Learning R Programming for Data Science

Discover why data science is a career of the future, its link to big data, and the data science process and toolbox, with R as your gateway.

Explore why data science is the career of the future, as data scientists decode patient records, derive value from the big data deluge, and communicate insights to decision makers.

Learn what data science is, how it unifies statistics, data analysis, and machine learning to extract insights from structured and unstructured data, including big data's volume, velocity, and variety.

Discover the data science process from raw data collection to output, handling varied sources. Clean and transform data, explore with visuals, model with statistics, and present reproducible research results.

Explore the data science toolbox by mastering R programming with the open source R software and its RStudio editor for coding, plotting, debugging, and workspace management.

Follow the data science process from getting and cleaning data and creating tidy data through exploratory data analysis, statistical inference, regression modelling, to machine learning and reproducible research.

Understand why data science matters now and why R serves as its backbone. Begin with the basics of R to start your journey as a data scientist.

R is the engine of data science. Use RStudio as coding environment on Windows, downloading and installing free open-source software and packages for data access, cleaning, analysis, and reporting.

Learn how to install R and RStudio on Windows by downloading R from CRAN, choosing 32 or 64 bit, running as administrator, and then installing RStudio Desktop with default options.

Take a tour of the four-panel RStudio interface, write and run code in the script area, view results in the console, and explore environment, history, files, packages, plots, and help.

Learn to perform arithmetic in R using RStudio, assign variables, and understand data types, while using # for comments and exploring operators like +, -, *, /, ^, and %%.

Learn to assign values to variables in R using the less-than and minus sign, store items like apples and oranges, and compute totals with addition, using the alt minus shortcut.

Explore basic data types in R by identifying numerics, integers, logicals, and characters. Check their class with the class function and practice assigning and inspecting my_numeric, my_character, and my_logical.

Learn to create and name one-dimensional vectors in R with c(), and practice boolean, numeric, and character vectors, including a five-day poker and roulette example.

Learn how to name vector elements in R using the names function, labeling poker_vector and roulette_vector with days of the week, and reuse a days_vector to streamline the process.

Explore how to perform element-wise vector addition in R, assign vectors to variables, compute daily totals and weekly sums, and compare poker versus roulette gains using simple operators.

Learn to select elements from a vector in R using square brackets, numeric indices, colon ranges, and element names, with examples like Monday to Friday and computing means.

Explore vector selection by comparison in R, using operators like >, <, ==, and != to create boolean vectors and subset poker and roulette winnings by condition.

Explore matrices in R: understand what a matrix is, how to perform matrix calculations, and how to create and access matrix elements by rows or columns using the matrix function.

Analyze matrices by constructing a matrix from three vectors of US and non-US box office revenues for first three Star Wars films, using c(...) to form the matrix in R.

Name the rows and columns of a matrix using rownames and colnames, then calculate row sums to produce a worldwide box office vector for three Star Wars movies.

Combine matrices and vectors by column or by row using cbind and rbind, and compute row and column sums with rowsums and colsums.

Learn to select matrix elements with square brackets in R, using all_wars_matrix to extract non-us revenue by column or row and compute means with mean().

Perform element-wise arithmetic on matrices in R to compute visitor counts from revenues. Use the Star Wars revenue and ticket prices matrices to derive average U.S. and non-U.S. visitors.

Explore how factors encode categorical variables in R, use the factor function to create a two-level gender factor, and print its elements and levels.

Explain nominal and ordinal categorical variables and show how to create and rename factor levels in R using levels, with attention to default alphabetical ordering.

Compare the summary function on a character vector and a factor to see how factorized data reveals counts per level, making analysis clearer with female and male categories.

Convert a speed vector to an ordered factor in R by using factor with ordered = true and levels, enabling ordinal comparisons of slow, fast, and very fast.

Explore data frames in R and understand their structure. See mtcars example with mile per gallon, cylinder, displacement, horsepower, and inspect data using head, tail, and str.

Learn to create a data frame in R with the data.frame function using four vectors to form eight planets and four features into planets_df, then inspect with head and str.

Learn to select data frame elements in r with square brackets, index rows and columns, and access diameter using names, indices, or the dollar sign in the planets data frame.

Learn to select rows based on specific column values, using conditional selection and the subset function to filter planets with rings or diameter less than one.

Learn how to sort a data frame in R using the order function, arranging planets by diameter from largest to smallest.

Discover why you need lists in R and how to create and populate them. See how lists store diverse objects—vectors, matrices, data frames, and even other lists—unlike fixed-length structures.

Create lists in R with list(), combining vec, mat, and df, and name components to form my_list and shining_list, as shown in ex6_1 and ex6_3.

Learn to select elements from a list in R by index, name, or the dollar sign, including selecting the data frame of reviews and the second and third rows.

Add elements to an R list with the c function, name them, and view the result with str to reveal components: movie name, actors, reviews, and year 1980.

Explore equality in R by using the double equal sign for comparisons, understand case sensitivity, and compare logicals with numerics through practical exercises on vectors and matrices.

Master relational operators in R, including greater than, less than, and their equal forms, with numeric, string, and logical comparisons. Explore true and false, and practice alphabetical ordering.

Apply vector-wise relational operators in R to two weekly profile view vectors for LinkedIn and Facebook, identifying days LinkedIn exceeds 15, remains at five or fewer, and surpasses Facebook.

Create a two-row matrix from LinkedIn and Facebook profile views, then compare it to 14 to identify days with views less than or equal to 14 in R.