Basics of R for Data Science

Learn how to import data in R by creating an empty data frame with column names and types, opening the dataset, entering records, and saving the data for analysis.

Learn to import csv data into R with a simple read function, then open a file and view the first six rows and columns.

Explore essential R functions for data analysis, including attaching and detaching datasets, accessing columns with the dollar sign, and inspecting structure with head, tail, dim, and length.

Explore data management in R, covering data type conventions, missing values, data creation and storage, dataset manipulation, sorting, selecting and dropping variables, and core operators for arithmetic and assignment.

Convert character data to dates in R with as.Date and the date function, format dates using %B, %b, %Y, and %A, and compute differences with Sys.Date().

Explore type conversions in R by identifying data formats and using as functions to convert between numeric, character, and logical values, and distinguishing vectors and data frames.

Learn to rename column names in a data frame using the rename function, with practical steps for mpg and displacement and handling missing values.

Learn how to sort numeric data in ascending and descending order, retrieve indices with order, and merge two data frames sharing a common column, followed by subsetting with logical conditions.

Explore basic math functions in data science, including square roots, absolute value, rounding, and precision. Learn logarithms, exponentials, and trigonometric and inverse hyperbolic functions for data transformations.

Explore how to construct a simple graph in R, interpret its output, and use basic graphs such as pie charts to analyze data.

Learn to plot in R using the plot function to build graphs from data, customize titles, axis labels, colors, and add multiple lines with the lines function.

Explore creating bar plots in R for data visualization, using a graphical user interface, loading data, and customizing colors, axes, and grouping by categories.

Learn to construct a pie chart in R with population and country data, add a main title, color slices with rainbow colors, and create a 3D version.

Construct a histogram in R to visualize population data, dividing the x axis into bins, labeling axes and title, and exploring frequency and probability with color options.

Read box plots in R to summarize data with minimum, maximum, mean, median, and quartiles. Use the interquartile range to identify outliers with the 1.5 IQR rule.

Learn the basics of descriptive statistics, construct and interpret frequency tables, and explore correlations in numerical data to understand data relationships.

Discover how descriptive statistics summarize data in R, using functions to compute min, max, mean, median, and standard deviation, and to inspect missing values, variables, and observations.

Explore constructing frequency and contingency tables in R to analyze factor variables, compute proportions with table and margin functions, and apply results to regression and confusion matrices.

Explore essential statistical functions in R for data analysis, including mean, median, standard deviation, range, and minimum and maximum values, plus vector handling, repetition, and text transformations such as uppercase.

explore how correlation reveals relationships between variables using Pearson and Spearman methods, and learn to compute correlations while handling missing data in a diabetes data example.

The analytics life cycle outlines six phases: discovery and data preparation, data understanding and transformation, model building, evaluation, communication, and deployment, including training and testing to deliver results.

Explores the four types of machine learning—supervised, unsupervised, semi supervised, and reinforcement—highlighting regression for continuous targets and classification for categorical targets, with examples like house prices and medical imaging.

Explore linear regression as a supervised learning technique for predicting continuous targets like salary from experience, and learn how least squares identifies the best fit line by minimizing error.

Evaluate a regression model by checking R-squared and adjusted R-squared, significance levels, standard error, and residuals, then use information criteria to assess predictive accuracy.

We implement linear regression to predict temperature from atmospheric features, after data cleaning, type conversion, and missing-value imputation, then select predictors via correlation and evaluate with train/test split and r-squared.