Course Outline

Basic overview of R and R Studio

  • R overview
  • R Studio Environment Windows
    • Script Editor Window
    • Data Environment
    • Console
    • Plots/Help/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 R Studio menu functions
    • Removing variables ls() command
  • Creating variables at the console prompt – single, vector, data frame
  • Naming vectors and matrices
  • Head and tail commands
  • Introduction to dim, length and class
  • Command line import (reading .csv and tab delimited .txt files)
  • Attaching and detaching data (advantages vs data.frame$)
  • Merging data using cbind and rbind

Exploratory Data Analysis

  • Summarising data
  • Summary command on both vectors and data frames
  • Sub-setting data using square brackets
    • summarising and creating new variables
  • Table and summary commands
  • Summary statistic commands
    • Mean
    • Median
    • Standard Deviation
    • Variance
    • Count & frequencies
    • Min & Max,
    • Quartiles
    • Percentiles
    • Correlation

Exporting data

    • Write table .txt
    • Write to a .csv file

R Workspace

  • Concept of Working Directories and Projects (menu driven and code – 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 command and ggplot2 package)

  • Bar Charts and Histograms
  • Boxplots
  • Line charts / time series
  • Scatter plots
  • Stem and leaf
  • Mosaic
  • Modifying plots
    • Titles
    • Legends
    • Axis
    • Plot Area
  • Exporting a plot to a third party application

Requirements

There are no specific requirements needed to attend this course.

 7 Hours

Number of participants



Price per participant

Testimonials (4)

Related Courses

Advanced Data Analysis with TIBCO Spotfire

14 Hours

Introduction to Spotfire

14 Hours

AI-Driven Data Analysis with TIBCO Spotfire X

14 Hours

Data Analysis with SQL, Python and Spotfire

14 Hours

TIBCO for Developers

21 Hours

TIBCO Statistica

14 Hours

Monitoring with Grafana

14 Hours

Advanced Grafana

14 Hours

Grafana and GLPI Administration

21 Hours

Grafana and Graphite

14 Hours

Advanced Elasticsearch and Kibana Administration

35 Hours

ELK: Elasticsearch, Logstash and Kibana for Administrators

14 Hours

Kibana: Essentials

14 Hours

Knowledge Discovery in Databases (KDD)

21 Hours

Introduction to Data Visualization with Tidyverse and R

7 Hours

Related Categories

1