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]
```

Originally posted at ujjwalkarn.me/