R Bootcamp

by Bilgecan Şen

Day 1

Part 1: Introduction to R
Part 2: Homogenous Data Strcutures
Part 3: Heterogenous Data Structures

Sources
Previous Contributors
- Ben Weinstein (2014)
- Mike McCann (2015)
- Nicole Kinlock (2016, 2017)

Part1: Introduction to R

The R Language

R is an integrated suite of software facilities for data manipulation, calculation and graphical display
Created by Ross Ihaka and Robert Gentlemen in 1996
Interpreted language not a compiled one
Development and distribution handled by several statisticians (R Core Development Team)
Freely distributed from Comprehensive R Archive Network (CRAN)
Available on MacOS, Windows and Linux

Why use R?

Flexible
Reproducible statistical analysis and data visulization
Vast user community

RStudio

Any text editor is good enough for an R script
Rstudio provides a more structured and streamlined experience
The screen is split four ways:
- Workspace: (Top left) Where scripts are written & saved
- Console: (Bottom left) Where commands are run
- Environment, History: (Top right) Where objects are stored
- Files, Plots, Packages, and Help: (Bottom right) View data, help

Running R

Type directly into the console (best when you don't want to save the code) or type into the script - then run (CTRL + ENTER)
A script is a plain text file with R commands in it. This will be where you save the code that you are writing - the file will end in the extension .R

Three components of R

Operators
Objects
Functions

Arithmetic operators

+ Addition
- Subtraction
* Multiplication
/ Division
^ Exponentiation
%% Modulus (finds remainder)
%/% Integer division (leaves off remainder)

Arithmetic operators

Examples

2+2

## [1] 4

7-2

## [1] 5

4^3

## [1] 64

9/2

## [1] 4.5

9%/%2

## [1] 4

9%%2

## [1] 1

Arithmetic operators

Examples

R obeys the standard order of operations.

2+5*2/3

## [1] 5.333333

2+(5*(2/3))

## [1] 5.333333

Logical Operators

< Less than
<= Less than or equal to
> Greater than
>= Greater than or equal to
== Exactly equal to
!= Not equal to
! NOT
| OR
& AND

Logical Operators

Examples

2<3

## [1] TRUE

6>=4

## [1] TRUE

(2+2)==5

## [1] FALSE

9<=15 & 10>11

## [1] FALSE

9<=15 | 10>11

## [1] TRUE

Objects

"Things" in R are stored in objects which have a name.
"Things" can be variables, data, functions, results, numbers, etc.
The "assign" operator: <-.

Objects

Assigning elements to objects

x <- 1
x

## [1] 1

x*2

## [1] 2

x + 4

## [1] 5

y <- x + 4
y

## [1] 5

x <- x + 4
x

## [1] 5

Objects

Arithmetic using objects

x <- 6
y <- 38
y-x

## [1] 32

x*y

## [1] 228

y%%x

## [1] 2

z <- y + x
z

## [1] 44

Objects

Arithmetic using objects

x <- 10
y <- 3
x==y

## [1] FALSE

x>y

## [1] TRUE

z <- x<=y
z

## [1] FALSE

Exercise 1.1

Pick a number, assign it to the object z
Double z and assign it to itself
Add 10 to z and assign it to itself
divide z by 2 and assign it to itself
Subtract the original number you started with from z and and assign it to itself
Is the last z you got equal to 5?

Objects

Assigning multiple elements to objects: `c()`

x <- c(1,2,3,4,5)
x

## [1] 1 2 3 4 5

y <- c(4,2)
y

## [1] 4 2

z <- c()
z

## NULL

Objects

Arithmetic with multiple elements

x <- c(1,2,3,4,5)
x + 1

## [1] 2 3 4 5 6

x*2

## [1]  2  4  6  8 10

y <- c(1,4,3,2,5)
x==y

## [1]  TRUE FALSE  TRUE FALSE  TRUE

x>y

## [1] FALSE FALSE FALSE  TRUE FALSE

Exercise 1.2

Create an object with 45, 3, 9, 99, and 0
Create another object with 66, 0, 1, 5, and 0
Sum these objects
Multiply them
Divide the first one with the second one
What results did you get?
How does R handle arithmetic operiations with vectors of length>1?

Functions

A function is a stored object that performs a task given some inputs (called arguments). R has many functions already available, but you can also write your own functions.
Functions are used in the format: name_of_function(inputs)
The output of a function can be saved to an object:
output <- name_of_function(inputs)

Functions

Use sum() to take the sum of all elements in an object:

x <- c(45,3,9,99,0)
sum(x)

## [1] 156

z <- sum(x)
z

## [1] 156

Use prod() to multiply all elements in an object:

prod(45,3,9,99,0)

## [1] 0

Use mean() to take the mean of all elements in an object:

mean(45,3,9,99,0)

## [1] 45

R Tip: The help system

Help files provide important information on what the function does, how it works, and they provide examples at the very bottom.

help(mean)

You can also use ? before a function name to view the help file

?mean  # Same as help(mean)

Use ?? to search for functions; e.g. search for any function whose help files contain the word "sequence"

??sequence

Exercise 1.3

What is the median of 34, 16, 105, and 27?
Remember: functions are often named intuitively.
What does the function range() do, and what is the sample example in the help file?
Is mean(4, 5) different than mean(c(4, 5))?

Part 2: Homogenous Data Structures

R has 7 types of object classes representing data
- vector (atomic vector)
- factor
- matrix
- array
- data frame
- list (recursive vector)
- ts

1. Vectors

Creating vectors

Every object we have crated so far is a vector
Vectors can be of any length
Single dimension

x <- 1
x <- c(5,6,7,8,9,10)
x

## [1]  5  6  7  8  9 10

x <- 5:10
x

## [1]  5  6  7  8  9 10

1. Vectors

Creating vectors

x <- seq(from = 5, to = 10, by = 1)
x

## [1]  5  6  7  8  9 10

is.vector(x)

## [1] TRUE

y <- c()
is.vector(y)

## [1] FALSE

z <- vector(length = 5, mode = "numeric")
z

## [1] 0 0 0 0 0

1. Vectors

Combining vectors

You can combine multiple vectors into a single vector

x <- c(1,2,3)
y <- c(4,5)
z <- c(x,y)
z

## [1] 1 2 3 4 5

1. Vectors

Naming vectors

You can name elements when you are creating the vector

z <- c(monday = 1, tuesday = 2, wednesday = 3, thursday = 4, friday = 5)
z

##    monday   tuesday wednesday  thursday    friday 
##         1         2         3         4         5

names(z)

## [1] "monday"    "tuesday"   "wednesday" "thursday"  "friday"

1. Vectors

Naming vectors

Or you can name the elements after you create the vector

z <- c(1,2,3,4,5)
names(z)

## NULL

# Names need to be a `character` vector 
names(z) <- c("monday", "tuesday", "wednesday", "thursday", "friday") 
names(z)

## [1] "monday"    "tuesday"   "wednesday" "thursday"  "friday"

##    monday   tuesday wednesday  thursday    friday 
##         1         2         3         4         5

z <- unname(z)
z

## [1] 1 2 3 4 5

1. Vectors

Vector subsetting: positional

You can access any element in the vector by putting its position in square brackets [ ]

height <- c(76, 72, 74, 74, 78)
height

## [1] 76 72 74 74 78

height[1] # extract the 1st element in the vector

## [1] 76

height[5] # extract the 5th element

## [1] 78

height[6] # There is no 6th element

## [1] NA

1. Vectors

Vector subsetting: positional

We can assign subsets to new objects.

x <- height[1]
x

## [1] 76

y <- height[5]
y

## [1] 78

We can subset multiple elements simultaneusly.

height[c(1,2,3)]

## [1] 76 72 74

height[1:3]

## [1] 76 72 74

1. Vectors

Vector subsetting: positional

You can also use vector indexing to return the same vector with certain elements missing using the - operator.

height <- c(76, 72, 74, 74, 78)

height[-1]

## [1] 72 74 74 78

height[c(-1,-5)]

## [1] 72 74 74

height_new <- height[-1]

1. Vectors

Vector subsetting: named

You can assign names to each element of the vector, and then extract the element by indexing based on the name.

temp <- c(monday = 28.1, tuesday = 28.5, wednesday = 29.0, thursday = 30.1, friday = 30.2)
temp

##    monday   tuesday wednesday  thursday    friday 
##      28.1      28.5      29.0      30.1      30.2

temp["wednesday"] # Always use "" when subsetting by name

## wednesday 
##        29

temp[3]

## wednesday 
##        29

# Try subseting with only wednesday (without ""). 
# What happens?

1. Vectors

Vector subsetting: logical

You can extract elements in a vector that meet specific criteria based on a logical expression.

y <- 5:50
y

##  [1]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [24] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

# extract all elements less than or equal to 10
y[y <= 10]

## [1]  5  6  7  8  9 10

# extract all elements less than 10 and not equal to 5
y[y < 10 & y != 5]

## [1] 6 7 8 9

1. Vectors

Vector subsetting: Simplifying vs Preserving

As a general rule single brackets [] in R has a preserving effect while subsetting data related objects.
Double brackets [[]], on the other hand, has a simpfiying effect in which it returns the simplest possible data structure that can represent the output.

1. Vectors

Vector subsetting: Simplifying vs Preserving

z <- c(monday = 1, tuesday = 2, wednesday = 3, thursday = 4, friday = 5)
z[1] # returns name of the element and the element itself

## monday 
##      1

z[[1]] # returns only the element

## [1] 1

z[1:3]

##    monday   tuesday wednesday 
##         1         2         3

z[[1:3]] # Can't subset multiple elements

## Error in z[[1:3]]: attempt to select more than one element in vectorIndex

Exercise 2.1

What are the 9th and 12th positions of the vector seq(1, 27, 0.5)?
Create a vector that includes all even number between 4 and 34 and name it a.
Extract all elements of a that are greater than or equal to 17.

Types of Data

All data related objects in R has three properties: length, type(mode), and attributes

x <- c(1,2,3,4,5)
y <- c("a", "b", "c","d","e")
length(x) # "length"" shows the number of elements in an object

## [1] 5

length(y)

## [1] 5

mode(x)

## [1] "numeric"

mode(y)

## [1] "character"

Types of Vectors

Also called storage mode or just mode
There are 6 vector types
- Numeric (Double)
- Integer
- Character
- Logical
- Complex
- Raw

Types of Vectors

Numeric and integer

x <- c(1,2,3,4,5)
is.double(x)

## [1] TRUE

y <- c(1.25, 3.755, 9.001)
is.double(y)

## [1] TRUE

z1 <- c(1L,2L,3L,4L,5L)
is.integer(z1)

## [1] TRUE

z2 <- as.integer(y)

typeof(1:3) # `:` creates an integer vector

## [1] "integer"

Types of Vectors

Character

x <- c("Bilgecan is the best TA")
x

## [1] "Bilgecan is the best TA"

length(x)

## [1] 1

y <- c("Bilgecan", "is", "the", "best", "TA")
y

## [1] "Bilgecan" "is"       "the"      "best"     "TA"

y[1]

## [1] "Bilgecan"

y[c(2,4)]

## [1] "is"   "best"

Types of Vectors

Logical

x <- c(1,2,3,4,5)
y <- x<3
y

## [1]  TRUE  TRUE FALSE FALSE FALSE

z <- c(TRUE, FALSE, T, F)
z

## [1]  TRUE FALSE  TRUE FALSE

Conversion of modes

To character

## [1] 1 2 3 4 5

x <- as.character(x)
x

## [1] "1" "2" "3" "4" "5"

z <- as.character(z)
z

## [1] "TRUE"  "FALSE" "TRUE"  "FALSE"

Conversion of modes

To numeric

x <- c("1","-2", "3.25", "A")
x <- as.numeric(x)

## Warning: NAs introduced by coercion

## [1]  1.00 -2.00  3.25    NA

y <- c(T,F,F,T,T)
y <- as.numeric(y)
y

## [1] 1 0 0 1 1

Conversion of modes

To logical

x <- c(0, 0, 5, 79, 3500)
x <- as.logical(x)
x

## [1] FALSE FALSE  TRUE  TRUE  TRUE

y <- c("TRUE", "F", "1", "0", "35000")
y <- as.logical(y)
y

## [1]  TRUE FALSE    NA    NA    NA

Exercise 2.2

Create a vector with TRUE, and 50
Create a vector with "A", and 1
Create a vector with TRUE and "C"
Did you notice a pattern? How is the mode of each vector you created above determined?

R Tip: Coercion

All elements of an atomic vector must be the same type, so when you attempt to combine different types they will be coerced to the most flexible type. Types from least to most flexible are: logical, integer, double, and character.

Attributes of vectors

The only formal attribute a vector can have is the name of its elements.

attributes(temp)

## $names
## [1] "monday"    "tuesday"   "wednesday" "thursday"  "friday"

attributes(x)

## NULL

Classes of vectors

For a vector object class and type(mode) are practically the same thing.

class(x)

## [1] "logical"

mode(x)

## [1] "logical"

typeof(x)

## [1] "logical"

2. Factors

A factor is a vector that can contain only predefined values, and is used to store categorical data.
Factors are built on top of integer vectors with two additional attributes:
- the class, “factor”, which makes them behave differently from regular integer vectors
- the levels, which defines the set of predefined values

2. Factors

Creating factors

x <- c(1,2,3,4,5)
x <- factor(x)
x

## [1] 1 2 3 4 5
## Levels: 1 2 3 4 5

z <- c("a", "b", "c", "d")
z <- as.factor(z)
z

## [1] a b c d
## Levels: a b c d

z <- factor(z, levels = c("c","b","a","d"), ordered = T)
z

## [1] a b c d
## Levels: c < b < a < d

2. Factors

Creating factors

# You can't assign values that is outside of its levels to a factor
z[6] <- "e"

## Warning in `[<-.factor`(`*tmp*`, 6, value = "e"): invalid factor level, NA
## generated

f <- c(z,x) # You can't combine factors
typeof(f)

## [1] "integer"

2. Factors

Attributes of factors

attributes(z)

## $levels
## [1] "c" "b" "a" "d"
## 
## $class
## [1] "ordered" "factor"

2. Factors

Factor subsetting: Simplifying vs Preserving

z[1:3] # preserving

## [1] a b c
## Levels: c < b < a < d

z[1:3, drop = T] # simplifying

## [1] a b c
## Levels: c < b < a

3. Matrices

Two dimensional data related object (a table)
Behaves very similiarly to vectors
Has an additional attribute: dim
dim gives number of row and number of columns that a matrix has

3. Matrices

Creating Matrices

m <- matrix(nrow = 2, ncol = 2)
m

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

m <- matrix(c(1,2,3,4), nrow = 2, ncol = 2)
m

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

m <- matrix(c(1,2,3,4), nrow = 2, ncol = 2, byrow = T)
m

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

is.matrix(m)

## [1] TRUE

3. Matrices

Creating Matrices

v1 <- c(1,2,3)
v2 <- c(4,5,6)

m <- cbind(v1, v2)
m

##      v1 v2
## [1,]  1  4
## [2,]  2  5
## [3,]  3  6

m <- rbind(v1, v2)
m

##    [,1] [,2] [,3]
## v1    1    2    3
## v2    4    5    6

is.matrix(m)

## [1] TRUE

3. Matrices

Naming rows and columns

names(m)

## NULL

colnames(m)

## NULL

rownames(m)

## [1] "v1" "v2"

colnames(m) <- c("a", "b", "c")
m

##    a b c
## v1 1 2 3
## v2 4 5 6

3. Matrices

Arithmetic

m <- matrix(c(1,2,3,4), nrow = 2, ncol = 2)
m + 1

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

m*2

##      [,1] [,2]
## [1,]    2    6
## [2,]    4    8

m2 <- matrix(c(5,6,7,8), nrow = 2, ncol = 2)
m + m2

##      [,1] [,2]
## [1,]    6   10
## [2,]    8   12

3. Matrices

Matrix multiplication

m*m2 # this is not matrix multiplication

##      [,1] [,2]
## [1,]    5   21
## [2,]   12   32

m %*% m2

##      [,1] [,2]
## [1,]   23   31
## [2,]   34   46

3. Matrices

Matrix subsetting: positional

january <- matrix(1:31, nrow = 5, ncol = 7, byrow = T)

## Warning in matrix(1:31, nrow = 5, ncol = 7, byrow = T): data length [31] is
## not a sub-multiple or multiple of the number of rows [5]

january

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,]    1    2    3    4    5    6    7
## [2,]    8    9   10   11   12   13   14
## [3,]   15   16   17   18   19   20   21
## [4,]   22   23   24   25   26   27   28
## [5,]   29   30   31    1    2    3    4

3. Matrices

Matrix subsetting: positional

january[1,2] # first row, second column

## [1] 2

january[5,4] # fifth row, fourth column

## [1] 1

january[1,]  # entire first row

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

january[,3]  # entire third column

## [1]  3 10 17 24 31

3. Matrices

Matrix subsetting: named

colnames(january) <- c("monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday")
rownames(january) <- c("week 1", "week 2", "week 3", "week 4", "week 5")
january

##        monday tuesday wednesday thursday friday saturday sunday
## week 1      1       2         3        4      5        6      7
## week 2      8       9        10       11     12       13     14
## week 3     15      16        17       18     19       20     21
## week 4     22      23        24       25     26       27     28
## week 5     29      30        31        1      2        3      4

3. Matrices

Matrix subsetting: named

january[,"monday"] # Remember to use "" when subsetting by name!

## week 1 week 2 week 3 week 4 week 5 
##      1      8     15     22     29

january[,"sunday"]

## week 1 week 2 week 3 week 4 week 5 
##      7     14     21     28      4

january["week 1",]

##    monday   tuesday wednesday  thursday    friday  saturday    sunday 
##         1         2         3         4         5         6         7

january["week 3", "thursday"]

## [1] 18

3. Matrices

Matrix subsetting: logical

january<15 # turns whole matrix to logical

##        monday tuesday wednesday thursday friday saturday sunday
## week 1   TRUE    TRUE      TRUE     TRUE   TRUE     TRUE   TRUE
## week 2   TRUE    TRUE      TRUE     TRUE   TRUE     TRUE   TRUE
## week 3  FALSE   FALSE     FALSE    FALSE  FALSE    FALSE  FALSE
## week 4  FALSE   FALSE     FALSE    FALSE  FALSE    FALSE  FALSE
## week 5  FALSE   FALSE     FALSE     TRUE   TRUE     TRUE   TRUE

3. Matrices

Matrix subsetting: logical

january[january<15]

##  [1]  1  8  2  9  3 10  4 11  1  5 12  2  6 13  3  7 14  4

index <- january[,"tuesday"]<15
index

## week 1 week 2 week 3 week 4 week 5 
##   TRUE   TRUE  FALSE  FALSE  FALSE

january[index, "tuesday"]

## week 1 week 2 
##      2      9

index <- january[,2]<15
january[index, 2]

## week 1 week 2 
##      2      9

3. Matrices

Matrix subsetting: Simplifying vs Preserving

Single brackets with a comma [,] has a simplfiying effect.

# Following returns only the elements stored in the matrix
january[1,2]

## [1] 2

january[1,]

##    monday   tuesday wednesday  thursday    friday  saturday    sunday 
##         1         2         3         4         5         6         7

january[,1]

## week 1 week 2 week 3 week 4 week 5 
##      1      8     15     22     29

3. Matrices

Matrix subsetting: Simplifying vs Preserving

Use drop=F to preserve the exact structure.

january[1,2, drop = F]

##        tuesday
## week 1       2

Exercise 2.3

Create two 5 by 2 matrices. One should include all the even numbers between 1 and 20. Other should include all the odd numbers between 1 and 20. Fill these matrices by row.
Combine these two matrices by row (rbind) and assing it to a new object.
Combine these two matrices by column (cbind) and assing it to a new object.

Exercise 2.4

january <- matrix(1:31, nrow = 5, ncol = 7, byrow = T)

## Warning in matrix(1:31, nrow = 5, ncol = 7, byrow = T): data length [31] is
## not a sub-multiple or multiple of the number of rows [5]

colnames(january) <- c("monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday")
rownames(january) <- c("week 1", "week 2", "week 3", "week 4", "week 5")

Remove Saturday and Sunday columns from January
You called in sick from 9th to 11th. Replace those days in the matrix with "sick". How has the other entries in the matrix changed?
Replace the remaining days with "healthy"
What do you get when you use length() on a matrix? Use also nrow() and ncol() on the same matrix. How are they related to length?

4. Arrays

Arrays are multidimensional
Behaves prcatically the same as vectors and matrices
Vectore are actually signle dimensional arrays and matrices are two dimensional arrays

4. Arrays

x <- array(c(1,2,3), dim = 3)
x

## [1] 1 2 3

is.vector(x)

## [1] FALSE

is.array(x)

## [1] TRUE

m <- array(c(1,2,3,4), dim = c(2,2))
m

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

is.array(m)

## [1] TRUE

is.matrix(m)

## [1] TRUE

4. Arrays

a <- array(c(1,2,3,4,5,6,7,8), dim = c(2,2,2))
a

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

4. Arrays

a[,,1]

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

a[1,2,]

## [1] 3 7

a[1,2,2]

## [1] 7

Part 3: Heterogenous Data Structures

5. Data frames

Most data you are going to be working with in R is going to be in a "table" form but will also include variables of different types. So matrices and vectors do not meet all our needs.
Data frames can be thought of as collection of vectors with different types.
There are many pre-loaded data sets in R. We'll be working with iris, famously used by R.A. Fisher in 1936.

5. Data frames

iris

##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

5. Data frames

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

is.data.frame(iris)

## [1] TRUE

class(iris)

## [1] "data.frame"

5. Data frames

Useful functions for dataframes and matrices

head() - see first 5 rows
tail() - see last 5 rows
dim() - dimensions (# rows, # columns)
nrow() - number of rows
ncol() - number of columns
str() - structure of any object
rownames() - row names
colnames() - column names

Exercise 3.1

trees and mtcars are other available data sets.

How many rows does the trees and mtcars data frames have?
How many columns? What are the column names?
Using the str() function, how many species does iris data set have?
What classes are each of the columns for the two data sets?

5. Data frames

Data frame subsetting

Subsetting data frames is very similar to subsetting matrices

iris[1,3]

## [1] 1.4

head(iris[,4])

## [1] 0.2 0.2 0.2 0.2 0.2 0.4

tail(iris[,"Species"])

## [1] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica

5. Data frames

Data frame subsetting

We can also use the dollar sign to call specific columns

head(iris$Petal.Width)

## [1] 0.2 0.2 0.2 0.2 0.2 0.4

head(iris[,4])

## [1] 0.2 0.2 0.2 0.2 0.2 0.4

5. Data frames

Data frame subsetting: Simplifying vs Preserving

Similar to matrices, but with important exceptions.

iris[3:5,4] # simplifying

## [1] 0.2 0.2 0.2

iris[3:5,4, drop = F] # preserving

##   Petal.Width
## 3         0.2
## 4         0.2
## 5         0.2

5. Data frames

Data frame subsetting: Simplifying vs Preserving

Calling the whole row or column has different effects in terms of simplifying vs preserving

iris[1,] # preserving

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa

iris[,1] # simplify

##   [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4
##  [18] 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5
##  [35] 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0
##  [52] 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8
##  [69] 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4
##  [86] 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8
## [103] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7
## [120] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7
## [137] 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9

Exercise 3.2

What is the 9th entry of the Sepal.Width column of the iris data frame?
Subset the 17th row of the iris data frame.
Create an object with the 1st, 4th and 7th rows of the iris data frame.
Use the seq() function to subset all odd rows in the iris data frame.
Remove the sepal width column from iris data frame, and assign this to an object.
What do you get when you use the length() function on a data frame? How is that different from a matrix?
Extract the rows of iris that has larger than 3 petal length.

5. Data frames

Creating data frames

d <- data.frame(x = c(5.6, 2.45, 7.09), y = c("a","b","c"), z = c(1,2,3))
d

##      x y z
## 1 5.60 a 1
## 2 2.45 b 2
## 3 7.09 c 3

str(d)

## 'data.frame':    3 obs. of  3 variables:
##  $ x: num  5.6 2.45 7.09
##  $ y: Factor w/ 3 levels "a","b","c": 1 2 3
##  $ z: num  1 2 3

5. Data frames

Creating data frames

d <- data.frame(x = c(5.6, 2.45, 7.09), y = c("a","b","c"), z = c(1,2,3), 
                stringsAsFactors = F)
str(d)

## 'data.frame':    3 obs. of  3 variables:
##  $ x: num  5.6 2.45 7.09
##  $ y: chr  "a" "b" "c"
##  $ z: num  1 2 3

5. Data frames

Creating data frames

d$t <- as.factor(c(10,20,30))
d

##      x y z  t
## 1 5.60 a 1 10
## 2 2.45 b 2 20
## 3 7.09 c 3 30

str(d)

## 'data.frame':    3 obs. of  4 variables:
##  $ x: num  5.6 2.45 7.09
##  $ y: chr  "a" "b" "c"
##  $ z: num  1 2 3
##  $ t: Factor w/ 3 levels "10","20","30": 1 2 3

5. Data frames

Combining data frames

d1 <- data.frame(x = c(5.6, 2.45, 7.09), y = c("a","b","c"), 
                 stringsAsFactors = F)
d2 <- data.frame(z = c(1,2,3), t = as.factor(c(10,20,30)))
d3 <- cbind(d1,d2)
str(d3)

## 'data.frame':    3 obs. of  4 variables:
##  $ x: num  5.6 2.45 7.09
##  $ y: chr  "a" "b" "c"
##  $ z: num  1 2 3
##  $ t: Factor w/ 3 levels "10","20","30": 1 2 3

5. Data frames

Combining data frames

# When binding two data frames by row, column names must match!
d4 <- rbind(d1,d2)

## Error in match.names(clabs, names(xi)): names do not match previous names

d2 <- data.frame(x = c(1,2,3), y = as.factor(c(10,20,30)))
d4 <- rbind(d1,d2)

# Remember coercison
str(d4)

## 'data.frame':    6 obs. of  2 variables:
##  $ x: num  5.6 2.45 7.09 1 2 3
##  $ y: chr  "a" "b" "c" "10" ...

Exercise 3.3

Create a two column data frame with c(1,2,3) and c(4,5,6,7). What was the result?
Create a data frame with c("a","b","c") and c(1,2,3) using dataframe directly. Create another one using cbind first and then dataframe (dataframe(cbind())). What is the difference?

Importing your own data

R is not a spreadsheet program, so it's not great for direct data entry. It's best to start with spreadsheets for data entry and storage, and to import spreadhseets into R for data visualization and analysis.
.csv (comma separated values) files are often the preferred format to import into R.
Before we do that, we will need to look at concept of your working directory.

Importing your own data

Working directory

Find out what your current working directory is

getwd()

## [1] "/Users/bilgecan/Documents/Bilgcan.github.io/RBootcamp"

This is the folder on your computer where R will look to open or write files.

Importing your own data

Working directory

Set your working directory

setwd("/Users/bilgecan/Desktop/")

Importing your own data

blue.hill <- read.csv("BlueHill.csv")
head(blue.hill)

# Reading data without changing working directory
blue.hill <- read.csv("/Users/bilgecan/Desktop/BlueHill.csv")

R Tip: Variable names

One of the largest sources of frustration with R can be importing data. Variable names often cause problems.
Do not have spaces in variable names
Use lower case letters
Abbreviate when appropriate

Average Height # BAD - this won't work
Average.Height # OK - this will work but isn't best practice (See style guide) 
average.height # BETTER - this will work, but will be slow to type repeatedly
avg.height     # GOOD!

Execise 3.4

Import the data frame BlueHill.csv and read it into R.
What is the mean daily temperature across all years and stations (MNTM)?
Subset the fourth column and assign it to an object. What is its class and mode?
Subset the first row and assign it to an object. What is its class? Does it have a single mode?
Considering the third and fourth questions, how is subsetting rows vs columns different for data frames compared to matrices?

Exporting data frames

After you've made changes (done subsetting, etc.), you may want to export the altered data frame.

# Write the file 
write.csv(iris, file = "iris.csv", row.names = FALSE)

# Check in your working directory for the new file
list.files()

6. Lists (Recursive Vectors)

List can include elements of any type and class simultaneusly.
A data frame is a list of vectors.
Unless specified, data in lists are not stored in a table format.

6. Lists

d <- data.frame(x = c(5.6, 2.45, 7.09), y = c("a","b","c"), z = as.factor(c(1,2,3)))
l <- list(x = c(5.6, 2.45, 7.09), y = c("a","b","c"), z = as.factor(c(1,2,3)))
l

## $x
## [1] 5.60 2.45 7.09
## 
## $y
## [1] "a" "b" "c"
## 
## $z
## [1] 1 2 3
## Levels: 1 2 3

typeof(l)

## [1] "list"

typeof(d)

## [1] "list"

6. Lists

Elements of a list can include other lists and data frames as well.

# Notice the similarity in the syntax with adding a column in a data frame
l$d <- d
l[2:4]

## $y
## [1] "a" "b" "c"
## 
## $z
## [1] 1 2 3
## Levels: 1 2 3
## 
## $d
##      x y z
## 1 5.60 a 1
## 2 2.45 b 2
## 3 7.09 c 3

6. Lists

# Listception
l$l <- l
l[4:5]

## $d
##      x y z
## 1 5.60 a 1
## 2 2.45 b 2
## 3 7.09 c 3
## 
## $l
## $l$x
## [1] 5.60 2.45 7.09
## 
## $l$y
## [1] "a" "b" "c"
## 
## $l$z
## [1] 1 2 3
## Levels: 1 2 3
## 
## $l$d
##      x y z
## 1 5.60 a 1
## 2 2.45 b 2
## 3 7.09 c 3

6. Lists

List subsetting

Simplfiyng vs Preserving differences have important practical consequences for lists.

# Brackets returns the elements of a given index as a list
l[1]

## $x
## [1] 5.60 2.45 7.09

typeof(l[1])

## [1] "list"

str(l[1])

## List of 1
##  $ x: num [1:3] 5.6 2.45 7.09

6. Lists

List subsetting

# double brackets return the elements in its original type and class
l[[1]]

## [1] 5.60 2.45 7.09

typeof(l[[1]])

## [1] "double"

str(l[[1]])

##  num [1:3] 5.6 2.45 7.09

v <- l[[1]]
v

## [1] 5.60 2.45 7.09

6. Lists

List naming

names(l)

## [1] "x" "y" "z" "d" "l"

l <- unname(l)
names(l)

## NULL

l[1:3]

## [[1]]
## [1] 5.60 2.45 7.09
## 
## [[2]]
## [1] "a" "b" "c"
## 
## [[3]]
## [1] 1 2 3
## Levels: 1 2 3

6. Lists

List naming

names(l)[1] <- "x"
names(l)[2] <- "y"
names(l[1])

## [1] "x"

names(l[[1]])

## NULL

names(l[[1]])[1] <- "a"
l[1]

## $x
##    a <NA> <NA> 
## 5.60 2.45 7.09

6. Lists

List naming

You can use $ to call elements from a list as well. This is the same as using [[]].

l$x

##    a <NA> <NA> 
## 5.60 2.45 7.09

l$y

## [1] "a" "b" "c"

Exercise 3.5

Create the list in the previous example:

d <- data.frame(x = c(5.6, 2.45, 7.09), 
                y = c("a","b","c"), 
                z = as.factor(c(1,2,3)))
l <- list(x = c(5.6, 2.45, 7.09), 
          y = c("a","b","c"), 
          z = as.factor(c(1,2,3)))
l2 <- list(a = c(5:10), b = c(500:600))
l$d <- d
l$l2 <- l2

Extract the first element of z in the list.
Extract the entire second column of d in the list.
Extract the 50th element of y in the l2 within l.
Multiply x in l with 2 by extracting it with [] and [[]]. Why do you think one works and the other does not?

General subsetting suggestions

Subset vectors and factors using []
Subset matrices, arrays, and data frames using [,]
Subset lists with [] if you want your output to be a list, or if you want to subset multiple elements from a list.
Subset lists with [[]] if you want to subset and use the actual data within the elements of a list.

Data Classes

If the object is a vector, it's class is its type(mode)
if the object is a matrix, array, data frame or list, its class is its data strcuture name

x <- c(1,2,3)
class(x)

## [1] "numeric"

y <- as.factor(x)
class(factor)

## [1] "function"

m <- matrix(c(1,2,3,4), nrow = 2, ncol = 2)
class(m)

## [1] "matrix"

R Bootcamp

by Bilgecan Şen

Day 1

Part1: Introduction to R

The R Language

Why use R?

RStudio

Running R

Three components of R

Arithmetic operators

Arithmetic operators

Examples

Arithmetic operators

Examples

Logical Operators

Logical Operators

Examples

Objects

Objects

Assigning elements to objects

Objects

Arithmetic using objects

Objects

Arithmetic using objects

Exercise 1.1

Objects

Assigning multiple elements to objects: c()

Objects

Arithmetic with multiple elements

Exercise 1.2

Functions

Functions

R Tip: The help system

Exercise 1.3

Part 2: Homogenous Data Structures

1. Vectors

Creating vectors

1. Vectors

Creating vectors

1. Vectors

Combining vectors

1. Vectors

Naming vectors

1. Vectors

Naming vectors

1. Vectors

Vector subsetting: positional

1. Vectors

Vector subsetting: positional

1. Vectors

Vector subsetting: positional

1. Vectors

Vector subsetting: named

1. Vectors

Vector subsetting: logical

1. Vectors

Vector subsetting: Simplifying vs Preserving

1. Vectors

Vector subsetting: Simplifying vs Preserving

Exercise 2.1

Types of Data

Types of Vectors

Types of Vectors

Numeric and integer

Types of Vectors

Character

Types of Vectors

Logical

Conversion of modes

To character

Conversion of modes

To numeric

Conversion of modes

To logical

Exercise 2.2

R Tip: Coercion

Attributes of vectors

Classes of vectors

2. Factors

2. Factors

Assigning multiple elements to objects: `c()`