RStudio::conf 2020

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# RStudio::conf 2020
## Reflections
### .left[<br> _Anna Krystalli_]
### .left[<em>2020-03-04, Sheffield R Users Group</em>]

---

# 👋 Hello

### me: **Dr Anna Krystalli**

- **Research Software Engineer**, _University of Sheffield_
    + <i class="fab  fa-twitter "></i> **@annakrystalli**
    + <i class="fab  fa-github "></i> **@annakrystalli**
    + <i class="fas  fa-envelope "></i> **a.krystalli[at]sheffield.ac.uk**
    
- **Editor [rOpenSci](http://onboarding.ropensci.org/)**

- **Co-organiser:** [Sheffield R Users group](https://www.meetup.com/SheffieldR-Sheffield-R-Users-Group/)

<br>

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layout: true

.footnote[<i class="fab  fa-twitter "></i> @annakrystalli]

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## Rstudio::conf 2020

##### <i class="fas  fa-map-pin "></i> &nbsp; San Francisco, USA  &nbsp; &nbsp;<i class="fas  fa-calendar "></i> &nbsp; 25 - 28 Jan

> ### 2 days of [Workshops](https://rviews.rstudio.com/2020/01/27/rstudio-conf-2020-workshopsr/)
>
> - All materials [available](https://github.com/rstudio-conf-2020)
> 
> ### 2 days of talks
>
> - All [recorded](https://resources.rstudio.com/rstudio-conf-2020)

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.center[
## Data visualisation
]

---

## [Data, visualization, and designing AI](https://resources.rstudio.com/rstudio-conf-2020/data-visualization-and-designing-ai-fernanda-viegas-and-martin-wattenberg)

#### _Fernanda Viegas & Martin Wattenberg_

---

## [Facets - KNOW YOUR DATA](https://pair-code.github.io/facets/)

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### Better data leads to better models.
]

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**Understanding your data is critical to building a powerful machine learning system.**

- **Facets Overview:** Get a sense of the shape of each feature of your dataset

- **Facets Dive.** explore individual observations  
]

---

## Facets Overview

Summary exploration of data

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]

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<img src="assets/rstudioconf-facets-overview2.png" width="1661" />
]

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## Facets Dive

Exploration of individual datasets

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]

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]
---

## Facets Dive - Quick Draw

[![](assets/rstudioconf-facets-dive-draw.png)](https://pair-code.github.io/facets/quickdraw.html)

---
## [People + AI Research (PAIR)](https://research.google/teams/brain/pair/)

PAIR is devoted to **advancing the research and design of people-centric AI systems**.

We're interested in the full spectrum of human interaction with machine intelligence, from supporting engineers to understanding everyday experiences with AI.

#### [People + AI Guidebook](https://pair.withgoogle.com/)

---
## [Auto-magic Package Development](https://resources.rstudio.com/rstudio-conf-2020/auto-magic-package-development-alicia-schep)

#### _Alicia Schep_

**`vlbuildr`**:  R functional interface for [Vega-Lite](https://vega.github.io/vega-lite/) graphics

---

### Vegalite

---

### Vegalite

---

## `vlbuildr` in action

```r
vl_chart() %>%
  vl_add_data(url = "https://vega.github.io/vega-editor/app/data/cars.json") %>%
  vl_mark_point() %>%
  vl_encode_x(field = "Horsepower", type = "quantitative") %>%
  vl_encode_y(field = "Miles_per_Gallon", type = "quantitative") %>%
  vl_encode_color(field = list("repeat" = "column"), type = "nominal") %>%
  vl_repeat_col("Origin", "Cylinders") %>%
  vl_resolve_scale_color("independent")
```
---

<div id="htmlwidget-78e01039cb43f9b53e5d" style="width:auto;height:auto;" class="vegawidget html-widget"></div>
<script type="application/json" data-for="htmlwidget-78e01039cb43f9b53e5d">{"x":{
  "chart_spec": {
    "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
    "data": {
      "url": "https://vega.github.io/vega-editor/app/data/cars.json"
    },
    "spec": {
      "mark": {
        "type": "point"
      },
      "encoding": {
        "x": {
          "field": {
            "repeat": "column"
          },
          "type": "quantitative"
        },
        "y": {
          "field": {
            "repeat": "row"
          },
          "type": "quantitative"
        },
        "color": {
          "value": "grey",
          "condition": {
            "selection": "brush",
            "type": "nominal",
            "field": "Origin"
          }
        }
      },
      "selection": {
        "brush": {
          "resolve": "global",
          "type": "interval"
        }
      }
    },
    "repeat": {
      "row": [
        "Horsepower",
        "Acceleration",
        "Miles_per_Gallon"
      ],
      "column": [
        "Miles_per_Gallon",
        "Acceleration",
        "Horsepower"
      ]
    }
  },
  "embed_options": {
    "defaultStyle": true,
    "renderer": "canvas"
  }
},"evals":[],"jsHooks":[]}</script>

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.center[
# the stuff of dreams
]

---

## [Shiny styling](https://resources.rstudio.com/rstudio-conf-2020/styling-shiny-apps-with-sass-and-bootstrap-4-joe-cheng) w `bootstraplib`

#### _Joe Cheng_

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## [Shiny styling](https://resources.rstudio.com/rstudio-conf-2020/styling-shiny-apps-with-sass-and-bootstrap-4-joe-cheng) w `bootstraplib`

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]

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]

---

---

## `bootstraplib` addin

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## [Reproducible shinyapps w `shinymeta`](https://resources.rstudio.com/rstudio-conf-2020/reproducible-shiny-apps-with-shinymeta-dr-carson-sievert)

#### _Carson Sievert_

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### Example `shinymeta`

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### `shinymeta` usage

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### `shinymeta` outputs

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]

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## [Interactivity and Programming in the Tidyverse](https://resources.rstudio.com/rstudio-conf-2020/interactivity-and-programming-in-the-tidyverse-lionel-henry)

#### _Lionel Henry_

advances in the tidy eval framework that make it easier to program around tidyverse pipelines without having to learn a lot of theory.

---
## Data-masking

> ### Idea of blending data with the workspace

```r
lm(Sepal.Length ~ Sepal.Width, data = iris)
```

```
## 
## Call:
## lm(formula = Sepal.Length ~ Sepal.Width, data = iris)
## 
## Coefficients:
## (Intercept)  Sepal.Width  
##      6.5262      -0.2234
```

---

## Data-masking

> #### Creates ambiguity between **data-variables** and **environment-variables**
 - unexpected masking by data-variables
 - data-variables that can't pass through functions

---

### Masking by data-variables

.pull-left[

```r
n <- 100
data.frame(x = 1) %>%
  mutate(y = x / n) %>%
  pull(y)
```

```
## [1] 0.01
```

]

.pull-right[

```r
data.frame(x = 1,
           n = 2) %>%
  mutate(y = x / n) %>%
  pull(y)
```

```
## [1] 0.5
```

]

---
### Masking by data-variables

#### Solution: Be explicit!

```r
data.frame(x = 1,
           n = 2) %>%
  mutate(y = .data$x / .env$n) 
```

```
##   x n    y
## 1 1 2 0.01
```

Use **`.env`** pronoun to refer to objects in environment and `.data` for data columns

---

### Passing data-variables through funtions

```r
mean_by <- function(data, by, var){
  data %>%
    group_by(by) %>%
    summarise(avg = mean(var))
}
```

```r
iris %>% mean_by(Species, Petal.Length)
```

```
## Error: Column `by` is unknown
```

---

### Passing data-variables through funtions

Tunnel data variable through env-variable with **`{{ }}`**

```r
mean_by <- function(data, by, var){
  data %>%
*   group_by({{ by }}) %>%
*   summarise(avg = mean({{ var }}))
}
```

```r
iris %>% mean_by(Species, Petal.Length)
```

```
## # A tibble: 3 x 2
##   Species      avg
##   <fct>      <dbl>
## 1 setosa      1.46
## 2 versicolor  4.26
## 3 virginica   5.55
```

---

### Programmable result name

Use glue string!

```r
mean_by <- function(data, by, var){
  data %>%
    group_by({{ by }}) %>%
*   summarise("{{ var }}" := mean({{ var }}))
}
```

```r
iris %>% mean_by(Species, Petal.Length)
```

```
## # A tibble: 3 x 2
*##   Species    Petal.Length
##   <fct>             <dbl>
## 1 setosa             1.46
## 2 versicolor         4.26
## 3 virginica          5.55
```
---

## Writting functions with data masking propagates it

<br>

> ### Users will need to be aware!

---

### Wrap tidy pipelines without data-masking?

```r
mean_by <- function(data, by, var){
  data %>%
*   group_by(.data[[by]]) %>%
*   summarise("{var}" := mean(.data[[var]]))
}
```

```r
iris %>% mean_by("Species", "Petal.Length")
```

```
## # A tibble: 3 x 2
##   Species    Petal.Length
##   <fct>             <dbl>
## 1 setosa             1.46
## 2 versicolor         4.26
## 3 virginica          5.55
```

---

## [Rstudio 1.3 Sneak Preview](https://resources.rstudio.com/rstudio-conf-2020/rstudio-1-3-sneak-preview-jonathan-mcpherson)

#### _Jonathan McPherson_

---

## Rstudio 1.3 Sneak Preview

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]

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<img src="assets/rstudioconf-1.3schk2.png" width="5376" />
]

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.center[
# masters of entertainment
]

---

## [Learning R with humorous side projects](https://resources.rstudio.com/rstudio-conf-2020/learning-r-with-humorous-side-projects-ryan-timpe)

#### _Ryan Timpe_

---

##### Q: Which season should I watch while playing the Golden Girls Drinking Game?

---

### Animated paths of Jurassic Park characters

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### Learn new tools!

<img src="assets/rstudioconf-humour-tools.png" width="70%" />
---

### [One R Markdown Document, Fourteen Demos](https://resources.rstudio.com/rstudio-conf-2020/one-r-markdown-document-fourteen-demosyihui-xie?wvideo=aj3deolth1)

#### _Yihui Xie_

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.center[
# feel good moments
]
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## R-ladies!

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Hello 👋 from <a href="https://twitter.com/hashtag/RLadies?src=hash&amp;ref_src=twsrc%5Etfw">#RLadies</a> at <a href="https://twitter.com/hashtag/rstudioconf?src=hash&amp;ref_src=twsrc%5Etfw">#rstudioconf</a>!!💜 <a href="https://t.co/lTUK2Ap9bc">pic.twitter.com/lTUK2Ap9bc</a></p>&mdash; R-Ladies Global (@RLadiesGlobal) <a href="https://twitter.com/RLadiesGlobal/status/1223082687759441920?ref_src=twsrc%5Etfw">January 31, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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## RStudio, PBC

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#### _J.J. Allaire_

]

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**Primary purpose:** [Create free and open-source software for data science, scientific research, and technical communication.](https://blog.rstudio.com/2020/01/29/rstudio-pbc/)

]
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## Thanks for 👀

### slides: **bit.ly/rstudioconf20**