R Programming:For Data Science With Real Exercises

Visit the official site, choose your operating system (Windows, Mac, or Linux), and download the appropriate R installer.

Install and use RStudio to provide a consistent interface for R across Mac, Windows, and Linux, with the console, identical coding, and projects, history, versioning, and graphics exporting features.

Open the R environment via desktop icons or terminal, script in RStudio, run lines with ctrl+enter, and define X <- 1:5 and Y <- c(6,7,8,9,10).

Learn to read data into R from spreadsheets by saving Excel as CSV, using read.csv with headers, handling missing values, and inspecting the resulting data frame with structure.

Export SPSS data to a comma-delimited csv, then import it into R with read commands, and explore packages that read SPSS files directly into a data frame with value labels.

Explore discovering, installing, loading, and updating R packages in RStudio, browse the package repository, view documentation and vignettes, and manage dependencies for data science workflows.

Learn to create bar charts for categorical variables in R by building frequency tables from raw data, plotting with barplot, and customizing orientation and colors for clear visualization.

Learn to create histograms for quantitative variables to screen data entry and understand distribution, using explicit data and variable names in R without attach, and customize colors and labels.

Create box plots for quantitative variables in R to inspect distributions, medians, and potential outliers. Use built-in functions with minimal preprocessing to customize labels and colors for clearer comparisons.

Create and customize frequency tables in R using a social network dataset, sort by frequency in decreasing order, and report proportions with two-decimal formatting.

Explore descriptive statistics for quantitative variables using both built‑in functions and a specialized package, applied to the social_network_gutsiest dataset. Compute the five‑number summary (min, first quartile, median, third quartile, max), along with the mean and handling of missing values, and learn how to summarize all variables at once for quick data insight.

Recode and transform variables to improve interpretability and analysis, using scale for z-scores, log transformations with zero handling, ranking for ordinalization, and if-else recoding to address skewness and outliers.

Explore how to create new variables by combining normal-distribution vectors into composites in R, using addition and multiplication to shape data for analysis.

Explore how to visualize group differences using simple bar charts of means and box plots by region, using a Google dataset to compare regional scores and interpret distributions in R.

Explain how to examine association between two quantitative variables by creating a scatterplot with the Google Correlate data, add a regression line and smoothing line, and interpret a positive trend.

Explore how to build and read scatterplot matrices in R to compare several quantitative variables at once, using correlation coefficients, density estimators, and overlaid normal distribution lines.

Explore three-dimensional data with an interactive scatterplot in R, color the dots red, label axes, and spin the plot to reveal relationships among x, y, and z variables.

Calculate pairwise correlations using the Pearson moment correlation coefficient and build a correlation matrix for a subset of quantitative variables, assessing significance with p-values and confidence intervals.

apply multiple regression in r to predict a single quantitative outcome from several predictors degree, state, facebook, and nba using lm, and interpret model output such as r-squared and f-statistics.

Create a crosstab of gender and site from social network data, compute cell and marginal frequencies and proportions, and apply a chi-squared test to reveal gender differences in site preferences.

Compare means with the t-test to see if states with NBA teams differ from those without on outcome; it reports means per group, a p-value, and a 95% confidence interval.

Explore how to compare multiple groups on a single quantitative outcome using one-way and two-way ANOVA in R, interpret F tests and p-values, and assess interaction effects.

Learn quick R language basics commands to perform essential data science tasks using R. Practice with real exercises to build foundational programming skills.

Read, access, and summarize data in R for data science, using real exercises from the course to reinforce practical data handling.

Master quick installation of the R language on Ubuntu Linux and dive into data science with real exercises.