Programming With R. Learn How To Program In R - Beginners

Master basic operators in R, including variables and negative numbers, exponentiation, rounding, and common functions such as trig, inverse trig, logs, and factorial.

Master vectors as objects in R by creating numeric, character, and boolean vectors with c or ranges, inspect with class, subset using square brackets and 1:3, and perform elementwise arithmetic.

Learn how to generate numeric sequences with the sequence function using from, to, and by, compare sequences with equals, and apply the repetition function to build vectors in R.

Explore basic statistical functions in R, including sum, length, min, max, range, mean, median, mad, variance, standard deviation, correlation, covariance, and the table function for frequency analysis of numeric vectors.

Learn to create and manipulate matrices in R using the matrix function, transpose, and subsetting, and explore dimensions, identity matrices via diag, and conformable operations.

Explore matrices and matrix operations in R by binding data with cbind and rbind, assigning column and row names with paste, and merging data sets while handling missing data.

Explore applying basic statistical functions to data matrices in R, including min, max, standard deviation, row and column means, the apply function, correlation matrices, and the summary function.

Learn to generate random numbers in R with runif, sample, and rnorm, using set.seed for reproducibility, and create vectors and matrices from uniform and normal values.

Explore in-depth string handling in R with substring and nchar, learn to paste with controlled separators, and use grep, sub, gsub, and strings split for search, replace, and split operations.

Learn to create, convert, and manipulate dates and times in R, using system date, system time, and POSIX time objects, and compute time differences for time series.

Learn to plot in R with the plot function, create random normal and uniform data, compute a cumulative sum, and visualize a random walk with lines, points, and color options.

Learn plotting arguments in R by plotting the cumulative sum across multiple lines with color, line type, and width; set y limits with range and add titles and axis labels.

Learn to create bar graphs and histograms in R using hair and eye color data, customize labels and density shading, and explore data layouts with transpose and beside.

Develop scatter plots in R with the Cars 93 data set, add regression lines and correlation information, and use pairs and order to compare variables by mpg city.

Explore probability plots with q-q plots and q-q norm to assess how data match distributions, scale data, and compare mpg, engine size, and chi-squared distributions.

Learn to combine and save multiple plots in R using par and layout, plot weight versus city miles per gallon and price, adjust margins, and export figures.

Explore how to create and manipulate arrays in R, including 3x3 matrices and higher-dimensional arrays, using attributes, dim, and subsetting to access and modify data.

Learn to create and manipulate lists in R, including subsetting with single and double brackets, naming components, using the dollar operator, and combining elements with unlist and c.

Explore data frames as a list of vectors forming a two-dimensional structure, including city data with names, population, average high temperature, and a continental boolean, plus subsetting and conversions.

Learn how to import data into R using read.csv and read.table, handling headers, separators, and strings as factors to load CSV and text files into data frames.

Learn to handle missing data in R by loading data with read.table, detecting and replacing missing values, and using na.omit and complete.cases for valid analyses.

Explore missing data handling in R using na.locf to carry forward last observations. Learn to use fill, manage NaN and Inf values, and preserve data frame structure with xts.

Learn to order and sort data frames in R using order, including multi-variable and decreasing sorts, and reorder columns, demonstrated with the petrol refinery data set.

Learn to subset and index data in R by creating new data sets from the cars 93 data set, selecting model and price, and filtering rows with the which function.

Merge data frames with the merge function, handling missing data and matching on city via by.x and by.y. Use match and %in% to filter, noting cartesian effects when names differ.

Explore how to examine data and files using functions like file.exists, file.remove, file.rename, and file.info. Learn to check file existence, size, attributes, and object sizes in the workspace.

Learn to perform descriptive statistics in R using the iris data set, load data sets, summarize variables, split by species, plot colored groups, and compare means with t tests.

Explore the apply family in R using apply and lapply on the iris data to compute max, min, and standard deviation for numeric variables; inspect split results and simplified outputs.

Learn to build and interpret linear models in R using lm and glm, inspect residuals and coefficients, assess R-squared and p-values, and visualize with multi-plot diagnostics and optional intercept adjustments.

Learn to extract linear model information in R by accessing coefficients and residuals, plot fitted versus actual values, and validate models with train-test splits and mean squared error.

Explore principal component analysis and how it expresses variance by converting data into uncorrelated components. Extract components with Comp or ProComp and view the rotation matrix, center, and scale.

Learn to create and subset xts time series objects in R, install and load the xts package, and use lag and diff with time indices.

Explore time series plots in R, including lag plots and cumulative sums, to relate current and lagged values; use the ACF and PACF to guide ARIMA lag selection.

Decompose a time series into trend, seasonal, and remainder components using STL, illustrated with Johnson and Johnson quarterly earnings data and seasonal adjustments.

Explore exponential smoothing with Holt-Winters on time series data, fitting, forecasting, and plotting predictions with confidence intervals and gamma options for ts and xts objects.

Explore rolling functions on a time series to compute rolling means and rolling max, apply roll apply for bollinger bands, and use width k and align options.

Explore how to set up and analyze ARIMA models for time series, including differencing to achieve stationarity, conducting ADF tests, and selecting orders with auto.arima.

Explore booleans and logical operators in R, then master if statements and vectorized if-else logic, including nested conditions, subsetting with logical tests, and common comparison operators.

Explore for loops in R, iterating over vectors, printing results, and nesting if statements; manage brackets, catch errors, and use next and break to skip or end iterations.

Learn to implement while loops, avoid infinite loops with break conditions, and use repeat loops in R to perform iterative tasks such as counting and printing numbers.

Master for loops in R to compute means of random numbers and build a vector of means efficiently, comparing appending versus preallocation and system.time for speed with large data.

Create user defined functions in R by naming the function, listing arguments, and returning a value. Handle errors, return multiple results as a list, and illustrate vectorized operations with inputs.

Learn to debug functions in R by flagging and stepping through code with debug and browser tools, including examining a mean function and tracing errors in loops and conditionals.

Explore recursive functions in r through fibonacci and factorial examples, compare slow, multi-output recursion with fast single-value implementations, and note performance considerations to avoid crashes.

Explore additional resources for learning R, including the Crann home page and manual, bloggers, the Google style guide, Stack Overflow, Inferno, and stat methods dot net for quick model insights.