Lecture 3: intro to R and dplyr
Gabriele Degola
October, 2021
Introduction to R
Load and explore data:
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 2str(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 ...Columns of the dataframe can be selected and manipulated easily:
mtcars$drat## [1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93
## [16] 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62
## [31] 3.54 4.11mtcars$drat + 1## [1] 4.90 4.90 4.85 4.08 4.15 3.76 4.21 4.69 4.92 4.92 4.92 4.07 4.07 4.07 3.93
## [16] 4.00 4.23 5.08 5.93 5.22 4.70 3.76 4.15 4.73 4.08 5.08 5.43 4.77 5.22 4.62
## [31] 4.54 5.11mtcars$drat > 3.5## [1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
## [25] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUEFilter observations according to the value of a feature (mind the comma at the end):
mtcars[mtcars$drat > 3.5,]## 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
## 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
## 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
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 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 2mtcars[mtcars$drat > 3.5 & mtcars$cyl == 6,]## 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
## 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
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6mtcars[mtcars$drat > 3.5,]$carb## [1] 4 4 1 2 2 4 4 1 2 1 1 4 1 2 2 4 6 8 2Compute feature statistics:
mean(mtcars$mpg); sd(mtcars$mpg)## [1] 20.09062## [1] 6.026948dplyr
library(dplyr)Use summarise to create a new dataframe summarising some variables:
mtcars %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg))## mpg_mean mpg_sd
## 1 20.09062 6.026948Use group_by to create different group according to the values of one or more variables:
mtcars %>% group_by(cyl) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg))## # A tibble: 3 x 3
## cyl mpg_mean mpg_sd
## <dbl> <dbl> <dbl>
## 1 4 26.7 4.51
## 2 6 19.7 1.45
## 3 8 15.1 2.56mtcars %>% group_by(cyl, am) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg))## # A tibble: 6 x 4
## # Groups: cyl [3]
## cyl am mpg_mean mpg_sd
## <dbl> <dbl> <dbl> <dbl>
## 1 4 0 22.9 1.45
## 2 4 1 28.1 4.48
## 3 6 0 19.1 1.63
## 4 6 1 20.6 0.751
## 5 8 0 15.0 2.77
## 6 8 1 15.4 0.566mtcars %>% group_by(cyl, carb) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg))## # A tibble: 9 x 4
## # Groups: cyl [3]
## cyl carb mpg_mean mpg_sd
## <dbl> <dbl> <dbl> <dbl>
## 1 4 1 27.6 5.55
## 2 4 2 25.9 3.81
## 3 6 1 19.8 2.33
## 4 6 4 19.8 1.55
## 5 6 6 19.7 NA
## 6 8 2 17.2 2.09
## 7 8 3 16.3 1.05
## 8 8 4 13.2 2.28
## 9 8 8 15 NASome standard deviation values are NaN. Why? Because there is a single observation having certains values for cyl and carb (for example, 6 and 6).
mtcars %>% group_by(cyl, carb) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg), num = n())## # A tibble: 9 x 5
## # Groups: cyl [3]
## cyl carb mpg_mean mpg_sd num
## <dbl> <dbl> <dbl> <dbl> <int>
## 1 4 1 27.6 5.55 5
## 2 4 2 25.9 3.81 6
## 3 6 1 19.8 2.33 2
## 4 6 4 19.8 1.55 4
## 5 6 6 19.7 NA 1
## 6 8 2 17.2 2.09 4
## 7 8 3 16.3 1.05 3
## 8 8 4 13.2 2.28 6
## 9 8 8 15 NA 1Note that n is a special function to be used in a data context. nrow returns the number of observations in a dataframe.
nrow(mtcars)## [1] 32ungroup is used to remove grouping and perform other operations on the whole dataframe. By itself, it does not do anything:
mtcars %>% group_by(cyl, carb) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg), num = n()) %>% ungroup()## # A tibble: 9 x 5
## cyl carb mpg_mean mpg_sd num
## <dbl> <dbl> <dbl> <dbl> <int>
## 1 4 1 27.6 5.55 5
## 2 4 2 25.9 3.81 6
## 3 6 1 19.8 2.33 2
## 4 6 4 19.8 1.55 4
## 5 6 6 19.7 NA 1
## 6 8 2 17.2 2.09 4
## 7 8 3 16.3 1.05 3
## 8 8 4 13.2 2.28 6
## 9 8 8 15 NA 1mutate adds new features, while preserving existing ones:
mtcars %>% group_by(cyl, carb) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg), num = n()) %>% ungroup() %>% mutate(mpg_gmean = mean(mpg_mean))## # A tibble: 9 x 6
## cyl carb mpg_mean mpg_sd num mpg_gmean
## <dbl> <dbl> <dbl> <dbl> <int> <dbl>
## 1 4 1 27.6 5.55 5 19.4
## 2 4 2 25.9 3.81 6 19.4
## 3 6 1 19.8 2.33 2 19.4
## 4 6 4 19.8 1.55 4 19.4
## 5 6 6 19.7 NA 1 19.4
## 6 8 2 17.2 2.09 4 19.4
## 7 8 3 16.3 1.05 3 19.4
## 8 8 4 13.2 2.28 6 19.4
## 9 8 8 15 NA 1 19.4mtcars %>% group_by(cyl, carb) %>% summarise(mpg_mean = mean(mpg), mpg_sd = sd(mpg), num = n()) %>% ungroup() %>% mutate(mpg_gmean = mean(mpg_mean), deviation = mpg_mean - mpg_gmean)## # A tibble: 9 x 7
## cyl carb mpg_mean mpg_sd num mpg_gmean deviation
## <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 4 1 27.6 5.55 5 19.4 8.22
## 2 4 2 25.9 3.81 6 19.4 6.54
## 3 6 1 19.8 2.33 2 19.4 0.386
## 4 6 4 19.8 1.55 4 19.4 0.386
## 5 6 6 19.7 NA 1 19.4 0.336
## 6 8 2 17.2 2.09 4 19.4 -2.21
## 7 8 3 16.3 1.05 3 19.4 -3.06
## 8 8 4 13.2 2.28 6 19.4 -6.21
## 9 8 8 15 NA 1 19.4 -4.36Plotting in R
cars## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85plot(cars)
plot(cars$speed, cars$dist)