chapter 2

02-intro_R.qmd

@@ -0,0 +1,97 @@
+# Introduction to R
+
+<div style="text-align:center;">
+```{r, echo = F}
+knitr::include_graphics("img/abacus.png")
+```
+</div>
+
+What you'll have learned by the end of the chapter: reading and writing,
+exploring (and optionally visualising) data.
+
+## Reading in data with R
+
+Your first job is to actually get the following datasets into an R session.
+
+First install the `{rio}` package (if you don't have it already), then download
+the following datasets:
+
+- [mtcars.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/mtcars.csv)
+- [mtcars.dta](https://github.com/b-rodrigues/modern_R/raw/master/datasets/mtcars.dta)
+- [mtcars.sas7bdat](https://github.com/b-rodrigues/modern_R/raw/master/datasets/mtcars.sas7bdat)
+- [multi.xlsx](https://github.com/b-rodrigues/modern_R/raw/master/datasets/multi.xlsx)
+
+Also download the following 4 `csv` files and put them in a directory called
+`unemployment`:
+
+- [unemp_2013.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/unemployment/unemp_2013.csv)
+- [unemp_2014.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/unemployment/unemp_2014.csv)
+- [unemp_2015.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/unemployment/unemp_2015.csv)
+- [unemp_2016.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/unemployment/unemp_2016.csv)
+
+Finally, download this one as well, but put it in a folder called `problem`:
+
+- [mtcars.csv](https://raw.githubusercontent.com/b-rodrigues/modern_R/master/datasets/problems/mtcars.csv)
+
+and take a look at chapter 3 of my other book, [Modern R with the
+{tidyverse}](https://b-rodrigues.github.io/modern_R/reading-and-writing-data.html)
+and follow along. This will teach you to import and export data.
+
+`{rio}` is some kind of wrapper around many packages. You can keep using
+`{rio}`, but it is also a good idea to know which packages are used under the
+hood by `{rio}`. For this, you can take a look at this
+[vignette](https://cran.r-project.org/web/packages/rio/vignettes/rio.html).
+
+If you need to import very large datasets (potentially several GBs), you might
+want to look at packages like `{vroom}` ([this
+benchmark](https://vroom.r-lib.org/articles/benchmarks.html#reading-delimited-files)
+shows a 1.5G csv file getting imported in seconds by `{vroom}`. For even larger
+files, take a look at `{arrow}` [here](https://arrow.apache.org/docs/r/). This
+package is able to efficiently read very large files (`csv`, `json`, `parquet`
+and `feather` formats).
+
+## A little aside on pipes
+
+Since R version 4.1, a forward pipe `|>` is included in the standard library of
+the language. It allows to do this:
+
+```{r}
+
+4 |>
+  sqrt()
+
+```
+
+Before R version 4.1, there was already a forward pipe, introduced with the
+`{magrittr}` package (and automatically loaded by many other packages from the
+*tidyverse*, like `{dplyr}`):
+
+```{r}
+library(dplyr)
+
+4 %>%
+  sqrt()
+
+```
+
+Both expressions above are equivalent to `sqrt(4)`. You will see why this is
+useful very soon. For now, just know this exists and try to get used to it.
+
+## Exploring and cleaning data with R
+
+Take a look at [chapter
+4](https://b-rodrigues.github.io/modern_R/descriptive-statistics-and-data-manipulation.html#a-first-taste-of-data-manipulation-with-dplyr)
+of my other book, ideally you should study the entirety of the chapter, but for
+our purposes you should really focus on sections 4.3, 4.4, 4.5.3, 4.5.4,
+(optionally 4.7) and 4.8.
+
+
+## Data visualization
+
+We're not going to focus on visualization due to lack of time. If you need to
+create graphs, read [chapter
+5](https://b-rodrigues.github.io/modern_R/graphs.html).
+
+## Further reading
+
+[R for Data Science](https://r4ds.had.co.nz/)

_quarto.yml

@@ -13,6 +13,7 @@ book:
   downloads: [pdf, epub]
   chapters:
     - index.qmd
+    - 02-intro_R.qmd
   page-navigation: true
 
 bibliography: references.bib