Reference
Last updated on 2024-10-03 | Edit this page
Reference
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:
-
- Brackets:
- 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
orhelp(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
, andInf
can only occur in complex, integer or numeric type vectors. Atomic vectors are the building blocks for all other data structures. ANULL
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).
-
?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.
-
?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.
-
?list()
Lists are a data structure designed to store data of different types.
-
?matrix()
Matrices are a data structure designed to store 2-dimensional data.
-
?data.frame
is a key data structure. It is alist
ofvectors
. -
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 firstn
elements (rows for two-dimensional objects) -
?tail
print the lastn
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.
-
extract rows and/or columns of
[[
double square brackets to extract items from lists.$
to access columns or list elements by namenegative indices skip elements
Control flow
- Use
if
condition to start a conditional statement,else if
condition to provide additional tests, andelse
to provide a default - The bodies of the branches of conditional statements must be indented.
- Use
==
to test for equality. -
%in%
will return aTRUE
/FALSE
indicating if there is a match between an element and a vector. -
X && Y
is only true if both X and Y areTRUE
. -
X || Y
is true if either X or Y, or both, areTRUE
. - Zero is considered
FALSE
; all other numbers are consideredTRUE
- 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 -
aes
thetics specify the data axes, shape, color, and data size -
geom
etry functions specify the type of plot, e.g.point
,line
,density
,box
-
geom
etry 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 -
aes
thetics apply to individual layers, or can be set for the whole plot insideggplot
. -
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 returnTRUE
if any element of a vector isTRUE
-
all()
will returnTRUE
if all elements of a vector areTRUE
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
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 number, 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.