将一些csv文件合并为一个不同数量的列

Question

I already loaded 20 csv files with function:

tbl = list.files(pattern="*.csv")
for (i in 1:length(tbl)) assign(tbl[i], read.csv(tbl[i]))

or

list_of_data = lapply(tbl, read.csv)

That how it looks like:

> head(tbl)
[1] "F1.csv"          "F10_noS3.csv"    "F11.csv"         "F12.csv"         "F12_noS7_S8.csv"
[6] "F13.csv"

I have to combine all of those files into one. Let's call it a master file but let's try with making a one table with all of the names. In all of those csv files is a column called "Accession". I would like to make a table of all "names" from all of those csv files. Of course many of the accessions can be repeated in different csv files. I would like to keep all of the data corresponding to the accession.

Some problems:

• Some of those "names" are the same and I don't want to duplicate them
• Some of those "names" are ALMOST the same. The difference is that there is name and after become the dot and the numer.
• The number of columns can be different is those csv files.

That's the screenshot showing how those data looks like: http://imageshack.com/a/img811/7103/29hg.jpg

Let me show you how it looks:

AT3G26450.1 <--
AT5G44520.2
AT4G24770.1
AT2G37220.2
AT3G02520.1
AT5G05270.1
AT1G32060.1
AT3G52380.1
AT2G43910.2
AT2G19760.1
AT3G26450.2 <--

<-- = Same sample, different names. Should be treated as one. So just ignore dot and a number after.

Is it possible to do ?

I couldn't do a dput(head) because it's even too big data set.

I tried to use such code:

all_data = do.call(rbind, list_of_data)
Error in rbind(deparse.level, ...) : 
The number of columns is not correct.


all_data$CleanedAccession = str_extract(all_data$Accession, "^[[:alnum:]]+")
all_data = subset(all_data, !duplicated(CleanedAccession))

I tried to do it for almost 2 weeks and I am not able to. So please help me.

Answer 1

Your questions seems to contain multiple subquestions. I encourage you to separate them.

The first thing you apparently need is to combine data frames with different columns. You can use rbind.fill from the plyr package:

library(plyr)
all_data = do.call(rbind.fill, list_of_data)

Answer 2

Here's an example using some tidyverse functions and a custom function that can combine multiple csv files with missing columns into one data frame:

library(tidyverse)

# specify the target directory
dir_path <- '~/test_dir/' 

# specify the naming format of the files. 
# in this case csv files that begin with 'test' and a single digit but it could be as just as simple as 'csv'
re_file <- '^test[0-9]\\.csv'

# create sample data with some missing columns 
df_mtcars <- mtcars %>% rownames_to_column('car_name')
write.csv(df_mtcars %>% select(-am), paste0(dir_path, 'test1.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-wt, -gear), paste0(dir_path, 'test2.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-cyl), paste0(dir_path, 'test3.csv'), row.names = FALSE)

# custom function that takes the target directory and file name pattern as arguments
read_dir <- function(dir_path, file_name){
  x <- read_csv(paste0(dir_path, file_name)) %>% 
    mutate(file_name = file_name) %>% # add the file name as a column              
    select(file_name, everything())   # reorder the columns so file name is first
  return(x)
}

# read the files from the target directory that match the naming format and combine into one data frame
df_panel <-
  list.files(dir_path, pattern = re_file) %>% 
  map_df(~ read_dir(dir_path, .))

# files with missing columns are filled with NAs.