[英]R: Change value in one column to a value based on another column if NA in one column
I have the following data:我有以下数据:
structure(list(Date = c("01.08.2018", "02.08.2018", "03.08.2018",
"04.08.2018", "31.08.2018", "06.04.2019", "07.04.2019", "08.04.2019",
"01.08.2018", "02.08.2018", "03.08.2018", "04.08.2018", "06.04.2019",
"07.04.2019", "08.04.2019", "01.08.2018", "02.08.2018", "03.08.2018",
"04.08.2018", "05.08.2018", "07.04.2019", "30.04.2019"), Name = c("A",
"A", "A", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B",
"B", "C", "C", "C", "C", "C", "C", "C"), Rating = c(1L, 1L, 1L,
3L, 3L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L,
5L, 5L, 5L), Size = c(1234L, 24123L, 23L, 1L, 23L, 3L, 23L, 4L,
323L, 3424L, 523L, 234L, 35L, 354L, 45L, 23L, 46L, 456L, 546L,
24L, 134L, 1L), Company = c("hello", "hello", "hello", "", "",
"bonjour", "bonjour", "bonjour", "", "", "hallo", "hallo", "hallo",
"hallo", "", "", "hallo", "hallo", "hallo", "", "", "hallo")), class = "data.frame", row.names = c(NA,
-22L))
First, I would like to add the sum of the column Size for each Company.首先,我想为每个公司添加列大小的总和。 For that I have the following code:
为此,我有以下代码:
Data <- Data %>%
group_by(Company, Date) %>%
dplyr:: mutate(Sum_Size = sum(Size))
Now with this code, R treats the NA values in the column Company as one group.现在使用此代码,R 将 Company 列中的 NA 值视为一组。 However, I don't want this to be one group.
但是,我不希望这是一组。 If the column Company is NA, then I would like to have the value of the column Size to be in the Sum_Size Column.
如果 Company 列是 NA,那么我希望 Size 列的值位于 Sum_Size 列中。
For that I have the following code:为此,我有以下代码:
Test <- Data %>%
dplyr:: mutate(Sum_Size=replace(Sum_Size, is.na(Company), Size))
However, the problem now is that with the code above, eg in row 9 Sum_Size is still the same as before and not equal to the value in column Size.但是,现在的问题是,对于上面的代码,例如第 9 行中的 Sum_Size 仍然与之前相同,并且不等于列 Size 中的值。 What do I need to adjust in the code to achieve my desired outcome?
我需要在代码中进行哪些调整才能达到我想要的结果?
A possible solution:一个可能的解决方案:
library(tidyverse)
df %>%
filter(Company != "") %>%
group_by(Company) %>%
mutate(Sum_Size = sum(Size)) %>%
bind_rows(df %>% filter(Company == "") %>% mutate(Sum_Size = Size))
#> # A tibble: 22 × 6
#> # Groups: Company [4]
#> Date Name Rating Size Company Sum_Size
#> <chr> <chr> <int> <int> <chr> <int>
#> 1 01.08.2018 A 1 1234 "hello" 25380
#> 2 02.08.2018 A 1 24123 "hello" 25380
#> 3 03.08.2018 A 1 23 "hello" 25380
#> 4 06.04.2019 A 4 3 "bonjour" 30
#> 5 07.04.2019 A 4 23 "bonjour" 30
#> 6 08.04.2019 A 4 4 "bonjour" 30
#> 7 03.08.2018 B 2 523 "hallo" 2195
#> 8 04.08.2018 B 2 234 "hallo" 2195
#> 9 06.04.2019 B 2 35 "hallo" 2195
#> 10 07.04.2019 B 1 354 "hallo" 2195
#> 11 02.08.2018 C 3 46 "hallo" 2195
#> 12 03.08.2018 C 3 456 "hallo" 2195
#> 13 04.08.2018 C 3 546 "hallo" 2195
#> 14 30.04.2019 C 5 1 "hallo" 2195
#> 15 04.08.2018 A 3 1 "" 1
#> 16 31.08.2018 A 3 23 "" 23
#> 17 01.08.2018 A 3 323 "" 323
#> 18 02.08.2018 B 3 3424 "" 3424
#> 19 08.04.2019 B 1 45 "" 45
#> 20 01.08.2018 C 1 23 "" 23
#> 21 05.08.2018 C 5 24 "" 24
#> 22 07.04.2019 C 5 134 "" 134
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