SEAI 2022 - R - Lab 2
Intro to R
Vincenzo Nardelli - vincnardelli@gmail.com - https://github.com/vincnardelli
Lab structure
- Intro
- Matrix computation
- Data manipulation with dplyr
- Graphs with ggplot2
Let’s start from the basics!
3 + 5
## [1] 8
12/7
## [1] 1.714286
result <- 3 + 5
result
## [1] 8
print(result)
## [1] 8
result <- result * 3.1415
print(result)
## [1] 25.132
vector = c(1, 3, 8, 13)
vector
## [1] 1 3 8 13
Unlike Python, the basic version of R allow operations between scalars and matrices without loading any external packages.
vector[1]
## [1] 1
The subset is very similar to Python but the index starts from 1 instead of 0.
Furthermore, in the case of multiple selection, the index starts from 1 and the second value representing the last element is included in the subset (unlike Python which is NOT included).
vector[1:3]
## [1] 1 3 8
vector[c(FALSE, TRUE, TRUE, FALSE)]
## [1] 3 8
vector < 3
## [1] TRUE FALSE FALSE FALSE
vector[vector < 3]
## [1] 1
1:10
## [1] 1 2 3 4 5 6 7 8 9 10
seq(from=1, to=10, by=1)
## [1] 1 2 3 4 5 6 7 8 9 10
Inside a vector we can insert just homogeneous class object.
class("a")
## [1] "character"
class(1)
## [1] "numeric"
c(1, "a")
## [1] "1" "a"
class(c(1, "a"))
## [1] "character"
1 - Matrix computation
a <- matrix(c(10, 8, 5, 12), nrow=2, ncol=2, byrow=TRUE)
a
## [,1] [,2]
## [1,] 10 8
## [2,] 5 12
dim(a)
## [1] 2 2
Subsetting in two dimension
a[1, ]
## [1] 10 8
a[, 1]
## [1] 10 5
a[1, 1]
## [1] 10
Operations with matrices
a + 2
## [,1] [,2]
## [1,] 12 10
## [2,] 7 14
a * 2
## [,1] [,2]
## [1,] 20 16
## [2,] 10 24
t(a)
## [,1] [,2]
## [1,] 10 5
## [2,] 8 12
b <- matrix(c(5, 3, 15, 6), ncol = 2, byrow = TRUE)
b
## [,1] [,2]
## [1,] 5 3
## [2,] 15 6
a + b
## [,1] [,2]
## [1,] 15 11
## [2,] 20 18
Element-wise multiplication
a * b
## [,1] [,2]
## [1,] 50 24
## [2,] 75 72
Matrix multiplication (matricial multiplication)
a %*% b
## [,1] [,2]
## [1,] 170 78
## [2,] 205 87
crossprod(a,b)
## [,1] [,2]
## [1,] 125 60
## [2,] 220 96
t(a) %*% b
## [,1] [,2]
## [1,] 125 60
## [2,] 220 96
Kronecker product
a %x% b
## [,1] [,2] [,3] [,4]
## [1,] 50 30 40 24
## [2,] 150 60 120 48
## [3,] 25 15 60 36
## [4,] 75 30 180 72
Determinant
det(a)
## [1] 80
Inverse of a matrix
solve(a)
## [,1] [,2]
## [1,] 0.1500 -0.100
## [2,] -0.0625 0.125
2 - Data manipulation with dplyr
Let’s start loading same data from R.
data(mtcars)
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
class(mtcars)
## [1] "data.frame"
A data.frame is an analog of a matrix which can contains a difference
classes of objects for each column. It is the perfect class to store the
data which we commonly use for our analysis.
dim(mtcars)
## [1] 32 11
colnames(mtcars)
## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"
summary(mtcars)
## 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
Subset a dataframe with base R
mtcars[1,]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
mtcars[1:3, ]
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
mtcars[, 1]
## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
mtcars$hp
## [1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
## [20] 65 97 150 150 245 175 66 91 113 264 175 335 109
Data loading
path = "https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv"
titanic = read.csv(path)
head(titanic)
## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S
## 6 330877 8.4583 Q
Now we will explore the use of dplyr in subsetting, manipulating and summarising our data frame. Most of the task that we will cover are possible also in base R without the use of others packages but dplyr simplify a lot the work.
R packages
How to install an R package from CRAN
#install.packages("dplyr")
Load package in the environment
library(dplyr)
##
## 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 variables (columns)
select(titanic, PassengerId, Age)
## PassengerId Age
## 1 1 22.00
## 2 2 38.00
## 3 3 26.00
## 4 4 35.00
## 5 5 35.00
## 6 6 NA
## 7 7 54.00
## 8 8 2.00
## 9 9 27.00
## 10 10 14.00
## 11 11 4.00
## 12 12 58.00
## 13 13 20.00
## 14 14 39.00
## 15 15 14.00
## 16 16 55.00
## 17 17 2.00
## 18 18 NA
## 19 19 31.00
## 20 20 NA
## 21 21 35.00
## 22 22 34.00
## 23 23 15.00
## 24 24 28.00
## 25 25 8.00
## 26 26 38.00
## 27 27 NA
## 28 28 19.00
## 29 29 NA
## 30 30 NA
## 31 31 40.00
## 32 32 NA
## 33 33 NA
## 34 34 66.00
## 35 35 28.00
## 36 36 42.00
## 37 37 NA
## 38 38 21.00
## 39 39 18.00
## 40 40 14.00
## 41 41 40.00
## 42 42 27.00
## 43 43 NA
## 44 44 3.00
## 45 45 19.00
## 46 46 NA
## 47 47 NA
## 48 48 NA
## 49 49 NA
## 50 50 18.00
## 51 51 7.00
## 52 52 21.00
## 53 53 49.00
## 54 54 29.00
## 55 55 65.00
## 56 56 NA
## 57 57 21.00
## 58 58 28.50
## 59 59 5.00
## 60 60 11.00
## 61 61 22.00
## 62 62 38.00
## 63 63 45.00
## 64 64 4.00
## 65 65 NA
## 66 66 NA
## 67 67 29.00
## 68 68 19.00
## 69 69 17.00
## 70 70 26.00
## 71 71 32.00
## 72 72 16.00
## 73 73 21.00
## 74 74 26.00
## 75 75 32.00
## 76 76 25.00
## 77 77 NA
## 78 78 NA
## 79 79 0.83
## 80 80 30.00
## 81 81 22.00
## 82 82 29.00
## 83 83 NA
## 84 84 28.00
## 85 85 17.00
## 86 86 33.00
## 87 87 16.00
## 88 88 NA
## 89 89 23.00
## 90 90 24.00
## 91 91 29.00
## 92 92 20.00
## 93 93 46.00
## 94 94 26.00
## 95 95 59.00
## 96 96 NA
## 97 97 71.00
## 98 98 23.00
## 99 99 34.00
## 100 100 34.00
## 101 101 28.00
## 102 102 NA
## 103 103 21.00
## 104 104 33.00
## 105 105 37.00
## 106 106 28.00
## 107 107 21.00
## 108 108 NA
## 109 109 38.00
## 110 110 NA
## 111 111 47.00
## 112 112 14.50
## 113 113 22.00
## 114 114 20.00
## 115 115 17.00
## 116 116 21.00
## 117 117 70.50
## 118 118 29.00
## 119 119 24.00
## 120 120 2.00
## 121 121 21.00
## 122 122 NA
## 123 123 32.50
## 124 124 32.50
## 125 125 54.00
## 126 126 12.00
## 127 127 NA
## 128 128 24.00
## 129 129 NA
## 130 130 45.00
## 131 131 33.00
## 132 132 20.00
## 133 133 47.00
## 134 134 29.00
## 135 135 25.00
## 136 136 23.00
## 137 137 19.00
## 138 138 37.00
## 139 139 16.00
## 140 140 24.00
## 141 141 NA
## 142 142 22.00
## 143 143 24.00
## 144 144 19.00
## 145 145 18.00
## 146 146 19.00
## 147 147 27.00
## 148 148 9.00
## 149 149 36.50
## 150 150 42.00
## 151 151 51.00
## 152 152 22.00
## 153 153 55.50
## 154 154 40.50
## 155 155 NA
## 156 156 51.00
## 157 157 16.00
## 158 158 30.00
## 159 159 NA
## 160 160 NA
## 161 161 44.00
## 162 162 40.00
## 163 163 26.00
## 164 164 17.00
## 165 165 1.00
## 166 166 9.00
## 167 167 NA
## 168 168 45.00
## 169 169 NA
## 170 170 28.00
## 171 171 61.00
## 172 172 4.00
## 173 173 1.00
## 174 174 21.00
## 175 175 56.00
## 176 176 18.00
## 177 177 NA
## 178 178 50.00
## 179 179 30.00
## 180 180 36.00
## 181 181 NA
## 182 182 NA
## 183 183 9.00
## 184 184 1.00
## 185 185 4.00
## 186 186 NA
## 187 187 NA
## 188 188 45.00
## 189 189 40.00
## 190 190 36.00
## 191 191 32.00
## 192 192 19.00
## 193 193 19.00
## 194 194 3.00
## 195 195 44.00
## 196 196 58.00
## 197 197 NA
## 198 198 42.00
## 199 199 NA
## 200 200 24.00
## 201 201 28.00
## 202 202 NA
## 203 203 34.00
## 204 204 45.50
## 205 205 18.00
## 206 206 2.00
## 207 207 32.00
## 208 208 26.00
## 209 209 16.00
## 210 210 40.00
## 211 211 24.00
## 212 212 35.00
## 213 213 22.00
## 214 214 30.00
## 215 215 NA
## 216 216 31.00
## 217 217 27.00
## 218 218 42.00
## 219 219 32.00
## 220 220 30.00
## 221 221 16.00
## 222 222 27.00
## 223 223 51.00
## 224 224 NA
## 225 225 38.00
## 226 226 22.00
## 227 227 19.00
## 228 228 20.50
## 229 229 18.00
## 230 230 NA
## 231 231 35.00
## 232 232 29.00
## 233 233 59.00
## 234 234 5.00
## 235 235 24.00
## 236 236 NA
## 237 237 44.00
## 238 238 8.00
## 239 239 19.00
## 240 240 33.00
## 241 241 NA
## 242 242 NA
## 243 243 29.00
## 244 244 22.00
## 245 245 30.00
## 246 246 44.00
## 247 247 25.00
## 248 248 24.00
## 249 249 37.00
## 250 250 54.00
## 251 251 NA
## 252 252 29.00
## 253 253 62.00
## 254 254 30.00
## 255 255 41.00
## 256 256 29.00
## 257 257 NA
## 258 258 30.00
## 259 259 35.00
## 260 260 50.00
## 261 261 NA
## 262 262 3.00
## 263 263 52.00
## 264 264 40.00
## 265 265 NA
## 266 266 36.00
## 267 267 16.00
## 268 268 25.00
## 269 269 58.00
## 270 270 35.00
## 271 271 NA
## 272 272 25.00
## 273 273 41.00
## 274 274 37.00
## 275 275 NA
## 276 276 63.00
## 277 277 45.00
## 278 278 NA
## 279 279 7.00
## 280 280 35.00
## 281 281 65.00
## 282 282 28.00
## 283 283 16.00
## 284 284 19.00
## 285 285 NA
## 286 286 33.00
## 287 287 30.00
## 288 288 22.00
## 289 289 42.00
## 290 290 22.00
## 291 291 26.00
## 292 292 19.00
## 293 293 36.00
## 294 294 24.00
## 295 295 24.00
## 296 296 NA
## 297 297 23.50
## 298 298 2.00
## 299 299 NA
## 300 300 50.00
## 301 301 NA
## 302 302 NA
## 303 303 19.00
## 304 304 NA
## 305 305 NA
## 306 306 0.92
## 307 307 NA
## 308 308 17.00
## 309 309 30.00
## 310 310 30.00
## 311 311 24.00
## 312 312 18.00
## 313 313 26.00
## 314 314 28.00
## 315 315 43.00
## 316 316 26.00
## 317 317 24.00
## 318 318 54.00
## 319 319 31.00
## 320 320 40.00
## 321 321 22.00
## 322 322 27.00
## 323 323 30.00
## 324 324 22.00
## 325 325 NA
## 326 326 36.00
## 327 327 61.00
## 328 328 36.00
## 329 329 31.00
## 330 330 16.00
## 331 331 NA
## 332 332 45.50
## 333 333 38.00
## 334 334 16.00
## 335 335 NA
## 336 336 NA
## 337 337 29.00
## 338 338 41.00
## 339 339 45.00
## 340 340 45.00
## 341 341 2.00
## 342 342 24.00
## 343 343 28.00
## 344 344 25.00
## 345 345 36.00
## 346 346 24.00
## 347 347 40.00
## 348 348 NA
## 349 349 3.00
## 350 350 42.00
## 351 351 23.00
## 352 352 NA
## 353 353 15.00
## 354 354 25.00
## 355 355 NA
## 356 356 28.00
## 357 357 22.00
## 358 358 38.00
## 359 359 NA
## 360 360 NA
## 361 361 40.00
## 362 362 29.00
## 363 363 45.00
## 364 364 35.00
## 365 365 NA
## 366 366 30.00
## 367 367 60.00
## 368 368 NA
## 369 369 NA
## 370 370 24.00
## 371 371 25.00
## 372 372 18.00
## 373 373 19.00
## 374 374 22.00
## 375 375 3.00
## 376 376 NA
## 377 377 22.00
## 378 378 27.00
## 379 379 20.00
## 380 380 19.00
## 381 381 42.00
## 382 382 1.00
## 383 383 32.00
## 384 384 35.00
## 385 385 NA
## 386 386 18.00
## 387 387 1.00
## 388 388 36.00
## 389 389 NA
## 390 390 17.00
## 391 391 36.00
## 392 392 21.00
## 393 393 28.00
## 394 394 23.00
## 395 395 24.00
## 396 396 22.00
## 397 397 31.00
## 398 398 46.00
## 399 399 23.00
## 400 400 28.00
## 401 401 39.00
## 402 402 26.00
## 403 403 21.00
## 404 404 28.00
## 405 405 20.00
## 406 406 34.00
## 407 407 51.00
## 408 408 3.00
## 409 409 21.00
## 410 410 NA
## 411 411 NA
## 412 412 NA
## 413 413 33.00
## 414 414 NA
## 415 415 44.00
## 416 416 NA
## 417 417 34.00
## 418 418 18.00
## 419 419 30.00
## 420 420 10.00
## 421 421 NA
## 422 422 21.00
## 423 423 29.00
## 424 424 28.00
## 425 425 18.00
## 426 426 NA
## 427 427 28.00
## 428 428 19.00
## 429 429 NA
## 430 430 32.00
## 431 431 28.00
## 432 432 NA
## 433 433 42.00
## 434 434 17.00
## 435 435 50.00
## 436 436 14.00
## 437 437 21.00
## 438 438 24.00
## 439 439 64.00
## 440 440 31.00
## 441 441 45.00
## 442 442 20.00
## 443 443 25.00
## 444 444 28.00
## 445 445 NA
## 446 446 4.00
## 447 447 13.00
## 448 448 34.00
## 449 449 5.00
## 450 450 52.00
## 451 451 36.00
## 452 452 NA
## 453 453 30.00
## 454 454 49.00
## 455 455 NA
## 456 456 29.00
## 457 457 65.00
## 458 458 NA
## 459 459 50.00
## 460 460 NA
## 461 461 48.00
## 462 462 34.00
## 463 463 47.00
## 464 464 48.00
## 465 465 NA
## 466 466 38.00
## 467 467 NA
## 468 468 56.00
## 469 469 NA
## 470 470 0.75
## 471 471 NA
## 472 472 38.00
## 473 473 33.00
## 474 474 23.00
## 475 475 22.00
## 476 476 NA
## 477 477 34.00
## 478 478 29.00
## 479 479 22.00
## 480 480 2.00
## 481 481 9.00
## 482 482 NA
## 483 483 50.00
## 484 484 63.00
## 485 485 25.00
## 486 486 NA
## 487 487 35.00
## 488 488 58.00
## 489 489 30.00
## 490 490 9.00
## 491 491 NA
## 492 492 21.00
## 493 493 55.00
## 494 494 71.00
## 495 495 21.00
## 496 496 NA
## 497 497 54.00
## 498 498 NA
## 499 499 25.00
## 500 500 24.00
## 501 501 17.00
## 502 502 21.00
## 503 503 NA
## 504 504 37.00
## 505 505 16.00
## 506 506 18.00
## 507 507 33.00
## 508 508 NA
## 509 509 28.00
## 510 510 26.00
## 511 511 29.00
## 512 512 NA
## 513 513 36.00
## 514 514 54.00
## 515 515 24.00
## 516 516 47.00
## 517 517 34.00
## 518 518 NA
## 519 519 36.00
## 520 520 32.00
## 521 521 30.00
## 522 522 22.00
## 523 523 NA
## 524 524 44.00
## 525 525 NA
## 526 526 40.50
## 527 527 50.00
## 528 528 NA
## 529 529 39.00
## 530 530 23.00
## 531 531 2.00
## 532 532 NA
## 533 533 17.00
## 534 534 NA
## 535 535 30.00
## 536 536 7.00
## 537 537 45.00
## 538 538 30.00
## 539 539 NA
## 540 540 22.00
## 541 541 36.00
## 542 542 9.00
## 543 543 11.00
## 544 544 32.00
## 545 545 50.00
## 546 546 64.00
## 547 547 19.00
## 548 548 NA
## 549 549 33.00
## 550 550 8.00
## 551 551 17.00
## 552 552 27.00
## 553 553 NA
## 554 554 22.00
## 555 555 22.00
## 556 556 62.00
## 557 557 48.00
## 558 558 NA
## 559 559 39.00
## 560 560 36.00
## 561 561 NA
## 562 562 40.00
## 563 563 28.00
## 564 564 NA
## 565 565 NA
## 566 566 24.00
## 567 567 19.00
## 568 568 29.00
## 569 569 NA
## 570 570 32.00
## 571 571 62.00
## 572 572 53.00
## 573 573 36.00
## 574 574 NA
## 575 575 16.00
## 576 576 19.00
## 577 577 34.00
## 578 578 39.00
## 579 579 NA
## 580 580 32.00
## 581 581 25.00
## 582 582 39.00
## 583 583 54.00
## 584 584 36.00
## 585 585 NA
## 586 586 18.00
## 587 587 47.00
## 588 588 60.00
## 589 589 22.00
## 590 590 NA
## 591 591 35.00
## 592 592 52.00
## 593 593 47.00
## 594 594 NA
## 595 595 37.00
## 596 596 36.00
## 597 597 NA
## 598 598 49.00
## 599 599 NA
## 600 600 49.00
## 601 601 24.00
## 602 602 NA
## 603 603 NA
## 604 604 44.00
## 605 605 35.00
## 606 606 36.00
## 607 607 30.00
## 608 608 27.00
## 609 609 22.00
## 610 610 40.00
## 611 611 39.00
## 612 612 NA
## 613 613 NA
## 614 614 NA
## 615 615 35.00
## 616 616 24.00
## 617 617 34.00
## 618 618 26.00
## 619 619 4.00
## 620 620 26.00
## 621 621 27.00
## 622 622 42.00
## 623 623 20.00
## 624 624 21.00
## 625 625 21.00
## 626 626 61.00
## 627 627 57.00
## 628 628 21.00
## 629 629 26.00
## 630 630 NA
## 631 631 80.00
## 632 632 51.00
## 633 633 32.00
## 634 634 NA
## 635 635 9.00
## 636 636 28.00
## 637 637 32.00
## 638 638 31.00
## 639 639 41.00
## 640 640 NA
## 641 641 20.00
## 642 642 24.00
## 643 643 2.00
## 644 644 NA
## 645 645 0.75
## 646 646 48.00
## 647 647 19.00
## 648 648 56.00
## 649 649 NA
## 650 650 23.00
## 651 651 NA
## 652 652 18.00
## 653 653 21.00
## 654 654 NA
## 655 655 18.00
## 656 656 24.00
## 657 657 NA
## 658 658 32.00
## 659 659 23.00
## 660 660 58.00
## 661 661 50.00
## 662 662 40.00
## 663 663 47.00
## 664 664 36.00
## 665 665 20.00
## 666 666 32.00
## 667 667 25.00
## 668 668 NA
## 669 669 43.00
## 670 670 NA
## 671 671 40.00
## 672 672 31.00
## 673 673 70.00
## 674 674 31.00
## 675 675 NA
## 676 676 18.00
## 677 677 24.50
## 678 678 18.00
## 679 679 43.00
## 680 680 36.00
## 681 681 NA
## 682 682 27.00
## 683 683 20.00
## 684 684 14.00
## 685 685 60.00
## 686 686 25.00
## 687 687 14.00
## 688 688 19.00
## 689 689 18.00
## 690 690 15.00
## 691 691 31.00
## 692 692 4.00
## 693 693 NA
## 694 694 25.00
## 695 695 60.00
## 696 696 52.00
## 697 697 44.00
## 698 698 NA
## 699 699 49.00
## 700 700 42.00
## 701 701 18.00
## 702 702 35.00
## 703 703 18.00
## 704 704 25.00
## 705 705 26.00
## 706 706 39.00
## 707 707 45.00
## 708 708 42.00
## 709 709 22.00
## 710 710 NA
## 711 711 24.00
## 712 712 NA
## 713 713 48.00
## 714 714 29.00
## 715 715 52.00
## 716 716 19.00
## 717 717 38.00
## 718 718 27.00
## 719 719 NA
## 720 720 33.00
## 721 721 6.00
## 722 722 17.00
## 723 723 34.00
## 724 724 50.00
## 725 725 27.00
## 726 726 20.00
## 727 727 30.00
## 728 728 NA
## 729 729 25.00
## 730 730 25.00
## 731 731 29.00
## 732 732 11.00
## 733 733 NA
## 734 734 23.00
## 735 735 23.00
## 736 736 28.50
## 737 737 48.00
## 738 738 35.00
## 739 739 NA
## 740 740 NA
## 741 741 NA
## 742 742 36.00
## 743 743 21.00
## 744 744 24.00
## 745 745 31.00
## 746 746 70.00
## 747 747 16.00
## 748 748 30.00
## 749 749 19.00
## 750 750 31.00
## 751 751 4.00
## 752 752 6.00
## 753 753 33.00
## 754 754 23.00
## 755 755 48.00
## 756 756 0.67
## 757 757 28.00
## 758 758 18.00
## 759 759 34.00
## 760 760 33.00
## 761 761 NA
## 762 762 41.00
## 763 763 20.00
## 764 764 36.00
## 765 765 16.00
## 766 766 51.00
## 767 767 NA
## 768 768 30.50
## 769 769 NA
## 770 770 32.00
## 771 771 24.00
## 772 772 48.00
## 773 773 57.00
## 774 774 NA
## 775 775 54.00
## 776 776 18.00
## 777 777 NA
## 778 778 5.00
## 779 779 NA
## 780 780 43.00
## 781 781 13.00
## 782 782 17.00
## 783 783 29.00
## 784 784 NA
## 785 785 25.00
## 786 786 25.00
## 787 787 18.00
## 788 788 8.00
## 789 789 1.00
## 790 790 46.00
## 791 791 NA
## 792 792 16.00
## 793 793 NA
## 794 794 NA
## 795 795 25.00
## 796 796 39.00
## 797 797 49.00
## 798 798 31.00
## 799 799 30.00
## 800 800 30.00
## 801 801 34.00
## 802 802 31.00
## 803 803 11.00
## 804 804 0.42
## 805 805 27.00
## 806 806 31.00
## 807 807 39.00
## 808 808 18.00
## 809 809 39.00
## 810 810 33.00
## 811 811 26.00
## 812 812 39.00
## 813 813 35.00
## 814 814 6.00
## 815 815 30.50
## 816 816 NA
## 817 817 23.00
## 818 818 31.00
## 819 819 43.00
## 820 820 10.00
## 821 821 52.00
## 822 822 27.00
## 823 823 38.00
## 824 824 27.00
## 825 825 2.00
## 826 826 NA
## 827 827 NA
## 828 828 1.00
## 829 829 NA
## 830 830 62.00
## 831 831 15.00
## 832 832 0.83
## 833 833 NA
## 834 834 23.00
## 835 835 18.00
## 836 836 39.00
## 837 837 21.00
## 838 838 NA
## 839 839 32.00
## 840 840 NA
## 841 841 20.00
## 842 842 16.00
## 843 843 30.00
## 844 844 34.50
## 845 845 17.00
## 846 846 42.00
## 847 847 NA
## 848 848 35.00
## 849 849 28.00
## 850 850 NA
## 851 851 4.00
## 852 852 74.00
## 853 853 9.00
## 854 854 16.00
## 855 855 44.00
## 856 856 18.00
## 857 857 45.00
## 858 858 51.00
## 859 859 24.00
## 860 860 NA
## 861 861 41.00
## 862 862 21.00
## 863 863 48.00
## 864 864 NA
## 865 865 24.00
## 866 866 42.00
## 867 867 27.00
## 868 868 31.00
## 869 869 NA
## 870 870 4.00
## 871 871 26.00
## 872 872 47.00
## 873 873 33.00
## 874 874 47.00
## 875 875 28.00
## 876 876 15.00
## 877 877 20.00
## 878 878 19.00
## 879 879 NA
## 880 880 56.00
## 881 881 25.00
## 882 882 33.00
## 883 883 22.00
## 884 884 28.00
## 885 885 25.00
## 886 886 39.00
## 887 887 27.00
## 888 888 19.00
## 889 889 NA
## 890 890 26.00
## 891 891 32.00
Subset observations (Rows)
filter(titanic, Age < 18)
## PassengerId Survived Pclass
## 1 8 0 3
## 2 10 1 2
## 3 11 1 3
## 4 15 0 3
## 5 17 0 3
## 6 23 1 3
## 7 25 0 3
## 8 40 1 3
## 9 44 1 2
## 10 51 0 3
## 11 59 1 2
## 12 60 0 3
## 13 64 0 3
## 14 69 1 3
## 15 72 0 3
## 16 79 1 2
## 17 85 1 2
## 18 87 0 3
## 19 112 0 3
## 20 115 0 3
## 21 120 0 3
## 22 126 1 3
## 23 139 0 3
## 24 148 0 3
## 25 157 1 3
## 26 164 0 3
## 27 165 0 3
## 28 166 1 3
## 29 172 0 3
## 30 173 1 3
## 31 183 0 3
## 32 184 1 2
## 33 185 1 3
## 34 194 1 2
## 35 206 0 3
## 36 209 1 3
## 37 221 1 3
## 38 234 1 3
## 39 238 1 2
## 40 262 1 3
## 41 267 0 3
## 42 279 0 3
## 43 283 0 3
## 44 298 0 1
## 45 306 1 1
## 46 308 1 1
## 47 330 1 1
## 48 334 0 3
## 49 341 1 2
## 50 349 1 3
## 51 353 0 3
## 52 375 0 3
## 53 382 1 3
## 54 387 0 3
## 55 390 1 2
## 56 408 1 2
## 57 420 0 3
## 58 434 0 3
## 59 436 1 1
## 60 446 1 1
## 61 447 1 2
## 62 449 1 3
## 63 470 1 3
## 64 480 1 3
## 65 481 0 3
## 66 490 1 3
## 67 501 0 3
## 68 505 1 1
## 69 531 1 2
## 70 533 0 3
## 71 536 1 2
## 72 542 0 3
## 73 543 0 3
## 74 550 1 2
## 75 551 1 1
## 76 575 0 3
## 77 619 1 2
## 78 635 0 3
## 79 643 0 3
## 80 645 1 3
## 81 684 0 3
## 82 687 0 3
## 83 690 1 1
## 84 692 1 3
## 85 721 1 2
## 86 722 0 3
## 87 732 0 3
## 88 747 0 3
## 89 751 1 2
## 90 752 1 3
## 91 756 1 2
## 92 765 0 3
## 93 778 1 3
## 94 781 1 3
## 95 782 1 1
## 96 788 0 3
## 97 789 1 3
## 98 792 0 2
## 99 803 1 1
## 100 804 1 3
## 101 814 0 3
## 102 820 0 3
## 103 825 0 3
## 104 828 1 2
## 105 831 1 3
## 106 832 1 2
## 107 842 0 2
## 108 845 0 3
## 109 851 0 3
## 110 853 0 3
## 111 854 1 1
## 112 870 1 3
## 113 876 1 3
## Name
## 1 Palsson, Master. Gosta Leonard
## 2 Nasser, Mrs. Nicholas (Adele Achem)
## 3 Sandstrom, Miss. Marguerite Rut
## 4 Vestrom, Miss. Hulda Amanda Adolfina
## 5 Rice, Master. Eugene
## 6 McGowan, Miss. Anna "Annie"
## 7 Palsson, Miss. Torborg Danira
## 8 Nicola-Yarred, Miss. Jamila
## 9 Laroche, Miss. Simonne Marie Anne Andree
## 10 Panula, Master. Juha Niilo
## 11 West, Miss. Constance Mirium
## 12 Goodwin, Master. William Frederick
## 13 Skoog, Master. Harald
## 14 Andersson, Miss. Erna Alexandra
## 15 Goodwin, Miss. Lillian Amy
## 16 Caldwell, Master. Alden Gates
## 17 Ilett, Miss. Bertha
## 18 Ford, Mr. William Neal
## 19 Zabour, Miss. Hileni
## 20 Attalah, Miss. Malake
## 21 Andersson, Miss. Ellis Anna Maria
## 22 Nicola-Yarred, Master. Elias
## 23 Osen, Mr. Olaf Elon
## 24 Ford, Miss. Robina Maggie "Ruby"
## 25 Gilnagh, Miss. Katherine "Katie"
## 26 Calic, Mr. Jovo
## 27 Panula, Master. Eino Viljami
## 28 Goldsmith, Master. Frank John William "Frankie"
## 29 Rice, Master. Arthur
## 30 Johnson, Miss. Eleanor Ileen
## 31 Asplund, Master. Clarence Gustaf Hugo
## 32 Becker, Master. Richard F
## 33 Kink-Heilmann, Miss. Luise Gretchen
## 34 Navratil, Master. Michel M
## 35 Strom, Miss. Telma Matilda
## 36 Carr, Miss. Helen "Ellen"
## 37 Sunderland, Mr. Victor Francis
## 38 Asplund, Miss. Lillian Gertrud
## 39 Collyer, Miss. Marjorie "Lottie"
## 40 Asplund, Master. Edvin Rojj Felix
## 41 Panula, Mr. Ernesti Arvid
## 42 Rice, Master. Eric
## 43 de Pelsmaeker, Mr. Alfons
## 44 Allison, Miss. Helen Loraine
## 45 Allison, Master. Hudson Trevor
## 46 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)
## 47 Hippach, Miss. Jean Gertrude
## 48 Vander Planke, Mr. Leo Edmondus
## 49 Navratil, Master. Edmond Roger
## 50 Coutts, Master. William Loch "William"
## 51 Elias, Mr. Tannous
## 52 Palsson, Miss. Stina Viola
## 53 Nakid, Miss. Maria ("Mary")
## 54 Goodwin, Master. Sidney Leonard
## 55 Lehmann, Miss. Bertha
## 56 Richards, Master. William Rowe
## 57 Van Impe, Miss. Catharina
## 58 Kallio, Mr. Nikolai Erland
## 59 Carter, Miss. Lucile Polk
## 60 Dodge, Master. Washington
## 61 Mellinger, Miss. Madeleine Violet
## 62 Baclini, Miss. Marie Catherine
## 63 Baclini, Miss. Helene Barbara
## 64 Hirvonen, Miss. Hildur E
## 65 Goodwin, Master. Harold Victor
## 66 Coutts, Master. Eden Leslie "Neville"
## 67 Calic, Mr. Petar
## 68 Maioni, Miss. Roberta
## 69 Quick, Miss. Phyllis May
## 70 Elias, Mr. Joseph Jr
## 71 Hart, Miss. Eva Miriam
## 72 Andersson, Miss. Ingeborg Constanzia
## 73 Andersson, Miss. Sigrid Elisabeth
## 74 Davies, Master. John Morgan Jr
## 75 Thayer, Mr. John Borland Jr
## 76 Rush, Mr. Alfred George John
## 77 Becker, Miss. Marion Louise
## 78 Skoog, Miss. Mabel
## 79 Skoog, Miss. Margit Elizabeth
## 80 Baclini, Miss. Eugenie
## 81 Goodwin, Mr. Charles Edward
## 82 Panula, Mr. Jaako Arnold
## 83 Madill, Miss. Georgette Alexandra
## 84 Karun, Miss. Manca
## 85 Harper, Miss. Annie Jessie "Nina"
## 86 Jensen, Mr. Svend Lauritz
## 87 Hassan, Mr. Houssein G N
## 88 Abbott, Mr. Rossmore Edward
## 89 Wells, Miss. Joan
## 90 Moor, Master. Meier
## 91 Hamalainen, Master. Viljo
## 92 Eklund, Mr. Hans Linus
## 93 Emanuel, Miss. Virginia Ethel
## 94 Ayoub, Miss. Banoura
## 95 Dick, Mrs. Albert Adrian (Vera Gillespie)
## 96 Rice, Master. George Hugh
## 97 Dean, Master. Bertram Vere
## 98 Gaskell, Mr. Alfred
## 99 Carter, Master. William Thornton II
## 100 Thomas, Master. Assad Alexander
## 101 Andersson, Miss. Ebba Iris Alfrida
## 102 Skoog, Master. Karl Thorsten
## 103 Panula, Master. Urho Abraham
## 104 Mallet, Master. Andre
## 105 Yasbeck, Mrs. Antoni (Selini Alexander)
## 106 Richards, Master. George Sibley
## 107 Mudd, Mr. Thomas Charles
## 108 Culumovic, Mr. Jeso
## 109 Andersson, Master. Sigvard Harald Elias
## 110 Boulos, Miss. Nourelain
## 111 Lines, Miss. Mary Conover
## 112 Johnson, Master. Harold Theodor
## 113 Najib, Miss. Adele Kiamie "Jane"
## Sex Age SibSp Parch Ticket Fare Cabin Embarked
## 1 male 2.00 3 1 349909 21.0750 S
## 2 female 14.00 1 0 237736 30.0708 C
## 3 female 4.00 1 1 PP 9549 16.7000 G6 S
## 4 female 14.00 0 0 350406 7.8542 S
## 5 male 2.00 4 1 382652 29.1250 Q
## 6 female 15.00 0 0 330923 8.0292 Q
## 7 female 8.00 3 1 349909 21.0750 S
## 8 female 14.00 1 0 2651 11.2417 C
## 9 female 3.00 1 2 SC/Paris 2123 41.5792 C
## 10 male 7.00 4 1 3101295 39.6875 S
## 11 female 5.00 1 2 C.A. 34651 27.7500 S
## 12 male 11.00 5 2 CA 2144 46.9000 S
## 13 male 4.00 3 2 347088 27.9000 S
## 14 female 17.00 4 2 3101281 7.9250 S
## 15 female 16.00 5 2 CA 2144 46.9000 S
## 16 male 0.83 0 2 248738 29.0000 S
## 17 female 17.00 0 0 SO/C 14885 10.5000 S
## 18 male 16.00 1 3 W./C. 6608 34.3750 S
## 19 female 14.50 1 0 2665 14.4542 C
## 20 female 17.00 0 0 2627 14.4583 C
## 21 female 2.00 4 2 347082 31.2750 S
## 22 male 12.00 1 0 2651 11.2417 C
## 23 male 16.00 0 0 7534 9.2167 S
## 24 female 9.00 2 2 W./C. 6608 34.3750 S
## 25 female 16.00 0 0 35851 7.7333 Q
## 26 male 17.00 0 0 315093 8.6625 S
## 27 male 1.00 4 1 3101295 39.6875 S
## 28 male 9.00 0 2 363291 20.5250 S
## 29 male 4.00 4 1 382652 29.1250 Q
## 30 female 1.00 1 1 347742 11.1333 S
## 31 male 9.00 4 2 347077 31.3875 S
## 32 male 1.00 2 1 230136 39.0000 F4 S
## 33 female 4.00 0 2 315153 22.0250 S
## 34 male 3.00 1 1 230080 26.0000 F2 S
## 35 female 2.00 0 1 347054 10.4625 G6 S
## 36 female 16.00 0 0 367231 7.7500 Q
## 37 male 16.00 0 0 SOTON/OQ 392089 8.0500 S
## 38 female 5.00 4 2 347077 31.3875 S
## 39 female 8.00 0 2 C.A. 31921 26.2500 S
## 40 male 3.00 4 2 347077 31.3875 S
## 41 male 16.00 4 1 3101295 39.6875 S
## 42 male 7.00 4 1 382652 29.1250 Q
## 43 male 16.00 0 0 345778 9.5000 S
## 44 female 2.00 1 2 113781 151.5500 C22 C26 S
## 45 male 0.92 1 2 113781 151.5500 C22 C26 S
## 46 female 17.00 1 0 PC 17758 108.9000 C65 C
## 47 female 16.00 0 1 111361 57.9792 B18 C
## 48 male 16.00 2 0 345764 18.0000 S
## 49 male 2.00 1 1 230080 26.0000 F2 S
## 50 male 3.00 1 1 C.A. 37671 15.9000 S
## 51 male 15.00 1 1 2695 7.2292 C
## 52 female 3.00 3 1 349909 21.0750 S
## 53 female 1.00 0 2 2653 15.7417 C
## 54 male 1.00 5 2 CA 2144 46.9000 S
## 55 female 17.00 0 0 SC 1748 12.0000 C
## 56 male 3.00 1 1 29106 18.7500 S
## 57 female 10.00 0 2 345773 24.1500 S
## 58 male 17.00 0 0 STON/O 2. 3101274 7.1250 S
## 59 female 14.00 1 2 113760 120.0000 B96 B98 S
## 60 male 4.00 0 2 33638 81.8583 A34 S
## 61 female 13.00 0 1 250644 19.5000 S
## 62 female 5.00 2 1 2666 19.2583 C
## 63 female 0.75 2 1 2666 19.2583 C
## 64 female 2.00 0 1 3101298 12.2875 S
## 65 male 9.00 5 2 CA 2144 46.9000 S
## 66 male 9.00 1 1 C.A. 37671 15.9000 S
## 67 male 17.00 0 0 315086 8.6625 S
## 68 female 16.00 0 0 110152 86.5000 B79 S
## 69 female 2.00 1 1 26360 26.0000 S
## 70 male 17.00 1 1 2690 7.2292 C
## 71 female 7.00 0 2 F.C.C. 13529 26.2500 S
## 72 female 9.00 4 2 347082 31.2750 S
## 73 female 11.00 4 2 347082 31.2750 S
## 74 male 8.00 1 1 C.A. 33112 36.7500 S
## 75 male 17.00 0 2 17421 110.8833 C70 C
## 76 male 16.00 0 0 A/4. 20589 8.0500 S
## 77 female 4.00 2 1 230136 39.0000 F4 S
## 78 female 9.00 3 2 347088 27.9000 S
## 79 female 2.00 3 2 347088 27.9000 S
## 80 female 0.75 2 1 2666 19.2583 C
## 81 male 14.00 5 2 CA 2144 46.9000 S
## 82 male 14.00 4 1 3101295 39.6875 S
## 83 female 15.00 0 1 24160 211.3375 B5 S
## 84 female 4.00 0 1 349256 13.4167 C
## 85 female 6.00 0 1 248727 33.0000 S
## 86 male 17.00 1 0 350048 7.0542 S
## 87 male 11.00 0 0 2699 18.7875 C
## 88 male 16.00 1 1 C.A. 2673 20.2500 S
## 89 female 4.00 1 1 29103 23.0000 S
## 90 male 6.00 0 1 392096 12.4750 E121 S
## 91 male 0.67 1 1 250649 14.5000 S
## 92 male 16.00 0 0 347074 7.7750 S
## 93 female 5.00 0 0 364516 12.4750 S
## 94 female 13.00 0 0 2687 7.2292 C
## 95 female 17.00 1 0 17474 57.0000 B20 S
## 96 male 8.00 4 1 382652 29.1250 Q
## 97 male 1.00 1 2 C.A. 2315 20.5750 S
## 98 male 16.00 0 0 239865 26.0000 S
## 99 male 11.00 1 2 113760 120.0000 B96 B98 S
## 100 male 0.42 0 1 2625 8.5167 C
## 101 female 6.00 4 2 347082 31.2750 S
## 102 male 10.00 3 2 347088 27.9000 S
## 103 male 2.00 4 1 3101295 39.6875 S
## 104 male 1.00 0 2 S.C./PARIS 2079 37.0042 C
## 105 female 15.00 1 0 2659 14.4542 C
## 106 male 0.83 1 1 29106 18.7500 S
## 107 male 16.00 0 0 S.O./P.P. 3 10.5000 S
## 108 male 17.00 0 0 315090 8.6625 S
## 109 male 4.00 4 2 347082 31.2750 S
## 110 female 9.00 1 1 2678 15.2458 C
## 111 female 16.00 0 1 PC 17592 39.4000 D28 S
## 112 male 4.00 1 1 347742 11.1333 S
## 113 female 15.00 0 0 2667 7.2250 C
The pipe function
The pipe function %>% helps to create a series of function to perform the data wrangling and makes the code more readable. For example if we want to select some columns and filter some rows we can write…
filter(select(titanic, PassengerId, Age), Age <18)
## PassengerId Age
## 1 8 2.00
## 2 10 14.00
## 3 11 4.00
## 4 15 14.00
## 5 17 2.00
## 6 23 15.00
## 7 25 8.00
## 8 40 14.00
## 9 44 3.00
## 10 51 7.00
## 11 59 5.00
## 12 60 11.00
## 13 64 4.00
## 14 69 17.00
## 15 72 16.00
## 16 79 0.83
## 17 85 17.00
## 18 87 16.00
## 19 112 14.50
## 20 115 17.00
## 21 120 2.00
## 22 126 12.00
## 23 139 16.00
## 24 148 9.00
## 25 157 16.00
## 26 164 17.00
## 27 165 1.00
## 28 166 9.00
## 29 172 4.00
## 30 173 1.00
## 31 183 9.00
## 32 184 1.00
## 33 185 4.00
## 34 194 3.00
## 35 206 2.00
## 36 209 16.00
## 37 221 16.00
## 38 234 5.00
## 39 238 8.00
## 40 262 3.00
## 41 267 16.00
## 42 279 7.00
## 43 283 16.00
## 44 298 2.00
## 45 306 0.92
## 46 308 17.00
## 47 330 16.00
## 48 334 16.00
## 49 341 2.00
## 50 349 3.00
## 51 353 15.00
## 52 375 3.00
## 53 382 1.00
## 54 387 1.00
## 55 390 17.00
## 56 408 3.00
## 57 420 10.00
## 58 434 17.00
## 59 436 14.00
## 60 446 4.00
## 61 447 13.00
## 62 449 5.00
## 63 470 0.75
## 64 480 2.00
## 65 481 9.00
## 66 490 9.00
## 67 501 17.00
## 68 505 16.00
## 69 531 2.00
## 70 533 17.00
## 71 536 7.00
## 72 542 9.00
## 73 543 11.00
## 74 550 8.00
## 75 551 17.00
## 76 575 16.00
## 77 619 4.00
## 78 635 9.00
## 79 643 2.00
## 80 645 0.75
## 81 684 14.00
## 82 687 14.00
## 83 690 15.00
## 84 692 4.00
## 85 721 6.00
## 86 722 17.00
## 87 732 11.00
## 88 747 16.00
## 89 751 4.00
## 90 752 6.00
## 91 756 0.67
## 92 765 16.00
## 93 778 5.00
## 94 781 13.00
## 95 782 17.00
## 96 788 8.00
## 97 789 1.00
## 98 792 16.00
## 99 803 11.00
## 100 804 0.42
## 101 814 6.00
## 102 820 10.00
## 103 825 2.00
## 104 828 1.00
## 105 831 15.00
## 106 832 0.83
## 107 842 16.00
## 108 845 17.00
## 109 851 4.00
## 110 853 9.00
## 111 854 16.00
## 112 870 4.00
## 113 876 15.00
As you can see the select function is nested in the filter function to keep the order we decided. Using pipes the code could be writed as:
titanic %>%
select(PassengerId, Age) %>%
filter(Age < 18)
## PassengerId Age
## 1 8 2.00
## 2 10 14.00
## 3 11 4.00
## 4 15 14.00
## 5 17 2.00
## 6 23 15.00
## 7 25 8.00
## 8 40 14.00
## 9 44 3.00
## 10 51 7.00
## 11 59 5.00
## 12 60 11.00
## 13 64 4.00
## 14 69 17.00
## 15 72 16.00
## 16 79 0.83
## 17 85 17.00
## 18 87 16.00
## 19 112 14.50
## 20 115 17.00
## 21 120 2.00
## 22 126 12.00
## 23 139 16.00
## 24 148 9.00
## 25 157 16.00
## 26 164 17.00
## 27 165 1.00
## 28 166 9.00
## 29 172 4.00
## 30 173 1.00
## 31 183 9.00
## 32 184 1.00
## 33 185 4.00
## 34 194 3.00
## 35 206 2.00
## 36 209 16.00
## 37 221 16.00
## 38 234 5.00
## 39 238 8.00
## 40 262 3.00
## 41 267 16.00
## 42 279 7.00
## 43 283 16.00
## 44 298 2.00
## 45 306 0.92
## 46 308 17.00
## 47 330 16.00
## 48 334 16.00
## 49 341 2.00
## 50 349 3.00
## 51 353 15.00
## 52 375 3.00
## 53 382 1.00
## 54 387 1.00
## 55 390 17.00
## 56 408 3.00
## 57 420 10.00
## 58 434 17.00
## 59 436 14.00
## 60 446 4.00
## 61 447 13.00
## 62 449 5.00
## 63 470 0.75
## 64 480 2.00
## 65 481 9.00
## 66 490 9.00
## 67 501 17.00
## 68 505 16.00
## 69 531 2.00
## 70 533 17.00
## 71 536 7.00
## 72 542 9.00
## 73 543 11.00
## 74 550 8.00
## 75 551 17.00
## 76 575 16.00
## 77 619 4.00
## 78 635 9.00
## 79 643 2.00
## 80 645 0.75
## 81 684 14.00
## 82 687 14.00
## 83 690 15.00
## 84 692 4.00
## 85 721 6.00
## 86 722 17.00
## 87 732 11.00
## 88 747 16.00
## 89 751 4.00
## 90 752 6.00
## 91 756 0.67
## 92 765 16.00
## 93 778 5.00
## 94 781 13.00
## 95 782 17.00
## 96 788 8.00
## 97 789 1.00
## 98 792 16.00
## 99 803 11.00
## 100 804 0.42
## 101 814 6.00
## 102 820 10.00
## 103 825 2.00
## 104 828 1.00
## 105 831 15.00
## 106 832 0.83
## 107 842 16.00
## 108 845 17.00
## 109 851 4.00
## 110 853 9.00
## 111 854 16.00
## 112 870 4.00
## 113 876 15.00
You can think a pipe as the command “then”. The previous algorithm can be interpreted as: - Load data then - Select Id and Age then - Filter Age less then 18
Make new variables
titanic %>%
select(PassengerId, Age) %>%
filter(!is.na(Age)) %>%
mutate(Age_Bracket = ifelse(Age < 18, 'Minor','Major'))
## PassengerId Age Age_Bracket
## 1 1 22.00 Major
## 2 2 38.00 Major
## 3 3 26.00 Major
## 4 4 35.00 Major
## 5 5 35.00 Major
## 6 7 54.00 Major
## 7 8 2.00 Minor
## 8 9 27.00 Major
## 9 10 14.00 Minor
## 10 11 4.00 Minor
## 11 12 58.00 Major
## 12 13 20.00 Major
## 13 14 39.00 Major
## 14 15 14.00 Minor
## 15 16 55.00 Major
## 16 17 2.00 Minor
## 17 19 31.00 Major
## 18 21 35.00 Major
## 19 22 34.00 Major
## 20 23 15.00 Minor
## 21 24 28.00 Major
## 22 25 8.00 Minor
## 23 26 38.00 Major
## 24 28 19.00 Major
## 25 31 40.00 Major
## 26 34 66.00 Major
## 27 35 28.00 Major
## 28 36 42.00 Major
## 29 38 21.00 Major
## 30 39 18.00 Major
## 31 40 14.00 Minor
## 32 41 40.00 Major
## 33 42 27.00 Major
## 34 44 3.00 Minor
## 35 45 19.00 Major
## 36 50 18.00 Major
## 37 51 7.00 Minor
## 38 52 21.00 Major
## 39 53 49.00 Major
## 40 54 29.00 Major
## 41 55 65.00 Major
## 42 57 21.00 Major
## 43 58 28.50 Major
## 44 59 5.00 Minor
## 45 60 11.00 Minor
## 46 61 22.00 Major
## 47 62 38.00 Major
## 48 63 45.00 Major
## 49 64 4.00 Minor
## 50 67 29.00 Major
## 51 68 19.00 Major
## 52 69 17.00 Minor
## 53 70 26.00 Major
## 54 71 32.00 Major
## 55 72 16.00 Minor
## 56 73 21.00 Major
## 57 74 26.00 Major
## 58 75 32.00 Major
## 59 76 25.00 Major
## 60 79 0.83 Minor
## 61 80 30.00 Major
## 62 81 22.00 Major
## 63 82 29.00 Major
## 64 84 28.00 Major
## 65 85 17.00 Minor
## 66 86 33.00 Major
## 67 87 16.00 Minor
## 68 89 23.00 Major
## 69 90 24.00 Major
## 70 91 29.00 Major
## 71 92 20.00 Major
## 72 93 46.00 Major
## 73 94 26.00 Major
## 74 95 59.00 Major
## 75 97 71.00 Major
## 76 98 23.00 Major
## 77 99 34.00 Major
## 78 100 34.00 Major
## 79 101 28.00 Major
## 80 103 21.00 Major
## 81 104 33.00 Major
## 82 105 37.00 Major
## 83 106 28.00 Major
## 84 107 21.00 Major
## 85 109 38.00 Major
## 86 111 47.00 Major
## 87 112 14.50 Minor
## 88 113 22.00 Major
## 89 114 20.00 Major
## 90 115 17.00 Minor
## 91 116 21.00 Major
## 92 117 70.50 Major
## 93 118 29.00 Major
## 94 119 24.00 Major
## 95 120 2.00 Minor
## 96 121 21.00 Major
## 97 123 32.50 Major
## 98 124 32.50 Major
## 99 125 54.00 Major
## 100 126 12.00 Minor
## 101 128 24.00 Major
## 102 130 45.00 Major
## 103 131 33.00 Major
## 104 132 20.00 Major
## 105 133 47.00 Major
## 106 134 29.00 Major
## 107 135 25.00 Major
## 108 136 23.00 Major
## 109 137 19.00 Major
## 110 138 37.00 Major
## 111 139 16.00 Minor
## 112 140 24.00 Major
## 113 142 22.00 Major
## 114 143 24.00 Major
## 115 144 19.00 Major
## 116 145 18.00 Major
## 117 146 19.00 Major
## 118 147 27.00 Major
## 119 148 9.00 Minor
## 120 149 36.50 Major
## 121 150 42.00 Major
## 122 151 51.00 Major
## 123 152 22.00 Major
## 124 153 55.50 Major
## 125 154 40.50 Major
## 126 156 51.00 Major
## 127 157 16.00 Minor
## 128 158 30.00 Major
## 129 161 44.00 Major
## 130 162 40.00 Major
## 131 163 26.00 Major
## 132 164 17.00 Minor
## 133 165 1.00 Minor
## 134 166 9.00 Minor
## 135 168 45.00 Major
## 136 170 28.00 Major
## 137 171 61.00 Major
## 138 172 4.00 Minor
## 139 173 1.00 Minor
## 140 174 21.00 Major
## 141 175 56.00 Major
## 142 176 18.00 Major
## 143 178 50.00 Major
## 144 179 30.00 Major
## 145 180 36.00 Major
## 146 183 9.00 Minor
## 147 184 1.00 Minor
## 148 185 4.00 Minor
## 149 188 45.00 Major
## 150 189 40.00 Major
## 151 190 36.00 Major
## 152 191 32.00 Major
## 153 192 19.00 Major
## 154 193 19.00 Major
## 155 194 3.00 Minor
## 156 195 44.00 Major
## 157 196 58.00 Major
## 158 198 42.00 Major
## 159 200 24.00 Major
## 160 201 28.00 Major
## 161 203 34.00 Major
## 162 204 45.50 Major
## 163 205 18.00 Major
## 164 206 2.00 Minor
## 165 207 32.00 Major
## 166 208 26.00 Major
## 167 209 16.00 Minor
## 168 210 40.00 Major
## 169 211 24.00 Major
## 170 212 35.00 Major
## 171 213 22.00 Major
## 172 214 30.00 Major
## 173 216 31.00 Major
## 174 217 27.00 Major
## 175 218 42.00 Major
## 176 219 32.00 Major
## 177 220 30.00 Major
## 178 221 16.00 Minor
## 179 222 27.00 Major
## 180 223 51.00 Major
## 181 225 38.00 Major
## 182 226 22.00 Major
## 183 227 19.00 Major
## 184 228 20.50 Major
## 185 229 18.00 Major
## 186 231 35.00 Major
## 187 232 29.00 Major
## 188 233 59.00 Major
## 189 234 5.00 Minor
## 190 235 24.00 Major
## 191 237 44.00 Major
## 192 238 8.00 Minor
## 193 239 19.00 Major
## 194 240 33.00 Major
## 195 243 29.00 Major
## 196 244 22.00 Major
## 197 245 30.00 Major
## 198 246 44.00 Major
## 199 247 25.00 Major
## 200 248 24.00 Major
## 201 249 37.00 Major
## 202 250 54.00 Major
## 203 252 29.00 Major
## 204 253 62.00 Major
## 205 254 30.00 Major
## 206 255 41.00 Major
## 207 256 29.00 Major
## 208 258 30.00 Major
## 209 259 35.00 Major
## 210 260 50.00 Major
## 211 262 3.00 Minor
## 212 263 52.00 Major
## 213 264 40.00 Major
## 214 266 36.00 Major
## 215 267 16.00 Minor
## 216 268 25.00 Major
## 217 269 58.00 Major
## 218 270 35.00 Major
## 219 272 25.00 Major
## 220 273 41.00 Major
## 221 274 37.00 Major
## 222 276 63.00 Major
## 223 277 45.00 Major
## 224 279 7.00 Minor
## 225 280 35.00 Major
## 226 281 65.00 Major
## 227 282 28.00 Major
## 228 283 16.00 Minor
## 229 284 19.00 Major
## 230 286 33.00 Major
## 231 287 30.00 Major
## 232 288 22.00 Major
## 233 289 42.00 Major
## 234 290 22.00 Major
## 235 291 26.00 Major
## 236 292 19.00 Major
## 237 293 36.00 Major
## 238 294 24.00 Major
## 239 295 24.00 Major
## 240 297 23.50 Major
## 241 298 2.00 Minor
## 242 300 50.00 Major
## 243 303 19.00 Major
## 244 306 0.92 Minor
## 245 308 17.00 Minor
## 246 309 30.00 Major
## 247 310 30.00 Major
## 248 311 24.00 Major
## 249 312 18.00 Major
## 250 313 26.00 Major
## 251 314 28.00 Major
## 252 315 43.00 Major
## 253 316 26.00 Major
## 254 317 24.00 Major
## 255 318 54.00 Major
## 256 319 31.00 Major
## 257 320 40.00 Major
## 258 321 22.00 Major
## 259 322 27.00 Major
## 260 323 30.00 Major
## 261 324 22.00 Major
## 262 326 36.00 Major
## 263 327 61.00 Major
## 264 328 36.00 Major
## 265 329 31.00 Major
## 266 330 16.00 Minor
## 267 332 45.50 Major
## 268 333 38.00 Major
## 269 334 16.00 Minor
## 270 337 29.00 Major
## 271 338 41.00 Major
## 272 339 45.00 Major
## 273 340 45.00 Major
## 274 341 2.00 Minor
## 275 342 24.00 Major
## 276 343 28.00 Major
## 277 344 25.00 Major
## 278 345 36.00 Major
## 279 346 24.00 Major
## 280 347 40.00 Major
## 281 349 3.00 Minor
## 282 350 42.00 Major
## 283 351 23.00 Major
## 284 353 15.00 Minor
## 285 354 25.00 Major
## 286 356 28.00 Major
## 287 357 22.00 Major
## 288 358 38.00 Major
## 289 361 40.00 Major
## 290 362 29.00 Major
## 291 363 45.00 Major
## 292 364 35.00 Major
## 293 366 30.00 Major
## 294 367 60.00 Major
## 295 370 24.00 Major
## 296 371 25.00 Major
## 297 372 18.00 Major
## 298 373 19.00 Major
## 299 374 22.00 Major
## 300 375 3.00 Minor
## 301 377 22.00 Major
## 302 378 27.00 Major
## 303 379 20.00 Major
## 304 380 19.00 Major
## 305 381 42.00 Major
## 306 382 1.00 Minor
## 307 383 32.00 Major
## 308 384 35.00 Major
## 309 386 18.00 Major
## 310 387 1.00 Minor
## 311 388 36.00 Major
## 312 390 17.00 Minor
## 313 391 36.00 Major
## 314 392 21.00 Major
## 315 393 28.00 Major
## 316 394 23.00 Major
## 317 395 24.00 Major
## 318 396 22.00 Major
## 319 397 31.00 Major
## 320 398 46.00 Major
## 321 399 23.00 Major
## 322 400 28.00 Major
## 323 401 39.00 Major
## 324 402 26.00 Major
## 325 403 21.00 Major
## 326 404 28.00 Major
## 327 405 20.00 Major
## 328 406 34.00 Major
## 329 407 51.00 Major
## 330 408 3.00 Minor
## 331 409 21.00 Major
## 332 413 33.00 Major
## 333 415 44.00 Major
## 334 417 34.00 Major
## 335 418 18.00 Major
## 336 419 30.00 Major
## 337 420 10.00 Minor
## 338 422 21.00 Major
## 339 423 29.00 Major
## 340 424 28.00 Major
## 341 425 18.00 Major
## 342 427 28.00 Major
## 343 428 19.00 Major
## 344 430 32.00 Major
## 345 431 28.00 Major
## 346 433 42.00 Major
## 347 434 17.00 Minor
## 348 435 50.00 Major
## 349 436 14.00 Minor
## 350 437 21.00 Major
## 351 438 24.00 Major
## 352 439 64.00 Major
## 353 440 31.00 Major
## 354 441 45.00 Major
## 355 442 20.00 Major
## 356 443 25.00 Major
## 357 444 28.00 Major
## 358 446 4.00 Minor
## 359 447 13.00 Minor
## 360 448 34.00 Major
## 361 449 5.00 Minor
## 362 450 52.00 Major
## 363 451 36.00 Major
## 364 453 30.00 Major
## 365 454 49.00 Major
## 366 456 29.00 Major
## 367 457 65.00 Major
## 368 459 50.00 Major
## 369 461 48.00 Major
## 370 462 34.00 Major
## 371 463 47.00 Major
## 372 464 48.00 Major
## 373 466 38.00 Major
## 374 468 56.00 Major
## 375 470 0.75 Minor
## 376 472 38.00 Major
## 377 473 33.00 Major
## 378 474 23.00 Major
## 379 475 22.00 Major
## 380 477 34.00 Major
## 381 478 29.00 Major
## 382 479 22.00 Major
## 383 480 2.00 Minor
## 384 481 9.00 Minor
## 385 483 50.00 Major
## 386 484 63.00 Major
## 387 485 25.00 Major
## 388 487 35.00 Major
## 389 488 58.00 Major
## 390 489 30.00 Major
## 391 490 9.00 Minor
## 392 492 21.00 Major
## 393 493 55.00 Major
## 394 494 71.00 Major
## 395 495 21.00 Major
## 396 497 54.00 Major
## 397 499 25.00 Major
## 398 500 24.00 Major
## 399 501 17.00 Minor
## 400 502 21.00 Major
## 401 504 37.00 Major
## 402 505 16.00 Minor
## 403 506 18.00 Major
## 404 507 33.00 Major
## 405 509 28.00 Major
## 406 510 26.00 Major
## 407 511 29.00 Major
## 408 513 36.00 Major
## 409 514 54.00 Major
## 410 515 24.00 Major
## 411 516 47.00 Major
## 412 517 34.00 Major
## 413 519 36.00 Major
## 414 520 32.00 Major
## 415 521 30.00 Major
## 416 522 22.00 Major
## 417 524 44.00 Major
## 418 526 40.50 Major
## 419 527 50.00 Major
## 420 529 39.00 Major
## 421 530 23.00 Major
## 422 531 2.00 Minor
## 423 533 17.00 Minor
## 424 535 30.00 Major
## 425 536 7.00 Minor
## 426 537 45.00 Major
## 427 538 30.00 Major
## 428 540 22.00 Major
## 429 541 36.00 Major
## 430 542 9.00 Minor
## 431 543 11.00 Minor
## 432 544 32.00 Major
## 433 545 50.00 Major
## 434 546 64.00 Major
## 435 547 19.00 Major
## 436 549 33.00 Major
## 437 550 8.00 Minor
## 438 551 17.00 Minor
## 439 552 27.00 Major
## 440 554 22.00 Major
## 441 555 22.00 Major
## 442 556 62.00 Major
## 443 557 48.00 Major
## 444 559 39.00 Major
## 445 560 36.00 Major
## 446 562 40.00 Major
## 447 563 28.00 Major
## 448 566 24.00 Major
## 449 567 19.00 Major
## 450 568 29.00 Major
## 451 570 32.00 Major
## 452 571 62.00 Major
## 453 572 53.00 Major
## 454 573 36.00 Major
## 455 575 16.00 Minor
## 456 576 19.00 Major
## 457 577 34.00 Major
## 458 578 39.00 Major
## 459 580 32.00 Major
## 460 581 25.00 Major
## 461 582 39.00 Major
## 462 583 54.00 Major
## 463 584 36.00 Major
## 464 586 18.00 Major
## 465 587 47.00 Major
## 466 588 60.00 Major
## 467 589 22.00 Major
## 468 591 35.00 Major
## 469 592 52.00 Major
## 470 593 47.00 Major
## 471 595 37.00 Major
## 472 596 36.00 Major
## 473 598 49.00 Major
## 474 600 49.00 Major
## 475 601 24.00 Major
## 476 604 44.00 Major
## 477 605 35.00 Major
## 478 606 36.00 Major
## 479 607 30.00 Major
## 480 608 27.00 Major
## 481 609 22.00 Major
## 482 610 40.00 Major
## 483 611 39.00 Major
## 484 615 35.00 Major
## 485 616 24.00 Major
## 486 617 34.00 Major
## 487 618 26.00 Major
## 488 619 4.00 Minor
## 489 620 26.00 Major
## 490 621 27.00 Major
## 491 622 42.00 Major
## 492 623 20.00 Major
## 493 624 21.00 Major
## 494 625 21.00 Major
## 495 626 61.00 Major
## 496 627 57.00 Major
## 497 628 21.00 Major
## 498 629 26.00 Major
## 499 631 80.00 Major
## 500 632 51.00 Major
## 501 633 32.00 Major
## 502 635 9.00 Minor
## 503 636 28.00 Major
## 504 637 32.00 Major
## 505 638 31.00 Major
## 506 639 41.00 Major
## 507 641 20.00 Major
## 508 642 24.00 Major
## 509 643 2.00 Minor
## 510 645 0.75 Minor
## 511 646 48.00 Major
## 512 647 19.00 Major
## 513 648 56.00 Major
## 514 650 23.00 Major
## 515 652 18.00 Major
## 516 653 21.00 Major
## 517 655 18.00 Major
## 518 656 24.00 Major
## 519 658 32.00 Major
## 520 659 23.00 Major
## 521 660 58.00 Major
## 522 661 50.00 Major
## 523 662 40.00 Major
## 524 663 47.00 Major
## 525 664 36.00 Major
## 526 665 20.00 Major
## 527 666 32.00 Major
## 528 667 25.00 Major
## 529 669 43.00 Major
## 530 671 40.00 Major
## 531 672 31.00 Major
## 532 673 70.00 Major
## 533 674 31.00 Major
## 534 676 18.00 Major
## 535 677 24.50 Major
## 536 678 18.00 Major
## 537 679 43.00 Major
## 538 680 36.00 Major
## 539 682 27.00 Major
## 540 683 20.00 Major
## 541 684 14.00 Minor
## 542 685 60.00 Major
## 543 686 25.00 Major
## 544 687 14.00 Minor
## 545 688 19.00 Major
## 546 689 18.00 Major
## 547 690 15.00 Minor
## 548 691 31.00 Major
## 549 692 4.00 Minor
## 550 694 25.00 Major
## 551 695 60.00 Major
## 552 696 52.00 Major
## 553 697 44.00 Major
## 554 699 49.00 Major
## 555 700 42.00 Major
## 556 701 18.00 Major
## 557 702 35.00 Major
## 558 703 18.00 Major
## 559 704 25.00 Major
## 560 705 26.00 Major
## 561 706 39.00 Major
## 562 707 45.00 Major
## 563 708 42.00 Major
## 564 709 22.00 Major
## 565 711 24.00 Major
## 566 713 48.00 Major
## 567 714 29.00 Major
## 568 715 52.00 Major
## 569 716 19.00 Major
## 570 717 38.00 Major
## 571 718 27.00 Major
## 572 720 33.00 Major
## 573 721 6.00 Minor
## 574 722 17.00 Minor
## 575 723 34.00 Major
## 576 724 50.00 Major
## 577 725 27.00 Major
## 578 726 20.00 Major
## 579 727 30.00 Major
## 580 729 25.00 Major
## 581 730 25.00 Major
## 582 731 29.00 Major
## 583 732 11.00 Minor
## 584 734 23.00 Major
## 585 735 23.00 Major
## 586 736 28.50 Major
## 587 737 48.00 Major
## 588 738 35.00 Major
## 589 742 36.00 Major
## 590 743 21.00 Major
## 591 744 24.00 Major
## 592 745 31.00 Major
## 593 746 70.00 Major
## 594 747 16.00 Minor
## 595 748 30.00 Major
## 596 749 19.00 Major
## 597 750 31.00 Major
## 598 751 4.00 Minor
## 599 752 6.00 Minor
## 600 753 33.00 Major
## 601 754 23.00 Major
## 602 755 48.00 Major
## 603 756 0.67 Minor
## 604 757 28.00 Major
## 605 758 18.00 Major
## 606 759 34.00 Major
## 607 760 33.00 Major
## 608 762 41.00 Major
## 609 763 20.00 Major
## 610 764 36.00 Major
## 611 765 16.00 Minor
## 612 766 51.00 Major
## 613 768 30.50 Major
## 614 770 32.00 Major
## 615 771 24.00 Major
## 616 772 48.00 Major
## 617 773 57.00 Major
## 618 775 54.00 Major
## 619 776 18.00 Major
## 620 778 5.00 Minor
## 621 780 43.00 Major
## 622 781 13.00 Minor
## 623 782 17.00 Minor
## 624 783 29.00 Major
## 625 785 25.00 Major
## 626 786 25.00 Major
## 627 787 18.00 Major
## 628 788 8.00 Minor
## 629 789 1.00 Minor
## 630 790 46.00 Major
## 631 792 16.00 Minor
## 632 795 25.00 Major
## 633 796 39.00 Major
## 634 797 49.00 Major
## 635 798 31.00 Major
## 636 799 30.00 Major
## 637 800 30.00 Major
## 638 801 34.00 Major
## 639 802 31.00 Major
## 640 803 11.00 Minor
## 641 804 0.42 Minor
## 642 805 27.00 Major
## 643 806 31.00 Major
## 644 807 39.00 Major
## 645 808 18.00 Major
## 646 809 39.00 Major
## 647 810 33.00 Major
## 648 811 26.00 Major
## 649 812 39.00 Major
## 650 813 35.00 Major
## 651 814 6.00 Minor
## 652 815 30.50 Major
## 653 817 23.00 Major
## 654 818 31.00 Major
## 655 819 43.00 Major
## 656 820 10.00 Minor
## 657 821 52.00 Major
## 658 822 27.00 Major
## 659 823 38.00 Major
## 660 824 27.00 Major
## 661 825 2.00 Minor
## 662 828 1.00 Minor
## 663 830 62.00 Major
## 664 831 15.00 Minor
## 665 832 0.83 Minor
## 666 834 23.00 Major
## 667 835 18.00 Major
## 668 836 39.00 Major
## 669 837 21.00 Major
## 670 839 32.00 Major
## 671 841 20.00 Major
## 672 842 16.00 Minor
## 673 843 30.00 Major
## 674 844 34.50 Major
## 675 845 17.00 Minor
## 676 846 42.00 Major
## 677 848 35.00 Major
## 678 849 28.00 Major
## 679 851 4.00 Minor
## 680 852 74.00 Major
## 681 853 9.00 Minor
## 682 854 16.00 Minor
## 683 855 44.00 Major
## 684 856 18.00 Major
## 685 857 45.00 Major
## 686 858 51.00 Major
## 687 859 24.00 Major
## 688 861 41.00 Major
## 689 862 21.00 Major
## 690 863 48.00 Major
## 691 865 24.00 Major
## 692 866 42.00 Major
## 693 867 27.00 Major
## 694 868 31.00 Major
## 695 870 4.00 Minor
## 696 871 26.00 Major
## 697 872 47.00 Major
## 698 873 33.00 Major
## 699 874 47.00 Major
## 700 875 28.00 Major
## 701 876 15.00 Minor
## 702 877 20.00 Major
## 703 878 19.00 Major
## 704 880 56.00 Major
## 705 881 25.00 Major
## 706 882 33.00 Major
## 707 883 22.00 Major
## 708 884 28.00 Major
## 709 885 25.00 Major
## 710 886 39.00 Major
## 711 887 27.00 Major
## 712 888 19.00 Major
## 713 890 26.00 Major
## 714 891 32.00 Major
Arrange data
titanic %>%
select(PassengerId, Age) %>%
filter(!is.na(Age)) %>%
mutate(Age_Bracket = ifelse(Age < 18, 'Minor','Major')) %>%
arrange(Age)
## PassengerId Age Age_Bracket
## 1 804 0.42 Minor
## 2 756 0.67 Minor
## 3 470 0.75 Minor
## 4 645 0.75 Minor
## 5 79 0.83 Minor
## 6 832 0.83 Minor
## 7 306 0.92 Minor
## 8 165 1.00 Minor
## 9 173 1.00 Minor
## 10 184 1.00 Minor
## 11 382 1.00 Minor
## 12 387 1.00 Minor
## 13 789 1.00 Minor
## 14 828 1.00 Minor
## 15 8 2.00 Minor
## 16 17 2.00 Minor
## 17 120 2.00 Minor
## 18 206 2.00 Minor
## 19 298 2.00 Minor
## 20 341 2.00 Minor
## 21 480 2.00 Minor
## 22 531 2.00 Minor
## 23 643 2.00 Minor
## 24 825 2.00 Minor
## 25 44 3.00 Minor
## 26 194 3.00 Minor
## 27 262 3.00 Minor
## 28 349 3.00 Minor
## 29 375 3.00 Minor
## 30 408 3.00 Minor
## 31 11 4.00 Minor
## 32 64 4.00 Minor
## 33 172 4.00 Minor
## 34 185 4.00 Minor
## 35 446 4.00 Minor
## 36 619 4.00 Minor
## 37 692 4.00 Minor
## 38 751 4.00 Minor
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## 710 117 70.50 Major
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## 712 494 71.00 Major
## 713 852 74.00 Major
## 714 631 80.00 Major
Group and summarise data
titanic %>%
select(PassengerId, Survived, Sex) %>%
group_by(Sex) %>%
summarise(survived_perc = mean(Survived))
## # A tibble: 2 × 2
## Sex survived_perc
## <chr> <dbl>
## 1 female 0.742
## 2 male 0.189
titanic %>%
select(PassengerId, Survived, Age) %>%
filter(!is.na(Age)) %>%
mutate(Age_Bracket = ifelse(Age < 18, 'Minor','Major')) %>%
group_by(Age_Bracket) %>%
summarise(survived_perc = mean(Survived))
## # A tibble: 2 × 2
## Age_Bracket survived_perc
## <chr> <dbl>
## 1 Major 0.381
## 2 Minor 0.540
titanic %>%
select(PassengerId, Survived, Age, Sex) %>%
filter(!is.na(Age)) %>%
mutate(Age_Bracket = ifelse(Age < 18, 'Minor','Major')) %>%
group_by(Age_Bracket, Sex) %>%
summarise(survived_perc = mean(Survived))
## `summarise()` has grouped output by 'Age_Bracket'. You can override using the
## `.groups` argument.
## # A tibble: 4 × 3
## # Groups: Age_Bracket [2]
## Age_Bracket Sex survived_perc
## <chr> <chr> <dbl>
## 1 Major female 0.772
## 2 Major male 0.177
## 3 Minor female 0.691
## 4 Minor male 0.397
titanic %>%
filter(Sex == 'male') %>%
group_by(Embarked) %>%
count()
## # A tibble: 3 × 2
## # Groups: Embarked [3]
## Embarked n
## <chr> <int>
## 1 C 95
## 2 Q 41
## 3 S 441
2 - Data visualization with ggplot2
#install.packages("ggplot2")
library(ggplot2)
Geom bar
ggplot(titanic, aes(x=Pclass, fill=Survived)) +
geom_bar() +
labs( y="Number of Passengers", x="Passenger Class", title="Titanic Survival Rate by Passenger Class")
titanic <- mutate(titanic, Survived = as.factor(Survived))
ggplot(titanic, aes(x=Pclass, fill=Survived)) +
geom_bar() +
labs( y="Number of Passengers", x="Ticket Class", title="Titanic Survival Rate by Passenger Class")
ggplot(titanic, aes(x=Sex, fill=Survived)) +
geom_bar() +
labs( y="Number of Passengers", title="Titanic Survival Rate by Gender by Passenger Class")+
facet_wrap(~Pclass)
ggplot(titanic, aes(x=Age, fill=Survived))+
geom_histogram(bins=20) +
labs(title="Survival Rate by Gender", y="Number of passengers", subtitle = "Distribution by age")
## Warning: Removed 177 rows containing non-finite values (stat_bin).
ggplot(titanic, aes(x=Age, fill=Survived))+
geom_histogram(bins=20) +
labs(title="Survival Rate by Gender", y="Number of passengers", subtitle = "Distribution by age, gender and ticket class")+
facet_grid(Sex~Pclass, scales="free")
## Warning: Removed 177 rows containing non-finite values (stat_bin).
titanic <- mutate(titanic, Pclass = as.factor(Pclass))
ggplot(data = titanic, aes(x=Pclass, y=Age)) +
geom_boxplot(alpha=0.7)
## Warning: Removed 177 rows containing non-finite values (stat_boxplot).
ggplot(data = titanic, aes(x=Age, y=Fare)) +
geom_point()
## Warning: Removed 177 rows containing missing values (geom_point).
ggplot(data = titanic, aes(x=Age, y=Fare, color=Pclass)) +
geom_point()
## Warning: Removed 177 rows containing missing values (geom_point).
