What is R and why use it for data analysis?
Setting up R and RStudio
RStudio interface and basic commands
Installing and using packages
Overview of the data analysis workflow
Variables and data types
Vectors, lists, matrices, and data frames
Control structures: if, else, for, while
Functions and scripting basics
Reading data from CSV, Excel, and text files
Using readr, readxl, and data.table
Handling missing data and outliers
Data type conversions
Working with dates and times (lubridate)
dplyrSelecting, filtering, and arranging data
Creating new variables (mutate)
Grouping and summarizing data (group_by, summarise)
Joining datasets (left_join, right_join, full_join)
tidyrLong vs wide formats
Using pivot_longer, pivot_wider
Combining and separating columns
Nesting and unnesting data
Summary statistics
Frequency tables and cross-tabulations
Correlation and covariance
Detecting anomalies and patterns
ggplot2Grammar of graphics concepts
Creating basic plots: bar charts, histograms, boxplots, line plots
Customizing plots (themes, labels, scales)
Faceting and multi-panel plots
Saving and exporting graphics
Descriptive statistics
Hypothesis testing (t-test, chi-square, ANOVA)
Linear regression analysis
Confidence intervals
Interpreting statistical results
Public datasets from healthcare, finance, marketing, etc.
Case study: Exploratory and inferential analysis
Practical hands-on exercises
Using R Markdown for reproducible reports
Creating dynamic documents (HTML, PDF, Word)
Basic introduction to Shiny (optional)
Exporting data and results
Define an analytical question
Clean and prepare data
Perform analysis and create visualizations
Document findings in an R Markdown report
Present final results
Connecting to databases (SQL in R)
Basic text analysis using tm or tidytext
Web scraping with rvest
Time series basics with forecast
R for Data Analyst roles
Recommended certifications (DataCamp, Coursera, edX)
Building a portfolio with R projects
Resume and interview preparation
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