Reference

Key Points

Introduction to R and RStudio	Use RStudio to write and run R programs. 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 (libraries).
Project Management With RStudio	Use RStudio to create and manage projects with consistent layout. Treat raw data as read-only. Treat generated output as disposable. Separate function definition and application.
Seeking Help	Use `help()` to get online help in R.
Data Structures	Use `read.csv` to read tabular data in R. The basic data types in R are double, integer, complex, logical, and character. Use factors to represent categories in R.
Exploring Data Frames	Use `cbind()` to add a new column to a data frame. Use `rbind()` to add a new row to a data frame. Remove rows from a data frame. Use `na.omit()` to remove rows from a data frame with `NA` values. Use `levels()` and `as.character()` to explore and manipulate factors. Use `str()`, `summary()`, `nrow()`, `ncol()`, `dim()`, `colnames()`, `rownames()`, `head()`, and `typeof()` to understand the structure of a data frame. Read in a csv file using `read.csv()`. Understand what `length()` of a data frame represents.
Subsetting Data	Indexing in R starts at 1, not 0. Access individual values by location using `[]`. Access slices of data using `[low:high]`. Access arbitrary sets of data using `[c(...)]`. Use logical operations and logical vectors to access subsets of data.
Control Flow	Use `if` and `else` to make choices. Use `for` to repeat operations.
Vectorization	Use vectorized operations instead of loops.
Functions Explained	Use `function` to define a new function in R. Use parameters to pass values into functions. Use `stopifnot()` to flexibly check function arguments in R. Load functions into programs using `source()`.
Data Wrangling with dplyr and tidyr	Use the `dplyr` package to manipulate dataframes. Use `select()` to choose variables from a dataframe. Use `filter()` to choose data based on values. Use `group_by()` and `summarize()` to work with subsets of data. Use `mutate()` to create new variables. Use the `tidyr` package to change the layout of dataframes. Use `pivot_wider()` to go from long to wide format. Use `pivot_longer()` to go from wide to long format.
Creating Publication-Quality Graphics with ggplot2	Use `ggplot2` to create plots. Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.
Producing Reports With knitr	Mix reporting written in R Markdown with software written in R. Specify chunk options to control formatting. Use `knitr` to convert these documents into PDF and other formats.
Command-Line Programs	Use `commandArgs(trailingOnly = TRUE)` to obtain a vector of the command-line arguments that a program was run with. Avoid silent failures. Use `file("stdin")` to connect to a program’s standard input. Use `cat(vec, sep = " ")` to write the elements of `vec` to standard output, one per line.
Writing Good Software	Keep your project folder structured, organized and tidy. Document what and why, not how. Break programs into short single-purpose functions. Write re-runnable tests. Don’t repeat yourself. Be consistent in naming, indentation, and other aspects of style.

Introduction to R and RStudio

Use the escape key to cancel incomplete commands or running code (Ctrl+C) if you’re using R from the shell.
Basic arithmetic operations follow standard order of precedence:
- Brackets: (, )
- Exponents: ^ or **
- Divide: /
- Multiply: *
- Add: +
- Subtract: -
Scientific notation is available, e.g: 2e-3
Anything to the right of a # is a comment, R will ignore this!
Functions are denoted by function_name(). Expressions inside the brackets are evaluated before being passed to the function, and functions can be nested.
Mathematical functions: exp, sin, log, log10, log2 etc.
Comparison operators: <, <=, >, >=, ==, !=
Use all.equal to compare numbers!
<- is the assignment operator. Anything to the right is evaluate, then stored in a variable named to the left.
ls lists all variables and functions you’ve created
rm can be used to remove them
When assigning values to function arguments, you must use =.

Project management with RStudio

To create a new project, go to File -> New Project
Install the packrat package to create self-contained projects
install.packages to install packages from CRAN
library to load a package into R
packrat::status to check whether all packages referenced in your scripts have been installed.

Seeking help

To access help for a function type ?function_name or help(function_name)
Use quotes for special operators e.g. ?"+"
Use fuzzy search if you can’t remember a name ‘??search_term’
CRAN task views are a good starting point.
Stack Overflow is a good place to get help with your code.
- ?dput will dump data you are working from so others can load it easily.
- sessionInfo() will give details of your setup that others may need for debugging.

Data structures

Individual values in R must be one of 5 data types, multiple values can be grouped in data structures.

Data types

typeof(object) gives information about an items data type.
There are 5 main data types:
- ?numeric real (decimal) numbers
- ?integer whole numbers only
- ?character text
- ?complex complex numbers
- ?logical TRUE or FALSE values
Special types:
- ?NA missing values
- ?NaN “not a number” for undefined values (e.g. 0/0).
- ?Inf, -Inf infinity.
- ?NULL a data structure that doesn’t exist
NA can occur in any atomic vector. NaN, and Inf can only occur in complex, integer or numeric type vectors. Atomic vectors are the building blocks for all other data structures. A NULL value will occur in place of an entire data structure (but can occur as list elements).

Basic data structures in R:

atomic ?vector (can only contain one type)
?list (containers for other objects)
?data.frame two dimensional objects whose columns can contain different types of data
?matrix two dimensional objects that can contain only one type of data.
?factor vectors that contain predefined categorical data.
?array multi-dimensional objects that can only contain one type of data

Remember that matrices are really atomic vectors underneath the hood, and that data.frames are really lists underneath the hood (this explains some of the weirder behaviour of R).

Vectors

?vector() All items in a vector must be the same type.
Items can be converted from one type to another using coercion.
The concatenate function ‘c()’ will append items to a vector.
seq(from=0, to=1, by=1) will create a sequence of numbers.
Items in a vector can be named using the names() function.

Factors

?factor() Factors are a data structure designed to store categorical data.
levels() shows the valid values that can be stored in a vector of type factor.

Lists

?list() Lists are a data structure designed to store data of different types.

Matrices

?matrix() Matrices are a data structure designed to store 2-dimensional data.

Data Frames

?data.frame is a key data structure. It is a list of vectors.
cbind() will add a column (vector) to a data.frame.
rbind() will add a row (list) to a data.frame.

Useful functions for querying data structures:

?str structure, prints out a summary of the whole data structure
?typeof tells you the type inside an atomic vector
?class what is the data structure?
?head print the first n elements (rows for two-dimensional objects)
?tail print the last n elements (rows for two-dimensional objects)
?rownames, ?colnames, ?dimnames retrieve or modify the row names and column names of an object.
?names retrieve or modify the names of an atomic vector or list (or columns of a data.frame).
?length get the number of elements in an atomic vector
?nrow, ?ncol, ?dim get the dimensions of a n-dimensional object (Won’t work on atomic vectors or lists).

Exploring Data Frames

read.csv to read in data in a regular structure
- sep argument to specify the separator
  - ”,” for comma separated
  - “\t” for tab separated
- Other arguments:
  - header=TRUE if there is a header row

Subsetting data

Elements can be accessed by:
- Index
- Name
- Logical vectors
[ single square brackets:
- extract single elements or subset vectors
- e.g.x[1] extracts the first item from vector x.
- extract single elements of a list. The returned value will be another list().
- extract columns from a data.frame
[ with two arguments to:
- extract rows and/or columns of
  - matrices
  - data.frames
  - e.g. x[1,2] will extract the value in row 1, column 2.
  - e.g. x[2,:] will extract the entire second column of values.
[[ double square brackets to extract items from lists.
$ to access columns or list elements by name
negative indices skip elements

Control flow

Use if condition to start a conditional statement, else if condition to provide additional tests, and else to provide a default
The bodies of the branches of conditional statements must be indented.
Use == to test for equality.
%in% will return a TRUE/FALSE indicating if there is a match between an element and a vector.
X && Y is only true if both X and Y are TRUE.
X || Y is true if either X or Y, or both, are TRUE.
Zero is considered FALSE; all other numbers are considered TRUE
Nest loops to operate on multi-dimensional data.

Creating publication quality graphics

figures can be created with the grammar of graphics:
- library(ggplot2)
- ggplot to create the base figure
- aesthetics specify the data axes, shape, color, and data size
- geometry functions specify the type of plot, e.g. point, line, density, box
- geometry functions also add statistical transforms, e.g. geom_smooth
- scale functions change the mapping from data to aesthetics
- facet functions stratify the figure into panels
- aesthetics apply to individual layers, or can be set for the whole plot inside ggplot.
- theme functions change the overall look of the plot
- order of layers matters!
- ggsave to save a figure.

Vectorization

Most functions and operations apply to each element of a vector
* applies element-wise to matrices
%*% for true matrix multiplication
any() will return TRUE if any element of a vector is TRUE
all() will return TRUE if all elements of a vector are TRUE

Functions explained

?"function"
Put code whose parameters change frequently in a function, then call it with different parameter values to customize its behavior.
The last line of a function is returned, or you can use return explicitly
Any code written in the body of the function will preferably look for variables defined inside the function.
Document Why, then What, then lastly How (if the code isn’t self explanatory)

Writing data

write.table to write out objects in regular format
set quote=FALSE so that text isn’t wrapped in " marks

Split-apply-combine

Use the xxply family of functions to apply functions to groups within some data.
the first letter, array , data.frame or list corresponds to the input data
the second letter denotes the output data structure
Anonymous functions (those not assigned a name) are used inside the plyr family of functions on groups within data.

Dataframe manipulation with dplyr

library(dplyr)
?select to extract variables by name.
?filter return rows with matching conditions.
?group_by group data by one of more variables.
?summarize summarize multiple values to a single value.
?mutate add new variables to a data.frame.
Combine operations using the ?"%>%" pipe operator.

Dataframe manipulation with tidyr

library(tidyr)
?pivot_longer convert data from wide to long format.
?pivot_wider convert data from long to wide format.
?separate split a single value into multiple values.
?unite merge multiple values into a single value.

Producing reports with knitr

Value of reproducible reports
Basics of Markdown
R code chunks
Chunk options
Inline R code
Other output formats

Best practices for writing good code

Program defensively, i.e., assume that errors are going to arise, and write code to detect them when they do.
Write tests before writing code in order to help determine exactly what that code is supposed to do.
Know what code is supposed to do before trying to debug it.
Make it fail every time.
Make it fail fast.
Change one thing at a time, and for a reason.
Keep track of what you’ve done.
Be humble

Glossary

argument: A value given to a function or program when it runs. The term is often used interchangeably (and inconsistently) with parameter.
assign: To give a value a name by associating a variable with it.
body: (of a function): the statements that are executed when a function runs.
comment: A remark in a program that is intended to help human readers understand what is going on, but is ignored by the computer. Comments in Python, R, and the Unix shell start with a # character and run to the end of the line; comments in SQL start with --, and other languages have other conventions.
comma-separated values: (CSV) A common textual representation for tables in which the values in each row are separated by commas.
delimiter: A character or characters used to separate individual values, such as the commas between columns in a CSV file.
documentation: Human-language text written to explain what software does, how it works, or how to use it.
floating-point number: A number containing a fractional part and an exponent. See also: integer.
for loop: A loop that is executed once for each value in some kind of set, list, or range. See also: while loop.
index: A subscript that specifies the location of a single value in a collection, such as a single pixel in an image.
integer: A whole number, such as -12343. See also: floating-point number.
library: In R, the directory(ies) where packages are stored.
package: A collection of R functions, data and compiled code in a well-defined format. Packages are stored in a library and loaded using the library() function.
parameter: A variable named in the function’s declaration that is used to hold a value passed into the call. The term is often used interchangeably (and inconsistently) with argument.
return statement: A statement that causes a function to stop executing and return a value to its caller immediately.
sequence: A collection of information that is presented in a specific order.
shape: An array’s dimensions, represented as a vector. For example, a 5×3 array’s shape is (5,3).
string: Short for “character string”, a sequence of zero or more characters.
syntax error: A programming error that occurs when statements are in an order or contain characters not expected by the programming language.
type: The classification of something in a program (for example, the contents of a variable) as a kind of number (e.g. floating-point, integer), string, or something else. In R the command typeof() is used to query a variables type.
while loop: A loop that keeps executing as long as some condition is true. See also: for loop.

R for Reproducible Scientific Analysis: Reference

Key Points

Reference

Glossary