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Minor clean-up translation vignette setup #531

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merged 4 commits into from
Jul 29, 2024
Merged

Minor clean-up translation vignette setup #531

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6caa14f
Minor clean-up translation vignette setup
IndrajeetPatil Jul 28, 2024
1c08fa6
Update tidyverse_translation.Rmd
IndrajeetPatil Jul 28, 2024
c16abb7
beautify tables
IndrajeetPatil Jul 28, 2024
a9cf93a
adress PR comments
IndrajeetPatil Jul 29, 2024
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63 changes: 34 additions & 29 deletions vignettes/tidyverse_translation.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ vignette: >
%\VignetteEngine{knitr::rmarkdown}
---

```{r message=FALSE, warning=FALSE, include=FALSE, eval = TRUE}
```{r setup, message=FALSE, warning=FALSE, include=FALSE, eval = TRUE}
library(knitr)
options(knitr.kable.NA = "")
knitr::opts_chunk$set(
Expand All @@ -21,16 +21,25 @@ knitr::opts_chunk$set(

pkgs <- c(
"dplyr",
"datawizard",
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No need to check if {datawizard} is available.

"tidyr"
)
all_deps_available <- all(vapply(pkgs, requireNamespace, quietly = TRUE, FUN.VALUE = logical(1L)))

# since we explicitely put eval = TRUE for some chunks, we can't rely on
# knitr::opts_chunk$set(eval = FALSE) at the beginning of the script. So we make
# a logical that is FALSE only if deps are not installed (cf easystats/easystats#317)
evaluate_chunk <- TRUE
if (all_deps_available) {
library(datawizard)
library(dplyr)
library(tidyr)
}

can_vignette_be_evaluated <- all_deps_available && getRversion() >= "4.1.0"

if (!all(vapply(pkgs, requireNamespace, quietly = TRUE, FUN.VALUE = logical(1L))) || getRversion() < "4.1.0") {
# Since we explicitly put `eval = TRUE` for some chunks, we can't rely on
# `knitr::opts_chunk$set(eval = FALSE)` at the beginning of the script.
# Therefore, we introduce a logical that is `FALSE` only if all suggested
# dependencies are not installed (cf easystats/easystats#317)
if (can_vignette_be_evaluated) {
evaluate_chunk <- TRUE
} else {
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I don't understand this. Why not put evaluate_chunk <- all_deps_available && getRversion() >= "4.1.0" and remove this if condition?

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Good point. No need for an extra level of indirection.

evaluate_chunk <- FALSE
}
```
Expand All @@ -39,7 +48,7 @@ This vignette can be referred to by citing the following:

Patil et al., (2022). datawizard: An R Package for Easy Data Preparation and Statistical Transformations. *Journal of Open Source Software*, *7*(78), 4684, https://doi.org/10.21105/joss.04684

```{css, echo=FALSE, eval = evaluate_chunk}
```{css, echo=FALSE, eval = TRUE}
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No need to eval CSS code conditionally. Doesn't hurt us if it is evaluated.

.datawizard, .datawizard > .sourceCode {
background-color: #e6e6ff;
}
Expand Down Expand Up @@ -84,10 +93,6 @@ This vignette is largely inspired from `{dplyr}`'s [Getting started vignette](ht
</div>

```{r, eval = evaluate_chunk}
library(dplyr)
library(tidyr)
library(datawizard)
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We always load all needed libraries in the setup chunk. Happy to revert this if you think this reduces readability of the vignette.

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I think it's better to show explicitly what packages are used so I'd like to keep this chunk (with eval=FALSE since they are loaded in setup)


data(efc)
efc <- head(efc)
```
Expand All @@ -97,20 +102,20 @@ efc <- head(efc)
Before we look at their *tidyverse* equivalents, we can first have a look at
`{datawizard}`'s key functions for data wrangling:

| Function | Operation |
| :---------------- | :------------------------------------------------ |
| `data_filter()` | [to select only certain observations](#filtering) |
| `data_select()` | [to select only a few variables](#selecting) |
| `data_modify()` | [to create variables or modify existing ones](#modifying) |
| `data_arrange()` | [to sort observations](#sorting) |
| `data_extract()` | [to extract a single variable](#extracting) |
| `data_rename()` | [to rename variables](#renaming) |
| `data_relocate()` | [to reorder a data frame](#relocating) |
| `data_to_long()` | [to convert data from wide to long](#reshaping) |
| `data_to_wide()` | [to convert data from long to wide](#reshaping) |
| `data_join()` | [to join two data frames](#joining) |
| `data_unite()` | [to concatenate several columns into a single one](#uniting) |
| `data_separate()` | [to separate a single column into multiple columns](#separating) |
| Function | Operation |
| :---------------- | :--------------------------------------------------------------- |
| `data_filter()` | [to select only certain observations](#filtering) |
| `data_select()` | [to select only a few variables](#selecting) |
| `data_modify()` | [to create variables or modify existing ones](#modifying) |
| `data_arrange()` | [to sort observations](#sorting) |
| `data_extract()` | [to extract a single variable](#extracting) |
| `data_rename()` | [to rename variables](#renaming) |
| `data_relocate()` | [to reorder a data frame](#relocating) |
| `data_to_long()` | [to convert data from wide to long](#reshaping) |
| `data_to_wide()` | [to convert data from long to wide](#reshaping) |
| `data_join()` | [to join two data frames](#joining) |
| `data_unite()` | [to concatenate several columns into a single one](#uniting) |
| `data_separate()` | [to separate a single column into multiple columns](#separating) |

Note that there are a few functions in `{datawizard}` that have no strict equivalent
in `{dplyr}` or `{tidyr}` (e.g `data_rotate()`), and so we won't discuss them in
Expand All @@ -124,7 +129,7 @@ Before we look at them individually, let's first have a look at the summary tabl
| :---------------- | :------------------------------------------------------------------ |
| `data_filter()` | `dplyr::filter()`, `dplyr::slice()` |
| `data_select()` | `dplyr::select()` |
| `data_modify()` | `dplyr::mutate()` |
| `data_modify()` | `dplyr::mutate()` |
| `data_arrange()` | `dplyr::arrange()` |
| `data_extract()` | `dplyr::pull()` |
| `data_rename()` | `dplyr::rename()` |
Expand All @@ -134,8 +139,8 @@ Before we look at them individually, let's first have a look at the summary tabl
| `data_join()` | `dplyr::inner_join()`, `dplyr::left_join()`, `dplyr::right_join()`, |
| | `dplyr::full_join()`, `dplyr::anti_join()`, `dplyr::semi_join()` |
| `data_peek()` | `dplyr::glimpse()` |
| `data_unite()` | `tidyr::unite()` |
| `data_separate()` | `tidyr::separate()` |
| `data_unite()` | `tidyr::unite()` |
| `data_separate()` | `tidyr::separate()` |

## Filtering {#filtering}

Expand Down
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