Statistical Computing with R: what's likely to come

Load the “igraph” package in R studio and do the basic SNA as follows with R scripts to knit HTML output: a) Define g as graph object with (1,2,3,4) as its elements b) Plot the g and interpret it carefully c) Define g1 as graph object with (“Sita”, “Ram”, “Rita”, “Gita”, “Gita”, “Sita”, “Sita”, “Gita”, “Anita”, “Rita”, “Ram”, “Sita”) as its elements d) Plot g1 with node color as green, node size as 20, link color as red and link size as 10 and interpret it e) Get degree, closeness and betweenness of g1 and interpret them carefully.

OR

Do as follows in R console and then to R Studio with R script to knit HTML outputs: a) Open R console and then go to Help and Manuals (in PDF) and open “An Introduction to R” file b) Save this file in the working directory and import this pdf file in R studio using “pdftools” package c) Perform pre-processing and create ‘corpus’ using “tm” package d) Find the most frequent terms and create histogram of the most frequent terms e) Create word cloud of the corpus, color it using rainbow or R Color Brewer package f) Perform topic modelling and interpret the result carefully

Explain social network analysis and describe its use in a real-life situation with: a) Nodes b) Links c) Attributes

Explain the followings with examples for R:

a) Reference range based on mean b) Reference range based on median c) Outliers and extreme values

Do as follows in R Studio and do as follows with R script to knit PDF output: a) Open R and then go to Help and "Manuals in PDF" and open "An Introduction to R" file b) Import this pdf file in R studio using "pdftools" package c) Perform pre-processing and create 'corpus' afterwards using "tm" package d) Find the most frequent terms and create histogram of the most frequent terms

OR

Do the following in R Studio using "mtcars" dataset with R script to knit PDF output: a) Get the bar plot of the "mpg" variable using ggplot2 package and interpret it carefully b) Get the boxplot of "mpg" variable using ggplot2 package and interpret it carefully c) Get scatterplot of "mpg" and "wt" variable using ggplot2 package and interpret it carefully d) Get appropriate correlation coefficient for "mpg" and "wt" and interpret it carefully

Do the following in R Studio using ggplot2 and dplyr packages and knit output as PDF file: a) Create a dataset with following variables: age (20-59 years), height (110 – 190 centimeters), weight (40-90 kg) with random 150 cases of each variable. Your roll number must be used to set the random seed. b) Compute body mass index (BMI) variable as: BMI = [(weight in kg) / (height in meter squared)] c) Create body mass index categories: <18, 18-24, 25-30, 30+ and label them as "underweight", "normal", "overweight" and "obese" respectively using dplyr package d) Show the percentage distribution of labelled BMI variable with pie chart using ggplot2 package

Load the "igraph" package in R studio and do the basic SNA as follows with R script and HTML output:

a) Define "g1" as graph object with ("R", "S", "S", "T", "T", "R", "R", "T", "U", "S") as its elements b) Plot "g1" with node color as green, node size as 30, link color as red and link size as 5 and interpret it c) Get degree, closeness and betweenness of g1 and interpret them carefully d) Get hub and communities of this data and interpret them carefully

OR

Do the following in R Studio using "airquality" dataset with R script:

a) Replace missing values of "Ozone" variable with median of this variable as corrected Ozone b) Get the histogram of the corrected Ozone variable using base R plot and interpret it carefully c) Get the boxplot of Wind variable using based R plot and interpret it carefully d) Get the Wind variable outliers using median and interquartile range and compare them with boxplot outlier values with justification

Do as follows in R Studio and do as follows with R script and HTML outputs:

a) Open R and go to Help and Manuals in PDF and open "An Introduction to R" file b) Import this pdf file in R using "pdftools" package c) Perform pre-processing and create 'corpus' afterwards d) Find the most frequent terms, create its bar diagram and interpret carefully

OR

Do the following in R Studio using "airquality" dataset with R script:

a) Get the boxplot of Temp variable using ggplot2 package and interpret it carefully b) Create class intervals of Temp variable using dplyr package and show it as frequency distribution c) Get pie chart of Temp variable class intervals using ggplot2 package and interpret it carefully d) Get scatter plot of corrected Temp and Wind variables using ggplot2 package and interpret it carefully

Use the “mtcars” dataset of tidiverse package and do as follows with R script to knit HTML output: a) Plot histogram of mpg variable and interpret it carefully b) Refine the histogram by filling the bars with “blue” color and changing number of bins to 10 c) Add a vertical abline at mean of the mpg variable d) Plot Q-Q plot of mpg variable, add normal Q-Q line of red color on it and interpret it carefully e) Plot density plot of mpg variable without the border, fill it with yellow color and interpret it

OR

Use the “air quality” dataset of R to do following using base R to knit HTML output with R script: a) Create line plot of “Temp” with “Day” as the row index and interpret it carefully b) Create bar plot of “Temp” variable after defining class intervals systematically c) Create histogram of “Temp” variable and compare it with the bar plot of “Temp” variable d) Plot Normal Q-Q plot of “Temp” variable and interpret it carefully e) Create a scatter plot of “Temp” and “Wind” variables and interpret it carefully

Use the "aq" datafile in R studio and do as follows with R script to produce HTML outputs: a) Create bar plot of "Month" variable and interpret it carefully b) Create histogram of "Temp" variable and interpret it carefully c) Create line plot of "Temp" and "Day" variables and interpret it carefully d) Create scatterplot of "Ozone" and "Solar.R" variables and interpret it carefully

OR

Use the "mpg" dataset of tidyverse package and do as follows with R script to knit HTML output: a) Plot histogram of hwy variable and interpret it carefully b) Add a vertical abline at mean and $\pm 2$ standard deviation of hwy variable and interpret it carefully c) Locate mode of hwy variable graphically and interpret it carefully d) Locate median of hwy variable graphical and interpret it carefully

Do the following in R Studio using ggplot2 package with R script to knit PDF output:

a) Create a dataset with following variables: age (10-99 years), sex (male/female), educational levels (No education/Primary/Secondary/Beyond secondary), socio-economic status (Low, Middle, High) and body mass index (14 – 38) with random 200 cases of each variable. Your roll number must be used to set the random seed.

b) Create scatter plot of age and body mass index variables using ggplot2 package and interpret the result carefully.

c) Create classes of body mass index variable as: <18, 18-24, 25-30, 30+ and show it as pie chart using ggplot2 package and interpret it carefully

d) Create histogram of age variable with bin size of 15 using the ggplot2 package and interpret it carefully

Do the following in R Studio with R script:

a) Create a dataset with following variables: age (18-99 years), sex (male/female), educational levels (No education/Primary/Secondary/Beyond secondary), socio-economic status (Low, Middle, High) and body mass index (14 – 38) with 150 random cases of each variable. Your exam roll number must be used to set the random seed. b) Show a sub-divided bar diagram of body mass index variable by sex and socio-economic variables separately with interpretations. c) Show multiple bar diagram of age variable with sex and educational level variables and interpret it carefully. d) Show boxplots of age and body mass index variable separately and interpret the results carefully. e) Create histogram of age and body mass index variable separately and interpret the results carefully.

Use the cleaned "AQ" file in R studio and do as follows with R Scripts and HTML outputs:

a) Get reference range of "Ozone" variable using mean and standard deviation b) Plot histogram of "Ozone" variable and show the outliers of "Ozone" with reference range limits c) Get reference range of "Ozone" variable using median and inter-quartile range d) Plot boxplot of "Ozone" variable and show the outliers of "Ozone" with reference range limits e) Write a summary of the results obtained from the histogram and boxplot

OR

Do as follows in R Studio and do as follows with R script and HTML outputs:

a) Open R and then go to Help and Manuals if PDF and open "An Introduction to R" file b) Import this pdf file in R using "pdftools" package c) Perform pre-processing and create 'corpus' afterwards d) Find the most frequent terms and create histogram of the most frequent e) Create word cloud of the corpus, color it using rainbow or R Color Brewer package f) Perform topic modelling and interpret the result carefully

Use the cleaned "AQ" object to do following in R Studio with R script to knit HTML output:

a) Create line plot of "Temp" with "Day" as the row index and interpret it carefully b) Create bar plot of "Temp" variable after defining class intervals systematically c) Create histogram of "Temp" variable and compare it with the bar plot of "Temp" variable d) Plot Normal Q-Q plot of "Temp" variable and interpret it carefully e) Create a scatter plot of "Temp" and "Wind" variables and interpret it carefully

Do the following in R Studio with tidy verse package using R Script to knit HTML output:

a) Define a tibble having country, year, cases and population variables with 10 random data each b) Transform this tibble to long format and interpret it carefully in terms of tidy data format c) Transform the cases variable as log of cases (LnCase) and population variable as log of population (LnPop) d) Create scatter plots of 1. Cases and population, 2. LnCase and population, 3. Cases and LnPop and 4. LnCase and LnPop in a single graph window and interpret it carefully

OR

Use the cleaned "AQ" file in R studio and do as follows with R Scripts and HTML outputs:

a) Get reference range of "Temp" variable using mean and standard deviation b) Plot histogram of "Temp" variable and show the outliers of "Temp" with reference range limits c) Get reference range of "Temp" variable using median and inter-quartile range d) Plot box plot of "Temp" variable and show the outliers of "Temp" with reference range limits e) Which measure of central tendency and dispersion should be used for this variable? Why?

Do the following in R Studio using ggplot2 package with R script:

(a) Create a dataset with following variables: age (18-99 years), sex (male/female), educational levels (No education/Primary/Secondary/Beyond secondary), socio-economic status (Low, Middle, High) and body mass index (14 – 38) with random 100 cases of each variable. Your roll number must be used to set the random seed. (b) Create a line chart of age variable using ggplot2 package and interpret the result carefully (c) Create scatter plot of age and body mass index variables using ggplot2 package and interpret the result carefully. (d) Create classes of body mass index variable as: <18, 18-24, 25-30, 30+ and show it as pie chart using ggplot2 package and interpret it carefully (e) Create classes of age variable as <15, 15-59 and 60+ and show it as bar diagram using the ggplot2 package and interpret it carefully

Do the following in R Studio with R script so that it can be knitted as PDF:

a) Prepare a column vector of miles per gallon (mpg) variable with random range between 10 to 50 of 500 values, do not forget to use your exam roll number as random seed to replicate the result a) Plot histogram of this "mpg" variable and interpret it carefully b) Refine the histogram by filling the bars with "blue" color and changing number of bins to 8 c) Add a vertical abline at the arithmetic mean of the mpg variable d) Plot Q-Q plot of mpg variable, add normal Q-Q line of red color on it and interpret it carefully e) Plot density plot of mpg variable without the border, fill it with yellow color and interpret it

OR

Use the "ggplot2" package and do as follow in R studio:

a) Define first layer of the ggplot object with diamond data, carat as x-axis and price as y-axis b) Add layer with geometric aesthetic as "point", statistics and position as "identity" c) Add layers with scale of y and x variables as continuous d) Add layer with coordinate system as Cartesian e) Add layer with appropriate title and interpret the resulting graph carefully

Import the "pollution.csv" file into R studio and do as follows with R script:

a) Check the structure of the data and explain class of each variable b) Change the attributes of "particulate matter", "date time" and "value" variables c) Get the summary of all the variables and replace the outliers as missing value d) Get summary statistics of "value" variables by "particulate matter" variable categories e) Write a summary of the results obtained in the earlier steps with interpretation and conclusion

Use the "pollution.csv" file imported and cleaned in R studio and do as follows with R script:

a) Create bar plot of "particulate matter" variable b) Create histogram of "value" variable c) Create line plot of "date time" and "value" variables d) Create histogram of "value" variable by particulate matter categories e) Write a summary of the results obtained in the earlier steps with interpretation and conclusion

Load the "term Doc Matrix.R data" file into R studio and do as follows with R script:

a) Define the term document matrix data object as matrix and store it as "m" object b) Define the frequencies of the terms using "row Sums" function and get the term frequencies c) Create a histogram of the term frequencies using ggplot2 package d) Create a histogram of the terms with 10 or more frequencies using ggplot2 package e) Create word cloud of term frequencies using word cloud package and interpret it carefully

OR

Load the "rdm Tweets.rdata" file in R studio and do as follows with "tm" and "tweetR" packages:

a) Convert twitter list as data frame and assign it as "df" object b) Create corpus using the "text" column of the data frame c) Perform pre-processing to clean the corpus for text mining d) Create term document matrix using the cleaned corpus e) Find the most frequent terms using the term document matrix f) Find the co-occurrence of the term "r" with filter of 0.1 and above.

Do the followings with R script in R Studio:

a) Define a column vector X with numbers between 1 and 30 b) Define another column vector Y with cubes of X c) Combine the two column vectors in a new data frame called DF d) Get plot X and Y variables and decide which type of relationship is seen e) Get the appropriate correlation coefficient for this plot and interpret it carefully

Use the "mtcars" dataset of R and do as follows:

a) Plot histogram of mpg variable and interpret it carefully b) Refine the histogram by filling the bars with "blue" color and changing number of bins to 10 c) Add a vertical abline at mean of the mpg variable d) Plot Q-Q plot of mpg variable, add normal Q-Q line of red color on it and interpret it carefully e) Plot density plot of mpg variable without the border, fill it with yellow color and interpret it

OR

Use the "ggplot2" package and do as follow in R studio:

a) Define first layer with diamond data, carat as x-axis and price as y-axis b) Add layer with geometric aesthetic as "point", statistics and position as "identity" c) Add layers with scale of y and x variables as continuous d) Add layer with coordinate system as Cartesian e) Add layer with appropriate title and interpret the resulting graph carefully

Explain the following concept with examples: a) Grammar of graphics – Wilkinson's approach b) Five number summary

Describe the following concepts with examples: a) Wilkinson's approach of grammar of graphics b) ggplot2 approach of grammar of graphics

Explain followings concepts with clear examples: a) Spearman's rank correlation b) Mann-Whitney U test

Explain the following concepts with focus on R software:

a) Boxplot with five number summaries with example b) Boxplot with outliers with example

Describe data visualization with focus on:

a) Concept of grammar of graphics with Wilkinson's approach

b) Layers in grammar of graphics with ggplot package's approach

c) Statistical transformations in grammar of graphics

Explain the following in R with example:

a) Nodes and edges b) Diameter c) Edge density

Explain the following concepts with focus on R software:

a) Test of normality b) Parametric tests c) Residual analysis

Explain the following concept with examples focusing on R software:

a) Measures of central tendency b) Measures of dispersion c) Measures of relative position

Explain the following concepts with examples focusing on R software:

a) Correlation b) Parametric tests c) Non-parametric tests