我有一个应该很容易解决的问题,但我根本无法解决。我有一个包含组和变量的庞大数据集。对于该变量,有些组为空(仅填充NA),有些包含值,但也包含NA。
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)
我想将所有NA都更改为零,但仅在包含信息的组中更改。
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)
我尝试使用group_by(ID)并以条件max(Value1)>“ replace”> = 0,但max()不能作为条件使用,或者不适用于NA。不幸的是,我在工作中经常需要这种条件,所以我也希望对任何建议有最好的选择,以便有选择地对待团体。
您可以使用简单的if语句,即。
library(dplyr)
data %>%
group_by(ID) %>%
mutate(Value1 = if (all(is.na(Value1))){Value1}else{replace_na(Value1, 0)})
给出,
# 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