Data Analytics with R from Scratch - Beginner

Explore RStudio architectures from local installation to server-based workflows, handling structured and unstructured data with ETL and analytical databases.

Explore the origination of R, an open-source vectorized programming language for data analysis, visualization, reporting, and statistical modeling, with RStudio as its programming front end.

Explore the architecture of R, comparing standalone local RStudio with RStudio server for enterprise data analysis, including flat files and ETL.

Explore the main file types in R, including R script, R markdown, text, C++ files, HTML outputs, and Shiny apps, and the four-window workspace in RStudio.

Explore the basic syntax of R, execute a hello world example, and create data frames for vectorized, table-like data using commands in the R console.

Explore the data types in R, including vectors, lists, matrices, arrays, factors, and data frames, and how variables reserve memory in the R Studio environment to store and classify values.

Discover how to create and manipulate vectors in R, including single and multiple element vectors, sequences, sorting, and basic operations, then explore lists and basic matrices.

Learn to create and manipulate matrices and arrays in R, access elements by row and column, perform arithmetic, and work with vectors, factors, and data frames.

Explore how to assign values to variables in R, inspect data types with class(), list workspace objects with ls, and remove unused variables with rm to optimize memory.

Explore the fundamentals of operators in R, including arithmetic, relational, logical, assignment, and miscellaneous operators, and learn how they manipulate vectors, arrays, lists, and data frames.

Explore loops and functions in R, including for and while loops, conditional testing, and user-defined and built-in functions, with practical use in RStudio and data frames.

Learn to create and manipulate strings in R from text data, using single or double quotes, and perform concatenation, case conversion, nchar counting, and substring extraction.

Create and manipulate data frames in R by constructing frames, reading and printing them, and merging or binding rows and columns, with attention to column names and join operations.

Improve business communication of analytics by using charts in R to convey results through pie charts, 3D pies, and bar charts with clear labels, colors, legends, and self-explanatory visuals.

Explore customizing bar charts in R with borders and colors, including grouped and stacked options, and use box plots and histograms to analyze distribution and outliers via interquartile range.

Learn to create and customize histograms, bar charts, line charts, and scatter plots in R from scratch, using numeric vectors and data frames, exploring distributions, trends, and variable relationships.

Explore statistical analysis and modeling in R, using mean, median, and mode with inbuilt functions and packages. Handle vectors and missing values, and apply trimming and data preparation.

Explains distribution functions in R, including normal distribution with its bell curve, mean and standard deviation, and how to use pnorm, dnorm, qnorm, rnorm, plus binomial and Poisson distributions.

Explore regression analysis, including linear and logistic regression, to model relationships between dependent and independent variables, and use R's lm to predict outcomes, estimate coefficients and intercept, and assess residuals.

Learn to build a linear regression model in R with lm to predict price from house type, review the lm summary, and predict for new data.

Explore multiple linear regression with multiple independent variables to predict house prices. Learn logistic regression for binary outcomes, using R's glm to estimate probabilities and classify transactions.

Explore decision trees and random forests in R for classification and regression, comparing single trees to ensembles with training and testing data and interpreting the most important factors.

Explore time series modeling in R, from mean and exponential smoothing to seasonal and ARIMA/ARMAX methods, using ts objects created with TTS, to forecast across time horizons.

Learn how a life insurance company uses predictive analysis on a 26-variable dataset to flag disease risk and price premiums, with R Markdown for presentation.

Explore and prepare data in R by converting variables to factors and ordered types, then analyze missing values, outliers, and relationships. Use logistic regression with feature selection and roc evaluation.