dplyr:mutate中的custum函数。使用完整矩阵而不是块?
考虑这个例子:dplyr:mutate中的custum函数。使用完整矩阵而不是块?,r,dplyr,magrittr,R,Dplyr,Magrittr,考虑这个例子: library(dplyr) library(magrittr) set.seed(123) grp_s <- round(runif(4, 1, 10)) group <- rep(1:length(grp_s), grp_s) dataF <- data.frame(grouping = group, var_a = runif(length(group)), var_b = runif(length(group)), var_c = runif(leng
library(dplyr)
library(magrittr)
set.seed(123)
grp_s <- round(runif(4, 1, 10))
group <- rep(1:length(grp_s), grp_s)
dataF <- data.frame(grouping = group, var_a = runif(length(group)), var_b = runif(length(group)), var_c = runif(length(group)))
compute_it <- function(var_a, var_b){
sum(var_a[var_b > .5], na.rm = TRUE)
}
dataF %<>%
group_by(grouping) %>%
mutate(fix_it = compute_it(var_a, var_b))
其中,上面使用了
compute\u it
。如何做到这一点?同样使用tidyr
和purrr
我们可以先使用do
或nest
:
library(tidyverse)
dataF %>%
group_by(grouping) %>%
do(fix_it = compute_it_2(.)) %>%
unnest()
给予:
也许您正在寻找
do(s=fix_it()
。或者使用dataF%%>%groupby(grouping)%%>%nest()%%>%mutate(s=map(data,fix_it2))
@Axeman:这些解决方案都不会返回带有新列fix_it_2的原始数据帧(它们返回必须解析的复杂数据结构),谢谢!最后一个解决方案是
library(tidyverse)
dataF %>%
group_by(grouping) %>%
do(fix_it = compute_it_2(.)) %>%
unnest()
# A tibble: 4 × 2
grouping fix_it
<int> <dbl>
1 1 2.4065483
2 2 0.9568333
3 3 0.0000000
4 4 1.8274955
dataF %>%
group_by(grouping) %>%
nest() %>%
mutate(fix_it = map_dbl(data, compute_it_2))
# A tibble: 4 × 3
grouping data fix_it
<int> <list> <dbl>
1 1 <tibble [4 × 3]> 2.4065483
2 2 <tibble [8 × 3]> 0.9568333
3 3 <tibble [5 × 3]> 0.0000000
4 4 <tibble [9 × 3]> 1.8274955
# A tibble: 26 × 5
grouping fix_it var_a var_b var_c
<int> <dbl> <dbl> <dbl> <dbl>
1 1 2.4065483 0.9404673 0.96302423 0.12753165
2 1 2.4065483 0.0455565 0.90229905 0.75330786
3 1 2.4065483 0.5281055 0.69070528 0.89504536
4 1 2.4065483 0.8924190 0.79546742 0.37446278
5 2 0.9568333 0.5514350 0.02461368 0.66511519
6 2 0.9568333 0.4566147 0.47779597 0.09484066
7 2 0.9568333 0.9568333 0.75845954 0.38396964
8 2 0.9568333 0.4533342 0.21640794 0.27438364
9 2 0.9568333 0.6775706 0.31818101 0.81464004
10 2 0.9568333 0.5726334 0.23162579 0.44851634
# ... with 16 more rows