# Practice tidying data

library(tidyverse)

Run the code below in your console to download this exercise as a set of R scripts.

usethis::use_course("uc-cfss/data-wrangling-tidy-data")

For each exercise, tidy the data frame. Before you write any code examine the structure of the data frame and mentally (or with pen-and-paper) sketch out what you think the tidy data structure should be.

## Race data

library(rcfss)
race
## # A tibble: 4 × 8
##   Name   50 100 150 200 250 300 350
##   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Carla   1.2   1.8   2.2   2.3   3     2.5   1.8
## 2 Mace    1.5   1.1   1.9   2     3.6   3     2.5
## 3 Lea     1.7   1.6   2.3   2.7   2.6   2.2   2.6
## 4 Karen   1.3   1.7   1.9   2.2   3.2   1.5   1.9

Important info:

• Name - pretty obvious
• 50:350 - column names define different lengths of time
• Cell values are scores associated with each name and length of time
Click for a hint

Tidy data structure

## # A tibble: 28 × 3
##    Name   Time Score
##    <chr> <dbl> <dbl>
##  1 Carla    50   1.2
##  2 Carla   100   1.8
##  3 Carla   150   2.2
##  4 Carla   200   2.3
##  5 Carla   250   3
##  6 Carla   300   2.5
##  7 Carla   350   1.8
##  8 Mace     50   1.5
##  9 Mace    100   1.1
## 10 Mace    150   1.9
## # … with 18 more rows

Click for the solution

pivot_longer(
data = race,
cols = -Name,
names_to = "Time",
values_to = "Score",
# ensure the Time column is stored as a numeric column
names_transform = parse_number
)
## # A tibble: 28 × 3
##    Name   Time Score
##    <chr> <dbl> <dbl>
##  1 Carla    50   1.2
##  2 Carla   100   1.8
##  3 Carla   150   2.2
##  4 Carla   200   2.3
##  5 Carla   250   3
##  6 Carla   300   2.5
##  7 Carla   350   1.8
##  8 Mace     50   1.5
##  9 Mace    100   1.1
## 10 Mace    150   1.9
## # … with 18 more rows

Except for the Name column, the remaining columns are actually one variable spread across multiple columns. The column names are a distinct variable, and the columns’ values are another variable. pivot_longer() is the appropriate function.

Because the column names are actually numeric values, we use names_transform = parse_number to coerce the new Time column into a numeric column. names_transform allows us to manually specify the column type for the names_to column. parse_number() is a function from the readr package for converting a character vector to a numeric vector, so names_transform = parse_number ensures the Time column is stored as a numeric column.

## # A tibble: 12 × 6
##       ID Test     Year  Fall Spring Winter
##    <dbl> <chr>   <dbl> <dbl>  <dbl>  <dbl>
##  1     1 Math     2008    15     16     19
##  2     1 Math     2009    12     13     27
##  3     1 Writing  2008    22     22     24
##  4     1 Writing  2009    10     14     20
##  5     2 Math     2008    12     13     25
##  6     2 Math     2009    16     14     21
##  7     2 Writing  2008    13     11     29
##  8     2 Writing  2009    23     20     26
##  9     3 Math     2008    11     12     22
## 10     3 Math     2009    13     11     27
## 11     3 Writing  2008    17     12     23
## 12     3 Writing  2009    14      9     31

This one is a bit tougher. Important info:

• The unit of analysis is ID-Year-Quarter. That is, in the tidy formulation each observation represents one individual during one quarter in a given year.
• Each test is unique. As in they should be treated as two separate variables.
Click for a hint

Tidy data structure

## # A tibble: 18 × 5
##       ID  Year Quarter  Math Writing
##    <dbl> <dbl> <chr>   <dbl>   <dbl>
##  1     1  2008 Fall       15      22
##  2     1  2008 Spring     16      22
##  3     1  2008 Winter     19      24
##  4     1  2009 Fall       12      10
##  5     1  2009 Spring     13      14
##  6     1  2009 Winter     27      20
##  7     2  2008 Fall       12      13
##  8     2  2008 Spring     13      11
##  9     2  2008 Winter     25      29
## 10     2  2009 Fall       16      23
## 11     2  2009 Spring     14      20
## 12     2  2009 Winter     21      26
## 13     3  2008 Fall       11      17
## 14     3  2008 Spring     12      12
## 15     3  2008 Winter     22      23
## 16     3  2009 Fall       13      14
## 17     3  2009 Spring     11       9
## 18     3  2009 Winter     27      31

Click for the solution

pivot_longer(
cols = c(Fall:Winter),
names_to = "Quarter",
values_to = "Score"
) %>%
pivot_wider(
names_from = Test,
values_from = Score
)
## # A tibble: 18 × 5
##       ID  Year Quarter  Math Writing
##    <dbl> <dbl> <chr>   <dbl>   <dbl>
##  1     1  2008 Fall       15      22
##  2     1  2008 Spring     16      22
##  3     1  2008 Winter     19      24
##  4     1  2009 Fall       12      10
##  5     1  2009 Spring     13      14
##  6     1  2009 Winter     27      20
##  7     2  2008 Fall       12      13
##  8     2  2008 Spring     13      11
##  9     2  2008 Winter     25      29
## 10     2  2009 Fall       16      23
## 11     2  2009 Spring     14      20
## 12     2  2009 Winter     21      26
## 13     3  2008 Fall       11      17
## 14     3  2008 Spring     12      12
## 15     3  2008 Winter     22      23
## 16     3  2009 Fall       13      14
## 17     3  2009 Spring     11       9
## 18     3  2009 Winter     27      31

In this example, the basic unit of observation is the test. Each individual takes two separate tests (Math or Writing) at multiple points in time: during each quarter (Fall, Winter, Spring) as well as in multiple years (2008 and 2009). So our final data frame should contain five columns: ID (identifying the student), Year (year the test was taken), Quarter (quarter in which the test was taken), Math (score on the math test), and Writing (score on the writing test).

Where can we begin? Initially we can make the data frame longer by making Fall, Winter, and Spring into a single column (we can use the inclusive select function : to gather these three columns):

pivot_longer(
cols = c(Fall:Winter),
names_to = "Quarter",
values_to = "Score"
)
## # A tibble: 36 × 5
##       ID Test     Year Quarter Score
##    <dbl> <chr>   <dbl> <chr>   <dbl>
##  1     1 Math     2008 Fall       15
##  2     1 Math     2008 Spring     16
##  3     1 Math     2008 Winter     19
##  4     1 Math     2009 Fall       12
##  5     1 Math     2009 Spring     13
##  6     1 Math     2009 Winter     27
##  7     1 Writing  2008 Fall       22
##  8     1 Writing  2008 Spring     22
##  9     1 Writing  2008 Winter     24
## 10     1 Writing  2009 Fall       10
## # … with 26 more rows

Good, but now we have observations spread across multiple rows. Remember that we want each test to be a separate variable. To do that, we can pivot_wider() those values across two columns.

pivot_longer(
cols = c(Fall:Winter),
names_to = "Quarter",
values_to = "Score"
) %>%
pivot_wider(
names_from = Test,
values_from = Score
)
## # A tibble: 18 × 5
##       ID  Year Quarter  Math Writing
##    <dbl> <dbl> <chr>   <dbl>   <dbl>
##  1     1  2008 Fall       15      22
##  2     1  2008 Spring     16      22
##  3     1  2008 Winter     19      24
##  4     1  2009 Fall       12      10
##  5     1  2009 Spring     13      14
##  6     1  2009 Winter     27      20
##  7     2  2008 Fall       12      13
##  8     2  2008 Spring     13      11
##  9     2  2008 Winter     25      29
## 10     2  2009 Fall       16      23
## 11     2  2009 Spring     14      20
## 12     2  2009 Winter     21      26
## 13     3  2008 Fall       11      17
## 14     3  2008 Spring     12      12
## 15     3  2008 Winter     22      23
## 16     3  2009 Fall       13      14
## 17     3  2009 Spring     11       9
## 18     3  2009 Winter     27      31

## Activities

activities
## # A tibble: 10 × 8
##    id    trt   work.T1 play.T1 talk.T1 work.T2 play.T2 talk.T2
##    <chr> <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 x1    cnt    0.652    0.865  0.536   0.275    0.354  0.0319
##  2 x2    cnt    0.568    0.615  0.0931  0.229    0.936  0.114
##  3 x3    tr     0.114    0.775  0.170   0.0144   0.246  0.469
##  4 x4    tr     0.596    0.356  0.900   0.729    0.473  0.397
##  5 x5    tr     0.358    0.406  0.423   0.250    0.192  0.834
##  6 x6    cnt    0.429    0.707  0.748   0.161    0.583  0.761
##  7 x7    tr     0.0519   0.838  0.823   0.0170   0.459  0.573
##  8 x8    tr     0.264    0.240  0.955   0.486    0.467  0.448
##  9 x9    cnt    0.399    0.771  0.685   0.103    0.400  0.0838
## 10 x10   cnt    0.836    0.356  0.501   0.802    0.505  0.219

This one is also pretty difficult, but if you think it through conceptually it is doable. The unit of analysis is a single individual (identified by id) observed at two different times (T1 and T2) performing different actions (work, play, talk, and total - note that total is not merely the sum of the first three values). Individuals in this experiment were assigned to either treatment or control (trt) and this information should be preserved in the final data frame.

Click for a hint

Tidy data structure

## # A tibble: 20 × 6
##    id    trt   time    work  play   talk
##    <chr> <chr> <chr>  <dbl> <dbl>  <dbl>
##  1 x1    cnt   T1    0.652  0.865 0.536
##  2 x1    cnt   T2    0.275  0.354 0.0319
##  3 x2    cnt   T1    0.568  0.615 0.0931
##  4 x2    cnt   T2    0.229  0.936 0.114
##  5 x3    tr    T1    0.114  0.775 0.170
##  6 x3    tr    T2    0.0144 0.246 0.469
##  7 x4    tr    T1    0.596  0.356 0.900
##  8 x4    tr    T2    0.729  0.473 0.397
##  9 x5    tr    T1    0.358  0.406 0.423
## 10 x5    tr    T2    0.250  0.192 0.834
## 11 x6    cnt   T1    0.429  0.707 0.748
## 12 x6    cnt   T2    0.161  0.583 0.761
## 13 x7    tr    T1    0.0519 0.838 0.823
## 14 x7    tr    T2    0.0170 0.459 0.573
## 15 x8    tr    T1    0.264  0.240 0.955
## 16 x8    tr    T2    0.486  0.467 0.448
## 17 x9    cnt   T1    0.399  0.771 0.685
## 18 x9    cnt   T2    0.103  0.400 0.0838
## 19 x10   cnt   T1    0.836  0.356 0.501
## 20 x10   cnt   T2    0.802  0.505 0.219

Click for the solution

This is a more complex operation. The basic problem is that we have variables stored in multiple columns (location, with possible values of work, play, and talk). We need to combine these columns into a single column for each variable. But what happens if we just make the data frame longer in this way?

pivot_longer(
data = activities,
cols = c(work.T1:talk.T2),
names_to = "variable",
values_to = "value"
)
## # A tibble: 60 × 4
##    id    trt   variable  value
##    <chr> <chr> <chr>     <dbl>
##  1 x1    cnt   work.T1  0.652
##  2 x1    cnt   play.T1  0.865
##  3 x1    cnt   talk.T1  0.536
##  4 x1    cnt   work.T2  0.275
##  5 x1    cnt   play.T2  0.354
##  6 x1    cnt   talk.T2  0.0319
##  7 x2    cnt   work.T1  0.568
##  8 x2    cnt   play.T1  0.615
##  9 x2    cnt   talk.T1  0.0931
## 10 x2    cnt   work.T2  0.229
## # … with 50 more rows

We’ve created a new problem! Actually, two problems:

1. We have a single observation stored across multiple rows: we want a single row for each id x trt pairing
2. We have two variables stored in a single column: variable contains the information on both location (work, play, and talk) as well as when the measurement was taken (T1 or T2)

The best approach is to fix the second problem by separating the columns, then spreading the different types of measurements back into their own columns.

pivot_longer(
data = activities,
cols = c(work.T1:talk.T2),
names_to = "variable",
values_to = "value"
) %>%
separate(variable, into = c("location", "time"))
## # A tibble: 60 × 5
##    id    trt   location time   value
##    <chr> <chr> <chr>    <chr>  <dbl>
##  1 x1    cnt   work     T1    0.652
##  2 x1    cnt   play     T1    0.865
##  3 x1    cnt   talk     T1    0.536
##  4 x1    cnt   work     T2    0.275
##  5 x1    cnt   play     T2    0.354
##  6 x1    cnt   talk     T2    0.0319
##  7 x2    cnt   work     T1    0.568
##  8 x2    cnt   play     T1    0.615
##  9 x2    cnt   talk     T1    0.0931
## 10 x2    cnt   work     T2    0.229
## # … with 50 more rows
pivot_longer(
data = activities,
cols = c(work.T1:talk.T2),
names_to = "variable",
values_to = "value"
) %>%
separate(variable, into = c("location", "time")) %>%
pivot_wider(names_from = location, values_from = value)
## # A tibble: 20 × 6
##    id    trt   time    work  play   talk
##    <chr> <chr> <chr>  <dbl> <dbl>  <dbl>
##  1 x1    cnt   T1    0.652  0.865 0.536
##  2 x1    cnt   T2    0.275  0.354 0.0319
##  3 x2    cnt   T1    0.568  0.615 0.0931
##  4 x2    cnt   T2    0.229  0.936 0.114
##  5 x3    tr    T1    0.114  0.775 0.170
##  6 x3    tr    T2    0.0144 0.246 0.469
##  7 x4    tr    T1    0.596  0.356 0.900
##  8 x4    tr    T2    0.729  0.473 0.397
##  9 x5    tr    T1    0.358  0.406 0.423
## 10 x5    tr    T2    0.250  0.192 0.834
## 11 x6    cnt   T1    0.429  0.707 0.748
## 12 x6    cnt   T2    0.161  0.583 0.761
## 13 x7    tr    T1    0.0519 0.838 0.823
## 14 x7    tr    T2    0.0170 0.459 0.573
## 15 x8    tr    T1    0.264  0.240 0.955
## 16 x8    tr    T2    0.486  0.467 0.448
## 17 x9    cnt   T1    0.399  0.771 0.685
## 18 x9    cnt   T2    0.103  0.400 0.0838
## 19 x10   cnt   T1    0.836  0.356 0.501
## 20 x10   cnt   T2    0.802  0.505 0.219

## Session Info

devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.2.0 (2022-04-22)
##  os       macOS Monterey 12.2.1
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/Chicago
##  date     2022-06-22
##  pandoc   2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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