teaching R:

# teaching R:
## five principles <br> 🔗 <a href="http://bit.ly/teach-r-5">bit.ly/teach-r-5</a>
### <br><br> dr. mine çetinkaya-rundel
### 7 april 2021

---

## Imagine you’re new to baking, and you’re in a baking class. I’m going to present two options for starting the class. Which one gives you **better sense** of the final product?

---

>### Today we’re going to make a 
>### pineapple and coconut sandwich 
>### sponge cake with these ingredients

---

>### Today we’re going to 
>### make a pineapple and 
>### coconut sandwich 
>### sponge cake

---

## .hand[ OK, hold on to that thought! ]

---

.pull-left-narrow[
  .huge-blue-number[2]
]
.pull-right-wide[
  .larger[
  design
  foundations
  ]
]

---

### .pink[ design foundation 1: ]

## backwards design

Set goals for educational curriculum before choosing instructional methods + forms of assessment

1. Identify desired results
1. Determine acceptable evidence
1. Plan learning experiences and instruction

---

## designing backwards

1. Identify desired **data analysis** results
1. Determine **building blocks**
1. Plan learning experiences and instruction

---

### .pink[ design foundation 2: ]

## 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE)

1. Teach statistical thinking.  
  - Teach statistics as an investigative process of problem-solving and decision making.
  - Give students experience with multivariable thinking [...] to answer challenging questions that require them to investigate and explore relationships among many variables.
1. Focus on conceptual understanding. 
1. Integrate real data with a context and purpose.
1. Foster active learning.
1. Use technology to explore concepts and analyse data.
1. Use assessments to improve and evaluate student learning.

---

## 2016 GAISE

.pull-left-wide[
1. Teach **statistical thinking**.  
  - Teach statistics as an investigative process of problem-solving and decision making.
  - Give students experience with multivariable thinking [...] to answer challenging questions that require them to investigate and explore relationships among many variables.
1. Focus on conceptual understanding. 
1. Integrate real data with a context and purpose.
1. Foster active learning.
1. Use technology to explore concepts and analyse data.
1. Use assessments to improve and evaluate student learning.
]
.pull-right-narrow[
.hand-blue[
NOT a commonly used subset of tests and intervals and produce them with hand calculations
]
]

---

## 2016 GAISE

.pull-left-wide[
1. Teach statistical thinking.  
  - Teach statistics as an investigative process of problem-solving and decision making.
  - Give students experience with multivariable thinking [...] to answer challenging questions that require them to **investigate and explore relationships among many variables**.
1. Focus on conceptual understanding. 
1. Integrate real data with a context and purpose.
1. Foster active learning.
1. Use technology to explore concepts and analyse data.
1. Use assessments to improve and evaluate student learning.
]
.pull-right-narrow[
.hand-blue[
Multivariate analysis requires the use of computing
]
]

---

## 2016 GAISE

.pull-left-wide[
1. Teach statistical thinking.  
  - Teach statistics as an investigative process of problem-solving and decision making.
  - Give students experience with multivariable thinking [...] to answer challenging questions that require them to investigate and explore relationships among many variables.
1. Focus on conceptual understanding. 
1. Integrate real data with a context and purpose.
1. Foster active learning.
1. **Use technology** to explore concepts and analyse data.
1. Use assessments to improve and evaluate student learning.
]
.pull-right-narrow[
.hand-blue[
NOT use technology that is only applicable in the intro course or that doesn’t follow good science principles
]
]

---

## 2016 GAISE

.pull-left-wide[
1. Teach statistical thinking.  
  - Teach statistics as an investigative process of problem-solving and decision making.
  - Give students experience with multivariable thinking [...] to answer challenging questions that require them to investigate and explore relationships among many variables.
1. Focus on conceptual understanding. 
1. Integrate real data with a context and purpose.
1. Foster active learning.
1. Use technology to explore concepts and **analyse data**.
1. Use assessments to improve and evaluate student learning.
]
.pull-right-narrow[
.hand-blue[
Data analysis isn’t just inference and modelling, it’s also data importing, cleaning, preparation, exploration, and visualization
]
]

---

---

## Which kitchen would you rather bake in?

.pull-left[
<img src="img/kitchen-inrepair.png" width="512" />
]
.pull-right[
<img src="img/kitchen-built.png" width="512" />
]

---

## Which kitchen would you rather bake in?

.pull-left[
<img src="index_files/figure-html/kitchen-inrepair-xed-1.png" width="512" />
]
.pull-right[
<img src="index_files/figure-html/kitchen-built-selected-1.png" width="544" />
]

---

.pull-left-wide[
  .right[
  .larger[
  cherish  
  day  
  one  
  ]  
  ]
]
.pull-right-narrow[
  .huge-pink-number[1]
]

---

.pull-left[
<img src="img/kitchen-inrepair.png" width="512" height="250" />
- Install R
- Install RStudio
- Install the following packages:
  - tidyverse
  - rmarkdown
  - ...
- Load these packages
- Install git
]
.pull-right[
<img src="img/kitchen-built.png" width="512" height="250" />
- Go to rstudio.cloud (or some other server based solution)
- Log in with your ID & pass

```r
> hello R!
```
]

---

## How do you prefer your cake recipes? Words only, or words & pictures?

.pull-left[
<img src="img/recipe-picture.png" width="512" />
]
.pull-right[
<img src="img/recipe-words.png" width="512" />
]

---

## How do you prefer your cake recipes? Words only, or words & pictures?

.pull-left[
<img src="index_files/figure-html/recipe-picture-selected-1.png" width="544" />
]
.pull-right[
<img src="index_files/figure-html/recipe-words-xed-1.png" width="512" />
]

---

.pull-left-wide[
  .right[
  .larger[
  start  
  with  
  cake  
  ]  
  ]
]
.pull-right-narrow[
  .huge-pink-number[2]
]

---

.pull-left[
<img src="img/recipe-picture.png" width="250" height="150" style="display: block; margin: auto;" />
- Open today's demo project
- Knit the document and discuss the visualisation you made with your neighbor
- Then, change `Turkey` to a different country, and plot again
]
.pull-right[
<img src="img/recipe-words.png" width="250" height="150" style="display: block; margin: auto;" />
.small[

```r
x <- 8
y <- "monkey"
z <- FALSE

class(x)
```

```
## [1] "numeric"
```

```r
class(y)
```

```
## [1] "character"
```

```r
class(z)
```

```
## [1] "logical"
```
]
]

---

[minecr.shinyapps.io/unvotes](https://minecr.shinyapps.io/unvotes/)

[<img src="img/unvotes-app.png" width="906" />](https://minecr.shinyapps.io/unvotes/)

---

## .hand[ with great examples, ]
## .hand[ comes a great amount of code... ]

---

## .hand[ but let’s focus on the task at hand... ]

- Open today's demo project
- Knit the document and discuss the visualisation you made with your neighbor
- Then, **.pink[ change `Turkey` to a different country, and plot again]**

---

```r
un_votes %>%
  filter(country %in% c("United States", "Turkey")) %>% 
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  group_by(country, year = year(date), issue) %>%
  summarize(
    votes = n(),
    perc_yes = mean(vote == "yes")
    ) %>%
  filter(votes > 5) %>%
  ggplot(mapping = aes(x = year, y = perc_yes, color = country)) +
    geom_point() +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
      title = "Percentage of Yes votes in the UN General Assembly",
      subtitle = "1946 to 2015",
      y = "% Yes", x = "Year", color = "Country"
    )
```
]

---

```r
un_votes %>%
* filter(country %in% c("United States", "Turkey")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  group_by(country, year = year(date), issue) %>%
  summarize(
    votes = n(),
    perc_yes = mean(vote == "yes")
    ) %>%
  filter(votes > 5) %>%
  ggplot(mapping = aes(x = year, y = perc_yes, color = country)) +
    geom_point() +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
      title = "Percentage of Yes votes in the UN General Assembly",
      subtitle = "1946 to 2015",
      y = "% Yes", x = "Year", color = "Country"
    )
```
]

---

```r
un_votes %>%
* filter(country %in% c("United States", "France")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  group_by(country, year = year(date), issue) %>%
  summarize(
    votes = n(),
    perc_yes = mean(vote == "yes")
    ) %>%
  filter(votes > 5) %>%
  ggplot(mapping = aes(x = year, y = perc_yes, color = country)) +
    geom_point() +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ issue) +
    labs(
      title = "Percentage of Yes votes in the UN General Assembly",
      subtitle = "1946 to 2015",
      y = "% Yes", x = "Year", color = "Country"
    )
```
]

---

## Which motivates you more to learn how to cook: perfectly chopped onions or ratatouille?

.pull-left[
<img src="img/chop-onions.png" width="512" />
]
.pull-right[
<img src="img/make-ratatouille.png" width="512" />
]

---

## Which motivates you more to learn how to cook: perfectly chopped onions or ratatouille?

.pull-left[
<img src="index_files/figure-html/chop-onions-xed-1.png" width="512" />
]
.pull-right[
<img src="index_files/figure-html/make-ratatouille-selected-1.png" width="544" />
]

---

.pull-left-wide[
  .right[
  .larger[
  skip  
  baby  
  steps  
  ]  
  ]
]
.pull-right-narrow[
  .huge-pink-number[3]
]

---

.pull-left[
<img src="img/chop-onions.png" width="250" height="150" style="display: block; margin: auto;" />
![](index_files/figure-html/unvotes-univariate-1.png)
]
.pull-right[
<img src="img/make-ratatouille.png" width="250" height="150" style="display: block; margin: auto;" />
.small[
<img src="index_files/figure-html/unvotes-multivariate-1.png" width="100%" />
]
]

---

## .hand[ non-trivial examples can be motivating, ]
## .hand[ but need to avoid ] 👇

.pull-left[
<img src="img/owl-step1.png" width="250" height="390" style="display: block; margin: auto 0 auto auto;" />
]
.pull-right[
<img src="img/owl-step2.png" width="250" height="390" style="display: block; margin: auto auto auto 0;" />
]

---

.center[
.three-column[
<img src="img/owl-step1.png" width="250" height="400" />
]
.three-column[
## .center[ .hand[ scaffold + layer ] ]
]
.three-column[
<img src="img/owl-step2.png" width="250" height="400" />
]
]

---

.discussion[
The following is used to create the multivariate visualisation from earlier. How much of the code would you show/hide when just starting teaching ggplot2?
]

```r
un_votes %>%
  filter(country %in% c("United States")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  mutate(importantvote = ifelse(importantvote == 0, "No", "Yes")) %>%
  ggplot(aes(y = importantvote, fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN", 
    subtitle = "By issue and importance of vote",
    x = "Important vote", y = "", fill = "Vote"
    ) +
  theme_minimal() +
  scale_fill_viridis_d(option = "E")
```
]

---

## Designing code snippets for teaching

- Write it out to your heart's desire and polish it

- Then, split into three parts:
  - 📦 **Pre-process:** Required, but isn't directly connected to / far off from learning goals of current lesson
  - 💔 **Stash:** Not required, and not directly connected to learning goals of current lesson 
    - Likely concepts that fit better into future lessons)
  - ✅ **Feature:** Heart of the lesson (and maybe a review of a previous lessons)
  
- Finally, decide on the pace at which to scaffold and layer

---

## 📦 Pre-process

We'll call the highlighted lines `us_votes`

```r
*un_votes %>%
* filter(country %in% c("United States")) %>%
* inner_join(un_roll_calls, by = "rcid") %>%
* inner_join(un_roll_call_issues, by = "rcid") %>%
* mutate(importantvote = ifelse(importantvote == 0, "No", "Yes")) %>%
  ggplot(aes(y = importantvote, fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN", 
    subtitle = "By issue and importance of vote",
    x = "Important vote", y = "", fill = "Vote"
    ) +
  theme_minimal() +
  scale_fill_viridis_d(option = "E")
```
]

---

```r
us_votes
```

```
## # A tibble: 5,718 x 14
##     rcid country country_code vote  session importantvote date       unres amend
##    <dbl> <chr>   <chr>        <fct>   <dbl> <chr>         <date>     <chr> <int>
##  1     6 United… US           no          1 No            1946-01-04 R/1/…     0
##  2     8 United… US           no          1 No            1946-01-05 R/1/…     1
##  3    11 United… US           yes         1 No            1946-02-05 R/1/…     0
##  4    11 United… US           yes         1 No            1946-02-05 R/1/…     0
##  5    18 United… US           no          1 No            1946-02-03 R/1/…     1
##  6    19 United… US           yes         1 No            1946-02-03 R/1/…     0
##  7    24 United… US           yes         1 No            1946-12-05 R/1/…     0
##  8    26 United… US           no          1 No            1946-12-06 R/1/…     0
##  9    27 United… US           yes         1 No            1946-12-06 R/1/…     0
## 10    28 United… US           yes         1 No            1946-12-06 R/1/…     0
## # … with 5,708 more rows, and 5 more variables: para <int>, short <chr>,
## #   descr <chr>, short_name <chr>, issue <fct>
```
]

---

## 💔 Stash

```r
un_votes %>%
  filter(country %in% c("United States")) %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid") %>%
  mutate(importantvote = ifelse(importantvote == 0, "No", "Yes")) %>%
  ggplot(aes(y = importantvote, fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN", 
    subtitle = "By issue and importance of vote",
    x = "Important vote", y = "", fill = "Vote"
    ) +
* theme_minimal() +
* scale_fill_viridis_d(option = "E")
```
]

---

## ✅ Feature

```r
us_votes %>%
  ggplot(aes(y = importantvote, fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN", 
    subtitle = "By issue and importance of vote",
    x = "Important vote", y = "", fill = "Vote"
    )
```

---

```r
*ggplot(data = us_votes)
```
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-4-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
* mapping = aes(x = importantvote,
*               fill = vote))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-5-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
  mapping = aes(x = importantvote, 
                fill = vote)) +
* geom_bar(position = "fill")
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-6-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
  mapping = aes(x = importantvote, 
                fill = vote)) +
  geom_bar(position = "fill") +
* facet_wrap(~ issue, ncol = 1)
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-7-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
  mapping = aes(x = importantvote, 
                fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
* labs(
*   title = "How the US voted in the UN",
*   subtitle = "By issue and importance of vote",
*   x = "Important vote",
*   y = ""
*   )
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
  mapping = aes(x = importantvote, 
                fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN",
    subtitle = "By issue and importance of vote",
    x = "Important vote",
    y = "",
*   fill = "Vote"
    ) 
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-9-1.png" width="100%" />
]

---

```r
ggplot(data = us_votes, 
  mapping = aes(y = importantvote, 
                fill = vote)) +
  geom_bar(position = "fill") +
  facet_wrap(~ issue, ncol = 1) +
  labs(
    title = "How the US voted in the UN", 
    subtitle = "By issue and importance of vote",
    x = "Important vote", 
    y = "", 
    fill = "Vote"
    )
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-10-1.png" width="100%" />
]

---

## ~~Skip~~ Re-insert baby steps

---

## Which is more likely to appeal to someone who has never tried broccoli?

.pull-left[
<img src="img/broccoli-raw.png" width="512" />
]
.pull-right[
<img src="img/broccoli-cooked.png" width="512" />
]

---

## Which is more likely to appeal to someone who has never tried broccoli?

.pull-left[
<img src="index_files/figure-html/broccoli-raw-xed-1.png" width="512" />
]
.pull-right[
<img src="index_files/figure-html/broccoli-cooked-selected-1.png" width="544" />
]

---

.pull-left-wide[
  .right[
  .larger[
  hide  
  the  
  veggies  
  ]  
  ]
]
.pull-right-narrow[
  .huge-pink-number[4]
]

---

.pull-left-narrow[
<img src="img/broccoli-raw.png" width="250" height="150" style="display: block; margin: auto;" />

Today we're going to do web scraping
- Using the **rvest** package
- And with the help of *regular expressions*
]
.pull-right-wide[
<img src="img/broccoli-cooked.png" width="250" height="150" style="display: block; margin: auto;" />

- Today we go from this to that
<img src="img/open-secrets-nc.png" width="687" />
- and do so in a way that is easy to replicate for another state
]

---

## .hand[ students will encounter lots of ]
## .hand[ new challenges along the way -- ]
## .hand[ let that happen, ]
## .hand[ and then provide a solution ]

---

## Start with a mini-lecture

- **Lesson:** Web scraping essentials for turning a structured table into a data frame in R.

---

## Follow up with a hands-on exercise

- **Lesson:** Web scraping essentials for turning a structured table into a data frame in R.
- **Ex 1:** Scrape the table off the web and save as a data frame.

---

## And a thought exercise

- **Lesson:** Web scraping essentials for turning a structured table into a data frame in R.
- **Ex 1:** Scrape the table off the web and save as a data frame.
- **Ex 2:** What other information do we need represented as variables in the data to obtain the desired facets?

---

## And finally, the veggies!

---

## If you are already taking a baking class, which will be easier to venture on to?

.pull-left[
<img src="img/make-pastries.png" width="512" />
]
.pull-right[
<img src="img/make-tacos.png" width="512" />
]

---

## If you are already taking a baking class, which will be easier to venture on to?

.pull-left[
<img src="index_files/figure-html/make-pastries-selected-1.png" width="544" />
]
.pull-right[
<img src="index_files/figure-html/make-tacos-xed-1.png" width="512" />
]

---

.pull-left-wide[
  .right[
  .larger[
  leverage  
  the  
  ecosystem  
  ]  
  ]
]
.pull-right-narrow[
  .huge-pink-number[5]
]

---

## Suppose...

Estimate the difference between the average evaluation score of male and female faculty.

```
## # A tibble: 463 x 5
##    score rank         ethnicity    gender bty_avg
##    <dbl> <fct>        <fct>        <fct>    <dbl>
##  1   4.7 tenure track minority     female    5   
##  2   4.1 tenure track minority     female    5   
##  3   3.9 tenure track minority     female    5   
##  4   4.8 tenure track minority     female    5   
##  5   4.6 tenured      not minority male      3   
##  6   4.3 tenured      not minority male      3   
##  7   2.8 tenured      not minority male      3   
##  8   4.1 tenured      not minority male      3.33
##  9   3.4 tenured      not minority male      3.33
## 10   4.5 tenured      not minority female    3.17
## # … with 453 more rows
```
]

---

.pull-left[
<img src="img/make-pastries.png" width="250" height="150" style="display: block; margin: auto;" />
.small[

```r
evals %>%
  specify(score ~ gender) %>%
  generate(reps = 1000, 
    type = "bootstrap") %>%
  calculate(stat = "diff in means", 
    order = c("male", "female")) %>%
  summarise(
    l = quantile(stat, 0.025), 
    u = quantile(stat, 0.975)
    )
```

```
## # A tibble: 1 x 2
##        l     u
##    <dbl> <dbl>
## 1 0.0407 0.236
```
]
]
.pull-right[
<img src="img/make-tacos.png" width="250" height="150" style="display: block; margin: auto;" />
.small[

```r
t.test(evals$score ~ evals$gender)
```

```
## 
## 	Welch Two Sample t-test
## 
## data:  evals$score by evals$gender
## t = -2.7507, df = 398.7, p-value = 0.006218
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.24264375 -0.04037194
## sample estimates:
## mean in group female   mean in group male 
##             4.092821             4.234328
```
]
]

---

## infer `$\in$` tidymodels

.pull-left-wide[
The objective of this package is to perform statistical inference using an expressive statistical grammar that coheres with the tidyverse design framework.
]
.pull-right-narrow[
<img src="img/infer-hex.png" width="553" />
]

---

```r
evals %>%
  specify(score ~ gender)
```

```
## Response: score (numeric)
## Explanatory: gender (factor)
## # A tibble: 463 x 2
##    score gender
##    <dbl> <fct> 
##  1   4.7 female
##  2   4.1 female
##  3   3.9 female
##  4   4.8 female
##  5   4.6 male  
##  6   4.3 male  
##  7   2.8 male  
##  8   4.1 male  
##  9   3.4 male  
## 10   4.5 female
## # … with 453 more rows
```
]

---

```r
set.seed(1234)
evals %>%
  specify(score ~ gender) %>%
  generate(reps = 1000, type = "bootstrap")
```

```
## Response: score (numeric)
## Explanatory: gender (factor)
## # A tibble: 463,000 x 3
## # Groups:   replicate [1,000]
##    replicate score gender
##        <int> <dbl> <fct> 
##  1         1   4   female
##  2         1   3.1 male  
##  3         1   5   male  
##  4         1   4.4 male  
##  5         1   3.5 female
##  6         1   4.5 female
##  7         1   4.5 male  
##  8         1   4.9 male  
##  9         1   4.4 male  
## 10         1   3.5 male  
## # … with 462,990 more rows
```
]

---

```r
set.seed(1234)
evals %>%
  specify(score ~ gender) %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "diff in means", order = c("male", "female"))
```

```
## # A tibble: 1,000 x 2
##    replicate     stat
##        <int>    <dbl>
##  1         1  0.230  
##  2         2  0.134  
##  3         3  0.100  
##  4         4  0.230  
##  5         5  0.128  
##  6         6  0.201  
##  7         7  0.168  
##  8         8  0.130  
##  9         9 -0.00490
## 10        10  0.123  
## # … with 990 more rows
```
]

---

```r
set.seed(1234)
evals %>%
  specify(score ~ gender) %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "diff in means", order = c("male", "female")) %>%
  visualise()
```

<img src="index_files/figure-html/infer-4-1.png" width="60%" />
]

---

```r
set.seed(1234)
evals %>%
  specify(score ~ gender) %>%
  generate(reps = 1000, type = "bootstrap") %>%
  calculate(stat = "diff in means", order = c("male", "female")) %>%
  summarise(l = quantile(stat, 0.025), u = quantile(stat, 0.975))
```

```
## # A tibble: 1 x 2
##        l     u
##    <dbl> <dbl>
## 1 0.0407 0.236
```
]

---

## One other way to "leverage the ecosystem"

Do it all in R!

- Slides with **xaringan**
- Course website with **blogdown**
- Course notes / textbook with **bookdown**
- A student dashboard with **flexdashboard**
- Git automation with **ghclass**
- Interactive tutorials with **learnr**
- ...

---

## .large-blue-number[5] design principles

.pink-number[1] cherish day one  
.pink-number[2] start with cake  
.pink-number[3] skip baby steps  
.pink-number[4] hide the veggies  
.pink-number[5] leverage the ecosystem

---

## .hand[ So, where do we go with all this? ]

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