Introduction to R

Use RStudio to write and run R programs.

Set up an RStudio project for each analysis you are performing.

R has the usual arithmetic operators and mathematical functions.

Use <- to assign values to variables.

Use ls() to list the variables in a program.

Use rm() to delete objects in a program.

Use install.packages() to install packages.

The basic data types in R are double, integer, complex, logical, and character.

Vectors are an ordered collection of data of the same type.

Create vectors with c().

Lists are an ordered collection of data that can be any type.

Use help() to get online help in R.

Use read_csv() or read_tsv() to read in plain text data

Use read_excel() from the readxl package to read in Excel files

For data to be tidy, each variable must be in its own column

For data to be tidy, each case must be in its own row

For data to be tidy, each value must be in its own cell

Dataframes (or tibbles in the tidyverse) are lists where each element is a vector of the same length

Use read_csv() to read comma separated files into a data frame

Use nrow(), ncol(), dim(), or colnames() to find information about a dataframe

Use head(), tail(), summary(), or glimpse() to inspect a dataframe’s content

Use select() to choose variables from a dataframe.

Helper functions make it easier to select the correct columns.

Use rename() to rename variables without dropping columns.

Use filter() to choose data based on values.

The logical operator %in% can filter data from a list of possible values

Use mutate() to create new variables from old ones.

You can create new variables using any function that returns a vector of the same length as the data frame.

Use group_by() to group your data based on a variable.

summarise() creates a new variable that provides a one row summary per group

n() is useful to count rows per group

bind_rows combines datasets that share the same variables

The join family of functions provide a complete range of methods to merge datasets that share common variables

Data analyses can be broken down into discrete stages

Most data analysis stages fit into a small number of types

Pipes pass their left hand side through as the first argument of the right hand side.

Pipes make your code more readable, but be careful of going overboard.

Use the tidyr package to change the layout of dataframes.

Use gather() to go from wide to long format.

Use spread() to go from long to wide format.

Real world data is messy

It takes time and care to prepare it for analysis and visualisation

Intermediate data objects do not need to be written to disk

Write data in an appropriate format

Write data to the most useful location

A script is a discrete unit of analysis

A script will be run in the context of an environment

Software (and compute) dependencies need to be considered