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r相关矩阵长格式

correlation dplyr r

1

Science11 · 技术社区 · 5 年前

我正试图估计这两者之间的相关性之间 (4601、4602等)

library("geepack")
data(dietox)
df <- dietox[, c("Weight", "Pig")]
head(df)

 Weight  Pig
67.00000 4601
76.59998 4601
86.50000 4601
91.59998 4601
98.59998 4601
30.09999 4602
38.29999 4602
44.50000 4602
.        .
.        .
.        .

这是我的密码

df %>%
group_by(Pig) %>%
cor()

我犯了一个错误, Error in cor(.) : 'x' must be numeric

1 回复 | 直到 5 年前

1

2

akrun 5 年前

也许,我们需要重塑为“宽”格式

library(dplyr)
library(tidyr)
library(data.table)
df %>%
    mutate(rn = rowid(Pig)) %>%
    pivot_wider(names_from = rn, values_from = Weight) %>% 
    column_to_rownames("Pig") %>% 
    as.matrix %>%
    cor

-输出

       1         2         3         4         5         6         7         8         9        10        11 12
1  1.0000000 0.9222855 0.9089571 0.8672937 0.8135320 0.7363923 0.7408283 0.7516862 0.7175035 0.6834182 0.7003925 NA
2  0.9222855 1.0000000 0.9558717 0.9157019 0.8600880 0.7859677 0.7955397 0.7830242 0.7404776 0.6852512 0.6953134 NA
3  0.9089571 0.9558717 1.0000000 0.9515235 0.8965352 0.8293146 0.8136445 0.7945452 0.7745692 0.7195725 0.7247124 NA
4  0.8672937 0.9157019 0.9515235 1.0000000 0.9577877 0.9188810 0.8950779 0.8803371 0.8581694 0.8064507 0.8104515 NA
5  0.8135320 0.8600880 0.8965352 0.9577877 1.0000000 0.9665819 0.9369499 0.9139555 0.8983066 0.8185975 0.8337903 NA
6  0.7363923 0.7859677 0.8293146 0.9188810 0.9665819 1.0000000 0.9568397 0.9327316 0.9280462 0.8538419 0.8557321 NA
7  0.7408283 0.7955397 0.8136445 0.8950779 0.9369499 0.9568397 1.0000000 0.9688745 0.9556239 0.8860879 0.8914012 NA
8  0.7516862 0.7830242 0.7945452 0.8803371 0.9139555 0.9327316 0.9688745 1.0000000 0.9657392 0.8894930 0.8929204 NA
9  0.7175035 0.7404776 0.7745692 0.8581694 0.8983066 0.9280462 0.9556239 0.9657392 1.0000000 0.9192849 0.9352723 NA
10 0.6834182 0.6852512 0.7195725 0.8064507 0.8185975 0.8538419 0.8860879 0.8894930 0.9192849 1.0000000 0.9358353 NA
11 0.7003925 0.6953134 0.7247124 0.8104515 0.8337903 0.8557321 0.8914012 0.8929204 0.9352723 0.9358353 1.0000000 NA
12        NA        NA        NA        NA        NA        NA        NA        NA        NA        NA        NA  1

out <- df %>% 
   mutate(rn = rowid(Pig)) %>% 
   pivot_wider(names_from = Pig, values_from = Weight) %>% 
   column_to_rownames("rn") %>%
    as.matrix %>%
    cor
 dim(out)
[1] 72 72