Remove an entire column from a data.frame in R

Does anyone know how to remove an entire column from a data.frame in R? For example if I am given this开发者_如何学Python data.frame:

> head(data)
   chr       genome region
1 chr1 hg19_refGene    CDS
2 chr1 hg19_refGene   exon
3 chr1 hg19_refGene    CDS
4 chr1 hg19_refGene   exon
5 chr1 hg19_refGene    CDS
6 chr1 hg19_refGene   exon

and I want to remove the 2nd column.

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You can set it to NULL.

> Data$genome <- NULL
> head(Data)
   chr region
1 chr1    CDS
2 chr1   exon
3 chr1    CDS
4 chr1   exon
5 chr1    CDS
6 chr1   exon

As pointed out in the comments, here are some other possibilities:

Data[2] <- NULL    # Wojciech Sobala
Data[[2]] <- NULL  # same as above
Data <- Data[,-2]  # Ian Fellows
Data <- Data[-2]   # same as above

You can remove multiple columns via:

Data[1:2] <- list(NULL)  # Marek
Data[1:2] <- NULL        # does not work!

Be careful with matrix-subsetting though, as you can end up with a vector:

Data <- Data[,-(2:3)]             # vector
Data <- Data[,-(2:3),drop=FALSE]  # still a data.frame

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To remove one or more columns by name, when the column names are known (as opposed to being determined at run-time), I like the subset() syntax. E.g. for the data-frame

df <- data.frame(a=1:3, d=2:4, c=3:5, b=4:6)

to remove just the a column you could do

Data <- subset( Data, select = -a )

and to remove the b and d columns you could do

Data <- subset( Data, select = -c(d, b ) )

You can remove all columns between d and b with:

Data <- subset( Data, select = -c( d : b )

As I said above, this syntax works only when the column names are known. It won't work when say the column names are determined programmatically (i.e. assigned to a variable). I'll reproduce this Warning from the ?subset documentation:

Warning:

This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like '[', and in particular the non-standard evaluation of argument 'subset' can have unanticipated consequences.

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(For completeness) If you want to remove columns by name, you can do this:

cols.dont.want <- "genome"
cols.dont.want <- c("genome", "region") # if you want to remove multiple columns

data <- data[, ! names(data) %in% cols.dont.want, drop = F]

Including drop = F ensures that the result will still be a data.frame even if only one column remains.

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The posted answers are very good when working with data.frames. However, these tasks can be pretty inefficient from a memory perspective. With large data, removing a column can take an unusually long amount of time and/or fail due to out of memory errors. Package data.table helps address this problem with the := operator:

library(data.table)
> dt <- data.table(a = 1, b = 1, c = 1)
> dt[,a:=NULL]
     b c
[1,] 1 1

I should put together a bigger example to show the differences. I'll update this answer at some point with that.

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There are several options for removing one or more columns with dplyr::select() and some helper functions. The helper functions can be useful because some do not require naming all the specific columns to be dropped. Note that to drop columns using select() you need to use a leading - to negate the column names.

Using the dplyr::starwars sample data for some variety in column names:

library(dplyr)

starwars %>% 
  select(-height) %>%                  # a specific column name
  select(-one_of('mass', 'films')) %>% # any columns named in one_of()
  select(-(name:hair_color)) %>%       # the range of columns from 'name' to 'hair_color'
  select(-contains('color')) %>%       # any column name that contains 'color'
  select(-starts_with('bi')) %>%       # any column name that starts with 'bi'
  select(-ends_with('er')) %>%         # any column name that ends with 'er'
  select(-matches('^v.+s$')) %>%       # any column name matching the regex pattern
  select_if(~!is.list(.)) %>%          # not by column name but by data type
  head(2)

# A tibble: 2 x 2
homeworld species
  <chr>     <chr>  
1 Tatooine  Human  
2 Tatooine  Droid 

You can also drop by column number:

starwars %>% 
  select(-2, -(4:10)) # column 2 and columns 4 through 10

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With this you can remove the column and store variable into another variable.

df = subset(data, select = -c(genome) )

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Using dplyR, the following works:

data <- select(data, -genome)

as per documentation found here https://www.marsja.se/how-to-remove-a-column-in-r-using-dplyr-by-name-and-index/#:~:text=select(starwars%2C%20%2Dheight)

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I just thought I'd add one in that wasn't mentioned yet. It's simple but also interesting because in all my perusing of the internet I did not see it, even though the highly related %in% appears in many places.

df <- df[ , -which(names(df) == 'removeCol')]

Also, I didn't see anyone post grep alternatives. These can be very handy for removing multiple columns that match a pattern.