Some R gotchas
Some potentially surprising results from R functions which are important to be aware of.
Here is a stackoverflow discussion with some more.
Even more: R programming for those coming from other languages
And more: aRrgh: a newcomer's (angry) guide to R
Dropped dimensions
For matrix:
m <- matrix(1:6, ncol=3)
m## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
m[1,] # vector## [1] 1 3 5
m[,1] # vector## [1] 1 2
m[1,,drop=FALSE] # matrix## [,1] [,2] [,3]
## [1,] 1 3 5
For data.frame:
df <- data.frame(a=1:3,b=4:6)
df## a b
## 1 1 4
## 2 2 5
## 3 3 6
df[1,] # data.frame## a b
## 1 1 4
df[,1] # vector## [1] 1 2 3
df[,1,drop=FALSE] # data.frame## a
## 1 1
## 2 2
## 3 3
stringsAsFactors: data.frame and read.table
df <- data.frame(a=c("10","11","12"))
as.numeric(df$a) + 1## [1] 2 3 4
df <- data.frame(a=c("10","11","12"), stringsAsFactors=FALSE)
as.numeric(df$a) + 1## [1] 11 12 13
Conventions, not so much
boxplot: names & horizontal
barplot: names.arg & horiz
stripchart: group.names & vertical
Logical operators
these are not 'gotchas' but good to remember:
c(TRUE,TRUE,FALSE) & c(FALSE,TRUE,TRUE) # element-wise## [1] FALSE TRUE FALSE
c(TRUE,TRUE,FALSE) && c(FALSE,TRUE,TRUE) # just the first## [1] FALSE
x <- "hi"
is.numeric(x) && x + 1 # evaluates left to right## [1] FALSE
is.numeric(x) & x + 1 # produces error## Error: non-numeric argument to binary operator
apply returns columns
m <- matrix(1:6, ncol=3)
m^2## [,1] [,2] [,3]
## [1,] 1 9 25
## [2,] 4 16 36
apply(m, 2, `^`, 2) # column-wise, ok## [,1] [,2] [,3]
## [1,] 1 9 25
## [2,] 4 16 36
apply(m, 1, `^`, 2) # gives back row-wise operation as columns## [,1] [,2]
## [1,] 1 4
## [2,] 9 16
## [3,] 25 36
Column names restricted characters
df <- data.frame("test-it-#1"=1:2)
df## test.it..1
## 1 1
## 2 2
make.names("test-it-#1") # this function is used## [1] "test.it..1"
Removing columns by name
df <- data.frame(a=1:2,b=3:4,c=5:6,d=7:8)
df[,-(2:3)] # numeric index ok ## a d
## 1 1 7
## 2 2 8
df[,-c("b","c")] # not character index## Error: invalid argument to unary operator
subset(df, select=-c(b,c)) # by name works here## a d
## 1 1 7
## 2 2 8
Safer to use seq_along and seq_len
x <- numeric(0)
1:length(x)## [1] 1 0
seq_len(length(x))## integer(0)
seq_along(x)## integer(0)
is.na and is.null
x <- c(1,2,NA)
which(x == NA)## integer(0)
which(is.na(x))## [1] 3
y <- NULL
y == NULL## logical(0)
is.null(y)## [1] TRUE
write.csv and read.csv don't agree
m <- matrix(1:4,ncol=2)
write.csv(m,file="matrix.csv")
read.csv("matrix.csv")## X V1 V2
## 1 1 1 3
## 2 2 2 4
read.csv("matrix.csv",row.names=1)## V1 V2
## 1 1 3
## 2 2 4
Formulas save variables in environment even if not referenced
f <- function() {
y <- 1:10
form <- ~ 1
form
}
get("y", environment(f()))## [1] 1 2 3 4 5 6 7 8 9 10