Learning Objectives

1. Recognize and use R and RStudio;

2. Use R as a calculator;

3. Manipulate objects in R;

4. Install and use R packages and functions;

5. Get help.

What is R?

Why should I become an useR?

• R is free, open source: built for you and for everyone;
• R is popular: a large enganged user-base fosters the continued development and the maintainance of statistical tools;
• R is powerful
  • You can program complex simulations
  • Use it on high performance clusters
• R supports extensions
• R runs on most operating systems
• R connects with other languages: C++, Java, Python, Julia, Stan and more!

Why should I become an useR?

Why use R?

Why use R?

• More and more scientists use it every year!
• As of October 2020, there are more than 16000 packages registered within the Comprehensive R Archive Network (CRAN) (and thousands more within Github repositories)!

A lot of features: customizable graphs

All of these graphs were made in R!

And what about RStudio?

RStudio is the most used Integrated Development Environment (IDE) for R.

It includes a console, a syntax-highlighting editor that supports direct code execution with tools for plotting, history, debugging and workspace management.

It integrates with R (and other programming languages) to provide a lot of useful features:

And what about RStudio?

RStudio is the most used Integrated Development Environment (IDE) for R.

It includes a console, a syntax-highlighting editor that supports direct code execution with tools for plotting, history, debugging and workspace management.

It integrates with R (and other programming languages) to provide a lot of useful features:

There are other IDE for R: Atom, Visual Studio, Jupyter notebook and Jupyter lab!

Challenge

First Challenge

Open RStudio.

Let us do this one together!

Note for Windows users

If the restriction unable to write on disk appears when you attempt to open RStudio or to install a package.

Do not worry!

We have the solution!

• Close the application.
• Right-click on your RStudio icon and click on "Execute as Administrator". This will provide file and directory writing rights to RStudio.

The RStudio Interface

The RStudio Console

• Usually, the first text you see within the Console pane is the R version RStudio is using.
  

• The Console is the place where R is waiting for you to tell it what to do, and is where it will communicate with you, showing the outcome of your command.
  

• Whenever R is ready to accept commands, it will show a > prompt.

Reading the Console

output
# [1] "This is the output"

What does the square brackets [ ] within the output mean?

The numbers within the brackets help you to locate the position of elements within the output.

seq(1, 100, by = 2)
#  [1]  1  3  5  7  9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
# [26] 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99

Error and Warning

Often, the Console will output Errors and Warning messages.

x <- c("2", -3, "end", 0, 4, 0.2)
as.numeric(x)
# Warning: NAs introduced by coercion
# [1]  2.0 -3.0   NA  0.0  4.0  0.2

x*10
# Error in x * 10: non-numeric argument to binary operator

Arithmetic Operators

• Additions and Subtractions

1 + 1
# [1] 2
10 - 1
# [1] 9

• Multiplications and Divisions

2 * 2
# [1] 4
8 / 2
# [1] 4

• Exponents

2^3
# [1] 8

Challenge

Use R to calculate the following equation:

$$2+16*24-56$$

Hint: The * symbol is used to multiply.

Challenge: Solution

Use R to calculate the following equation:

$$2+16*24-56$$

It would look like this in R:

2 + 16 * 24 - 56
# [1] 330

Challenge

Use R to calculate the following equation:

$$2+16*24-56 / (2+1)-457$$

Hint: Think about the order of the operation.

Challenge: Solution

Use R to calculate the following equation:

$$2+16*24-56 / (2+1)-457$$

It would look like this in R:

2 + 16 * 24 - 56 / (2 + 1) - 457
# [1] -89.66667

Note that R respects the order of the operations

Still using R for arithmetic operations

What is the area of a circle with a radius of $5\ cm$?

Still using R for arithmetic operations

What is the area of a circle with a radius of $5\ cm$?

3.1416 * 5^2
# [1] 78.54

pi * 5^2
# [1] 78.53982

R: an object-oriented environment

You can assign information to named objects using the assignment operator <-.

The information is assigned to the name that is pointed by the assignment operator <-.

See the examples below:

money_talks <- "ACDC"
money_talks
# [1] "ACDC"

9 -> my_birthday_month
my_birthday_month
# [1] 9

Careful! There is no space between the less than (<) and minus (-) signs.

One can assign values using = instead of the <- operator. We caution against using = to assign values to objects because the = is allowed at the top level only and also determines subexpressions.

Naming objects: a few rules

Objects names can only include:

1. Objects names must always begin with a letter.
2. R is case-sensitive: Data_1 is different than data_1.
3. You cannot use special characters (@, /, #, etc.).
4. Object names must be unique: data_1 <- 1 will overwrite any previously objects named data_1

Good practice when naming objects and writing code

Short and explicit names are preferred.

• Naming a variable var is not very informative.

You can separate words within a name using underscores ( _ ) or dots ( . ).

• avg_richness or avg.richness are easier to read than avgrichness.

Avoid using names of existing functions or constants (e.g., c, table, T, matrix)

Add spaces around operators (=, +, -, <-, etc.) to make the code more readable.

Always put a space after a comma, and never before (like in regular English).

mean_x <- (2 + 6) / 2
mean_x
# [1] 4

meanx<-(2+6)/2
meanx
# [1] 4

Challenge

Create an object with a name (of your choice) that starts with a number. What happens?

Challenge: Solution

Create an object with a name (of your choice) that starts with a number. What happens?

Creating an object name that starts with a number returns the following error:

Error: unexpected symbol in "your object name"

Challenge

Create an object with a value of 1 + 1.718282 (e or Euler's number) and name it euler_value.

Challenge: Solution

Create an object with a value of 1 + 1.718282 (e or Euler's number) and name it euler_value.

euler_value <- 1 + 1.718282
euler_value
# [1] 2.718282

What has happened in your RStudio window when you created this object?

The RStudio Environment

The Environment panel shows you all the objects you have defined in your current workspace.

Core data types in R

Data types define how the values are stored in R.

We can obtain the type and mode of an object using the functions typeof(). The core data types are:

Numeric-type with integer and double values

(x <- 1.1)
# [1] 1.1
typeof(x)
# [1] "double"

(y <- 2L)
# [1] 2
typeof(y)
# [1] "integer"

Character-type (always between " ")

z <- "You are becoming very good in this!"
typeof(z)
# [1] "character"

Logical-type

t <- TRUE
typeof(t)
# [1] "logical"

f <- FALSE
typeof(f)
# [1] "logical"

Data structure in R: scalars

Until this moment, we have create objects that had just one element inside them:

x <- 1.1
x
# [1] 1.1

euler_value <- 1 + 1.718282
euler_value
# [1] 2.718282

An object that has just a single value or unit like a number or a text string is called a scalar.

a <- 100
b <- 3 / 100
c <- (a + b) / b

d <- "species"
e <- "genus"
f <- "When is the next pause again?"

By creating combinations of scalars, we can create data with different structures in R. We are getting there!

Data structure in R: vectors

A vector object is just a combination of several scalars stored as a single object.

Like scalars, vectors can be of numeric-, logical-, character-types, but never a mix of them!

Data structure in R: vectors

A vector object is just a combination of several scalars stored as a single object.

Like scalars, vectors can be of numeric-, logical-, character-types, but never a mix of them!

There are many ways to create vectors in R. Here are some we are going to see:

Creating vectors with c()

The c() function (c stands for concatenate, meaning bring them together) combines several scalars as arguments, which are separated by commas, and returns a vector containing them:

vector <- c(value1, value2, ...)

Let us use the c() function to create vectors of different types:

num_vector <- c(1, 4, 32, -76, -4)
num_vector
# [1]   1   4  32 -76  -4

char_vector <- c("blue", 
                 "red", 
                 "green")
char_vector
# [1] "blue"  "red"   "green"

Logical vector

bool_vector <- c(TRUE, TRUE, FALSE) # or c(T, T, F)
bool_vector
# [1]  TRUE  TRUE FALSE

Creating vectors of sequential values: a:b, seq(), rep()

The a:b takes two numeric scalars a and b as arguments, and returns a vector of numbers from the starting point a to the ending point b, in steps of 1 unit:

1:8
# [1] 1 2 3 4 5 6 7 8

7.5:1.5
# [1] 7.5 6.5 5.5 4.5 3.5 2.5 1.5

seq() allows us to create a sequence, like a:b, but also allows us to specify either the size of the steps (the by argument), or the total length of the sequence (the length.out argument):

seq(from = 1, to = 10, by = 2)
# [1] 1 3 5 7 9

seq(from = 20, to = 2, by = -2)
#  [1] 20 18 16 14 12 10  8  6  4  2

rep() allows you to repeat a scalar (or vector) a specified number of times, or to a desired length:

rep(x = 1:3, each = 2, times = 2)
#  [1] 1 1 2 2 3 3 1 1 2 2 3 3

rep(x = c(1, 2), each = 3)
# [1] 1 1 1 2 2 2

Challenge

Let's practice:

1. Create a vector containing the first 5 odd numbers, starting from 1
2. Name it odd_n
3. You can use any of the previous functions we have previously learned!

Challenge: Solution

Let's practice:

1. Create a vector containing the first 5 odd numbers, starting from 1
2. Name it odd_n
3. You can use any of the previous functions we have previously learned!

Solution:

odd_n <- c(1, 3, 5, 7, 9)

or

odd_n <- seq(from = 1, to = 9, by = 2)
odd_n
# [1] 1 3 5 7 9

Operations using vectors

Let us begin with the following objects:

x <- c(1:5)
y <- 6

Remember that the colon symbol : combines all values between the first and the second number in steps of 1. c(1:5) or 1:5 is equivalent to c(1, 2, 3, 4, 5)

What happens when we add and multiply the two objects together?

x + y
# [1]  7  8  9 10 11

x * y
# [1]  6 12 18 24 30

Excellent! We have learned a lot! How about a short break?

Data structure in R: matrices

We have learned that scalars contain one element, and that vectors contain more than one scalar of the same type!

Matrices are nothing but a bunch of vectors stacked together!

While vectors have one dimension, matrices have two dimensions, determined by rows and columns.

Finally, like vectors and scalars matrices can contain only one type of data: numeric, character, or logical.

Creating matrices using matrix(), cbind(), rbind()

There are many ways to create your own matrix. Let us start with a simple one:

matrix(data = 1:10,
       nrow = 5,
       ncol = 2)
#      [,1] [,2]
# [1,]    1    6
# [2,]    2    7
# [3,]    3    8
# [4,]    4    9
# [5,]    5   10

matrix(data = 1:10,
       nrow = 2,
       ncol = 5)
#      [,1] [,2] [,3] [,4] [,5]
# [1,]    1    3    5    7    9
# [2,]    2    4    6    8   10

We can also combine multiple vectors using cbind() and rbind():

nickname <- c("kat", "gab", "lo")
animal <- c("dog", "mouse", "cat")

rbind(nickname, 
      animal)
#          [,1]  [,2]    [,3] 
# nickname "kat" "gab"   "lo" 
# animal   "dog" "mouse" "cat"

cbind(nickname, animal)
#      nickname animal 
# [1,] "kat"    "dog"  
# [2,] "gab"    "mouse"
# [3,] "lo"     "cat"

Operations with matrices

Similarly as in the case of vectors, operations with matrices work just fine:

(mat_1 <- matrix(data = 1:9,
       nrow = 3,
       ncol = 3))
#      [,1] [,2] [,3]
# [1,]    1    4    7
# [2,]    2    5    8
# [3,]    3    6    9

(mat_2 <- matrix(data = 9:1,
       nrow = 3,
       ncol = 3))
#      [,1] [,2] [,3]
# [1,]    9    6    3
# [2,]    8    5    2
# [3,]    7    4    1

The product of the matrices is:

mat_1 * mat_2
#      [,1] [,2] [,3]
# [1,]    9   24   21
# [2,]   16   25   16
# [3,]   21   24    9

Challenge

It is your time to get your hands dirty!

1. Create an object containing a matrix with 2 rows and 3 columns, with values from 1 to 6, sorted per column.
2. Create another object with a matrix with 2 rows and 3 columns, with the names of six animals you like.
3. Create a third object with 4 rows and 2 columns:
   • in the first column, include the numbers from 2 to 5; and,
   • in the second column, include the first name of participants in this workshop.
4. Compare them and tell us what differences have you detected (despite their values).

Remember that text strings must always be surrounded by quote marks (" ").

Remember that values or arguments must be separated by commas if they are inside a function, e.g. c("one", "two", "three").

Challenge

It is your time to get your hands dirty!

1. Create an object containing a matrix with 2 rows and 3 columns, with values from 1 to 6, sorted per column.
2. Create another object with a matrix with 2 rows and 3 columns, with the names of six animals you like.

(step_1 <- matrix(data = 1:6,
       nrow = 2,
       ncol = 3))
#      [,1] [,2] [,3]
# [1,]    1    3    5
# [2,]    2    4    6

(step_2 <- matrix(
  data = c("cheetah", 
           "tiger", 
           "ladybug",
           "deer",
           "monkey",
           "crocodile"),
  nrow = 2,
  ncol = 3))
#      [,1]      [,2]      [,3]       
# [1,] "cheetah" "ladybug" "monkey"   
# [2,] "tiger"   "deer"    "crocodile"

Challenge

It is your time to get your hands dirty!

1. Create a third object with 4 rows and 2 columns:
   • in the first column, include the numbers from 2 to 5; and,
   • in the second column, include the first name of participants in this workshop.
2. Compare them and tell us what differences have you detected (despite their values).

step_1
#      [,1] [,2] [,3]
# [1,]    1    3    5
# [2,]    2    4    6

step_2
#      [,1]      [,2]      [,3]       
# [1,] "cheetah" "ladybug" "monkey"   
# [2,] "tiger"   "deer"    "crocodile"

step_3 <- cbind(c(2:5), 
                c("linley", 
                  "jessica", 
                  "joe",
                  "emma"))

step_3
#      [,1] [,2]     
# [1,] "2"  "linley" 
# [2,] "3"  "jessica"
# [3,] "4"  "joe"    
# [4,] "5"  "emma"

Data structure in R: data frames

Differently than a matrix, a data frame can contain numeric, character, and logical columns (or vectors).

Data structure in R: data frames

Data frames resemble a lot the usual Excel tables that we use in our research!

One of the ways of representing this table in R, is to create vectors:

site_id <- c("A1.01", "A1.02", "B1.01", "B1.02")
soil_pH <- c(5.6, 7.3, 4.1, 6.0)
num_sp <- c(17, 23, 15, 7)
fertilised <- c("yes", "yes", "no", "no")

We then combine them using data.frame():

soil_fertilisation_data <- data.frame(site_id, soil_pH, num_sp, fertilised)

Data structure in R: data frames

Data frames resemble a lot the usual Excel tables that we use in our research!

soil_fertilisation_data looks like this!

soil_fertilisation_data
#   site_id soil_pH num_sp fertilised
# 1   A1.01     5.6     17        yes
# 2   A1.02     7.3     23        yes
# 3   B1.01     4.1     15         no
# 4   B1.02     6.0      7         no

Note how the data frame integrated the name of the objects as column names!

Indexing objects in R

We have our information stored as a vector, a matrix, or a data.frame in R.

We will probably be interested in accessing and even subsetting the data based on some criteria.

Let's start with a pretty basic one: the square brackets [ ] and [, ]

We can indicate the position of the values we want to see between the brackets. This is often called indexing or slicing.

Let us see how to index a vector object in R.

Indexing vectors

To index an element within a vector using [], we need to write position number of the element within the brackets [].

For instance, here is our odd_n object:

(odd_n <- seq(1,9, by = 2))
# [1] 1 3 5 7 9

To obtain the value in the second position, we do as follows:

odd_n[2]
# [1] 3

We can also obtain values for multiple positions within a vector with c():

odd_n[c(2, 4)]
# [1] 3 7

And, we can remove values pertaining to particular positions from a vector using the minus (-) sign before the position value:

odd_n[-c(1, 2)]
# [1] 5 7 9

odd_n[-4]
# [1] 1 3 5 9

Challenge

Using the vector num_vector and our indexing abilities:

1. Extract the 4th value;
2. Extract the 1st and 3rd values;
3. Extract all values except for the 2nd and the 4th;
4. Extract from the 6th to the 10th value.

num_vector <- c(1, 4, 3, 98, 32, -76, -4)

Challenge: Solution

Using the vector num_vector and our indexing abilities:

1. Extract the 4th value

num_vector[4]
# [1] 98

1. Extract the 1st and 3rd values

num_vector[c(1, 3)]
# [1] 1 3

1. Extract all values except for the 2nd and the 4th

num_vector[c(-2, -4)]
# [1]   1   3  32 -76  -4

1. Extract from the 6th to the 10th value.

num_vector[6:10]
# [1] -76  -4  NA  NA  NA

What happened there? What is that NA?

Indexing from matrices and data frames

To index a data frame you must specify the position of values within two dimensions: within the row and columns, as in:

data_frame_name[row_number, column_number]

my_df[1, ]

my_df[, 3]

my_df[2, 4]

Indexing matrices and data frames by variable names

Remember that our soil_fertilisation_data data frame had column names?

soil_fertilisation_data
#   site_id soil_pH num_sp fertilised
# 1   A1.01     5.6     17        yes
# 2   A1.02     7.3     23        yes
# 3   B1.01     4.1     15         no
# 4   B1.02     6.0      7         no

We can subset columns from it using the column names:

soil_fertilisation_data[ , c("site_id", "soil_pH")]
#   site_id soil_pH
# 1   A1.01     5.6
# 2   A1.02     7.3
# 3   B1.01     4.1
# 4   B1.02     6.0

And, also subset columns from it using $:

soil_fertilisation_data$site_id
# [1] "A1.01" "A1.02" "B1.01" "B1.02"

Note: $ only works for data frames!

Statement testing with logical operators

You might remember about the logical data types, which contains only TRUE and FALSE values.

We can use operators to obtain TRUE or FALSE values for a type of testing. See examples below:

Indexing with logical operators

We can use conditions to select values:

odd_n[odd_n > 4]
# [1] 5 7 9

It is also possible to match a character string.

char_vector <- c("blue", "red", "green")

char_vector[char_vector == "blue"]
# [1] "blue"

Statement testing with logical functions

There are also ways in R that allows us to test conditions!

We can for, instance, test if values within a vector or a matrix are numeric:

char_vector
# [1] "blue"  "red"   "green"
is.numeric(char_vector)
# [1] FALSE

odd_n
# [1] 1 3 5 7 9
is.numeric(odd_n)
# [1] TRUE

Or, whether they are of the character type:

char_vector
# [1] "blue"  "red"   "green"
is.character(char_vector)
# [1] TRUE

odd_n
# [1] 1 3 5 7 9
is.character(odd_n)
# [1] FALSE

And, also, if they are vectors:

char_vector
# [1] "blue"  "red"   "green"
is.vector(char_vector)
# [1] TRUE

Challenge

Explore the difference between these two lines of code:

char_vector == "blue"
char_vector[char_vector == "blue"]

Challenge: Solution

Explore the difference between these 2 lines of code:

char_vector == "blue"
# [1]  TRUE FALSE FALSE

In this line of code, you test a logical statement. For each entry in the char_vector, R checks whether the entry is equal to blue or not.

char_vector[char_vector == "blue"]
# [1] "blue"

In this above line, we asked R to extract all values within the char_vector vector that are exactly equal to blue.

Challenge

1. Extract the num_sp column from soil_fertilisation_data and multiply its value by the first four values of num_vec.

2. After that, write a statement that checks if the values you obtained are greater than 25.

Challenge: Solution

1. Extract the num_sp column from soil_fertilisation_data and multiply its value by the first four values of num_vec.

soil_fertilisation_data$num_sp * num_vector[c(1:4)]
# [1]  17  92  45 686

or

soil_fertilisation_data[, 3] * num_vector[c(1:4)]
# [1]  17  92  45 686

1. After that, write a statement that checks if the values you obtained are greater than 25.

(soil_fertilisation_data$num_sp * num_vector[c(1:4)]) > 25
# [1] FALSE  TRUE  TRUE  TRUE

Other kinds of data structure: arrays and lists

We focused here mainly on vectors and data frames.

While we will discuss discuss about arrays and lists in other workshops, you can already have an idea what these types of object structure are.

Any wild guesses?

Short review about data structure in R

Functions

Using functions

To use (or to call) a function, the command must be structured properly, following the "grammar rules" of the R language: the syntax.

function_name(argument1 = value, argument2 = value, ..., argument4 = value)

Using functions: arguments

Arguments are values and instructions the function needs to run.

Objects storing these values and instructions can be used in functions:

a <- 3
b <- 5
sum(a, b)
# [1] 8

mean(soil_fertilisation_data$num_sp)
# [1] 15.5

Challenge

1. Create a vector a that contains all the numbers from 1 to 5.
2. Create an object b that has a value of 2.
3. Add a and b together using the basic + operator and save the result in an object called result_add.
4. Add a and b together using the sum function and save the result in an object called result_sum.
5. Are result_add and result_sum different?
6. Add 5 to result_sum using the sum() function.

Challenge: Solution

1. Are result_add and result_sum different?

a <- c(1:5)
b <- 2
result_add <- a + b
result_sum <- sum(a, b)

1. Add 5 to result_sum using the sum() function.

result_add
# [1] 3 4 5 6 7
result_sum
# [1] 17
sum(result_sum, 5)
# [1] 22

The operation + on the vector a adds 2 to each element. The result is a vector.

The function sum() concatenates all the values provided and then sum them. It is the same as doing 1 + 2 + 3 + 4 + 5 + 2.

Arguments

Each argument has a name, which may be used during a function call.

For instance, the first arguments of the matrix() function are:

matrix(data, nrow, ncol)

We can create a matrix using:

matrix(data = 1:12, nrow = 3, ncol = 4)
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    7   10
# [2,]    2    5    8   11
# [3,]    3    6    9   12

We can also execute a function when omitting argument names, however the order of the values will matter:

matrix(data = 1:12, 3, 4)
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    7   10
# [2,]    2    5    8   11
# [3,]    3    6    9   12

matrix(1:12, 4, 3)
#      [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2    6   10
# [3,]    3    7   11
# [4,]    4    8   12

Challenge

plot is a function that draws a graph of y as a function of x. It requires two arguments names x and y. What are the differences between the following lines?

a <- 1:100
b <- a^2
plot(a, b, type = "l")
plot(b, a, type = "l")
plot(x = a, y = b, type = "l")
plot(y = b, x = a, type = "l")

The argument type of the function plot let you choose the type of graph you want. Try it without this argument.

Challenge: Solution

plot is a function that draws a graph of y as a function of x. It requires two arguments names x and y. What are the differences between the following lines?

Challenge: Solution

plot(a, b, type = "l")

plot(b, a, type = "l")

The shape of the plot changes, as we did not provide the argument's names, the order is important.

Challenge: Solution

plot(x = a, y = b, type = "l")

plot(y = b, x = a, type = "l")

Same as plot(a, b, type = "l"). The argument names are provided, the order is not important.

R Packages

Packages group functions and/or datasets that share a similar theme, e.g. statistics, spatial analysis, plotting.

Anyone can develop packages and make them available to others.

Many packages available through the Comprehensive R Archive Network (CRAN) and now many more on GitHub.

Guess how many packages are available (not only within the CRAN)?

How many R packages?

rdrr.io visited on March, 10th 2020

Installing R packages

To install packages in your Computer, use the function install.packages().

install.packages("package_name")

Installing a package is essential to use it, but there is one more step: loading it.

You can load a package into your workspace using the library() function.

library(package_name)

Installing your first R package: ggplot2

Let us install a popular visualization package called ggplot.

install.packages("ggplot2")

Installing package into '/home/labo/R/x86_64-redhat-linux-gnu-library/3.3'
(as 'lib' is unspecified)

Now we will use the function qplot from the package

qplot(1:10, 1:10)

Did you get this error?

## Error: could not find function "qplot"

Loading your first R package: ggplot2

We need to gain access the functions that are available within the installed package. To do this, load ggplot2 using the library() function.

library(ggplot2)

Now we can draw the graph

qplot(1:10, 1:10)

The ggplot2 package will be covered in Workshop #3: Introduction to ggplot2.

Finding functions within packages

WOW! R is so great! So many functions to do what I want!

But... how do I find them?

To find a function that does something specific in your installed packages, you can use ?? followed by a search term.

Let us say we want to create a sequence of odd numbers between 0 and 10 as we did earlier. We can search in our packages all the functions with the word "sequence" in them:

??sequence

Search results

Search results

Search results

Getting help with functions

OK! So let us use the seq function!

But wait... how does it work? What arguments does it need?

To find information about a function in particular, use ?

  ?seq

Help pages

Description

• function_name {package_name}
• Description: a short description of what the function does.

Usage

• How to call the function
• If name = value is present, a default value is provided if the argument is missing. The argument becomes optional.
• Other related functions described in this help page

Arguments

• Description of all the arguments and what they are used for

Details

• A detailed description of how the functions work and their characteristics

Value, See Also, and Examples

• A description of the return value

• Other related functions that can be useful

• Reproducible examples

Challenge

1. Create a sequence of even numbers from 0 to 10 using the seq function.

2. Create an unsorted vector of your favourite numbers, then sort your vector in reverse order.

Challenge: Solutions

1. Create a sequence of even numbers from 0 to 10 using the seq function.

seq(from = 0, to = 10, by = 2)
# [1]  0  2  4  6  8 10

seq(0, 10, 2)
# [1]  0  2  4  6  8 10

1. Create an unsorted vector of your favorite numbers, then sort your vector in reverse order.

numbers <- c(2, 4, 22, 6, 26)
sort(numbers, decreasing = TRUE)
# [1] 26 22  6  4  2

Other ways to get help

Usually, your best source of information will be your favorite search engine!

Here are some tips on how to use them efficiently:

• Search in English;
• Use the keyword R at the beginning of your search;
• Define precisely what you are looking for;
• Learn to read discussion forums, such as StackOverflow. Chances are other people already had your problem and asked about it!
• Do not hesitate to search again using different keywords!

Challenge

Find the appropriate functions to perform the following operations:

• Square root
• Calculate the mean of numbers
• Combine two data frames by columns
• List available objects in your workspace

Challenge: Solutions

Find the appropriate functions to perform the following operations:

• Square root
  • sqrt()
• Calculate the mean of numbers
  • mean()
• Combine two data frames by columns
  • cbind()
• List available objects in your workspace
  • ls()

Cheat 4ever

Lots of cheat sheets are available online.

Open it directly from RStudio: Help $\rightarrow$ Cheatsheets

Cheatsheet 4ever

Some useful R books

Some useful R websites