我有一个包含许多列的数据框(或者不管是小标题),我只想对其中的7个应用一个函数(比如rowSums),但是我不想弄糟其他的。诀窍是我想按管道顺序执行此操作 -创建(或读取数据) -应用功能 -之后的可选操作
这是我要在前三列上进行求和的数据框上的可复制示例。
data <- data.frame("v1" = runif(10, 0, 10), "v2" = runif(10, 0 ,10), "v3" = runif(10, 0 ,10), "v4" = rep("some_charchter", 10))
我通常会做的方式是
data$sum <- rowSums(data[,1:3])
但是我想要这样的东西
data <- data.frame("v1" = runif(10, 0, 10), "v2" = runif(10, 0 ,10), "v3" = runif(10, 0 ,10), "v4" = rep("some_charchter", 10)) %>%
mutate(sum = rowSums())
感谢您的帮助!
答案 0 :(得分:2)
您可以使用.
在管道内访问数据对象。因此mutate(sum = rowSums(.[, 1:3]))
可以达到目的:
data <- data.frame("v1" = runif(10, 0, 10), "v2" = runif(10, 0 ,10), "v3" = runif(10, 0 ,10), "v4" = rep("some_charchter", 10)) %>%
mutate(sum = rowSums(.[, 1:3]))
data
v1 v2 v3 v4 sum
1 2.280871 0.1981815 7.5349128 some_charchter 10.013965
2 1.250208 7.6687056 0.6193483 some_charchter 9.538262
3 6.782954 3.6973201 2.7694021 some_charchter 13.249677
4 3.809574 6.8641731 3.1271489 some_charchter 13.800896
5 9.339726 4.4571677 5.4489081 some_charchter 19.245802
6 6.623371 3.9594287 0.6025072 some_charchter 11.185307
7 6.843193 1.3548732 3.1826649 some_charchter 11.380731
8 2.377099 7.5661778 9.6320561 some_charchter 19.575333
9 3.582874 2.1485691 8.2970807 some_charchter 14.028524
10 4.565336 3.7073800 0.3355328 some_charchter 8.608248
答案 1 :(得分:1)
另一种选择是根据您在选定列上的返回类型使用pmap_*
的变体形式。
library(dplyr)
library(purrr)
data %>% mutate(sum = pmap_dbl(list(v1, v2, v3), sum))
# v1 v2 v3 v4 sum
#1 1.13703411 6.935913 3.1661245 some_charchter 11.239072
#2 6.22299405 5.449748 3.0269337 some_charchter 14.699676
#3 6.09274733 2.827336 1.5904600 some_charchter 10.510543
#4 6.23379442 9.234335 0.3999592 some_charchter 15.868088
#5 8.60915384 2.923158 2.1879954 some_charchter 13.720308
#6 6.40310605 8.372956 8.1059855 some_charchter 22.882048
#7 0.09495756 2.862233 5.2569755 some_charchter 8.214166
#8 2.32550506 2.668208 9.1465817 some_charchter 14.140295
#9 6.66083758 1.867228 8.3134505 some_charchter 16.841516
#10 5.14251141 2.322259 0.4577026 some_charchter 7.922473
数据
set.seed(1234)
data <- data.frame("v1" = runif(10, 0, 10), "v2" = runif(10, 0 ,10),
"v3" = runif(10, 0 ,10), "v4" = rep("some_charchter", 10))
答案 2 :(得分:1)
我们可能还需要进行轻微的类型检查,以使流程良好地自动化
library(tidyverse)
data %>%
mutate(Sum = select_if(., is.numeric) %>%
reduce(`+`))
# v1 v2 v3 v4 Sum
#1 1.13703411 6.935913 3.1661245 some_charchter 11.239072
#2 6.22299405 5.449748 3.0269337 some_charchter 14.699676
#3 6.09274733 2.827336 1.5904600 some_charchter 10.510543
#4 6.23379442 9.234335 0.3999592 some_charchter 15.868088
#5 8.60915384 2.923158 2.1879954 some_charchter 13.720308
#6 6.40310605 8.372956 8.1059855 some_charchter 22.882048
#7 0.09495756 2.862233 5.2569755 some_charchter 8.214166
#8 2.32550506 2.668208 9.1465817 some_charchter 14.140295
#9 6.66083758 1.867228 8.3134505 some_charchter 16.841516
#10 5.14251141 2.322259 0.4577026 some_charchter 7.922473
注意:这将是矢量化解决方案,类似于@symbolrush的rowSums
解决方案
set.seed(1234)
data <- data.frame("v1" = runif(10, 0, 10), "v2" = runif(10, 0 ,10),
"v3" = runif(10, 0 ,10), "v4" = rep("some_charchter", 10))