YoVDO

R Essential Training: Wrangling and Visualizing Data

Offered By: LinkedIn Learning

Tags

Statistics & Probability Courses Data Science Courses Data Analysis Courses Data Visualization Courses Tidyverse Courses Data Wrangling Courses Data Modeling Courses ggplot2 Courses

Course Description

Overview

Taking your R coding skills to the next level by wrangling, visualizing, and modeling data.

Syllabus

Introduction
  • Make your data make sense
  • Using the exercise files
1. What Is R?
  • R in context
  • Data science with R: A case study
2. Getting Started
  • Installing R
  • Environments for R
  • Installing RStudio
  • Navigating the RStudio environment
  • Entering data
  • Data types and structures
  • Comments and headers
  • Packages for R
  • The tidyverse
  • Piping commands with %>%
3. Importing Data
  • R's built-in datasets
  • Exploring sample datasets with pacman
  • Importing data from a spreadsheet
  • Importing XML data
  • Importing JSON data
  • Saving data in native R formats
4. Visualizing Data with ggplot2
  • Introduction to ggplot2
  • Using colors in R
  • Using color palettes
  • Creating bar charts
  • Creating histograms
  • Creating box plots
  • Creating scatterplots
  • Creating multiple graphs
  • Creating cluster charts
5. Wrangling Data
  • Creating tidy data
  • Using tibbles
  • Using data.table
  • Converting data from wide to tall and from tall to wide
  • Converting data from tables to rows
  • Working with dates and times
  • Working with list data
  • Working with XML data
  • Working with categorical variables
  • Filtering cases and subgroups
6. Recoding Data
  • Recoding categorical data
  • Recoding quantitative data
  • Transforming outliers
  • Creating scale scores by counting
  • Creating scale scores by averaging
7. An R for Data Science Case Study
  • Data science with R: A case study
8. Exploring Data
  • Computing frequencies
  • Computing descriptive statistics
  • Computing correlations
  • Creating contingency tables
  • Conducting a principal component analysis
  • Conducting an item analysis
  • Conducting a confirmatory factor analysis
9. Analyzing Data
  • Comparing proportions
  • Comparing one mean to a population: One-sample t-test
  • Comparing paired means: Paired samples t-test
  • Comparing two means: Independent samples t-test
  • Comparing multiple means: One-factor analysis of variance
  • Comparing means with multiple categorical predictors: Factorial analysis of variance
10. Predicting Outcomes
  • Predicting outcomes with linear regression
  • Predicting outcomes with lasso regression
  • Predicting outcomes with quantile regression
  • Predicting outcomes with logistic regression
  • Predicting outcomes with Poisson or log-linear regression
  • Assessing predictions with blocked-entry models
11. Clustering and Classifying Cases
  • Grouping cases with hierarchical clustering
  • Grouping cases with k-means clustering
  • Classifying cases with k-nearest neighbors
  • Classifying cases with decision tree analysis
  • Creating ensemble models with random forest classification
Conclusion
  • Next steps

Taught by

Barton Poulson

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