Graphs in R - Data Visualization with R Programming Language

Explore two base plots exercises using the quakes data: two histograms of magnitude and depth on one sheet, and a horizontal, notched box plot of price by clarity.

Learn how to create nested bar plots in R using the plotrix package, by specifying a grouping formula and visualizing data grouped by sex and nationality.

Explore three dimensional pie charts in r, customize explode effects, borders, and shading, and compare with fan plots to clarify data using labels and values.

Explore Gantt charts in R to visualize tasks with start and end times using posix time formats, assign high or low priorities, and render colored bars on a monthly x-axis.

Create gap plots in R with the plot tricks library, adjusting y-axis gaps and color groups. Add a table to the plot to decode the color coding.

Apply line clip to add horizontal, vertical, and slope lines and regression fits in plots in R. Create circular text, circles, and zoom.

Learn plotting with plotrix: build a three-dimensional pie chart using revenue values, practice a gap plot with two sequences and a legend, and create a scatterplot from the interlinks dataset.

Demonstrate practical plotting with the Plotrix package in R by solving exercises on pie 3D charts, gap plots, and Stirling's trapping scatterplot, including tables and labeled axes.

Explore ggplot2 as an alternative to base graphics, master the grammar of graphics, and use the quick plot workflow (qplot) with layering and integrated legends.

Explore plotting options in R with qplot using the iris data, including line, scatter, and path plots, colored by species, smoothed with lm, and with confidence intervals removed.

Create histograms with qplot, adjust binwidth and breaks, and generate density plots with facets, using iris data to explore color, size, shape, and alpha aesthetics.

Master ggplot basics with Chichi plot layers, specifying data and mappings, and adding layers with the plus sign; explore histogram, density, and bar, noting layer order and position mapping.

Explore scatter plots with Jiechi plot in ggplot, using a data frame with x, y, and group, and layer aesthetics like color, shape, and size while customizing legends and scales.

Format text in ggplot by adding titles with ggtitle or labs, customize axis labels via theme, and annotate text with x and y coordinates, size, and color gradients.

Learn to create and customize histograms in ggplot for R, using geom_histogram and density layers, adjusting bin width and bandwidth, and comparing iris groups.

Create and customize box plots with ggplot in R, using the iris dataset grouped by species; adjust outliers, flip orientation, add mean points, and distinguish setting from mapping for legends.

Discover the Chichi plot system as an all-in-one plotting framework with layers, integrated legends, and ready-made plots such as histograms, box plots, and scatter plots.

Practice ggplot2 by building a scatterplot of miles per gallon versus horsepower from the base package dataset, with cylinder count, then add a title, annotation, smoother, and a faceted histogram.

Learn ggplot2 techniques for visualizing car data with mpg, horsepower, and cylinder, including custom two-line titles, a scatter plot with arrows and a smoothing line, and faceted histograms.

Create xy plots and bar charts using lattice in R, grouping by factors such as species and survival, and practice with iris and Titanic datasets.

Demonstrate shingles to split iris petal length into four overlapping levels, create panel plots with a color bar, and enhance with panel functions for grids and regression lines.

Create box plots and violin plots in R to compare distributions across iris species, using panel layouts, density options, and histograms. Assess data distribution with q-q plots.

Practice creating lattice plots in R: an x y plot of mpg versus horsepower grouped by cylinder with legend via auto.key, and two-panel mpg histogram split by horsepower with equal.count.