Lecture solutions

This section contains solutions to the reshaping practical at the end of the ‘Data manipulation’ lecture.

1. Reshape the fish_encounters dataset to ‘wide’ format, such that each column represents a different monitoring station.

NoteSolution

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.1     ✔ stringr   1.5.2
✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

head(fish_encounters)

# A tibble: 6 × 3
  fish  station  seen
  <fct> <fct>   <int>
1 4842  Release     1
2 4842  I80_1       1
3 4842  Lisbon      1
4 4842  Rstr        1
5 4842  Base_TD     1
6 4842  BCE         1

fish_encounters |>
  pivot_wider(
    names_from = station,
    values_from = seen
  )

# A tibble: 19 × 12
   fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE   MAW
   <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> <int>
 1 4842        1     1      1     1       1     1     1     1     1     1     1
 2 4843        1     1      1     1       1     1     1     1     1     1     1
 3 4844        1     1      1     1       1     1     1     1     1     1     1
 4 4845        1     1      1     1       1    NA    NA    NA    NA    NA    NA
 5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA
 6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA    NA
 7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
 8 4850        1     1     NA     1       1     1     1    NA    NA    NA    NA
 9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
11 4855        1     1      1     1       1    NA    NA    NA    NA    NA    NA
12 4857        1     1      1     1       1     1     1     1     1    NA    NA
13 4858        1     1      1     1       1     1     1     1     1     1     1
14 4859        1     1      1     1       1    NA    NA    NA    NA    NA    NA
15 4861        1     1      1     1       1     1     1     1     1     1     1
16 4862        1     1      1     1       1     1     1     1     1    NA    NA
17 4863        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
18 4864        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
19 4865        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA

2. After reshaping, is this dataset ‘tidy’? Why?

NoteSolution

No. It was already tidy, before reshaping.

• Before: station and seen in separate columns.
• After: station stored in the column headers; values in the cells.

3. Reshape the world_bank_pop dataset to ‘long’ format, such that it contains three columns: country, indicator, year, and value.

head(world_bank_pop)

# A tibble: 6 × 20
  country indicator       `2000`  `2001`  `2002`  `2003`  `2004`  `2005`  `2006`
  <chr>   <chr>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 ABW     SP.URB.TOTL     4.16e4 4.20e+4 4.22e+4 4.23e+4 4.23e+4 4.24e+4 4.26e+4
2 ABW     SP.URB.GROW     1.66e0 9.56e-1 4.01e-1 1.97e-1 9.46e-2 1.94e-1 3.67e-1
3 ABW     SP.POP.TOTL     8.91e4 9.07e+4 9.18e+4 9.27e+4 9.35e+4 9.45e+4 9.56e+4
4 ABW     SP.POP.GROW     2.54e0 1.77e+0 1.19e+0 9.97e-1 9.01e-1 1.00e+0 1.18e+0
5 AFE     SP.URB.TOTL     1.16e8 1.20e+8 1.24e+8 1.29e+8 1.34e+8 1.39e+8 1.44e+8
6 AFE     SP.URB.GROW     3.60e0 3.66e+0 3.72e+0 3.71e+0 3.74e+0 3.81e+0 3.81e+0
# ℹ 11 more variables: `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <dbl>,
#   `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, `2014` <dbl>, `2015` <dbl>,
#   `2016` <dbl>, `2017` <dbl>

longer <- world_bank_pop |>
  pivot_longer(
    `2000`:`2017`,
    names_to = "year",
    values_to = "value",
    names_transform = parse_number
  )

3. Reshape the table2 dataset such that there are separate columns for cases and population.

head(table2)

# A tibble: 6 × 4
  country      year type           count
  <chr>       <dbl> <chr>          <dbl>
1 Afghanistan  1999 cases            745
2 Afghanistan  1999 population  19987071
3 Afghanistan  2000 cases           2666
4 Afghanistan  2000 population  20595360
5 Brazil       1999 cases          37737
6 Brazil       1999 population 172006362

table2 |>
  pivot_wider(names_from = type,
              values_from = count)

# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583