[英]Using the unite function in R and removing duplicated values
I'm trying to use the unite
function in R to concatenate values across columns, but also deduplicate the values.我正在尝试使用 R 中的
unite
函数来跨列连接值,但也对值进行重复数据删除。 How can I accomplish this?我怎样才能做到这一点?
Here is the input data:这是输入数据:
input <- tibble(
id = c('aa', 'ss', 'dd', 'qq'),
'2017' = c('tv', NA, NA, 'web'),
'2018' = c('tv', 'web', NA, NA),
'2019' = c(NA, 'web', 'book', 'tv')
)
# A tibble: 4 x 4
id `2017` `2018` `2019`
<chr> <chr> <chr> <chr>
1 aa tv tv NA
2 ss NA web web
3 dd NA NA book
4 qq web NA tv
The desired output with the ALL column is: ALL 列所需的输出是:
> output
# A tibble: 4 x 5
id `2017` `2018` `2019` ALL
<chr> <chr> <chr> <chr> <chr>
1 aa tv tv NA tv
2 ss NA web web web
3 dd NA NA book book
4 qq web NA tv web, tv
Similar questions exist here on SO, but since you are after a unite
solution and I couldn't find any that specifically use unite
, here we go: SO 上也存在类似的问题,但是由于您正在寻求
unite
解决方案,而我找不到任何专门使用unite
解决方案,因此我们开始:
Using unite
使用
unite
input %>% unite(ALL, -id, sep = ", ", remove = FALSE, na.rm = TRUE)
## A tibble: 4 x 5
# id ALL `2017` `2018` `2019`
# <chr> <chr> <chr> <chr> <chr>
#1 aa tv tv NA NA
#2 ss web NA web NA
#3 dd book NA NA book
#4 qq web, tv web NA tv
To recover the exact column order of your expected output, you can add a %>% select(names(input), ALL)
.要恢复预期输出的确切列顺序,您可以添加
%>% select(names(input), ALL)
。
Alternatively, using nest
或者,使用
nest
input %>%
group_by(id) %>%
nest() %>%
mutate(ALL = map_chr(data, ~toString(unlist(.x[!is.na(unlist(.x))])))) %>%
unnest(data)
## A tibble: 4 x 5
## Groups: id [4]
# id `2017` `2018` `2019` ALL
# <chr> <chr> <chr> <chr> <chr>
#1 aa tv NA NA tv
#2 ss NA web NA web
#3 dd NA NA book book
#4 qq web NA tv web, tv
Or the base R way (as in How to create new column with all non-NA values from multiple other columns? ):或基本 R 方式(如如何使用来自多个其他列的所有非 NA 值创建新列? ):
input$ALL <- apply(input[, -1], 1, function(x) toString(x[!is.na(x)]))
input
# A tibble: 4 x 5
# id `2017` `2018` `2019` ALL
# <chr> <chr> <chr> <chr> <chr>
#1 aa tv NA NA tv
#2 ss NA web NA web
#3 dd NA NA book book
#4 qq web NA tv web, tv
I am not sure if deduplicating is possible with unite
, however you can use apply
row-wise.我不确定
unite
是否可以进行重复数据删除,但是您可以按行apply
。
input$ALL <- apply(input[-1], 1, function(x) toString(na.omit(unique(x))))
Or a tidyverse
way could be using pmap
或者一种
tidyverse
方式可能是使用pmap
library(tidyverse)
input %>%
mutate(ALL = pmap_chr(select(., -id), ~toString(unique(na.omit(c(...))))))
# id `2017` `2018` `2019` ALL
# <chr> <chr> <chr> <chr> <chr>
#1 aa tv tv NA tv
#2 ss NA web web web
#3 dd NA NA book book
#4 qq web NA tv web, tv
Or getting the data in long format and then joining或者以长格式获取数据然后加入
input %>%
pivot_longer(cols = -id, values_drop_na = TRUE) %>%
group_by(id) %>%
summarise(ALL = toString(unique(value))) %>%
left_join(input)
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