Master RStudio: Beginner to Advanced

Build a solid foundation in statistics before using RStudio by learning to summarize data, interpret results, and understand measures of central tendency and data distribution.

Explore the foundations of statistics for R, distinguishing descriptive and inferential statistics, and learn how data types, populations, samples, parameters, and histograms guide analysis.

Master data summarization and interpretation by learning mean, median, and mode; dispersion, z scores, probability, sampling methods, and the central limit theorem.

Explore key statistical tools to test ideas and uncover patterns in data, including chi-square, t tests, ANOVA, linear and logistic regression, nonparametric tests, and Bayesian thinking with RStudio.

Explore the RStudio interface and learn the difference between the R language and the RStudio IDE, including the four panes, script editor, console, and running code.

Learn to write and save R scripts in RStudio, create new scripts, run code from the editor and console, and assign values to variables.

Install and load packages in R to unlock powerful data tools, using tidyverse. Learn how install vs load works, create tibble data frames, and view data in the environment.

Discover how to create and manipulate data frames in R from vectors, inspect structure with str and summary, view in the environment tab, and import csv with tidyverse tools.

Learn to clean, reshape, and explore data with dplyr from the tidyverse, using filter, select, mutate, and arrange, and master the pipe workflow for efficient data wrangling in R.

Master RStudio by visualizing data with ggplot2 from the tidyverse, building bar plots, histograms, and scatter plots using grammar of graphics, aesthetics, and conditional colors.

Explore cleaning, reshaping, and data exploration with the dplyr package from the tidyverse, using filter, select, mutate, arrange, and the pipe to streamline workflows.

Visualize data with ggplot2 in the tidyverse, building bar plots, histograms, and scatter plots, mapping aesthetics, adding labels, colors by status, and minimal styling.

Learn to create and knit an R Markdown report in RStudio by combining narrative text, code, and plots into an HTML document. Load tidyverse to summarize data and generate plots.

Learn to import data into R from CSV and Excel using tidyverse and read_csv/read_excel. View, summarize, and inspect structure with the environment pane to explore datasets.

Clean messy data in R with dplyr and tidyverse by standardizing names, trimming spaces, converting empty strings to na, and filtering out missing grades.

Learn to summarize and group data in R with dplyr. Read Excel files, compute averages and counts by subject and class, and derive min, max, and standard deviation.

Visualize data with bar and line charts in R using ggplot2 to compare average scores across classes and subjects. Customize colors and themes to help tell stories.

Learn to save your plots as image files and export data frames as spreadsheets using ggsave and write.csv, while organizing outputs with folders and file paths for easy sharing.

Filter rows and select columns with tidyverse in RStudio to turn large data sets into focused insights, then arrange results for clear interpretation.

Learn to merge datasets by column with left join and stack them with bind_rows using tidyverse, handling missing values (na) and unmatched rows for real-world data from multiple sources.

Explore faceted plots in R with facetwrap to compare groups side by side, by class or subject, using grid layouts for clear visual storytelling.

Transform data between wide and long formats using pivot longer and pivot wider in R with tidyverse, enabling ggplot2 visualization, modeling, and clean reporting.

Group and summarize data in R using tidyverse to compute average scores by subject, count students by class, and identify highest and lowest scores by group.

Visualize group summaries in R with bar charts to compare average scores across subjects and classes, using error bars for spread from minimum to maximum, standard deviation, and confidence intervals.

Master RStudio by learning how to save plots as PNG or PDF and export summaries as CSV, using ggsave and getwd to manage files from the mtcars data.