YoVDO

Learning the R Tidyverse

Offered By: LinkedIn Learning

Tags

Tidyverse Courses Data Visualization Courses R Programming Courses Statistical Analysis Courses Data Summarization Courses Data Transformation Courses

Course Description

Overview

Learn to integrate the tidyverse into your R workflow and get new tools for importing, filtering, visualizing, and modeling research and statistical data.

Syllabus

Introduction
  • Welcome
  • What you should know
  • Exercise files
1. Getting Started with the tidyverse
  • What is the tidyverse?
  • Why use the tidyverse?
  • Strengths of the tidyverse
  • Set up R and RStudio for the tidyverse
  • Maintain the tidyverse
  • Prevent issues with plyr and dplyr
2. Being Tidy with RStudio Projects
  • Why should you use projects in RStudio?
  • Disable auto-saving of RData for reproducibility
  • Create a new project
3. Introducing the %>% Operator
  • What is the %>% operator?
  • Identify where to use %>%
  • Signficance of %>%
  • Alternate options to %>%
4. Importing, Modifying, and Filtering Data
  • Separate raw and clean data folders
  • Import .xlsx files with readxl in R
  • Import .csv files with readr into R
  • Is it a data frame or a tibble?
  • Select and filter data
  • Convert strings to dates with mutate
  • Separating columns into multiple columns
  • Filter out NA values
  • Export .csv files with readr
  • Export .rdata objects for later
5. Summarizing and Tabulating Data in the tidyverse
  • Sample data and cross-validation with dplyr
  • Categorizing data with group_by
  • Count members of subgroups within groups with n()
  • Cumulative sums and more: cumsum, cumall, and cumany
  • Create group summaries
  • Remember to ungroup before moving on
6. Wide and Long Data
  • Identify if data is wide or long
  • The benefits of long (or tidy) data
  • Convert data from wide to long
  • Convert data from long to wide
7. select(), select_(), !!!, and Non-Standard Evaluation
  • Non-standard evaluation and programming with the tidyverse
  • Compare group_by and group_by_
  • Tidy evaluation, quo, and !!
Conclusion
  • Next steps

Taught by

Charlie Joey Hadley

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