This post covers the content and exercises for Ch 10: Tibbles from R for Data Science. The chapter teaches how to use the tidyverse version of data frames called tibbles.

10.5 Exercises

library(tidyverse)
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'tibble' was built under R version 3.4.2
## Warning: package 'tidyr' was built under R version 3.4.2
## Warning: package 'purrr' was built under R version 3.4.2
## Warning: package 'dplyr' was built under R version 3.4.2
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
  1. How can you tell if an object is a tibble? (Hint: try printing mtcars, which is a regular data frame).
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
as_tibble(mtcars)
## # A tibble: 32 x 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##  * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
##  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
##  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
##  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
##  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
##  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
##  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
##  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
##  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## # ... with 22 more rows
  • One indication is that the rownames are removed from tibbles. When printing to the console it is obvious since the variable classes are displayed under the variable names.
  1. Compare and contrast the following operations on a data.frame and equivalent tibble. What is different? Why might the default data frame behaviours cause you frustration?
df <- data.frame(abc = 1, xyz = "a")
df$x
## [1] a
## Levels: a
df[, "xyz"]
## [1] a
## Levels: a
df[, c("abc", "xyz")]
##   abc xyz
## 1   1   a
df <- tibble(abc = 1, xyz = "a")
df$x
## Warning: Unknown or uninitialised column: 'x'.
## NULL
df[, "xyz"]
## # A tibble: 1 x 1
##     xyz
##   <chr>
## 1     a
df[, c("abc", "xyz")]
## # A tibble: 1 x 2
##     abc   xyz
##   <dbl> <chr>
## 1     1     a
  • data.frame allows for the partial matching from $x to xyz
  • data.frame converts the text to a factor
  • data.frame returns a vector when selecting a single column, while tibble maintains the information in a data frame
  1. If you have the name of a variable stored in an object, e.g. var <- “mpg”, how can you extract the reference variable from a tibble?
var <- "mpg"
mtcars[[var]]
##  [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
## [15] 10.4 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
## [29] 15.8 19.7 15.0 21.4
  1. Practice referring to non-syntactic names in the following data frame by:

    1. Extracting the variable called 1.

    2. Plotting a scatterplot of 1 vs 2.

    3. Creating a new column called 3 which is 2 divided by 1.

    4. Renaming the columns to one, two and three.

annoying <- tibble(
  `1` = 1:10,
  `2` = `1` * 2 + rnorm(length(`1`))
)

# 1
annoying$`1`
##  [1]  1  2  3  4  5  6  7  8  9 10
# 2
annoying %>% 
        ggplot(aes(x = `1`, y = `2`)) +
        geom_point()

# 3
annoying$`3` <- with(annoying, `2` / `1`)
annoying$`3`
##  [1] 2.430725 1.269464 1.735472 1.743951 2.008373 2.177681 2.043334
##  [8] 2.006540 1.843430 2.024131
# 4
(annoying <- rename(annoying, one = `1`, two = `2`, three = `3`))
## # A tibble: 10 x 3
##      one       two    three
##    <int>     <dbl>    <dbl>
##  1     1  2.430725 2.430725
##  2     2  2.538928 1.269464
##  3     3  5.206415 1.735472
##  4     4  6.975805 1.743951
##  5     5 10.041863 2.008373
##  6     6 13.066083 2.177681
##  7     7 14.303339 2.043334
##  8     8 16.052323 2.006540
##  9     9 16.590871 1.843430
## 10    10 20.241313 2.024131
  1. What does tibble::enframe() do? When might you use it?
enframe(1:3)
## # A tibble: 3 x 2
##    name value
##   <int> <int>
## 1     1     1
## 2     2     2
## 3     3     3
c(a = 5, b = 7)
## a b 
## 5 7
enframe(c(a = 5, b = 7))
## # A tibble: 2 x 2
##    name value
##   <chr> <dbl>
## 1     a     5
## 2     b     7
  • converts named vectors to data frames
  1. What option controls how many additional column names are printed at the footer of a tibble?
print(as_tibble(mtcars), n_extra = 1)
## # A tibble: 32 x 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##  * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
##  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
##  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
##  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
##  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
##  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
##  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
##  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
##  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## # ... with 22 more rows