将data.frame列拆分为其他列

Question

我有一个很大的data.frame有一些列，但我的第9列是由分号分隔的数据：

    gtf$V9
1                 gene_id CUFF.1; transcript_id CUFF.1.1; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;
2  gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 1; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;
3  gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 2; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;
4  gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 3; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;

所以我想把这个列切换到其他列和merge这个稍后用data.frame的其他部分（第9列之前的其他列）。

我尝试了一些没有结果的代码：

head(gtf$V9, sep = ";",stringsAsFactors = FALSE) 

要么

new_df <- matrix(gtf$V9, ncol=7, byrow=TRUE) # sep = ";"

与as.data.frame，data.frame或as.matrix相同

我也尝试过qazxsw poi并将其导入包括qazxsw poi，但write.csv太大而且我的电脑滞后..

有什么建议？

Answer 1

另一种选择是使用sep=";"-package（也加载data.frame）。使用：

splitstackshape

得到：

data.table

Answer 2

你可以在

library(splitstackshape)
cSplit(cSplit(df, 'V9', sep = ';', direction = 'long'),
       'V9', sep = ' ')[, dcast(.SD, cumsum(V9_1 == 'gene_id') ~ V9_1)]

内做

   V9_1  conf_hi  conf_lo       cov exon_number         FPKM     frac gene_id transcript_id
1:    1 9.805420 4.347062 25.616962          NA 7.0762407256 1.000000  CUFF.1      CUFF.1.1
2:    2 9.805420 4.347062 25.616962           1 7.0762407256 1.000000  CUFF.1      CUFF.1.1
3:    3 9.805420 4.347062 25.616962           2 7.0762407256 1.000000  CUFF.1      CUFF.1.1
4:    4 9.805420 4.347062 25.616962           3 7.0762407256 1.000000  CUFF.1      CUFF.1.1

如果你知道V9中有多少个对象，你可以在它上面进行for循环

strsplit()

如果您不知道V9有多少个对象，那么只需在gtf $ V9中的sapply()上运行

for (i in 1:number_of_max_objects_in_V9) {
 gtf[ncol(gtf)+1] = sapply(1:nrow(gtf), function(x) strsplit(gtf$V9[x],',')[[1]][i])
}

，如下所示：

str_count

Answer 3

,

您可以使用行ID（

library(stringr)
number_of_max_objects_in_V9 <- max(sapply(1:nrow(gtf), function(x) str_count(gtf$V9,',')))

）将此数据集连接回初始数据集。您还需要在原始数据集中创建

# example dataset (only variable of interest included)
df = data.frame(V9=c("gene_id CUFF.1; transcript_id CUFF.1.1; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;",
                "gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 1; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;",
                "gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 2; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;",
                "gene_id CUFF.1; transcript_id CUFF.1.1; exon_number 3; FPKM 7.0762407256; frac 1.000000; conf_lo 4.347062; conf_hi 9.805420; cov 25.616962;"),
                stringsAsFactors = F)

library(dplyr)
library(tidyr)

df %>%
  mutate(id = row_number()) %>%                  # flag row ids (will need those to reshape data later)                
  separate_rows(V9, sep="; ") %>%                # split strings and create new rows
  separate(V9, c("name","value"), sep=" ") %>%   # separate column name from value
  mutate(value = gsub(";","",value)) %>%         # remove ; when necessary
  spread(name, value)                            # reshape data

#   id  conf_hi  conf_lo       cov exon_number         FPKM     frac gene_id transcript_id
# 1  1 9.805420 4.347062 25.616962        <NA> 7.0762407256 1.000000  CUFF.1      CUFF.1.1
# 2  2 9.805420 4.347062 25.616962           1 7.0762407256 1.000000  CUFF.1      CUFF.1.1
# 3  3 9.805420 4.347062 25.616962           2 7.0762407256 1.000000  CUFF.1      CUFF.1.1
# 4  4 9.805420 4.347062 25.616962           3 7.0762407256 1.000000  CUFF.1      CUFF.1.1

。