Hands-On R Programming: Build Real World Data Projects

Explore the basics of conditional statements in R by writing if-else logic, testing x for positive, negative, or zero, and handling even/odd cases with print outputs.

Explore for and while loops in R, learn to print iterations, manage a loop counter, iterate over vectors, and control loops with a break statement.

Explore creating and using functions in R, including defining simple functions, passing parameters, default arguments, and returning multiple values such as mean and sum of vectors.

Explore how to pass arguments to functions in R and manage scope with local and global variables, using a multiply example that returns 20 and demonstrates access to globals.

Explore data manipulation with dplyr by filtering players who scored over 100 runs, selecting columns, mutating new variables, and arranging results in a real-world cricket data frame.

Tidy a 2d messy data set in R using tidyr pivot_longer and pivot_wider, reshaping between long and wide formats for easier analysis.

Create data frames and perform inner join, left join, and full join to merge employee and salary data using dplyr.

Master R programming by creating scatter plots, bar plots, line plots, and histograms to visualize a sample dataset and compare time, score, and category values.

Learn to customize plot aesthetics in R by adjusting colors, labels, and themes for bar and scatter plots with ggplot2, using sample data and color by category or group.

Create interactive plots in r using a popular package, including bar and line plots with hover info, using a sample data set of categories, values, and scores.

Learn to compute descriptive statistics in R by calculating mean, median, standard deviation, and quartiles on a real data set, with practical coding demonstrations.

Master hypothesis testing in R programming by performing one-sample and two-sample t tests and chi-squared tests to compare observed and expected frequencies on real data.

Explore regression analysis in R by building sample data and applying simple linear and multiple regression to model relationships, interpret p-values and r-squared, and make predictions on new data.