Teaching an Introductory Data Science course with tidyverse workshop

# Teaching an Introductory Data Science course with tidyverse workshop
## Packages and External Datasets
### Dr. Mine Dogucu
### 2021-12-15

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<div class="my-header"></div>

<div class="my-footer"> 
 CC BY-NC-ND 4.0 <a href="https://mdogucu.ics.uci.edu">Mine Dogucu</a></div>

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## Goals

- R packages
- Importing Data
- Projects Workflow

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__Default__

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.footnote[Microsoft products have Copyright. Images used based on [fair use](https://www.microsoft.com/en-us/legal/copyright/default.aspx) for educational purposes.]

__Optional__

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## R packages

When you download R, you actually download base R.

But there are MANY optional packages you can download.

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## R packages

There are more than 15000 R packages.

Good part: There is an R package for (almost) everything, from complex statistical modeling packages to baby names.

Bad part: At the beginning it can feel overwhelming.

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## R packages

All this time we have actually been using R packages.

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## R packages

What do R packages have? All sorts of things but mainly

- functions

- datasets

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## R packages

Try running the following code:

```r
beep()
```

```
## Error in beep(): could not find function "beep"
```

Why are we seeing this error?

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## Using `install.packages()`

In your **Console**, install the beepr package

```r
install.packages("beepr")
```

We do this in the Console because we only need to do it once.

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## Using Packages pane

Packages Pane > Install

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## Letting RStudio Install

If you save your file and using a package RStudio will tell you that you have not installed the package.

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class:inverse middle

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## Using beep() from beepr

```r
library(beepr)
beep()
```

More common usage. 
Useful if you are going to use multiple functions from the same package.
E.g. we have used many functions (ggplot, aes, geom_...) from the ggplot2 package. In such cases, usual practice is to put the library name in the first R chunk in the .Rmd file.

]

```r
beepr::beep()
```
Useful when you are going to use a function once or few times. Also useful if there are any conflicts. For instance if there is some other package in your environment that has a beep() function that prints the word beep, you would want to distinguish the beep function from the beepr package and the beep function from the other imaginary package. 
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## Open Source

Any one around the world can create R packages.

Good part: We are able to do pretty much anything R because someone from around the world has developed the package and shared it.

Bad part: The language can be inconsistent.

Good news: We have tidyverse.

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## Tidyverse

>The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. 
                  tidyverse.org
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## Tidyverse

In short, tidyverse is a family of packages. From practical stand point, you can install many tidyverse packages at once (and you did this). By doing that you installed all the following packages.

- ggplot2
- dplyr
- tidyr
- readr
- purrr
- tibble
- stringr
- forcats

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We can also load the tidyverse packages all at the same time.

```r
library(tidyverse)
```

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## Fun fact

```r
library(magrittr)
```

<img src="img/pipe-logo.png" width="40%" style="display: block; margin: auto;" />
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.right-panel[
[Treachery of Images](https://en.wikipedia.org/wiki/The_Treachery_of_Images#/media/File:MagrittePipe.jpg) by René Magritte

.footnote[Image for Treachery of Images is from University of Alabama [website](https://tcf.ua.edu/Classes/Jbutler/T311/Modernism.htm) and used under fair use for educational purposes.]
]

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Three principles of naming files

- machine readable
- human readable
- plays well with default ordering (e.g. alphabetical and numerical ordering)

(Jenny Bryan)

for the purposes of this class an additional principle is that file names follow

- tidyverse style (all lower case letters, words separated by HYPHEN)

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- README file is the first file users read. In our case a user might be our future self, a teammate, or (if open source) anyone.

- There can be multiple README files within a single directory: e.g. for the general project folder and then for a data subfolder. Data folder README's can possibly contain codebook (data dictionary).

- It should be brief but detailed enough to help user navigate.

- a README should be up-to-date (e.g. from proposal to presentation stage of final projects they need to be updated).

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## README examples

- [ICS 80 website](https://github.com/ics80-fa21/website)
- [Museum of Modern Art Collection](https://github.com/MuseumofModernArt/collection)
- [R package bayesrules](https://github.com/bayes-rules/bayesrules)

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## Importing .csv Data

```r
readr::read_csv("dataset.csv")
```

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## Importing Excel Data

```r
readxl::read_excel("dataset.xlsx")
```

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## Importing Excel Data

```r
readxl::read_excel("dataset.xlsx", sheet = 2)
```

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## Importing SAS, SPSS, Stata Data

```r
library(haven)
# SAS
read_sas("dataset.sas7bdat")
# SPSS
read_sav("dataset.sav")
# Stata
read_dta("dataset.dta")
```

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## Where is the dataset file?

Importing data will depend on where the dataset is on your computer. However we use the help of `here::here()` function. 
This function sets the working directory to the project folder (i.e. where the `.Rproj` file is).

```r
read_csv(here::here("data/dataset.csv"))
```

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## Schedule for the Day

__10:00 - 10:15 Introduction and Setup__  
__10:15 - 11:15 Introduction to Toolkit and Data Basics__
__11:20 - 12:30 Data Visualization__  
__1:00 - 1:45 Data Wrangling__   
__1:45 - 2:15 Packages and External Datasets__  
2:15 - 2:30 Wrap Up