R Programming

R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.

1. PROGRAMMING by Bs Ram

2. Why Do We Need Analytics ?

3. Why Do We Need Analytics? Data Analytics helps organizations harness their data and use it to identify new opportunities. DATA ANALYTICS Cost Reduction Faster, Better Decision Organization Analysis Better Marketing & Product Analysis

4. What is Business analytics? Business Analytics examines large and different types of data to uncover hidden patterns, correlations and other insights.

5. What is Data Visualization? Visualization allows us visual access to huge amounts of data in easily digestible visuals. Well designed data graphics are usually the simplest and at the same time, the most powerful.

6. Why R? Apart from being used as statistical language, it can also be used a programming language for analytical purposes. Apart from being one of the most dominant analytics tools, R also is one of the most popular tools used for data visualization Programming and Statistical Language Data Analysis and Visualization

7. Why R? R is a simple and easy to learn, read & write R is an example of a FLOSS (Free / Libre and Open Source Software) which means one can freely distribute copies of this software, read it’s source code, modify it, etc. Simple and Easy to Learn Free and Open Source Easy 2 Learn

8. R Installation Base https://cran.r-project.org/bin/windows/base / Download R 3.5.0 for Windows (62 megabytes, 32/64 bit) Windows Ubuntu Press Ctrl+Alt+T to open Terminal Then execute  sudo apt-get update   After that,  sudo apt-get install r-base

9. R Installation R Studio Windows Ubuntu sudo apt-get install gdebi -core wget https://download1.rstudio.org/rstudio-1.0.44-amd64.deb sudo gdebi rstudio-1.0.44-amd64.deb rm rstudio-1.0.44-amd64.deb https:// www.rstudio.com/products/rstudio/download /

10. Download Here

11. R Installation R Studio Windows Ubuntu sudo apt-get install gdebi -core wget https://download1.rstudio.org/rstudio-1.0.44-amd64.deb sudo gdebi rstudio-1.0.44-amd64.deb rm rstudio-1.0.44-amd64.deb https:// www.rstudio.com/products/rstudio/download /

12. Module

13. Variables Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory. R Basics A = 2 S <- “AU TVS Centre for Quality Management” TURE -> Y Memory A = 2 S <- “AU TVS Centre for Quality Management” Y = TURE

14. Operator R Basics

15. Operator R Basics

16. Operator R Basics

17. Operator R Basics

18. Arithmetic Operators R Basics >2+3 5 >+2 >3-1 2 >-2 >2*3 6 >6/3 2 >5%%2 1 >7%/%3 2 >2^3 8

19. Arithmetic Operators R Basics

20. R Basics

21. Data Types R Basics

22. Data Types R Basics

23. Vectors R Basics

24. Vectors R Basics

25. Vectors : Sequence Operations R Basics

26. List R Basics

27. List Sequence operations R Basics

28. Array R Basics

29. Array R Basics

30. Matrices R Basics

31. Matrices R Basics

32. Matrices R Basics

33. Factors R Basics

34. Factors R Basics

35. Data Frames R Basics

36. Data Frames R Basics

37. Flow Control Statement R Basics

38. Flow Control R Basics

39. Flow Control Statement R Basics

40. Loops R Basics

41. Loops R Basics

42. Repeat Statement R Basics

43. While Statement R Basics

44. For Statement R Basics

45. For Statement R Basics

46. Brake Statement R Basics Brake;

47. Data Visualization R Basics

48. Data Visualization R Basics

49. Data Visualization R Basics

50. Data Visualization R Basics

51. Data Visualization R Basics

52. Data Visualization R Basics

53. Data Visualization R Basics

54. Data Visualization R Basics

55. Data Visualization R Basics

56. Data Visualization R Basics

57. Data Visualization R Basics

58. Data Visualization R Basics

59. Data Visualization R Basics

60. Data Visualization R Basics

61. Function

62. Function Syntax: functionName <- function(inputs) { body of the function which returns something} You must run/submit the function before you can use it Note that variable naming within a function is protected, and not altered by whatever your script is doing

63. Function # tests if a number if positive pos = function(x) { if(x >= 0) out = 1 if(x < 0) out = 0 return(out) } Input Output

64. Function > pos = function(x) { + if(x >= 0) out = 1 + if(x < 0) out = 0 + return(out) + } > pos (5) [1] 1 > pos (-5) [1] 0 Run the function first

65. Data Manipulation

66. Importing Data Into R

67. Open your file

68. Reading data from txt|csv files: R base functions R base functions for importing data:  read.table (),  read.delim (),  read.csv (),  read.csv2 () Reading a local file Reading a file from internet

69. Reading data from txt|csv files: R base functions

70. Fast Reading of Data From txt|csv Files into R: readr package Functions for reading txt|csv files:  read_delim (),  read_tsv (),  read_csv (), read_csv2() Reading a file Reading a local file Reading a file from internet In the case of parsing problems Specify column types Reading lines from a file:  read_lines () Read whole file:  read_file ()

71. Fast Reading of Data From txt|csv Files into R: readr package

72. Reading data From Excel Files ( xls|xlsx ) into R

73. Exporting Data From R

74. Exporting Data From R Export Data From R to txt|csv|Excel files Writing data from R to a txt|csv file: R base functions Fast Writing of Data From R to txt|csv Files: readr package Writing data from R to Excel files ( xls|xlsx ) Saving data into R data format: RDATA and RDS Create and format word and powerpoint documents using R and ReporteRs package : Create and format Word documents Create a Word document from a template file Add a table into a Word document Create and format PowerPoint documents Create an editable graph from R software to PowerPoint

75. Writing data from R to a txt|csv file: R base functions R R base functions for writing data:  write.table (),  write.csv (),  write.csv2 () Writing data to a file

76. Fast writing of Data From R to txt|csv Files: readr package   Installing and loading readr :  install.packages (“ readr ”) readr functions for writing data:  write_tsv (),  write_csv () Writing data to a file

77. Fast writing of Data From R to txt|csv Files: readr package  

78. Writing data from R to Excel files ( xls|xlsx )   Installing xlsx package:  install.packages (“ xlsx ”) Using xlsx package:  write.xlsx ()

79. Writing data from R to Excel files ( xls|xlsx )  

80. Saving data into R data format: RDATA and RDS   Save one object to a file:  saveRDS (object, file),  readRDS (file) Save multiple objects to a file:  save (data1, data2, file),  load (file) Save your entire workspace:  save.image (),  load ()

81. Saving data into R data format: RDATA and RDS   Saving and restoring one single R object:

82. Saving data into R data format: RDATA and RDS   2. Saving and restoring one or more R objects :

83. Saving data into R data format: RDATA and RDS   3. Saving and restoring your entire workspace

84. Create and format Word documents with R and ReporteRs package  ReporteRs  package, by David Gohel , provides easy to use functions to  write  and  format   Word documents . It can be also used to generate  Word document from a template file  with logos, fonts, etc. ReporteRs is Java-based solution, so it works on Windows, Linux and Mac OS systems. Install and load the ReporteRs R package Create a simple Word document Add texts : title and paragraphs of texts Format the text of a Word document using R software Add plots and images Add a table Add lists : ordered and unordered lists Add a footnote to a Word document Add R scripts Add a table of contents into a Word document

85. Create a Word document from a template file with R and ReporteRs package   Quick introduction to ReporteRs package Create a Word document using a template file data <- iris [ 1 : 5 , ] data

86. Add a simple table

87. Create a Word document from a template file with R and ReporteRs package   Quick introduction to ReporteRs package Create a Word document using a template file data <- iris [ 1 : 5 , ] data

88. Packages

89. dplyr data.table ggplot2 reshape2 readr tidyr lubridate Packages ggplot2 is a graphical package. But, it generally helps in visualizing data ( distributions, correlations) and making manipulations accordingly. 

90. dplyr Package by  Hadley Wickham .  filter – It filters the data based on a condition select – It is used to select columns of interest from a data set arrange – It is used to arrange data set values on ascending or descending order mutate – It is used to create new variables from existing variables summarise (with group_by ) – It is used to perform analysis by commonly used operations such as min, max, mean count etc This package has everything (almost) to accelerate your data manipulation efforts. It is known best for data exploration and transformation. It’s chaining syntax makes it highly adaptive to use. 

91. dplyr Package by  Hadley Wickham .  I have used 2 pre-installed R data sets namely mtcars and iris.

92. dplyr Package by  Hadley Wickham .  #read data > head( mydata )

93. dplyr Package by  Hadley Wickham .  #creating a local dataframe . Local data frame are easier to read

94. dplyr Package by  Hadley Wickham .  #now data will be in tabular structure > mynewdata

95. dplyr Package by  Hadley Wickham .  > myirisdata

96. dplyr Package by  Hadley Wickham .  #use filter to filter data with required condition filter( mynewdata , cyl > 4 & gear > 4 ) filter

97. dplyr Package by  Hadley Wickham .  #use filter to filter data with required condition filter( mynewdata , cyl > 4)

98. dplyr Package by  Hadley Wickham .  > filter( myirisdata , Species %in% c(' setosa ', ' virginica '))

99. dplyr Package by  Hadley Wickham .  >select( mynewdata , cyl,mpg,hp ) #use select to pick columns by name select

100. dplyr Package by  Hadley Wickham .  > select( mynewdata , - cyl , -mpg ) #here you can use (-) to hide columns

101. dplyr Package by  Hadley Wickham .  #hide a range of columns > select( mynewdata , -c( cyl,mpg ))

102. dplyr Package by  Hadley Wickham .  #select series of columns > select( mynewdata , cyl:gear )

103. dplyr Package by  Hadley Wickham .  #chaining or pipelining - a way to perform multiple operations mynewdata %>% select( cyl , wt , gear) %>% filter( wt > 2)

104. dplyr Package by  Hadley Wickham .  #arrange can be used to reorder rows > mynewdata %>% select( cyl , wt , gear)%>% arrange( wt )

105. dplyr Package by  Hadley Wickham .  #Or > mynewdata %>% select( cyl , wt , gear)%>% arrange( desc ( wt ))

106. dplyr Package by  Hadley Wickham .  New variable > newvariable <- mynewdata %>% mutate( newvariable = mpg* cyl ) this is used to find insights from data > myirisdata %>% group_by (Species)%>% summarise (Average = mean( Sepal.Length , na.rm = TRUE)) Summarise -

107. dplyr Package by  Hadley Wickham .  > myirisdata %>% group_by (Species )%>% summarise_each (funs(mean , n()), Sepal.Length , Sepal.Width )

108. dplyr Package by  Hadley Wickham .  # you can rename the variables using rename command > mynewdata %>% rename(miles = mpg) Rename

109. data.table Package Leave your traditional ways of sub setting rows and columns and use this package. With minimum coding, you can do much more. Using data.table helps in reducing computing time as compared to data.frame . #load data > data (" airquality ") > mydata <- airquality > head(airquality,6) --- > data(iris ) > myiris <- iris

110. data.table Package # load package > library( data.table ) > mydata <- data.table ( mydata ) > mydata

111. data.table Package > myiris <- data.table ( myiris ) > myiris

112. data.table Package #subset rows - select 2nd to 4th row > mydata [2:4,]

113. data.table Package # select columns with particular values > > myiris [Species == ' setosa ']

114. data.table Package #select columns with multiple values. This will give you columns with Setosa # and virginica species > myiris [Species %in% c(' setosa ', ' virginica ')] # select columns. Returns a vector > mydata [,Temp]

115. data.table Package > mydata [,.( Temp,Month )] #returns sum of selected column > mydata [,sum(Ozone, na.rm = TRUE)] [ 1]4887 #returns sum and standard deviation > mydata [,.(sum(Ozone, na.rm = TRUE), sd (Ozone, na.rm = TRUE))]

116. data.table Package # print and plot > myiris [,{print( Sepal.Length ) > plot( Sepal.Width ) NULL}]

117. data.table Package # print and plot > myiris [,{print( Sepal.Length ) > plot( Sepal.Width ) NULL}]

118. data.table Package #grouping by a variable > myiris [,.( sepalsum = sum( Sepal.Length )), by=Species]

119. ggplot2 Package ggplot offers a whole new world of colors and patterns. If you are a creative soul, you would love this package till depth . Chart patterns covers almost every type of data representation except maps. ggplot is enriched with customized features to make your visualization better and better. > library(ggplot2 ) > library( gridExtra ) > df <- ToothGrowth

120. ggplot2 Package > df$dose <- as.factor ( df$dose ) > head( df )

121. ggplot2 Package #boxplot > bp <- ggplot ( df , aes (x = dose, y = len , color = dose)) + geom_boxplot () + theme( legend.position = 'none') > bp

122. ggplot2 Package # add gridlines > bp + background_grid (major = " xy ", minor = 'none ') > bp

123. ggplot2 Package # scatterplot > sp <- ggplot (mpg, aes (x = cty , y = hwy , color = factor( cyl )))+ geom_point (size = 2.5) > sp

124. ggplot2 Package # barplot > bp <- ggplot (diamonds, aes (clarity, fill = cut)) + geom_bar () +theme( axis.text.x = element_text (angle = 70, vjust = 0.5)) > bp

125. ggplot2 Package # compare two plots > plot_grid ( sp , bp , labels = c("A","B"), ncol = 2, nrow = 1 )

126. ggplot2 Package #histogram > ggplot (diamonds, aes (x = carat)) + geom_histogram ( binwidth = 0.25, fill = ' steelblue ')+ scale_x_continuous (breaks= seq (0,3, by=0.5))

127. reshape2 Package this package is useful in reshaping data R base functions consist of ‘Aggregation’ option using which data can be reduced and rearranged into smaller forms, but with reduction in amount of information. Aggregation includes tapply , by and aggregate base functions. The reshape package overcome these problems.  melt  This function converts data from wide format to long format. It’s a form of restructuring where multiple categorical columns are ‘melted’ into unique rows.

128. reshape2 Package #create a data > ID <- c(1,2,3,4,5) > Names <- c('Joseph',' Matrin ','Joseph','James',' Matrin ') > DateofBirth <- c(1993,1992,1993,1994,1992) > Subject<- c(' Maths ','Biology','Science',' Psycology ','Physics') > thisdata <- data.frame (ID, Names, DateofBirth , Subject) > data.table ( thisdata )

129. reshape2 Package #load package > install.packages ('reshape2') > library(reshape2) #melt > mt <- melt( thisdata , id=(c(' ID','Names '))) > mt

130. reshape2 Package cast  : This function converts data from long format to wide format. It starts with melted data and reshapes into long format. It’s just the reverse of  melt  function. It has two functions namely,  dcast  and  acast . dcast returns a data frame as output. acast returns a vector/matrix/array as the output. #cast > mcast <- dcast ( mt , DateofBirth + Subject ~ variable) > mcast

131. readr Package ‘ readr ’ helps in reading various forms of data into R. With 10x faster speed. Here, characters are never converted to factors(so no more stringAsFactors = FALSE). This package can replace the traditional read.csv() and read.table () base R functions . Delimited files withread_delim (), read_csv (), read_tsv (), andread_csv2(). Fixed width files with read_fwf (), and read_table (). Web log files with read_log () > install.packages (' readr ') > library( readr ) > read_csv ('test.csv', col_names = TRUE)

132. readr Package > read_csv ("iris.csv", col_types = list( Sepal.Length = col_double (), Sepal.Width = col_double (), Petal.Length = col_double (), Petal.Width = col_double (), Species = col_factor (c(" setosa ", " versicolor ", " virginica ")) )) > read_csv ("iris.csv", col_types = list( Species = col_factor (c(" setosa ", " versicolor ", " virginica ")) )

133. tidyr Package This package can make your data look ‘tidy’. It has 4 major functions to accomplish this task. Needless to say, if you find yourself stuck in data exploration phase, you can use them anytime (along with dplyr ).  gather() – it ‘gathers’ multiple columns. Then, it converts them into key:value pairs. This function will transform wide from of data to long form. You can use it as in alternative to ‘melt’ in reshape package. spread() – It does reverse of gather. It takes a key:value pair and converts it into separate columns. separate() – It splits a column into multiple columns. unite() – It does reverse of separate. It unites multiple columns into single column

134. tidyr Package #load package > library( tidyr ) #create a dummy data set > names <- c('A','B','C','D','E','A','B') > weight <- c(55,49,76,71,65,44,34) > age <- c(21,20,25,29,33,32,38) > Class <- c('Maths','Science','Social','Physics','Biology','Economics','Accounts')

135. tidyr Package #create data frame > tdata <- data.frame (names, age, weight, Class) > tdata

136. tidyr Package #using gather function > long_t <- tdata %>% gather(Key, Value, weight:Class ) > long_t

137. tidyr Package #create a data set > Humidity <- c(37.79, 42.34, 52.16, 44.57, 43.83, 44.59) > Rain <- c(0.971360441, 1.10969716, 1.064475853, 0.953183435, 0.98878849, 0.939676146) > Time <- c("27/01/2015 15:44","23/02/2015 23:24", "31/03/2015 19:15", "20/01/2015 20:52", "23/02/2015 07:46", "31/01/2015 01:55 ") #build a data frame > d_set <- data.frame (Humidity, Rain, Time) #using separate function we can separate date, month, year > separate_d <- d_set %>% separate(Time, c('Date', ' Month','Year ')) > separate_d

138. tidyr Package # using unite function - reverse of separate > unite_d <- separate_d %>% unite(Time, c(Date, Month, Year), sep = "/") > unite_d

139. tidyr Package #using spread function - reverse of gather > wide_t <- long_t %>% spread(Key, Value) > wide_t

140. Lubridate Package Lubridate package reduces the pain of working of data time variable in R. The inbuilt function of this package offers a nice way to make easy parsing in dates and times . > install.packages (' lubridate ') > library( lubridate ) #current date and time > now () [ 1] "2015-12-11 13:23:48 IST“ #assigning current date and time to variable n_time > n_time <- now()

141. Lubridate Package #using update function > n_update <- update( n_time , year = 2013, month = 10) > n_update [ 1] "2013-10-11 13:24:28 IST" #add days, months, year, seconds > d_time <- now() > d_time + ddays (1) [1] "2015-12-12 13:24:54 IST" > d_time + dweeks (2) [1] "2015-12-12 13:24:54 IST" > d_time + dyears (3) [1] "2018-12-10 13:24:54 IST" > d_time + dhours (2) [1] "2015-12-11 15:24:54 IST" > d_time + dminutes (50) [1] "2015-12-11 14:14:54 IST" > d_time + dseconds (60) [1] "2015-12-11 13:25:54 IST" #extract date,time > n_time$hour <- hour(now()) > n_time$minute <- minute(now()) > n_time$second <- second(now()) > n_time$month <- month(now()) > n_time$year <- year(now())

142. Lubridate Package #check the extracted dates in separate columns > new_data <- data.frame ( n_time$hour , n_time$minute , n_time$second , n_time$month , n_time$year ) > new_data

143. Thank you! PROGRAMMING by Bs Ram