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Overview

R is a versatile, open source programming language that was specifically designed for data analysis. R is extremely useful for data management, statistics and analyzing data.

This tutorial should be seem more as a reference on the basics of R and not a tutorial for learning to use R. Here we define many of the basics, however, this can get overwhelming if you are brand new to R.

R Skill Level: beginner or intermediate – a refresher on basics

Objectives

After completing this tutorial, you will be able to:

  • Use basic R syntax
  • Explain the concepts of objects and assignment
  • Explain the concepts of vector and data types
  • Describe why you would or would not use factors
  • Use basic few functions

Things You’ll Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.


Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

The Very Basics of R

R is a versatile, open source programming language that was specifically designed for data analysis. R is extremely useful for data management, statistics and analyzing data.

Cool Fact: R was inspired by the programming language S.

R is:

  • Open source software under a GNU General Public License (GPL).
  • A good alternative to commercial analysis tools. R has over 5,000 user contributed packages (as of 2014) and is widely used both in academia and industry.
  • Available on all platforms.
  • Not just for statistics, but also general purpose programming.
  • Supported by a large and growing community of peers.

Introduction to R

You can use R alone or with a user interace like RStudio to write your code. Some people prefer RStudio as it provides a graphic interface where you can see what objects have been created and you can also set variables like your working directory, using menu options.

Learn more about RStudio with thier online learning materials.

We want to use R to create code and a workflow is more reproducible. We can document everything that we do. Our end goal is not just to “do stuff” but to do it in a way that anyone can easily and exactly replicate our workflow and results – this includes ourselves in 3 months when the paper reviews come back!

Code & Comments in R

Everything you type into an R script is code, unless you demark it otherwise.

Anything to the right of a # is ignored by R. Use these comments within the code to describe what it is that you code is doing. Comment liberally in your R scripts. This will help you when you return to it and will also help others understand your scripts and analyses.

# this is a comment. It allows text that is ignored by the program.
# for clean, easy to read comments, use a space between the # and text. 

# there is a line of code below this comment
 a <- 1+2

Basic Operations in R

Let’s take a few moments to play with R. You can get output from R simply by typing in math

# basic math
3 + 5

## [1] 8

12/7

## [1] 1.714286

or by typing words, with the command writeLines(). Words that you want to be recognized as text (as opposed to a field name or other text that signifies an object) must be enclosed within quotes.

# have R write words

writeLines("hello world")

## hello world

We can assign our results to an object and name the object. Objects names cannot contain spaces.

# assigning values to objects 
b <- 60 * 60

hours <- 365 * 24

# object names can't contain spaces.  Use a period, underscore, or camelCase to 
# create longer names
temp_HARV <- 90

The result of the operation on the right hand side of <- is assigned to an object with the name specified on the left hand side of <-. The result could be any type of R object, including your own functions (see the Build & Work With Functions in R tutorial.

Assignment Operator: Drop the Equals Sign

The assignment operator is <-. It assigns values on the right to objects on the left. It is similar to = but there are some subtle differences. Learn to use <- as it is good programming practice. Using = in place of <- can lead to issues down the line.

# this is preferred syntax
a <- 1+2 

# this is NOT preferred syntax
a = 1+2 

Typing Tip: If you are using RStudio, you can use a keyboard shortcut for the assignment operator: Windows/Linux: “Alt” + “-“ or Mac: “Option” + “-“.

List All Objects in the Environment

Some functions are the same as in other languages. These might be familiar from command line.

  • ls(): to list objects in your current environment.
  • rm(): remove objects from your current environment.

Now try them in the console.

# assign value "5" to object "x"
x <- 5
ls()

##  [1] "a"           "add.date"    "b"           "base.url"    "codeDir"    
##  [6] "dat"         "dir"         "dirs"        "f"           "fig.path"   
## [11] "files"       "gitRepoPath" "hours"       "imagePath"   "input"      
## [16] "m"           "m2"          "m2_row"      "m3"          "mdFile"     
## [21] "n"           "o"           "p"           "postsDir"    "rmd.files"  
## [26] "subDir"      "temp_HARV"   "wd"          "x"           "x1"         
## [31] "x2"          "y"           "z"

# remove x
rm(x)

# what is left?
ls()

##  [1] "a"           "add.date"    "b"           "base.url"    "codeDir"    
##  [6] "dat"         "dir"         "dirs"        "f"           "fig.path"   
## [11] "files"       "gitRepoPath" "hours"       "imagePath"   "input"      
## [16] "m"           "m2"          "m2_row"      "m3"          "mdFile"     
## [21] "n"           "o"           "p"           "postsDir"    "rmd.files"  
## [26] "subDir"      "temp_HARV"   "wd"          "x1"          "x2"         
## [31] "y"           "z"

# remove all objects
rm(list = ls())

ls()

## character(0)

Using rm(list=ls()), you combine several functions to remove all objects. If you typed x on the console now you will get Error: object 'x' not found'.

Data Types and Structures

To make the best of the R language, you’ll need a strong understanding of the basic data types and data structures and how to operate on those. These are the objects you will manipulate on a day-to-day basis in R. Dealing with object conversions is one of the most common sources of frustration for beginners.

First, everything in R is an object. But there are different types of objects. One of the basic differences in in the data structures which are different ways data are stored.

R has many different data structures. These include

  • atomic vector
  • list
  • matrix
  • data frame
  • array

These data structures vary by the dimensionality of the data and if they can handle data of differnt types (homogenous vs heterogeneous).

Dimensions Homogenous Heterogeneous
1-D atomic vector list
2-D matrix data frame
none array  

Vectors

A vector is the most common and basic data structure in R and is the workhorse of R. Technically, vectors can be one of two types:

  • atomic vectors
  • lists

although the term “vector” most commonly refers to the atomic types not to lists.

Atomic Vectors

R has 6 atomic vector types.

  • character
  • numeric (real or decimal)
  • integer
  • logical
  • complex
  • raw (not discussed in this tutorial)

By atomic, we mean the vector only holds data of a single type.

  • character: "a", "swc"
  • numeric: 2, 15.5
  • integer: 2L (the L tells R to store this as an integer)
  • logical: TRUE, FALSE
  • complex: 1+4i (complex numbers with real and imaginary parts)

R provides many functions to examine features of vectors and other objects, for example

  1. typeof() - what is it?
  2. length() - how long is it? What about two dimensional objects?
  3. attributes() - does it have any metadata?

Let’s look at some examples:

# assign word "april" to x"
x <- "april"

# return the type of the object
typeof(x)

## [1] "character"

# 
attributes(x)

## NULL

# assign all values 1 to 10 to the object y
y <- 1:10
y

##  [1]  1  2  3  4  5  6  7  8  9 10

typeof(y)

## [1] "integer"

# how many 
length(y)

## [1] 10

# 
z <- as.numeric(y)
z

##  [1]  1  2  3  4  5  6  7  8  9 10

typeof(z)

## [1] "double"

A vector is a collection of elements that are most commonly character, logical, integer or numeric.

You can create an empty vector with vector(). (By default the mode is logical. You can be more explicit as shown in the examples below.) It is more common to use direct constructors such as character(), numeric(), etc.

x <- vector()
    
# Create vector with a length and type
vector("character", length = 10)

##  [1] "" "" "" "" "" "" "" "" "" ""

# create character vector with length of 5
character(5)  

## [1] "" "" "" "" ""

# numeric vector length=5
numeric(5)

## [1] 0 0 0 0 0

# logical vector length=5
logical(5)

## [1] FALSE FALSE FALSE FALSE FALSE

# create a list with combine `c()`
x <- c(1, 2, 3)
x

## [1] 1 2 3

length(x)

## [1] 3

typeof(x)

## [1] "double"

x is a numeric vector. These are the most common kind. They are numeric objects and are treated as double precision real numbers (they can store decimal points). To explicitly create integers (no decimal points), add an L to each (or coerce to the integer type using as.integer().

# a numeric vector with integers (L)
x1 <- c(1L, 2L, 3L)
x1

## [1] 1 2 3

typeof(x1)

## [1] "integer"

# or using as.integer()
x2 <- as.integer(x)
typeof(x2)

## [1] "integer"

You can also have logical vectors.

# logical vector 
y <- c(TRUE, TRUE, FALSE, FALSE)
y

## [1]  TRUE  TRUE FALSE FALSE

typeof(y)

## [1] "logical"

Finally, you can have character vectors.

# character vector
z <- c("Sarah", "Tracy", "Jon")
z

## [1] "Sarah" "Tracy" "Jon"

typeof(z)

## [1] "character"

length(z)

## [1] 3

# what class is it
class(z)

## [1] "character"

# what is the structure
str(z)

##  chr [1:3] "Sarah" "Tracy" "Jon"

You can also add to a list

	z <- c(z, "Annette")
	z

## [1] "Sarah"   "Tracy"   "Jon"     "Annette"

More examples of how to create vectors

  • x <- c(0.5, 0.7)
  • x <- c(TRUE, FALSE)
  • x <- c(“a”, “b”, “c”, “d”, “e”)
  • x <- 9:100
  • x <- c(1 + (0+0i), 2 + (0+4i))

You can also create vectors as a sequence of numbers.

# simple series 
1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

# use seq() 'sequence'
seq(10)

##  [1]  1  2  3  4  5  6  7  8  9 10

# specify values for seq()
seq(from = 1, to = 10, by = 0.1)

##  [1]  1.0  1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9  2.0  2.1  2.2  2.3
## [15]  2.4  2.5  2.6  2.7  2.8  2.9  3.0  3.1  3.2  3.3  3.4  3.5  3.6  3.7
## [29]  3.8  3.9  4.0  4.1  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1
## [43]  5.2  5.3  5.4  5.5  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5
## [57]  6.6  6.7  6.8  6.9  7.0  7.1  7.2  7.3  7.4  7.5  7.6  7.7  7.8  7.9
## [71]  8.0  8.1  8.2  8.3  8.4  8.5  8.6  8.7  8.8  8.9  9.0  9.1  9.2  9.3
## [85]  9.4  9.5  9.6  9.7  9.8  9.9 10.0

You can also get non-numeric outputs.

  • Inf is infinity. You can have either positive or negative infinity.
  • NaN means Not a Number. It’s an undefined value.

Try it out in the console.

# infinity return
1/0

## [1] Inf

# non numeric return
0/0

## [1] NaN

Objects can have attributes. Attribues are part of the object. These include:

  • names: the field or variable name within the object
  • dimnames:
  • dim:
  • class:
  • attributes: this contain metadata

You can also glean other attribute-like information such as length() (works on vectors and lists) or number of characters nchar() (for character strings).

# length of an object
length(1:10)

## [1] 10

length(x)

## [1] 3

# number of characters in a text string
nchar("NEON Data Skills")

## [1] 16

Heterogeneous Data - Mixing Types?

When you mix types, R will create a resulting vector that is the least common denominator. The coercion will move towards the one that’s easiest to coerce to.

Guess what the following do:

  • m <- c(1.7, “a”)
  • n <- c(TRUE, 2)
  • o <- c(“a”, TRUE)

Were you correct?

n <- c(1.7, "a")
n

## [1] "1.7" "a"

o <- c(TRUE, 2)
o

## [1] 1 2

p <- c("a", TRUE)
p

## [1] "a"    "TRUE"

This is called implicit coercion. You can also coerce vectors explicitly using the as.<class_name>.

# making values numeric
as.numeric(c("1")

# make values charactor
as.character(1:2)

# make values 
as.factor("male","female")

## Error: <text>:5:1: unexpected symbol
## 4: # make values charactor
## 5: as.character
##    ^

Matrix

In R, matrices are an extension of the numeric or character vectors. They are not a separate type of object but simply an atomic vector with dimensions; the number of rows and columns.

# create an empty matrix that is 2x2
m <- matrix(nrow = 2, ncol = 2)
m

##      [,1] [,2]
## [1,]   NA   NA
## [2,]   NA   NA

# what are the dimensions of m
dim(m)

## [1] 2 2

Matrices in R are by default filled column-wise. You can also use the byrow argument to specify how the matrix is filled.

# create a matrix. Notice R fills them by columns
m2 <- matrix(1:6, nrow = 2, ncol = 3)
m2

##      [,1] [,2] [,3]
## [1,]    1    3    5
## [2,]    2    4    6

m2_row <- matrix(c(1:6), nrow = 2, ncol = 3, byrow = TRUE)
m2_row

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

dim() takes a vector and transform into a matrix with 2 rows and 5 columns. Another way to shape your matrix is to bind columns cbind() or rows rbind().

# create vector with 1:10
m3 <- 1:10
m3

##  [1]  1  2  3  4  5  6  7  8  9 10

class(m3)

## [1] "integer"

# set the dimensions so it becomes a matrix
dim(m3) <- c(2, 5)
m3

##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10

class(m3)

## [1] "matrix"

# create matrix from two vectors
x <- 1:3
y <- 10:12

# cbind will bind the two by column
cbind(x, y)

##      x  y
## [1,] 1 10
## [2,] 2 11
## [3,] 3 12

# rbind will bind the two by row
rbind(x, y)

##   [,1] [,2] [,3]
## x    1    2    3
## y   10   11   12

List

In R, lists act as containers. Unlike atomic vectors, the contents of a list are not restricted to a single mode and can encompass any mixture of data types. Lists are sometimes called generic vectors, because the elements of a list can by of any type of R object, even lists containing further lists. This property makes them fundamentally different from atomic vectors.

A list is different from an atomic vector because each element can be a different type – it can contain heterogeneous data types.

Create lists using list() or coerce other objects using as.list(). An empty list of the required length can be created using vector()

x <- list(1, "a", TRUE, 1 + (0+4i))
x

## [[1]]
## [1] 1
## 
## [[2]]
## [1] "a"
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] 1+4i

class(x)

## [1] "list"

x <- vector("list", length = 5)  ## empty list
length(x)

## [1] 5

x[[1]]

## NULL

x <- 1:10
x <- as.list(x)

Questions:

  1. What is the class of x[1]?
  2. What about x[[1]]?

Try it out.

xlist <- list(a = "Karthik Ram", b = 1:10, data = head(iris))
xlist

$a
 
$b

$data

## Error: <text>:5:1: unexpected '$'
## 4: 
## 5: $
##    ^
  1. What is the length of this object? What about its structure?
  • Lists can be extremely useful inside functions. You can “staple” together lots of different kinds of results into a single object that a function can return.
  • A list does not print to the console like a vector. Instead, each element of the list starts on a new line.
  • Elements are indexed by double brackets. Single brackets will still return a(nother) list.

Factors

Factors are special vectors that represent categorical data. Factors can be ordered or unordered and are important for modelling functions such as lm() and glm() and also in plot() methods. Once created, factors can only contain a pre-defined set values, known as levels.

Factors are stored as integers that have labels associated the unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood. You need to be careful when treating them like strings. Some string methods will coerce factors to strings, while others will throw an error.

  • Sometimes factors can be left unordered. Example: male, female.
  • Other times you might want factors to be ordered (or ranked). Example: low, medium, high.
  • Underlying it’s represented by numbers 1, 2, 3.
  • They are better than using simple integer labels because factors are what are called self describing. male and female is more descriptive than 1s and 2s. Helpful when there is no additional metadata.

Which is male? 1 or 2? You wouldn’t be able to tell with just integer data. Factors have this information built in.

Factors can be created with factor(). Input is often a character vector.

x <- factor(c("yes", "no", "no", "yes", "yes"))
x

## [1] yes no  no  yes yes
## Levels: no yes

table(x) will return a frequency table counting the number of elements in each level.

If you need to convert a factor to a character vector, simply use

as.character(x)

## [1] "yes" "no"  "no"  "yes" "yes"

To convert a factor to a numeric vector, go via a character. Compare

f <- factor(c(1, 5, 10, 2))

as.numeric(f)  ## wrong!

## [1] 1 3 4 2

as.numeric(as.character(f))

## [1]  1  5 10  2

In modeling functions, it is important to know what the baseline level is. This is the first factor but by default the ordering is determined by alphanumerical order of elements. You can change this by speciying the levels (another option is to use the function relevel()).

x <- factor(c("yes", "no", "yes"), levels = c("yes", "no"))
x

## [1] yes no  yes
## Levels: yes no

Data Frame

A data frame is a very important data type in R. It’s pretty much the de facto data structure for most tabular data and what we use for statistics.

  • A data frame is a special type of list where every element of the list has same length.
  • Data frames can have additional attributes such as rownames(), which can be useful for annotating data, like subject_id or sample_id. But most of the time they are not used.

Some additional information on data frames:

  • Usually created by read.csv() and read.table().
  • Can convert to matrix with data.matrix() (preferred) or as.matrix()
  • Coercion will be forced and not always what you expect.
  • Can also create with data.frame() function.
  • Find the number of rows and columns with nrow(dat) and ncol(dat), respectively.
  • Rownames are usually 1, 2, …, n.

Manually Create Data Frames

You can manually create a data frame using data.frame.

# create a dataframe
dat <- data.frame(id = letters[1:10], x = 1:10, y = 11:20)
dat

##    id  x  y
## 1   a  1 11
## 2   b  2 12
## 3   c  3 13
## 4   d  4 14
## 5   e  5 15
## 6   f  6 16
## 7   g  7 17
## 8   h  8 18
## 9   i  9 19
## 10  j 10 20

Useful Data Frame Functions

  • head() - shown first 6 rows
  • tail() - show last 6 rows
  • dim() - returns the dimensions
  • nrow() - number of rows
  • ncol() - number of columns
  • str() - structure of each column
  • names() - shows the names attribute for a data frame, which gives the column names.

See that it is actually a special list:

list()

## list()

is.list(iris)

## [1] TRUE

class(iris)

## [1] "data.frame"

A recap of the different data types

Dimensions Homogenous Heterogeneous
1-D atomic vector list
2-D matrix data frame
none array  

Indexing

Vectors have positions, these positions are ordered and can be called using object[index]

# index
letters[2]

## [1] "b"

Functions

A function is an R object that takes inputs to perform a task. Functions take in information and may return desired outputs.

output <- name_of_function(inputs)

# create a list of 1 to 10
x <- 1:10 

# the sum of all x
y <- sum(x)
y

## [1] 55

Help

All functions come with a help screen. It is critical that you learn to read the help screens since they provide important information on what the function does, how it works, and usually sample examples at the very bottom. You can use help() or more simply ??()

# call up a help search
help.start()

# help (documentation) for a package
?? ggplot2

# help for a function
? sum()

You can’t ever learn all of R as it is ever changing with new packages and new tools, but once you have the basics and know how to find help to do the things that you want to do, you’ll be able to use R in your science.

Sample Data

R comes with sample data sets. You will often find these as the date sets in documentation files or responses to inquires on public forums like StackOverflow. To see all available sample data sets you can type in data() to the console.

Packages in R

R comes with a set of functions or commands that perform particular sets of calculations. For example, in the equation 1+2, R knows that the “+” means to add the two numbers, 1 and 2 together. However, you can expand the capability of R by installing packages that contain suites of functions and compiled code that you can also use in your code.

Go to the next tutorial Packages in R.


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