Flexibility & reproducibility

• R Markdown allows the user to integrate R code into a report

• When data changes or code changes, so does the report

• No more need to copy-and-paste graphics, tables, or numbers

• Creates reproducible reports

  • Anyone who has your R Markdown (.Rmd) file and input data can re-run your analysis and get the exact same results (tables, figures, summaries)

• Can output report in HTML (default), Microsoft Word, or PDF

An example

This example shows an R Markdown (.Rmd) file opened in the Source pane of RStudio:

• To turn an Rmd file into a report, click the Knit HTML button in the Source pane menu bar
• The results will appear in a Preview window, as shown on the right
• You can knit into html (default), MS Word, and pdf format
• These lecture notes are also created in RStudio (using html_document as the output format)

R code chunks

• To integrate R output into your report, you need to use R code chunks

• All of the code that appears in between the “triple back-ticks” gets executed when you Knit

Data building blocks

You’ll encounter different kinds of data types

• Booleans Direct binary values: TRUE or FALSE in R

• Integers: whole numbers (positive, negative or zero)

• Characters fixed-length blocks of bits, with special coding; strings = sequences of characters

• Floating point numbers: a fraction (with a finite number of bits) times an exponent, like \(1.87 \times {10}^{6}\)

• Missing or ill-defined values: NA, NaN, etc.

Operators (functions)

You can use R as a very, very fancy calculator

7 + 5 # Addition

[1] 12

7 - 5 # Subtraction

[1] 2

7 * 5 # Multiplication

[1] 35

7 ^ 5 # Exponentiation

[1] 16807

7 / 5 # Division

[1] 1.4

7 %% 5 # Modulus

[1] 2

7 %/% 5 # Integer division 

[1] 1

Comparisons are also binary operators; they take two objects, like numbers, and give a Boolean

7 > 5

[1] TRUE

7 < 5

[1] FALSE

7 >= 7

[1] TRUE

7 <= 5

[1] FALSE

7 == 5

[1] FALSE

7 != 5

[1] TRUE

Boolean operators

Basically “and” and “or”:

(5 > 7) & (6*7 == 42)

[1] FALSE

(5 > 7) | (6*7 == 42)

[1] TRUE

(will see special doubled forms, && and ||, later)

More types

• typeof() function returns the type

• is.foo() functions return Booleans for whether the argument is of type foo

• as.foo() (tries to) “cast” its argument to type foo — to translate it sensibly into a foo-type value

Special case: as.factor() will be important later for telling R when numbers are actually encodings and not numeric values. (E.g., 1 = High school grad; 2 = College grad; 3 = Postgrad) ##

typeof(7)

[1] "double"

is.numeric(7)

[1] TRUE

is.na(7)

[1] FALSE

is.character(7)

[1] FALSE

is.character("7")

[1] TRUE

is.character("seven")

[1] TRUE

is.na("seven")

[1] FALSE

Variables

We can give names to data objects; these give us variables

A few variables are built in:

pi

[1] 3.141593

Variables can be arguments to functions or operators, just like constants:

pi*10

[1] 31.41593

cos(pi)

[1] -1

Assignment operator

Most variables are created with the assignment operator, <- or =

time.factor <- 12
time.factor

[1] 12

time.in.years = 2.5
time.in.years * time.factor

[1] 30

The assignment operator also changes values:

time.in.months <- time.in.years * time.factor
time.in.months

[1] 30

time.in.months <- 45
time.in.months

[1] 45

• Using names and variables makes code: easier to design, easier to debug, less prone to bugs, easier to improve, and easier for others to read

• Avoid “magic constants”; use named variables

• Use descriptive variable names

  • Good: num.students <- 35
  • Bad: ns <- 35

The workspace

What names have you defined values for?

ls()

[1] "time.factor"    "time.in.months" "time.in.years" 

Getting rid of variables:

rm("time.in.months")
ls()

[1] "time.factor"   "time.in.years"

First data structure: vectors

• Group related data values into one object, a data structure

• A vector is a sequence of values, all of the same type

• c() function returns a vector containing all its arguments in order

students <- c("Sean", "Louisa", "Frank", "Farhad", "Li")
midterm <- c(80, 90, 93, 82, 95)

• Typing the variable name at the prompt causes it to display

students

[1] "Sean"   "Louisa" "Frank"  "Farhad" "Li"    

Indexing

• vec[1] is the first element, vec[4] is the 4th element of vec

students

[1] "Sean"   "Louisa" "Frank"  "Farhad" "Li"    

students[4]

[1] "Farhad"

• vec[-4] is a vector containing all but the fourth element

students[-4]

[1] "Sean"   "Louisa" "Frank"  "Li"    

Vector arithmetic

Operators apply to vectors “pairwise” or “elementwise”:

final <- c(78, 84, 95, 82, 91) # Final exam scores
midterm # Midterm exam scores

[1] 80 90 93 82 95

midterm + final # Sum of midterm and final scores

[1] 158 174 188 164 186

(midterm + final)/2 # Average exam score

[1] 79 87 94 82 93

course.grades <- 0.4*midterm + 0.6*final # Final course grade
course.grades

[1] 78.8 86.4 94.2 82.0 92.6

Pairwise comparisons

Is the final score higher than the midterm score?

midterm 

[1] 80 90 93 82 95

final

[1] 78 84 95 82 91

final > midterm

[1] FALSE FALSE  TRUE FALSE FALSE

Boolean operators can be applied elementwise:

(final < midterm) & (midterm > 80)

[1] FALSE  TRUE FALSE FALSE  TRUE

Functions on vectors

Functions on vectors

course.grades

[1] 78.8 86.4 94.2 82.0 92.6

mean(course.grades) # mean grade

[1] 86.8

median(course.grades)

[1] 86.4

sd(course.grades) # grade standard deviation

[1] 6.625708

More functions on vectors

sort(course.grades)

[1] 78.8 82.0 86.4 92.6 94.2

max(course.grades) # highest course grade

[1] 94.2

min(course.grades) # lowest course grade

[1] 78.8

Referencing elements of vectors

students

[1] "Sean"   "Louisa" "Frank"  "Farhad" "Li"    

Vector of indices:

students[c(2,4)]

[1] "Louisa" "Farhad"

Vector of negative indices

students[c(-1,-3)]

[1] "Louisa" "Farhad" "Li"    

More referencing

which() returns the TRUE indexes of a Boolean vector:

course.grades

[1] 78.8 86.4 94.2 82.0 92.6

a.threshold <- 90 # A grade = 90% or higher
course.grades >= a.threshold # vector of booleans

[1] FALSE FALSE  TRUE FALSE  TRUE

a.students <- which(course.grades >= a.threshold) # Applying which() 
a.students

[1] 3 5

students[a.students] # Names of A students

[1] "Frank" "Li"   

Named components

You can give names to elements or components of vectors

students

[1] "Sean"   "Louisa" "Frank"  "Farhad" "Li"    

names(course.grades) <- students # Assign names to the grades
names(course.grades)

[1] "Sean"   "Louisa" "Frank"  "Farhad" "Li"    

course.grades[c("Sean", "Frank","Li")] # Get final grades for 3 students

 Sean Frank    Li 
 78.8  94.2  92.6 

Note the labels in what R prints; these are not actually part of the value

Useful RStudio tips

What

A hands-on worksheet where you practice basic R within Markdown, in Rstudio. 514-Lab1.Rmd 514-Lab1.html

When

[[was due Thu 2/4]] new due date: Due Fri 2/5

Where

submit on google classroom \[\leftarrow\]make sure you’re logged into your IIT account to access.

Instructions

[These are copied from Google classroom.]

• you are to start to work on this worksheet in groups in class,

• Then you are to finish it up by yourself and submit.

• As for each submission for this course, you need to submit at least two files, and preferably three:

  1. the source .Rmd file
  2. the knitted html
  3. the knitted pdf.
  • 1 and 2 are required.
  • 3 is optional but desired (it is much easier for me to grade it, that’s the reason why). You can create pdf by changing “html_document” to “pdf_document” in the output specification in your .Rmd file.

Installing and loading packages

Just like every other programming language you may be familiar with, R’s capabilities can be greatly extended by installing additional “packages” and “libraries”.

To install a package, use the install.packages() command. You’ll want to run the following commands to get the necessary packages for today’s lab:

install.packages("rmdformats")
install.packages("ggplot2")
install.packages("knitr")

You only need to install packages once. Once they’re installed, you may use them by loading the libraries using the library() command. For today’s lab, you’ll want to run the following code

library(ggplot2) # graphics library
library(knitr)   # contains kable() function

options(scipen = 4)  # Suppresses scientific notation

Full info page on Markdown

Link to extensive resource with many examples you can reference as needed.