R语言基础入门(5) rowwise按行进行数据处理

本节来介绍tdplyr中的一个重要函数rowwise，可以通过它对数据按行进行处理
https://dplyr.tidyverse.org/articles/rowwise.html

依然还是使用我们熟悉的iris数据集

library(tidyverse)

iris %>% as_tibble()

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 

可以看到有5列其中一列为因子其余四列为数值，进行计算时只需要数值列

按行计算均值

方法1

iris %>%  as_tibble() %>% select(-Species) %>%
  mutate(.,mean=rowMeans(.))

   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean
          <dbl>       <dbl>        <dbl>       <dbl> <dbl>
 1          5.1         3.5          1.4         0.2  2.55
 2          4.9         3            1.4         0.2  2.38
 3          4.7         3.2          1.3         0.2  2.35

方法2

根据逻辑判断只选择了数值列

iris %>% as_tibble() %>% select_if(is.numeric) %>%
  mutate(.,mean=rowMeans(.))

   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean
          <dbl>       <dbl>        <dbl>       <dbl> <dbl>
 1          5.1         3.5          1.4         0.2  2.55
 2          4.9         3            1.4         0.2  2.38
 3          4.7         3.2          1.3         0.2  2.35

方法3

iris %>% as_tibble() %>% select_if(is.numeric) %>% 
  rowwise() %>% 
  mutate(mean = mean(c_across(Sepal.Length:Petal.Width)))

通过rowwise说明对数据按行进行处理，c_across选择多列

方法4

iris %>% as_tibble() %>% 
  rowwise() %>%
  mutate(mean = rowMeans(across(where(is.numeric))))

通过across函数只选择了数值列，此函数异常强大，灵活使用能使代码简洁无比

按行统计最小值

iris %>% as_tibble() %>% 
  rowwise() %>%
  mutate(min = min(across(where(is.numeric))))

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   min
          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <dbl>
 1          5.1         3.5          1.4         0.2 setosa    0.2
 2          4.9         3            1.4         0.2 setosa    0.2
 3          4.7         3.2          1.3         0.2 setosa    0.2
 4          4.6         3.1          1.5         0.2 setosa    0.2

按行统计最大值

iris %>% as_tibble() %>%
  rowwise() %>%
  mutate(max = max(across(where(is.numeric))))

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   max
          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <dbl>
 1          5.1         3.5          1.4         0.2 setosa    5.1
 2          4.9         3            1.4         0.2 setosa    4.9
 3          4.7         3.2          1.3         0.2 setosa    4.7
 4          4.6         3.1          1.5         0.2 setosa    4.6

按行求和

iris %>% as_tibble() %>%
  rowwise() %>%
  mutate(sum = sum(across(where(is.numeric))))

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   sum
          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <dbl>
 1          5.1         3.5          1.4         0.2 setosa   10.2
 2          4.9         3            1.4         0.2 setosa    9.5
 3          4.7         3.2          1.3         0.2 setosa    9.4
 4          4.6         3.1          1.5         0.2 setosa    9.4

按行计算标准差

iris %>% as_tibble() %>%
  rowwise() %>%
  mutate(sd = sd(across(where(is.numeric))))

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    sd
          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <dbl>
 1          5.1         3.5          1.4         0.2 setosa   2.18
 2          4.9         3            1.4         0.2 setosa   2.04
 3          4.7         3.2          1.3         0.2 setosa   2.00
 4          4.6         3.1          1.5         0.2 setosa   1.91

统计每行中某值出现的次数

iris %>%  as_tibble() %>% select(-Species) %>%
  mutate(.,n=rowSums(. > 3))

   Sepal.Length Sepal.Width Petal.Length Petal.Width     n
          <dbl>       <dbl>        <dbl>       <dbl> <dbl>
 1          5.1         3.5          1.4         0.2     2
 2          4.9         3            1.4         0.2     1
 3          4.7         3.2          1.3         0.2     2
 4          4.6         3.1          1.5         0.2     2