我有这个数据帧。
Sub <- c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2)
trial <-c(1,1,1,1,2,2,2,2,2,2,1,1,1,1,2,2,2,2,2,2)
One <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
Two <- c(1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1)
Three <- c(2,0,0,1,3,0,0,0,0,1,7,8,0,0,0,1,1,1,1,0)
Four <- c(3,4,5,4,3,4,5,6,7,8,6,5,4,5,6,7,6,5,6,5)
Five <- c(3,4,5,4,6,7,5,4,3,2,3,4,5,4,3,5,7,4,3,5)
Six <- c(3,4,5,4,6,7,5,4,3,2,3,4,5,4,3,5,7,4,3,5)
Seven <- c(3,4,5,4,9,7,5,4,3,2,3,4,5,4,3,5,7,4,3,5)
dat <- data.frame(Sub, trial, One, Two, Three, Four, Five, Six, Seven)
我创建了这个函数来计算变量之间的相关性。
fun <- function(a,b,c,d,e,f,g) {
v = cor(a,b)
v1 = cor(a,c)
v2 = cor(a,d)
v3 = cor(a,e)
v4 = cor(a,f)
v5 = cor(a,g)
return(c(v,v1,v2,v3,v4,v5))
}
我需要将此函数应用于我的数据集的每组(Sub，trial(。
dat %>%
group_by(Sub,trial) %>%
summarize(as.data.frame(matrix(fun(One, Two, Three, Four, Five, Six, Seven), nr = 1)))
然而，我得到了这个结果：
Sub trial V1 V2 V3 V4 V5 V6
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA NA NA NA NA NA
2 1 2 NA NA NA NA NA NA
3 2 1 NA NA NA NA NA NA
4 2 2 NA NA NA NA NA NA
分组/试验很好。但我得到了其他变量的NA结果。
你有什么建议吗？
谢谢。
user@user438383的解决方案是正确的。
得到NA的原因与应用函数无关。
当你收到标准偏差为零的警告时，你可以考虑：R-警告消息："；在cor(…(中：标准偏差为零"；
这里有一个例子：
# generate a list of dataframes with your groups:
my_list <- dat %>%
group_by(Sub, trial) %>%
group_split()
[[1]]
# A tibble: 5 x 9
Sub trial One Two Three Four Five Six Seven
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 1 2 3 3 3 3
2 1 1 1 0 0 4 4 4 4
3 1 1 1 0 0 5 5 5 5
4 1 1 1 0 1 4 4 4 4
5 1 1 1 1 7 6 3 3 3
[[2]]
# A tibble: 6 x 9
Sub trial One Two Three Four Five Six Seven
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2 1 1 3 3 6 6 9
2 1 2 1 0 0 4 7 7 7
3 1 2 1 0 0 5 5 5 5
4 1 2 1 0 0 6 4 4 4
5 1 2 1 0 0 7 3 3 3
6 1 2 1 0 1 8 2 2 2
[[3]]
# A tibble: 3 x 9
Sub trial One Two Three Four Five Six Seven
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 1 1 0 8 5 4 4 4
2 2 1 1 0 0 4 5 5 5
3 2 1 1 0 0 5 4 4 4
[[4]]
# A tibble: 6 x 9
Sub trial One Two Three Four Five Six Seven
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2 2 1 1 0 6 3 3 3
2 2 2 1 1 1 7 5 5 5
3 2 2 1 1 1 6 7 7 7
4 2 2 1 0 1 5 4 4 4
5 2 2 1 0 1 6 3 3 3
6 2 2 1 1 0 5 5 5 5
现在将cor应用于第一组
my_list[[1]] %>%
summarise(across(Two:Seven, ~cor(One, .)))
# gives:
# A tibble: 1 x 6
Two Three Four Five Six Seven
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 NA NA NA NA NA NA
Warning messages:
1: In cor(One, Two) : Standardabweichung ist Null
2: In cor(One, Three) : Standardabweichung ist Null
3: In cor(One, Four) : Standardabweichung ist Null
4: In cor(One, Five) : Standardabweichung ist Null
5: In cor(One, Six) : Standardabweichung ist Null
6: In cor(One, Seven) : Standardabweichung ist Null
# or correlation of two columns only One and two of group one
cor(my_list[[1]]$One, my_list[[1]]$Two)
# gives:
[1] NA
Warning message:
In cor(my_list[[1]]$One, my_list[[1]]$Two) : Standardabweichung ist Null
mtcars数据集的外推示例：
mtcars %>%
relocate(cyl, vs, everything()) %>%
group_by(cyl, vs) %>%
summarise(across(hp:carb, ~cor(., mpg)))
cyl vs hp drat wt qsec am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 0 NA NA NA NA NA NA NA
2 4 1 -0.522 0.466 -0.721 -0.296 0.557 0.442 -0.189
3 6 0 -1 1 -0.101 0.931 NA -1 -1
4 6 1 -0.248 -0.249 -0.936 -0.0424 NA -0.442 -0.442
5 8 0 -0.284 0.0479 -0.650 -0.104 0.0496 0.0496 -0.394
Warning messages:
1: In cor(am, mpg) : Standardabweichung ist Null
2: In cor(am, mpg) : Standardabweichung ist Null