R Programming For Beginners

Explore the fundamentals of R programming for beginners, including its open source nature and basic setup, and learn variables, packages, charts, statistics, and workspace concepts.

Learn how to install R programming and set up the development environment, explore the console, create projects with working directories, manage packages, and understand workspace concepts for data analysis.

Learn the basics of R programming, from console and script use to arithmetic and output. Explore core data structures (vectors, matrices, data frames, lists), variables, assignment, c(), and workspace management.

Learn to manage R packages and libraries, install and update packages, load them with library, and work with data sets as you read data, generate sequences, and prepare charts.

Explore bar charts for one variable in R, starting from loading datasets and plotting frequency values. Compare with pie charts and customize axis labels and colors to clearly convey patterns.

Learn to create a pie chart for a single variable from a dataset, customize colors and order, adjust the start angle and clockwise distribution, and include legends for clarity.

Learn to create histograms in R for interval data, loading datasets, choosing breaks, colors, and density to reveal distribution. Superimpose normal distribution lines and interpret the mean to assess shape.

Explore how to create and customize boxplots in R, including notches, whiskers, outliers, colors, and labeled axes, to analyze distribution visually.

Learn descriptive statistics in R, including frequency tables, data replication for categorical values, and using summary and describe to compute min, max, mean, median, and standard deviation.

Explore techniques to modify data for deeper insights, including handling outliers with transformations and box plots, creating composite variables, and managing missing data in R.

Learn core data structures in r, from vectors and lists to matrices and data frames, including indexing, naming, homogeneous vs heterogeneous elements, and factors for categorical data.

Explore data frames in R for beginners, creating frames with unique column names and consistent row counts, using numeric, factor, and character types, and summarizing salary and start date data.

Create a multiple line chart in R using numeric attributes from orange trees, set x and y ranges, plot the data, and customize with colors, line types, and a legend.

Learn to create 2d and 3d scatter plots in R using height and weight as attributes, with x, y, and z axes and meaningful labels.

Learn to read and interpret data in R by selecting cases and subgroups, filtering with conditions, and computing means and aggregates to reveal insights and build charts.

Learn to create grouped charts in R by building side-by-side bar plots, customizing colors, and positioning legends. Analyze data with means and box plots using aggregate functions for comparing groups.

Explore scatter plots to visualize associations between two variables, using speed and stopping distance, and learn to add linear regression lines and variations with an R package.

Explore the loop functions in R, including applying a function to each element of a list, simplifying the result, and the split-apply approach for grouped analyses.

Explore how to simulate data in R by generating random numbers from normal, Poisson, and binomial distributions, control reproducibility with seeds, and summarize and visualize results.

Learn to create clustered bar charts in R to compare means across multiple variables using cluster analysis, with side-by-side bars and legend customization.

Learn to create scatter plots by groups, using two categorical predictors and a single quantitative outcome, with group-specific regression lines and iris species examples.

Explore profiling R code to measure how time is spent across program parts, using the profiler, system.time, readLines, and the summary of time across functions to guide optimization.

Explore correlations in R programming for beginners, using default and single correlation methods on data sets, including the Swiss fertility indicators, with packages for correlation matrices and inference.

Explore bivariate regression by building and saving a simple two-variable model, then analyze it with histograms, scatter plots, and a concise summary of outputs.

Compare means between two groups and within paired observations using two-sample and paired t-tests, load the sleep data, check distribution with histograms, and interpret p-values.

Learn to perform cluster analysis in R, determine the number of clusters using k-means, and visualize clusters with plots and dendrograms to reveal homogeneous groups.