---
title: "tableHTML"
author: "Clemens Zauchner"
date: "`r Sys.Date()`"
output: html_document
vignette: >
  %\VignetteIndexEntry{conditional formatting on columns}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

When presenting data in a table, you might want to put emphasis on certain areas of your table. `add_css_column()` 
allows you to apply the same CSS to all rows in a column. E.g. you can change the background color of a column. 
What you cannot do though, is change the color of the column if a condition is met. This is why 
`add_css_conditional_column()` was implemented. You can choose from a range of conditionals, equalities and 
inequalities ("==", "!=", ">", ">=", "<", "<="), "min", "max", "top_n", "bottom_n", 
  "between", "contains")

### Highlight specific value in a column

The most basic use case of the function is highlighting a specific value in a column.
In this case it will apply green background to all values that are equal to 21.4 in column 'mpg'.

```{r example_1}
library(tableHTML)
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = '==', 
                             value = 21.4, 
                             css = list('background-color', 'green'), 
                             columns = 'mpg')
```

### Highlight specific value in multiple columns
It is possible to apply the same condition to multiple columns in one function call.

```{r example_2}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = '==', 
                             value = 3.15,
                             css = list('background-color', 'steelblue'), 
                             columns = c('drat', 'wt'))
```

### Chaining
As usual, the function can be chained

```{r example_3}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = '==', 
                             value = 21.4, 
                             css = list('background-color', 'green'), 
                             columns = 'mpg') %>%
  add_css_conditional_column(conditional = '==',
                             value = 3.15,
                             css = list('background-color', 'steelblue'), 
                             columns = c('drat', 'wt'))
```

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The function can also be chained changing the same column

```{r example_4}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = '==', 
                             value = 21.4, 
                             css = list('background-color', 'green'), 
                             columns = 'mpg') %>%
  add_css_conditional_column(conditional = '==', 
                             value = 15.2, 
                             css = list('background-color', 'steelblue'), 
                             columns = 'mpg')
```

\

You may want to apply styles depending on quantiles and use several conditionals to achieve this.

```{r example_5}
 qu_25_75 <- quantile(mtcars$disp, c(0.25, 0.75))
 
 tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
   add_css_conditional_column(conditional = "<",
                              value = qu_25_75[1], 
                              css = list('background-color', "green"),
                              columns = c("disp")) %>%
   add_css_conditional_column(conditional = "between",
                              between = qu_25_75, 
                              css = list('background-color', "orange"), 
                              columns = c("disp")) %>%
   add_css_conditional_column(conditional = ">", 
                              value = qu_25_75[2], 
                              css = list('background-color', "red"), 
                              columns = c("disp"))
```

### Applying function on multiple columns
If you want to apply conditional formatting to multiple columns, you might want to compare the columns individually or together.
E.g. you might want to find the minimum value in columns 'disp' and 'hp'. If you want to find the minimum in both columns, 
use `same_scale = TRUE`:

```{r example_6}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = 'min', 
                             css = list('background-color', 'orange'), 
                             columns = c('disp', 'hp'),
                             same_scale = TRUE)
```

\

If you want to highlight the minimum in 'disp' and the minimum in 'hp' columns, use `same_scale = FALSE`:

```{r example_7}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = 'min', 
                             css = list('background-color', 'orange'), 
                             columns = c('disp', 'hp'),
                             same_scale = FALSE)
```

\

If you want to find the minimum values in every column you can use `same_scale = FALSE`

```{r example_8}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>% 
  add_css_conditional_column(conditional = "min", 
                             css = list('background-color', "green"),
                             columns = seq_along(mtcars),
                             same_scale = FALSE)
```


### Equalities and inequalities

When evaluating equalities or inequalities there is a choice of all common operators. A `value` argument 
needs to be specified.

```{r example_9}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = "==",
                             value = 14.3, 
                             css = list('background-color', "steelblue"), 
                             columns = 1) %>%
  add_css_conditional_column(conditional = "!=", 
                             value = 8,
                             css = list('background-color', "mediumvioletred"), 
                             columns = 2) %>%
  add_css_conditional_column(conditional = ">",
                             value = 440,
                             css = list('background-color', "orange"),
                             columns = 3) %>%
  add_css_conditional_column(conditional = ">=", 
                             value = 264,
                             css = list('background-color', "green"),
                             columns = 4) %>%
  add_css_conditional_column(conditional = "<",
                             value = 3, 
                             css = list('background-color', "yellow"),
                             columns = 5) %>%
  add_css_conditional_column(conditional = "<=", 
                             value = 2.20, 
                             css = list('background-color', "lightgray"), 
                             columns = 6)
  

```

### Highest and lowest values

You can use 'min' and 'max' to highlight the minimum or maximum value in a column (or columns). 'bottom_n' and 'top_n'
are similar functions, only that they allow to hightlight n values, where 'bottom_n' with `n = 1` is an equivalent
to 'min' and 'top_n' with `n = 1` is an equivalent to 'max': 

Notice that you can use `same_scale` to define the context for a function:

```{r example_10}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = "min",
                             css = list('background-color', "steelblue"), 
                             columns = 1) %>%
  add_css_conditional_column(conditional = "max", 
                             css = list('background-color', "mediumvioletred"), 
                             columns = 2) %>%
  add_css_conditional_column(conditional = "bottom_n",
                             n = 5, 
                             css = list('background-color', "green"), 
                             columns = c(3, 4), 
                             same_scale = FALSE) %>% 
  add_css_conditional_column(conditional = "top_n",
                             n = 5, 
                             css = list('background-color', "orange"), 
                             columns = c(5, 6)) 
```

### Between

The between operator can be used to format values that are in a defined range. The operator is very much like
SQL with inclusive lower and upper bound. You need to provide a vector with two elements in `between`. 

```{r example_11}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = "between",
                             between = c(15, 25),
                             css = list('background-color', "steelblue"), 
                             columns = 1)
```

\

Again, the 'between' conditional can be applied to multiple columns in one function call. 

```{r example_12}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = "between", 
                             between = c(20, 22), 
                             css = list('background-color', "steelblue"), 
                             columns = c(1, 7))

```

### Character columns - Pattern matching

If you want to highlight elements that contain a specific substring, you can use 'contains'. The function
will check if a pattern or regular expression can be found (Note: case sensitive). The R coecion rules apply: if you use it on numeric columns, 
they will be evaluated as character. 

```{r example_13}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(conditional = "contains", 
                             value = "[0-9]", 
                             css = list('background-color', "steelblue"), 
                             columns = "rownames") %>%
  add_css_conditional_column(conditional = "contains", 
                             value = "Honda",
                             css = list('background-color', "silver"), 
                             columns = "rownames")
```

### Update to sorting factors with levels

In the old version 1.1.0 of tableHTML, the default order of factor levels was alphabetic,
because the data was parsed from the HTML. Since version 2.0.0, 
the data comes from the tableHTML objects attributes, so the factors and levels are preserved.
The `levels` argument is therfore deprecated.

```{r example_20}
df <- data.frame(factor_alphabetic = c('d', 'a', 'e', 'a', 'd', 'd', 'a', 'c', 'd', 'a'),
                 factor_ordered = c('D', 'A', 'E', 'A', 'D', 'D', 'A', 'C', 'D', 'A'),
                 stringsAsFactors = TRUE)

df$factor_ordered <- factor(df, levels = c('B', 'D', 'A', 'E', 'C'))

tableHTML(df, 
          rownames = FALSE) %>%
  add_css_conditional_column(color_rank_theme = 'White-Green',
                             columns = 1) %>%
  add_css_conditional_column(color_rank_theme = 'White-Green', 
                             columns = 2)
```

### Color rank 

A common conditional formatting usecase is to apply a color rank to columns. There are a few of them pre-defined:

```{r example_14}
tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(color_rank_theme = "RAG", columns = 1) %>%
  add_css_conditional_column(color_rank_theme = "Spectral", columns = 2) %>%
  add_css_conditional_column(color_rank_theme = "Rainbow", columns = 3) %>%
  add_css_conditional_column(color_rank_theme = "White-Green", columns = 4) %>%
  add_css_conditional_column(color_rank_theme = "White-Blue", columns = 5) %>%
  add_css_conditional_column(color_rank_theme = "White-Red", columns = 6) 
```

\

You can reverese the color by using `decreasing = TRUE`

```{r example_15}
tableHTML(mtcars,
          widths = rep(100, 12)) %>%
  add_css_conditional_column(color_rank_theme = "RAG", 
                             columns = 1, 
                             decreasing = TRUE) 
```

\

Color ranks can be applied to multiple columns with one function call as well and 
the context can be set using `same_scale`:

```{r example_16}
tableHTML(data.frame(a = 1:20, b = rep(1:5, 4), c = 1:20, d = rep(1:5, 4)), 
          width = rep(80, 4),
          second_headers = list(c(2, 2), 
                                c("same_scale = TRUE",
                                  "same_scale = FALSE")),
          rownames = FALSE) %>%
  add_css_conditional_column(color_rank_theme = "RAG",
                             columns = c(1, 2), 
                             decreasing = FALSE, 
                             same_scale = TRUE) %>%
  add_css_conditional_column(color_rank_theme = "RAG", 
                             columns = c(3, 4), 
                             decreasing = FALSE, 
                             same_scale = FALSE)
```

### Using custom CSS

You can also provide custom css. The css needs to be a named list where the name corresponds to the 
name of the column the css should be applied to. The elements of that named list a vector of 
css style attributes and a list of style attribute values. For each style attribute you need to
have a list of attribute values and you need to have an attribute value for every row in the column. 

You can use `make_css_color_rank_theme()` with specific colors that you wish to apply. You can provide
your own colors here or use palettes from e.g. RColorBrewer, as long as they are a valid argument to `col2rgb()`.

```{r example_17}
color_rank_css <- 
  make_css_color_rank_theme(list(qsec = mtcars$qsec),
                             colors = RColorBrewer::brewer.pal(9, "Set1"))

tableHTML(mtcars,
          widths = c(140, rep(45, 11))) %>%
  add_css_conditional_column(color_rank_theme =  "Custom", 
                             color_rank_css = color_rank_css, 
                             columns = 7)
```


### Integration with other tableHTML functions

The functions `add_css_column()` and `add_css_conditinal_column()` can be combined. 

```{r example_18} 
tableHTML(mtcars,
          widths = c(120, 200, rep(100, 11)),
          row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3'))) %>%
 add_theme('rshiny-blue') %>%
 add_css_column(css = list('border', '1px solid'), columns = 1) %>%
 add_css_conditional_column(color_rank_theme = "RAG", columns = 1) %>%
 add_css_conditional_column(conditional = "contains",
                            value = "1", 
                            css = list(c('color', 'font-size', 'border'), 
                                       c('steelblue', '20', '1px solid steelblue')),
                            columns = "row_groups") %>%
 add_css_conditional_column(conditional = "contains",
                            value = "2", 
                            css = list(c('color', 'font-size', 'border'),
                                       c('royalblue', '30', '1px solid royalblue')),
                            columns = "row_groups") %>%
 add_css_conditional_column(conditional = "contains",
                            value = "3", 
                            css = list(c('color', 'border'),
                                       c('navy', '1px solid navy')),
                            columns = "row_groups")
```

\

In the example below you can see how to combine a few other functions from the `add_css_` family. First, 
a tableHTML is created with rownames, an outside boarder, specified column widths, row groups, and 
second headers. 
CSS is then applied to the second header, the header, every second row, a color rank is added to the 
row groups, and in addition conditional formatting if the row groups contain specific numbers. 

```{r example_19}
tableHTML(mtcars, 
          border = 2,
          rownames = TRUE, 
          widths = c(80, 140, rep(50, 11)),
          row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
          second_headers = list(c(3, 4, 6), c('col1', 'col2', 'col3'))) %>%
  add_css_second_header(css = list(c('background-color', 'color', 'height'),
                                   c('#2E5894', 'white', '50px')),
                        second_headers = 1:3) %>%
  add_css_header(css = list(c('transform', 'height'),
                            c('rotate(-45deg)', '50px')),
                 headers = 3:13) %>%
  add_css_row(css = list('background-color', '#f2f2f2'), 
              rows = even(3:34)) %>%
  add_css_conditional_column(color_rank_css = 
                               make_css_color_rank_theme(list(row_groups = 1:3),
                                                          colors = c('#00b200',
                                                                     '#007f00',
                                                                     '#004c00'),
                                                          css_property = 'color'),
                             columns = 'row_groups') %>%
  add_css_conditional_column(conditional = "contains",
                             value = "1", 
                             css = list('background-color', '#F5F5F5'),
                             columns = "row_groups") %>%
  add_css_conditional_column(conditional = "contains",
                             value = "2", 
                             css = list('background-color', '#D0D0D0'),
                             columns = "row_groups") %>%
  add_css_conditional_column(conditional = "contains",
                             value = "3", 
                             css = list('background-color', '#A9A9A9'),
                             columns = "row_groups") %>%
  add_css_conditional_column(color_rank_theme = 'RAG', 
                             columns = 4)
```


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