Quick Refresher from Previous Classes

✅ Why Programming

✅ Why

✅ RStudio IDE

❌ Basic

❌ Data Types in

RStudio Again

Working Directory

• Every task [and possibly project] can be handled

  • As manually organized Folders 🤔
    
  • As project 😄

• If you know how to handle the program then both possible

• Project is a bit easier way

• In this course manual creation is recommended to let you learn the structure

Manual Folder Creation and Planning

• Create a directory structure for this course such as

• MIS207/ 📂

  • data/ 📁
    • *.csv *.txt, *.xlsx ,
      
  • images/ 📂
    • .jpg, *.png, *.bmp, *.svg
      
  • code/ 📁
    • calculater.r, calendar.r, test.r
      
  • HW/ 📁
    • HWTemplate.rmd, firstHW.rmd, importEx.rmd

• All addresses are relative (we will see examples)

RStudio Project Creation (For the Curious )

• Create an RStudio Project

• Click New Directory

RStudio Create New Project

• Select folder location where this subdirectory is going to be created, and, write directory name

RStudio Create New Project

RStudio New Project Opened

RStudio Project Options

RStudio Project Options

For More Details please visit RStudio User Guide, Project section https://docs.posit.co/ide/user/ide/guide/code/projects.html

R Basic Syntax: R prompt

Default R prompt is >

Can be changed with options() function

options(prompt="R> ")  
options(prompt="> ")  

R Basic Syntax: “Continuation: +”

“+” is continuation for incomplete line

library(tidyverse)
dat <- tibble(names = c("smile","school","office","blush","smirk","heart_eyes"),
              emoji = map_chr(names, emo::ji)) %>% 
  tibble::rowid_to_column("n")

R Basic Syntax: “Comment #”

All of the text, values, assignments etc. will be interpreted as comment after # symbol

x = 3#5
x

x = #35

# x = 35

nothing happens

R Basic Syntax: Assignment

Recommended

x <- 3  

or (?)

x = 3  

• Simply;
  • left-hand side: variable name (x)
    
  • assignment operator: <- or =
    
  • right-hand side: value (3)

x <- 3 # recommended  
x      # Retrieval

## [1] 3

x = 5 # not recommended 
x      # Retrieval 

## [1] 5

7 -> x # not recommended
x      # Retrieval

## [1] 7

R Basic Syntax: Retrieval

x   

• Note: Variable (object) names are case sensitive

X  

• Legal R Names: Variable (object) names can be combinations of letters, numbers, periods (.) and underscores (_)

• We will discuss naming in programming style guide section

R Basic Syntax: Built in Objects

• R has several built in Constants, Functions,.., objects in general

• For example c, T, F, t, pi

• Avoid any assignment to built in objects

T 

## [1] TRUE

F 

## [1] FALSE

pi

## [1] 3.141593

exp(1) # Euler's number, or e, is a mathematical constant  

## [1] 2.718282

R Basic Syntax: Spaces

• R ignores extra spaces between object names and operators

3 + 5  

## [1] 8

4  +  9 

## [1] 13

x <- 7+8 # not recommended
x <- 7 + 8 # recommended
x <- 7  +  8 # not recommended

• Warning: Spaces cannot be placed between the < and - in the assignment operator “<-”, or in the object names

What happens here?

x < - 5 + 3

## [1] FALSE

R as Calculator: Operators: Basic Calculations

• Arithmetic Operators: +, -, *, /, ^, %% (modulo), %/% (integer division)

1 + 1 # addition 

## [1] 2

45 - 25 # subtraction

## [1] 20

2 * 5 # multiplication   

## [1] 10

10 / 5 # division 

## [1] 2

2^3 # to the power  

## [1] 8

8^(1/3) # to the power  

## [1] 2

4^0.5 # to the power  

## [1] 2

11%%3  # mod 

## [1] 2

11%/%3  # integer division 

## [1] 3

Scientific Notation: e

1e3

## [1] 1000

2.1e2

## [1] 210

R as Calculator: Order of Operations

From highest to lowest

+ Parentheses: (, )
+ Exponents: ^ or **
+ Multiply: *
+ Divide: /
+ Add: +
+ Subtract: -

# * and - 
12*5-30

## [1] 30

20-100*2

## [1] -180

# * and / 
12*5/30

## [1] 2

5/30*12

## [1] 2

20*100/2

## [1] 1000

# (), *, /, + 
(3+5)*4-5*6

## [1] 2

4*(25-20)/10

## [1] 2

(4+(5*(2^2))) # hard to read

## [1] 24

4+5*2^2     # clear, if you remember the rules

## [1] 24

4+5*(2^2)    # if you forget some rules, this might help

## [1] 24

# very small numbers  
4/100000 # [1] 4e-05 means 4x10^(-5)

## [1] 4e-05

12e3  # 12x10^3  

## [1] 12000

R as Calculator

• Inf means non-finite numeric value

10/0

## [1] Inf

-10/0

## [1] -Inf

• Nan means not a number

-10/0 + 1/0

## [1] NaN

R as Calculator: Comparisons

Logical operators are operators that return TRUE and FALSE values

• Some operators are for comparisons

# Equality  
1==1 # == read as "is equal to"  

## [1] TRUE

# Inequality  
1!=1 # != read as "is not equal to"  

## [1] FALSE

1!=2

## [1] TRUE

12345 != 3425 

## [1] TRUE

# greater than or equal to
1 >= 1  

## [1] TRUE

1 >= -2

## [1] TRUE

12345 >= 3425 

## [1] TRUE

# less than or equal to
1 <= 1  

## [1] TRUE

1 <= 2

## [1] TRUE

12345 <= 3425 

## [1] FALSE

# less than 
12345 < 3425 

## [1] FALSE

# greater than 
12345 > 3425 

## [1] TRUE

# if there is a match or not
12345 %in% c(3425, 12, 12345) 

## [1] TRUE

R as Calculator: Logical Operators

R as Calculator: More About TRUE and FALSE

TRUE + TRUE # = ? 
TRUE + FALSE # = ?
TRUE - TRUE # = ?
!TRUE # = ?
!FALSE # = ?
TRUE & FALSE # = ?  
TRUE | FALSE # = ? 
TRUE * FALSE # = ? 
TRUE == TRUE # = ? 
TRUE == 1 # = ? 
TRUE == 0 # = ? 
TRUE == -1 # = ?
FALSE == 0 # = ? 
FALSE == -1 # = ?
FALSE == 0.5 # = ?

R as Calculator: More About TRUE and FALSE

TRUE + TRUE 

## [1] 2

TRUE + FALSE 

## [1] 1

TRUE - TRUE 

## [1] 0

!TRUE 

## [1] FALSE

!FALSE

## [1] TRUE

TRUE & FALSE 

## [1] FALSE

TRUE | FALSE

## [1] TRUE

TRUE * FALSE

## [1] 0

TRUE == TRUE 

## [1] TRUE

TRUE == 1 

## [1] TRUE

TRUE == 0 

## [1] FALSE

TRUE == -1 

## [1] FALSE

FALSE == 0 

## [1] TRUE

FALSE == -1

## [1] FALSE

FALSE == 0.5

## [1] FALSE

R as a calculator: Mathematical Functions

trigonometric functions

sin(1)  

## [1] 0.841471

sin(pi)  

## [1] 1.224606e-16

sin(pi/2)  

## [1] 1

cos(1)

## [1] 0.5403023

cos(pi)

## [1] -1

cos(pi/2)

## [1] 6.123032e-17

tan(1)

## [1] 1.557408

tan(pi)

## [1] -1.224647e-16

tan(pi/2)

## [1] 1.633124e+16

atan2(1, 1)

## [1] 0.7853982

tan(atan2(1, 1))

## [1] 1

atan(2/1) == atan2(2,1)

## [1] TRUE

tan(atan(8/4)) # slope of line passing through (x=0,0) and (x=4,8)

## [1] 2

R as a calculator: Mathematical Functions

Logarithms and Exponential

# natural logarithm
log(1)  

## [1] 0

log(exp(1))  # see exp(1) :)

## [1] 1

# to base 10 
log10(10)

## [1] 1

log10(0.1)

## [1] -1

# to base 2 
log2(2^5)

## [1] 5

log2(1/2^5)

## [1] -5

log(-0.5) # Not a Number, NaN

## [1] NaN

log2(-0.5) # Not a Number, NaN

## [1] NaN

log10(-0.5) # Not a Number, NaN  

## [1] NaN

log(0) # -Inf 

## [1] -Inf

# Exponential 
exp(-Inf)

## [1] 0

exp(0)

## [1] 1

exp(1)

## [1] 2.718282

exp(sin(1))

## [1] 2.319777

R as as a calculator: Precision

Computers may represent decimal numbers with a certain degree of precision. Two numbers which look the same when printed out by R, may actually have different underlying representations and therefore be different by a small margin of error (called Machine numeric tolerance). If numbers are not integers, use all.equal function

pi 

## [1] 3.141593

all.equal(3.141593,pi)

## [1] "Mean relative difference: 1.102658e-07"

all.equal(1.234,1.234)

## [1] TRUE

all.equal(1.234,1.2345)

## [1] "Mean relative difference: 0.0004051864"

Variables

Note: Recall <- or -> are assignment operators

x <- 1/10  

1/5 -> y

x 

## [1] 0.1

y

## [1] 0.2

x >= y  

## [1] FALSE

all.equal(x,y)

## [1] "Mean relative difference: 1"

# type: will see later 
class(x) 

## [1] "numeric"

typeof(x)

## [1] "double"

x + y  

## [1] 0.3

x * y  

## [1] 0.02

x / y 

## [1] 0.5

x^y

## [1] 0.6309573

z=10
z

## [1] 10

z^2

## [1] 100

z*x/y

## [1] 5

Vectorization Teaser

: is Colon operator; from:to in steps of 1 or -1

c(): Combine Values into a Vector

# Basic  
1:10

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

17:25 

## [1] 17 18 19 20 21 22 23 24 25

pi:6 # real number 

## [1] 3.141593 4.141593 5.141593

6:pi # integer number 

## [1] 6 5 4

2^(1:5) # element-wise 

## [1]  2  4  8 16 32

x <- 5

x^(3:5)  

## [1]  125  625 3125

y <- 1:5 

2^y  

## [1]  2  4  8 16 32

x^y

## [1]    5   25  125  625 3125

y^x

## [1]    1   32  243 1024 3125

(1:3)^(2:4)

## [1]  1  8 81

(1:3)^(2:7) # shorter recycled :)

## [1]    1    8   81    1   64 2187

z <- 2:4
z

## [1] 2 3 4

y

## [1] 1 2 3 4 5

y^z # observe warning  

## [1]   1   8  81  16 125

l <- c(1,3,7,10)
l

## [1]  1  3  7 10

lstr <- c("one","two","three")
lstr

## [1] "one"   "two"   "three"

lstr2 <- c(lstr,2) # observe 2
lstr2 

## [1] "one"   "two"   "three" "2"

lstr3 <- c(lstr,l) # observe numeric values
lstr3

## [1] "one"   "two"   "three" "1"     "3"     "7"     "10"

rbind(l,c(2,4,6,8)) # creates a 2-d data, 2 by 4  

##   [,1] [,2] [,3] [,4]
## l    1    3    7   10
##      2    4    6    8

rbind(l,2,4,6,8) # creates a 2-d data, 5 by 4  

##   [,1] [,2] [,3] [,4]
## l    1    3    7   10
##      2    2    2    2
##      4    4    4    4
##      6    6    6    6
##      8    8    8    8

Combining Number, Logical and Character: Teaser

TRUE:4 # what happens to TRUE

## [1] 1 2 3 4

T:4 # same 

## [1] 1 2 3 4

4:FALSE

## [1] 4 3 2 1 0

F:4

## [1] 0 1 2 3 4

c(T,T,F,T)

## [1]  TRUE  TRUE FALSE  TRUE

c(T,4,F,T) # observe 

## [1] 1 4 0 1

c("Ali", "Veli", "Hatice")

## [1] "Ali"    "Veli"   "Hatice"

c("Ali", "Veli", "Hatice", TRUE)

## [1] "Ali"    "Veli"   "Hatice" "TRUE"

c("Ali", "Veli", "Hatice", TRUE, 4)

## [1] "Ali"    "Veli"   "Hatice" "TRUE"   "4"

Environment

ls()

##  [1] "Description" "l"           "l_table_1"   "l_table_2"   "lstr"       
##  [6] "lstr2"       "lstr3"       "Operator"    "x"           "y"          
## [11] "z"

ls(all.names=TRUE)

##  [1] ".Random.seed" "Description"  "l"            "l_table_1"    "l_table_2"   
##  [6] "lstr"         "lstr2"        "lstr3"        "Operator"     "x"           
## [11] "y"            "z"

ls # a function is an object in R :)  

## function (name, pos = -1L, envir = as.environment(pos), all.names = FALSE, 
##     pattern, sorted = TRUE) 
## {
##     if (!missing(name)) {
##         pos <- tryCatch(name, error = function(e) e)
##         if (inherits(pos, "error")) {
##             name <- substitute(name)
##             if (!is.character(name)) 
##                 name <- deparse(name)
##             warning(gettextf("%s converted to character string", 
##                 sQuote(name)), domain = NA)
##             pos <- name
##         }
##     }
##     all.names <- .Internal(ls(envir, all.names, sorted))
##     if (!missing(pattern)) {
##         if ((ll <- length(grep("[", pattern, fixed = TRUE))) && 
##             ll != length(grep("]", pattern, fixed = TRUE))) {
##             if (pattern == "[") {
##                 pattern <- "\\["
##                 warning("replaced regular expression pattern '[' by  '\\\\['")
##             }
##             else if (length(grep("[^\\\\]\\[<-", pattern))) {
##                 pattern <- sub("\\[<-", "\\\\\\[<-", pattern)
##                 warning("replaced '[<-' by '\\\\[<-' in regular expression pattern")
##             }
##         }
##         grep(pattern, all.names, value = TRUE)
##     }
##     else all.names
## }
## <bytecode: 0x000001972cc9fdc8>
## <environment: namespace:base>

rm(x) # remove x

rm(z) # remove z

rm(list=ls()) # Remove all (do not use assignment "<-" here) 

Packages

• There are 22447 packages available on cran

• Some/Many/All of these can be downloaded and installed on the local library folder

• .libpaths(): gets/sets the library trees within which packages are looked for

.libPaths()

## [1] "C:/Users/CANKAYA/AppData/Local/R/win-library/4.5"
## [2] "C:/Program Files/R/R-4.5.1/library"

.Library

## [1] "C:/PROGRA~1/R/R-45~1.1/library"

Package Installation

Some important functions for package installation and call:

• install.packages(), devtools::install_github() - later, library(), require() (designed to use in a function)

# installing a package 
install.packages("ggplot2") 

# installing more than one package  
pkgs <- c("ggplot2","gapminder","dplyr")
install.packages(pkgs) 

library(ggplot2)
detach("package:ggplot2")

require(ggplot2)

Package Installation

• Function arguments

 install.packages(pkgs, lib, repos = getOption("repos"),
                 contriburl = contrib.url(repos, type),
                 method, available = NULL, destdir = NULL,
                 dependencies = NA, type = getOption("pkgType"),
                 configure.args = getOption("configure.args"),
                 configure.vars = getOption("configure.vars"),
                 clean = FALSE, Ncpus = getOption("Ncpus", 1L),
                 verbose = getOption("verbose"),
                 libs_only = FALSE, INSTALL_opts, quiet = FALSE,
                 keep_outputs = FALSE, ...)
                 

• install.packages("ggplot2") and install.packages(pkgs="ggplot2") are both valid usage. install.packages("ggplot2") states that the first argument is "ggplot2"

• Arguments match either by names, i.e., install.packages(pkgs="ggplot2") or by positions install.packages("ggplot2")

• To use a function from a package, first of all package should be loaded and attached using either library(ggplot2) or require(ggplot2)

Detach Packages

• To detach objects, including package attached by library() or require() functions, from the search path, detach() function can be used

detach(name, pos = 2L, unload = FALSE, character.only = FALSE,
       force = FALSE)

library(ggplot2)
detach(package:ggplot2)

Imprtant Packages to Install

• Some packages that are necessary for this course are collected in the name of tidyverse

• Do visit tidyverse web site https://www.tidyverse.org/

• The complete tidyverse can be installed by install.packages("tidyverse")

Help

• ?<function name> open help page

• Example: ?lm: for lm() function

lm {stats}  R Documentation
Fitting Linear Models
Description
lm is used to fit linear models, including multivariate ones. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although aov may provide a more convenient interface for these).

Usage
lm(formula, data, subset, weights, na.action,
   method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE,
   singular.ok = TRUE, contrasts = NULL, offset, ...)

• For operators ?"<operator>"

• Example: ?“*” for multiplication

Arithmetic {base}   R Documentation
Arithmetic Operators
Description
These unary and binary operators perform arithmetic on numeric or complex vectors (or objects which can be coerced to them).

Usage
+ x
- x
x + y
x - y
x * y
x / y
x ^ y
x %% y
x %/% y
Arguments

• ??<function name> is help from all packages

• Example: ??left_join

• Example: help("anytime", try.all.packages = TRUE)

• From a specific package: help("anytime", package = anytime)

• Getting help for specific package, for example, help(package=anytime) (if anytime package is installed)

Other Resources

• Books: Try to visit available books online

• Manuals/Documents

  • Visit R-Project, website
    
  • Click Packages and find the package
    
  • If vignette(s) is(are) available click it(they), for example, anytime package vignette
    
  • Click reference manual if needed, for example, anytime reference manual

• Contributed Documents on website

  • Visit R-Project, website

  • Click Contributed

• Visit R-Journal website and browse articles, R-Journal

• Visit Journal of Statistical Software website and browse articles, Journal of Statistical Software

• …

• Community Websites/Blogs/etc

  • Visit Posit (R-Studio) Community Website, Posit, R-Studio Community

  • Visit stackoverflow website stackoverflow

  • R-bloggers

  • for Lecture Notes, short documents etc., for more…