04z-PRACTICE-03-04.qmd

---
title: "Practice with Modules 03 and 04"
format: html
html-table-processing: none
---

```{r}
library(gt)
library(tidyverse)
```

Practice time again. Here we can experiment with the `fmt_*()` and `cols_*()` functions.

### **gt** functions from module `03`

- `fmt_number()`
- `fmt_integer()`
- `fmt_scientific()`
- `fmt_engineering()`
- `fmt_percent()`
- `fmt_currency()`
- `fmt_date()`
- `fmt_time()`
- `fmt_datetime()`
- `fmt_markdown()`
- `fmt_missing()`
- `data_color()`
- `info_locales()`
- `info_currencies()`
- `info_date_style()`
- `info_time_style()`
- `info_paletteer()`

### From module `04`

- `cols_align()`
- `cols_label()`
- `cols_width()`
- `cols_move()`
- `cols_hide()`
- `cols_merge_range()`
- `cols_merge_uncert()`
- `cols_merge_n_pct()`
- `cols_merge()`

The formatting functions (`fmt_*()` + `data_color()`), information functions (`info_*()`), and functions that work with columns (`cols_*()`) were looked at. Below are a few examples for practicing.

#### Suggestions for practice

The `fmt_number()` function allows us to format numbers. This is important as tables often contain number with specific formatting requirements. There are many options for formatting numbers. One suggestion for working with `fmt_number()` is to modify the options given to it. Working with just the `num` column from `exibble` and exposing all of the function's default options, go ahead and change things and re-run the code. 

```{r}
exibble |>
  dplyr::select(num) |>
  gt() |>
  fmt_number(
    columns = num,
    rows = everything(),
    decimals = 2,
    n_sigfig = NULL,
    drop_trailing_zeros = FALSE,
    drop_trailing_dec_mark = TRUE,
    use_seps = TRUE,
    scale_by = 1,
    suffixing = FALSE,
    pattern = "{x}",
    sep_mark = ",",
    dec_mark = ".",
    locale = NULL
  )
```


The `data_color()` function allows us to add color to cells. Try using different `method` values (`"numeric"`, `"bin"`, `"quantile"`, etc.), change the `palette`, and try using a non-`NULL` `domain` (hint: should be a vector of length 2). After this, try using a `fmt_*()` function that nicely formats these population values.

```{r}
countrypops |>
  dplyr::filter(country_name == "United States") |>
  dplyr::select(-contains("code")) |>
  tail(10) |>
  gt(rowname_col = "year", groupname_col = "country_name") |>
  tab_options(column_labels.hidden = TRUE) |>
  data_color(
    columns = population,
    method = "numeric",
    palette = c("black", "orange", "blue", "green"),
    domain = NULL
  )
```


Setting column widths can be admittedly confusing. It uses browser logic to great extent and there are really a lot of possible outcomes. Try combinations of setting the overall width (`tab_options(table.width = ...)`) and using the `pct()` and `px()` helpers to define percentage and pixel widths. These interactions are occasionally surprising but there is some helpful documentation on all the interactions at https://github.com/rstudio/gt/blob/master/tests/gt-examples/01-html-script/html-17-table_width_specifications.R.

```{r}
exibble |>
  dplyr::select(num, char, date, datetime, row) |>
  gt() |>
  tab_options(table.width = pct(100)) |>
  cols_width(
    num ~ pct(20),
    ends_with("r") ~ px(100),
    starts_with("date") ~ px(200),
    everything() ~ pct(30)
  )
```


Lastly, some information might do us good. The information functions (`info_*()`) can provide us with a helpful reference when we need it most.

Many `fmt_*()` functions have a `locale` option for locale-specific formatting. Here is information on which locales are supported:

```{r}
info_locales()
```

The `fmt_currency()` function has support for many currencies through its `currency` option. Here is information on which currencies are supported, their codes, and a preview of their formatting:

```{r}
info_currencies()
```

The `fmt_date()`, `fmt_time()`, and `fmt_datetime()` functions use numbered styles as a shortcut for specifying date and time styles. A reference for those can be accessed with:

```{r}
info_date_style()
```

and

```{r}
info_time_style()
```

You may want to use a color palette with `data_color()`. Pretty much every color palette package in R is contained in the **paletteer** package. This info function shows them all in a **gt** table:

```{r}
info_paletteer()
```