Comprehensive Graphics Visualizations with R

Explore base graphics in R Studio by loading a dataset of two thousand observations, plotting two variables with Cartesian and formula notation, and compare data access methods.

Compare base graphics with ggplot using quick plot, color, and labels; add a regression line and plot two datasets on one graph, noting syntax and capability differences.

Scale axes for two datasets by setting x and y ranges, plot red and blue points with regression lines, and place an interactive legend using PCH symbols.

Rename the x and y columns in D and D-1, bind the rows into D2, and plot the data with quick plot, a linear model, and confidence intervals.

Explore plotting characters in R using nested loops, pch values, and character expansion to label 0–25. Read datasets with header=true and examine a sleep deprivation experiment and reaction time.

Create unique subject numbers, merge datasets, and visualize patterns with color by subject and plotting characters in R to reveal individual performance trends.

Explore base graphics capabilities in R, compare them to the ggplot2 grammar of graphics, and preview lattice graphics or trellised plots, while practicing scatterplots with grass dataset and text annotations.

This lecture compares base graphics to ggplot in R, showing how to build and annotate base plots, color points by a third variable, and manage legends.

Learn to add text to scatter plots and maps in R by converting factors to characters, preparing location data, and plotting town names at correct coordinates.

Learn to enrich plots by merging place data, labeling map points with text, and comparing base and quick plot approaches, including scaling coordinates and using curves.

Learn to add shapes to plots in R by interactively drawing rectangles and arrows with the locator function, including suppressing axes and creating user defined functions for customization.

Define and call an interactive drawing function to add arrows and polygons to plots. Explore default and named arguments, lazy evaluation, and vectors, lists, and data frames.

Interactively add shapes to plots in R by defining user created functions to draw polygons and labels, color areas lavender and red, and illustrate a normal distribution with base graphics.

Explore fitting smooth nonlinear curves to a fishery dataset in R using nonlinear least squares, plotting predictions, comparing to a theoretical model, and adding a legend.

Explore adding nonlinear fits to plots by building and combining predicted and theoretical data frames, then visualize with ggplot, color lines and label axes; apply multi-panel plotting on jawbone data.

Explore four nonlinear fitting methods for plots: loess, lowess, generalized additive models, and polynomial models, by estimating and labeling each line against age-related data.

Predict y from deer age using lm and polynomial terms, add lines to plots for linear, polynomial, and cubic fits, and visually compare to select the best model.

Learn to add nonlinear fits to plots in R using G-G plot and Q plot, visualize 95 percent confidence bounds, and adjust window span and data order for accurate lines.

Convert the month variable to a factor and use box plots to compare temperature distributions across months. Identify medians, quartiles, and outliers to understand non-parametric summaries of the data.

Explore box plots for biomass across clipping treatments, using notches to gauge median differences and skirts overlap, then apply tapply for means and bar plots with confidence intervals.

Analyze biomass by clipping treatment using notched box plots to indicate likely differences, then compare means with t apply and bar plots showing 95% confidence intervals across five clipping levels.

Compute confidence intervals for eight factor levels using box plots with notches; compare means via analysis of variance and tukey hsd; end with histograms of a poison distribution.

Explore histograms for Poisson-distributed data, using breaks to center bars on whole numbers and compare base and quick plot visuals. Learn to interpret index plots and spot outliers.

Create time series plots in base graphics to compare total, male, and female deaths by year, and illustrate pie chart labeling and interpretability.

Analyze strip charts of orchard spray data under a Latin square design to compare treatments and row-column positions, and use pairs plots to study radiation, temperature, wind, and ozone.

Day 3 covers g-g plot equivalents and lattice graphics, presenting a six-panel conditioning plot with shingles for ozone data, exploring how wind and temperature affect ozone levels.

Explore shingle plots and copplots to analyze the ozone–wind relationship across temperature ranges, and learn to craft interaction plots for a split yield design with irrigation and fertilizer densities.

Analyze interactions using interaction plots for fertilizer nitrogen and potassium across irrigation levels. Explore trellis (lattice) xy plots conditioned by gender and box-and-whisker plots for factorial designs analysis of variance.

Master box and whiskers plots and design plots in R, plotting growth rate as a function of water, Daphnia, and detergent across trellised panels to reveal interactions.

Explore the effects sizes graph in the trellised suite to analyze anova results with factors like Daphnia, water, and detergent, visualizing interactions, confidence intervals, and growth rates.

Learn to use bubble plots and sunflower plots to display two-way data with a third variable, encoding FR through symbol size and shape across PH and hay.

Explore trellis graphics with histogram in the lattice package, conditioning histograms by factor variables using the GCSE chemistry scores dataset to reveal distribution shapes.

Explore density plots in R using lattice graphics, grouping by score and adding autokey legends to display separate density curves for each group.

Explore a technical overview of lattice graphics in R, loading oats yield data, and constructing an xyplot conditioned on variety and block, with dimension, subscripting, and panel updates.

Update plots with the update function, adjust aspect ratio and layout for readability, add breaks with between, and use autokey with groups by year in the barley lattice plot.

Create and reuse lattice plot components with a key to fix a coding error, then visualize univariate distributions with density plots and kernels, plus flow cytometry data using lattice extra.

Explore density plots and histograms of the log of fse h conditioned on days, with a zero reference line and 50-bin histograms, plus q-q plots by score and gender.

Apply a box Cox transformation to improve normality and then use q-q plots and ECDF to compare transformed scores by gender.

Compare distributions with q-q plots against theoretical and empirical distributions, revealing higher mean and lower variance for females in test scores, with box and whiskers and a box-cox transform.

Explore violin plots and strip plots to visualize relative frequencies, depths, and magnitudes in earthquakes, using panel=violin, jitter, color, and alpha to reveal patterns.

Explore multiway tables, dot plots, and bar charts in R to analyze Virginia 1940 death rates by age group and urban/rural sex categories.

Explore how to create bar charts from a two-way table of counts to a proportion table for postdoc data, adjusting label positions and abbreviating panel names for readability.

Advance dot plots by defining a median-based index condition and using pre-painted panel functions, then visualize GCSE scores by gender with crosstabs and x y plots.

Investigate titanic survival patterns using latus and ggplot by building multivariate bar charts of class, sex, and age, with stacked panels and scaled comparisons.

Create and update a Titanic bar chart, apply a panel function to add a grid, and visualize Fiji earthquakes with XY plots, depth cuts, ISO aspect, and depth-based strip names.

Explains creating advanced xy plots with grouping by color, automatic quantile breaks, and labeled axes, using depth color scales and reverse ordering to reveal quakes.

Explore advanced xy plots of earthquake data in R using lattice, shading by depth, and creating shingles to split magnitudes into four overlapping levels, with panel functions and grids.

Explore elaborated xy plots of earthquake data in R, using depth breaks, color scales, legends, axis labeling, log scales, and smoothing with loess and local regression.

Explore elaborating xy plot with shingles to visualize overlapping levels, fit curved and linear regression lines, and compare earthquake and SeaTac weather datasets using lattice in R.

Update xy plots with panel functions and add right-hand scales for extra variables. Explore descriptive scatterplot matrices with grouped data and labeled axes from crime and car datasets.

Learn ggplot2 and the grammar of graphics, employing layered geoms, quick plots, range layouts, and color by cut to explore diamonds data with log transformations.

Learn to use alpha to control transparency in ggplot visuals. Explore smooth curves and generalized additive models with cubic splines to estimate trends and confidence intervals.

Explore ggplot smooths with spline models, compare linear fits and five degrees of freedom splines, and visualize distributions using jitter, box plots, histograms, and density plots.

vary bin width in ggplot histograms to reveal distribution shapes, map density lines to color, and build stacked histograms and bar charts with weights.

Explore time series and scatter visualizations in ggplot2 using the economics and mpg datasets, plotting unemployment and the unemployment-to-population ratio over date with year-based color and varied geoms.

Learn to create ggplot graphs with mileage by displacement, adding a smooth lm regression line by cylinder group, faceting by year, and customizing legends and coordinates.