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I have listed some useful functions below:
with()
The with( ) function applys an expression to a dataset. It is similar to DATA= in SAS.
# with(data, expression) # example applying a t-test to a data frame mydata with(mydata, t.test(y ~ group))
Please look at other examples here and here.
by()
The by( ) function applys a function to each level of a factor or factors. It is similar to BY processing in SAS.
# by(data, factorlist, function) # example obtain variable means separately for # each level of byvar in data frame mydata by(mydata, mydatat$byvar, function(x) mean(x))
Please look here for more details.
do.call()
do.call calls a function with a list of arguments, lapply applies a function to each element of the list
do.call(sum, list(c(1,2,4,1,2), na.rm = TRUE))
#10
lapply(c(1,2,4,1,2), function(x) x + 1)
#2
#3
#5
#2
#3
do.call("+",list(4,5))
#9
More examples here.
more()
more() is a user-defined function that is helpful in printing out a large object. Taken from here.
#to print out an object such as data.frame mydf 20 lines at a time, use:
more(mydf)
#where more() is defined as
more <- function(expr, lines=20) {
out <- capture.output(expr)
n <- length(out)
i <- 1
while( i < n ) {
j <- 0
while( j < lines && i <= n ) {
cat(out[i],"\n")
j <- j + 1
i <- i + 1
}
if(i<n){
rl <- readline()
if( grepl('^ *q', rl, ignore.case=TRUE) ) i <- n
if( grepl('^ *t', rl, ignore.case=TRUE) ) i <- n - lines + 1
if( grepl('^ *[0-9]', rl) ) i <- as.numeric(rl)/10*n + 1
}
}
invisible(out)
}
options()
options() can be used to increase the limit for max.print in R. More info here.
options(max.print=1000000)
To check which columns in the data frame df have missing values
colnames(df)[colSums(is.na(df)) > 0]
The cover photo of this blog post is taken from https://visualstudiomagazine.com/Articles/2016/04/01/Program-Defined-Functions-in-R.aspx?Page=1
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