R is a programming language and software environment that can be used for graphic representation, statistical analysis, and reporting. This R Programming course is intended to provide a thorough understanding of R’s operation and environment setup. Participants will learn R concepts such as variables, operators, data types, etc. During this course, you will learn how to name, access, and manipulate list elements and how to convert lists to vectors. Participants will learn how to work with various files in R, including CSV files, Excel files, binary files, XML files, and JSON files. Delegates will be able to create boxplots and scatterplots after completing this course.
Prerequisites
There are no prerequisites to enrol in this course, but a general understanding of the programming language can benefit.
What will you gain after this course
In R, grasp the concepts of data reshaping and regression.
Discover how to name, access, and manipulate list elements.
Gain in-depth knowledge of R programming and how to set up the R environment.
Jobs you can get with Your R Programming Qualification
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Who is this certification for?
This course is open to anyone who wants to improve their knowledge of the R programming language.
This course is perfect for Business and IT Professionals, Survey Researchers, Risk Analysts, Data Scientists, and Data Engineers.
Small Groups
With small groups of students, our instructors can work closely with each student.
Flexible Class Schedules
Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.
Experienced Instructors
Our instructors follow a modified are personalized approach to engage students during class
Hi-Tech Lab Facilities
Our students can access our lab facilities anytime for practical experience during and after studies.
Syllabus
Module 1: Introduction to R
Overview of R
What is R?
Installing R and RStudio
RStudio interface overview
Basic syntax and operations in R
R data types: Numeric, Integer, Complex, Character, Logical, and Factor
Variables and assignments in R
Basic R Functions
Using built-in R functions
Writing custom functions in R
Function arguments and return values
Understanding function scopes
Module 2: Data Structures in R
Vectors and Lists
Creating vectors and lists
Accessing and manipulating elements
Vectorized operations
Matrices and Arrays
Creating and accessing matrices and arrays
Matrix operations
Applying functions across matrices
Data Frames
Creating data frames
Accessing and modifying data frame columns
Subsetting and filtering data frames
Merging and joining data frames
Factors
What are factors in R?
Factor levels and ordering
Using factors for categorical data analysis
Module 3: Data Import/Export
Importing Data
Reading data from CSV, Excel, and text files
Using read.csv(), read.table(), readxl, and tidyverse packages
Importing data from databases (SQL, etc.)
Exporting Data
Writing data to CSV and Excel files
Saving R objects using save(), saveRDS()
Using write.csv(), write.xlsx()
Module 4: Data Manipulation with R
Data Cleaning and Preprocessing
Handling missing data (NA values)
Data imputation methods
Removing duplicates
Renaming and transforming variables
Data Wrangling with dplyr
Introduction to dplyr for data manipulation
Selecting, filtering, and arranging data
Mutating and summarizing data
Grouping and aggregating data
Reshaping Data
Reshaping data using reshape(), melt(), and cast()
Pivot tables using tidyr
Combining and splitting data frames
Module 5: Data Visualization in R
Basic Plotting with Base R
Plot types: scatter, bar, histogram, boxplot, and line plots
Customizing base R plots (axes, labels, colors)
Advanced Visualization with ggplot2
Introduction to ggplot2
Creating static plots: scatter, line, box plots, histograms, bar charts
Customizing ggplot2 plots: aesthetics, themes, and labels
Faceting and multi-panel plots
Creating interactive plots with plotly
Geospatial Visualization
Working with spatial data
Visualizing geospatial data with ggmap, leaflet, and deck.gl
Plotting geographical maps and choropleth maps
Module 6: Statistical Analysis in R
Descriptive Statistics
Summary statistics (mean, median, mode, range)
Variability measures: variance, standard deviation
Skewness, kurtosis, and normality tests
Hypothesis Testing
T-tests, chi-squared tests, and ANOVA
p-values, confidence intervals, and effect sizes
Regression Analysis
Simple and multiple linear regression
Logistic regression
Model diagnostics and validation
Correlation and Covariance
Calculating and interpreting correlations (Pearson, Spearman)
Covariance and its relationship to correlation
Module 7: Introduction to Machine Learning with R
Supervised Learning
Linear regression and classification models (e.g., decision trees, random forests)
Cross-validation and model evaluation (ROC, confusion matrix)
Unsupervised Learning
K-means clustering and hierarchical clustering
Principal Component Analysis (PCA)
Model Tuning
Hyperparameter tuning using caret and tuneR
Grid search and cross-validation techniques
Module 8: Advanced Topics in R
Advanced Data Manipulation with data.table
Introduction to data.table
Efficient data manipulation with data.table
Joining, aggregating, and subsetting large datasets
Time Series Analysis
Working with time series data
Time series decomposition and forecasting
ARIMA models for time series prediction
Text Mining and Natural Language Processing
Introduction to text mining in R
Tokenization, stemming, and stop words
Sentiment analysis using tidytext and tm packages
Big Data Analytics with SparkR
Introduction to SparkR for distributed computing
Processing large datasets using Spark in R
Parallel computing and optimization techniques
Module 9: R in Reproducible Research
R Markdown and Reporting
Introduction to R Markdown for dynamic report generation
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