[英]Replacing NAs with zeros only in non-empty groups
I have a problem that should be easy to solve but I simply cannot figure it out.我有一个应该很容易解决的问题,但我就是想不通。 I have a huge dataset with groups and a variable.
我有一个包含组和变量的庞大数据集。 Some groups are empty for this variable (filled only with NAs) and some contains values but also NAs.
对于此变量,有些组是空的(仅填充 NA),有些包含值但也包含 NA。
For example:例如:
ID <- c("A1","A1","A1","A1","B1","B1","B1","B1", "B1", "C1", "C1", "C1")
Value1 <- c(0,2,1,1,NA,1,1,NA,1,NA,NA,NA)
data <- data.frame(ID, Value1)
I would like to change all NAs to zeros but only in groups that otherwise contain information.我想将所有 NA 更改为零,但仅限于包含信息的组。
So like this:所以像这样:
ID <- c("A1","A1","A1","A1","B1","B1","B1","B1","B1","C1","C1","C1")
Value1 <- c(0,2,1,1,0,1,1,0,1,NA,NA,NA)
I tried to use group_by(ID) and "replace" with the condition max(Value1)>=0 but either max() doesn't work as a condition or it doesn't work with NAs.我尝试使用 group_by(ID) 并在条件 max(Value1)>=0 下“替换”,但 max() 不能作为条件使用,或者不能与 NA 一起使用。 Unfortunately I would need this kind of conditioning often in my work so I would also appreciate any suggestions on which are the best packages to treat groups selectively.
不幸的是,我在工作中经常需要这种调节,所以我也很感激任何关于哪些是选择性地对待群体的最佳方案的建议。
You can use a simple if` statement, ie您可以使用一个简单的 if` 语句,即
library(dplyr)
library(tidyr)
data %>%
group_by(ID) %>%
mutate(Value1 = if (all(is.na(Value1))){Value1}else{replace_na(Value1, 0)})
which gives,这使,
# A tibble: 12 x 2 # Groups: ID [3] ID Value1 <fct> <dbl> 1 A1 0 2 A1 2 3 A1 1 4 A1 1 5 B1 0 6 B1 1 7 B1 1 8 B1 0 9 B1 1 10 C1 NA 11 C1 NA 12 C1 NA
Here is a base R solution这是一个基本的 R 解决方案
dfout <- Reduce(rbind,
lapply(split(data,data$ID),
function(v) {if (!all(is.na(v$Value1))) v$Value1[is.na(v$Value1)]<- 0; v}))
such that这样
> dfout
ID Value1
1 A1 0
2 A1 2
3 A1 1
4 A1 1
5 B1 0
6 B1 1
7 B1 1
8 B1 0
9 B1 1
10 C1 NA
11 C1 NA
12 C1 NA
With dplyr
:随着
dplyr
:
data %>%
group_by(ID) %>%
mutate(Value1 = ifelse(any(!is.na(Value1)) & is.na(Value1), 0, Value1))
# A tibble: 12 x 2
# Groups: ID [3]
ID Value1
<fct> <dbl>
1 A1 0
2 A1 2
3 A1 1
4 A1 1
5 B1 0
6 B1 1
7 B1 1
8 B1 0
9 B1 1
10 C1 NA
11 C1 NA
12 C1 NA
Using data.table
使用
data.table
setDT(data)
data[, Value1 := if (all(is.na(Value1))) NA else replace(Value1, is.na(Value1), 0), by = ID]
ID Value1
1: A1 0
2: A1 2
3: A1 1
4: A1 1
5: B1 0
6: B1 1
7: B1 1
8: B1 0
9: B1 1
10: C1 NA
11: C1 NA
12: C1 NA
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