• The source codes and necessary documentations are available at the repository: (https://github.com/mdtuhinsheikh/introR_ucsas2019).
• The workshop slides are available at the link.
• Prerequisites:
  • R and Rstudio have been installed.

• Third year Ph.D. Student in Statistics, UConn.
• Research Interests:
  • Bayesian biostatistics
  • Modelling informative dropout process
  • Machine learning and deep learning
  • Microbiome data analysis
• Future goals:
  • Contribute to the broader field of biostatistics and statistical learning.

• Have little to no experience with R.
• Enthusiastic attitude towards learning R programming.

• By the end of this workshop, the participants should have a basic understanding of
  • What R can do and whether you want to use it in the future
  • The ecosystem of R programming
  • Data structure and basic R functions
  • Import, export, and data management using R

• R Basics
• File System and Data Structure
• Read and Write Data
• Data Management
• Brief overview of “dplyr” package

• R is a free software environment for statistical computing and graphics
• R is a full-featured statistical analysis package (like SAS, SPSS, etc.) that allows to:
  • import, export, and manipulate data
  • represent data graphically
  • analyse and report
• Why the Name “R”?
  • Initial of the names of two originators: Ross Ihaka and Robert Gentleman
• More details: https://www.r-project.org.

• R is completely free to use and is an open source
• R is compatible with major operating systems (e.g. Windows, macOS, Linux).
• R has a huge library of user-contributed packages with
  • frequent release and
  • active development
• R is flexible to use
• Popular in academia and industry
• R makes data analysis fun!

• RStudio is an integrated development environment for the R programming language.
• Let us introduce with the RStudio interface.

5+7                          # 12

exp(5)                       # 148.4132   

log(2)                       # 0.6931472

• If you know the exact name of the function, then try ?function_name, e.g.,
  

?mean                       # Search mean function

• If you know specific words then help.search("text"), e.g.,
  

help.search("print") 

• If you do not know the exact function names, Google search engine or Stack Overflow are always excellent resources.

• We can assign values to a variable in R by using <- or = or ->. For example,

x <- "Hello World"
x                 # [1] "Hello World"
x = 5
x                 # [1] 5
5 -> x
x                 # [1] 5

• For combining elements or creating vectors, we can use c() and [] is used for subsetting and indexing.

x <- c(2, 5, 8)       # [1] 2 5 8
x[2]                  # [1] 5
x[1:2]                # [1] 2 5
x[1]**2               # [1] 4
exp(x)                # [1]    7.389056  148.413159 2980.957987
x[1] %% 2             # [1] 0
x[1] == 2             # [1] TRUE
x[1] < 2              # [1] FALSE
x[1] != 2             # [1] FALSE

• For atomic operators:

vector(), length(), names(), setNames(), rep(), rep.int(), rep_len(),
seq(), seq.int(), seq_len(), seq.along(), %in%, match(), rev(), head(), 
tail(), paste(), paste0(), typeof(), is.character(), as.character(), 
is.numeric(), as.numeric(), is.logical(), as.logical(), ...

• Quiz: Let us try ?rep to get to know about this function and then use this function to print {1 2 3 1 2 3} and {1 1 2 2 3 3}.

rep(c(1, 2, 3), times = 2)                 # 1 2 3 1 2 3
rep(c(1, 2, 3), each = 2)                  # 1 1 2 2 3 3

• For ordering and subsetting:

sort(), order(), rank(), quantile(),
duplicated(), unique(),
table(), split(), cut().

• Quiz: Consider we have \(x = (5, 5, 3, 3, 10, 20)\)
  • Length of this vector?
  • What are the unique values of \(x\)
  • Sort the values of \(x\)
  • Frequency distribution of \(x\)

x <- c(3, 3, 5, 5, 10, 20)  
length(x)        # 6
unique(x)        # 5 3 10 20
sort(x)          # 3 5 10 20 
table(x)         # 3 5 10 20 
                 # 2 2  1  1 

• The common forms of data types are as follows:

• Basic R data structures are
  • 1d: vector or list
  • 2d: matrix or data frame
  • nd: array

• Vector: a single entity consisting of a collection of numbers or characters.
  • The vector index can used by [].

x <- c(10.4, 5.6, 3.1, 6.4, 21.7)
x[2]          # Second element 5.6

• List: can list vectors or elements with unequal dimension or different data types.
  • The indexing for list can be used by [[]].

x <- list(c(1, 2), c(5, 8, 9))
x[[1]]        # [1] 1 2
x[[2]]        # [1] 5 8 9

• Array: multiply subscripted collection of data entries

x <- array(1:12, dim = c(3,2,2))
x[ , , 1] ##      [,1] [,2]
          ## [1,]    1    4
          ## [2,]    2    5
          ## [3,]    3    6

• Matrix: two-dimensional array

x <- matrix(1:6, nrow = 2)
x

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

• Let us learn arithmatic operations of matrix by solving the following quiz
• Quiz: Let \(A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}\) and \(B = \begin{bmatrix} 10 & 20 \\ 30 & 40 \end{bmatrix}\).
  • What are the dimensions of \(A\) and \(B\)?
  • Find the determinant of \(A\) and \(B\).
  • Evaluate \(AB\), \(A^{-1}\), and \(B^{-1}\).

• What are the dimensions of \(A\) and \(B\)?

A <- matrix(c(1, 2, 3, 4), ncol = 2, byrow = TRUE)
B <- matrix(c(10, 20, 30, 40), ncol = 2, byrow = TRUE)

dim(A)
dim(B)

• Find the determinant of \(A\) and \(B\).

det(A)
det(B)

• Evaluate \(AB\), \(A^{-1}\), and \(B^{-1}\).

A %*% B
solve(A)
solve(B)

• Basic Structure:

function_name <- function(arguments) {
  # Some operation
}

• Example: function of evaluating absolute value

find_abs <- function(x) {
  if (x > 0) {
    return(x)
  }
  x * -1
}
find_abs(-5)       # 5

• Write a function that takes \(x\) and \(y\) as input and evaluates \(x^y\).

pow <- function(x, y) {
  result <- x^y
  return(result)
}
pow(2, 3)            # 8

• If we want to repeat a task, we can use for, while, etc. statements.

for(i in 1:5){
  index <- i
  print(index)
}

i=1
while(i <= 5){
  index <- i
  print(index)
  i = i + 1
}

• Useful arithmatic operators and R syntax

• When we want to control how the program should work, we use if statement.

for(i in 1:5){
  if(i != 2){
    index <- i
    print(index)
  }
}

• Loop and conditioning can always be significant component of a R function.

• Is matrix multiplication \(AB\) possible? Use the same \(A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}\) and \(B = \begin{bmatrix} 10 & 20 \\ 30 & 40 \end{bmatrix}\). Write an R program using if condition.

A <- matrix(c(1, 2, 3, 4), ncol = 2, byrow = TRUE)
B <- matrix(c(10, 20, 30, 40), ncol = 2, byrow = TRUE)

dim(A)
dim(B)

if (dim(A)[2] == dim(B)[1]){
  print("The multiplication is possible")
} else {
  print("The multiplication is not possible")
}

• Basic syntax:
  • install a package:

install.packages("package_name")

• load a package:

library("package_name")

• Example: install and load “Lahman” package

install.packages("Lahman")
library(Lahman)

• Basic syntax:

data(package = "package_name")

• Example:

data(package = "Lahman")

• Read data syntex:

read.table(file = "location_of_file", header = TRUE)

• Write or save data syntex:

write.table(dataName, file = "location_of_file")

• Some of the useful functions for reading and writing data

read.csv(), read.csv2(), write.csv(), write.csv2(), etc.

• We already know how to search a dataset in a package
• To read a particular data from the package

data("dataName", package = "package_name")

• Quiz: Read “Batting” data from “Lahman” package

data("Batting", package = "Lahman")
# the data will be loaded if no error shows

• To learn about the variables

str("Batting")
#'data.frame':  105861 obs. of  22 variables:
# $ playerID: chr  "abercda01" "addybo01" "allisar01" "allisdo01" ...
# $ yearID  : int  1871 1871 1871 1871 1871 1871 1871 1871 1871 ...
# $ stint   : int  1 1 1 1 1 1 1 1 1 1 ...
# $ teamID  : Factor w/ 149 levels "ALT","ANA","ARI",..: 136 111 39 ...
# $ lgID    : Factor w/ 7 levels "AA","AL","FL",..: 4 4 4 4 4 4 4 4 ...
# $ G       : int  1 25 29 27 25 12 1 31 1 18 ...
# $ AB      : int  4 118 137 133 120 49 4 157 5 86 ...
# $ R       : int  0 30 28 28 29 9 0 66 1 13 ...
# $ H       : int  0 32 40 44 39 11 1 63 1 13 ...
#...

• More detailed description of the variables can be found at the link.
• Using this example we will learn data frame and data management.
• Display few rows of the data

head(Batting)
#   playerID yearID stint teamID lgID  G  AB  R  H X2B X3B HR RBI ...
# 1 abercda01   1871     1    TRO   NA  1   4  0  0   0   0  0   0 ...
# 2  addybo01   1871     1    RC1   NA 25 118 30 32   6   0  0  13 ...
# 3 allisar01   1871     1    CL1   NA 29 137 28 40   4   5  0  19 ...
# 4 allisdo01   1871     1    WS3   NA 27 133 28 44  10   2  2  27 ...
# 5 ansonca01   1871     1    RC1   NA 25 120 29 39  11   3  0  16 ...
# 6 armstbo01   1871     1    FW1   NA 12  49  9 11   2   1  0   5 ...

• Indexing works similar to matrix

Batting[, 1:4]
#    playerID yearID stint teamID
# 1 abercda01   1871     1    TRO
# 2  addybo01   1871     1    RC1
# 3 allisar01   1871     1    CL1
# 4 allisdo01   1871     1    WS3
# 5 ansonca01   1871     1    RC1
# 6 armstbo01   1871     1    FW1
# ...

• We can also display or work with variable(s) by using $ or mentioning the name of variable inside the indexing.

Batting$playerID
Batting[, c("playerID")]
# Displayes all rows of playerID variable
Batting[1:10, c("playerID", "yearID")]
# Displayes 10 rows of playerID and yearID variables

• Create new variable

Batting$CS_SO <- with(Batting, CS + SO)
head(Batting[, c("CS", "SO", "CS_SO")])
#   CS SO CS_SO
# 1  0  0     0
# 2  1  0     1
# 3  1  5     6
# 4  1  2     3
# 5  2  1     3
# 6  1  1     2

• Display data from 1871 only
• Find the frequency of unique number of players, teams, and year information
• Create a separate dataframe for year 1872 only.

• Conditioning on the dataframe

Batting[Batting$yearID == "1871", ]   # Displays data from 1871 only
# Displays data for aardsda01 from 1871
Batting[Batting$yearID == "1871" & Batting$playerID == "aardsda01", ]   

• Unique number of players, teams, and year information:

length(unique(Batting$playerID))        # [1] 19428
length(unique(Batting$teamID))          # [1] 149
length(unique(Batting$yearID))          # [1] 148

• Alternative way: using apply() function

apply(Batting, 2, function(x) {length(unique(x))})
# playerID   yearID    stint   teamID ...
#    19428      148        5      149 ...

• Create new dataframe

Batting_1872 <- Batting[Batting$yearID == "1872", ]

• Requirements

install.packages("dplyr")
library(dplyr)

• Let us read “Salary” data from “Lahman” package

data("Salaries", package = "Lahman")
head(Salaries)
#   yearID teamID lgID  playerID salary
# 1   1985    ATL   NL barkele01 870000
# 2   1985    ATL   NL bedrost01 550000
# 3   1985    ATL   NL benedbr01 545000
# 4   1985    ATL   NL  campri01 633333
# 5   1985    ATL   NL ceronri01 625000
# 6   1985    ATL   NL chambch01 800000

• Let us create a subset of “Salary” data without “lgID”

salaries <- Salaries %>%
             select(playerID, yearID, teamID, salary)
head(salaries)
#    playerID yearID teamID salary
# 1 barkele01   1985    ATL 870000
# 2 bedrost01   1985    ATL 550000
# ...

• We want to add salary info of the players in our Batting data

batting <- left_join(Batting, salaries, 
                by =c("playerID", "yearID", "teamID"))

• Mean salary

mean(batting$salary, na.rm =  TRUE)

• Arrange the data with respect to multiple variables in order

batting <- batting %>% arrange(playerID, yearID, stint)
#   playerID yearID stint teamID lgID  ...  salary
#1 aardsda01   2004     1    SFN   NL  ...  300000
#2 aardsda01   2006     1    CHN   NL  ...   NA
#3 aardsda01   2007     1    CHA   AL  ...  387500
#4 aardsda01   2008     1    BOS   AL  ...  403250
#5 aardsda01   2009     1    SEA   AL  ...  419000
#6 aardsda01   2010     1    SEA   AL  ...  2750000

• Filter the data with eiligible players after the year 1899 and with a minimum of 20 base on balls.
• Arrange the eligible players by decreasing order of base on balls.

• Filter the data with eiligible players after the year 1899 and with a minimum of 20 base on balls

eligibleHitters <- batting %>%
                     filter(yearID >= 1900 & BB > 20)

• Arrange the eligible players by decreasing order of base on balls.

goodPlayers <- eligibleHitters %>%
                     arrange(desc(BB))

• R Programming for Data Science by Roger D. Peng.
• An Introduction to R by W. N. Venables, D. M. Smith and the R Core Team.
• Advanced R by Hadley Wickham.

• Good luck with coding R.
• Feel free to contact for further queries: mdtuhin.sheikh@uconn.edu.