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Course Outline
Basic Overview of R and RStudio
- Overview of R
- RStudio Environment on Windows
- Script Editor Window
- Data Environment
- Console
- Plots, Help, and Packages
Working with Data
- Introduction to vectors and matrices (data.frame)
- Different types of variables
- Numeric, Integer, Factor, etc.
- Changing variable types
- Importing data using RStudio menu functions
- Removing variables using the ls() command
- Creating variables at the console prompt – single values, vectors, and data frames
- Naming vectors and matrices
- Using head and tail commands
- Introduction to dim, length, and class functions
- Command-line data import (reading .csv and tab-delimited .txt files)
- Attaching and detaching data (advantages compared to data.frame$)
- Merging data using cbind and rbind
Exploratory Data Analysis
- Summarizing data
- Using the summary command on both vectors and data frames
- Sub-setting data using square brackets
- Summarizing and creating new variables
- Using table and summary commands
- Summary statistic commands
- Mean
- Median
- Standard Deviation
- Variance
- Count and frequencies
- Min and Max
- Quartiles
- Percentiles
- Correlation
Exporting Data
- Writing tables to .txt files
- Writing to .csv files
R Workspace
- Concept of working directories and projects (menu-driven and code-based – setwd())
Introduction to R Scripts
- Creating R scripts
- Saving scripts
- Workspace images
Concepts of Packages
- Installing packages
- Loading packages into memory
Plotting Data (using standard default R plot commands and the ggplot2 package)
- Bar charts and histograms
- Boxplots
- Line charts and time series
- Scatter plots
- Stem and leaf plots
- Mosaic plots
- Modifying plots
- Titles
- Legends
- Axes
- Plot area
- Exporting plots to third-party applications
Requirements
- No prior experience with R is required.
- Basic familiarity with programming or data analysis concepts is helpful but not mandatory.
Target Audience
- Data analysts and statisticians beginning their journey with R.
- Researchers and academics exploring data manipulation and visualization.
- Professionals transitioning into data science roles.
7 Hours
Testimonials (4)
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna - Birmingham City University
Course - Foundation R
Background knowledge and 'provenance' of trainer.
Francis McGonigal - Birmingham City University
Course - Foundation R
The trainer was very concern about individual understanding.
Muhammad Surajo Sanusi - Birmingham City University
Course - Foundation R
I genuinely enjoyed the hands passed exercises.
