12 Reshaping

(AST230) R for Data Science

Author

Md Rasel Biswas

Long- and wide-format data

Wide-format data

year Adelie Chinstrap Gentoo
2007 50 26 34
2008 50 18 46
2009 52 24 44

Long-format data

year species n
2007 Adelie 50
2007 Chinstrap 26
2007 Gentoo 34
2008 Adelie 50
2008 Chinstrap 18
2008 Gentoo 46
2009 Adelie 52
2009 Chinstrap 24
2009 Gentoo 44

Wide format → Long format data

  • pivot_longer() function converts an wide format data to a long format data

  • It is required to mention which columns (variables) should be combined into a single variable and it will return two new variables based on the column names and values of the selected columns

    • The first variable will contain the names of the selected columns

    • The second variable will contain the values of the selected columns

Long-format → Wide-format

  • The syntax of the function pivot_longer()

    • data

    • cols \rightarrow selected variables

    • names_to \rightarrow selected variable (column) names

    • values_to \rightarrow A character vector specifying the new column to create from the information stored in names_to argument

  • Wide-format data
wdat
# A tibble: 3 × 4
   year Adelie Chinstrap Gentoo
  <int>  <int>     <int>  <int>
1  2007     50        26     34
2  2008     50        18     46
3  2009     52        24     44
  • Long-format data
wdat %>% 
  pivot_longer( 
    cols = Adelie:Gentoo, 
    names_to = "species",  
    values_to = "body_mass"
    ) 
# A tibble: 9 × 3
   year species   body_mass
  <int> <chr>         <int>
1  2007 Adelie           50
2  2007 Chinstrap        26
3  2007 Gentoo           34
4  2008 Adelie           50
5  2008 Chinstrap        18
6  2008 Gentoo           46
7  2009 Adelie           52
8  2009 Chinstrap        24
9  2009 Gentoo           44

  • pivot_wider() function converts a long-format data to an wide-format data

  • It is required to mention which columns (variables) should be combined and it will create two new variables based on the column names and values of the selected columns

    • The first variable will contain the names of the selected columns

    • The second variable will contain the values of the selected columns

  • The syntax of the function pivot_wider()

    • data

    • id_cols \rightarrow unique idetifier of a column

    • names_from \rightarrow selected variable names

    • values_from \rightarrow

  • Long format data
penguins %>%
  count(year, species)
# A tibble: 9 × 3
   year species       n
  <int> <fct>     <int>
1  2007 Adelie       50
2  2007 Chinstrap    26
3  2007 Gentoo       34
4  2008 Adelie       50
5  2008 Chinstrap    18
6  2008 Gentoo       46
7  2009 Adelie       52
8  2009 Chinstrap    24
9  2009 Gentoo       44
  • Wide format data
penguins %>%
  count(year, species) %>%
  pivot_wider(
    names_from = species,
    values_from = n)
# A tibble: 3 × 4
   year Adelie Chinstrap Gentoo
  <int>  <int>     <int>  <int>
1  2007     50        26     34
2  2008     50        18     46
3  2009     52        24     44

Practice 2.1

  1. Starting with penguins, find counts of observation by species, island and year.

  2. Starting with penguins, filter to only keep Adelie and Gentoo penguins, then find counts by species and sex.

  3. Add a new column to penguins called year that contains:

    • “Year 1” if the year is 2007

    • “Year 2” if the year is 2008

    • “Year 3” if the year is 2009

  1. Starting with penguins, only keep observations for chinstrap penguins, then only keep the flipper_length_mm and body_mass_g variables.

  • Add a new column called fm_ratio that contains the ratio of flipper length to body mass for each penguin.

  • Next, add another column named ratio_bin which contains the word “high” if fm_ratio is greater than or equal to 0.05, “low” if the ratio is less than 0.05, and “no record” if anything else (e.g. NA).