R入門2
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Rの入門2, 簡単にdata frameを操作する
mtcarsデータセット
特にロードする必要もなく、デフォルトで入っている。
mtcars mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
%>% パイプを使ってデーターセットのサマリーを調べる。
library(dplyr)
mtcars %>% summary() mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
mtcars data frameの構造を表示
print(str(mtcars))'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...
NULL
carbカラムのデータタイプを調べる
print(typeof(mtcars$carb))[1] "double"
carbカラムにユニークな値
print(unique(mtcars$carb))[1] 4 1 2 3 6 8
carbカラムに1は７個、2は10個などの情報を出す
print(table(mtcars$carb)) 1 2 3 4 6 8
7 10 3 10 1 1
quantile()関数を使って、mpg列の四分位点を調べる
print(quantile(mtcars$mpg)) 0% 25% 50% 75% 100%
10.400 15.425 19.200 22.800 33.900
boxplotで簡単に箱ひげ図を作成することができる。
boxplot(mtcars$mpg)
mpg列の分散と標準偏差を求める。
print(var(mtcars$mpg))
print(sd(mtcars$mpg))[1] 36.3241
[1] 6.026948
選択された各列に対して分散を計算する。
print(mtcars %>% select(mpg, disp) %>% summarise_all(funs(var))) mpg disp
1 36.3241 15360.8
airqualityデータセット
airquality| Ozone | Solar.R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41 | 190 | 7.4 | 67 | 5 | 1 |
| 36 | 118 | 8.0 | 72 | 5 | 2 |
| 12 | 149 | 12.6 | 74 | 5 | 3 |
| 18 | 313 | 11.5 | 62 | 5 | 4 |
| NA | NA | 14.3 | 56 | 5 | 5 |
| 28 | NA | 14.9 | 66 | 5 | 6 |
| 23 | 299 | 8.6 | 65 | 5 | 7 |
| 19 | 99 | 13.8 | 59 | 5 | 8 |
| 8 | 19 | 20.1 | 61 | 5 | 9 |
| NA | 194 | 8.6 | 69 | 5 | 10 |
| 7 | NA | 6.9 | 74 | 5 | 11 |
| 16 | 256 | 9.7 | 69 | 5 | 12 |
| 11 | 290 | 9.2 | 66 | 5 | 13 |
| 14 | 274 | 10.9 | 68 | 5 | 14 |
| 18 | 65 | 13.2 | 58 | 5 | 15 |
| 14 | 334 | 11.5 | 64 | 5 | 16 |
| 34 | 307 | 12.0 | 66 | 5 | 17 |
| 6 | 78 | 18.4 | 57 | 5 | 18 |
| 30 | 322 | 11.5 | 68 | 5 | 19 |
| 11 | 44 | 9.7 | 62 | 5 | 20 |
| 1 | 8 | 9.7 | 59 | 5 | 21 |
| 11 | 320 | 16.6 | 73 | 5 | 22 |
| 4 | 25 | 9.7 | 61 | 5 | 23 |
| 32 | 92 | 12.0 | 61 | 5 | 24 |
| NA | 66 | 16.6 | 57 | 5 | 25 |
| NA | 266 | 14.9 | 58 | 5 | 26 |
| NA | NA | 8.0 | 57 | 5 | 27 |
| 23 | 13 | 12.0 | 67 | 5 | 28 |
| 45 | 252 | 14.9 | 81 | 5 | 29 |
| 115 | 223 | 5.7 | 79 | 5 | 30 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 96 | 167 | 6.9 | 91 | 9 | 1 |
| 78 | 197 | 5.1 | 92 | 9 | 2 |
| 73 | 183 | 2.8 | 93 | 9 | 3 |
| 91 | 189 | 4.6 | 93 | 9 | 4 |
| 47 | 95 | 7.4 | 87 | 9 | 5 |
| 32 | 92 | 15.5 | 84 | 9 | 6 |
| 20 | 252 | 10.9 | 80 | 9 | 7 |
| 23 | 220 | 10.3 | 78 | 9 | 8 |
| 21 | 230 | 10.9 | 75 | 9 | 9 |
| 24 | 259 | 9.7 | 73 | 9 | 10 |
| 44 | 236 | 14.9 | 81 | 9 | 11 |
| 21 | 259 | 15.5 | 76 | 9 | 12 |
| 28 | 238 | 6.3 | 77 | 9 | 13 |
| 9 | 24 | 10.9 | 71 | 9 | 14 |
| 13 | 112 | 11.5 | 71 | 9 | 15 |
| 46 | 237 | 6.9 | 78 | 9 | 16 |
| 18 | 224 | 13.8 | 67 | 9 | 17 |
| 13 | 27 | 10.3 | 76 | 9 | 18 |
| 24 | 238 | 10.3 | 68 | 9 | 19 |
| 16 | 201 | 8.0 | 82 | 9 | 20 |
| 13 | 238 | 12.6 | 64 | 9 | 21 |
| 23 | 14 | 9.2 | 71 | 9 | 22 |
| 36 | 139 | 10.3 | 81 | 9 | 23 |
| 7 | 49 | 10.3 | 69 | 9 | 24 |
| 14 | 20 | 16.6 | 63 | 9 | 25 |
| 30 | 193 | 6.9 | 70 | 9 | 26 |
| NA | 145 | 13.2 | 77 | 9 | 27 |
| 14 | 191 | 14.3 | 75 | 9 | 28 |
| 18 | 131 | 8.0 | 76 | 9 | 29 |
| 20 | 223 | 11.5 | 68 | 9 | 30 |
print(str(airquality))'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
NULL
construct 9 number of equally-spaced bins for us by using the cut function. 間隔は同じで、その間隔に入っている数字の数は違うかも。table関数でみることができる
print(cut(airquality$Temp, 9)) [1] (65.1,69.7] (69.7,74.2] (69.7,74.2] (60.6,65.1] (56,60.6] (65.1,69.7]
[7] (60.6,65.1] (56,60.6] (60.6,65.1] (65.1,69.7] (69.7,74.2] (65.1,69.7]
[13] (65.1,69.7] (65.1,69.7] (56,60.6] (60.6,65.1] (65.1,69.7] (56,60.6]
[19] (65.1,69.7] (60.6,65.1] (56,60.6] (69.7,74.2] (60.6,65.1] (60.6,65.1]
[25] (56,60.6] (56,60.6] (56,60.6] (65.1,69.7] (78.8,83.3] (78.8,83.3]
[31] (74.2,78.8] (74.2,78.8] (69.7,74.2] (65.1,69.7] (83.3,87.9] (83.3,87.9]
[37] (78.8,83.3] (78.8,83.3] (83.3,87.9] (87.9,92.4] (83.3,87.9] (92.4,97]
[43] (87.9,92.4] (78.8,83.3] (78.8,83.3] (78.8,83.3] (74.2,78.8] (69.7,74.2]
[49] (60.6,65.1] (69.7,74.2] (74.2,78.8] (74.2,78.8] (74.2,78.8] (74.2,78.8]
[55] (74.2,78.8] (74.2,78.8] (74.2,78.8] (69.7,74.2] (78.8,83.3] (74.2,78.8]
[61] (78.8,83.3] (83.3,87.9] (83.3,87.9] (78.8,83.3] (83.3,87.9] (78.8,83.3]
[67] (78.8,83.3] (87.9,92.4] (87.9,92.4] (87.9,92.4] (87.9,92.4] (78.8,83.3]
[73] (69.7,74.2] (78.8,83.3] (87.9,92.4] (78.8,83.3] (78.8,83.3] (78.8,83.3]
[79] (83.3,87.9] (83.3,87.9] (83.3,87.9] (69.7,74.2] (78.8,83.3] (78.8,83.3]
[85] (83.3,87.9] (83.3,87.9] (78.8,83.3] (83.3,87.9] (87.9,92.4] (83.3,87.9]
[91] (78.8,83.3] (78.8,83.3] (78.8,83.3] (78.8,83.3] (78.8,83.3] (83.3,87.9]
[97] (83.3,87.9] (83.3,87.9] (87.9,92.4] (87.9,92.4] (87.9,92.4] (87.9,92.4]
[103] (83.3,87.9] (83.3,87.9] (78.8,83.3] (78.8,83.3] (78.8,83.3] (74.2,78.8]
[109] (78.8,83.3] (74.2,78.8] (74.2,78.8] (74.2,78.8] (74.2,78.8] (69.7,74.2]
[115] (74.2,78.8] (78.8,83.3] (78.8,83.3] (83.3,87.9] (87.9,92.4] (92.4,97]
[121] (92.4,97] (92.4,97] (92.4,97] (87.9,92.4] (87.9,92.4] (92.4,97]
[127] (92.4,97] (83.3,87.9] (83.3,87.9] (78.8,83.3] (74.2,78.8] (74.2,78.8]
[133] (69.7,74.2] (78.8,83.3] (74.2,78.8] (74.2,78.8] (69.7,74.2] (69.7,74.2]
[139] (74.2,78.8] (65.1,69.7] (74.2,78.8] (65.1,69.7] (78.8,83.3] (60.6,65.1]
[145] (69.7,74.2] (78.8,83.3] (65.1,69.7] (60.6,65.1] (69.7,74.2] (74.2,78.8]
[151] (74.2,78.8] (74.2,78.8] (65.1,69.7]
9 Levels: (56,60.6] (60.6,65.1] (65.1,69.7] (69.7,74.2] ... (92.4,97]
# それぞれの区間内にどれだけ入っているかを調べる
print(table(cut(airquality$Temp, 9))) (56,60.6] (60.6,65.1] (65.1,69.7] (69.7,74.2] (74.2,78.8] (78.8,83.3]
8 10 14 16 26 35
(83.3,87.9] (87.9,92.4] (92.4,97]
22 15 7
carbは１である確率は0.2185, これはPMF(probability mass function)と呼ばれる
print(table(mtcars$carb))
print(table(mtcars$carb) / length(mtcars$carb)) 1 2 3 4 6 8
7 10 3 10 1 1
1 2 3 4 6 8
0.21875 0.31250 0.09375 0.31250 0.03125 0.03125
11と12月のフライトを表示する。
カッコをつけることによって、結果をnov_decに保存して、さらに表示する。
library(nycflights13)
(nov_dec <- filter(flights, month %in% c(11, 12)))distanceの大きい順でソートする。
arrange(flights, desc(distance))yearからdayまでの列を除去して、表示する。
select(flights, -(year:day))destとdistance列を先頭に移動して、表示する。
select(flights, dest, distance, everything())tailnum列名をtail_numに変更する。
rename(flights, tail_num= tailnum)mutateを使って、新しい変数added_columeを追加する.
mutate(flights, added_colume = distance + 60)transmuteを使って新しい作った変数added_columeだけを残して表示する。
transmute(flights, added_colume = distance + 60)