在dplyr中跨多個列傳播變量

Question

假設我有以下數據集：

df <- read.table(header=TRUE, text="
politics_collapse question_id mean_confidence mean_accuracy mean_importance
Democrat arms_manufacturing_company 24.00000 0.0000000 1.000000
Democrat black_panther 48.50000 0.0000000 1.500000
Democrat stranger_things_universe 55.50000 0.2500000 2.500000
Democrat the_office 37.66667 0.6666667 1.666667
Democrat tupac 80.33333 1.0000000 2.000000
Democrat uber_ceo 39.60000 0.8000000 2.600000
Republican arms_manufacturing_company 37.00000 1.0000000 1.000000
Republican black_panther 45.00000 1.0000000 2.000000
Republican stranger_things_universe 33.00000 1.0000000 3.000000")

我正在嘗試在mean_confidence, mean_accuracy, and mean_importance列中傳播politics_collapse列。 結果輸出將是一個mean_confidence_democrat ， mean_accuracy_democrat和mean_importance_democrat ......對於共和黨來說也是如此。

像這樣：

df <- read.table(header=TRUE, text="
question_id mean_confidence_democrat mean_accuracy_democrat mean_importance_democrat mean_confidence_republican mean_accuracy_republican mean_importance_republican
arms_manufacturing_company 
black_panther 
stranger_things_universe 
the_office 
tupac 
uber_ceo 
arms_manufacturing_company 
black_panther 
stranger_things_universe")

顯然，每行中都有數值。

我在這里看到了這個小插圖： https ： //community.rstudio.com/t/spread-with-multiple-value-columns/5378建議使用全新的“樞軸功能”，但我無法弄清楚如何獲得他們去工作。 我還嘗試嵌套值，傳播它們和取消嵌套，並且沒有讓它工作。

Answer 1

這可能是您正在尋找的：

library(tidyverse)

df %>%
  gather("metric", "score", mean_confidence, mean_accuracy, mean_importance) %>%
  mutate(metric = paste0(metric, "_", politics_collapse)) %>%
  select(-politics_collapse) %>%
  spread(metric, score)

                 question_id mean_accuracy_Democrat mean_accuracy_Republican mean_confidence_Democrat mean_confidence_Republican mean_importance_Democrat
1 arms_manufacturing_company              0.0000000                        1                 24.00000                         37                 1.000000
2              black_panther              0.0000000                        1                 48.50000                         45                 1.500000
3   stranger_things_universe              0.2500000                        1                 55.50000                         33                 2.500000
4                 the_office              0.6666667                       NA                 37.66667                         NA                 1.666667
5                      tupac              1.0000000                       NA                 80.33333                         NA                 2.000000
6                   uber_ceo              0.8000000                       NA                 39.60000                         NA                 2.600000
  mean_importance_Republican
1                          1
2                          2
3                          3
4                         NA
5                         NA
6                         NA

Answer 2

在新的pivot功能正式發布之前，這是使用tidyr執行此操作的簡單方法：

df %>% 
    tidyr::gather(variable, value, mean_confidence, mean_accuracy, mean_importance) %>%
    tidyr::unite(new_columns, politics_collapse, variable) %>%
    tidyr::spread(new_columns, value)

贈送：

                 question_id Democrat_mean_accuracy Democrat_mean_confidence Democrat_mean_importance Republican_mean_accuracy Republican_mean_confidence Republican_mean_importance
1 arms_manufacturing_company              0.0000000                 24.00000                 1.000000                        1                         37                          1
2              black_panther              0.0000000                 48.50000                 1.500000                        1                         45                          2
3   stranger_things_universe              0.2500000                 55.50000                 2.500000                        1                         33                          3
4                 the_office              0.6666667                 37.66667                 1.666667                       NA                         NA                         NA
5                      tupac              1.0000000                 80.33333                 2.000000                       NA                         NA                         NA
6                   uber_ceo              0.8000000                 39.60000                 2.600000                       NA                         NA                         NA