Data Visualization with R and ggplot2

This section builds the foundations for constructing plots with ggplot2, covering grammar of graphics, data layer, aesthetic mapping, geometries, facets, statistics, coordinates, scales, themes, and exporting.

Master the grammar of graphics and the ggplot2 library, building plots with seven layers: data, aesthetics, geometries, facets, statistics, coordinates, and theme.

Keep raw data for plotting, avoid aggregation, and organize data as a data frame with variables in columns, observations in rows, and values in cells, using ggplot datasets like diamonds.

Create a data frame in r studio, install and load ggplot2 and deep layer, and perform data wrangling with mutate and case_when for a future ggplot plot.

Learn how aesthetics map data variables to graph properties in ggplot2, mapping x and y, and use color, size, shape, fill, and transparency while avoiding visual noise.

Explore geoms in ggplot2, learn to layer geometries with the plus operator, and create scatter, histogram, bar, and box visuals with control over size and transparency.

Explore facets in ggplot2, splitting a plot into subplots by one or two categorical variables using facet_grid or facet_wrap with the same coordinate system and optional free scales.

Explore how statistical layers in ggplot2 transform data, add summaries, and adjust positions, enabling regression lines, histograms, and other stat options across facets.

Master coordinates and scales in data visualization with R ggplot2, learning cartesian coordinates, polar and map coordinate systems, and how continuous and discrete scales translate data to visuals.

Explore how to tailor non-data elements in ggplot2 with the theme function and theme sets, override defaults, and adjust axis text, titles, margins, and facet strips for polished visuals.

Assign the plot to an object, create a figure folder, and export it to disk with a save command specifying the path, size in centimeters, and dpi.

Learn the grammar of graphics by building and exporting plots, refining data mapping, geometries, scales, and themes. Apply color by transmission and adapt axes for discrete and continuous variables.

Follow an R ggplot2 walkthrough to build, modify, and save four plots from a dataset, demonstrating static versus mapped colors, jittered positions, and theme adjustments.

Explore two data-wrangling functions in widely used libraries, perform data preparation, manipulate variables and rows, and master piping, pivoting, long-to-wide and wide-to-long transformations, and separating or uniting columns.

Prepare tidy data for visualization by mastering data wrangling and cleaning with essential functions from deep layer and tyler, including long from wide and wide to long transformations.

Learn to manipulate variables in a flight data frame using select, mutate, and rename; filter columns with contains and begins with, and create time-based variables in hours with rounding.

Learn to manipulate rows in a data frame with filter, distinct, sample, and range sort, extracting subsets, removing duplicates, and ordering by column values.

Learn to summarize and group data in R using summarize and group_by, performing mean, standard deviation, and median per group to create concise summary tables.

Learn to use the forward pipe operator to chain data frame operations—filter, select, group_by, summarize, and arrange—demonstrating American Airlines flight data.

Explore pivoting between wide and long data formats using tidyr's pivot_longer and pivot_wider, and reshape data for modeling and visualization.

Learn to separate and unite columns in R with separate and unite, splitting a column into year, month, and day, or merging three columns into a date with a dash.

Create integrated data transformations and visualizations in R with ggplot2, showing total distance and flights per carrier, highlighting top five carriers with histograms and monthly flight trends.

explore data transformation in R to recode flight cancellations, summarize by carrier and date, and create bar and area plots of flights by cancellation status.

Walk through an assignment in R using ggplot2, loading libraries, creating tables, transforming data, and building plots to visualize flight data.

Explore exploratory data analysis with the diamonds dataset, learning to visualize single and multiple variables using histograms, frequency polygons, area plots, and bar plots for continuous and discrete data.

Exploratory data analysis uses numerical and graphical methods, including visualization, to learn data and generate questions. Look for variation within variables and correlations between variables to guide an iterative understanding.

Explore the diamonds dataset in R using ggplot2, inspect variables such as carat, price, cut, color, clarity, and dimensions, and create smaller samples to visualize distributions and outliers.

Explore dot plots of diamond carat using ggplot2 in R, adjusting bin width, dot size, and stack direction to reveal the distribution and outliers, and export the final plot.

Learn to create and customize a price histogram for diamonds with ggplot2, adjust bin width and breaks, and compare it to a density plot using color, fill, and line options.

Explore frequency polygons for diamond length, width, and depth distributions using ggplot2, addressing bandwidth, density, and missing values, with subplots via plot grid and the blood package.

Explore area plots for continuous variables by visualizing diamond depth with area and density options. Adjust bins and kernels to compare counts and density estimates, and merge subplots for export.

Explore discrete variables with bar plots in ggplot2 by counting frequencies for diamond cut, color, and clarity. Build a multi-variable bar plot from the diamonds dataset using count statistics.

Perform exploratory data analysis with diamonds and cards datasets using ggplot2, create histograms, dot plots, and area plots, reshape from wide to long, and complete the assignment.

Clean and visualize fuel economy data in R: convert character columns to factors, summarize variables, and build ggplot2 histograms and a long-form area plot of city versus highway mpg.

Walk through an assignment on visualizing engine displacement frequencies with ggplot2, exporting final figures, and assembling a multi-plot grid of manufacturer, cylinders, transmission, and fuel type.

Explore combining two variables in data visualization with R and ggplot2, using scatter plots, smoothing lines, box plots, and comparisons of discrete variables to understand diamond price relationships before modeling.

Explore scatter plots in R with ggplot2 by mapping carat to x and price to y, and use transparency, size, color, and shape to reveal nonlinear relationships and outliers.

Learn to fit a linear model predicting price from carat in R and draw a regression line with ggplot2, using log-transform axes and smoothing options.

Explore how a rug plot adds density cues to a two-variable scatter plot, with lines on chosen sides and adjustable length to reveal data patterns.

Explore visualizing continuous bivariate data in R with ggplot2 using heat maps, two-dimensional densities, and hex bin maps; map price vs length in the diamonds data, adjusting bins and colors.

Explains how a boxplot compares one discrete and one continuous variable, visualizing median, quartiles, IQR, and outliers across diamond cuts with prices.

Learn how to create violin plots in ggplot2 to compare price distributions across diamond colors and clarities, and adjust scale, bandwidth, and add quantiles for detailed density insights.

learn to compare two discrete variables using bar plots with color or fill to show subgroup counts, explore stack, dodge, and normalized 'fill' positions, and distinguish absolute from relative counts.

Explore matrix plots that compare all pairs of variables in a data frame, with diagonal distributions and visualizations such as histograms, density plots, and the correlation coefficient.

Explore visualizing two variables in R with ggplot2, using scatter plots with smoothing lines, heat maps, hexbin, and matrix plots, plus an assignment with cars and diamonds data.

Explore data visualization with r and ggplot2 in walkthrough, creating scatter plots with a linear model smoothing line and correlation, plus box, violin, and heat map visuals saved to disk.

Explore data visualization with R and ggplot2 through exercises on scatter plots, bar plots, and grid layouts using car data. Include cylinders, drive time, fuel type, transmission, and engine displacement.

Explore multi-variable visualizations by mapping color, size, and shape beyond two variables, using continuous and discrete color scales, wraps, and faceted grids, plus a benchmarked diamond price prediction model.

Map color to a continuous volume variable in a scatter plot, using R. Explore gradient and diverging color scales to reveal patterns in the data.

Apply a custom color palette to a discrete variable in ggplot2 with scale_color_manual and explore how color and clarity relate to diamond price across cuts in a multi-panel plot.

Learn how to map a continuous variable to point size and a discrete variable to point shape in ggplot2, using the diamonds dataset, with scaling, breaks, and custom shapes.

Explore facet wrap in ggplot2 to create small multiples of scatter plots by color and cut, with adjustable scales, labels, and strip backgrounds for clear, comparative visualizations.

Discover how facet_grid creates a grid of panels for small multiples in plots. Adjust rows or columns, set scales and labels, and explore a diamond example across cuts and clarities.

Explore mapping multiple graphical elements in a ggplot to reveal how price relates to cut, color, and clarity in diamonds, while balancing color and size to minimize visual noise.

Explore diamond price prediction with linear regression, visualize results for various predictor combinations (carat, volume, cut, color, clarity), and assess models with 80/20 cross-validated train-test splits using RMSE.

Train and test multiple diamond price models using cross-validation and mean squared error to compare predictors like carat, color, and clarity, and visualize model performance.

Use a script of functions to generate all possible diamond price models with chosen predictors. Split data into training and testing sets with adjustable ratios for evaluation.

Explore mapping color for continuous variables and shape for discrete ones in ggplot2, build facets and scatter plots, and develop models to predict highway mpg from car features.

Watch an assignment walkthrough on data visualization with R and ggplot2. Build and customize plots, map aesthetics to car attributes, and run, train, and compare predictive models with train-test splits.

Use markdown to wrap up analysis and build a lightweight html report of data visualization work. Create sections for datasets, one-variable and multi-variable analysis, and price prediction models.

Explore rmarkdown, a lightweight markup language in R for reproducible documents with code and text. Render LaTeX, Beamer slides, and HTML with the rmarkdown package as you begin your report.

Explore the diamonds dataset by loading data, tuning knitr chunks, and generating an R Markdown report; learn to inspect variables, their relationships, and prepare data for modeling and visualization.

Explore exploratory data analysis in R with ggplot2, learning to build reports with figures, tables, and plots for multiple variables, including discrete and continuous data.

Create a two-variable visualization workflow by plotting price against selected features with scatter plots, then compare distributions using box plots and bar plots, loading the necessary libraries.

Explore multiple variables by creating a three-subplot visualization in R using ggplot2, plotting price versus volume across discrete variables, with color and clarity adjustments.

Create the price prediction models section by building models, selecting a target price, and evaluating accuracy with RMSE. Present results in a report with tables using kableExtra.

Learn to customize html output for data visualization reports by creating and applying css templates, adjusting fonts, margins, and table of contents, and integrating markdown and templates for polished reports.

Learn to build markdown reports, create plots, and develop price prediction models using the diamonds dataset and cars dataset, with a guided markdown report walkthrough of assignments.

Walk through an assignment in r, using markdown templates to explore the diamonds and cars datasets, create single and multiple-variable plots, and build a highway consumption model.

Explore a range of plots in data visualization with R and ggplot2, from pie and doughnut charts to time-series, heat maps, parallel coordinates, and coronavirus data maps.

Explore creating a pie chart in R with ggplot2 by transforming color to a factor, mapping fill, applying polar coordinates, and refining the plot with theme and axis adjustments.

Learn how to build a donut chart in R using ggplot2, including data frame setup, calculating percentages, customizing labels, colors, and polar coordinates for a polished visualization.

Visualize time series with a line chart in ggplot2 using economics data to plot unemployment and indicators, format date axis, reshape to long format, and apply a log scale.

Learn to build a waterfall chart in R with ggplot2, computing monthly unemployment changes using lag and differencing, and color coding increases in red and decreases in lime green.

Learn to build radar spider charts in ggplot2, using iris flower measurements and cars data to compare groups by species and transmission, and explore interactive options.

Learn to build parallel coordinates plots from iris data using tidy data wrangling and ggplot2 layers, transforming wide to long, calculating relative measures, and styling for clear comparison across species.

Create a heat map in ggplot2 by building a combined car key from manufacture and model, selecting displacement and miles, and applying a max-normalized color scale.

Explore creating a mosaic plot in R to visualize relationships among discrete variables, using Titanic data to compare class, sex, age, and survival with the mosaic package.

Prepare and clean a coronavirus dataset with infections, deaths, and recoveries by country for map visualization, merge coordinates, and prep for plotting in the next video.

Learn to create world maps with ggplot2 using the maps package and a world database, plotting infected, killed, and recovered counts with borders, longitude–latitude coordinates, and facets.

Explore advanced data visualization techniques in R and ggplot2. Create time series visuals, heat maps, mosaic plots, parallel coordinates, and country maps from bitcoin prices and coronavirus data.

Walks through importing bitcoin price data, transforming dates, and building a bitcoin daily closing price line chart and a waterfall price chart with color coding for increases and decreases.

Assignment walkthrough guides cleaning and aggregating coronavirus data in R, summing infected, recovered, and killed by country and date, creating a country word cloud and a continent-based parallel coordinates plot.

An assignment walkthrough showing how to filter data to China, compute week numbers and days, rank weeks, and build a ggplot2 map of infection spread across Chinese provinces.

This section explores creating and saving custom themes, using built-in themes, and applying annotations, labels, legends, and advanced color plots with subplots, plus highlighting with gghighlight.

Design and apply custom themes in ggplot2 by building from built-in themes, testing with multiple color schemes, and adjusting margins, titles, and axis text.

Explore ggplot2 theme customization in R by adjusting axis text and ticks, grid lines, and panel backgrounds. Learn to style legends and apply element_line and element_rect settings for visuals.

Learn how to annotate ggplot2 plots in R with custom text labels, mapping data to x and y, and adjusting size, color, and fonts to clearly label outliers and insights.

Learn to annotate and label ggplot2 visuals by generating two normal distributions, mapping x and y, and adding mean labels and decade annotations for unemployment data.

Master legend control in ggplot2 by toggling visibility, mapping color, size, and shape to multiple variables, and guiding legends to minimize clutter while preserving clarity in data visualizations.

Learn to arrange multiple ggplot2 plots with cowplot by building grids, customizing labels, alignments, relative widths and heights, and adding titles with ggdraw and draw_label.

Utilize gghighlight in R to mark specific random walk time series in ggplot2, applying multi-criteria highlighting, labeling, and faceted visuals.

Apply gghighlight to histograms of the diamonds data to emphasize subsets such as carat greater than 3 or 2.5 with price over 15000, using optional labels and facets.

Learn to create a custom ggplot2 theme, apply built-in and additional themes, add annotations and labels, and arrange subplots with grid and highlight libraries.

Learn to build and customize ggplot2 visualizations in R, covering data preparation, long-to-wide transformations, and plotting multiple assets—including cryptocurrency prices and corona data set—with themes, labels, and a logarithmic scale.

Explore replicating published figures from 538 using its data package in R, practicing line, bar, and dot plots to match original visuals.

Reproduce FiveThirtyEight-style visualizations in R with ggplot2, including a congress-age line chart by party and a state bar plot of speeding collisions, using the 538 data package.

Load the fiveThirtyEight congress age data, compute average age by party over term start years, and render a ggplot2 split line chart with custom labels, colors, and theme.

Customize the plot theme by adjusting the panel background and grid colors. Tune borders, axis ticks, and fonts to resemble the original figure from challenge 1.

Explore building a ggplot2 bar chart of state driving data, converting percentages to counts, reshaping data to long format, renaming districts, and customizing legends and themes for clarity.

Apply ggplot2 customizations to visualize speeding-related driver crashes by reversing axes, tuning themes, suppressing grids, refining axis text, legend placement, and bar transparency.

Explore reproducing a 538 figure about four Tarantino movies using R and ggplot2, including profanity counts and deaths, with guidance on subplots and labeling.

Create and prepare a data frame of movie events, then build a ggplot2 visualization with profanity and death events by minute, using subplots and custom scales.

Learn to build a multi-plot visualization in R with ggplot2 by composing themed subplots, configuring legends, margins, and titles, and arranging them in a grid using film data examples.