I have an R data frame with 6 columns, and I want to create a new dataframe that only has three of the columns.
Assuming my data frame is df
, and I want to extract columns A
, B
, and E
, this is the only command I can figure out:
data.frame(df$A,df$B,df$E)
Is there a more compact way of doing this?
You can subset using a vector of column names. I strongly prefer this approach over those that treat column names as if they are object names (e.g. subset()
), especially when programming in functions, packages, or applications.
# data for reproducible example
# (and to avoid confusion from trying to subset `stats::df`)
df <- setNames(data.frame(as.list(1:5)), LETTERS[1:5])
# subset
df[c("A","B","E")]
Note there's no comma (i.e. it's not df[,c("A","B","C")]
). That's because df[,"A"]
returns a vector, not a data frame. But df["A"]
will always return a data frame.
str(df["A"])
## 'data.frame': 1 obs. of 1 variable:
## $ A: int 1
str(df[,"A"]) # vector
## int 1
Thanks to David Dorchies for pointing out that df[,"A"]
returns a vector instead of a data.frame, and to Antoine Fabri for suggesting a better alternative (above) to my original solution (below).
# subset (original solution--not recommended)
df[,c("A","B","E")] # returns a data.frame
df[,"A"] # returns a vector
Using the dplyr package, if your data.frame is called df1
:
library(dplyr)
df1 %>%
select(A, B, E)
This can also be written without the %>%
pipe as:
select(df1, A, B, E)
df1 %>% select(A, B, E) %>% rowMeans(.)
. See the documentation for the %>%
pipe by typing ?magrittr::`%>%`
This is the role of the subset()
function:
> dat <- data.frame(A=c(1,2),B=c(3,4),C=c(5,6),D=c(7,7),E=c(8,8),F=c(9,9))
> subset(dat, select=c("A", "B"))
A B
1 1 3
2 2 4
c("A", "B")
is a vector, not a list.
There are two obvious choices: Joshua Ulrich's df[,c("A","B","E")]
or
df[,c(1,2,5)]
as in
> df <- data.frame(A=c(1,2),B=c(3,4),C=c(5,6),D=c(7,7),E=c(8,8),F=c(9,9))
> df
A B C D E F
1 1 3 5 7 8 9
2 2 4 6 7 8 9
> df[,c(1,2,5)]
A B E
1 1 3 8
2 2 4 8
> df[,c("A","B","E")]
A B E
1 1 3 8
2 2 4 8
For some reason only
df[, (names(df) %in% c("A","B","E"))]
worked for me. All of the above syntaxes yielded "undefined columns selected".
Where df1 is your original data frame:
df2 <- subset(df1, select = c(1, 2, 5))
dplyr
. It uses base::subset
, and is identical to Stephane Laurent's answer except that you use column numbers instead of column names.
You can also use the sqldf
package which performs selects on R data frames as :
df1 <- sqldf("select A, B, E from df")
This gives as the output a data frame df1
with columns: A, B ,E.
You can use with
:
with(df, data.frame(A, B, E))
df<- dplyr::select ( df,A,B,C)
Also, you can assign a different name to the newly created data
data<- dplyr::select ( df,A,B,C)
[
and subset are not substitutable:
[
does return a vector if only one column is selected.
df = data.frame(a="a",b="b")
identical(
df[,c("a")],
subset(df,select="a")
)
identical(
df[,c("a","b")],
subset(df,select=c("a","b"))
)
drop=FALSE
. Example: df[,c("a"),drop=F]
Success story sharing
object of type 'closure' is not subsettable
.df
.df
is also a function in the stats package.-"A"
is a syntax error. And?Extract
says, "i
,j
,...
can also be negative integers, indicating elements/slices to leave out of the selection."> df[,c("A")]
[1] 1
. Usingsubset
doesn't have this disadvantage.