R for Data Science

class: center, middle, inverse, title-slide

# R for Data Science
## Workshop 1
### Mark Neal
### DairyNZ
### 2021/03/03

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xaringan::inf_mr()

background-image: url(https://www.dairynz.co.nz/media/5789262/new_fallback_banner_1920x600_april2018.png)

???

Image credit: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Sharingan_triple.svg)

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# How would you arrange these tasks to get work done?

.pull-left[
- Communicate results
- Model
- Visualise]

.pull-right[
- Tidy data
- Import data
- Transform data]

---

# Here's what R for Data Science proposes

.center[![workflow](https://d33wubrfki0l68.cloudfront.net/795c039ba2520455d833b4034befc8cf360a70ba/558a5/diagrams/data-science-explore.png)]

## So where should we start?

### Homework 1: Read Introduction
Only a few pages, good context and easy to read
https://r4ds.had.co.nz/introduction.html

---

# Who are you?

## Homework 2: Intro and Learning persona

.left[
- Kia ora, my name is ...?
- I do ...?
- I am learning about R because ...?
- My relevant experience is [no programming/some statistics at uni/...]
- Optional fun fact: In 1996 I said the internet is a waste of time.
]

---

# My goals

## Get Masters students up to speed
--

## Best science workflows of any NZ research group

## Fill my R and tidyverse gaps

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# Teaching and learning
--

### Learning is Cognitive AND Social
--

### Active learning is better

### Reduce cognitive load

### Get Feedback

Homework 3: Provide some feedback

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# Methods we will use

- Spaced practise
- Retrieval practise (homework, repetition) 
- Elaboration (Explain to self and others) 
- Concrete examples (then generalise to principles)

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# Benefits of realtime coding

- It slows me down

- You get to see my workflow

- You get to see my mistakes...

- ...and hopefully how I fix them

- You can quickly compare your code, your neighbours, mine and R4DS to troubleshoot

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# Back to work!

## Rstudio

## livecoder (browser)

## R for Data Science (https://r4ds.had.co.nz/data-visualisation.html)

## Teams (commentary and help)

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# Homework 1: Read intro
### Answer these questions (25 min)
- Describe the steps in the basic data analysis cycle.
- Explain the relative strengths and weaknesses of visualization and modeling.
- Explain when techniques beyond those described in these lessons may be needed.
- Explain the differences between hypothesis generation and hypothesis confirmation.
- Describe and install the prerequisites for these lessons.
- Explain where and how to get help.

---

### Homework 2: 
Post Learning Persona on Yammer (5 min)
### Homework 3: 
Post feedback on Yammer (5 min)
### Homework 4: 
Print and keep copy of ggplot cheatsheet (5 min)
### Homework 5: 
Post a plot on Yammer (10 min)
### Homework 6: 
Finish ggplot chapter if you haven't already (?)

---

# R Code

```r
# a boring regression
fit = lm(dist ~ 1 + speed, data = cars)
coef(summary(fit))
```

```
#               Estimate Std. Error   t value     Pr(>|t|)
# (Intercept) -17.579095  6.7584402 -2.601058 1.231882e-02
# speed         3.932409  0.4155128  9.463990 1.489836e-12
```

```r
dojutsu = c('地爆天星', '天照', '加具土命', '神威', '須佐能乎', '無限月読')
grep('天', dojutsu, value = TRUE)
```

```
# character(0)
```

---

# R Plots

```r
par(mar = c(4, 4, 1, .1))
plot(cars, pch = 19, col = 'darkgray', las = 1)
abline(fit, lwd = 2)
```

![](test-Xaringan-presentation_files/figure-html/cars-1.svg)

---

# Tables

If you want to generate a table, make sure it is in the HTML format (instead of Markdown or other formats), e.g.,

```r
knitr::kable(head(iris), format = 'html')
```

<table>
 <thead>
  <tr>
   <th style="text-align:right;"> Sepal.Length </th>
   <th style="text-align:right;"> Sepal.Width </th>
   <th style="text-align:right;"> Petal.Length </th>
   <th style="text-align:right;"> Petal.Width </th>
   <th style="text-align:left;"> Species </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 5.1 </td>
   <td style="text-align:right;"> 3.5 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.9 </td>
   <td style="text-align:right;"> 3.0 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.7 </td>
   <td style="text-align:right;"> 3.2 </td>
   <td style="text-align:right;"> 1.3 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.6 </td>
   <td style="text-align:right;"> 3.1 </td>
   <td style="text-align:right;"> 1.5 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5.0 </td>
   <td style="text-align:right;"> 3.6 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5.4 </td>
   <td style="text-align:right;"> 3.9 </td>
   <td style="text-align:right;"> 1.7 </td>
   <td style="text-align:right;"> 0.4 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
</tbody>
</table>

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# Some Tips

- There are several ways to build incremental slides. See [this presentation](https://slides.yihui.org/xaringan/incremental.html) for examples.

- The option `highlightLines: true` of `nature` will highlight code lines that start with `*`, or are wrapped in `{{ }}`, or have trailing comments `#<<`;

```yaml
    output:
      xaringan::moon_reader:
        nature:
          highlightLines: true
    ```

See examples on the next page.

---

# Some Tips

An example of using the trailing comment `#<<` to highlight lines:

````markdown
```{r tidy=FALSE}
library(ggplot2)
ggplot(mtcars) + 
  aes(mpg, disp) + 
  geom_point() +   #<<
  geom_smooth()    #<<
```
````

Output:

```r
library(ggplot2)
ggplot(mtcars) + 
  aes(mpg, disp) + 
* geom_point() +
* geom_smooth()
```

---