It is a scripting language, which helps statisticians and data scientists to develop code and test individual statistical models for efficient data analysis.R comes with very good data visualization features supporting potting and graphs using graphical packages like ggplot2.It has a wide range of statistical library support like T-test, linear regression, logistic regression, time-series data analysis.R is a reliable programming language for Statistical Analysis.Importance of Statistical Analysis with R language This T-test shows the Alternate hypothesis is true in the hypothesis testing process.It also shows the mean value of 31.52205. In this T-test, the P-value is
And the percentage value is the confidence interval. The p-value is the probability value significant to the null hypothesis.T-test shows the statistical inferences and the confidence interval.test(sampleDat$G) is the syntax we will apply on the R Studio console.We will use G variable data of data frame sample data for t-test.t,test() function used in R to process the t-test.Next, we will discuss the t-test which is the statistical hypothesis testing process using R.Apart from the Scatter plot, there are several other functions and plots like histograms, line plots, and boxplots are being used for Bivariate data analysis.This scatter plot represents the graph for bivariate analysis.Plot(sampleData$G~sampleData$W,col='blue') In this example, we are creating a scatter diagram or scatter plot for G and W variable using.Symbol ‘~’ is used for bivariate analysis in R.It helps to identify the correlation and patterns between the two variables.This statistical analysis is a comparison between two variables present in that data set.Next, we will discuss bivariate statistical analysis with R.Also, we n plat this frequency table using plot function in R using >.This function is very useful while doing statistical categorical variables. The frequency table shows the value 20 has a maximum frequency in the data.We can use Table function to create a frequency table which shows the number of frequency of the data in the variable using table(sampleData$W).default() command to view overall data distribution The data can be plotted as a histogram using hist.For example, to view the statistical summary of W variable, we will use.A particular variable can be assessed from the data frame using $ symbol.R data frames are an efficient data store reference,.Next, we will discuss univariate data analysis.The summary of basketball data set shows the Variable G has min value 24.00, Max values 40.00, the median value is 31.00 and the mean value is 31.52.Next, we will use a summary() command to do basic statistical analysis which will show the Min, Max, Mean, median, and the inter quartile range information about the data set for each quantitative variable.To check the dataset imported correctly and review the few top lines of data use head() command in R.Next will import the data set using read.csv() command and assign to a data frame called SampleData as the following the syntax.Following is a screenshot of R Studio with setwd() and getwd() functions.getwd() to check the present working directory.setwd() is used in R to set the working directory.The first step is to set the working directory which will be used as the preferred location to read and write datasets.This section will do hands-on using R studio for college basketball dataset.The practical approach of statistical analysis with R The data file we will use is ‘cbb.csv’ which is college basketball dataset,.We will be downloading an open-source data set from for this demonstration.csv file to R studio for Statistical analysis. Once the Environment is ready, the next step is to import the data set to R workspace.Once the R studio is installed, it can be directly used to develop R script which will work on the installed version of the R language.R Studio can be downloaded and installed from.R Studio provides GUI support along with some enterprise-ready features like Syntax hiliting, debugging, packages, and workspace management.
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