[英]How to call list elements for calculation of a new data.frame column
Given a grouped data.frame
and a list
containing total numbers referring to another characteristic for each group (70 for group 1, 90 for group 2):给定一个分组的data.frame
和一个包含引用每个组的另一个特征的总数的list
(第 1 组为 70,第 2 组为 90):
group <- c(1,1,1,1,2,2,2,2,2)
n<- c(2,4,10,2,4,5,2,8,9)
df <- data.frame(group, n) %>%
group_by(group)
mylist <- list(70, 90)
How can I add a new column to the data.frame
that reflects the proportion of each n
in mylist
for the respective group given by n/mylist[[i]]*100
?如何向data.frame
添加一个新列,以反映n/mylist[[i]]*100
给出的各个组的mylist
中每个n
的比例?
I thought about using map_dbl
to iterate over the list elements, however, I can't get my head around how to call these commands in mutate
(something like df %>% mutate ("Percent" = n / map_dbl (mylist, .)*100)
) doing the percent calculation to finally make it look like this:我考虑过使用map_dbl
来遍历列表元素,但是,我无法理解如何在mutate
中调用这些命令(类似于df %>% mutate ("Percent" = n / map_dbl (mylist, .)*100)
) 进行百分比计算,最终使它看起来像这样:
df$percent %>% c (2.9, 5.7, 14.3, 2.9, 4.4, 5.6, 2.2., 8.9, 10.0)
df
What would be an elegant way to call the list
elements to include them into the calculation?调用list
元素以将它们包含在计算中的优雅方法是什么?
Perhaps this也许这
df %>% mutate(p = n/map_dbl(group, ~mylist[[.]]) * 100)
Basically, mapping group to pull out the selected element of mylist.基本上,映射组以拉出 mylist 的选定元素。
You might also consider using a join.您也可以考虑使用联接。
I know it doesn't use purrr
, but how about just rowwise()
?我知道它不使用purrr
,但是rowwise()
怎么样?
library(dplyr)
df %>%
rowwise %>%
mutate(percent = n / mylist[[group]] * 100)
## A tibble: 9 x 3
# group n percent
# <dbl> <dbl> <dbl>
#1 1 2 2.86
#2 1 4 5.71
#3 1 10 14.3
#4 1 2 2.86
#5 2 4 4.44
#6 2 5 5.56
#7 2 2 2.22
#8 2 8 8.89
#9 2 9 10
You can represent your list data as data.frame first to make it easier to work with.您可以首先将列表数据表示为 data.frame 以使其更易于使用。
library(dplyr)
library(data.table)
group <- c(1,1,1,1,2,2,2,2,2)
n<- c(2,4,10,2,4,5,2,8,9)
df <- data.frame(group, n) %>%
group_by(group)
setDT(df)
mylist <- data.table(
group = c(1 ,2),
other.metric = c(70, 90)
)
dt <- merge(df, mylist, by = "group")
dt[, n_share := n / other.metric * 100]
dt
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