Authoring

class: title-slide, left, middle
background-image: url("images/renoir.jpg")
background-position: right
background-size: contain
background-color: #0071B8

.pull-left[

# Authoring

## R / Medicine 2021

### Alison Hill &#183; RStudio

#### [r3-rmedicine.netlify.app](https://r3-rmedicine.netlify.app)
]

---
class: middle, center

# <i class="fas fa-cloud"></i>

# Go here and log in (free):

https://rstd.io/r3-cloud

---
layout: true

<div class="my-footer"><span>https://rstd.io/r3-cloud</span></div>

---

# Your turn

**WARM-UP**: open `02-draft.qmd` and read the source file. It has several figures coded using the `ggplot2` package. Look at the source and answer the following questions:

1. What is the output **format**?

1. Are there any output **options**?

1. Are any `knitr` **execution** options set?

1. Anything in the code look foreign to you?

Finally, render this file. Anything in the output (or not in the output) surprise you?

---
class: middle, center, inverse

# Figures

---

# Including plots

.pull-left[

Will this print?

```r
# PLOT: Age distribution -------------------------------------------------------
ggplot(mockdata, aes(age)) +
  geom_histogram(color = 'white',
                 fill = scico(1, 
                              begin = .3, 
                              palette = "berlin"),
                 bins = 20) +
  labs(x = "Age", 
       y = "Count") +
  scale_y_continuous(
    breaks = scales::pretty_breaks()
  )
```

]

.pull-right[

]

---

# Including plots

.pull-left[

What about this?

```r
# PLOT: Age distribution -------------------------------------------------------
age_histogram <- 
  ggplot(mockdata, aes(age)) +
  geom_histogram(color = 'white',
                 fill = scico(1, 
                              begin = .3, 
                              palette = "berlin"),
                 bins = 20) +
  labs(x = "Age", 
       y = "Count") +
  scale_y_continuous(
    breaks = scales::pretty_breaks()
  )
```

]

.pull-right[

]

---

# Including plots

.pull-left[

What about this?

]

.pull-right[

]

???

so, how did we get a figure into R Markdown?

Answer: it has to print!

---

# Chunk options for plots

- fig resolution
- fig size (`fig.width`, `fig.height`, `fig.asp`)
- [fig "device"](https://r4ds.had.co.nz/graphics-for-communication.html#other-important-options)

See also:

https://r4ds.had.co.nz/graphics-for-communication.html#figure-sizing

.footnote[https://yihui.name/knitr/options/#plots]

---

# out.width

.pull-left[

````
```{r}
#| out.width="70%"
age_histogram
```
````

]

.pull-right[

````
```{r}
#| out.width="10%"
age_histogram
```
````

]
---

# fig-cap

.pull-left[

````
```{r fig-age} 
#| echo: FALSE
#| fig-cap: 'Age distributions'
age_histogram
```
````

]

.pull-right[

**Fig. 1:** Age distributions

]

---

# Chunk labels

.pull-left[

````
```{r}
#| label: peek
#| echo: FALSE
#| results: 'hide'
glimpse(mockdata)
```
````

Place either:

+ On a separate line after `#| label:`
+ In chunk header after `r`

]

.pull-right[

+ Careful! No duplicate chunk labels

````
    ```{r peek}
    head(mockdata)
    ```
    ````

```
    Error in parse_block(g[-1], g[1], params.src) : 
      duplicate label 'peek'
    Calls: <Anonymous> ... process_file -> split_file -> lapply -> FUN -> parse_block
    Execution halted
    ```

]

???

how can we make it easier on ourselves to explore the code in here?

show how to add chunk labels and view in IDE interactively

---
class: middle, center

# Chunks as house plants or crops?

.footnote[https://masalmon.eu/2017/08/08/chunkpets/]

---
class: middle, center

# A good chunk label

.pull-left[

### Good

`myplot`

`my-plot`

`myPlot`

`myplot1`

]

.pull-right[

### Bad

`my_plot`

`my plot`

everything else!

]

---
background-color: #fff
class: middle, center

# Think: kebabs, not snakes

.pull-left[
![](images/kebab.png)
]

.pull-right[
![](images/snake.png)
]
---

# Cross-reference a figure

.pull-left[

See Figure `@fig-age`.

````
```{r fig-age}
#| echo: FALSE
#| fig-cap: 'Age distributions'
age_histogram
```
````

]

.pull-right[

See Figure [1]().

.center[

<div class="figure">
<img src="02-authoring_files/figure-html/unnamed-chunk-11-1.png" alt="Age distributions" width="70%" />
<p class="caption">Age distributions</p>
</div>

]

---
class: middle, center

# Key takeaway

Once you have:

✔️ A labeled chunk that produces a plot

✔️ AND the plot label starts with `fig-`

✔️ AND a figure caption using `fig-cap` (**not `fig.cap`**)

Then you can have:

### Cross-references! In any format.

---
class: inverse, middle

# Live code demo

We open up `02-draft.Rmd` and take the static image and together we:

1. Label that chunk like `fig-bmj`

1. Add a cross-reference like `see Figure @fig-bmj`

1. Try again with `counts-by-site` code chunk: `see Figure @fig-counts-by-site` _(watch what happens if try to cross-reference without a caption, or use a chunk label that does not start with `fig-`!)_

---
class: middle, center, inverse

#  Layouts

---
class: middle, center

# The line-up

---
class: inverse, middle

# Live code demo

We take these plots and use:

1. Tabsets `::: {.panel-tabset}`

1. Panels with `#| layout-ncol: 2`

---
class: middle, center, inverse

# Tables

---
class: center, middle

# Two table types

.pull-left[

### for your eyes mainly

![](images/tables-you.jpg)

]

.pull-right[

### for other people's eyes too

![](images/tables-them.jpg)
]

---

.pull-left[

![](images/imani-_TuIdedBPwQ-unsplash.jpg)
]

.pull-right[
![](images/gear_shift.jpg)
]

.footnote[Images from [Unsplash](https://unsplash.com/photos/_TuIdedBPwQ) and [Wikipedia](https://commons.wikimedia.org/wiki/File:FIAT_500L_gear_shift.jpg)]

---
class: middle, center, inverse

# EDA Tables

---

# Let's try using data now

```r
mockdata <- read_csv(here::here("static/slides/data/mockdata.csv")) %>%
  mutate(fu_fct = fct_recode(as.factor(fu_stat), 
                             "Lived" = "1", 
                             "Died" = "2")) %>% 
  mutate_at(vars(starts_with("ae_")), ~as.factor(.))
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   .default = col_double(),
#>   arm = col_character(),
#>   sex = col_character(),
#>   race = col_character(),
#>   age_ord = col_character(),
#>   site = col_character(),
#>   country = col_character(),
#>   ethnicity = col_character(),
#>   name = col_character(),
#>   first_name = col_character(),
#>   last_name = col_character()
#> )
#> ℹ Use `spec()` for the full column specifications.
```

---

# EDA tables: `janitor`

Good for 1- and 2-way frequency tables with categorical variables

Count number of participants by `arm`...

.pull-left[

```r
mockdata %>% 
  tabyl(arm)
#>        arm   n   percent
#>     A: IFL 428 0.2855237
#>  F: FOLFOX 691 0.4609740
#>    G: IROX 380 0.2535023
```

]

.pull-right[

```r
mockdata %>% 
  tabyl(arm) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting()
#>        arm    n percent
#>     A: IFL  428   28.6%
#>  F: FOLFOX  691   46.1%
#>    G: IROX  380   25.4%
#>      Total 1499  100.0%
```

]

---

Add by status (`fu_fct`) too.

.pull-left[

````
```{r}
#| results: 'asis'
mockdata %>% 
  tabyl(arm, fu_fct) %>%
  knitr::kable()
```
````

|arm       | Lived| Died|
|:---------|-----:|----:|
|A: IFL    |    18|  410|
|F: FOLFOX |    99|  592|
|G: IROX   |    26|  354|

]

.pull-right[

````
```{r}
#| results: 'asis'
mockdata %>% 
  tabyl(arm, fu_fct) %>% 
  adorn_totals("row") %>%
  adorn_percentages("row") %>%
  adorn_pct_formatting(digits = 2) %>%
  adorn_ns() %>%
  knitr::kable()
```
````

|arm       |Lived        |Died          |
|:---------|:------------|:-------------|
|A: IFL    |4.21%  (18)  |95.79%  (410) |
|F: FOLFOX |14.33%  (99) |85.67%  (592) |
|G: IROX   |6.84%  (26)  |93.16%  (354) |
|Total     |9.54% (143)  |90.46% (1356) |

]

---
class: your-turn

# Your turn

## Fix this `tabyl`

Find this section:
```markdown
<--TODO: make this print better with knitr::kable + code chunks-->
```

Make this `tabyl` print nicely when knitted.

```r
mockdata %>% 
  tabyl(arm) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting() 
```

_psst: answers on next slide..._

---
class: your-turn

# Answer

````
```{r}
#| results: 'asis'
mockdata %>% 
  tabyl(arm) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting() %>% 
  knitr::kable()
```
````

|arm       |    n|percent |
|:---------|----:|:-------|
|A: IFL    |  428|28.6%   |
|F: FOLFOX |  691|46.1%   |
|G: IROX   |  380|25.4%   |
|Total     | 1499|100.0%  |

---
class: middle

.left-column[

]

.right-column[
# the gt package

<https://gt.rstudio.com/>
]

---
background-image: url(https://gt.rstudio.com/reference/figures/gt_parts_of_a_table.svg)
background-size: contain

---
background-image: url(https://gt.rstudio.com/reference/figures/gt_workflow_diagram.svg)
background-size: contain

---
class: middle

.pull-left[

```r
mockdata %>%
  count(arm) %>% 
  gt::gt()
```

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#coiargkoka .gt_table {
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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">arm</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">n</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">A: IFL</td>
<td class="gt_row gt_right">428</td></tr>
    <tr><td class="gt_row gt_left">F: FOLFOX</td>
<td class="gt_row gt_right">691</td></tr>
    <tr><td class="gt_row gt_left">G: IROX</td>
<td class="gt_row gt_right">380</td></tr>
  </tbody>
  
  
</table>
</div>
]

.pull-right[

```r
mockdata %>%
  count(arm, sex) %>% 
  pivot_wider(names_from = sex, 
              values_from = n) %>% 
  gt::gt()
```

<div id="eelfajfqmm" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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}

#eelfajfqmm .gt_table {
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}

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  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#eelfajfqmm .gt_title {
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  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#eelfajfqmm .gt_subtitle {
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}

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<table class="gt_table">
  
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      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Female</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Male</th>
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<td class="gt_row gt_right">277</td></tr>
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<td class="gt_row gt_right">152</td>
<td class="gt_row gt_right">228</td></tr>
  </tbody>
  
  
</table>
</div>

]

---
class: middle

.left-column[

![](http://www.danieldsjoberg.com/gtsummary/reference/figures/logo.png)
]

.right-column[
# the gtsummary package

<http://www.danieldsjoberg.com/gtsummary/index.html>
]

---
class: middle

# Simple frequency table

```r
library(gtsummary)
mockdata %>%
  select(arm) %>% 
  tbl_summary()
```

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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N = 1,499</strong><sup class="gt_footnote_marks">1</sup></th>
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  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">arm</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">A: IFL</td>
<td class="gt_row gt_center">428 (29%)</td></tr>
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<td class="gt_row gt_center">380 (25%)</td></tr>
  </tbody>
  
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      <td colspan="2">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

---

## Cross-tables

.pull-left[

```r
mockdata %>%
  select(fu_fct, arm) %>% 
  tbl_summary(by = fu_fct)
```

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  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Lived</strong>, N = 143<sup class="gt_footnote_marks">1</sup></th>
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  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">arm</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">A: IFL</td>
<td class="gt_row gt_center">18 (13%)</td>
<td class="gt_row gt_center">410 (30%)</td></tr>
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<td class="gt_row gt_center">99 (69%)</td>
<td class="gt_row gt_center">592 (44%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">G: IROX</td>
<td class="gt_row gt_center">26 (18%)</td>
<td class="gt_row gt_center">354 (26%)</td></tr>
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        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

.pull-right[

```r
mockdata %>%
  select(fu_fct, arm) %>% 
  tbl_cross(
    row = arm,
    col = fu_fct,
    percent = "cell",
    label = fu_fct ~ "Follow-up")
```

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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">A: IFL</td>
<td class="gt_row gt_center">18 (1.2%)</td>
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<td class="gt_row gt_center">428 (29%)</td></tr>
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<td class="gt_row gt_center">691 (46%)</td></tr>
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<td class="gt_row gt_center">143 (9.5%)</td>
<td class="gt_row gt_center">1,356 (90%)</td>
<td class="gt_row gt_center">1,499 (100%)</td></tr>
  </tbody>
  
  
</table>
</div>
]

---
class: middle

## A `tbl_summary()`

.left-column[

```r
mockdata %>% 
  select(arm, sex) %>% 
  tbl_summary(by = arm) %>% 
  add_p() %>% 
  add_overall()
```
]

.right-column[
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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Overall</strong>, N = 1,499<sup class="gt_footnote_marks">1</sup></th>
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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-value</strong><sup class="gt_footnote_marks">2</sup></th>
    </tr>
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  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">sex</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">0.2</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">583 (39%)</td>
<td class="gt_row gt_center">151 (35%)</td>
<td class="gt_row gt_center">280 (41%)</td>
<td class="gt_row gt_center">152 (40%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">916 (61%)</td>
<td class="gt_row gt_center">277 (65%)</td>
<td class="gt_row gt_center">411 (59%)</td>
<td class="gt_row gt_center">228 (60%)</td>
<td class="gt_row gt_center"></td></tr>
  </tbody>
  
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        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          n (%)
          <br />
        </p>
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>2</em>
          </sup>
           
          Pearson's Chi-squared test
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

]

---
class: middle

# A bigger `tbl_summary()`

.left-column[

```r
mockdata %>% 
  select(arm, 
         starts_with("ae")) %>% 
  tbl_summary(by = arm)  
```
]

.right-column[
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  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">ae_low_wbc</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">0</td>
<td class="gt_row gt_center">354 (83%)</td>
<td class="gt_row gt_center">551 (80%)</td>
<td class="gt_row gt_center">347 (91%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td>
<td class="gt_row gt_center">74 (17%)</td>
<td class="gt_row gt_center">140 (20%)</td>
<td class="gt_row gt_center">33 (8.7%)</td></tr>
    <tr><td class="gt_row gt_left">ae_neuropathy</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">0</td>
<td class="gt_row gt_center">354 (83%)</td>
<td class="gt_row gt_center">580 (84%)</td>
<td class="gt_row gt_center">347 (91%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td>
<td class="gt_row gt_center">74 (17%)</td>
<td class="gt_row gt_center">111 (16%)</td>
<td class="gt_row gt_center">33 (8.7%)</td></tr>
    <tr><td class="gt_row gt_left">ae_diarrhea</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">0</td>
<td class="gt_row gt_center">339 (79%)</td>
<td class="gt_row gt_center">546 (79%)</td>
<td class="gt_row gt_center">333 (88%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td>
<td class="gt_row gt_center">89 (21%)</td>
<td class="gt_row gt_center">145 (21%)</td>
<td class="gt_row gt_center">47 (12%)</td></tr>
    <tr><td class="gt_row gt_left">ae_vomiting</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">0</td>
<td class="gt_row gt_center">339 (79%)</td>
<td class="gt_row gt_center">571 (83%)</td>
<td class="gt_row gt_center">333 (88%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td>
<td class="gt_row gt_center">89 (21%)</td>
<td class="gt_row gt_center">120 (17%)</td>
<td class="gt_row gt_center">47 (12%)</td></tr>
    <tr><td class="gt_row gt_left">ae_blood_clot</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">0</td>
<td class="gt_row gt_center">411 (96%)</td>
<td class="gt_row gt_center">658 (95%)</td>
<td class="gt_row gt_center">365 (96%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">1</td>
<td class="gt_row gt_center">17 (4.0%)</td>
<td class="gt_row gt_center">33 (4.8%)</td>
<td class="gt_row gt_center">15 (3.9%)</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="4">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

]

---

# Your turn: Part 2 of 2

Now go through `02-draft-revised.Rmd` and read the source, then knit.

.pull-left[

```r
# demographics-----------------------------
age_density 
age_boxplot 
demo_tab

# treatment response @ follow-up-----------
surv_pct_plot 
surv_days_plot 
fu_tab

# adverse events---------------------------
ae_pct_plot
ae_tab
```
]

.pull-right[
1. Add new code chunks containing tables and plots where you want.

1. Edit the code chunk options as you need to.

1. Add headers and other Markdown formatting as you see fit.

1. Feel free to add figure captions and in text cross-references.
]

---
class: middle, center

# ⏱

# Time for a break!