n
{dplyr}
The number of observations in the current group.
sum
{base}
Get sum of numeric values or a vector
slice
{dplyr}
Select rows by position
rm
{base}
Remove objects
table
{base}
Obtain frequency table of a variable/cross-tabulation of two variables
write_dta
{haven}
Write Stata files
as.character
{base}
Coerce a vector to character
factor
{base}
Create a factor
summarise
{dplyr}
collapse the dataset to a summary statistic. Usually used with group_by()
desc
{dplyr}
Arrange in descending order
summarize
{dplyr}
Collapse the dataset to a summary statistic (American-spelling alias of summarise)
ordered
{dplyr}
Create an ordered factor
quantile
{stats}
Obtain empirical quantiles of a vector
cut
{base}
Convert Numeric to Factor
read_dta
{haven}
Read a .dta file (Stata data)
mean
{base}
Get mean of a vector
filter
{dplyr}
Filter out rows of a data frame according to logical vector
separate
{tidyr}
Split columns
spread
{tidyr}
Spread a key-value pair across multiple columns
recode
{dplyr}
Recode a variable
c
{base}
Combine values/vectors into a vector
as.numeric
{base}
Coerce a vector to numeric
write.csv
{utils}
write a csv file to a data frame
factor
{base}
Create factor variables
arrange
{dplyr}
Sort values of data frame according to a variable/combination of variables
group_by
{dplyr}
Group tibble/data.frame by a factor variable. All further tidyverse operations are performed group-wise
read.csv
{utils}
Read a csv file to data frame. Specify stringsAsFactors = FALSE to keep all string columns as characters
mutate
{dplyr}
Create new variables
setwd
{base}
Set Working Directory
select
{dplyr}
Select columns from a tibble/data frame
ls
{base}
Return a vector of character strings giving the names of the objects in the specified environment
data.frame
{base}
Create a data.frame from vectors
save
{base}
Save R objects
ifelse
{base}
Return a or b depending on the value of test
is.na
{base}
Check if a value is NA/elements of vector are NA
library
{base}
Load an R package
The end!