Thank you, the csvtk spread is super useful. I usually import into R using lapply then Reduce using merge but this might be easier.

Hi,

Just thought I'd share 2 methods for when each file contains multiple samples. Thanks to Ram for providing lapply solution.

# load libraries
library(readr)
library(dplyr)
library(purrr) # for reduce()

# create sample data
file_1 <- tibble(name = c("A", "B", "C"),
             sample_1 = c(1, 2, 3),
             sample_2 = c(4, 5, 6),
             sample_3 = c(7, 8, 9))

file_2 <- tibble(name = c("A", "B", "C"),
             sample_4 = c(10, 20, 30),
             sample_5 = c(40, 50, 60),
             sample_6 = c(70, 80, 90))

file_3 <- tibble(name = c("A", "B", "C"),
             sample_7 = c(100, 200, 300),
             sample_8 = c(400, 500, 600),
             sample_9 = c(700, 800, 900))

directory = "path/to/files"

write_tsv(file_1, paste(directory, "/", "file_1.tsv", sep = "")) 
write_tsv(file_2, paste(directory, "/", "file_2.tsv", sep = "")) 
write_tsv(file_3, paste(directory, "/", "file_3.tsv", sep = "")) 

# Store files
files <- list.files(path = directory, pattern = "*.tsv", full.names = TRUE)
files

Method 1: For Loop

# Import first file
data <- read_tsv(files[1])
data

# Remove first file
files <- files[-1] 
files

# Join remaining files
for (x in files) {
  y <- read_tsv(x)
  data <- full_join(data, y, join_by(name))
}
data

Method 2: lapply

data <- lapply(files, read_tsv) # change to read_csv as needed
data <- data |> reduce(full_join, by = "name")
data

I need assistance don't know what to do next after signing up