04-modify-columns.Rmd

---
title: "Modifying Columns"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(gt)
library(tidyverse)
```

## Intro

The `cols_*()` functions allow for modifications that act on entire columns. This includes alignment of the data in columns (`cols_align()`), hiding columns from view (`cols_hide()`), re-labeling the column labels (`cols_label()`), merging two columns together (`cols_merge*()`), moving columns around (`cols_move*()`), and using a column name delimiter to create labels in the column spanner (`cols_split_delim()`).

------

### Functions in this module

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

------


### `cols_align()`: Set the alignment of columns

``` r
cols_align(
  data,
  align = c("auto", "left", "center", "right"),
  columns = everything()
)
```

The individual alignments of columns (which includes the column labels and all of their data cells) can be modified. We have the option to align text to the `left`, the `center`, and the `right`. In a less explicit manner, we can allow **gt** to automatically choose the alignment of each column based on the data type (with the `auto` option).

##### EXAMPLE

Use `countrypops` to create a **gt** table. Align the `population` column data to the left.

```{r}
countrypops %>%
  dplyr::select(-contains("code")) %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_align(
    align = "left",
    columns = population
  )
```

------

### `cols_label()`: Relabel one or more columns

``` r
cols_label(
  .data,
  ...,
  .list = list2(...)
)
```

Column labels can be modified from their default values (the names of the columns from the input table data). When you create a **gt** table object using `gt()`, column names effectively become the column labels. While this serves as a good first approximation, column names aren't often appealing as column labels in a **gt** output table. The `cols_label()` function provides the flexibility to relabel one or more columns and we even have the option to use the `md()` of `html()` helper functions for rendering column labels from Markdown or using HTML.

##### EXAMPLES

Use `countrypops` to create a **gt** table; label all the table's columns to present better.

```{r}
countrypops %>%
  dplyr::select(-contains("code")) %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_label(
    country_name = "Name",
    year = "Year",
    population = "Population"
  )
```


Use `countrypops` to create a **gt** table; label columns as before but make them bold with markdown formatting.

```{r}
countrypops %>%
  dplyr::select(-contains("code")) %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_label(
    country_name = md("**Name**"),
    year = md("**Year**"),
    population = md("**Population**")
  )
```

------

### `cols_width()`: Set the widths of columns

``` r
cols_width(
  .data,
  ...,
  .list = list2(...)
)
```

Manual specifications of column widths can be performed using the
`cols_width()` function. We choose which columns get specific widths (in
pixels, usually by use of the `px()` helper function). Width assignments are supplied in `...` through two-sided formulas, where the left-hand side
defines the target columns and the right-hand side is a single width value in pixels.

##### EXAMPLES

Use `exibble` to create a **gt** table. With named arguments in `...`, we can specify the exact widths for table columns (using `everything()` will capture all remaining columns).

```{r}
exibble %>%
  dplyr::select(
    num, char, date,
    datetime, row
  ) %>%
  gt() %>%
  cols_width(
    num ~ px(150),
    ends_with("r") ~ px(100),
    starts_with("date") ~ px(200),
    everything() ~ px(60)
  )
```

------

### `cols_move()`: Move one or more columns

``` r
cols_move(
  data,
  columns,
  after
)
```

On those occasions where you need to move columns this way or that way, we can make use of the `cols_move()` function. While it's true that the movement of columns can be done upstream of **gt**'s API, it is much easier and less error prone to use the function provided here. The movement procedure here takes one or more specified columns (in the `columns` argument) and places them to the right of a different column (the `after` argument). The ordering of the columns to be moved is preserved, as is the ordering of all other columns in the table.

##### EXAMPLE

Use `countrypops` to create a **gt** table; With the remaining columns, position `population` after `country_name`.

```{r}
countrypops %>%
  dplyr::select(-contains("code")) %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_move(
    columns = population,
    after = country_name
  )
```

------

### `cols_hide()`: Hide one or more columns

``` r
cols_hide(
  data,
  columns
)
```

The `cols_hide()` function allows us to hide one or more columns from appearing in the final output table. While it's possible and often desirable to omit columns from the input table data before introduction to the `gt()` function, there can be cases where the data in certain columns is useful (as a column reference during formatting of other columns) but the final display of those columns is not necessary.

##### EXAMPLES

Use `countrypops` to create a **gt** table; Hide the columns `country_code_2` and `country_code_3`.

```{r}
countrypops %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_hide(columns = c(country_code_2, country_code_3))
```


Use `countrypops` to create a **gt** table and use the `population` column to provide the conditional placement of footnotes, then hide that column and one other.

```{r}
countrypops %>%
  dplyr::filter(country_name == "Mongolia") %>%
  tail(5) %>%
  gt() %>%
  cols_hide(columns = c(country_code_3, population)) %>%
  tab_footnote(
    footnote = "Population above 3,000,000.",
    locations = cells_body(
      columns = year,
      rows = population > 3000000
    )
  )
```

------

### `cols_merge_range()`: Merge two columns to a value range column

``` r
cols_merge_range(
  data,
  col_begin,
  col_end,
  sep = "--",
  autohide = TRUE
)
```

The `cols_merge_range()` function is a specialized variant of the `cols_merge()` function. It operates by taking a two columns that constitute a range of values (`col_begin` and `col_end`) and merges them into a single column. What results is a column containing both values separated by a long dash (e.g., `12.0 — 20.0`). The column specified in `col_end` is dropped from the output table

##### EXAMPLE

Use `gtcars` to create a **gt** table, keeping only the `model`, `mpg_c`, and `mpg_h` columns; merge the mpg columns together as a single range column (which is labeled as `MPG`, in italics).

```{r}
gtcars %>%
  dplyr::select(model, starts_with("mpg")) %>%
  dplyr::slice(1:8) %>%
  gt() %>%
  cols_merge_range(
    col_begin = mpg_c,
    col_end = mpg_h
  ) %>%
  cols_label(
    mpg_c = md("*MPG*")
  )
```

------

### `cols_merge_uncert()`: Merge two columns to a value & uncertainty column

``` r
cols_merge_uncert(
  data,
  col_val,
  col_uncert,
  sep = " +/- ",
  autohide = TRUE
)
```

The `cols_merge_uncert()` function is a specialized variant of the `cols_merge()` function. It operates by taking a base value column (`col_val`) and an uncertainty column (`col_uncert`) and merges them into a single column. What results is a column with values and associated uncertainties (e.g., `12.0 ± 0.1`), and, the column specified in `col_uncert` is dropped from the output table.

##### EXAMPLE

Use `exibble` to create a **gt** table, keeping only the `currency` and `num` columns; merge columns into one with a base value and uncertainty (after formatting the `num` column).

```{r}
exibble %>%
  dplyr::select(currency, num) %>%
  dplyr::slice(1:7) %>%
  gt() %>%
  fmt_number(
    columns = num,
    decimals = 3,
    use_seps = FALSE
  ) %>%
  cols_merge_uncert(
    col_val = currency,
    col_uncert = num
  ) %>%
  cols_label(currency = "value + uncert.")
```

------

### `cols_merge_n_pct()`: Merge two columns to combine counts and percentages

``` r
cols_merge_uncert(
  data,
  col_n,
  col_pct,
  autohide = TRUE
)
```

The `cols_merge_n_pct()` function is a specialized variant of the `cols_merge()` function. It operates by taking two columns that constitute both a count (`col_n`) and a fraction of the total population (`col_pct`) and merges them into a single column. What results is a column containing both counts and their associated percentages (e.g., `12 (23.2%)`). The column specified in `col_pct` is dropped from the output table.

##### EXAMPLE

Use `pizzaplace` to create a **gt** table that displays the counts and percentages of the top 3 pizzas sold by pizza category in 2015. The `cols_merge_n_pct()` function is used to merge the `n` and `frac` columns (and the `frac` column is formatted using `fmt_percent()`).

```{r}
pizzaplace %>%
  dplyr::group_by(name, type, price) %>%
  dplyr::summarize(
    n = dplyr::n(),
    frac = n/nrow(.),
    .groups = "drop"
  ) %>%
  dplyr::arrange(type, dplyr::desc(n)) %>%
  dplyr::group_by(type) %>%
  dplyr::slice_head(n = 3) %>%
  gt(
    rowname_col = "name",
    groupname_col = "type"
  ) %>%
  fmt_integer(n) %>%
  fmt_percent(frac) %>%
  cols_merge_n_pct(
    col_n = n,
    col_pct = frac
  ) %>%
  fmt_currency(price) %>%
  cols_label(
    n = md("*N*<br>(%)"),
    price = md("Price<br>*(USD)*")
  ) %>%
  tab_style(
    style = cell_text(font = "monospace"),
    locations = cells_stub()
  ) %>%
  tab_stubhead(md("Cat. and  \nPizza Code")) %>%
  tab_header(title = "Top 3 Pizzas Sold by Category in 2015") %>%
  tab_options(table.width = px(512))
```

------

### `cols_merge()`: Merge data from two or more columns to a single column

``` r
cols_merge(
  data,
  columns,
  hide_columns = columns[-1],
  pattern = paste0("{", seq_along(columns), "}", collapse = " ")
)
```

This function takes input from two or more columns and allows the contents to be merged them into a single column, using a pattern that specifies the formatting. We can specify which columns to merge together in the `columns` argument. The string-combining pattern is given in the `pattern` argument. The first column in the `columns` series operates as the target column (i.e., will undergo mutation) whereas all following `columns` will be untouched.

##### EXAMPLE

Use `sp500` to create a **gt** table; merge the `open` & `close` columns together, and, the `low` & `high` columns (putting an em dash between both); rename the columns.

```{r}
sp500 %>%
  dplyr::slice(50:55) %>%
  dplyr::select(-volume, -adj_close) %>%
  gt() %>%
  cols_merge(
    columns = c(open, close),
    hide_columns = close,
    pattern = "{1} - {2}"
  ) %>%
  cols_merge(
    columns = c(low, high),
    hide_columns = high,
    pattern = "{1} - {2}"
  ) %>%
  cols_label(
    open = "open/close",
    low = "low/high"
  )
```

------

### SUMMARY

1. Values in columns can be aligned with the `cols_align()` function.
2. Column labels can be set with `cols_label()` (they otherwise inherit column names from the input dataset).
3. The widths of columns can precisely sized with `cols_width()` (HTML output only, for now); use the `px()` and `pct()` helpers to make this easier.
4. Didn't arrange columns in the original dataset? Second chance with `cols_move()`.
5. Columns you don't want to show can be 'hidden' (not shown) with `cols_hide()`; this is good when you a column for something but don't want to display it in the end.
6. Create ranged values in a column using two different columns and `cols_merge_range()`.
7. Create values with uncertainties with two columns and `cols_merge_uncert()`.
8. Merge multiple columns in an arbitrary way with `cols_merge()` and a pattern (`{1} + {2}...`)