Mastering in Visualization with R programming

Improve decision making with data visualization by ensuring data quality and context, understanding the audience, and using clustering to handle large data and outliers.

Explore the open-source R programming language, its powerful data analysis and visualization capabilities, and its extensible graphics and package ecosystem for statistics, data mining, and machine learning.

Explore basic math operations and variable concepts in R, covering binary, continuous, discrete, and categorical variables, and navigate the RStudio interface to import data, run code, and manage packages.

Explore vectors and matrices in R: create various vector types, form numeric matrices, index elements, generate sequences, and perform arithmetic and basic matrix operations.

Explore how lists and data frames work in R, creating a mixed-type list and a two-dimensional data frame with equal-length columns. Compare their structures, types, and usage in analysis.

Create factors from vectors with levels such as male and female, filter data using subset, and summarize datasets with functions like summary and describe.

Mastering in visualization with R programming demonstrates the plot function for plotting objects and customizing colors, labels, axis limits, margins, and line styles using par, pch, and xlab.

Learn how to add colors to graphs in R using rgb color specification, color names, and numeric indices to create readable, interactive bar plots.

Explore discrete color palettes in R for bar plots. Apply rainbow, heat.colors, terrain.colors, cm.colors, and gray.colors for clearer visuals.

Explore color palettes from the RColorBrewer package, including sequential, qualitative, and diverging schemes, learn to select palettes and apply them to bar plots for clear, aesthetically pleasing visuals.

Explore the types of graphs, including line graphs and bar charts, and learn how independent and dependent variables, along with quantitative and qualitative data, guide graph selection.

Explore line graphs to display data points with straight line segments, track changes over time, and compare crime rates in 1983 and 1993 using time series analysis in R.

Learn how scatter plots visualize the relationship between two quantitative variables, identifying positive, negative, or no correlation by examining the pattern of points on the x-y plane.

Explore bar plots in R to visualize categorical data, compare frequencies across categories, customize margins and borders, and create horizontal or grouped bar plots using arthritis data as examples.

Explore how histograms in R visualize data distributions, using bins and frequencies to compare class outputs, assess process capability, and create overlapping histograms with the mpg dataset.

Learn to create and customize pie charts in R to show the proportion of nominal or ordinal categories and compare parts of a whole, with labeling, colors, and percentage calculations.

Master the table plot in R to visualize diamond attributes—price, carat, x, y, z, color, and clarity—while handling missing values in the diamond dataset.

Master box plots to summarize data distributions, showing median, quartiles, and outliers. Learn to compare groups with color, notches, and orientation using the schooldays dataset example.

Learn to build and customize organizational charts in R using a dataset of regions, countries, states, and cities; explore hierarchical structures and browser-based visualizations.

Learn to build and compare directed and undirected network graphs in R using a data frame with source, destination, and weight, and construct graphs with igraph.

Learn to use qplot in R for quick plots from the diamonds dataset, mapping x, y, color, and shape with geometric objects, and add labels and a smooth line.

Learn to create line graphs, scatter plots, histograms, box plots, and bar charts with ggplot2 in R, using a 50-observation dataset featuring illiteracy, population, and region.

Explore ggthemes in R to apply themes like economist and Excel to ggplot2, control colors and backgrounds, and compare styles across plots.

Explore faceted panels to partition data by one or two grouping variables using facet_wrap and facet_grid in ggplot2.

Discover how to implement and customize legends in ggplot2 graphs, explaining what each color or box represents and controlling legend order, title, and placement.

Learn to annotate plots in R with a plotting package, adding labels, colors, and sizes to scatterplots such as eruptions versus waiting times for clearer data visualization.

Create an area chart in R by plotting quantitative data from a data frame and highlighting the area between axes with colors, textures, and hatchings.

Learn how the plotrix package accelerates R plotting with ready-to-use functions for color scaling, text placement, and charts like Gantt, zoom, nested bar, radial bar, fan, and pie charts.

Create nested bar charts that break down a data frame by categorical variables, using Titanic data and aggregate functions, with color coding by class, age, and sex to show proportions.

Create a Gantt chart in R using the graphics package by building a data frame with start and end times, then customize colors, borders, and labels for each task.

Learn to create a zoom-in plot in R with a left and right half, using 100 random x and y values, and customize title, x and y labels, and color.

Create a fan plot in R by loading libraries, building a continent data frame, and drawing overlapping sectors with radians angles and labeled continent names.

Master the creation of a 3d pie chart in R by grouping region data, counting occurrences, and labeling each sector with region names, then customize colors for clear visualization.

Learn to add a table to a plot in R by preparing a data frame, mapping x and y, and displaying row names with color and dividers for clarity.

Explore radial plots in R to visualize multiple variables on spokes with a common origin, customizing labels, grid lines, and polygon fills using radial.plot.

Introduction to the googleVis package shows how it interfaces with Google Charts to create interactive visualizations from data frames in a browser.

Create a combination chart in R with the Google package that blends bars and a line to compare projected and actual sales, with profit shown as a red line.

Create a three-dimensional bubble chart in R using googleVis package to compare quantitative variables, with sales on the x axis, expenses on the y axis, and profit as bubble size.

Explore gauge charts in R programming using Google Charts to visualize a single key measure, such as city population, with a speedometer-like gauge and color ranges from green to red.

Create Google Maps in R with the Google Apps package, using a data frame of 47 observations and 8 variables (timestamp, latitude, longitude); configure map type and interactive markers.

Create a timeline chart that visualizes resource use over time, with start and end dates, using a data frame and the G-Wiz timeline package for events like conference room bookings.

Explore treemaps as space-constrained visualizations of hierarchical data, using area and color to represent value and enabling interactive drill-down into regional structures.

Explore calendar charts to display daily activity across multiple years, using a dataset with date and average temperature from 2002–2004, implemented with a Google visualization package.

Learn to plot two-set Venn diagrams in R using a diagram package, with customizable colors, transparency, and labels. Export the diagrams as image files for easy sharing.

Learn to generate a word cloud from text data in R using the workload package, including text cleaning and colorful visualization of word frequencies.