CheatSheet

The goal of this appendix is to give a easy reference to basic manipulation functions that are often used and should always be readily accessible.

Importing Data and Loading Packages

Function	Meaning
`data("DataName", package="PackageName")`	Load the data set `DataName` which is found in the package `PackageName`
`library(PackageName)`	Load the package `PackageName` to be used.
`read.csv("filename.csv")`	Read a .csv file. This result needs to be saved or else it is just printed

Function	Meaning
`ifelse( logicalTest, TrueResult, FalseResult )`	Creates a vector of output, where elements are either the `TrueResult` or `FalseResult` based on the corresponding outcome in the `logicalTest` vector

In the examples below, df stands for an arbitrary data frame that we are applying the functions to.

Function	Meaning
`data.frame(x= , y=.)`	Creates a data frame “by hand” with one column per input.
`tibble(x= , y= )`	Creates a tibble “by hand” with one column per input.
`tribble( ~x, ~y, 1, 2)`	Creates a tibble “by hand”, but with row-wise specification.
`df %>% add_row(x=3, y=5)`	Add a single row to the `df` data frame. Any column with unspecified data is filled with `NA`.
`df1 %>% bind_rows(df2)`	Stack data frames `df1` and `df2`
`df %>% select(ColumnNames)`	Subset `df` and return a data frame with the columns specified.
`df %>% filter(logicalTest)`	Subset `df` and return a data frame with the rows that satisfy the logical expression
`df %>% mutate( New= )`	Create (or update) a column `New` with some manipulation of `Old` column. A common manipulation is to use an `ifelse()` command to update only particular rows.

These functions will modify an input data frame df

Function	Meaning
`group_by(df, Column1, Column2)`	Create a grouped tibble with groups defined by all unique combinations of `Column1` and `Column2`
`summarize(df, Function(Column1))`	Apply `Function` to `Column1` and return a data frame with just a single row. This is quite powerful when applied to a grouped tibble as it will result in a single row per group.
`df %>% pivot_wider(names_from=, values_from=)`	Create a wide data set from a long format
`df %>% pivot_longer(names_to, values_to)`	Create a long data set from a wide format