R Essential Training: Wrangling and Visualizing Data
Offered By: LinkedIn Learning
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
- R in context
- Data science with R: A case study
- 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 %>%
- 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
- 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
- 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
- Recoding categorical data
- Recoding quantitative data
- Transforming outliers
- Creating scale scores by counting
- Creating scale scores by averaging
- Data science with R: A case study
- Computing frequencies
- Computing descriptive statistics
- Computing correlations
- Creating contingency tables
- Conducting a principal component analysis
- Conducting an item analysis
- Conducting a confirmatory factor analysis
- 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
- 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
- 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
- Next steps
Taught by
Barton Poulson
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