R Programming Easy As ABC: For The Beginner Data Scientist

Install the base of R on Windows by downloading from CRAN, running the installer, and configuring language, install location, components, startup options, and the start-menu folder.

Download and install R Studio from the official rstudio.com, choose the Windows version, run the installer, then complete the setup with default locations.

Learn how to locate and launch RStudio on Windows or Mac, open the console, access environment history, workspace, and packages, and prepare the interface for your first session.

Explore the R layout by writing code in the script editor, running it in the console, and manage objects, Iris data, plots, and the global workspace.

Explore how packages expand R's capabilities and how the library stores and organizes them. Install and activate packages, recognize active ones with a checkmark, and distinguish user and system libraries.

Install and activate the tidyverse package in R using install.packages and library. Remember: install once, then call library in each session, and use tidyverse for data visualization and data manipulation.

Explore R for data science with a free, beginner-friendly book resource, plus a purchasable hard copy, to guide you through your data science journey in this course.

Discover how to get help in R programming using the question mark, help(), the help icon, and community resources like the art studio community and Stack Overflow.

Explore essential R tips and tricks, including hashtags for comments, the assignment operator and double equals for comparisons, and practice with lowercase variable names for clear documentation and output.

Introduce data types in R, covering characters, doubles, integers, and logicals, with strings in quotation marks, decimals for doubles, and true or false booleans.

Learn R data types: character, double, integer, and logical, using the assignment operator and the typeof function, then inspect the global environment to identify variables.

Explore vectors, matrices, lists, and data frames as core data structures in R, noting vectors are one-dimensional, matrices are two-dimensional, lists hold mixed types, and data frames support per-column types.

Explore vectors as one-dimensional, same-type containers that form the foundation for all other data structures, supporting characters, doubles, integers, and more.

Explore vectors as one-dimensional data structures in R, created with c(), and learn how implicit coercion can change integer, double, character, and logical types within a vector.

Practice along in R by plotting one-dimensional X and Y vectors with the plot function, selecting plot symbols, and fitting a linear model to reveal their relationship.

Learn explicit coercion in R using as functions to convert a vector from double to character and then to integer, contrasting with implicit coercion.

Explore vectors and character data types in R, create a color vector, count elements with length and characters with nchar, and concatenate values into a single string with paste.

Master string manipulation in R by pasting name components, extracting substrings to reveal first, middle, and last names, and using stringr for email extraction via pattern matching.

Explore R vectors and numbers by using sequence, unit (uniform), sample, and replicate functions to generate fixed or unique datasets of integers and doubles.

Learn how booleans in R work, with true and false values and their numeric counterparts, and perform vectorized comparisons of vectors using operators like ==, >, and !=.

Explore vectorization for parallel processing of two vectors to boost efficiency without loops, and learn vector recycling, where the longest vector dictates length and multiples matter.

Learn how to subset vectors in R using positive and negative indices, including the c function for non-sequential positions, and remove elements to manipulate vector data types.

Explore matrices in R, a two-dimensional structure with rows and columns that holds a single data type, and learn how to create matrices using the matrix function.

Create matrices in R with the matrix function and binding methods using the same data type. Learn to load data by row or by column, setting rows, columns, and names.

Learn to create matrices in R by binding vectors as columns with cbind and as rows with rbind, and to assign row and column names using rownames and colnames.

Learn to create matrices in R and subset them by rows and columns. Explore extracting specific values, selecting non-sequential rows with c(), using sequences, and removing columns with negative indices.

Explore lists in R as a one-dimensional structure that can hold multiple data types and even other data structures.

Discover the R list, a versatile one-dimensional container that holds multiple data types and other data structures using list(), including a matrix, and see its role as data frames' foundation.

Learn to create a multi-dimensional array in R, a data type restricted structure like vectors and matrices, using a 3x3 array of 1 to 9.

Explore the data frame, a two-dimensional structure of rows and columns that holds multiple data types, including characters, numbers, and logicals, and learn to create and use data frames.

Learn to import an external dataset into R using read and the file chooser, inspect with head and tail, and summarize with summary, controlling strings with stringsAsFactors.

Create a data frame in R by combining vectors with the data.frame function, turning vectors into columns; data frame one has three observations and four variables.

Learn how to access built-in datasets in R using data(). Discover which packages supply datasets and how to practice with open data from the datasets package and others.

Learn to subset data frames in R using base indexing, names, and dollar signs, and compare with DPR functions like select, filter, slice, and sample.

Master data preparation in r by cleaning incomplete data, reformatting and renaming columns, and creating date features using the air quality dataset from new york city 1973 as an example.

Identify continuous versus categorical data, using real examples like ozone in air quality data. Create histograms, box plots, scatterplots, and bar plots to reveal distributions, skewness, quartiles, means, and relationships.

Learn how to manipulate data with dplyr functions using the air quality dataset, applying filter, select, slice, sample, arrange, rename, mutate, and group_by with summarize and pipe.

Learn to group by and summarize data using the pipe, chaining group by and summarize for efficient aggregation, such as total ozone by weekday.

Explore hands-on data manipulation in R by comparing base R operations with the tidyverse, using filter, group by, and summarize to count by gender and filter true values.