featurette

class: inverse, left, bottom
background-image: url(https://images.unsplash.com/photo-1502740479091-635887520276?auto=format&fit=crop&q=80&w=1469&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D)
background-size: cover
# .Large[# A featurette: How do you retrieve R help file as text?]
## .small[Featuring baptnz@vis.social's R help retrieval]
#### .tiny[Gina Reynolds | 2023-11-02 |Image credit: Zhen H, Upsplash]
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Title

---

count: false

.panel1-feature-auto[

```r
# tidytuesday data
*library(magrittr)
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

*tmp <- tempfile()
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

*help(mtcars, package = datasets)
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

help(mtcars, package = datasets) %>%
* as.character()
```
]
 
.panel2-feature-auto[

```
## [1] "/Library/Frameworks/R.framework/Resources/library/datasets/help/mtcars"
```
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

help(mtcars, package = datasets) %>%
  as.character() %>%
* utils:::.getHelpFile()
```
]
 
.panel2-feature-auto[

```
## \title{Motor Trend Car Road Tests}\name{mtcars}\alias{mtcars}\keyword{datasets}\description{
##   The data was extracted from the 1974 \emph{Motor Trend} US magazine,
##   and comprises fuel consumption and 10 aspects of
##   automobile design and performance for 32 automobiles (1973--74
##   models).
## }\usage{mtcars}\format{
##   A data frame with 32 observations on 11 (numeric) variables.
##   \tabular{rll}{
##     [, 1] \tab mpg  \tab Miles/(US) gallon \cr
##     [, 2] \tab cyl  \tab Number of cylinders \cr
##     [, 3] \tab disp \tab Displacement (cu.in.) \cr
##     [, 4] \tab hp   \tab Gross horsepower \cr
##     [, 5] \tab drat \tab Rear axle ratio \cr
##     [, 6] \tab wt   \tab Weight (1000 lbs) \cr
##     [, 7] \tab qsec \tab 1/4 mile time \cr
##     [, 8] \tab vs   \tab Engine (0 = V-shaped, 1 = straight) \cr
##     [, 9] \tab am   \tab Transmission (0 = automatic, 1 = manual) \cr
##     [,10] \tab gear \tab Number of forward gears \cr
##     [,11] \tab carb \tab Number of carburetors
##   }
## }\note{
##   Henderson and Velleman (1981) comment in a footnote to Table 1:
##   \sQuote{Hocking [original transcriber]'s noncrucial coding of the
##     Mazda's rotary engine as a straight six-cylinder engine and the
##     Porsche's flat engine as a V engine, as well as the inclusion of the
##     diesel Mercedes 240D, have been retained to enable direct comparisons
##     to be made with previous analyses.}
## }\source{
##   Henderson and Velleman (1981),
##   Building multiple regression models interactively.
##   \emph{Biometrics}, \bold{37}, 391--411.
## }\examples{
## require(graphics)
## pairs(mtcars, main = "mtcars data", gap = 1/4)
## coplot(mpg ~ disp | as.factor(cyl), data = mtcars,
##        panel = panel.smooth, rows = 1)
## ## possibly more meaningful, e.g., for summary() or bivariate plots:
## mtcars2 <- within(mtcars, {
##    vs <- factor(vs, labels = c("V", "S"))
##    am <- factor(am, labels = c("automatic", "manual"))
##    cyl  <- ordered(cyl)
##    gear <- ordered(gear)
##    carb <- ordered(carb)
## })
## summary(mtcars2)
## }
```
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

help(mtcars, package = datasets) %>%
  as.character() %>%
  utils:::.getHelpFile() %>%
* tools:::Rd2latex(Rd = ., out = tmp)
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

help(mtcars, package = datasets) %>%
  as.character() %>%
  utils:::.getHelpFile() %>%
  tools:::Rd2latex(Rd = ., out = tmp)

*pandoc::pandoc_convert(file = tmp,
*                      from = "latex",
*                      to = 'markdown')
```
]
 
.panel2-feature-auto[

```
## The data was extracted from the 1974 *Motor Trend* US magazine, and
## comprises fuel consumption and 10 aspects of automobile design and
## performance for 32 automobiles (1973--74 models).
## 
##     mtcars
## 
## A data frame with 32 observations on 11 (numeric) variables.
## 
## Henderson and Velleman (1981) comment in a footnote to Table 1: 'Hocking
## \[original transcriber\]'s noncrucial coding of the Mazda's rotary
## engine as a straight six-cylinder engine and the Porsche's flat engine
## as a V engine, as well as the inclusion of the diesel Mercedes 240D,
## have been retained to enable direct comparisons to be made with previous
## analyses.'
## 
## Henderson and Velleman (1981), Building multiple regression models
## interactively. *Biometrics*, , 391--411.
## 
## require(graphics) pairs(mtcars, main = \"mtcars data\", gap = 1/4)
## coplot(mpg   disp \| as.factor(cyl), data = mtcars, panel =
## panel.smooth, rows = 1) \#\# possibly more meaningful, e.g., for
## summary() or bivariate plots: mtcars2 \<- within(mtcars, vs \<-
## factor(vs, labels = c(\"V\", \"S\")) am \<- factor(am, labels =
## c(\"automatic\", \"manual\")) cyl \<- ordered(cyl) gear \<-
## ordered(gear) carb \<- ordered(carb) ) summary(mtcars2)
```
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(magrittr)

tmp <- tempfile()

help(mtcars, package = datasets) %>%
  as.character() %>%
  utils:::.getHelpFile() %>%
  tools:::Rd2latex(Rd = ., out = tmp)

pandoc::pandoc_convert(file = tmp,
                       from = "latex",
                       to = 'markdown') ->
* md
```
]
 
.panel2-feature-auto[

]

---

Also: possible alternative route https://yihui.org/rd2roxygen/

---

### Check out flipbookr, used to build this featurette

- https://github.com/EvaMaeRey/flipbookr
- discussion: https://github.com/EvaMaeRey/flipbookr/blob/master/docs/draft_jasa_submission.pdf

---

### Check out more featurettes

- https://EvaMaeRey.github.io/featurette