R4DS: tidyverse and beyond
前言
I R for data science
1 dplyr: Data transformation
2 tibble: Modern data frames
3 readr: Data import
4 lubridate: Dates and times
5 forcats: factor
6 tidyr: Tidy data
7 purrr: Functional programming
8 Relational data
9 broom: Tidy representation of models
II Importing
10 vroom: Fast reading of delimited files
11 Reading in data from other formats
12 Useful APIs
- 12.1 WDI
  - 12.1.1 WDIsearch()
  - 12.1.2 WDI
- 12.2 ipumsr
III Exploring and Wrangling
13 Data summary
14 Janitor
- 14.1 cleaning
  - 14.1.1 clean_names
- 14.2 Exploring
IV Miscellaneous tools
15 Advanced relational data
16 Categorical data (facotr)
17 Dealing with missing values
References
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R for data science: tidyverse and beyond

16 Categorical data (facotr)