英文:
How to coalesce columns that have NA using dplyr?
问题
我正在寻找一种使用dplyr组合具有多个列中的NA的行的方法。我还没有找到解决这个问题的方法。我对R还很陌生，提前道歉。
我想要将这个示例数据框更改为如下所示：
# Groups: RID, FlankerCongruence, NoiseLevel
RID FlankerCongruence TrialStim.ACC NoiseLevel TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 1 0 678
2 101 Congruent 1 0 909
3 101 Congruent 1 0 928
我首先尝试使用以下方法：
coalesce_by_column <- function(noMeancombinedNoiseTable.data) {
return(dplyr::coalesce(!!! as.list(noMeancombinedNoiseTable.data)))
}
noMeancombinedNoiseTable.data <- noMeancombinedNoiseTable.data %>%
group_by(RID, FlankerCongruence, NoiseLevel) %>%
arrange(RID, FlankerCongruence, NoiseLevel) %>%
summarise_all(coalesce_by_column)
但是这将列汇总为以下形式：
RID FlankerCongruence NoiseLevel TrialStim.ACC TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 0 1 678
有什么建议吗？
英文:
I am looking to combine rows that have NA across multiple columns using dplyr. I have not been able to find a way around this problem. I am still new to R, so apologies in advance.
I want to change this example data frame:
# Groups: RID, FlankerCongruence, NoiseLevel
RID FlankerCongruence TrialStim.ACC NoiseLevel TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 1 0 NA
2 101 Congruent 1 0 NA
3 101 Congruent 1 0 NA
4 101 Congruent NA 0 678
5 101 Congruent NA 0 909
6 101 Congruent NA 0 928
into something that looks like this:
# Groups: RID, FlankerCongruence, NoiseLevel
RID FlankerCongruence TrialStim.ACC NoiseLevel TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 1 0 678
2 101 Congruent 1 0 909
3 101 Congruent 1 0 928
I first tried using this:
coalesce_by_column <- function(noMeancombinedNoiseTable.data) {
return(dplyr::coalesce(!!! as.list(noMeancombinedNoiseTable.data)))
}
noMeancombinedNoiseTable.data <- noMeancombinedNoiseTable.data %>%
group_by(RID, FlankerCongruence, NoiseLevel) %>%
arrange(RID, FlankerCongruence, NoiseLevel) %>%
summarise_all(coalesce_by_column)
But that summarized columns like this:
RID FlankerCongruence NoiseLevel TrialStim.ACC TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 0 1 678
Any suggestions??
答案1
得分: 2
我们可以按列进行分组，重新排列其他列中的NAs，并保留至少有一个非NA值的行。
library(dplyr)
noMeancombinedNoiseTable.data %>%
group_by(RID, FlankerCongruence, NoiseLevel) %>%
mutate(across(everything(), ~ .x[order(!is.na(.x))])) %>%
filter(if_any(everything(), ~ !is.na(.x))) %>%
ungroup
输出结果：
# A tibble: 3 × 5
RID FlankerCongruence TrialStim.ACC NoiseLevel TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 1 0 678
2 101 Congruent 1 0 909
3 101 Congruent 1 0 928
数据
noMeancombinedNoiseTable.data <- structure(list(RID = c(101L, 101L,
101L, 101L, 101L, 101L), FlankerCongruence = c("Congruent",
"Congruent", "Congruent", "Congruent", "Congruent", "Congruent"
), TrialStim.ACC = c(1L, 1L, 1L, NA, NA, NA), NoiseLevel = c(0L,
0L, 0L, 0L, 0L, 0L), TrialStim.RT = c(NA, NA, NA, 678L, 909L,
928L)), class = "data.frame", row.names = c("1", "2", "3", "4",
"5", "6"))
英文:
We could group by the columns, realign the NAs in other columns and keep the rows with at least one non-NA value
library(dplyr)
noMeancombinedNoiseTable.data %>%
group_by(RID, FlankerCongruence, NoiseLevel) %>%
mutate(across(everything(), ~ .x[order(!is.na(.x))])) %>%
filter(if_any(everything(), ~ !is.na(.x))) %>%
ungroup
-output
# A tibble: 3 × 5
RID FlankerCongruence TrialStim.ACC NoiseLevel TrialStim.RT
<int> <chr> <int> <int> <int>
1 101 Congruent 1 0 678
2 101 Congruent 1 0 909
3 101 Congruent 1 0 928
data
noMeancombinedNoiseTable.data <- structure(list(RID = c(101L, 101L,
101L, 101L, 101L, 101L), FlankerCongruence = c("Congruent",
"Congruent", "Congruent", "Congruent", "Congruent", "Congruent"
), TrialStim.ACC = c(1L, 1L, 1L, NA, NA, NA), NoiseLevel = c(0L,
0L, 0L, 0L, 0L, 0L), TrialStim.RT = c(NA, NA, NA, 678L, 909L,
928L)), class = "data.frame", row.names = c("1", "2", "3", "4",
"5", "6"))
</details>
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