Pivot wider

The function pivot_wider() widens data, converting it from long to wide.

When data is widened:

The number of rows decreases.
The number of columns increases.

To widen data two columns are chosen.

A names column is chosen. These names are used to name the widened columns.
A values column is chosen. These values populate the widened columns.

Dataset

For demonstration we’ll load the fisheries_long_tbl data from the mgrtibbles package (hyperlink includes install instructions).

#Load package
library("mgrtibbles")
#mammal_sleep_tbl tibble for demonstration
mgrtibbles::fisheries_long_tbl

# A tibble: 14,674 × 3
   Entity       Year Metric_tons
   <chr>       <dbl>       <dbl>
 1 Afghanistan  1969         400
 2 Afghanistan  1970         400
 3 Afghanistan  1971         500
 4 Afghanistan  1972         500
 5 Afghanistan  1973         500
 6 Afghanistan  1974         500
 7 Afghanistan  1975         600
 8 Afghanistan  1976         600
 9 Afghanistan  1977         600
10 Afghanistan  1978         600
# ℹ 14,664 more rows

Long to wide

Pivot the long tibble to a wide tibble.

The two options provided below are:

names_from=: Column in long data to use as the widened column names.
values_from=: Column in long data to use as the widened values.

fisheries_long_tbl |> tidyr::pivot_wider(names_from=Year, values_from=Metric_tons) |>
    #View first 11 columns with select
    dplyr::select(1:11)

# A tibble: 264 × 11
   Entity  `1969` `1970` `1971` `1972` `1973` `1974` `1975` `1976` `1977` `1978`
   <chr>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1 Afgha…     400 4   e2 5   e2 5   e2 5   e2 5   e2 6   e2 6   e2 6   e2 6   e2
 2 Afric… 3078461 2.48e6 2.36e6 2.63e6 2.78e6 2.74e6 2.49e6 2.20e6 2.16e6 2.12e6
 3 Afric…  850772 9.61e5 1.13e6 1.20e6 1.20e6 1.24e6 1.28e6 1.26e6 1.29e6 1.31e6
 4 Alban…    7000 8   e3 8   e3 8   e3 8.00e3 8.45e3 8.41e3 8.38e3 8.34e3 8.81e3
 5 Alger…   23150 2.42e4 2.37e4 2.83e4 3.12e4 3.58e4 3.77e4 3.51e4 4.35e4 3.41e4
 6 Ameri…       0 0      0      0      1   e2 8.2 e1 1.36e2 1.13e2 2.02e2 1.93e2
 7 Andor…      NA 0      0      0      0      0      0      0      0      0     
 8 Angola  425200 3.75e5 3.24e5 6.07e5 4.79e5 4.00e5 1.61e5 8.15e4 1.20e5 1.26e5
 9 Antig…     800 9   e2 9   e2 1.2 e3 1.5 e3 1.59e3 1.60e3 1.61e3 1.82e3 1.62e3
10 Arab …  634609 6.88e5 7.05e5 7.53e5 9.21e5 8.99e5 8.64e5 9.34e5 9.05e5 9.10e5
# ℹ 254 more rows