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