7.3 purrr (map)
map(f_values, f_c)
## [[1]]
## [1] -17.77778
##
## [[2]]
## [1] 0
##
## [[3]]
## [1] 100
##
## [[4]]
## [1] -40mtcars
## 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 2map(mtcars, class)
## $mpg
## [1] "numeric"
##
## $cyl
## [1] "numeric"
##
## $disp
## [1] "numeric"
##
## $hp
## [1] "numeric"
##
## $drat
## [1] "numeric"
##
## $wt
## [1] "numeric"
##
## $qsec
## [1] "numeric"
##
## $vs
## [1] "numeric"
##
## $am
## [1] "numeric"
##
## $gear
## [1] "numeric"
##
## $carb
## [1] "numeric"map_chr(mtcars, class)
## mpg cyl disp hp drat wt qsec vs
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## am gear carb
## "numeric" "numeric" "numeric"map_dbl(mtcars, mean)
## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs am gear carb
## 0.437500 0.406250 3.687500 2.812500map(mtcars, summary)
## $mpg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90
##
## $cyl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.000 4.000 6.000 6.188 8.000 8.000
##
## $disp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 71.1 120.8 196.3 230.7 326.0 472.0
##
## $hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 52.0 96.5 123.0 146.7 180.0 335.0
##
## $drat
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.760 3.080 3.695 3.597 3.920 4.930
##
## $wt
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.513 2.581 3.325 3.217 3.610 5.424
##
## $qsec
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 14.50 16.89 17.71 17.85 18.90 22.90
##
## $vs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4375 1.0000 1.0000
##
## $am
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4062 1.0000 1.0000
##
## $gear
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 3.000 4.000 3.688 4.000 5.000
##
## $carb
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.812 4.000 8.0007.3.1 Exercise
- Compute the mean of every column in mtcars.
- Determine the type of each column in nycflights13::flights.
- Compute the number of unique values in each column of iris. (Hint: you may want to write a function)
- Generate 10 random normals from distributions with means of -10, 0, 10, and 100.
7.3.1.1 Solutions
# 1. Compute the mean of every column in mtcars
purrr::map_dbl(mtcars, mean)
## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs am gear carb
## 0.437500 0.406250 3.687500 2.812500# 2. Determine the type of each column in nycflights13::flights.
purrr::map_chr(nycflights13::flights, class)
## Error: Result 19 must be a single string, not a character vector of length 2purrr::map(nycflights13::flights, class)
## $year
## [1] "integer"
##
## $month
## [1] "integer"
##
## $day
## [1] "integer"
##
## $dep_time
## [1] "integer"
##
## $sched_dep_time
## [1] "integer"
##
## $dep_delay
## [1] "numeric"
##
## $arr_time
## [1] "integer"
##
## $sched_arr_time
## [1] "integer"
##
## $arr_delay
## [1] "numeric"
##
## $carrier
## [1] "character"
##
## $flight
## [1] "integer"
##
## $tailnum
## [1] "character"
##
## $origin
## [1] "character"
##
## $dest
## [1] "character"
##
## $air_time
## [1] "numeric"
##
## $distance
## [1] "numeric"
##
## $hour
## [1] "numeric"
##
## $minute
## [1] "numeric"
##
## $time_hour
## [1] "POSIXct" "POSIXt"# Compute the number of unique values in each column of iris.
count_unique <- function(x) {
return(length(unique(x)))
}
purrr::map_int(iris, count_unique)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 35 23 43 22 3purrr::map_int(iris, function(x) length(unique(x)))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 35 23 43 22 3purrr::map_int(iris, ~ length(unique(.)))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 35 23 43 22 3# Generate 10 random normals from distributions with means of -10, 0, 10, and 100.
purrr::map(c(-10, 0, 10, 100), ~ rnorm(n = 10, mean = .))
## [[1]]
## [1] -8.629042 -10.564698 -9.636872 -9.367137 -9.595732 -10.106125
## [7] -8.488478 -10.094659 -7.981576 -10.062714
##
## [[2]]
## [1] 1.3048697 2.2866454 -1.3888607 -0.2787888 -0.1333213 0.6359504
## [7] -0.2842529 -2.6564554 -2.4404669 1.3201133
##
## [[3]]
## [1] 9.693361 8.218692 9.828083 11.214675 11.895193 9.569531 9.742731
## [8] 8.236837 10.460097 9.360005
##
## [[4]]
## [1] 100.45545 100.70484 101.03510 99.39107 100.50496 98.28299 99.21554
## [8] 99.14909 97.58579 100.03612