Extracting specific columns from a data frame

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)

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

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