[英]Compute row-wise counts using across or c_across
I'd like to ask a question inspired by this question asked years ago here in stack overflow我想问一个问题, 这个问题的灵感来自几年前在堆栈溢出中提出的这个问题
given the data frame: input_df给定数据框:input_df
num_col_1 num_col_2 text_col_1 text_col_2
1 1 4 yes yes
2 2 5 no yes
3 3 6 no <NA>
this chunk of code这段代码
library(dplyr)
df %>%
mutate(sum_yes = rowSums(.[c("text_col_1", "text_col_2")] == "yes"))
will produce this new dataframe将生产这个新的 dataframe
> output_df
num_col_1 num_col_2 text_col_1 text_col_2 sum_yes
1 1 4 yes yes 2
2 2 5 no yes 1
3 3 6 no <NA> 0
The question is, how do you do the same with modern dplyr verbs across and c_across ?问题是,你如何对现代的 dplyr 动词 cross 和c_across做同样的事情?
thank you.谢谢你。
1) c_across Here c_across
returns a one row tibble containing the columns indicated by its argument. 1) c_across这里
c_across
返回一个包含由其参数指示的列的单行 tibble。
library(dplyr)
input_df %>%
rowwise %>%
mutate(sum = sum( c_across(starts_with("text")) == "yes", na.rm = TRUE)) %>%
ungroup
giving:给予:
# A tibble: 3 x 5
num_col_1 num_col_2 text_col_1 text_col_2 sum
<int> <int> <chr> <chr> <int>
1 1 4 yes yes 2
2 2 5 no yes 1
3 3 6 no <NA> 0
2) across This gives the same result. 2)cross这给出了相同的结果。
across
returns a tibble with only the columns indicated by its argument. cross 返回一个仅包含其参数指示
across
列的小标题。
input_df %>%
mutate(sum = rowSums( across(starts_with("text")) == "yes", na.rm = TRUE))
In case it is of interest to sum the scores corresponding to the yes values:如果有兴趣将对应于 yes 值的分数求和:
3) c_across 3) c_across
library(dplyr)
input_df %>%
rowwise %>%
mutate(sum = sum( c_across(starts_with("num")) *
(c_across(starts_with("text")) == "yes"), na.rm = TRUE)) %>%
ungroup
giving:给予:
# A tibble: 3 x 5
num_col_1 num_col_2 text_col_1 text_col_2 sum
<int> <int> <chr> <chr> <int>
1 1 4 yes yes 5
2 2 5 no yes 5
3 3 6 no <NA> 0
4) across The output is the same as (3). 4)跨output同(3)。
input_df %>%
mutate(sum = rowSums(across(starts_with("num")) *
(across(starts_with("text")) == "yes"), na.rm = TRUE))
The input in reproducible form:可重现形式的输入:
Lines <- " num_col_1 num_col_2 text_col_1 text_col_2
1 1 4 yes yes
2 2 5 no yes
3 3 6 no NA"
input_df <- read.table(text = Lines)
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