检查 R 中一个数据帧中的两列是否具有相同的值 [除了 NA 之外]

Question

我有 1 个数据帧，j：

Chr|Pos|A0|A1|rsID|Beta-A1|P|info|maf|se|rsid  
1|16021|C|T|NA|0.410|0.26|0.842|0.01|NA|rs1163602158   
1|17483|C|T|rs845637483|-0.356|0.32|0.856|0.01|NA|rs845637483    
1|19250|T|C|NA|-0.255|0.54|0.812|0.01|NA|rs7465843777   
1|39402|T|TCAA|NA|-0.873|0.37|0.821|0.01|NA|rs2746475333   
1|39883|G|C|NA|0.195|0.59|0.808|0.01|NA|rs2726463882

我想检查 rsID 和 rsid 中​​的行是否与前一列中的 NA 相同

所以我可以做

table(ifelse(j$rsID==j$rsid,"Yes","No"))

No      Yes
701232 18207968

而且我可以做

table(is.na(j$rsID))

FALSE     TRUE
18909200  2550533

table(is.na(j$rsid))

   FALSE
21459733

所以我可以看到那里有 701232 个不匹配的实例，但这些并不是全部，因为 NA，因为 NA 的数量比不匹配的实例多（2550533）？

有没有更好/更干净的方法来做到这一点，以便我可以更好地了解这一点？

谢谢

Answer 1

可以删除Na，然后过滤它们不相等的地方：

library(dplyr)
library(tidyr)
j %>%
  drop_na(rsID, rsid) %>%
  filter(rsID != rsid) # Or == instead of != to keep where they are equal

Answer 2

另一个 dplyr 选项

j %>%
  rowwise() %>%
  mutate(duplicate = anyDuplicated(na.omit(c(rsid, rsID)))) %>%
  mutate(duplicate = ifelse(duplicate > 1, "Yes", "No")) %>% count(duplicate)

输出

# A tibble: 2 x 2
# Rowwise: 
  duplicate     n
  <chr>     <int>
1 No            4
2 Yes           1

Answer 3

我们可以使用基础R

with(na.omit(j[c('rsID', 'rsid')]),table(ifelse(rsID == rsid, "Yes", "No"))  )

Answer 4

# Load dplyr library
library(dplyr, warn.conflicts = FALSE, quietly = TRUE)

# you already have j defined so this step is only for this demo
j <- tibble(Chr = c(1, 1, 1, 1, 1), 
            Pos = c(16021, 17483, 19250, 39402, 39883), 
            A0 = c("C", "C", "T", "T", "G"), 
            A1 = c("T", "T", "C", "TCAA", "C"), 
            rsID = c(NA, "rs845637483", NA, NA, NA), 
            `Beta-A1` = c(0.41, -0.356, -0.255, -0.873, 0.195), P = c(0.26,0.32, 0.54, 0.37, 0.59), 
            info = c(0.842, 0.856, 0.812, 0.821, 0.808), 
            maf = c(0.01, 0.01, 0.01, 0.01, 0.01), se = c(NA, NA, NA, NA, NA), 
            rsid = c("rs1163602158", "rs845637483", "rs7465843777","rs2746475333", "rs2726463882"))

# create a column is_same and use count()
j %>% 
  mutate(is_same = if_else(rsid == rsID, "Yes", "No", "No")) %>% 
  count(is_same)
#> # A tibble: 2 x 2
#>   is_same     n
#>   <chr>   <int>
#> 1 No          4
#> 2 Yes         1