->1673 " " 15142253 / R: 1641P17->1657P24 "
## [9] " 14954524 / R: 1411 ->1564 " " 14150362 / R: 1365 ->1544 "
## [11] " 12581589 / R: 1712 ->1696 " " 12681257 / R: 1663 ->1670 "
## [13] " 15082995 / R: 1666 ->1662 " " 10131499 / R: 1610 ->1618 "
## [15] " 15619130 / R: 1220P13->1416P20 " " 10295068 / R: 1604 ->1613 "
## [17] " 10297702 / R: 1629 ->1610 " " 11342094 / R: 1600 ->1600 "
## [19] " 14862333 / R: 1564 ->1570 " " 14529060 / R: 1595 ->1569 "
## [21] " 15495066 / R: 1563P22->1562 " " 12405534 / R: 1555 ->1529 "
## [23] " 15030142 / R: 1363 ->1371 " " 13469010 / R: 1229 ->1300 "
## [25] " 12486656 / R: 1745 ->1681 " " 15131520 / R: 1579 ->1564 "
## [27] " 14476567 / R: 1552 ->1539 " " 14882954 / R: 1507 ->1513 "
## [29] " 15323285 / R: 1602P6 ->1508P12 " " 12577178 / R: 1522 ->1444 "
## [31] " 15131618 / R: 1494 ->1444 " " 14073750 / R: 1441 ->1433 "
## [33] " 14691842 / R: 1449 ->1421 " " 15051807 / R: 1399 ->1400 "
## [35] " 14601397 / R: 1438 ->1392 " " 14773163 / R: 1355 ->1367 "
## [37] " 15489571 / R: 980P12->1077P17 " " 15108523 / R: 1423 ->1439 "
## [39] " 12923035 / R: 1436P23->1413 " " 14892710 / R: 1348 ->1346 "
## [41] " 15761443 / R: 1403P5 ->1341P9 " " 14462326 / R: 1332 ->1256 "
## [43] " 14101068 / R: 1283 ->1244 " " 15323504 / R: 1199 ->1199 "
## [45] " 15372807 / R: 1242 ->1191 " " 15490981 / R: 377P3 ->1076P10 "
## [47] " 12533115 / R: 1362 ->1341 " " 14369165 / R: 1382 ->1335 "
## [49] " 12531685 / R: 1291P12->1259P17 " " 14773178 / R: 1056 ->1111 "
## [51] " 15205474 / R: 1011 ->1097 " " 14918803 / R: 935 ->1092 "
## [53] " 12578849 / R: 1393 ->1359 " " 12836773 / R: 1270 ->1200 "
## [55] " 15412571 / R: 1186 ->1163 " " 14679887 / R: 1153 ->1140 "
## [57] " 15113330 / R: 1092 ->1079 " " 14700365 / R: 917 -> 941 "
## [59] " 12841036 / R: 853 -> 878 " " 14579262 / R: 967 -> 984 "
## [61] " 15771592 / R: 955P11-> 979P18 " " 15219542 / R: 1530 ->1535 "
## [63] " 15057092 / R: 1175 ->1125 " " 15006561 / R: 1163 ->1112 "pattern <- " \\d\\d\\d\\d?( |P)"
pre_rating_final <- lapply(pre_rating, function(x) str_extract(x, pattern))pre_rating_final## [[1]]
## [1] " 1794 "
##
## [[2]]
## [1] " 1553 "
##
## [[3]]
## [1] " 1384 "
##
## [[4]]
## [1] " 1716 "
##
## [[5]]
## [1] " 1655 "
##
## [[6]]
## [1] " 1686 "
##
## [[7]]
## [1] " 1649 "
##
## [[8]]
## [1] " 1641P"
##
## [[9]]
## [1] " 1411 "
##
## [[10]]
## [1] " 1365 "
##
## [[11]]
## [1] " 1712 "
##
## [[12]]
## [1] " 1663 "
##
## [[13]]
## [1] " 1666 "
##
## [[14]]
## [1] " 1610 "
##
## [[15]]
## [1] " 1220P"
##
## [[16]]
## [1] " 1604 "
##
## [[17]]
## [1] " 1629 "
##
## [[18]]
## [1] " 1600 "
##
## [[19]]
## [1] " 1564 "
##
## [[20]]
## [1] " 1595 "
##
## [[21]]
## [1] " 1563P"
##
## [[22]]
## [1] " 1555 "
##
## [[23]]
## [1] " 1363 "
##
## [[24]]
## [1] " 1229 "
##
## [[25]]
## [1] " 1745 "
##
## [[26]]
## [1] " 1579 "
##
## [[27]]
## [1] " 1552 "
##
## [[28]]
## [1] " 1507 "
##
## [[29]]
## [1] " 1602P"
##
## [[30]]
## [1] " 1522 "
##
## [[31]]
## [1] " 1494 "
##
## [[32]]
## [1] " 1441 "
##
## [[33]]
## [1] " 1449 "
##
## [[34]]
## [1] " 1399 "
##
## [[35]]
## [1] " 1438 "
##
## [[36]]
## [1] " 1355 "
##
## [[37]]
## [1] " 980P"
##
## [[38]]
## [1] " 1423 "
##
## [[39]]
## [1] " 1436P"
##
## [[40]]
## [1] " 1348 "
##
## [[41]]
## [1] " 1403P"
##
## [[42]]
## [1] " 1332 "
##
## [[43]]
## [1] " 1283 "
##
## [[44]]
## [1] " 1199 "
##
## [[45]]
## [1] " 1242 "
##
## [[46]]
## [1] " 377P"
##
## [[47]]
## [1] " 1362 "
##
## [[48]]
## [1] " 1382 "
##
## [[49]]
## [1] " 1291P"
##
## [[50]]
## [1] " 1056 "
##
## [[51]]
## [1] " 1011 "
##
## [[52]]
## [1] " 935 "
##
## [[53]]
## [1] " 1393 "
##
## [[54]]
## [1] " 1270 "
##
## [[55]]
## [1] " 1186 "
##
## [[56]]
## [1] " 1153 "
##
## [[57]]
## [1] " 1092 "
##
## [[58]]
## [1] " 917 "
##
## [[59]]
## [1] " 853 "
##
## [[60]]
## [1] " 967 "
##
## [[61]]
## [1] " 955P"
##
## [[62]]
## [1] " 1530 "
##
## [[63]]
## [1] " 1175 "
##
## [[64]]
## [1] " 1163 "pre_rating_final <- unlist(pre_rating_final)
pre_rating_final <- lapply(pre_rating_final, function(x) str_trim(x, "both"))
pre_rating_final <- lapply(pre_rating_final, function(x) gsub("P","", x))
pre_rating_final <-unlist(pre_rating_final)final <-data.frame(names = names, state = state, total_points = total_points, pre_rating = pre_rating_final)
final$index <- 1:nrow(final)
final <- final %>% relocate(index, .before = names)
final <- transform(final, total_points = as.numeric(total_points), pre_rating = as.numeric(pre_rating))
final## index names state total_points pre_rating
## 1 1 GARY HUA ON 6.0 1794
## 2 2 DAKSHESH DARURI MI 6.0 1553
## 3 3 ADITYA BAJAJ MI 6.0 1384
## 4 4 PATRICK H SCHILLING MI 5.5 1716
## 5 5 HANSHI ZUO MI 5.5 1655
## 6 6 HANSEN SONG OH 5.0 1686
## 7 7 GARY DEE SWATHELL MI 5.0 1649
## 8 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 9 STEFANO LEE ON 5.0 1411
## 10 10 ANVIT RAO MI 5.0 1365
## 11 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 12 KENNETH J TACK MI 4.5 1663
## 13 13 TORRANCE HENRY JR MI 4.5 1666
## 14 14 BRADLEY SHAW MI 4.5 1610
## 15 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 16 MIKE NIKITIN MI 4.0 1604
## 17 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 18 DAVID SUNDEEN MI 4.0 1600
## 19 19 DIPANKAR ROY MI 4.0 1564
## 20 20 JASON ZHENG MI 4.0 1595
## 21 21 DINH DANG BUI ON 4.0 1563
## 22 22 EUGENE L MCCLURE MI 4.0 1555
## 23 23 ALAN BUI ON 4.0 1363
## 24 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 26 MAX ZHU ON 3.5 1579
## 27 27 GAURAV GIDWANI MI 3.5 1552
## 28 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507
## 29 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 30 GEORGE AVERY JONES ON 3.5 1522
## 31 31 RISHI SHETTY MI 3.5 1494
## 32 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 33 JADE GE MI 3.5 1449
## 34 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 36 SIDDHARTH JHA MI 3.5 1355
## 37 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 38 BRIAN LIU MI 3.0 1423
## 39 39 JOEL R HENDON MI 3.0 1436
## 40 40 FOREST ZHANG MI 3.0 1348
## 41 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 42 JARED GE MI 3.0 1332
## 43 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 45 DEREK YAN MI 3.0 1242
## 46 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 47 ERIC WRIGHT MI 2.5 1362
## 48 48 DANIEL KHAIN MI 2.5 1382
## 49 49 MICHAEL J MARTIN MI 2.5 1291
## 50 50 SHIVAM JHA MI 2.5 1056
## 51 51 TEJAS AYYAGARI MI 2.5 1011
## 52 52 ETHAN GUO MI 2.5 935
## 53 53 JOSE C YBARRA MI 2.0 1393
## 54 54 LARRY HODGE MI 2.0 1270
## 55 55 ALEX KONG MI 2.0 1186
## 56 56 MARISA RICCI MI 2.0 1153
## 57 57 MICHAEL LU MI 2.0 1092
## 58 58 VIRAJ MOHILE MI 2.0 917
## 59 59 SEAN M MC CORMICK MI 2.0 853
## 60 60 JULIA SHEN MI 1.5 967
## 61 61 JEZZEL FARKAS ON 1.5 955
## 62 62 ASHWIN BALAJI MI 1.0 1530
## 63 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 64 BEN LI MI 1.0 1163all_rds## rd1 rd2 rd3 rd4 rd5 rd6 rd7
## 1 39 21 18 14 7 12 4
## 2 63 58 4 17 16 20 7
## 3 8 61 25 21 11 13 12
## 4 23 28 2 26 5 19 1
## 5 45 37 12 13 4 14 17
## 6 34 29 11 35 10 27 21
## 7 57 46 13 11 1 9 2
## 8 3 32 14 9 47 28 19
## 9 25 18 59 8 26 7 20
## 10 16 19 55 31 6 25 18
## 11 38 56 6 7 3 34 26
## 12 42 33 5 38 NA 1 3
## 13 36 27 7 5 33 3 32
## 14 54 44 8 1 27 5 31
## 15 19 16 30 22 54 33 38
## 16 10 15 NA 39 2 36 NA
## 17 48 41 26 2 23 22 5
## 18 47 9 1 32 19 38 10
## 19 15 10 52 28 18 4 8
## 20 40 49 23 41 28 2 9
## 21 43 1 47 3 40 39 6
## 22 64 52 28 15 NA 17 40
## 23 4 43 20 58 17 37 46
## 24 28 47 43 25 60 44 39
## 25 9 53 3 24 34 10 47
## 26 49 40 17 4 9 32 11
## 27 51 13 46 37 14 6 NA
## 28 24 4 22 19 20 8 36
## 29 50 6 38 34 52 48 NA
## 30 52 64 15 55 31 61 50
## 31 58 55 64 10 30 50 14
## 32 61 8 44 18 51 26 13
## 33 60 12 50 36 13 15 51
## 34 6 60 37 29 25 11 52
## 35 46 38 56 6 57 52 48
## 36 13 57 51 33 NA 16 28
## 37 NA 5 34 27 NA 23 61
## 38 11 35 29 12 NA 18 15
## 39 1 54 40 16 44 21 24
## 40 20 26 39 59 21 56 22
## 41 59 17 58 20 NA NA NA
## 42 12 50 57 60 61 64 56
## 43 21 23 24 63 59 46 55
## 44 NA 14 32 53 39 24 59
## 45 5 51 60 56 63 55 58
## 46 35 7 27 50 64 43 23
## 47 18 24 21 61 8 51 25
## 48 17 63 NA 52 NA 29 35
## 49 26 20 63 64 58 NA NA
## 50 29 42 33 46 NA 31 30
## 51 27 45 36 57 32 47 33
## 52 30 22 19 48 29 35 34
## 53 NA 25 NA 44 NA 57 NA
## 54 14 39 61 NA 15 59 64
## 55 62 31 10 30 NA 45 43
## 56 NA 11 35 45 NA 40 42
## 57 7 36 42 51 35 53 NA
## 58 31 2 41 23 49 NA 45
## 59 41 NA 9 40 43 54 44
## 60 33 34 45 42 24 NA NA
## 61 32 3 54 47 42 30 37
## 62 55 NA NA NA NA NA NA
## 63 2 48 49 43 45 NA NA
## 64 22 30 31 49 46 42 54rd1_pre_rating <- list()
for (i in seq_along(all_rds$rd1)) {
if (is.na(all_rds$rd1[i])) {
rd1_pre_rating[i] <- NA
} else {
rd1_pre_rating[i] <- final$pre_rating[final$index == all_rds$rd1[i]]
}
}rd1_pre_rating## [[1]]
## [1] 1436
##
## [[2]]
## [1] 1175
##
## [[3]]
## [1] 1641
##
## [[4]]
## [1] 1363
##
## [[5]]
## [1] 1242
##
## [[6]]
## [1] 1399
##
## [[7]]
## [1] 1092
##
## [[8]]
## [1] 1384
##
## [[9]]
## [1] 1745
##
## [[10]]
## [1] 1604
##
## [[11]]
## [1] 1423
##
## [[12]]
## [1] 1332
##
## [[13]]
## [1] 1355
##
## [[14]]
## [1] 1270
##
## [[15]]
## [1] 1564
##
## [[16]]
## [1] 1365
##
## [[17]]
## [1] 1382
##
## [[18]]
## [1] 1362
##
## [[19]]
## [1] 1220
##
## [[20]]
## [1] 1348
##
## [[21]]
## [1] 1283
##
## [[22]]
## [1] 1163
##
## [[23]]
## [1] 1716
##
## [[24]]
## [1] 1507
##
## [[25]]
## [1] 1411
##
## [[26]]
## [1] 1291
##
## [[27]]
## [1] 1011
##
## [[28]]
## [1] 1229
##
## [[29]]
## [1] 1056
##
## [[30]]
## [1] 935
##
## [[31]]
## [1] 917
##
## [[32]]
## [1] 955
##
## [[33]]
## [1] 967
##
## [[34]]
## [1] 1686
##
## [[35]]
## [1] 377
##
## [[36]]
## [1] 1666
##
## [[37]]
## [1] NA
##
## [[38]]
## [1] 1712
##
## [[39]]
## [1] 1794
##
## [[40]]
## [1] 1595
##
## [[41]]
## [1] 853
##
## [[42]]
## [1] 1663
##
## [[43]]
## [1] 1563
##
## [[44]]
## [1] NA
##
## [[45]]
## [1] 1655
##
## [[46]]
## [1] 1438
##
## [[47]]
## [1] 1600
##
## [[48]]
## [1] 1629
##
## [[49]]
## [1] 1579
##
## [[50]]
## [1] 1602
##
## [[51]]
## [1] 1552
##
## [[52]]
## [1] 1522
##
## [[53]]
## [1] NA
##
## [[54]]
## [1] 1610
##
## [[55]]
## [1] 1530
##
## [[56]]
## [1] NA
##
## [[57]]
## [1] 1649
##
## [[58]]
## [1] 1494
##
## [[59]]
## [1] 1403
##
## [[60]]
## [1] 1449
##
## [[61]]
## [1] 1441
##
## [[62]]
## [1] 1186
##
## [[63]]
## [1] 1553
##
## [[64]]
## [1] 1555rds_pre_rating <-data.frame(rd1_rating = final[c(all_rds$rd1),]$pre_rating, rd2_rating = final[c(all_rds$rd2),]$pre_rating, rd3_rating = final[c(all_rds$rd3),]$pre_rating, rd4_rating = final[c(all_rds$rd4),]$pre_rating, rd5_rating = final[c(all_rds$rd5),]$pre_rating, rd6_rating = final[c(all_rds$rd6),]$pre_rating, rd7_rating = final[c(all_rds$rd7),]$pre_rating)
rds_pre_rating## rd1_rating rd2_rating rd3_rating rd4_rating rd5_rating rd6_rating rd7_rating
## 1 1436 1563 1600 1610 1649 1663 1716
## 2 1175 917 1716 1629 1604 1595 1649
## 3 1641 955 1745 1563 1712 1666 1663
## 4 1363 1507 1553 1579 1655 1564 1794
## 5 1242 980 1663 1666 1716 1610 1629
## 6 1399 1602 1712 1438 1365 1552 1563
## 7 1092 377 1666 1712 1794 1411 1553
## 8 1384 1441 1610 1411 1362 1507 1564
## 9 1745 1600 853 1641 1579 1649 1595
## 10 1604 1564 1186 1494 1686 1745 1600
## 11 1423 1153 1686 1649 1384 1399 1579
## 12 1332 1449 1655 1423 NA 1794 1384
## 13 1355 1552 1649 1655 1449 1384 1441
## 14 1270 1199 1641 1794 1552 1655 1494
## 15 1564 1604 1522 1555 1270 1449 1423
## 16 1365 1220 NA 1436 1553 1355 NA
## 17 1382 1403 1579 1553 1363 1555 1655
## 18 1362 1411 1794 1441 1564 1423 1365
## 19 1220 1365 935 1507 1600 1716 1641
## 20 1348 1291 1363 1403 1507 1553 1411
## 21 1283 1794 1362 1384 1348 1436 1686
## 22 1163 935 1507 1220 NA 1629 1348
## 23 1716 1283 1595 917 1629 980 377
## 24 1507 1362 1283 1745 967 1199 1436
## 25 1411 1393 1384 1229 1399 1365 1362
## 26 1291 1348 1629 1716 1411 1441 1712
## 27 1011 1666 377 980 1610 1686 NA
## 28 1229 1716 1555 1564 1595 1641 1355
## 29 1056 1686 1423 1399 935 1382 NA
## 30 935 1163 1220 1186 1494 955 1056
## 31 917 1186 1163 1365 1522 1056 1610
## 32 955 1641 1199 1600 1011 1579 1666
## 33 967 1663 1056 1355 1666 1220 1011
## 34 1686 967 980 1602 1745 1712 935
## 35 377 1423 1153 1686 1092 935 1382
## 36 1666 1092 1011 1449 NA 1604 1507
## 37 NA 1655 1399 1552 NA 1363 955
## 38 1712 1438 1602 1663 NA 1600 1220
## 39 1794 1270 1348 1604 1199 1563 1229
## 40 1595 1579 1436 853 1563 1153 1555
## 41 853 1629 917 1595 NA NA NA
## 42 1663 1056 1092 967 955 1163 1153
## 43 1563 1363 1229 1175 853 377 1186
## 44 NA 1610 1441 1393 1436 1229 853
## 45 1655 1011 967 1153 1175 1186 917
## 46 1438 1649 1552 1056 1163 1283 1363
## 47 1600 1229 1563 955 1641 1011 1745
## 48 1629 1175 NA 935 NA 1602 1438
## 49 1579 1595 1175 1163 917 NA NA
## 50 1602 1332 1449 377 NA 1494 1522
## 51 1552 1242 1355 1092 1441 1362 1449
## 52 1522 1555 1564 1382 1602 1438 1399
## 53 NA 1745 NA 1199 NA 1092 NA
## 54 1610 1436 955 NA 1220 853 1163
## 55 1530 1494 1365 1522 NA 1242 1283
## 56 NA 1712 1438 1242 NA 1348 1332
## 57 1649 1355 1332 1011 1438 1393 NA
## 58 1494 1553 1403 1363 1291 NA 1242
## 59 1403 NA 1411 1348 1283 1270 1199
## 60 1449 1399 1242 1332 1229 NA NA
## 61 1441 1384 1270 1362 1332 1522 980
## 62 1186 NA NA NA NA NA NA
## 63 1553 1382 1291 1283 1242 NA NA
## 64 1555 1522 1494 1291 377 1332 1270rds_pre_rating <- rds_pre_rating %>% rowwise() %>% mutate(mean = mean(c(rd1_rating,rd2_rating,rd3_rating,rd4_rating,rd5_rating,rd6_rating,rd7_rating), na.rm = TRUE))
rds_pre_rating$mean <- round(rds_pre_rating$mean, 0)
rds_pre_rating## # A tibble: 64 × 8
## # Rowwise:
## rd1_rating rd2_rating rd3_rating rd4_rating rd5_rating rd6_rating rd7_rating
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1436 1563 1600 1610 1649 1663 1716
## 2 1175 917 1716 1629 1604 1595 1649
## 3 1641 955 1745 1563 1712 1666 1663
## 4 1363 1507 1553 1579 1655 1564 1794
## 5 1242 980 1663 1666 1716 1610 1629
## 6 1399 1602 1712 1438 1365 1552 1563
## 7 1092 377 1666 1712 1794 1411 1553
## 8 1384 1441 1610 1411 1362 1507 1564
## 9 1745 1600 853 1641 1579 1649 1595
## 10 1604 1564 1186 1494 1686 1745 1600
## # ℹ 54 more rows
## # ℹ 1 more variable: mean <dbl>final$average_opponent_pre_rating <- rds_pre_rating$mean
final <- final[-c(1)]
final## names state total_points pre_rating
## 1 GARY HUA ON 6.0 1794
## 2 DAKSHESH DARURI MI 6.0 1553
## 3 ADITYA BAJAJ MI 6.0 1384
## 4 PATRICK H SCHILLING MI 5.5 1716
## 5 HANSHI ZUO MI 5.5 1655
## 6 HANSEN SONG OH 5.0 1686
## 7 GARY DEE SWATHELL MI 5.0 1649
## 8 EZEKIEL HOUGHTON MI 5.0 1641
## 9 STEFANO LEE ON 5.0 1411
## 10 ANVIT RAO MI 5.0 1365
## 11 CAMERON WILLIAM MC LEMAN MI 4.5 1712
## 12 KENNETH J TACK MI 4.5 1663
## 13 TORRANCE HENRY JR MI 4.5 1666
## 14 BRADLEY SHAW MI 4.5 1610
## 15 ZACHARY JAMES HOUGHTON MI 4.5 1220
## 16 MIKE NIKITIN MI 4.0 1604
## 17 RONALD GRZEGORCZYK MI 4.0 1629
## 18 DAVID SUNDEEN MI 4.0 1600
## 19 DIPANKAR ROY MI 4.0 1564
## 20 JASON ZHENG MI 4.0 1595
## 21 DINH DANG BUI ON 4.0 1563
## 22 EUGENE L MCCLURE MI 4.0 1555
## 23 ALAN BUI ON 4.0 1363
## 24 MICHAEL R ALDRICH MI 4.0 1229
## 25 LOREN SCHWIEBERT MI 3.5 1745
## 26 MAX ZHU ON 3.5 1579
## 27 GAURAV GIDWANI MI 3.5 1552
## 28 SOFIA ADINA STANESCU-BELLU MI 3.5 1507
## 29 CHIEDOZIE OKORIE MI 3.5 1602
## 30 GEORGE AVERY JONES ON 3.5 1522
## 31 RISHI SHETTY MI 3.5 1494
## 32 JOSHUA PHILIP MATHEWS ON 3.5 1441
## 33 JADE GE MI 3.5 1449
## 34 MICHAEL JEFFERY THOMAS MI 3.5 1399
## 35 JOSHUA DAVID LEE MI 3.5 1438
## 36 SIDDHARTH JHA MI 3.5 1355
## 37 AMIYATOSH PWNANANDAM MI 3.5 980
## 38 BRIAN LIU MI 3.0 1423
## 39 JOEL R HENDON MI 3.0 1436
## 40 FOREST ZHANG MI 3.0 1348
## 41 KYLE WILLIAM MURPHY MI 3.0 1403
## 42 JARED GE MI 3.0 1332
## 43 ROBERT GLEN VASEY MI 3.0 1283
## 44 JUSTIN D SCHILLING MI 3.0 1199
## 45 DEREK YAN MI 3.0 1242
## 46 JACOB ALEXANDER LAVALLEY MI 3.0 377
## 47 ERIC WRIGHT MI 2.5 1362
## 48 DANIEL KHAIN MI 2.5 1382
## 49 MICHAEL J MARTIN MI 2.5 1291
## 50 SHIVAM JHA MI 2.5 1056
## 51 TEJAS AYYAGARI MI 2.5 1011
## 52 ETHAN GUO MI 2.5 935
## 53 JOSE C YBARRA MI 2.0 1393
## 54 LARRY HODGE MI 2.0 1270
## 55 ALEX KONG MI 2.0 1186
## 56 MARISA RICCI MI 2.0 1153
## 57 MICHAEL LU MI 2.0 1092
## 58 VIRAJ MOHILE MI 2.0 917
## 59 SEAN M MC CORMICK MI 2.0 853
## 60 JULIA SHEN MI 1.5 967
## 61 JEZZEL FARKAS ON 1.5 955
## 62 ASHWIN BALAJI MI 1.0 1530
## 63 THOMAS JOSEPH HOSMER MI 1.0 1175
## 64 BEN LI MI 1.0 1163
## average_opponent_pre_rating
## 1 1605
## 2 1469
## 3 1564
## 4 1574
## 5 1501
## 6 1519
## 7 1372
## 8 1468
## 9 1523
## 10 1554
## 11 1468
## 12 1506
## 13 1498
## 14 1515
## 15 1484
## 16 1386
## 17 1499
## 18 1480
## 19 1426
## 20 1411
## 21 1470
## 22 1300
## 23 1214
## 24 1357
## 25 1363
## 26 1507
## 27 1222
## 28 1522
## 29 1314
## 30 1144
## 31 1260
## 32 1379
## 33 1277
## 34 1375
## 35 1150
## 36 1388
## 37 1385
## 38 1539
## 39 1430
## 40 1391
## 41 1248
## 42 1150
## 43 1107
## 44 1327
## 45 1152
## 46 1358
## 47 1392
## 48 1356
## 49 1286
## 50 1296
## 51 1356
## 52 1495
## 53 1345
## 54 1206
## 55 1406
## 56 1414
## 57 1363
## 58 1391
## 59 1319
## 60 1330
## 61 1327
## 62 1186
## 63 1350
## 64 1263write.csv(final,"C:\\Users\\Steve\\Documents\\Masters Program\\project3_final_data.csv")