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Fix typos (#936)

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Jakob Krigovsky 2021-03-26 14:12:18 +01:00 committed by GitHub
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2
data-import.Rmd
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@ -311,7 +311,7 @@ fruit <- c("apple", "banana")
parse_factor(c("apple", "banana", "bananana"), levels = fruit)
```
But if you have many problematic entries, it's often easier to leave as character vectors and then use the tools you'll learn about in [strings](#readr-strings) and [factors](#readr-factors) to clean them up.
But if you have many problematic entries, it's often easier to leave them as character vectors and then use the tools you'll learn about in [strings](#readr-strings) and [factors](#readr-factors) to clean them up.
### Dates, date-times, and times {#readr-datetimes}

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data-tidy.Rmd
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@ -690,7 +690,7 @@ names(who)
2. The next letter gives the sex of TB patients.
The dataset groups cases by males (`m`) and females (`f`).
3. The remaining numbers gives the age group.
3. The remaining numbers give the age group.
The dataset groups cases into seven age groups:
- `014` = 0 -- 14 years old

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factors.Rmd
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@ -23,7 +23,7 @@ library(tidyverse)
If you want to learn more about factors, I recommend reading Amelia McNamara and Nicholas Horton's paper, [*Wrangling categorical data in R*](https://peerj.com/preprints/3163/).
This paper lays out some of the history discussed in [*stringsAsFactors: An unauthorized biography*](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) and [*stringsAsFactors = \<sigh\>*](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh), and compares the tidy approaches to categorical data outlined in this book with base R methods.
An early version of the paper help motivate and scope the forcats package; thanks Amelia & Nick!
An early version of the paper helped motivate and scope the forcats package; thanks Amelia & Nick!
## Creating factors

2
workflow-projects.Rmd
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@ -130,7 +130,7 @@ Notice you get back to where you left off: it's the same working directory and c
Because you followed my instructions above, you will, however, have a completely fresh environment, guaranteeing that you're starting with a clean slate.
In your favorite OS-specific way, search your computer for `diamonds.pdf` and you will find the PDF (no surprise) but *also the script that created it* (`diamonds.R`).
This is huge win!
This is a huge win!
One day you will want to remake a figure or just understand where it came from.
If you rigorously save figures to files **with R code** and never with the mouse or the clipboard, you will be able to reproduce old work with ease!