I need to extract many columns from a dataset. I have a very large csv file with thousands of columns and rows, and I read it into R using:

mydata <- read.csv(file = "file.csv",header = TRUE,sep = ",",row.names = 1)

Each column is a gene name. I know how to extract specific columns from my R data.frame by using the basic code like this:

dataset[ , "GeneName1", "GeneName2"]

But my question is, how do I pull hundreds of gene names? Too many to type in? They are listed in a txt file. I'm new, so please go easy on jargon and abbreviations.

  • $\begingroup$ try dataset[, 1:100] or melt(dataset), ?melt $\endgroup$
    – Valentas
    Apr 3, 2019 at 14:35

2 Answers 2


You can also subset a dataframe in base R like this:

# Some dataframe
df = data.frame(a=c(1,2),b=c(1,2),c=c(1,2),d=c(1,2),e=c(1,2))

# List of column names to select
colnamelist = c("a","b","c")

# Subset dataframe based on list of wanted columns
df = df[,colnames(df) %in% colnamelist]

Result will be:

> names(df)
[1] "a" "b" "c"

The logic is: Select from a dataframe df[row,column], where you say the column names to be selected should be in a list colnames(df) %in% colnamelist.


Hopefully I've understood your question correctly.

Assuming your text file looks like this?


You can read that in using the readLines() function:

cols <- readLines("name_of_text_file")

Which returns cols as a vector of those names:

> cols
[1] "GeneName1" "GeneName2"

Which can then be used to subset the data frame as per your example:

mydata[ , cols]
  • $\begingroup$ Thank you, Jerb, that worked! However, I get this error if there is a gene in my cols list that is not found in the dataset. Error in [.data.frame(sensory, , cols) : undefined columns selected Do you know what I could add to the script to make it skip over any genes it does not find? $\endgroup$
    – julie
    Apr 8, 2019 at 11:59
  • 1
    $\begingroup$ No probs. You could subset cols with an %in% expression thus: mydata[, cols[cols %in% names(mydata)]] $\endgroup$
    – Jerb
    Apr 8, 2019 at 12:45

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