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Grid

Source: vignettes/extras/grid.Rmd
grid.Rmd

Usage

Create a table from a data.frame with datagrid():

datagrid(rolling_stones_50)

Column sizing

Default column width above is "fit", to (try to) fit container width (depending on the number of columns), other alternatives are:

  • "guess: to guess minimum width according to content and column names.
datagrid(rolling_stones_50, colwidths = "guess")
  • "auto": to set width according to content, excluding column names.
datagrid(rolling_stones_50, colwidths = "auto")
  • NULL: to do nothing and use default behavior.
datagrid(rolling_stones_50, colwidths = NULL)

It’s also possible to set width in pixel for columns with grid_columns(), here we set width for first two variables to 150px:

datagrid(rolling_stones_50) %>% 
  grid_columns(columns = c("Number", "Year"), width = 150)

Height

Default height of table created is 600px (in markdown and shiny), you can use a different value by using:

datagrid(rolling_stones_50, height = "300px")

To display all rows in table use bodyHeight argument (a bit long, not displayed here):

datagrid(rolling_stones_50, bodyHeight = "auto")

An other solution to control row displayed is to use pagination.

Pagination

Activate pagination by indicating the number of rows to display:

datagrid(rolling_stones_50, pagination = 8)

Sort

Sort is enabled by default, you can disable it using sortable argument:

datagrid(rolling_stones_50, sortable = FALSE)

To enable sorting on a specific set of columns, use grid_columns():

datagrid(rolling_stones_50, sortable = FALSE) %>% 
  grid_columns(
    columns = 1:2, 
    sortable = TRUE
  )

Filter

You can allow filtering rows in the table with:

datagrid(rolling_stones_50, filters = TRUE)

You can customize filter type with grid_filters():

datagrid(rolling_stones_50) %>% 
  grid_filters(
    columns = "Artist", 
    showApplyBtn = TRUE, 
    showClearBtn = TRUE, 
    type = "text"
  )

Formatting content

Format a column with an R function, like label_* functions from scales package:

data <- data.frame(
  col_num = rnorm(12),
  col_currency = sample(1:1e6, 12, TRUE),
  col_percentage = sample(1:100, 12, TRUE) / 100,
  col_date = sample(Sys.Date() + 0:364, 12),
  col_time = Sys.time() + sample.int(86400 * 365, 12),
  col_logical = sample(c(TRUE, FALSE), 12, TRUE),
  stringsAsFactors = FALSE
)

library(scales)
datagrid(data, colwidths = "fit", bodyHeight = "auto") %>% 
  grid_format(
    "col_percentage", label_percent(accuracy = 1)
  ) %>%
  grid_format(
    "col_currency", label_dollar(prefix = "$", big.mark = ",")
  ) %>%
  grid_format(
    "col_num", label_number(accuracy = 0.01)
  ) %>% 
  grid_format(
    "col_date", label_date(format = "%d/%m/%Y")
  ) %>% 
  grid_format(
    "col_time", label_date(format = "%d/%m/%Y %H:%M")
  ) %>% 
  grid_format(
    "col_logical", function(value) {
      lapply(
        X = value,
        FUN = function(x) {
          if (x)
            shiny::icon("check")
          else
            shiny::icon("times")
        }
      )
    }
  )

It’s also possible to format column with a JavaScript function:

datagrid(data, colwidths = "fit", bodyHeight = "auto") %>% 
  grid_format(
    column = "col_percentage",
    formatter = JS("function(obj) {return (obj.value*100).toFixed(0) + '%';}")
  )

Edit content

Editor options

You can edit data in table and retrieve back results in Shiny, double click on any cell to see different options:

edit <- data.frame(
  col_text = month.name,
  col_select = month.name,
  col_checkbox = month.abb,
  col_radio = month.name,
  col_month = format(as.Date("2020-01-01") + 0:11*31, format = "%Y-%m"),
  col_date = as.Date("2020-01-01") + 0:11*31
)
datagrid(edit, bodyHeight = "auto") %>% 
  grid_editor("col_text", type = "text") %>% 
  grid_editor("col_select", type = "select", choices = month.name) %>% 
  grid_editor("col_checkbox", type = "checkbox", choices = month.abb) %>% 
  grid_editor("col_radio", type = "radio", choices = month.name) %>% 
  grid_editor_date("col_month", type = "month", format = "yyyy-MM") %>% 
  grid_editor_date("col_date", type = "date")

In Shiny, retrieve data edited with input$<outputId>_data.

Validation

When grid is editable you can set some rules to validate what values user can enter:

validate <- data.frame(
  col_text = c("a", "b", "a", NA, "c"),
  col_number = sample(1:10, 5),
  col_mail = c("victor@mail.com", "victor", NA, "victor@mail", "victor.fr")
)

datagrid(validate, bodyHeight = "auto") %>%
  grid_editor(
    "col_text", type = "text",
    validation = validateOpts(required = TRUE, unique = TRUE)
  ) %>%
  grid_editor(
    "col_number", type = "number",
    validation = validateOpts(min = 0, max = 5)
  ) %>%
  grid_editor(
    "col_mail", type = "text",
    validation = validateOpts(
      regExp = "^([a-zA-Z0-9_\\-\\.]+)@([a-zA-Z0-9_\\-\\.]+)\\.([a-zA-Z]{2,5})$"
    )
  )

In Shiny, retrieve validation errors with input$<outputId>_validation, example above will return:

#>   row     column    error
#> 1   1   col_text   UNIQUE
#> 2   1 col_number      MAX
#> 3   2   col_mail   REGEXP
#> 4   3   col_text   UNIQUE
#> 5   3 col_number      MAX
#> 6   4   col_text REQUIRED
#> 7   4   col_mail   REGEXP
#> 8   5   col_mail   REGEXP

Selection

In Shiny applications, you can select multiple rows with checkbox, single row with radio button or select cells by holding and selecting an area.

# Checkbox
datagrid(rolling_stones_50) %>%
  grid_selection_row(
    inputId = "sel_check",
    type = "checkbox"
  )

# Radio
datagrid(rolling_stones_50) %>%
  grid_selection_row(
    inputId = "sel_radio",
    type = "radio"
  )

Row & Column groups

Group columns under a common label:

datagrid(ps3_games[order(ps3_games$Publisher), c(1, 4, 5:8)], colwidths = "guess") %>%
  grid_columns(
    columns = c("NA_Sales", "EU_Sales", "JP_Sales", "Other_Sales"),
    header = c("North America", "Europe", "Japan", "Other")
  ) %>%
  grid_complex_header(
    "Sales"= c("NA_Sales", "EU_Sales", "JP_Sales", "Other_Sales")
  )

Group rows with common values:

datagrid(ps3_games[order(ps3_games$Publisher), c(1, 4, 5:8)], colwidths = "guess") %>%
  grid_row_merge(columns = "Publisher")

Summary

Add a summary area to the bottom of the grid to display statistics computed on column:

datagrid(ps3_games[, c(1, 5:8)], colwidths = "guess") %>% 
  grid_summary(
    column = c("NA_Sales", "EU_Sales", "JP_Sales", "Other_Sales"),
    stat = "sum",
    label = "Total: "
  )

Styling grid

Color bar

Add a color bar depending on the value in a cell

datagrid(ps3_games[, c(1, 5:6)], colwidths = "guess") %>%
  grid_colorbar(
    column = "NA_Sales",
    label_outside = TRUE,
    label_width = "30px"
  ) %>%
  grid_colorbar(
    column = "EU_Sales",
    bar_bg = "#BF616A",
    height = "20px"
  )

Color cells

Add custom CSS styles to cells identified by an expression:

datagrid(ps3_games[, c(1, 4, 5:6)], colwidths = "guess") %>%
  grid_style_cell(
    startsWith(Name, "Call of"),
    column = "Name",
    background = "#F781BE",
    fontWeight = "bold"
  )

Color rows

In the same way you can apply styles to an entire row:

datagrid(ps3_games[, c(1, 4, 5:6)], colwidths = "guess") %>%
  grid_style_row(
    Publisher == "Electronic Arts",
    background = "#ff4747",
    color = "white"
  )

Color columns

Set styles on an entire columns based on its values, here for example we use scales::col_numeric() to create a color gradient:

library(scales)
datagrid(ps3_games[, c(1, 5:6)], colwidths = "guess") %>%
  grid_style_column(
    column = "NA_Sales",
    background = col_numeric("Blues", domain = c(0, 10))(NA_Sales),
    fontWeight = "bold",
    color = ifelse(NA_Sales > 5, "white", "black")
  ) %>%
  grid_style_column(
    column = "EU_Sales",
    background = col_numeric("Blues", domain = c(0, 10))(EU_Sales),
    fontWeight = "bold",
    color = ifelse(EU_Sales > 5, "white", "black")
  )

Sparkline

You can embed sparkline charts in a grid, using an HTMLwidget capable of generating such type of charts. Here we use a nested data.frame, with data.frame inside list columns, then we use a function taking one of these data.frames to generate a chart.

library(apexcharter)
datagrid(met_paris) %>%
  grid_complex_header(
    "Le Bourget meteorological data" = names(met_paris)
  ) %>%
  grid_columns(
    columns = "month", width = 150
  ) %>%
  grid_sparkline(
    column = "temp",
    renderer = function(data) {
      apex(data, aes(date, temp), type = "area") %>%
        ax_chart(sparkline = list(enabled = TRUE)) %>%
        ax_yaxis(min = -5, max = 30)
    }
  ) %>%
  grid_sparkline(
    column = "rh",
    renderer = function(data) {
      apex(data, aes(date, rh), type = "column") %>%
        ax_chart(sparkline = list(enabled = TRUE)) %>%
        ax_yaxis(min = 0, max = 100)
    }
  )

Theme

It’s possible to customize theme used to create tables, theme is set globally and will be applied to all table created after its declaration. In a rmarkdown document (like this one), theme is unique for all tables, click buttons to switch between a custom theme and the default one, you will see that all tables are styled.

set_grid_theme(
  row.even.background = "#ddebf7",
  cell.normal.border = "#9bc2e6",
  cell.normal.showVerticalBorder = TRUE,
  cell.normal.showHorizontalBorder = TRUE,
  cell.header.background = "#5b9bd5",
  cell.header.text = "#FFF",
  cell.selectedHeader.background = "#013ADF",
  cell.focused.border = "#013ADF"
)

datagrid(rolling_stones_50)