For R, how do I row bind an unknown number of items in list?

[Skye]

I am new to R. I have many csv files that I want to append into a single CSV file.

I have the following issues in my below code:

1. After the 3rd line Error in read.table(file = file, header = header, sep = sep, quote = quote, : argument "file" is missing, with no default **
2. I don't know the number of files in the folder to rowbind. So how do I amend this line of code to rowbind an unknown number of files? bind_rows(tables [1], tables [2], tables[3])

Thank you!

library(dplyr)
files <- list.files(path = "C:/Users/...",pattern="*.csv", full.names = T)
tables <- lapply(files, read.csv(header=False))
merged <- bind_rows(tables [1], tables [2], tables[3])
write.csv(merged, file = "C:/.../files_merged.csv", row.names = FALSE)

[andresrcs]

I would recommend using something like this instead

library(tidyverse)

list_of_files <- list.files(path = "C:/Users/...",
                            pattern = "\\.csv$",
                            full.names = TRUE)
df <- list_of_files %>% 
  map_dfr(read.csv, header = FALSE)

write.csv(df, file = "C:/.../files_merged.csv", row.names = FALSE)

[Skye]

Hi andresrcs,

Thanks for the reply. But I get the error whether I use rbind or map_dfr. The values below the column head gets affixed with 

My csv files contain different number of rows & columns, and some rows are just text.

Warning messages:

1: In bind_rows_(x, .id) : Unequal factor levels: coercing to character

2: In bind_rows_(x, .id) :

binding character and factor vector, coercing into character vector

3: In bind_rows_(x, .id) :

binding character and factor vector, coercing into character vector

I read that rbindlist can solve the above issue of factors with unequal levels and character cols automatically. But I can't install the package data.table

[nirgrahamuk]

how about, instead of read.csv use readr::read_csv , this wont guess factors, so you will get character columns that can be rowbound, then as a final step you can convert final columns of your choice to factor where those are your preferred types.