tidyr로 데이터 재구조화하기
Jeroen Boeye
Head of Machine Learning, Faktion
who_df
# A tibble: 362 x 3 country metric value <chr> <chr> <dbl> 1 Afghanistan life_exp 62.7 2 Afghanistan pct_obese 5.5 3 Albania life_exp 76.4 4 Albania pct_obese 21.7 # ... with 358 more rows
who_df %>% pivot_wider(names_from = metric, values_from = value)
# A tibble: 181 x 3 country life_exp pct_obese <chr> <dbl> <dbl> 1 Afghanistan 62.7 5.5 2 Albania 76.4 21.7 3 Algeria 76.4 27.4 4 Angola 62.6 8.2 # ... with 177 more rows
who_long_df %>% pivot_wider(names_from = metric, values_from = value, names_prefix = "national_")
# A tibble: 181 x 3 country national_life_exp national_pct_obese <chr> <dbl> <dbl> 1 Afghanistan 62.7 5.5 2 Albania 76.4 21.7 3 Algeria 76.4 27.4 4 Angola 62.6 8.2 # ... with 177 more rows
sideways_df
# A tibble: 2 x 5 variable `1969` `1970` `1971` `1972` <chr> <int> <int> <int> <int> 1 people_on_moon 4 0 4 4 2 nuclear_bombs 82 85 59 62
sideways_df %>% pivot_longer(-variable, names_to = "year", names_transform = list(year = as.integer))
# A tibble: 8 x 3 variable year value <chr> <int> <int> 1 people_on_moon 1969 4 2 people_on_moon 1970 0 3 people_on_moon 1971 4 4 people_on_moon 1972 4 5 nuclear_bombs 1969 82 6 nuclear_bombs 1970 85 7 nuclear_bombs 1971 59 8 nuclear_bombs 1972 62
sideways_df %>% pivot_longer(-variable, names_to = "year", names_transform = list(year = as.integer)) %>% pivot_wider(names_from = variable, values_from = value)
# A tibble: 4 x 3 year people_on_moon nuclear_bombs <int> <int> <int> 1 1969 4 82 2 1970 0 85 3 1971 4 59 4 1972 4 62
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