📊 Data & Tech Skills
Data Science: R Basics
A practical, beginner-friendly path to using R for data science. You will learn core R syntax, data structures, importin...
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1. Getting Started with R and RStudio1 views
Install tools, navigate RStudio, manage projects, and learn how to run and organize R code.
15 topics (15 versions)
1.1What is R and where it fits
1.2Installing R
1.3Installing RStudio
1.4Touring RStudio interface
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1.5Setting working directory
1.6Using the Console and Script editor
1.7Running code and sourcing scripts
1.8R help and documentation
1.9Installing packages from CRAN
1.10Updating R and packages
1.11Using RStudio projects
1.12Keyboard shortcuts essentials
1.13Using the Environment and History panes
1.14Managing options and .Rprofile
1.15Finding and using vignettes
2. R Language Fundamentals7 views
Master core syntax, operators, and rules that govern how R code executes and evaluates.
15 topics (15 versions)
2.1Expressions and statements
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2.2Objects and assignment operators
2.3Naming conventions
2.4Basic arithmetic and precedence
2.5Comparison and logical operators
2.6Vectorized operations
2.7Special values NA NaN Inf
2.8Coercion and type checking
2.9Sequence and repetition
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2.10Indexing basics
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2.11Recycling rules
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2.12Comments and code style
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2.13Getting and setting attributes
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2.14Working directory and paths
2.15Saving and loading workspaces
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3. Data Types and Structures14 views
Understand atomic types and the primary data structures used in R for data analysis.
15 topics (15 versions)
3.1Atomic types numeric integer logical character
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3.2Complex and raw types
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3.3Vectors lists matrices data frames
3.4Type inspection functions
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3.5Creating atomic vectors
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3.6Factors vs characters
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3.7S3 vs S4 vs R6 overview
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3.8Attributes names dim class
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3.9Copy on modify semantics
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3.10Memory vs reference behavior
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3.11Missing data basics
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3.12Overview of tibbles
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3.13Data frame vs tibble differences
3.14Subsetting semantics
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3.15Object size and memory usage
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4. Vectors and Factors12 views
Work fluently with vectors and categorical data, including indexing and transformations.
15 topics (15 versions)
4.1Creating vectors c seq rep
4.2Indexing by position
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4.3Logical indexing
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4.4Named vectors
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4.5Sorting ordering ranking
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4.6Matching and membership
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4.7Unique and duplicates
4.8Vector arithmetic recycling
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4.9Handling NAs in vectors
4.10Character vectors basics
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4.11Regular expressions overview
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4.12Creating and using factors
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4.13Releveling and reordering factors
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4.14Ordered factors
4.15Label management and forcats basics
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5. Matrices, Arrays, and Lists14 views
Create, index, and manipulate higher-dimensional data and flexible list containers.
15 topics (15 versions)
5.1Creating matrices matrix
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5.2Binding rows and columns
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5.3Matrix indexing
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5.4Matrix operations
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5.5Applying functions over margins
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5.6Creating arrays
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5.7Array indexing
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5.8List creation list
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5.9Named and nested lists
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5.10List subsetting techniques
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5.11Recursing through lists
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5.12Lists to data frames
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5.13Flattening and unlisting
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5.14Lapply sapply on lists
5.15Matrix vs data frame differences
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6. Data Frames and Tibbles0 views
Operate on tabular data structures central to most data science workflows.
15 topics (15 versions)
6.1Creating data frames
6.2Reading built in datasets
6.3Inspecting structure and summary
6.4Column types and conversion
6.5Row and column subsetting
6.6Filtering rows base R
6.7Adding and removing columns
6.8Sorting and ranking rows
6.9Handling missing values in frames
6.10Tibbles creation tibble tribble
6.11Printing and glimpse
6.12Rowwise and columnwise ops
6.13Renaming columns
6.14Row names vs explicit id
6.15Combining frames rbind cbind
7. Importing, Exporting, and Reading Data0 views
Read, write, and connect to common data formats and data sources.
15 topics (15 versions)
7.1Reading CSV with readr
7.2Base R read table and read csv
7.3Writing CSV and TSV
7.4Reading Excel with readxl
7.5Writing Excel with openxlsx
7.6Reading JSON with jsonlite
7.7Reading XML with xml2
7.8Reading RDS and RData
7.9Connecting to databases DBI
7.10Using odbc and RPostgres
7.11Querying with dplyr dbplyr
7.12Reading web data httr rvest
7.13Handling encodings and locales
7.14Efficient parsing and column types
7.15Data validation on import
8. Data Cleaning and Transformation with dplyr0 views
Transform, filter, summarize, and join data using a consistent grammar.
15 topics (15 versions)
8.1Introduction to tidyverse
8.2The pipe operator
8.3select to choose columns
8.4filter to subset rows
8.5arrange to sort rows
8.6mutate to add transform columns
8.7transmute to keep new columns only
8.8summarise and across
8.9group by and ungroup
8.10window functions
8.11joins inner left right full
8.12semi and anti joins
8.13bind rows and bind cols
8.14case when and if else
8.15rowwise operations
9. Reshaping Data with tidyr0 views
Tidy datasets and restructure tables for analysis and modeling.
15 topics (15 versions)
9.1Tidy data principles
9.2pivot longer
9.3pivot wider
9.4separate and unite
9.5nest and unnest
9.6expand and complete
9.7fill and replace na
9.8crossing and grid expansion
9.9chop and unchop
9.10rectangling from JSON
9.11list columns basics
9.12multiple keys and spec columns
9.13handling duplicate keys
9.14relational reshaping strategies
9.15reshaping performance tips
10. Working with Dates, Strings, and Categories0 views
Handle time, text, and categorical variables robustly and consistently.
15 topics (15 versions)
10.1Date and POSIXct basics
10.2Parsing dates with lubridate
10.3Time zones and daylight saving
10.4Date arithmetic and rounding
10.5Periods durations intervals
10.6Character encodings and UTF 8
10.7String manipulation with stringr
10.8Pattern detection and extraction
10.9Regular expressions practice
10.10Cleaning text data
10.11Factor engineering with forcats
10.12Collapsing and lumping levels
10.13Ordered categorical handling
10.14Labeling and recoding values
10.15Formatting for display
11. Exploratory Data Analysis and Visualization with ggplot215 views
Explore data patterns and communicate insights using the grammar of graphics.
15 topics (15 versions)
11.1Grammar of graphics basics
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11.2ggplot aesthetics and mappings
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11.3Geoms for points lines bars
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11.4Statistical transformations
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11.5Scales and guides
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11.6Faceting with wrap and grid
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11.7Themes and theme elements
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11.8Labels titles and annotations
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11.9Color palettes and viridis
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11.10Coordinate systems and flips
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11.11Position adjustments
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11.12Dealing with overplotting
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11.13Saving plots ggsave
11.14Plotting time series
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11.15Plotting distributions
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12. Control Flow, Functions, and Apply Family11 views
Write clear, reusable code using control structures, functions, and iteration tools.
15 topics (15 versions)
12.1if else and switch
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12.2for while and repeat
12.3next and break
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12.4Function syntax and scope
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12.5Arguments defaults and dots
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12.6Return values and invisibility
12.7Closures and lexical scoping
12.8Documenting with roxygen2
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12.9Vectorize and mapply
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12.10apply lapply sapply
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12.11vapply and replicate
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12.12purrr map family
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12.13Anonymous functions
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12.14Error handling tryCatch
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12.15Debugging with browser
13. Reproducibility, Projects, and Packages2 views
Structure analyses for reliability using projects, dependency management, and reporting.
15 topics (15 versions)
13.1RStudio projects workflow
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13.2Working directories best practices
13.3Script organization and sourcing
13.4Using renv for dependencies
13.5Version control with Git
13.6GitHub integration in RStudio
13.7Quarto and R Markdown basics
13.8Parameterized reports
13.9Caching and targets overview
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13.10Data dictionaries and metadata
13.11Packrat vs renv migration
13.12Writing your first package
13.13Testing with testthat
13.14Documentation with pkgdown
13.15Sharing and publishing results
14. Basics of Statistical Modeling in R14 views
Build and evaluate simple models using base R and tidy modeling tools.
15 topics (15 versions)
14.1Sampling and random numbers
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14.2Distributions and density functions
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14.3Descriptive statistics
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14.4Hypothesis testing basics
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14.5t tests and nonparametrics
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14.6Correlation and covariance
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14.7Simple linear regression
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14.8Multiple linear regression
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14.9Model diagnostics
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14.10Categorical predictors with factors
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14.11Logistic regression overview
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14.12Train test split and CV
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14.13Metrics RMSE MAE accuracy
14.14broom for tidy models
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14.15Visualizing model results
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15. Working with Larger Data and Performance0 views
Scale analyses and write faster code with efficient tools and techniques.
15 topics (15 versions)
15.1data table essentials
15.2Efficient reading with vroom
15.3Memory profiling and gc
15.4Vectorization strategies
15.5Preallocation and growing
15.6Benchmarking with microbenchmark
15.7Parallel basics future
15.8Efficient joins and keys
15.9Chunked processing
15.10Arrow and feather
15.11Using duckdb
15.12Database backed dplyr
15.13Writing fast Rcpp overview
15.14Profiling with profvis
15.15Best practices for speed