mutate_each：标准化data.frame行

Question

如何使用dplyr标准化数据框的每一行，使行元素总和为一行？

我更喜欢使用mutate_each，但结果不正确，因为我在下面指定了它们。有什么问题？

library(dplyr)
iris %>%select(-Species)%>%mutate_each(funs(./sum(.)))%>%head()

  Sepal.Length Sepal.Width Petal.Length Petal.Width
1  0.005818597 0.007631923  0.002483591 0.001111729
2  0.005590416 0.006541648  0.002483591 0.001111729
3  0.005362236 0.006977758  0.002306191 0.001111729
4  0.005248146 0.006759703  0.002660990 0.001111729
5  0.005704507 0.007849978  0.002483591 0.001111729
6  0.006160867 0.008504143  0.003015789 0.002223457

Answer 1

mutate_each不按行计算sum列，除了已弃用之外，您应该使用mutate_all代替;要标准化每一行，您可以先计算行总和，然后按行划分所有列：

row_sum = rowSums(select(iris, -Species))
iris %>% select(-Species) %>% mutate_all(~ ./row_sum) %>% head()

#  Sepal.Length Sepal.Width Petal.Length Petal.Width
#1    0.5000000   0.3431373    0.1372549  0.01960784
#2    0.5157895   0.3157895    0.1473684  0.02105263
#3    0.5000000   0.3404255    0.1382979  0.02127660
#4    0.4893617   0.3297872    0.1595745  0.02127660
#5    0.4901961   0.3529412    0.1372549  0.01960784
#6    0.4736842   0.3421053    0.1491228  0.03508772

如果您更喜欢单个烟斗：

iris %>% 
    mutate(row_sum = rowSums(select(., 1:4))) %>% 
    mutate_at(1:4, ~ ./row_sum) %>% 
    select(-row_sum) %>% head()

#  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1    0.5000000   0.3431373    0.1372549  0.01960784  setosa
#2    0.5157895   0.3157895    0.1473684  0.02105263  setosa
#3    0.5000000   0.3404255    0.1382979  0.02127660  setosa
#4    0.4893617   0.3297872    0.1595745  0.02127660  setosa
#5    0.4901961   0.3529412    0.1372549  0.01960784  setosa
#6    0.4736842   0.3421053    0.1491228  0.03508772  setosa

Answer 2

mutate_if解决方案

RS <- rowSums(iris[,1:4])
iris %>%
  mutate_if(is.numeric, funs(. / RS))

输出

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1    0.5000000   0.3431373    0.1372549  0.01960784  setosa
2    0.5157895   0.3157895    0.1473684  0.02105263  setosa
3    0.5000000   0.3404255    0.1382979  0.02127660  setosa
4    0.4893617   0.3297872    0.1595745  0.02127660  setosa
5    0.4901961   0.3529412    0.1372549  0.01960784  setosa
# etc