---
title: "Introduction to Tiny Variable Labels"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to Tiny Variable Labels}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

Variable labels are useful in many data-analysis contexts, but R does not provide
variable labels in its base distribution. Several R packages introduced (sometimes conflicting)
implementations (e.g., **Hmisc**, **haven**, **sjlabelled**), but these packages come with extensive
dependencies.
Following the philosophy of a *tiny* dependency graph (the [tinyverse](https://www.tinyverse.org) philosophy),
**tinylabels** set out to provide functionality for variable labels without depending
on any non-base R packages, while also being as compatible as possible with other implementations.
Another deliberate choice is that **tinylabels** only implements *variable* labels, not *value* labels.

With a tiny dependency graph and a small code base, we hope that **tinylabels** may become an
excellent choice for package developers, as well as for data analysts who care about dependencies.

Function overview:

- Assign variable label: `variable_label()<-`
- Extract variable label: `variable_label()`
- Remove labels and `tiny_labelled` class: `unlabel()`
- Assign labels in a piped workflow: `label_variables()`


## Basic Usage

Assign a variable label to a vector:

```{r}
library(tinylabels)

x <- rnorm(6)
variable_label(x) <- "Values randomly drawn from a standard-normal distribution"
x
```
```{r}
# Extract the variable label from a vector (e.g., a numeric vector)
variable_label(x)
```


It is also possible to assign variable labels to (all or a subset of) the columns of a data frame.
For instance, consider the built-in data set `npk`:

```{r}
# View original data set ----
str(npk)
```

```{r}
# Assign labels to the built-in data set 'npk' ----
variable_label(npk) <- c(
  N = "Nitrogen"
  , P = "Phosphate"
  , yield = "Pea yield"
)

# View the altered data set ----
str(npk)
```
Each labelled column now has an attribute `label` that contains the respective variable label,
and is now of class `tiny_labelled` that extends the simple vector classes (e.g., logical, integer, double, etc.)

You can access the variable labels by using

```{r}
variable_labels(npk)
```

The return value of `variable_label()` applied to a data frame is a named list,
where each list element corresponds to a column of the data frame.
For columns that do not have a variable label, the corresponding list element is NULL.

For data frames, the right-hand side of the assignment has to be a **named** list or vector.
When trying to set columns that are not present in the data frame, a meaningful error  message is thrown:

```{r error = TRUE, purl = FALSE}
variable_label(npk) <- c(wrong_column_name = "A supposedly terrific label")
```

If you want to remove labels (and the corresponding S3 class) from a vector *or all columns of a data frame*,
you may use function `unlabel`:

```{r}
# Return as a simple factor ----
unlabel(npk$N)

# Remove all labels (and class 'tiny_labelled') from all columns ----
npk <- unlabel(npk)
str(npk)
```



## Supporting the *tidyverse*

Developing **tinylabels**,
we aimed at supporting the *tidyverse* while also keeping the package's dependency graph as tiny as possible.
For this reason, *tidyverse* packages are not imported.
However, if you have already installed the **vctrs** package on your computer,
**tinylabels** will dynamically register methods for `vctrs::vec_ptype2()` and `vctrs::vec_cast()` that are necessary to play well with the *tidyverse*.
With these methods, the *tidyverse*'s data manipulation functions become rather explicit about how they handle labels.

For instance, If you try to combine two vectors that have different labels,
only  the label of the first vector is retained -- but in addition, you will receive a meaningful warning message.
Consider, for instance, `dplyr::bind_rows()` applied to two data frames with non-matching variable labels:

```{r}
data_1 <- data_2 <- data.frame(
  x = rnorm(10)
  , y = rnorm(10)
)

variable_label(data_1) <- c(x = "Label for x", y = "Label for y")
variable_label(data_2) <- c(x = "Label for x", y = "Another label for y")
```

```{r}
library(dplyr)
combined_data <- bind_rows(data_1, data_2)
variable_label(combined_data)
```

To further support *tidyverse*-ish code,
we also wrote the function `label_variables()` that is intended to be used in conjunction with
the *tidyverse*'s pipe operator:

```{r}
test <- npk %>%
  group_by(N, P) %>%
  summarize(yield = mean(yield), .groups = "keep") %>%
  label_variables(N = "Nitrogen", P = "Phosphate", yield = "Average yield")

variable_labels(test)
```