我尝试做的是选择每个独特的Mean（或CellLine，相同的想法）并创建一个名为AVGMOrder的新列，计算48个唯一的组。我不确定为什么我会收到这个错误，说组的大小是错误的。
感谢您的帮助！
> xist.df %>% group_by(Mean) %>% dplyr::mutate(AVGMOrder = seq(unique(Mean)))
# A tibble: 240 x 8
# Groups: Mean [48]
CpG geneID CellLine Meth OrigOrder Sex Mean AVGMOrder
<chr> <chr> <fct> <dbl> <chr> <chr> <dbl> <int>
1 cg03554089 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.455 286339 Female 0.511 1
2 cg12653510 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.491 286340 Female 0.511 1
3 cg05533223 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.515 286341 Female 0.511 1
4 cg11717280 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.489 286342 Female 0.511 1
5 cg20698282 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.605 286343 Female 0.511 1
6 cg03554089 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.491 376195 Female 0.519 1
7 cg12653510 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.542 376196 Female 0.519 1
8 cg05533223 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.483 376197 Female 0.519 1
9 cg11717280 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.503 376198 Female 0.519 1
10 cg20698282 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.574 376199 Female 0.519 1
# ... with 230 more rows
> unique(xist.df$Mean)
[1] 0.5110429 0.5185945 0.5299138 0.5319983 0.5333054 0.5465974 0.5484405 0.5518451 0.5631779 0.5647687 0.5736542
[12] 0.5741134 0.5803745 0.5839757 0.6864615 0.6990654 0.6994218 0.7478772 0.7986107 0.8016629 0.8204100 0.8239762
[23] 0.8281310 0.8311557 0.8375466 0.8405810 0.8460025 0.8513457 0.8514124 0.8583415 0.8587972 0.8596317 0.8597244
[34] 0.8632049 0.8642843 0.8656732 0.8661410 0.8679203 0.8707371 0.8710717 0.8816540 0.8823595 0.8827582 0.8852854
[45] 0.8856669 0.8900214 0.8903854 0.8915359
> xist.df %>% group_by(Mean) %>% dplyr::mutate(AVGMOrder = seq(unique(xist.df$Mean)))
Error in mutate_impl(.data, dots) :
Column `AVGMOrder` must be length 5 (the group size) or one, not 48
对评论的回应：
> xist.df %>% group_by(Mean) %>% dplyr::mutate(AVGMOrder = row_number())
# A tibble: 240 x 8
# Groups: Mean [48]
CpG geneID CellLine Meth OrigOrder Sex Mean AVGMOrder
<chr> <chr> <fct> <dbl> <chr> <chr> <dbl> <int>
1 cg03554089 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.455 286339 Female 0.511 1
2 cg12653510 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.491 286340 Female 0.511 2
3 cg05533223 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.515 286341 Female 0.511 3
4 cg11717280 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.489 286342 Female 0.511 4
5 cg20698282 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.605 286343 Female 0.511 5
6 cg03554089 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.491 376195 Female 0.519 1
7 cg12653510 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.542 376196 Female 0.519 2
8 cg05533223 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.483 376197 Female 0.519 3
9 cg11717280 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.503 376198 Female 0.519 4
10 cg20698282 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.574 376199 Female 0.519 5
# ... with 230 more rows
编辑评论。我希望每个唯一的组都是单个值。 group_by在这里是错误的功能吗？
答案 0 :(得分：1)
我决定使用以下解决方法：
xist.df <- xist.df %>% dplyr::arrange(Mean)
order <- as.data.frame(unique(xist.df$Mean))
order$AVGMOrder <- seq(rownames(order))
colnames(order) <- c("Mean", "AVGMOrder")
xist.df <- left_join(xist.df, order)
xist.df
输出如下：
> order <- as.data.frame(unique(xist.df$Mean))
> order$AVGMOrder <- seq(rownames(order))
> order
unique(xist.df$Mean) AVGMOrder
1 0.5110429 1
2 0.5185945 2
3 0.5299138 3
4 0.5319983 4
5 0.5333054 5
6 0.5465974 6
7 0.5484405 7
8 0.5518451 8
9 0.5631779 9
10 0.5647687 10
> colnames(order) <- c("Mean", "AVGMOrder")
> test <- left_join(xist.df, order)
Joining, by = "Mean"
> test
# A tibble: 240 x 8
# Groups: CellLine [?]
CpG geneID CellLine Meth OrigOrder Sex Mean AVGMOrder
<chr> <chr> <fct> <dbl> <chr> <chr> <dbl> <int>
1 cg03554089 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.455 286339 Female 0.511 1
2 cg12653510 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.491 286340 Female 0.511 1
3 cg05533223 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.515 286341 Female 0.511 1
4 cg11717280 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.489 286342 Female 0.511 1
5 cg20698282 XIST iPS__HDF51IPS5_passage6_Female____156.440.1.1 0.605 286343 Female 0.511 1
6 cg03554089 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.491 376195 Female 0.519 2
7 cg12653510 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.542 376196 Female 0.519 2
8 cg05533223 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.483 376197 Female 0.519 2
9 cg11717280 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.503 376198 Female 0.519 2
10 cg20698282 XIST iPS__HDF51IPS10_passage37_Female____161.900.1.2 0.574 376199 Female 0.519 2
# ... with 230 more rows