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Gapminder data frame in various forms

Source: R/gapminder.R
gap_simple.Rd

The main data frame from the gapminder package in three forms:

  1. gap_simple, same as gapminder::gapminder

  2. gap_nested, nested by country and continent

  3. gap_split, split by country

Usage

gap_simple

gap_nested

gap_split

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1704 rows and 6 columns.

Examples

gap_simple
#> # A tibble: 1,704 × 6
#>    country     continent  year lifeExp      pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#>  1 Afghanistan Asia       1952    28.8  8425333      779.
#>  2 Afghanistan Asia       1957    30.3  9240934      821.
#>  3 Afghanistan Asia       1962    32.0 10267083      853.
#>  4 Afghanistan Asia       1967    34.0 11537966      836.
#>  5 Afghanistan Asia       1972    36.1 13079460      740.
#>  6 Afghanistan Asia       1977    38.4 14880372      786.
#>  7 Afghanistan Asia       1982    39.9 12881816      978.
#>  8 Afghanistan Asia       1987    40.8 13867957      852.
#>  9 Afghanistan Asia       1992    41.7 16317921      649.
#> 10 Afghanistan Asia       1997    41.8 22227415      635.
#> # … with 1,694 more rows
gap_nested
#> # A tibble: 142 × 3
#>    country     continent data             
#>    <fct>       <fct>     <list>           
#>  1 Afghanistan Asia      <tibble [12 × 4]>
#>  2 Albania     Europe    <tibble [12 × 4]>
#>  3 Algeria     Africa    <tibble [12 × 4]>
#>  4 Angola      Africa    <tibble [12 × 4]>
#>  5 Argentina   Americas  <tibble [12 × 4]>
#>  6 Australia   Oceania   <tibble [12 × 4]>
#>  7 Austria     Europe    <tibble [12 × 4]>
#>  8 Bahrain     Asia      <tibble [12 × 4]>
#>  9 Bangladesh  Asia      <tibble [12 × 4]>
#> 10 Belgium     Europe    <tibble [12 × 4]>
#> # … with 132 more rows

str(gap_split, max.level = 1, list.len = 10)
#> List of 142
#>  $ Afghanistan             : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Albania                 : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Algeria                 : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Angola                  : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Argentina               : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Australia               : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Austria                 : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Bahrain                 : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Bangladesh              : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ Belgium                 : tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>   [list output truncated]
str(gap_split[[1]])
#> tibble [12 × 6] (S3: tbl_df/tbl/data.frame)
#>  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
#>  $ year     : int [1:12] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
#>  $ lifeExp  : num [1:12] 28.8 30.3 32 34 36.1 ...
#>  $ pop      : int [1:12] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
#>  $ gdpPercap: num [1:12] 779 821 853 836 740 ...