Experiment-1 Introduction: Understanding Data types; importing/exporting data Aim: The purpose of this experiment is to learn the input data types, various arithmetic operations of dataset and importing/exporting data in R Procedure: Step by step procedure to conduct the required experiment – 1. Input and creation of dataset using R 2. Perform various arithmetic operations on the dataset using R 3. Explore various types of data import using R Introduction to R R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Statistical Features of R Basic Statistics: The most common basic statistics terms are the mean, mode, and median. These are all known as “Measures of Central Tendency.” So using the R language we can measure central tendency very easily. Static graphics: R is rich with facilities for creating and developing interesting static graphics. R contains functionality for many plot types including graphic maps, mosaic plots, biplots, and the list goes on. Probability distributions: Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distribution such as Binomial Distribution, Normal Distribution, Chi-squared Distribution and many more. Data analysis: It provides a large, coherent and integrated collection of tools for data analysis. R Packages: One of the major features of R is it has a wide availability of libraries. R has CRAN(Comprehensive R Archive Network), which is a repository holding more than 100000 packages. Programming in R Since R is much similar to other widely used languages syntactically, it is easier to code and learn in R. Programs can be written in R in any of the widely used IDE like R Studio, Rattle, Tinn-R , etc., After writing the program save the file with the extension .r . To run the program use the following command on the command line: R file_name.r To install R on Windows OS Go to the CRAN website. https://cran.r-project.org/ Click on "Download R for Windows". Click on "install R for the first time" link to download the R executable (.exe) file. Run the R executable file to start installation, and allow the app to make changes to your device. Select the installation language. Install RStudio If you want to work with R in your local machine, installing R is not enough. R does not come with a GUI-based platform. Most users install a separate IDE which allows them to interact with R. It gives them additional functionality such as help, preview, etc. The most popular IDE for the R programming language is RStudio . You can follow these steps to install RStudio on your Windows machine. Visit https://www.rstudio.com/products/rstudio/download/#download to download the free version of RStudio for any platform you want. Once the download is completed, you need to open the executable file to start the installation process. An installation wizard will appear on the screen. Click on the next button. On the next prompt, it will ask you to select the start menu folder for shortcut creation. Click on the install button. Once the installation is completed, click on Finish. You have now successfully installed RStudio in your local machine. R Online Compilers Another way to run R programs is to simply use an online environment. You don't have to go through the hassles of installing R and RStudio in this case. There are lots of competitive R compilers that you can find in a single Google search. The most commonly used online R compilers are: JDoodle online R Editor Paiza.io online R Compiler IdeaOne R Compiler The four RStudio Windows Codes and Results # Generate data 1:10 ## [1] 1 2 3 4 5 6 7 8 9 10 # Assign variable name to the value X=10; X<-10; 10->X; # To combine numeric values into a vector c(1,2,5) ## [1] 1 2 5 #Arithmetic operations of vectors are performed member wise. a = c(1, 3, 5, 7) b = c(2, 4, 6, 8) #addition a+b ## [1] 3 7 11 15 #subtraction a-b ## [1] -1 -1 -1 -1 #constant multiplication 5*a ## [1] 5 15 25 35 #product a*b ## [1] 2 12 30 56 #division a/b ## [1] 0.5000000 0.7500000 0.8333333 0.8750000 # character object is used to represent string values in R X=as.character(5.2) X ## [1] "5.2" #Concatenation of strings paste("Baa", "Baa", "Black", "Sheep") ## [1] "Baa Baa Black Sheep" Installing an R Package R packages provide a powerful mechanism for extending the functionality of R R packages can be obtained from CRAN or other repositories The install.packages() can be used to install packages at the R console Eg. install.packages("moments") This command downloads the moments package from CRAN and installs it on your computer Any packages on which this package depends will also be downloaded and installed Multiple R packages can be installed at once with a single call to install.packages() Eg. install.packages(c("moments", "ggplot2", "devtools")) Loading R Packages Installing a package does not make it immediately available to you in R; it must load the package. The library() function loads packages that have been installed so that you may access the functionality in the package Importing Data Importing data into R is a necessary step that, at times, can become time intensive. To ease this task, the RStudio includes new features to import data from: csv, xls, xlsx, sav, dta, por, sas and stata files. The data import features can be accessed from the environment pane or from the tools menu. The importers are grouped into 3 categories: Text data, Excel data and statistical data. To access this feature, use the "Import Dataset" dropdown from the "Environment" pane: Or through the "File" menu, followed by the "Import Dataset" submenu: Importing data from Text and CSV files Importing "From Text (readr)" files allows you to import CSV files and in general, character delimited files using the readr package. This Text importer provides support to: Import from the file system or a url Change column data types Skip or include-only columns Rename the data set Skip the first N rows Use the header row for column names Trim spaces in names Change the column delimiter Encoding selection Select quote, escape, comment and NA identifiers Or read.csv(file.choose()) can be used through R-console. Importing data from Excel files The Excel importer provides support to: Import from the file system or a url Change column data types Skip columns Rename the data set Select an specific Excel sheet Skip the first N rows Select NA identifiers Conclusion: Installation, input, output, import and various arithmetic operations have been explored in R Experiment-2 Computing Summary Statistics /plotting and visualizing data using Tabulation and Graphical Representations Aim: The purpose of this experiment is to learn the different alignment of data set and various graphical representations in R Procedure: Step by step procedure to conduct the required experiment – 1. Arrangement of data using various R functions 2. Visualize the data set using various R functions Code and Results: #creating a vector empid empid=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) empid ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # creating a vector age age=c(30,37,45,32,50,60,35,32,34,43,32,30,43,50,60) age ## [1] 30 37 45 32 50 60 35 32 34 43 32 30 43 50 60 # creating a vector gender gender=c(0,1,0,1,1,1,0,0,1,0,0,1,1,0,0) gender ## [1] 0 1 0 1 1 1 0 0 1 0 0 1 1 0 0 # creating a vector status status=c(1,1,2,2,1,1,1,2,2,1,2,1,2,1,2) status ## [1] 1 1 2 2 1 1 1 2 2 1 2 1 2 1 2 # reating a data frame (Combining vectors) empinfo=data.frame(empid,age,gender,status) empinfo ## empid age gender status ## 1 1 30 0 1 ## 2 2 37 1 1 ## 3 3 45 0 2 ## 4 4 32 1 2 ## 5 5 50 1 1 ## 6 6 60 1 1 ## 7 7 35 0 1 ## 8 8 32 0 2 ## 9 9 34 1 2 ## 10 10 43 0 1 ## 11 11 32 0 2 ## 12 12 30 1 1 ## 13 13 43 1 2 ## 14 14 50 0 1 ## 15 15 60 0 2 # labeling character to numeric empinfo$gender=factor(empinfo$gender,labels=c("male","female")) empinfo$gender ## [1] male female male female female female male male female male ## [11] male female female male male ## Levels: male female empinfo$status=factor(empinfo$status,labels=c("staff","faculty")) empinfo$status ## [1] staff staff faculty faculty staff staff staff faculty faculty ## [10] staff faculty staff faculty staff faculty ## Levels: staff faculty empinfo ## empid age gender status ## 1 1 30 male staff ## 2 2 37 female staff ## 3 3 45 male faculty ## 4 4 32 female faculty ## 5 5 50 female staff ## 6 6 60 female staff ## 7 7 35 male staff ## 8 8 32 male faculty ## 9 9 34 female faculty ## 10 10 43 male staff ## 11 11 32 male faculty ## 12 12 30 female staff ## 13 13 43 female faculty ## 14 14 50 male staff ## 15 15 60 male faculty # Extract male data male=subset(empinfo,empinfo$gender=="male") male ## empid age gender status ## 1 1 30 male staff ## 3 3 45 male faculty ## 7 7 35 male staff ## 8 8 32 male faculty ## 10 10 43 male staff ## 11 11 32 male faculty ## 14 14 50 male staff ## 15 15 60 male faculty # Extract female data female=subset(empinfo, empinfo$gender=='female') female ## empid age gender status ## 2 2 37 female staff ## 4 4 32 female faculty ## 5 5 50 female staff ## 6 6 60 female staff ## 9 9 34 female faculty ## 12 12 30 female staff ## 13 13 43 female faculty # summary statistics for empinfo data summary(empinfo) ## empid age gender status ## Min. : 1.0 Min. :30.00 male :8 staff :8 ## 1st Qu.: 4.5 1st Qu.:32.00 female:7 faculty:7 ## Median : 8.0 Median :37.00 ## Mean : 8.0 Mean :40.87 ## 3rd Qu.:11.5 3rd Qu.:47.50 ## Max. :15.0 Max. :60.00 # summary statistics of male,female and age summary(male) ## empid age gender status ## Min. : 1.000 Min. :30.00 male :8 staff :4 ## 1st Qu.: 6.000 1st Qu.:32.00 female:0 faculty:4 ## Median : 9.000 Median :39.00 ## Mean : 8.625 Mean :40.88 ## 3rd Qu.:11.750 3rd Qu.:46.25 ## Max. :15.000 Max. :60.00 summary(female) ## empid age gender status ## Min. : 2.000 Min. :30.00 male :0 staff :4 ## 1st Qu.: 4.500 1st Qu.:33.00 female:7 faculty:3 ## Median : 6.000 Median :37.00 ## Mean : 7.286 Mean :40.86 ## 3rd Qu.:10.500 3rd Qu.:46.50 ## Max. :13.000 Max. :60.00 summary(age) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 30.00 32.00 37.00 40.87 47.50 60.00 # creating table (one-way) table1=table(empinfo$gender) table1 ## ## male female ## 8 7 table2=table(empinfo$status) table2 ## ## staff faculty ## 8 7 # creating table (two-way) table3=table(empinfo$gender, empinfo$status) table3 ## ## staff faculty ## male 4 4 ## female 4 3 # Graphical representation (scatterplot) plot(empinfo$age,type="l",main="Age of employees",xlab="empid",ylab="age in years",col="blue") # Graphical representation (Pie chart) pie(table1) # Graphical representation (Bar plot) barplot(table3,beside=T,xlim=c(1,15),ylim=c(0,5),col=c("blue", "red")) legend("topright",legend=rownames(table3),fill=c('blue','red'),bty="n") # Graphical representation (Box plot) boxplot(empinfo$age~empinfo$status,col=c('red','blue')) Conclusion: Different alignment of data set and various graphical representations in R have been explored and executed. Experiment-3 Applying correlation and simple linear regression model to real data set; computing and interpreting the coefficient of determination Aim: To understand the simple correlation and linear regression with computation and interpretation Introduction The most commonly used techniques for investigating the relationship between two quantitative variables are correlation and linear regression. Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation. Correlation: A correlation coefficient is a statistical measure of the degree to which changes to the value of one variable predict change to the value of another. When the fluctuation of one variable reliably predicts a similar fluctuation in another variable, there’s often a tendency to think that means that the change in one causes the change in the other. Regression: Regression analysis is a statistical tool to study the nature and extent of functional relationship between two or more variables and to estimate (or predict) the unknown values of dependent variable from the known values of independent variable. Simple Linear Regression: Simple linear regression model we have the following two regression lines: 1. Regression line of Y on X: This line gives the probable value of Y (Dependent variable) for any given value of X (Independent variable). Regression line of Y on X : Y – Ẏ = byx (X – Ẋ ) OR : Y = a + bX 2. Regression line of X on Y: This line gives the probable value of X (Dependent variable) for any given value of Y (Independent variable). Regression line of X on Y : X – Ẋ = bxy (Y – Ẏ ) OR : X = a + bY In the above two regression lines or regression equations, there are two regression parameters, which are “a” and “b”. Here “a” is unknown constant and “b” which is also denoted as “byx” or “bxy”, is also another unknown constant popularly called as regression coefficient. Hence, these “a” and “b” are two unknown constants (fixed numerical values) which determine the position of the line completely. Procedure: Input/Import the data set Determine the correlation and regression line using R functions Visualize the regression line using R functions Code and Result: # Problem-1 # Import the inbuilt data set "cars" data=cars data ## speed dist ## 1 4 2 ## 2 4 10 ## 3 7 4 ## 4 7 22 ## 5 8 16 ## 6 9 10 ## 7 10 18 ## 8 10 26 ## 9 10 34 ## 10 11 17 ## 11 11 28 ## 12 12 14 ## 13 12 20 ## 14 12 24 ## 15 12 28 ## 16 13 26 ## 17 13 34 ## 18 13 34 ## 19 13 46 ## 20 14 26 ## 21 14 36 ## 22 14 60 ## 23 14 80 ## 24 15 20 ## 25 15 26 ## 26 15 54 ## 27 16 32 ## 28 16 40 ## 29 17 32 ## 30 17 40 ## 31 17 50 ## 32 18 42 ## 33 18 56 ## 34 18 76 ## 35 18 84 ## 36 19 36 ## 37 19 46 ## 38 19 68 ## 39 20 32 ## 40 20 48 ## 41 20 52 ## 42 20 56 ## 43 20 64 ## 44 22 66 ## 45 23 54 ## 46 24 70 ## 47 24 92 ## 48 24 93 ## 49 24 120 ## 50 25 85 # Summary of the data set summary(data) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00 # Variance of "speed" v1=var(data$speed) v1 ## [1] 27.95918 # Variance of "dist" v2=var(data$dist) v2 ## [1] 664.0608 # Covariance between "speed" and "dist" covariance=cov(data$speed,data$dist) covariance ## [1] 109.9469 #or covariance=var(data$speed,data$dist) covariance ## [1] 109.9469 # correlation coefficient using Pearson's formula corr=covariance/(sd(data$speed)*sd(data$dist)) corr ## [1] 0.8068949 # or corr=cor(data$speed,data$dist) corr ## [1] 0.8068949 # Test for association between paired samples cor.test(data$speed,data$dist) ## ## Pearson's product-moment correlation ## ## data: data$speed and data$dist ## t = 9.464, df = 48, p-value = 1.49e-12 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## 0.6816422 0.8862036 ## sample estimates: ## cor ## 0.8068949 cor.test(data$speed,data$dist,method="pearson") ## ## Pearson's product-moment correlation ## ## data: data$speed and data$dist ## t = 9.464, df = 48, p-value = 1.49e-12 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## 0.6816422 0.8862036 ## sample estimates: ## cor ## 0.8068949 cor.test(data$speed,data$dist,method="spearman") ## ## Spearman's rank correlation rho ## ## data: data$speed and data$dist ## S = 3532.8, p-value = 8.825e-14 ## alternative hypothesis: true rho is not equal to 0 ## sample estimates: ## rho ## 0.8303568 # Visualize the samples plot(data$speed,data$dist) # Linear Regression model of "speed" with respect to "dist" regression1=lm(data$speed~data$dist) regression1 ## ## Call: ## lm(formula = data$speed ~ data$dist) ## ## Coefficients: ## (Intercept) data$dist ## 8.2839 0.1656 # Visualize linear regression line abline(regression1) summary(regression1) ## ## Call: ## lm(formula = data$speed ~ data$dist) ## ## Residuals: ## Min 1Q Median 3Q Max ## -7.5293 -2.1550 0.3615 2.4377 6.4179 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 8.28391 0.87438 9.474 1.44e-12 *** ## data$dist 0.16557 0.01749 9.464 1.49e-12 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 3.156 on 48 degrees of freedom ## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438 ## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12 # Linear Regression model of "dist" with respect to "speed" regression2=lm(data$dist~data$speed) regression2 ## ## Call: ## lm(formula = data$dist ~ data$speed) ## ## Coefficients: ## (Intercept) data$speed ## -17.579 3.932 abline(regression2) summary(regression2) ## ## Call: ## lm(formula = data$dist ~ data$speed) ## ## Residuals: ## Min 1Q Median 3Q Max ## -29.069 -9.525 -2.272 9.215 43.201 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -17.5791 6.7584 -2.601 0.0123 * ## data$speed 3.9324 0.4155 9.464 1.49e-12 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 15.38 on 48 degrees of freedom ## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438 ## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12