dplyr：独特与独特之间的区别

本文介绍了dplyr：独特与独特之间的区别的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

当使用不重复和唯一时，结果行的数量似乎不同。我正在使用的数据集非常庞大。希望代码可以理解。

Seems the number of resulting rows is different when using distinct vs unique. The data set I am working with is huge. Hope the code is OK to understand.

dt2a <- select(dt, mutation.genome.position, mutation.cds, primary.site, sample.name, mutation.id) %>% group_by(mutation.genome.position, mutation.cds, primary.site) %>% mutate(occ = nrow(.)) %>% select(-sample.name) %>% distinct() dim(dt2a) [1] 2316382 5 ## Using unique instead dt2b <- select(dt, mutation.genome.position, mutation.cds, primary.site, sample.name, mutation.id) %>% group_by(mutation.genome.position, mutation.cds, primary.site) %>% mutate(occ = nrow(.)) %>% select(-sample.name) %>% unique() dim(dt2b) [1] 2837982 5

这是我正在使用的文件：

This is the file I am working with:

sftp：//sftp-cancer.sanger.ac.uk/files/grch38/cosmic/ v72 / CosmicMutantExport.tsv.gz

sftp://sftp-cancer.sanger.ac.uk/files/grch38/cosmic/v72/CosmicMutantExport.tsv.gz

dt = fread(fl)

推荐答案

这似乎是 group_by 考虑这种情况的结果

This appears to be a result of the group_by Consider this case

dt<-data.frame(g=rep(c("a","b"), each=3), v=c(2,2,5,2,7,7)) dt %>% group_by(g) %>% unique() # Source: local data frame [4 x 2] # Groups: g # # g v # 1 a 2 # 2 a 5 # 3 b 2 # 4 b 7 dt %>% group_by(g) %>% distinct() # Source: local data frame [2 x 2] # Groups: g # # g v # 1 a 2 # 2 b 2 dt %>% group_by(g) %>% distinct(v) # Source: local data frame [4 x 2] # Groups: g # # g v # 1 a 2 # 2 a 5 # 3 b 2 # 4 b 7

使用 distinct（） 没有指出要区分哪些变量，而是使用了分组变量。

When you use distinct() without indicating which variables to make distinct, it appears to use the grouping variable.