Mastering Data Visualization with R

Explore basic data types and data structures—vectors, factors, lists, matrices, and data frames—then examine R base datasets and the ggplot2 Diamonds dataset to demonstrate visualization.

Explore built-in R datasets from the base package using data(), like mtcars and chickwts, and learn to inspect data frames with class, dim, names, str, head, tail, and summary.

Install the ggplot2 package, load it in the R session, and explore its datasets such as diamonds and mpg, then list all available ggplot2 datasets with data(packages='ggplot2').

Discover how to plan visualizations in R by selecting the right plot for each data type, from histogram and dot plot to scatter and density plots.

Explore visualizing a single continuous variable in R using base package commands with chickwts chicken weights, through histogram, density plot, and box-and-whisker plots.

Visualize a single discrete variable using a bar chart to show feed frequency in chicken data. Identify gaps to distinguish bar charts from histograms.

Visualize the relationship between two continuous variables with scatter plots, using mpg versus displacement or cylinder size in cars to show output depends on input.

Learn how to plot one continuous and one discrete variable with R, using box and whisker, pie, dot, and strip charts to compare chicken weight across feed types.

Visualize two discrete variables, gender and smoking status, using mosaic plots or stacked bar charts to compare counts and reveal any associations.

Master time series plotting with line charts in R using base commands, illustrate Lake Huron data over years, and introduce histograms, density plots, and box-and-whisker plots.

Identify data types, continuous and discrete, and select suitable plots from twelve options; use base R commands to draw twelve plots, starting with a histogram for a single continuous variable.

Plot the chicken weight distribution with hist(chickwts$weight) in R, then customize with xlab, ylab, main, breaks, and color schemes.

Use a density plot in R as a smooth alternative to histograms by computing density(chickwts$weight) and overlaying it on a histogram to reveal distribution and peaks around 220 and 300.

Explore how box and whisker plots visualize the distribution of a single continuous variable, showing the median, first and third quartiles, and potential outliers using chicken weight.

Plot discrete data with bar plots, using tables to count feed types and cylinder counts in mtcars, and explore single and two discrete variable charts, including stacked bars.

Explore how to create a scatter plot in base R to study the relationship between two continuous variables, using speed and distance, with axis labels and jitter for overlapping points.

Enhance scatter plots in R by adding lines, text, and a trend line using abline, lm, text, and mtext for clear visualization.

visualize a continuous weight by a discrete feed group using box and whisker plots to compare weights and reveal medians, quartiles, and outliers across feeds in the chickwts dataset.

Learn how to create a pie chart in base R using a defects vector and named labels, compare it with a bar plot, and customize colors with the col argument.

Use the base R dot chart to show individual weights by feed, compare it with a box plot, and apply groups to reveal feed effects (casein, sunflowers, meat meal).

Use stripchart() in R to collapse individual chicken weights by feed into a single strip, contrasting with dot charts and other charts for discrete vs. continuous data.

Explore mosaic plots to visualize the relationship between discrete variables, using ggplot2's mpg data set to compare car class and cylinder counts.

Learn to create a stacked bar chart for two discrete variables using mtcars data, comparing cylinder counts by vs type (v vs s) in a single chart.

Explore how to read time series data and reveal trends using line charts in R, using the Lake Huron dataset and the plot command to visualize time-based sequences.

Explore how to install and use the lattice package to create high-quality plots with multiple windows, enabling clearer, segmented data visualization.

Explore lattice plots in R with the lattice package for trellis graphs and multivariate relationships, highlighting better defaults than base plots.

Plot histograms with the lattice package by using histogram(~ weight | feed, data = chickwts) to compare weight distributions across feed types, highlighting clearer, more presentable results than base histograms.

Plot histograms with the lattice package to compare mpg across cylinders, automatic vs manual, and weight in the mtcars dataset, including vertical layouts and equal-count splits.

Explore multivariate relationships by using lattice plots to split histograms by weight and automatic versus manual, revealing how mpg varies across gear and weight categories.

Master density plots in lattice, using ~ weight with chicken weight data, and compare with base R density(), grouping by feed with | and removing points via plot.points = FALSE.

Demonstrates creating lattice density plots of miles per gallon in mtcars by cylinder groups and by transmission, using layout = c(1,3) and color options.

Master the lattice bwplot box-and-whisker plots in R, comparing weight by feed, with notch options and flexible layouts, and explore violin plots via panel.violin.

Learn to create bar charts with the lattice package in R, using barchart on chicken weight by feed and mtcars, including multiple windows by gears and by am.

Explore scatter plots with lattice: normal scatter, scatter matrix, and 3d plots, using xyplot with grid, regression and smooth lines, and grouping by gears and am.

Explore scatter plot matrix in lattice by using splom to compare multiple variables, show selective columns, and add smooth curves for mtcars and the chicken weight dataset.

Create 3D-style scatter visuals in R using lattice's cloud command. Plot miles per gallon on the z-axis against displacement and weight from mtcars, with color by cylinder groups.

Explore dot plots and strip plots in lattice, comparing dotplot and dotchart, using the formula feed ~ weight, and show how box separation works before moving to ggplot2.

Learn ggplot2, the most powerful R visualization tool, through a diamond project that links price to the 4C’s—carat, color, cut, and clarity—across ggplot layers.

Learn to build plots layer by layer with the ggplot2 package, based on the grammar of graphics, using data, aesthetics, geoms, facets, statistics, coordinate system, and themes across seven layers.

Explore building a histogram with ggplot2 by layering data, aesthetics, and geom for the mtcars dataset, adjusting bins or bin width, and saving plots as objects.

Visualize a single continuous variable with ggplot2 geoms like histogram, density, and area. Customize color, fill, alpha, and bin settings guided by the ggplot2 cheat sheet.

Explore the diamond data set through the three ggplot layers: data, aesthetics, and geom. Learn how carat, cut, color, and clarity, along with depth and table, influence pricing and visuals.

Analyze the diamonds dataset to examine how carat size relates to price using histograms, scatter plots, and smooth lines; then create box plots by carat groups and plan budget-based filtering.

Learn to filter the diamonds data set by carat ranges in R (0.29-0.31), create filtered subsets, and visualize price distributions with histograms using ggplot.

Analyze diamond price by cut using scatter and box plots, add a smooth line, map color by cut, and filter a 0.29–0.31 carat dataset to ideal cuts for clearer insights.

Analyze how diamond color influences price using a box plot in R, narrowing the big dataset to D color with ideal cut before evaluating clarity in the next step.

Filter the diamonds data to ideal cut, D color, and VS1 clarity; use a box plot and histogram to identify the cheapest option under 550 dollars, a 0.29 carat diamond.

Explore ggplot's aesthetics layer and how to map carat and price in a diamond scatter plot, using shape for cut and color for clarity, with geom_point and geom_smooth.

Explore mapping aesthetics in box plots and bar charts using a filtered d2931 dataset (carat 0.29–0.31), coloring or filling by clarity, and compare bar positions like dodge, fill, and stack.

Explore the geom layer in ggplot, from 37 geometries to histograms and line plots, and map aesthetics while adjusting binwidth, fill, color, size, and alpha with a cheat sheet.

Master the facets layer in ggplot to create multiple panels by cut and clarity, using facet grid or facet wrap on a scatter plot of carat and price.

Explore the statistics layer in ggplot using geom_smooth to fit lines with lm, gam, or loess, customize the line and error band, and combine with facet grids for comparisons.

Learn how the coord layer in ggplot changes scale and zooms into a plot using coord_cartesian and xlim, while preserving the fitted line.

Master the scaling and coordinate options in ggplot, adjusting ratio to squeeze or expand axes, using coord_flip, and exploring Cartesian to polar transformations and pie chart applications.

Explore polar coordinates in ggplot by transforming a bar chart into a pie chart using coord_polar, theta = y, and fill by clarity.

Explore axis transformation in ggplot by transforming data with log10, scaling axes, and applying coordinate system changes via coord_trans to linearize nonlinear relationships.

Master the themes layer in ggplot to transform graph presentation using built-in themes like theme_light and theme_dark, explore extended options from ggthemes, and add labels with labs.

Explore quick data visualization with qplot in ggplot2, learning how to create histograms, scatter plots, density, box plots, and color or size aesthetics using mtcars data.