创建现有类别的新分组并汇总新组

Question

我有一个数据框，目前有一列包含 8 种能量类型。我想将该列聚合成这样的两种类型。

1. 能源类型水电、地热、风能、太阳能、抽水蓄能是“可再生能源”类型

2. 其他3种是“不可再生能源”

创建了这组新的类别后，我想按国家/地区分组，然后为数据框中的每个“年份”列求和，这些列的名称类似于 energyProd_2016

数据集的当前外观如下

top10ProducersMod <- 
    structure(list(country = c("DE", "DE", "DE", "DE", "DE", "DE", 
    "DE", "DE", "ES", "ES", "ES", "ES", "ES", "ES", "ES", "ES", "FR", 
    "FR", "FR", "FR", "FR", "FR", "FR", "FR", "IT", "IT", "IT", "IT", 
    "IT", "IT", "IT", "IT", "PL", "PL", "PL", "PL", "PL", "PL", "PL", 
    "PL", "SE", "SE", "SE", "SE", "SE", "SE", "SE", "SE", "UK", "UK", 
    "UK", "UK", "UK", "UK", "UK", "UK", "NO", "NO", "NO", "NO", "NO", 
    "NO", "NO", "NO", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", 
    "UA", "UA", "UA", "UA", "UA", "UA", "UA", "UA"), country_name = c("Germany", 
    "Germany", "Germany", "Germany", "Germany", "Germany", "Germany", 
    "Germany", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
    "Spain", "Spain", "France", "France", "France", "France", "France", 
    "France", "France", "France", "Italy", "Italy", "Italy", "Italy", 
    "Italy", "Italy", "Italy", "Italy", "Poland", "Poland", "Poland", 
    "Poland", "Poland", "Poland", "Poland", "Poland", "Sweden", "Sweden", 
    "Sweden", "Sweden", "Sweden", "Sweden", "Sweden", "Sweden", "United Kingdom", 
    "United Kingdom", "United Kingdom", "United Kingdom", "United Kingdom", 
    "United Kingdom", "United Kingdom", "United Kingdom", "Norway", 
    "Norway", "Norway", "Norway", "Norway", "Norway", "Norway", "Norway", 
    "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", 
    "Turkey", "Ukraine", "Ukraine", "Ukraine", "Ukraine", "Ukraine", 
    "Ukraine", "Ukraine", "Ukraine"), type = c("Conventional thermal", 
    "Nuclear", "Hydro", "Pumped hydro power", "Wind", "Solar", "Geothermal", 
    "Other", "Conventional thermal", "Nuclear", "Hydro", "Pumped hydro power", 
    "Wind", "Solar", "Geothermal", "Other", "Conventional thermal", 
    "Nuclear", "Hydro", "Pumped hydro power", "Wind", "Solar", "Geothermal", 
    "Other", "Conventional thermal", "Nuclear", "Hydro", "Pumped hydro power", 
    "Wind", "Solar", "Geothermal", "Other", "Conventional thermal", 
    "Nuclear", "Hydro", "Pumped hydro power", "Wind", "Solar", "Geothermal", 
    "Other", "Conventional thermal", "Nuclear", "Hydro", "Pumped hydro power", 
    "Wind", "Solar", "Geothermal", "Other", "Conventional thermal", 
    "Nuclear", "Hydro", "Pumped hydro power", "Wind", "Solar", "Geothermal", 
    "Other", "Conventional thermal", "Nuclear", "Hydro", "Pumped hydro power", 
    "Wind", "Solar", "Geothermal", "Other", "Conventional thermal", 
    "Nuclear", "Hydro", "Pumped hydro power", "Wind", "Solar", "Geothermal", 
    "Other", "Conventional thermal", "Nuclear", "Hydro", "Pumped hydro power", 
    "Wind", "Solar", "Geothermal", "Other"), energyProd_2016 = c(390141, 
    80038, 25690, 5451, 78218, 38098, 165, 1805, 108210, 56100, 39180, 
    3410, 47712, 13051, 0, 91, 60607.854, 384008.125, 64879.956, 
    4786.979, 21472.916, 8657.27, 87.933, 559.515, 190121, 0, 43784.622, 
    1825.204, 17523, 21757, 5867, 650, 136945, 0, 2591, 474, 12279, 
    124, 0, 64, 14621, 60524, 61764, 119, 15479, 143, 0, 0, 203165.188, 
    65149.08, 8286.51, 2948.89, 37262.699, 10410.948, 0, 0, 3212, 
    0, 143442, 999, 2116, 0, 0, 297, 174166, 0, 66686, 0, 15381, 
    1013, 4010, 681, 65415, 75931, 9025, 1633, 949, 490, 0, 79), 
        energyProd_2017 = c(376128, 72155, 25888, 5910, 103707, 39401, 
        157, 1623, 126885, 55540, 20708, 2249, 47929, 13778, 0, 78, 
        68922.949, 379093.955, 54432.864, 5070.791, 24710.749, 9572.843, 
        119.937, 673.71, 199722.444, 0, 37556.721, 1825.96, 17565.332, 
        24016.821, 5821.46, 582.871, 137068.099, 0, 2999.86, 448.193, 
        14574.192, 165.463, 0, 61.73, 15003, 63008, 64676, 25, 17609, 
        230, 0, 0, 189296.528, 63886.86, 8723.43, 2862.01, 50003.654, 
        11524.87, 0, 0, 3233, 0, 142276, 1157, 2852, 0, 0, 281, 199910.043, 
        0, 57823.851, 0, 17793.167, 2817.837, 5101.487, 811.134, 
        54041.9, 80295.3, 8812, 1559, 974, 737.6, 0, 45), energyProd_2018 = c(320437.701, 
        72274.133, 20041.878, 6661.083, 112174, 46184, 165.25, 522.751, 
        110084.238, 53270.8, 36083.636, 2009.408, 49495.207, 12137.315, 
        0, 0, 53956.44, 393153.254, 68706.552, 9082.043, 26134.02, 
        9651.478, 84.084, 0, 185046, 0, 49275, 1632, 17318, 22887, 
        5708, 0, 138957.387, 0, 2353.336, 402.36, 12487.345, 298.432, 
        0, 0, 14869.986, 65801.155, 60977.602, 0, 16638.345, 0, 0, 
        0, 180837.905, 59097.753, 7678.659, 2515.998, 56903.961, 
        12857.349, 0, 0, 3457.28, 0, 139512.026, 3490.918, 3875.881, 
        0, 0, 0, 195153.546, 0, 59865.351, 0, 19825.697, 7459.485, 
        6275.373, 703.126, 60830.7, 84397.9, 11920.2, 0, 1185.9, 
        737.8, 0, 0)), row.names = c(NA, -80L), class = c("tbl_df", 
    "tbl", "data.frame"))

我希望它是这样的

DE Germany Renewable Energy      amount1  amount2 amount3
DE Germany Non-Renewable Energy  amount4  amount5 amount6

Answer 1

一种选择是：

library(tidyverse)
top10ProducersMod %>% 
  mutate(type2 = if_else(type %in% c('Hydro', 'Geothermal', 'Wind', 'Solar', 'Pumped hydro power'), 'Renewable_Energy', 'Non-Renewable_Energy')) %>%
  pivot_longer(-c(country:type, type2), names_to = 'energy_year', values_to = 'prod') %>%
  group_by(country, country_name, type2, energy_year) %>%
  summarise(prod = sum(prod)) %>%
  pivot_wider(values_from = prod, names_from = energy_year)

# country country_name type2      energyProd_2016 energyProd_2017 energyProd_2018
# <chr>   <chr>        <chr>                <dbl>           <dbl>           <dbl>
# 1 DE      Germany      Non-Renew~         471984          449906          393235.
# 2 DE      Germany      Renewable~         147622          175063          185226.
# 3 ES      Spain        Non-Renew~         164401          182503          163355.
# 4 ES      Spain        Renewable~         103353           84664           99726.
# 5 FR      France       Non-Renew~         445175.         448691.         447110.
# 6 FR      France       Renewable~          99885.          93907.         113658.

Answer 2

使用data.table，为Energy类型创建分组列，然后对多列求和：

library(data.table)

setDT(top10ProducersMod)

# make a grouping column
top10ProducersMod[, Energy := ifelse(type %in% c("Hydro", "Geothermal", "Wind", "Solar", "Pumped hydro power"), 
                                     "Renewable Energy", "Non-Renewable Energy") ]

# then group by columns, and sum on selected columns
top10ProducersMod[, lapply(.SD, sum), by = .(country, country_name, Energy), .SDcols = 4:6 ]
#     country   country_name               Energy energyProd_2016 energyProd_2017 energyProd_2018
# 1:      DE        Germany Non-Renewable Energy       471984.00       449906.00       393234.59
# 2:      DE        Germany     Renewable Energy       147622.00       175063.00       185226.21
# 3:      ES          Spain Non-Renewable Energy       164401.00       182503.00       163355.04
# 4:      ES          Spain     Renewable Energy       103353.00        84664.00        99725.57
...

Answer 3

还有dplyr，但没有旋转...

library(dplyr)

df %>% 
   mutate(renewable = case_when(
      type == "Hydro" ~ "Renewable Energy",
      type == "Geothermal" ~ "Renewable Energy",
      type == "Wind" ~ "Renewable Energy",
      type == "Solar" ~ "Renewable Energy",
      type == "Pumped hydro power" ~ "Renewable Energy",
      TRUE ~ "Non-Renewable Energy")
      ) %>%
   group_by(country, country_name, renewable) %>%
   summarise(across(starts_with("energyProd_"), sum, .names = "{.col}"))

#> `summarise()` regrouping output by 'country', 'country_name' (override with `.groups` argument)
#> # A tibble: 20 x 6
#> # Groups:   country, country_name [10]
#>    country country_name renewable energyProd_2016 energyProd_2017
#>    <chr>   <chr>        <chr>               <dbl>           <dbl>
#>  1 DE      Germany      Non-Rene…         471984          449906 
#>  2 DE      Germany      Renewabl…         147622          175063 
#>  3 ES      Spain        Non-Rene…         164401          182503 
#>  4 ES      Spain        Renewabl…         103353           84664 
#>  5 FR      France       Non-Rene…         445175.         448691.
#>  6 FR      France       Renewabl…          99885.          93907.
#>  7 IT      Italy        Non-Rene…         190771          200305.
#>  8 IT      Italy        Renewabl…          90757.          86786.
#>  9 NO      Norway       Non-Rene…           3509            3514 
#> 10 NO      Norway       Renewabl…         146557          146285 
#> 11 PL      Poland       Non-Rene…         137009          137130.
#> 12 PL      Poland       Renewabl…          15468           18188.
#> 13 SE      Sweden       Non-Rene…          75145           78011 
#> 14 SE      Sweden       Renewabl…          77505           82540 
#> 15 TR      Turkey       Non-Rene…         174847          200721.
#> 16 TR      Turkey       Renewabl…          87090           83536.
#> 17 UA      Ukraine      Non-Rene…         141425          134382.
#> 18 UA      Ukraine      Renewabl…          12097           12083.
#> 19 UK      United King… Non-Rene…         268314.         253183.
#> 20 UK      United King… Renewabl…          58909.          73114.
#> # … with 1 more variable: energyProd_2018 <dbl>