RStudio Bootcamp- for Data Management, Statistics & graphics

Learn input techniques in RStudio, including importing native and fixed-format data, reading CSV, Excel, and SPSS files, and using header options and directory location with read functions.

Discover output techniques in RStudio, including importing datasets, naming columns (x1, x2, x3), and exporting to csv or excel using library functions and write commands.

Learn to derive variables and manipulate data in a dataset by creating new variables, converting units, renaming fields, and converting between numeric and string types.

Explore merging, combining, subsetting, and reshaping datasets in R, using subset filters, selecting variables, converting between wide and long formats, and binding or concatenating data frames.

Create date and time variables and extract today's date, weekday, month, and year. Compute the quarter from the date and build a time variable to capture timestamps.

Explore probability distributions and random number generation in RStudio, including normal distribution, probability density functions, z-values, and seed-based simulations.

Explore mathematical functions in RStudio, applying min, max, mean, range, standard deviation, absolute value, log, modulo, trig, rounding, truncation, complex numbers, and matrix functions on a used car sales dataset.

Explore matrix operations in the RStudio bootcamp: create matrices, index by rows and columns, apply transpose, dimensions, and multiplication. Also learn inverse, determinant, diagonal matrices, and eigenvalues with eigenvectors.

Master programming an operating system interface in R, using for loops and if/else on a used car sales dataset, and interact with the OS via system commands and directory management.

Master summary statistics in RStudio with real data, calculating mean, median, variance, quartiles, skewness, kurtosis, box plots, and performing t-tests, ANOVA, and proportion tests.

Explore bivariate statistics by analyzing two variables with correlation, including Pearson and Spearman methods, and apply chi square tests for categorical data, then use t tests for continuous variables.

Explore regression basics: distinguish simple and multiple regression, model sales from expense using linearity, interpret coefficients, residuals, and p-values, and assess with R-squared and adjusted R-squared.

Explore linear regression and ANOVA in RStudio, fitting models with numerical predictors, interpreting coefficients, R-squared, p-values, and handling categorical variables and large data.

Analyze residuals and diagnostic plots for price regression, using predicted vs actual values, residuals, scatter plots, BP test, and R-squared.

Learn binary logistic regression to predict a yes/no outcome using numeric and non-numeric predictors. Use the probability formula with a 0.5 threshold and interpret confusion matrix and F1.

Learn binary logistic regression in R Studio using Titanic data to model survived as a yes/no outcome, fit a binomial glm, and predict survival probabilities on train and test data.

Learn to model counts and rates with Poisson regression in R Studio, using population offsets to analyze lung cancer cases across age groups and cities.

Explore factor analysis in R Studio by converting Likert data to numeric, extracting factors, examining loadings and communality, and naming factors like quality of life, housing, and crime.

Explore survival analysis in R using Kaplan-Meier plots to estimate time-to-event and handle censored data, and compare covariates with Cox proportional hazards models.

Learn Cox proportional hazards regression, a semi-parametric survival model for time-to-event data with censoring. Interpret coefficients and hazard ratios, assess concordance, and compare variables like gender and age.

Explore univariate plots in RStudio, learning to visualize a single variable with bar plots, histograms, stem-and-leaf plots, dot plots, and box plots for both numeric and categorical data.

Explore bivariate plots using scatterplots, hex bin density, and density plots to analyze two variables like age and price, revealing negative correlations and data density patterns.

Explore spatial data visualization by plotting Starbucks store locations on OpenStreetMap using maps and latitude-longitude, then analyze interactions with price plots and circular data representations.

Demonstrate circular plotting in R using the circular package on a von Mises distribution (kappa = 3) with 150 bins, then assess normality via a Q-Q plot.

plot a roc curve using a 40 threshold to classify students as pass or fail, and assess true positive rate, false positive rate, sensitivity, and specificity.

Explore graphical options and configuration in R, including annotating plots, customizing lines and colors, adding legends and regression lines, exporting publication-ready figures from used car sales data.

Explore adjusting plotting options and parameters in R, including margins, axis limits, labels, colors, point and line sizes, and exporting figures for publication.