5523

R Markdown

key_crop_yields <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv') %>% 
  janitor::clean_names()

## Parsed with column specification:
## cols(
##   Entity = col_character(),
##   Code = col_character(),
##   Year = col_double(),
##   `Wheat (tonnes per hectare)` = col_double(),
##   `Rice (tonnes per hectare)` = col_double(),
##   `Maize (tonnes per hectare)` = col_double(),
##   `Soybeans (tonnes per hectare)` = col_double(),
##   `Potatoes (tonnes per hectare)` = col_double(),
##   `Beans (tonnes per hectare)` = col_double(),
##   `Peas (tonnes per hectare)` = col_double(),
##   `Cassava (tonnes per hectare)` = col_double(),
##   `Barley (tonnes per hectare)` = col_double(),
##   `Cocoa beans (tonnes per hectare)` = col_double(),
##   `Bananas (tonnes per hectare)` = col_double()
## )

## Warning in FUN(X[[i]], ...): strings not representable in native encoding will
## be translated to UTF-8

## Warning in FUN(X[[i]], ...): unable to translate '<U+00C4>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00D6>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00E4>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00F6>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00DF>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00C6>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00E6>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00D8>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00F8>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00C5>' to native encoding

## Warning in FUN(X[[i]], ...): unable to translate '<U+00E5>' to native encoding

dplyr::glimpse(key_crop_yields)

## Rows: 13,075
## Columns: 14
## $ entity                         <chr> "Afghanistan", "Afghanistan", "Afgha...
## $ code                           <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "...
## $ year                           <dbl> 1961, 1962, 1963, 1964, 1965, 1966, ...
## $ wheat_tonnes_per_hectare       <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.97...
## $ rice_tonnes_per_hectare        <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.72...
## $ maize_tonnes_per_hectare       <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.44...
## $ soybeans_tonnes_per_hectare    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ potatoes_tonnes_per_hectare    <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.80...
## $ beans_tonnes_per_hectare       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ peas_tonnes_per_hectare        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ cassava_tonnes_per_hectare     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ barley_tonnes_per_hectare      <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.08...
## $ cocoa_beans_tonnes_per_hectare <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ bananas_tonnes_per_hectare     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, ...

map_df <- map_data("world")
dplyr::glimpse(map_df)

## Rows: 99,338
## Columns: 6
## $ long      <dbl> -69.89912, -69.89571, -69.94219, -70.00415, -70.06612, -7...
## $ lat       <dbl> 12.45200, 12.42300, 12.43853, 12.50049, 12.54697, 12.5970...
## $ group     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...
## $ order     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18...
## $ region    <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Ar...
## $ subregion <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...

ggplot(map_df, aes(long, lat, group = group)) +
  geom_polygon(color = "white") +   
  coord_equal() + 
  theme_void()