Lecture 1

class: center, middle, inverse, title-slide

# Lecture 1
## R/RStudio basics
### Jung-Jin Lee
### Jan 5, 2021

---

## Help on slides

At any time, press `h` to open `Help`.

---

## What is statistics?

- Statistics is the science of learning from data

- The goal of statistics is to summarize data in a way that allows for
easy descriptions or inferences of the data
  - Data visualization and computing are important parts of statistics

- R, a language and environment for statistical computing and graphics, will be used as a main tool throughout the course

---

## Why R?

- Specialized for data analysis

- Many packages have been developed and are available

- Excellent support for data visualization

- Free!

---

## Installing R/RStudio

- To install R, go to https://www.r-project.org/ and follow instruction there
    - Click on CRAN under Download on the left
    - Choose the closest mirror site (e.g. http://lib.stat.cmu.edu/R/CRAN/)
    - Download R for your platform (Windows/Mac/Linux)
    
- To install RStudio (an integrated development environment for R), go to https://www.rstudio.com/ and follow instruction

- Also, RStudio Cloud is available at https://rstudio.cloud/

---

## RStudio IDE overview

---

## Additional software and libraries

- (Not essential) If at all possible, install `$\TeX$` using one of the following options:
    - MiKTeX (http://miktex.org): recommended for Windows users
    - MacTeX (http://tug.org/mactex): recommended for Mac users
    
- Open RStudio, and go to Tools `$\rightarrow$` Install Packages... and install the following libraries using default options:
    - `tidyverse`
    - `tinytex` (unnecessary if you installed `$\TeX$`)

---

## Using R console as a calculator

```r
5 + 3
```

```
## [1] 8
```

```r
# everything after # (pound sign) is ignored
# useful for comments
2 * 5 # product of 2 and 5
```

```
## [1] 10
```

```r
3^2
```

```
## [1] 9
```

```r
3**2
```

```
## [1] 9
```

---

## R project setup

- Make a directory that will be used for the class (e.g. C:/Users/jungl/MLAS_530S_2021)
  - For easy access, all the data files and scripts used in the class are assumed to be saved in this directory

- File `$\rightarrow$` New Project `$\rightarrow$` Existing Directory `$\rightarrow$` Create Project from Existing Directory `$\rightarrow$` Choose C:/Users/jungl/MLAS_530S_2021

- You can start RStudio by opening the .Rproj file
  - Running `getwd()` in console shows the current working directory, which should be the class directory you made previously.

- If you have not installed `$\TeX$`, run following (it may take some time) in console - this is necessary to generate pdf output:

```r
tinytex::install_tinytex()
```

---

## R scripts / R Markdown / Others

- File `$\rightarrow$` New File `$\rightarrow$` R Script for a plain R script

- A part of an R script is copied into the R console and run
  - Can be saved for future use

- File `$\rightarrow$` New File `$\rightarrow$` R Markdown for an R markdown files

- Mixture of plain text and R codes (called *chunks*)
  - Great for generating report (preferred for **homework submission!**)
  - Can be rendered into (HTML/PDF/Word
  - Useful chunk options: `message = F, echo = F, eval = F`
  - Inline coding can be done using a pair of backticks and `r`: ` `r 2+3`\`

- R script and R markdown files can also be developed into presentation (`xaringan`), web application (`Shiny`), blog (`blogdown`), and book (`bookdown`)

- Not covered in this course

---

## Inline/Console output control

---

## Variables

Variables can be used to store numerical values.

- A variable can be a combination of letters (case sensitive), digits, period (.) and underscore (_).
- It must start with a letter or a period. If it starts with a period, it cannot be followed by a digit.
- Reserved words in R, such as `TRUE`, cannot be used as variables.

```r
Num1 <- 5 
Num2 <- 3 
sum_num <- Num1 + Num2
prod.num <- Num1 * Num2
*print(sum_num)
```

```
## [1] 8
```

```r
print(prod.num)
```

```
## [1] 15
```

---

## Characters

Variables can also be used to store character values.

```r
first_name <- "John" # use quotation marks for character strings
last_name <- 'Smith' # single quotation also works
print(first_name) # print() can be omitted, but encouraged
```

```
## [1] "John"
```

```r
print(last_name)
```

```
## [1] "Smith"
```

---

## Logical values

```r
TF1 <- T # upper case T, not t
TF2 <- F # upper case F, not f
print(TF1)
```

```
## [1] TRUE
```

```r
print(TF2)
```

```
## [1] FALSE
```

```r
TF3 <- TRUE # can use TRUE instead of T
TF4 <- FALSE # can use FALSE instead of F
print(TF3)
```

```
## [1] TRUE
```

```r
print(TF4)
```

```
## [1] FALSE
```

---

## Logical values, continued

```r
p <- 4; q <- 6 # semicolon (;) can be used to separate lines
call1 <- p > 5
call2 <- q > 5
call3 <- q > 6
call4 <- q >= 6
print(call1)
```

```
## [1] FALSE
```

```r
print(call2)
```

```
## [1] TRUE
```

```r
print(call3)
```

```
## [1] FALSE
```

```r
print(call4)
```

```
## [1] TRUE
```

---

## Combination of logical values

```r
p <- 4; q <- 6 
*print(p < 5 & q < 7) # & means "and": TRUE only if both are TRUE
```

```
## [1] TRUE
```

```r
print(p < 5 & q < 5) 
```

```
## [1] FALSE
```

```r
*print(p < 5 | q < 5) # | means "or": FALSE only if both are FALSE
```

```
## [1] TRUE
```

```r
*print(!(p < 5)) # ! means "not"
```

```
## [1] FALSE
```

---

## Functions

One or more values can produce new one using R functions.

```r
a <- 1; b <- 3 
*s <- sum(a, b) # sum() is a function that computes the sum
print(s)
```

```
## [1] 4
```

```r
first_name <- "John" 
last_name <- "Smith"
# paste() puts together multiple character strings
*full_name <- paste(first_name, last_name)
print(full_name)
```

```
## [1] "John Smith"
```

For detailed usage of a function, type `help(function_name)`, e.g. `help(paste)` or `?function_name`, e.g. `?paste`.

---

## Vectors

Multiple numerical values or characters can be saved as a single variable using an R function `c()`.

```r
*vec1 <- c(2, 3, 5, 7, 1, 4)
print(vec1)
```

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

```r
vec2 <- c("Joe", "waited", "for", "the", "train")
print(vec2)
```

```
## [1] "Joe"    "waited" "for"    "the"    "train"
```

```r
*vec3 <- vec2 %in% c("Joe", "for") # %in% determines membership
print(vec3)
```

```
## [1]  TRUE FALSE  TRUE FALSE FALSE
```

---

## Functions applied to a vector -- part I

```r
*length(vec1) # number of components of a vector
```

```
## [1] 6
```

```r
sum(vec1)
```

```
## [1] 22
```

**Exercise**: sort the numbers 2, 3, 5, 7, 1, 4 from the smallest to the largest and vice versa.

```r
*sort(vec1)
```

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

```r
sort(vec1, decreasing = T)
```

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

---

## Functions applied to a vector -- part II

```r
collapse1 <- paste(vec2, collapse = " "); print(collapse1)
```

```
## [1] "Joe waited for the train"
```

```r
collapse2 <- paste(vec2, collapse = "_"); print(collapse2)
```

```
## [1] "Joe_waited_for_the_train"
```

**Exercise**: produce the following output

```
## [1] "Joewaitedforthetrain"
```

```r
collapse3 <- paste(vec2, collapse = ""); print(collapse3)
```

```
## [1] "Joewaitedforthetrain"
```

---

## Functions applied to a vector -- part III

```r
print(vec1)
```

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

Which elements are greater than 3?

```r
vec4 <- vec1 > 3
print(vec4)
```

```
## [1] FALSE FALSE  TRUE  TRUE FALSE  TRUE
```

How many elements of `vec1` are greater than 3?

```r
sum(vec4) # in R, TRUE is 1, FALSE is 0
```

```
## [1] 3
```

---

## Named vector

Each member of a vector can be given a name:

```r
age_Simpsons <- c(36, 34, 10, 8, 1)
print(age_Simpsons)
```

```
## [1] 36 34 10  8  1
```

```r
*names(age_Simpsons) # currently no names are given to each component
```

```
## NULL
```

```r
# Now assign names to each member of the vector
names(age_Simpsons) <- c("Homer", "Marge", "Bart", "Lisa", "Maggie")
print(age_Simpsons)
```

```
##  Homer  Marge   Bart   Lisa Maggie 
##     36     34     10      8      1
```

---

## Vector manipulation

Use brackets `[]` to extract components of a vector.

```r
*a <- vec1[3]
print(a)
```

```
## [1] 5
```

```r
b <- vec2[c(1, 5)]
print(b)
```

```
## [1] "Joe"   "train"
```

**Exercise**: extract the last element of `vec1` without viewing its elements.

```r
vec1[length(vec1)]
```

```
## [1] 4
```

---

## Vector manipulation, continued

Recall the named vector, `age_Simpsons`:

```r
print(age_Simpsons)
```

```
##  Homer  Marge   Bart   Lisa Maggie 
##     36     34     10      8      1
```

What are the ages of the kids?

```r
age_Simpsons[c(3, 4, 5)]
```

```
##   Bart   Lisa Maggie 
##     10      8      1
```

```r
age_Simpsons[c("Bart", "Lisa", "Maggie")]
```

```
##   Bart   Lisa Maggie 
##     10      8      1
```

---

## Vector manipulation, continued

Components of a vector can be extracted using logical values:

```r
age_Simpsons[c(TRUE, TRUE, FALSE, TRUE, FALSE)]
```

```
## Homer Marge  Lisa 
##    36    34     8
```

```r
male <- c("Homer", "Bart")
age_Simpsons[names(age_Simpsons) %in% male]
```

```
## Homer  Bart 
##    36    10
```

---

## Vector manipulation, continued

Modifying a vector:

```r
m1 <- c("Apple", "Bananana", "Cherry")
m1[2] <- "Banana"
print(m1)
```

```
## [1] "Apple"  "Banana" "Cherry"
```

Joining vectors:

```r
m2 <- c("Pear", "Watermelon")
m3 <- c(m1, m2)
print(m3)
```

```
## [1] "Apple"      "Banana"     "Cherry"     "Pear"       "Watermelon"
```

---

## Special vectors -- part I

**Exercise**: compute the sum `$1 + 2 + 3 + \cdots + 10$`.

```r
w1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
sum(w1)
```

```
## [1] 55
```

Colon (:) can be used to generate a vector consisting of consecutive numbers.

```r
w2 <- 1:10
print(w2)
```

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

```r
sum(w2) 
```

```
## [1] 55
```

---

## Special vectors -- part II

**Exercise**: compute the sum `$2 + 4 + 6 +\cdots + 20$`.

```r
# one can use a function seq()
# type ?seq to get help
*w3 <- seq(2, 20, by = 2)
sum(w3)
```

```
## [1] 110
```

Alternatively, one can use the previous vector:

```r
w4 <- 2*w1 # product of a single number and a vector
print(w4)
```

```
##  [1]  2  4  6  8 10 12 14 16 18 20
```

```r
sum(w4)
```

```
## [1] 110
```

---

## Recycling in R

Operation between a single value and a vector.

```r
num3 <- 3
vec5 <- 1:5
vec6 <- vec5 + num3
print(vec6)
```

```
## [1] 4 5 6 7 8
```

```r
*vec7 <- paste0("Visitor_", vec5)
print(vec7)
```

```
## [1] "Visitor_1" "Visitor_2" "Visitor_3" "Visitor_4" "Visitor_5"
```

```r
vec8 <- 5 + 2*c(-1, 1)
print(vec8)
```

```
## [1] 3 7
```

---

## Recycling in R, continued

**Exercise**: Change "Female" to "F" for consistency.

```r
gender <- c("M", "F", "Female", "M", "Female")
```

```r
# solution using hardcoding
sol1 <- gender
sol1[c(3, 5)] <- "F"
print(sol1)
```

```
## [1] "M" "F" "F" "M" "F"
```

```r
# solution without using hardcoding
sol2 <- gender
sol2[sol2 == "Female"] <- "F"
print(sol2)
```

```
## [1] "M" "F" "F" "M" "F"
```

---

## Data frame

Vectors of an equal length can be combined to form a data frame.

```r
# use of the function data.frame()
first_name <- c("Lisa", "John", "Chuck", "Matt")
last_name <- c("Simpson", "Smith", "Williams", "June")
age_yrs <- c(8, 42, 81, 23)
*book <- data.frame(first = first_name,
*                  last = last_name,
*                  age = age_yrs)
print(book)
```

```
##   first     last age
## 1  Lisa  Simpson   8
## 2  John    Smith  42
## 3 Chuck Williams  81
## 4  Matt     June  23
```

```r
*dim(book) # dimensions of a data frame: dim()
```

```
## [1] 4 3
```

---

## Handling data frames

```r
# extract a single element from a data frame
book[2, 3]
```

```
## [1] 42
```

```r
# extract a column: use $
*age_extracted <- book$age
print(age_extracted)
```

```
## [1]  8 42 81 23
```

```r
# extract a row
book[4, ]
```

```
##   first last age
## 4  Matt June  23
```

---

## Retrieving a column from a data frame

```r
# use column number
method1 <- book[,1]
print(method1)
```

```
## [1] "Lisa"  "John"  "Chuck" "Matt"
```

```r
# use "$" to extract a column
method2 <- book$first
print(method2)
```

```
## [1] "Lisa"  "John"  "Chuck" "Matt"
```

---

## Adding a variable in a data frame

```r
gender <- c("Female", "Male", "Male", "Unknown")
book$sex <- gender
print(book)
```

```
##   first     last age     sex
## 1  Lisa  Simpson   8  Female
## 2  John    Smith  42    Male
## 3 Chuck Williams  81    Male
## 4  Matt     June  23 Unknown
```

Recycling applies to a data frame:

```r
book$remark <- "friend"
print(book)
```

```
##   first     last age     sex remark
## 1  Lisa  Simpson   8  Female friend
## 2  John    Smith  42    Male friend
## 3 Chuck Williams  81    Male friend
## 4  Matt     June  23 Unknown friend
```

---

## Reading a data file

Download a [file](heights.txt) (Chrome users: Menu `$\rightarrow$` More Tools `$\rightarrow$` Save Page As) and move it to the class directory.

```r
# "heights.txt" is a space-separated file with a header
# 1375 observations of UK mom/daughter age height pairs
*ht <- read.table(file = "heights.txt", header = TRUE, sep = " ")
```

A glance at a data frame:

```r
*dim(ht); nrow(ht); ncol(ht) # dimension, number of rows/columns
```

```
## [1] 1375    2
```

```
## [1] 1375
```

```
## [1] 2
```

```r
names(ht) # variable(column) names
```

```
## [1] "Mheight" "Dheight"
```

---

## Inspecting a data frame

```r
*head(ht) # first few rows (6 rows by default)
```

```
##   Mheight Dheight
## 1    59.7    55.1
## 2    58.2    56.5
## 3    60.6    56.0
## 4    60.7    56.8
## 5    61.8    56.0
## 6    55.5    57.9
```

```r
*tail(ht) # last few rows
```

```
##      Mheight Dheight
## 1370    69.5    70.4
## 1371    69.1    70.1
## 1372    65.0    71.6
## 1373    66.3    71.4
## 1374    70.8    71.0
## 1375    63.0    73.1
```

---

## List

A list is a collection of different types of elements:

```r
*li <- list(num = 1.6,
           seq = 1:5, 
           names = c("Bart", "Lisa", "Homer"), 
           uk.heihgt = ht) 
names(li)
```

```
## [1] "num"       "seq"       "names"     "uk.heihgt"
```

.pull-left[

```r
li$num
```

```
## [1] 1.6
```

```r
li$names
```

```
## [1] "Bart"  "Lisa"  "Homer"
```
]

.pull-right[

```r
head(li$uk.heihgt)
```

```
##   Mheight Dheight
## 1    59.7    55.1
## 2    58.2    56.5
## 3    60.6    56.0
## 4    60.7    56.8
## 5    61.8    56.0
## 6    55.5    57.9
```
]