Learning the R Tidyverse
Offered By: LinkedIn Learning
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
- 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
- Why should you use projects in RStudio?
- Disable auto-saving of RData for reproducibility
- Create a new project
- What is the %>% operator?
- Identify where to use %>%
- Signficance of %>%
- Alternate options to %>%
- 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
- 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
- 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
- Non-standard evaluation and programming with the tidyverse
- Compare group_by and group_by_
- Tidy evaluation, quo, and !!
- Next steps
Taught by
Charlie Joey Hadley
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