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Source  : c7a3d57
Branch  : main
Author  : pull[bot] <39814207+pull[bot]@users.noreply.github.com>
Time    : 2024-01-22 10:44:04 +0000
Message : [pull] main from swcarpentry:main (#8)

* add instructor note, closes swcarpentry#870

* Update/improve links to "Other great resources" in two episodes

* [actions] update 16 packages

* Update 05-data-structures-part2.Rmd

Updated the url to the gapminder.csv in Challenge 3.

* [actions] update 13 packages

* fix typo

---------

Co-authored-by: Hao Ye <[email protected]>
Co-authored-by: Matthieu Bruneaux <[email protected]>
Co-authored-by: zkamvar <[email protected]>
Co-authored-by: louise-ord <[email protected]>
Co-authored-by: Naupaka Zimmerman <[email protected]>
Co-authored-by: Sehrish Kanwal <[email protected]>
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2 changes: 1 addition & 1 deletion 05-data-structures-part2.md
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Expand Up @@ -539,7 +539,7 @@ Check out `?source` to find out more.


```r
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv", destfile = "data/gapminder_data.csv")
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/main/episodes/data/gapminder_data.csv", destfile = "data/gapminder_data.csv")
gapminder <- read.csv(file = "data/gapminder_data.csv")
```

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8 changes: 4 additions & 4 deletions 13-dplyr.md
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Expand Up @@ -653,10 +653,10 @@ lifeExp_2countries_bycontinents <- gapminder %>%

## Other great resources

- [R for Data Science](https://r4ds.had.co.nz/)
- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
- [Introduction to dplyr](https://dplyr.tidyverse.org/)
- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/)
- [R for Data Science](https://r4ds.hadley.nz/) (online book)
- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) (pdf file)
- [Introduction to dplyr](https://dplyr.tidyverse.org/) (online documentation)
- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/) (online video)

:::::::::::::::::::::::::::::::::::::::: keypoints

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8 changes: 4 additions & 4 deletions 14-tidyr.md
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Expand Up @@ -589,10 +589,10 @@ There and back again!

## Other great resources

- [R for Data Science](https://r4ds.had.co.nz/index.html)
- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
- [Introduction to tidyr](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html)
- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/)
- [R for Data Science](https://r4ds.hadley.nz/) (online book)
- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) (pdf file)
- [Introduction to tidyr](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) (online documentation)
- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/) (online video)

:::::::::::::::::::::::::::::::::::::::: keypoints

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15 changes: 15 additions & 0 deletions instructor-notes.md
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Expand Up @@ -69,6 +69,21 @@ To prevent the learners from having to re-enter their password each time they pu
$ git config --global credential.helper 'cache --timeout=10000000'
```

## RStudio Color Preview

RStudio has a feature to preview the color for certain named colors and hexadecimal colors. This may confuse or distract learners (and instructors) who are not expecting it.

Mainly, this is likely to come up during the episode on "Data Structures" with the following code block:

```r
cats <- data.frame(coat = c("calico", "black", "tabby"),
weight = c(2.1, 5.0, 3.2),
likes_string = c(1, 0, 1))
```

This option can be turned off and on in the following menu setting:
Tools -> Global Options -> Code -> Display -> Enable preview of named and hexadecimal colors (under "Syntax")

## Pulling in Data

The easiest way to get the data used in this lesson during a workshop is to have
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8 changes: 4 additions & 4 deletions md5sum.txt
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