getwd()
## [1] "C:/Users/taetaetae/Documents/R/NEW_Kaggle/5th_tele"
setwd("C:/Users/taetaetae/Documents/R/NEW_Kaggle/5th_tele")
#https://www.kaggle.com/becksddf/churn-in-telecoms-dataset/kernels
tele_raw <- read.csv("tele.csv")
str(tele_raw)
## 'data.frame': 3333 obs. of 21 variables:
## $ state : Factor w/ 51 levels "AK","AL","AR",..: 17 36 32 36 37 2 20 25 19 50 ...
## $ account.length : int 128 107 137 84 75 118 121 147 117 141 ...
## $ area.code : int 415 415 415 408 415 510 510 415 408 415 ...
## $ phone.number : Factor w/ 3333 levels "327-1058","327-1319",..: 1927 1576 1118 1708 111 2254 1048 81 292 118 ...
## $ international.plan : Factor w/ 2 levels "no","yes": 1 1 1 2 2 2 1 2 1 2 ...
## $ voice.mail.plan : Factor w/ 2 levels "no","yes": 2 2 1 1 1 1 2 1 1 2 ...
## $ number.vmail.messages : int 25 26 0 0 0 0 24 0 0 37 ...
## $ total.day.minutes : num 265 162 243 299 167 ...
## $ total.day.calls : int 110 123 114 71 113 98 88 79 97 84 ...
## $ total.day.charge : num 45.1 27.5 41.4 50.9 28.3 ...
## $ total.eve.minutes : num 197.4 195.5 121.2 61.9 148.3 ...
## $ total.eve.calls : int 99 103 110 88 122 101 108 94 80 111 ...
## $ total.eve.charge : num 16.78 16.62 10.3 5.26 12.61 ...
## $ total.night.minutes : num 245 254 163 197 187 ...
## $ total.night.calls : int 91 103 104 89 121 118 118 96 90 97 ...
## $ total.night.charge : num 11.01 11.45 7.32 8.86 8.41 ...
## $ total.intl.minutes : num 10 13.7 12.2 6.6 10.1 6.3 7.5 7.1 8.7 11.2 ...
## $ total.intl.calls : int 3 3 5 7 3 6 7 6 4 5 ...
## $ total.intl.charge : num 2.7 3.7 3.29 1.78 2.73 1.7 2.03 1.92 2.35 3.02 ...
## $ customer.service.calls: int 1 1 0 2 3 0 3 0 1 0 ...
## $ churn : Factor w/ 2 levels "False","True": 1 1 1 1 1 1 1 1 1 1 ...
head(tele_raw)
## state account.length area.code phone.number international.plan
## 1 KS 128 415 382-4657 no
## 2 OH 107 415 371-7191 no
## 3 NJ 137 415 358-1921 no
## 4 OH 84 408 375-9999 yes
## 5 OK 75 415 330-6626 yes
## 6 AL 118 510 391-8027 yes
## voice.mail.plan number.vmail.messages total.day.minutes total.day.calls
## 1 yes 25 265.1 110
## 2 yes 26 161.6 123
## 3 no 0 243.4 114
## 4 no 0 299.4 71
## 5 no 0 166.7 113
## 6 no 0 223.4 98
## total.day.charge total.eve.minutes total.eve.calls total.eve.charge
## 1 45.07 197.4 99 16.78
## 2 27.47 195.5 103 16.62
## 3 41.38 121.2 110 10.30
## 4 50.90 61.9 88 5.26
## 5 28.34 148.3 122 12.61
## 6 37.98 220.6 101 18.75
## total.night.minutes total.night.calls total.night.charge
## 1 244.7 91 11.01
## 2 254.4 103 11.45
## 3 162.6 104 7.32
## 4 196.9 89 8.86
## 5 186.9 121 8.41
## 6 203.9 118 9.18
## total.intl.minutes total.intl.calls total.intl.charge
## 1 10.0 3 2.70
## 2 13.7 3 3.70
## 3 12.2 5 3.29
## 4 6.6 7 1.78
## 5 10.1 3 2.73
## 6 6.3 6 1.70
## customer.service.calls churn
## 1 1 False
## 2 1 False
## 3 0 False
## 4 2 False
## 5 3 False
## 6 0 False
summary(tele_raw)
## state account.length area.code phone.number
## WV : 106 Min. : 1.0 Min. :408.0 327-1058: 1
## MN : 84 1st Qu.: 74.0 1st Qu.:408.0 327-1319: 1
## NY : 83 Median :101.0 Median :415.0 327-3053: 1
## AL : 80 Mean :101.1 Mean :437.2 327-3587: 1
## OH : 78 3rd Qu.:127.0 3rd Qu.:510.0 327-3850: 1
## OR : 78 Max. :243.0 Max. :510.0 327-3954: 1
## (Other):2824 (Other) :3327
## international.plan voice.mail.plan number.vmail.messages
## no :3010 no :2411 Min. : 0.000
## yes: 323 yes: 922 1st Qu.: 0.000
## Median : 0.000
## Mean : 8.099
## 3rd Qu.:20.000
## Max. :51.000
##
## total.day.minutes total.day.calls total.day.charge total.eve.minutes
## Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 0.0
## 1st Qu.:143.7 1st Qu.: 87.0 1st Qu.:24.43 1st Qu.:166.6
## Median :179.4 Median :101.0 Median :30.50 Median :201.4
## Mean :179.8 Mean :100.4 Mean :30.56 Mean :201.0
## 3rd Qu.:216.4 3rd Qu.:114.0 3rd Qu.:36.79 3rd Qu.:235.3
## Max. :350.8 Max. :165.0 Max. :59.64 Max. :363.7
##
## total.eve.calls total.eve.charge total.night.minutes total.night.calls
## Min. : 0.0 Min. : 0.00 Min. : 23.2 Min. : 33.0
## 1st Qu.: 87.0 1st Qu.:14.16 1st Qu.:167.0 1st Qu.: 87.0
## Median :100.0 Median :17.12 Median :201.2 Median :100.0
## Mean :100.1 Mean :17.08 Mean :200.9 Mean :100.1
## 3rd Qu.:114.0 3rd Qu.:20.00 3rd Qu.:235.3 3rd Qu.:113.0
## Max. :170.0 Max. :30.91 Max. :395.0 Max. :175.0
##
## total.night.charge total.intl.minutes total.intl.calls total.intl.charge
## Min. : 1.040 Min. : 0.00 Min. : 0.000 Min. :0.000
## 1st Qu.: 7.520 1st Qu.: 8.50 1st Qu.: 3.000 1st Qu.:2.300
## Median : 9.050 Median :10.30 Median : 4.000 Median :2.780
## Mean : 9.039 Mean :10.24 Mean : 4.479 Mean :2.765
## 3rd Qu.:10.590 3rd Qu.:12.10 3rd Qu.: 6.000 3rd Qu.:3.270
## Max. :17.770 Max. :20.00 Max. :20.000 Max. :5.400
##
## customer.service.calls churn
## Min. :0.000 False:2850
## 1st Qu.:1.000 True : 483
## Median :1.000
## Mean :1.563
## 3rd Qu.:2.000
## Max. :9.000
##
names(tele_raw)
## [1] "state" "account.length"
## [3] "area.code" "phone.number"
## [5] "international.plan" "voice.mail.plan"
## [7] "number.vmail.messages" "total.day.minutes"
## [9] "total.day.calls" "total.day.charge"
## [11] "total.eve.minutes" "total.eve.calls"
## [13] "total.eve.charge" "total.night.minutes"
## [15] "total.night.calls" "total.night.charge"
## [17] "total.intl.minutes" "total.intl.calls"
## [19] "total.intl.charge" "customer.service.calls"
## [21] "churn"
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.1
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#subset 만들기 select, filter
(iris_fil_spe <- filter(iris, Species == "setosa")) #setosa 종만 뽑아라
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(iris_fil_spe_sl5 <- filter(iris, Species == "setosa" & Sepal.Length > 5))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 5.4 3.9 1.7 0.4 setosa
## 3 5.4 3.7 1.5 0.2 setosa
## 4 5.8 4.0 1.2 0.2 setosa
## 5 5.7 4.4 1.5 0.4 setosa
## 6 5.4 3.9 1.3 0.4 setosa
## 7 5.1 3.5 1.4 0.3 setosa
## 8 5.7 3.8 1.7 0.3 setosa
## 9 5.1 3.8 1.5 0.3 setosa
## 10 5.4 3.4 1.7 0.2 setosa
## 11 5.1 3.7 1.5 0.4 setosa
## 12 5.1 3.3 1.7 0.5 setosa
## 13 5.2 3.5 1.5 0.2 setosa
## 14 5.2 3.4 1.4 0.2 setosa
## 15 5.4 3.4 1.5 0.4 setosa
## 16 5.2 4.1 1.5 0.1 setosa
## 17 5.5 4.2 1.4 0.2 setosa
## 18 5.5 3.5 1.3 0.2 setosa
## 19 5.1 3.4 1.5 0.2 setosa
## 20 5.1 3.8 1.9 0.4 setosa
## 21 5.1 3.8 1.6 0.2 setosa
## 22 5.3 3.7 1.5 0.2 setosa
(iris_sel_sl.pl.spe <-select(iris, Sepal.Width, Petal.Length, Species)) #sp, pl, species 열만 뽑아라
## Sepal.Width Petal.Length Species
## 1 3.5 1.4 setosa
## 2 3.0 1.4 setosa
## 3 3.2 1.3 setosa
## 4 3.1 1.5 setosa
## 5 3.6 1.4 setosa
## 6 3.9 1.7 setosa
## 7 3.4 1.4 setosa
## 8 3.4 1.5 setosa
## 9 2.9 1.4 setosa
## 10 3.1 1.5 setosa
## 11 3.7 1.5 setosa
## 12 3.4 1.6 setosa
## 13 3.0 1.4 setosa
## 14 3.0 1.1 setosa
## 15 4.0 1.2 setosa
## 16 4.4 1.5 setosa
## 17 3.9 1.3 setosa
## 18 3.5 1.4 setosa
## 19 3.8 1.7 setosa
## 20 3.8 1.5 setosa
## 21 3.4 1.7 setosa
## 22 3.7 1.5 setosa
## 23 3.6 1.0 setosa
## 24 3.3 1.7 setosa
## 25 3.4 1.9 setosa
## 26 3.0 1.6 setosa
## 27 3.4 1.6 setosa
## 28 3.5 1.5 setosa
## 29 3.4 1.4 setosa
## 30 3.2 1.6 setosa
## 31 3.1 1.6 setosa
## 32 3.4 1.5 setosa
## 33 4.1 1.5 setosa
## 34 4.2 1.4 setosa
## 35 3.1 1.5 setosa
## 36 3.2 1.2 setosa
## 37 3.5 1.3 setosa
## 38 3.6 1.4 setosa
## 39 3.0 1.3 setosa
## 40 3.4 1.5 setosa
## 41 3.5 1.3 setosa
## 42 2.3 1.3 setosa
## 43 3.2 1.3 setosa
## 44 3.5 1.6 setosa
## 45 3.8 1.9 setosa
## 46 3.0 1.4 setosa
## 47 3.8 1.6 setosa
## 48 3.2 1.4 setosa
## 49 3.7 1.5 setosa
## 50 3.3 1.4 setosa
## 51 3.2 4.7 versicolor
## 52 3.2 4.5 versicolor
## 53 3.1 4.9 versicolor
## 54 2.3 4.0 versicolor
## 55 2.8 4.6 versicolor
## 56 2.8 4.5 versicolor
## 57 3.3 4.7 versicolor
## 58 2.4 3.3 versicolor
## 59 2.9 4.6 versicolor
## 60 2.7 3.9 versicolor
## 61 2.0 3.5 versicolor
## 62 3.0 4.2 versicolor
## 63 2.2 4.0 versicolor
## 64 2.9 4.7 versicolor
## 65 2.9 3.6 versicolor
## 66 3.1 4.4 versicolor
## 67 3.0 4.5 versicolor
## 68 2.7 4.1 versicolor
## 69 2.2 4.5 versicolor
## 70 2.5 3.9 versicolor
## 71 3.2 4.8 versicolor
## 72 2.8 4.0 versicolor
## 73 2.5 4.9 versicolor
## 74 2.8 4.7 versicolor
## 75 2.9 4.3 versicolor
## 76 3.0 4.4 versicolor
## 77 2.8 4.8 versicolor
## 78 3.0 5.0 versicolor
## 79 2.9 4.5 versicolor
## 80 2.6 3.5 versicolor
## 81 2.4 3.8 versicolor
## 82 2.4 3.7 versicolor
## 83 2.7 3.9 versicolor
## 84 2.7 5.1 versicolor
## 85 3.0 4.5 versicolor
## 86 3.4 4.5 versicolor
## 87 3.1 4.7 versicolor
## 88 2.3 4.4 versicolor
## 89 3.0 4.1 versicolor
## 90 2.5 4.0 versicolor
## 91 2.6 4.4 versicolor
## 92 3.0 4.6 versicolor
## 93 2.6 4.0 versicolor
## 94 2.3 3.3 versicolor
## 95 2.7 4.2 versicolor
## 96 3.0 4.2 versicolor
## 97 2.9 4.2 versicolor
## 98 2.9 4.3 versicolor
## 99 2.5 3.0 versicolor
## 100 2.8 4.1 versicolor
## 101 3.3 6.0 virginica
## 102 2.7 5.1 virginica
## 103 3.0 5.9 virginica
## 104 2.9 5.6 virginica
## 105 3.0 5.8 virginica
## 106 3.0 6.6 virginica
## 107 2.5 4.5 virginica
## 108 2.9 6.3 virginica
## 109 2.5 5.8 virginica
## 110 3.6 6.1 virginica
## 111 3.2 5.1 virginica
## 112 2.7 5.3 virginica
## 113 3.0 5.5 virginica
## 114 2.5 5.0 virginica
## 115 2.8 5.1 virginica
## 116 3.2 5.3 virginica
## 117 3.0 5.5 virginica
## 118 3.8 6.7 virginica
## 119 2.6 6.9 virginica
## 120 2.2 5.0 virginica
## 121 3.2 5.7 virginica
## 122 2.8 4.9 virginica
## 123 2.8 6.7 virginica
## 124 2.7 4.9 virginica
## 125 3.3 5.7 virginica
## 126 3.2 6.0 virginica
## 127 2.8 4.8 virginica
## 128 3.0 4.9 virginica
## 129 2.8 5.6 virginica
## 130 3.0 5.8 virginica
## 131 2.8 6.1 virginica
## 132 3.8 6.4 virginica
## 133 2.8 5.6 virginica
## 134 2.8 5.1 virginica
## 135 2.6 5.6 virginica
## 136 3.0 6.1 virginica
## 137 3.4 5.6 virginica
## 138 3.1 5.5 virginica
## 139 3.0 4.8 virginica
## 140 3.1 5.4 virginica
## 141 3.1 5.6 virginica
## 142 3.1 5.1 virginica
## 143 2.7 5.1 virginica
## 144 3.2 5.9 virginica
## 145 3.3 5.7 virginica
## 146 3.0 5.2 virginica
## 147 2.5 5.0 virginica
## 148 3.0 5.2 virginica
## 149 3.4 5.4 virginica
## 150 3.0 5.1 virginica
(iris_sepal <- select(iris, starts_with("Sepal")))
## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0
(iris_samp <- sample_frac(iris, 0.5, replace = TRUE)) #이건 나중에 샘플링해서 데이터셋 나눌때 쓰기도
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.5 2.4 3.7 1.0 versicolor
## 2 6.3 2.8 5.1 1.5 virginica
## 3 7.0 3.2 4.7 1.4 versicolor
## 4 5.6 3.0 4.1 1.3 versicolor
## 5 5.4 3.9 1.3 0.4 setosa
## 6 5.4 3.7 1.5 0.2 setosa
## 7 5.8 2.7 3.9 1.2 versicolor
## 8 6.4 3.1 5.5 1.8 virginica
## 9 5.1 3.8 1.5 0.3 setosa
## 10 5.6 3.0 4.5 1.5 versicolor
## 11 5.7 2.8 4.5 1.3 versicolor
## 12 5.9 3.0 5.1 1.8 virginica
## 13 6.4 3.1 5.5 1.8 virginica
## 14 5.9 3.2 4.8 1.8 versicolor
## 15 5.1 2.5 3.0 1.1 versicolor
## 16 5.5 2.3 4.0 1.3 versicolor
## 17 5.9 3.2 4.8 1.8 versicolor
## 18 5.1 3.8 1.5 0.3 setosa
## 19 5.9 3.0 4.2 1.5 versicolor
## 20 4.9 3.1 1.5 0.2 setosa
## 21 7.7 3.0 6.1 2.3 virginica
## 22 5.6 2.8 4.9 2.0 virginica
## 23 6.9 3.1 4.9 1.5 versicolor
## 24 6.7 3.1 4.7 1.5 versicolor
## 25 5.0 2.3 3.3 1.0 versicolor
## 26 6.7 3.1 4.4 1.4 versicolor
## 27 5.8 2.7 3.9 1.2 versicolor
## 28 7.4 2.8 6.1 1.9 virginica
## 29 5.7 2.9 4.2 1.3 versicolor
## 30 5.5 2.6 4.4 1.2 versicolor
## 31 5.0 3.4 1.6 0.4 setosa
## 32 7.7 2.6 6.9 2.3 virginica
## 33 5.8 2.7 3.9 1.2 versicolor
## 34 6.4 3.2 4.5 1.5 versicolor
## 35 6.8 2.8 4.8 1.4 versicolor
## 36 4.9 3.6 1.4 0.1 setosa
## 37 7.4 2.8 6.1 1.9 virginica
## 38 6.7 3.3 5.7 2.1 virginica
## 39 5.1 3.5 1.4 0.3 setosa
## 40 6.3 2.9 5.6 1.8 virginica
## 41 6.5 3.0 5.2 2.0 virginica
## 42 5.7 2.8 4.5 1.3 versicolor
## 43 6.1 2.8 4.7 1.2 versicolor
## 44 6.7 2.5 5.8 1.8 virginica
## 45 5.0 2.0 3.5 1.0 versicolor
## 46 5.1 3.3 1.7 0.5 setosa
## 47 5.9 3.0 5.1 1.8 virginica
## 48 6.1 2.8 4.7 1.2 versicolor
## 49 6.3 2.8 5.1 1.5 virginica
## 50 6.2 2.9 4.3 1.3 versicolor
## 51 5.8 2.7 5.1 1.9 virginica
## 52 6.4 2.7 5.3 1.9 virginica
## 53 4.7 3.2 1.6 0.2 setosa
## 54 5.8 2.7 4.1 1.0 versicolor
## 55 5.2 3.4 1.4 0.2 setosa
## 56 5.5 2.3 4.0 1.3 versicolor
## 57 5.5 4.2 1.4 0.2 setosa
## 58 4.8 3.4 1.9 0.2 setosa
## 59 6.7 3.0 5.2 2.3 virginica
## 60 5.6 3.0 4.1 1.3 versicolor
## 61 6.5 3.2 5.1 2.0 virginica
## 62 7.7 2.6 6.9 2.3 virginica
## 63 4.9 2.5 4.5 1.7 virginica
## 64 6.1 3.0 4.6 1.4 versicolor
## 65 6.0 2.7 5.1 1.6 versicolor
## 66 7.2 3.0 5.8 1.6 virginica
## 67 5.5 2.3 4.0 1.3 versicolor
## 68 6.4 3.1 5.5 1.8 virginica
## 69 5.5 2.4 3.7 1.0 versicolor
## 70 6.2 2.9 4.3 1.3 versicolor
## 71 6.9 3.2 5.7 2.3 virginica
## 72 5.6 2.8 4.9 2.0 virginica
## 73 6.4 2.8 5.6 2.2 virginica
## 74 5.2 4.1 1.5 0.1 setosa
## 75 5.0 3.6 1.4 0.2 setosa
#tele_example
names(tele_raw)
## [1] "state" "account.length"
## [3] "area.code" "phone.number"
## [5] "international.plan" "voice.mail.plan"
## [7] "number.vmail.messages" "total.day.minutes"
## [9] "total.day.calls" "total.day.charge"
## [11] "total.eve.minutes" "total.eve.calls"
## [13] "total.eve.charge" "total.night.minutes"
## [15] "total.night.calls" "total.night.charge"
## [17] "total.intl.minutes" "total.intl.calls"
## [19] "total.intl.charge" "customer.service.calls"
## [21] "churn"
tele_charge_cust <- tele_raw %>%
select(contains("charge"), c("state", "churn", "customer.service.calls"))
summary(tele_charge_cust)
## total.day.charge total.eve.charge total.night.charge total.intl.charge
## Min. : 0.00 Min. : 0.00 Min. : 1.040 Min. :0.000
## 1st Qu.:24.43 1st Qu.:14.16 1st Qu.: 7.520 1st Qu.:2.300
## Median :30.50 Median :17.12 Median : 9.050 Median :2.780
## Mean :30.56 Mean :17.08 Mean : 9.039 Mean :2.765
## 3rd Qu.:36.79 3rd Qu.:20.00 3rd Qu.:10.590 3rd Qu.:3.270
## Max. :59.64 Max. :30.91 Max. :17.770 Max. :5.400
##
## state churn customer.service.calls
## WV : 106 False:2850 Min. :0.000
## MN : 84 True : 483 1st Qu.:1.000
## NY : 83 Median :1.000
## AL : 80 Mean :1.563
## OH : 78 3rd Qu.:2.000
## OR : 78 Max. :9.000
## (Other):2824
tele_median_up <- tele_charge_cust %>%
filter(total.day.charge > median(tele_charge_cust$total.day.charge, na.rm = T), total.eve.charge > 17.12, total.night.charge > 9.050)
median(tele_charge_cust$total.day.charge, na.rm = T)
## [1] 30.5
summary(tele_median_up)
## total.day.charge total.eve.charge total.night.charge total.intl.charge
## Min. :30.55 Min. :17.13 Min. : 9.06 Min. :0.000
## 1st Qu.:33.52 1st Qu.:18.41 1st Qu.: 9.66 1st Qu.:2.240
## Median :36.96 Median :19.80 Median :10.49 Median :2.780
## Mean :38.14 Mean :20.43 Mean :10.76 Mean :2.749
## 3rd Qu.:41.17 3rd Qu.:21.83 3rd Qu.:11.54 3rd Qu.:3.290
## Max. :59.64 Max. :29.89 Max. :15.97 Max. :4.750
##
## state churn customer.service.calls
## IN : 15 False:297 Min. :0.00
## CT : 14 True :114 1st Qu.:1.00
## VA : 14 Median :1.00
## KS : 13 Mean :1.53
## VT : 13 3rd Qu.:2.00
## FL : 12 Max. :9.00
## (Other):330
#잠깐, 커스터머 콜을 팩터로 변경해서 그림한번 그려보자
str(tele_median_up)
## 'data.frame': 411 obs. of 7 variables:
## $ total.day.charge : num 38 37.1 31.4 44 38.4 ...
## $ total.eve.charge : num 18.8 29.6 29.9 18.9 17.1 ...
## $ total.night.charge : num 9.18 9.57 9.71 14.69 11.08 ...
## $ total.intl.charge : num 1.7 2.03 2.35 3.02 2.78 3.19 2.11 1.84 3.56 3.4 ...
## $ state : Factor w/ 51 levels "AK","AL","AR",..: 2 20 19 50 31 4 13 16 50 38 ...
## $ churn : Factor w/ 2 levels "False","True": 1 1 1 1 1 2 1 1 1 1 ...
## $ customer.service.calls: int 0 3 1 0 1 1 3 1 1 3 ...
tele_median_up$customer.service.calls <- as.factor(tele_median_up$customer.service.calls)
str(tele_median_up)
## 'data.frame': 411 obs. of 7 variables:
## $ total.day.charge : num 38 37.1 31.4 44 38.4 ...
## $ total.eve.charge : num 18.8 29.6 29.9 18.9 17.1 ...
## $ total.night.charge : num 9.18 9.57 9.71 14.69 11.08 ...
## $ total.intl.charge : num 1.7 2.03 2.35 3.02 2.78 3.19 2.11 1.84 3.56 3.4 ...
## $ state : Factor w/ 51 levels "AK","AL","AR",..: 2 20 19 50 31 4 13 16 50 38 ...
## $ churn : Factor w/ 2 levels "False","True": 1 1 1 1 1 2 1 1 1 1 ...
## $ customer.service.calls: Factor w/ 8 levels "0","1","2","3",..: 1 4 2 1 2 2 4 2 2 4 ...
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.6.1
tele_median_up %>% ggplot(aes(x = customer.service.calls, y = churn)) + geom_bar(stat = "identity")
#새로운 변수 만들고 mutate, 그룹별로 묶어서 group_by, 요약하고 summarise, 정렬 arrange
(mutate(iris, sepal = Sepal.Length + Sepal.Width)) #sepal 길이 넓이 더한 sepal이라는 열을 만들어라.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species sepal
## 1 5.1 3.5 1.4 0.2 setosa 8.6
## 2 4.9 3.0 1.4 0.2 setosa 7.9
## 3 4.7 3.2 1.3 0.2 setosa 7.9
## 4 4.6 3.1 1.5 0.2 setosa 7.7
## 5 5.0 3.6 1.4 0.2 setosa 8.6
## 6 5.4 3.9 1.7 0.4 setosa 9.3
## 7 4.6 3.4 1.4 0.3 setosa 8.0
## 8 5.0 3.4 1.5 0.2 setosa 8.4
## 9 4.4 2.9 1.4 0.2 setosa 7.3
## 10 4.9 3.1 1.5 0.1 setosa 8.0
## 11 5.4 3.7 1.5 0.2 setosa 9.1
## 12 4.8 3.4 1.6 0.2 setosa 8.2
## 13 4.8 3.0 1.4 0.1 setosa 7.8
## 14 4.3 3.0 1.1 0.1 setosa 7.3
## 15 5.8 4.0 1.2 0.2 setosa 9.8
## 16 5.7 4.4 1.5 0.4 setosa 10.1
## 17 5.4 3.9 1.3 0.4 setosa 9.3
## 18 5.1 3.5 1.4 0.3 setosa 8.6
## 19 5.7 3.8 1.7 0.3 setosa 9.5
## 20 5.1 3.8 1.5 0.3 setosa 8.9
## 21 5.4 3.4 1.7 0.2 setosa 8.8
## 22 5.1 3.7 1.5 0.4 setosa 8.8
## 23 4.6 3.6 1.0 0.2 setosa 8.2
## 24 5.1 3.3 1.7 0.5 setosa 8.4
## 25 4.8 3.4 1.9 0.2 setosa 8.2
## 26 5.0 3.0 1.6 0.2 setosa 8.0
## 27 5.0 3.4 1.6 0.4 setosa 8.4
## 28 5.2 3.5 1.5 0.2 setosa 8.7
## 29 5.2 3.4 1.4 0.2 setosa 8.6
## 30 4.7 3.2 1.6 0.2 setosa 7.9
## 31 4.8 3.1 1.6 0.2 setosa 7.9
## 32 5.4 3.4 1.5 0.4 setosa 8.8
## 33 5.2 4.1 1.5 0.1 setosa 9.3
## 34 5.5 4.2 1.4 0.2 setosa 9.7
## 35 4.9 3.1 1.5 0.2 setosa 8.0
## 36 5.0 3.2 1.2 0.2 setosa 8.2
## 37 5.5 3.5 1.3 0.2 setosa 9.0
## 38 4.9 3.6 1.4 0.1 setosa 8.5
## 39 4.4 3.0 1.3 0.2 setosa 7.4
## 40 5.1 3.4 1.5 0.2 setosa 8.5
## 41 5.0 3.5 1.3 0.3 setosa 8.5
## 42 4.5 2.3 1.3 0.3 setosa 6.8
## 43 4.4 3.2 1.3 0.2 setosa 7.6
## 44 5.0 3.5 1.6 0.6 setosa 8.5
## 45 5.1 3.8 1.9 0.4 setosa 8.9
## 46 4.8 3.0 1.4 0.3 setosa 7.8
## 47 5.1 3.8 1.6 0.2 setosa 8.9
## 48 4.6 3.2 1.4 0.2 setosa 7.8
## 49 5.3 3.7 1.5 0.2 setosa 9.0
## 50 5.0 3.3 1.4 0.2 setosa 8.3
## 51 7.0 3.2 4.7 1.4 versicolor 10.2
## 52 6.4 3.2 4.5 1.5 versicolor 9.6
## 53 6.9 3.1 4.9 1.5 versicolor 10.0
## 54 5.5 2.3 4.0 1.3 versicolor 7.8
## 55 6.5 2.8 4.6 1.5 versicolor 9.3
## 56 5.7 2.8 4.5 1.3 versicolor 8.5
## 57 6.3 3.3 4.7 1.6 versicolor 9.6
## 58 4.9 2.4 3.3 1.0 versicolor 7.3
## 59 6.6 2.9 4.6 1.3 versicolor 9.5
## 60 5.2 2.7 3.9 1.4 versicolor 7.9
## 61 5.0 2.0 3.5 1.0 versicolor 7.0
## 62 5.9 3.0 4.2 1.5 versicolor 8.9
## 63 6.0 2.2 4.0 1.0 versicolor 8.2
## 64 6.1 2.9 4.7 1.4 versicolor 9.0
## 65 5.6 2.9 3.6 1.3 versicolor 8.5
## 66 6.7 3.1 4.4 1.4 versicolor 9.8
## 67 5.6 3.0 4.5 1.5 versicolor 8.6
## 68 5.8 2.7 4.1 1.0 versicolor 8.5
## 69 6.2 2.2 4.5 1.5 versicolor 8.4
## 70 5.6 2.5 3.9 1.1 versicolor 8.1
## 71 5.9 3.2 4.8 1.8 versicolor 9.1
## 72 6.1 2.8 4.0 1.3 versicolor 8.9
## 73 6.3 2.5 4.9 1.5 versicolor 8.8
## 74 6.1 2.8 4.7 1.2 versicolor 8.9
## 75 6.4 2.9 4.3 1.3 versicolor 9.3
## 76 6.6 3.0 4.4 1.4 versicolor 9.6
## 77 6.8 2.8 4.8 1.4 versicolor 9.6
## 78 6.7 3.0 5.0 1.7 versicolor 9.7
## 79 6.0 2.9 4.5 1.5 versicolor 8.9
## 80 5.7 2.6 3.5 1.0 versicolor 8.3
## 81 5.5 2.4 3.8 1.1 versicolor 7.9
## 82 5.5 2.4 3.7 1.0 versicolor 7.9
## 83 5.8 2.7 3.9 1.2 versicolor 8.5
## 84 6.0 2.7 5.1 1.6 versicolor 8.7
## 85 5.4 3.0 4.5 1.5 versicolor 8.4
## 86 6.0 3.4 4.5 1.6 versicolor 9.4
## 87 6.7 3.1 4.7 1.5 versicolor 9.8
## 88 6.3 2.3 4.4 1.3 versicolor 8.6
## 89 5.6 3.0 4.1 1.3 versicolor 8.6
## 90 5.5 2.5 4.0 1.3 versicolor 8.0
## 91 5.5 2.6 4.4 1.2 versicolor 8.1
## 92 6.1 3.0 4.6 1.4 versicolor 9.1
## 93 5.8 2.6 4.0 1.2 versicolor 8.4
## 94 5.0 2.3 3.3 1.0 versicolor 7.3
## 95 5.6 2.7 4.2 1.3 versicolor 8.3
## 96 5.7 3.0 4.2 1.2 versicolor 8.7
## 97 5.7 2.9 4.2 1.3 versicolor 8.6
## 98 6.2 2.9 4.3 1.3 versicolor 9.1
## 99 5.1 2.5 3.0 1.1 versicolor 7.6
## 100 5.7 2.8 4.1 1.3 versicolor 8.5
## 101 6.3 3.3 6.0 2.5 virginica 9.6
## 102 5.8 2.7 5.1 1.9 virginica 8.5
## 103 7.1 3.0 5.9 2.1 virginica 10.1
## 104 6.3 2.9 5.6 1.8 virginica 9.2
## 105 6.5 3.0 5.8 2.2 virginica 9.5
## 106 7.6 3.0 6.6 2.1 virginica 10.6
## 107 4.9 2.5 4.5 1.7 virginica 7.4
## 108 7.3 2.9 6.3 1.8 virginica 10.2
## 109 6.7 2.5 5.8 1.8 virginica 9.2
## 110 7.2 3.6 6.1 2.5 virginica 10.8
## 111 6.5 3.2 5.1 2.0 virginica 9.7
## 112 6.4 2.7 5.3 1.9 virginica 9.1
## 113 6.8 3.0 5.5 2.1 virginica 9.8
## 114 5.7 2.5 5.0 2.0 virginica 8.2
## 115 5.8 2.8 5.1 2.4 virginica 8.6
## 116 6.4 3.2 5.3 2.3 virginica 9.6
## 117 6.5 3.0 5.5 1.8 virginica 9.5
## 118 7.7 3.8 6.7 2.2 virginica 11.5
## 119 7.7 2.6 6.9 2.3 virginica 10.3
## 120 6.0 2.2 5.0 1.5 virginica 8.2
## 121 6.9 3.2 5.7 2.3 virginica 10.1
## 122 5.6 2.8 4.9 2.0 virginica 8.4
## 123 7.7 2.8 6.7 2.0 virginica 10.5
## 124 6.3 2.7 4.9 1.8 virginica 9.0
## 125 6.7 3.3 5.7 2.1 virginica 10.0
## 126 7.2 3.2 6.0 1.8 virginica 10.4
## 127 6.2 2.8 4.8 1.8 virginica 9.0
## 128 6.1 3.0 4.9 1.8 virginica 9.1
## 129 6.4 2.8 5.6 2.1 virginica 9.2
## 130 7.2 3.0 5.8 1.6 virginica 10.2
## 131 7.4 2.8 6.1 1.9 virginica 10.2
## 132 7.9 3.8 6.4 2.0 virginica 11.7
## 133 6.4 2.8 5.6 2.2 virginica 9.2
## 134 6.3 2.8 5.1 1.5 virginica 9.1
## 135 6.1 2.6 5.6 1.4 virginica 8.7
## 136 7.7 3.0 6.1 2.3 virginica 10.7
## 137 6.3 3.4 5.6 2.4 virginica 9.7
## 138 6.4 3.1 5.5 1.8 virginica 9.5
## 139 6.0 3.0 4.8 1.8 virginica 9.0
## 140 6.9 3.1 5.4 2.1 virginica 10.0
## 141 6.7 3.1 5.6 2.4 virginica 9.8
## 142 6.9 3.1 5.1 2.3 virginica 10.0
## 143 5.8 2.7 5.1 1.9 virginica 8.5
## 144 6.8 3.2 5.9 2.3 virginica 10.0
## 145 6.7 3.3 5.7 2.5 virginica 10.0
## 146 6.7 3.0 5.2 2.3 virginica 9.7
## 147 6.3 2.5 5.0 1.9 virginica 8.8
## 148 6.5 3.0 5.2 2.0 virginica 9.5
## 149 6.2 3.4 5.4 2.3 virginica 9.6
## 150 5.9 3.0 5.1 1.8 virginica 8.9
(transmute(iris, sepal = Sepal.Length + Sepal.Width)) #똑같은데 저기 쓰인 애들 버리고
## sepal
## 1 8.6
## 2 7.9
## 3 7.9
## 4 7.7
## 5 8.6
## 6 9.3
## 7 8.0
## 8 8.4
## 9 7.3
## 10 8.0
## 11 9.1
## 12 8.2
## 13 7.8
## 14 7.3
## 15 9.8
## 16 10.1
## 17 9.3
## 18 8.6
## 19 9.5
## 20 8.9
## 21 8.8
## 22 8.8
## 23 8.2
## 24 8.4
## 25 8.2
## 26 8.0
## 27 8.4
## 28 8.7
## 29 8.6
## 30 7.9
## 31 7.9
## 32 8.8
## 33 9.3
## 34 9.7
## 35 8.0
## 36 8.2
## 37 9.0
## 38 8.5
## 39 7.4
## 40 8.5
## 41 8.5
## 42 6.8
## 43 7.6
## 44 8.5
## 45 8.9
## 46 7.8
## 47 8.9
## 48 7.8
## 49 9.0
## 50 8.3
## 51 10.2
## 52 9.6
## 53 10.0
## 54 7.8
## 55 9.3
## 56 8.5
## 57 9.6
## 58 7.3
## 59 9.5
## 60 7.9
## 61 7.0
## 62 8.9
## 63 8.2
## 64 9.0
## 65 8.5
## 66 9.8
## 67 8.6
## 68 8.5
## 69 8.4
## 70 8.1
## 71 9.1
## 72 8.9
## 73 8.8
## 74 8.9
## 75 9.3
## 76 9.6
## 77 9.6
## 78 9.7
## 79 8.9
## 80 8.3
## 81 7.9
## 82 7.9
## 83 8.5
## 84 8.7
## 85 8.4
## 86 9.4
## 87 9.8
## 88 8.6
## 89 8.6
## 90 8.0
## 91 8.1
## 92 9.1
## 93 8.4
## 94 7.3
## 95 8.3
## 96 8.7
## 97 8.6
## 98 9.1
## 99 7.6
## 100 8.5
## 101 9.6
## 102 8.5
## 103 10.1
## 104 9.2
## 105 9.5
## 106 10.6
## 107 7.4
## 108 10.2
## 109 9.2
## 110 10.8
## 111 9.7
## 112 9.1
## 113 9.8
## 114 8.2
## 115 8.6
## 116 9.6
## 117 9.5
## 118 11.5
## 119 10.3
## 120 8.2
## 121 10.1
## 122 8.4
## 123 10.5
## 124 9.0
## 125 10.0
## 126 10.4
## 127 9.0
## 128 9.1
## 129 9.2
## 130 10.2
## 131 10.2
## 132 11.7
## 133 9.2
## 134 9.1
## 135 8.7
## 136 10.7
## 137 9.7
## 138 9.5
## 139 9.0
## 140 10.0
## 141 9.8
## 142 10.0
## 143 8.5
## 144 10.0
## 145 10.0
## 146 9.7
## 147 8.8
## 148 9.5
## 149 9.6
## 150 8.9
(group_by(iris, Species)) #종별로 묶어라
## # A tibble: 150 x 5
## # Groups: Species [3]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ... with 140 more rows
(summarise(iris, avg = mean(Sepal.Length))) #sl의 평균을 구하라
## avg
## 1 5.843333
(summarise_each(iris, funs(mean))) #각 열별로 mean 구해라
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
## Warning in mean.default(Species): argument is not numeric or logical:
## returning NA
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.843333 3.057333 3.758 1.199333 NA
(arrange(iris, Sepal.Length)) #sepal 길이로 오름차순 정렬
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 4.3 3.0 1.1 0.1 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 4.4 3.0 1.3 0.2 setosa
## 4 4.4 3.2 1.3 0.2 setosa
## 5 4.5 2.3 1.3 0.3 setosa
## 6 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 4.6 3.6 1.0 0.2 setosa
## 9 4.6 3.2 1.4 0.2 setosa
## 10 4.7 3.2 1.3 0.2 setosa
## 11 4.7 3.2 1.6 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.8 3.4 1.9 0.2 setosa
## 15 4.8 3.1 1.6 0.2 setosa
## 16 4.8 3.0 1.4 0.3 setosa
## 17 4.9 3.0 1.4 0.2 setosa
## 18 4.9 3.1 1.5 0.1 setosa
## 19 4.9 3.1 1.5 0.2 setosa
## 20 4.9 3.6 1.4 0.1 setosa
## 21 4.9 2.4 3.3 1.0 versicolor
## 22 4.9 2.5 4.5 1.7 virginica
## 23 5.0 3.6 1.4 0.2 setosa
## 24 5.0 3.4 1.5 0.2 setosa
## 25 5.0 3.0 1.6 0.2 setosa
## 26 5.0 3.4 1.6 0.4 setosa
## 27 5.0 3.2 1.2 0.2 setosa
## 28 5.0 3.5 1.3 0.3 setosa
## 29 5.0 3.5 1.6 0.6 setosa
## 30 5.0 3.3 1.4 0.2 setosa
## 31 5.0 2.0 3.5 1.0 versicolor
## 32 5.0 2.3 3.3 1.0 versicolor
## 33 5.1 3.5 1.4 0.2 setosa
## 34 5.1 3.5 1.4 0.3 setosa
## 35 5.1 3.8 1.5 0.3 setosa
## 36 5.1 3.7 1.5 0.4 setosa
## 37 5.1 3.3 1.7 0.5 setosa
## 38 5.1 3.4 1.5 0.2 setosa
## 39 5.1 3.8 1.9 0.4 setosa
## 40 5.1 3.8 1.6 0.2 setosa
## 41 5.1 2.5 3.0 1.1 versicolor
## 42 5.2 3.5 1.5 0.2 setosa
## 43 5.2 3.4 1.4 0.2 setosa
## 44 5.2 4.1 1.5 0.1 setosa
## 45 5.2 2.7 3.9 1.4 versicolor
## 46 5.3 3.7 1.5 0.2 setosa
## 47 5.4 3.9 1.7 0.4 setosa
## 48 5.4 3.7 1.5 0.2 setosa
## 49 5.4 3.9 1.3 0.4 setosa
## 50 5.4 3.4 1.7 0.2 setosa
## 51 5.4 3.4 1.5 0.4 setosa
## 52 5.4 3.0 4.5 1.5 versicolor
## 53 5.5 4.2 1.4 0.2 setosa
## 54 5.5 3.5 1.3 0.2 setosa
## 55 5.5 2.3 4.0 1.3 versicolor
## 56 5.5 2.4 3.8 1.1 versicolor
## 57 5.5 2.4 3.7 1.0 versicolor
## 58 5.5 2.5 4.0 1.3 versicolor
## 59 5.5 2.6 4.4 1.2 versicolor
## 60 5.6 2.9 3.6 1.3 versicolor
## 61 5.6 3.0 4.5 1.5 versicolor
## 62 5.6 2.5 3.9 1.1 versicolor
## 63 5.6 3.0 4.1 1.3 versicolor
## 64 5.6 2.7 4.2 1.3 versicolor
## 65 5.6 2.8 4.9 2.0 virginica
## 66 5.7 4.4 1.5 0.4 setosa
## 67 5.7 3.8 1.7 0.3 setosa
## 68 5.7 2.8 4.5 1.3 versicolor
## 69 5.7 2.6 3.5 1.0 versicolor
## 70 5.7 3.0 4.2 1.2 versicolor
## 71 5.7 2.9 4.2 1.3 versicolor
## 72 5.7 2.8 4.1 1.3 versicolor
## 73 5.7 2.5 5.0 2.0 virginica
## 74 5.8 4.0 1.2 0.2 setosa
## 75 5.8 2.7 4.1 1.0 versicolor
## 76 5.8 2.7 3.9 1.2 versicolor
## 77 5.8 2.6 4.0 1.2 versicolor
## 78 5.8 2.7 5.1 1.9 virginica
## 79 5.8 2.8 5.1 2.4 virginica
## 80 5.8 2.7 5.1 1.9 virginica
## 81 5.9 3.0 4.2 1.5 versicolor
## 82 5.9 3.2 4.8 1.8 versicolor
## 83 5.9 3.0 5.1 1.8 virginica
## 84 6.0 2.2 4.0 1.0 versicolor
## 85 6.0 2.9 4.5 1.5 versicolor
## 86 6.0 2.7 5.1 1.6 versicolor
## 87 6.0 3.4 4.5 1.6 versicolor
## 88 6.0 2.2 5.0 1.5 virginica
## 89 6.0 3.0 4.8 1.8 virginica
## 90 6.1 2.9 4.7 1.4 versicolor
## 91 6.1 2.8 4.0 1.3 versicolor
## 92 6.1 2.8 4.7 1.2 versicolor
## 93 6.1 3.0 4.6 1.4 versicolor
## 94 6.1 3.0 4.9 1.8 virginica
## 95 6.1 2.6 5.6 1.4 virginica
## 96 6.2 2.2 4.5 1.5 versicolor
## 97 6.2 2.9 4.3 1.3 versicolor
## 98 6.2 2.8 4.8 1.8 virginica
## 99 6.2 3.4 5.4 2.3 virginica
## 100 6.3 3.3 4.7 1.6 versicolor
## 101 6.3 2.5 4.9 1.5 versicolor
## 102 6.3 2.3 4.4 1.3 versicolor
## 103 6.3 3.3 6.0 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.3 2.7 4.9 1.8 virginica
## 106 6.3 2.8 5.1 1.5 virginica
## 107 6.3 3.4 5.6 2.4 virginica
## 108 6.3 2.5 5.0 1.9 virginica
## 109 6.4 3.2 4.5 1.5 versicolor
## 110 6.4 2.9 4.3 1.3 versicolor
## 111 6.4 2.7 5.3 1.9 virginica
## 112 6.4 3.2 5.3 2.3 virginica
## 113 6.4 2.8 5.6 2.1 virginica
## 114 6.4 2.8 5.6 2.2 virginica
## 115 6.4 3.1 5.5 1.8 virginica
## 116 6.5 2.8 4.6 1.5 versicolor
## 117 6.5 3.0 5.8 2.2 virginica
## 118 6.5 3.2 5.1 2.0 virginica
## 119 6.5 3.0 5.5 1.8 virginica
## 120 6.5 3.0 5.2 2.0 virginica
## 121 6.6 2.9 4.6 1.3 versicolor
## 122 6.6 3.0 4.4 1.4 versicolor
## 123 6.7 3.1 4.4 1.4 versicolor
## 124 6.7 3.0 5.0 1.7 versicolor
## 125 6.7 3.1 4.7 1.5 versicolor
## 126 6.7 2.5 5.8 1.8 virginica
## 127 6.7 3.3 5.7 2.1 virginica
## 128 6.7 3.1 5.6 2.4 virginica
## 129 6.7 3.3 5.7 2.5 virginica
## 130 6.7 3.0 5.2 2.3 virginica
## 131 6.8 2.8 4.8 1.4 versicolor
## 132 6.8 3.0 5.5 2.1 virginica
## 133 6.8 3.2 5.9 2.3 virginica
## 134 6.9 3.1 4.9 1.5 versicolor
## 135 6.9 3.2 5.7 2.3 virginica
## 136 6.9 3.1 5.4 2.1 virginica
## 137 6.9 3.1 5.1 2.3 virginica
## 138 7.0 3.2 4.7 1.4 versicolor
## 139 7.1 3.0 5.9 2.1 virginica
## 140 7.2 3.6 6.1 2.5 virginica
## 141 7.2 3.2 6.0 1.8 virginica
## 142 7.2 3.0 5.8 1.6 virginica
## 143 7.3 2.9 6.3 1.8 virginica
## 144 7.4 2.8 6.1 1.9 virginica
## 145 7.6 3.0 6.6 2.1 virginica
## 146 7.7 3.8 6.7 2.2 virginica
## 147 7.7 2.6 6.9 2.3 virginica
## 148 7.7 2.8 6.7 2.0 virginica
## 149 7.7 3.0 6.1 2.3 virginica
## 150 7.9 3.8 6.4 2.0 virginica
(iris %>% group_by(Species) %>%
mutate(sepal = Sepal.Length + Sepal.Width) %>%
summarise(avg = mean(sepal)) %>%
arrange(desc(avg))) #종별로 sepal 평균을 구하고, 내림차순 정렬해라
## # A tibble: 3 x 2
## Species avg
## <fct> <dbl>
## 1 virginica 9.56
## 2 versicolor 8.71
## 3 setosa 8.43
#tele_example
(tele_median_up %>%
group_by(state) %>%
mutate(sum_charge = total.day.charge + total.eve.charge + total.night.charge) %>%
summarise(avg = mean(sum_charge)) %>%
arrange(desc(avg)))
## # A tibble: 51 x 2
## state avg
## <fct> <dbl>
## 1 OK 79.1
## 2 MS 77.0
## 3 NY 74.7
## 4 SC 74.5
## 5 MD 73.3
## 6 NJ 71.8
## 7 WA 71.7
## 8 MO 71.7
## 9 HI 71.4
## 10 PA 71.1
## # ... with 41 more rows