library(ROvis.plotly) # nolint: missing_package_linter.

Introduction

This vignette demonstrates the use of accessibility functions for interactive plotly and ggplotly plots. These functions are designed to improve keyboard and screen reader accessibility for various types of interactive charts.

Note: The example plots in this vignette are for demonstration only and do not themselves comply with the Rijksoverheid style requirements.

Key Accessibility Features

Keyboard navigation: Users can focus bars, points, and legend items using Tab and Arrow keys.
Accessible tooltips: Tooltip content is made available for screen readers and keyboard navigation.
Screen reader support: Meaningful ARIA labels are provided for plot elements.
Legend accessibility: Legends can be reached and toggled via keyboard.
Toolbar control: The plotly modebar (toolbar) can be disabled for improved accessibility.

The functions covered in this vignette are:

ro_ply_keyboard_nav_barchart()
ro_ply_keyboard_nav_bargraph()
ro_ply_keyboard_nav_trendline()
ro_ply_keyboard_nav_linechart()
ro_ply_keyboard_nav_legend()
ro_ply_disable_toolbar()

Keyboard accessibility - ggplotly and plotly plots

Each section below demonstrates how to add accessibility to a ggplotly or plotly graph using the functions from this package. For each plot type, you will see an example with and without accessibility enhancements.

Horizontal Barchart

This section demonstrates how to make a horizontal bar chart created with ggplotly or plotly keyboard accessible. The example shows how to use ro_ply_keyboard_nav_barchart() to enable keyboard navigation and accessible tooltips for all bars.

Example data

data <- tidyr::tibble(
  expand.grid(`Age group` = c("A", "B", "C", "D", "E"), `Sex` = c("Man", "Vrouw")),
  `Number of Cases` = sample(1:50, 10, TRUE)
)

ggplotly - Horizontal Barchart

library(ggplot2)
library(plotly)
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout
 
p <- ggplot(data, aes(
  x = `Number of Cases`,
  y = `Age group`,
  fill = Sex,
  text = paste0(
    "Leeftijdsgroep: <b>", `Age group`, "</b><br>",
    "Geslacht: <b>", Sex, "</b><br>",
    "Aantal cases: <b>", `Number of Cases`, "</b>"
  )
)) +
  geom_col(position = "dodge") +
  labs(
    x = "Aantal cases",
    y = "Leeftijdsgroep",
    fill = "Geslacht"
  ) 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_barchart(fig)
fig

plotly - Horizontal Barchart

library(plotly)
 
fig <- plot_ly(data = data,
  x = ~`Number of Cases`,              
  y = ~`Age group`,                    
  color = ~`Sex`,          
  type = "bar",
  orientation = "h",                   # horizontal bars
  hovertext  = ~paste0(
    "Leeftijdsgroep: <b>", `Age group`, "</b><br>",
    "Geslacht: <b>", `Sex`, "</b><br>",
    "Aantal cases: <b>", `Number of Cases`, "</b>"
  ),
  text  = ~paste0(
    "Leeftijdsgroep: <b>", `Age group`, "</b><br>",
    "Geslacht: <b>", `Sex`, "</b><br>",
    "Aantal cases: <b>", `Number of Cases`, "</b>"
  ),    
  textposition = "none",
  hoverinfo = "text"
) 
# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_barchart(fig)
fig

Grouped Bar Graph / Column Chart

This section demonstrates how to make a column chart created with ggplotly or plotly keyboard accessible. The example shows how to use ro_ply_keyboard_nav_barchart() to enable keyboard navigation and accessible tooltips for all bars.

Example data

age_levels <- c(
  "0", "1-9", "10-19", "20-29",
  "30-39", "40-49", "50-59",
  "60-69", "70-79", "80-89", "90+"
)

base_data <- tidyr::expand_grid(
  `Age group` = age_levels,
  Sex = c("Men", "Women")
) |>
  dplyr::mutate(
    `Number of Cases` = sample(1:50, length(age_levels) * 2, TRUE)
  )

summary_data <- base_data |>
  dplyr::summarize(
    `Number of Cases` = sum(`Number of Cases`),
    .by = `Age group`
  ) |>
  dplyr::mutate(Sex = "Total")

data <- dplyr::bind_rows(base_data, summary_data) |>
  dplyr::mutate(
    `Age group` = factor(`Age group`, levels = age_levels),
    Sex = factor(Sex, levels = c("Men", "Women", "Total"))
  )

ggplotly Grouped Bar Graph / Column Chart

p <- ggplot(data, aes(
  x = `Age group`,
  y = `Number of Cases`,
  fill = Sex,
  text = paste0(
    "Geslacht: <b>", Sex, "</b>",
    "<br>Leeftijdsgroep: <b>", `Age group`,
    "</b><br>Aantal cases: <b>",
    format(round(`Number of Cases`, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
  )
)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.7) +
  labs(x = "Leeftijdsgroep", y = "Aantal cases", fill = "Geslacht")

# Convert to interactive plotly plot
fig <- ggplotly(p, tooltip = "text")
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

plotly Grouped Bar Graph / Column Chart

fig <- plot_ly(
  data = data,
  x = ~`Age group`,
  y = ~`Number of Cases`,
  type = "bar",
  color = ~Sex,
  text = ~paste0(
    "Geslacht: <b>", Sex, "</b>",
    "<br>Leeftijdsgroep: <b>", `Age group`,
    "</b><br>Aantal cases: <b>", 
    format(round(`Number of Cases`, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
  ),
  textposition = "none",
  hoverinfo = "text"
) |>
  layout(barmode = "group")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

Bar Graph

This section demonstrates how to make a bar graph created with ggplotly or plotly keyboard accessible. The example shows how to use ro_ply_keyboard_nav_barchart() to enable keyboard navigation and accessible tooltips for all bars.

Example data

data <- tidyr::tibble(
  Sex = factor(c("Vrouw", "Man"), levels = c("Vrouw", "Man")),
  n = sample(1e6:5e6, 2)
)

ggplotly Bar Graph

library(ggplot2) library(plotly)

p <- ggplot(data, aes(x = Sex, y = n, fill = Sex,
                      text = paste0(
                        "Geslacht: <b>", Sex, "</b>",
                        "</b><br>Aantal cases: <b>",
                        format(round(n, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
                      ))) +
  geom_col(width = 0.6) +
  labs(x = "Geslacht", y = "Aantal cases") +
  theme_minimal()

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

plotly Bar Graph

fig <- plot_ly(
  data = data,
  x = ~Sex,
  y = ~n,
  type = "bar",
  color = ~Sex,
  text = ~paste0(
    "Geslacht: <b>", Sex, "</b>",
    "</b><br>Aantal cases: <b>", 
    format(round(n, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
  ),
  hoverinfo = "text",
  width = 0.6
) 

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

Stacked Bar Graph

This section demonstrates how to make a stacked bar graph created with ggplotly or plotly keyboard accessible. The example shows how to use ro_ply_keyboard_nav_barchart() to enable keyboard navigation and accessible tooltips for all bars.

Example data

agegroups <- c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+")
sex <- c("Vrouw", "Man", "Onbekend")

data <- expand.grid(
  Agegroup = agegroups,
  Sex = sex,
  stringsAsFactors = FALSE
) |>
  tibble::as_tibble() |>
  dplyr::rowwise() |>
  dplyr::mutate(
    n = if (Sex == "Onbekend") sample(200:3000, 1) else sample(200000:700000, 1)
  ) |>
  dplyr::ungroup() |>
  dplyr::mutate(Sex = factor(Sex, levels = c("Vrouw", "Man", "Onbekend")))

ggplotly Stacked Bar Graph

library(ggplot2)
library(plotly)
 
p <- ggplot(data, aes(x = Agegroup, y = n, fill = Sex,
                      text = paste0(
                        "Aantal cases: <b>", format(n, big.mark = ".", decimal.mark = ",", scientific = FALSE),
                        "</b><br>Leeftijdsgroep: <b>", Agegroup,
                        "</b><br>Geslacht: <b>", Sex
                      ))) +
  geom_col(width = 0.7, position = "stack") +
  labs(x = "Leeftijdsgroep", y = "Aantal cases", fill = "Geslacht")

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

plotly Stacked Bar Graph

library(plotly)
 
fig <- plot_ly(
  data = data, # your tibble
  x = ~Agegroup,
  y = ~n,
  color = ~Sex,
  type = "bar",
  text = ~paste0(
    "Aantal cases: <b>", format(n, big.mark = ".", decimal.mark = ",", scientific = FALSE),
    "</b><br>Leeftijdsgroep: <b>", Agegroup,
    "</b><br>Geslacht: <b>", Sex
  ),
  textposition = "none",
  hoverinfo = "text"
) |> 
  layout(barmode = "stack")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_bargraph(fig)
fig

Proportional Stacked Bar Graph

This section demonstrates how to make a proportional stacked bar graph created with plotly keyboard accessible. The example shows how to use ro_ply_keyboard_nav_barchart() to enable keyboard navigation and accessible tooltips for all bars.

Note: Proportional stacked bar graphs are not supported via ggplotly, only via plotly.

Example data

age_cat <- c("0-19", "20-39", "40-59", "60+")
sex <- c("Vrouw", "Man")
period <- c("Na", "Voor")

data <- expand.grid(
  age_cat = age_cat,
  omicron_period = period,
  stringsAsFactors = FALSE
) |>
  tibble::as_tibble() |>
  dplyr::rowwise() |>
  dplyr::mutate(
    # Simuleer totaal aantal cases per groep
    total_n = sample(50000:1200000, 1),
    # Simuleer random verhouding tussen vrouw/man
    vrouw_prop = runif(1, 0.45, 0.55),
    man_prop = 1 - vrouw_prop,
    n_vrouw = as.integer(total_n * vrouw_prop),
    n_man = total_n - n_vrouw
  ) |>
  dplyr::ungroup() |>
  # Zet weer om naar long format per sex
  tidyr::pivot_longer(
    cols = c(n_vrouw, n_man),
    names_to = "Sex",
    values_to = "n",
    names_prefix = "n_"
  ) |>
  dplyr::group_by(age_cat, omicron_period) |>
  dplyr::mutate(
    prop = n / sum(n),
    bar_label = paste0(age_cat, " (", omicron_period, ")"),
    prop_label = paste0(format(round(prop * 100, 1), decimal.mark = ","), "%")
  ) |>
  dplyr::ungroup() |>
  dplyr::select(age_cat, Sex, omicron_period, n, prop, bar_label, prop_label)

# Order the labels
data <- data |>
  dplyr::mutate(
    Sex = dplyr::recode(Sex, "vrouw" = "Vrouw", "man" = "Man")
  )
bar_label_levels <- as.vector(outer(age_cat, period, function(a, p) paste0(a, " (", p, ")")))
data <- data |>
  dplyr::mutate(
    bar_label = factor(bar_label, levels = bar_label_levels),
    Sex = factor(Sex, levels = c("Vrouw", "Man"))
  ) |>
  dplyr::arrange(bar_label, Sex) |>
  dplyr::select(age_cat, Sex, omicron_period, n, prop, bar_label, prop_label)

# Show the structure of the data
str(data)
#> tibble [16 × 7] (S3: tbl_df/tbl/data.frame)
#>  $ age_cat       : chr [1:16] "0-19" "0-19" "20-39" "20-39" ...
#>  $ Sex           : Factor w/ 2 levels "Vrouw","Man": 1 2 1 2 1 2 1 2 1 2 ...
#>  $ omicron_period: chr [1:16] "Na" "Na" "Na" "Na" ...
#>  $ n             : int [1:16] 377825 426895 175689 185337 475958 552204 374982 361982 138322 122726 ...
#>  $ prop          : num [1:16] 0.47 0.53 0.487 0.513 0.463 ...
#>  $ bar_label     : Factor w/ 8 levels "0-19 (Na)","20-39 (Na)",..: 1 1 2 2 3 3 4 4 5 5 ...
#>  $ prop_label    : chr [1:16] "47%" "53%" "48,7%" "51,3%" ...

plotly Proportional Stacked Bar Graph

library(plotly)
 
fig <- plot_ly(
  data = data,
  y = ~bar_label,
  x = ~prop,
  color = ~Sex,
  orientation = "h",
  type = "bar",
  text = ~paste0(
    "Leeftijdsgroep: <b>", age_cat, "<br></b>",
    "Omicron periode: <b>", omicron_period, "<br></b>",
    "Geslacht: <b>", Sex, "<br></b>",
    "Percentage: <b>", prop_label, "<br></b>"
  ),
  textposition = "auto",
  hovertemplate = ~paste0(
    "Leeftijdsgroep: <b>", age_cat, "<br></b>",
    "Omicron periode: <b>", omicron_period, "<br></b>",
    "Geslacht: <b>", Sex, "<br></b>",
    "Percentage: <b>", prop_label, "<br></b>"
  ),
  hoverinfo = "text"
) |> layout(barmode = "stack")

fig <- ro_ply_keyboard_nav_barchart(fig)
fig

Trendline

This section demonstrates how to make a trendline created with ggplotly or plotly keyboard accessible. The ro_ply_keyboard_nav_trendline() function allows users to focus on individual points (not lines), with tooltips and ARIA labels for screen readers.

Example data

dates <- seq(as.Date("2021-10-04"), by = "day", length.out = 100)
n <- round(2500 + 700 * sin(seq_along(dates) / 25) + seq_along(dates) * 3 + rnorm(length(dates), 0, 150))
moving_average <- stats::filter(n, rep(1 / 7, 7), sides = 2)
text <- paste0(
  "Dag: <b>", format(dates, "%d-%m-%Y"), "</b><br>",
  "Aantal cases: <b>", format(n, big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b><br>",
  "7-daags gemiddelde: <b>", format(round(moving_average, 0), 
                                    big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
)

data <- tibble::tibble(
  Date_statistics = dates,
  n = n,
  moving_average = as.numeric(moving_average),
  text = text
)

ggplotly Trendline

p <- ggplot(data, aes(x = Date_statistics, y = n, text = text, group = 1)) +
  geom_line() +
  geom_point() 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_trendline(fig)
fig

plotly Trendline

library(plotly)
 
fig <- plot_ly()
fig <- fig |>
  plotly::add_trace(
    data = data,
    x = ~Date_statistics,
    y = ~n,
    type = "scatter",
    mode = "markers",
    name = "Aantal cases",
    text = ~text,
    hoverinfo = "text"
  )
# Add line for moving average
fig <- fig |>
  plotly::add_trace(
    data = data,
    x = ~Date_statistics,
    y = ~moving_average,
    type = "scatter",
    mode = "lines",
    name = "7-daags gemiddelde",
    text = ~text,
    hoverinfo = "text"
  )

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_trendline(fig)
fig

Grouped Trendline

This section demonstrates how to make a grouped trendline created with ggplotly or plotly keyboard accessible. The ro_ply_keyboard_nav_trendline() function allows users to focus on individual points (not lines), with tooltips and ARIA labels for screen readers.

Example data

categories <- LETTERS[1:5]  # "A", "B", "C", "D", "E"
weeks <- seq(as.Date("2023-01-02"), as.Date("2023-05-29"), by = "week")

data <- tidyr::expand_grid(
  week = weeks,
  Category = categories
) |> 
  dplyr::mutate(
    n = dplyr::case_when(
      Category == "A" ~ round(100 + 10 * as.numeric(week - min(week)) / 7 + stats::rnorm(dplyr::n(), 0, 15)),
      Category == "B" ~ round(50 + 2 * as.numeric(week - min(week)) / 7 + stats::rnorm(dplyr::n(), 0, 7)),
      Category == "C" ~ round(80 + 5 * sin(dplyr::row_number() / 4) + stats::rnorm(dplyr::n(), 0, 10)),
      Category == "D" ~ round(200 - 5 * as.numeric(week - min(week)) / 7 + stats::rnorm(dplyr::n(), 0, 18)),
      Category == "E" ~ round(70 + 8 * cos(dplyr::row_number() / 5) + stats::rnorm(dplyr::n(), 0, 12))
    )
  )
data <- data |>
  dplyr::mutate(
    text = paste0(
      "Week: <b>", format(week, "%d-%m-%Y"), "</b><br>",
      "Categorie: <b>", Category, "</b><br>",
      "Aantal cases: <b>", format(n, big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
    )
  )
cat_names <- unique(data$Category)

ggplotly Grouped Trendline

library(ggplot2)
library(plotly)
 
p <- ggplot(data, aes(x = week, y = n, color = Category, group = Category, text = text)) +
  geom_line() +
  geom_point() 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_trendline(fig)
fig

plotly Grouped Trendline

library(plotly)
 


fig <- plot_ly()
for (cat in cat_names) {
  subset <- data[data$Category == cat, ]
  fig <- fig |>
    plotly::add_trace(
      data = subset,
      x = ~week,
      y = ~n,
      type = "scatter",
      mode = "lines+markers",
      name = cat,
      text = ~text,
      hoverinfo = "text"
    )
}

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_trendline(fig)
fig

Line Chart

This section demonstrates how to make a line chart created with ggplotly or plotly keyboard accessible. The ro_ply_keyboard_nav_linechart() adds keyboard focus to lines, and shows a focus-point.

Example data

base_data <- tidyr::tibble(expand.grid(
  Jaar = 2001:2020,
  Geslacht = c("Man", "Vrouw")
)) |>
  dplyr::mutate(value = sample(1:50, dplyr::n(), TRUE))

total_data <- base_data |>
  dplyr::group_by(Jaar) |>
  dplyr::summarize(value = sum(value), .groups = "drop") |>
  dplyr::mutate(Geslacht = "Totaal")

data <- dplyr::bind_rows(base_data, total_data) |>
  dplyr::mutate(Geslacht = factor(Geslacht, levels = c("Man", "Vrouw", "Totaal")))

ggplotly Line Chart

ro_ply_keyboard_nav_linechart

library(ggplot2)
library(plotly)
 

p <- ggplot(data, aes(x = Jaar, y = value, color = Geslacht, group = Geslacht,
                      text = paste0(
                        "Geslacht: <b>", Geslacht,
                        "</b><br>Jaar: <b>", Jaar,
                        "</b><br>Aantal cases: <b>", value
                      ))) +
  geom_line() 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_linechart(fig)
fig

plotly Line Chart

library(plotly)
 

fig <- plot_ly()

for (geslacht in levels(data$Geslacht)) {
  subset <- data |> filter(Geslacht == geslacht)
  
  fig <- fig |>
    add_trace(
      data = subset,
      x = ~Jaar,
      y = ~value,
      type = "scatter",
      mode = "lines",
      name = geslacht,
      text = paste0(
        "Geslacht: <b>", subset$Geslacht,
        "</b><br>Jaar: <b>", subset$Jaar,
        "</b><br>Aantal cases: <b>", subset$value
      ),
      hoverinfo = "text"
    )
}

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_linechart(fig)
fig

Keyboard accessibility - ggplotly and plotly legends

By default, plotly legends are not keyboard accessible. The ro_ply_keyboard_nav_legend() function makes it possible to reach and toggle legend items using the keyboard, with visual focus indication and ARIA attributes for screen readers.

Note: For stacked bar graphs for ggplot, it is advised to disable the legend filtering (also with the mouse) altogether, as the image is not re-rendered after filtering, which can lead to ‘floating’ bars.

Example data and plot (horizontal barchart from above)

The example data is used from the horizontal barchart as shown above.

data <- tidyr::tibble(
  expand.grid(`Age group` = c("A", "B", "C", "D", "E"), `Sex` = c("Man", "Vrouw")),
  `Number of Cases` = sample(1:50, 10, TRUE)
) 
library(ggplot2)
library(plotly)
 
p <- ggplot(data, aes(
  x = `Number of Cases`,
  y = `Age group`,
  fill = Sex,
  text = paste0(
    "Leeftijdsgroep: <b>", `Age group`, "</b><br>",
    "Geslacht: <b>", Sex, "</b><br>",
    "Aantal cases: <b>", `Number of Cases`, "</b>"
  )
)) +
  geom_col(position = "dodge") +
  labs(
    x = "Aantal cases",
    y = "Leeftijdsgroep",
    fill = "Geslacht"
  ) 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_barchart(fig)

After the figure is made, the legend can be made accessible.

# Add the keyboard accessibility to the legend
fig <- ro_ply_keyboard_nav_legend(fig)
fig

The plotly modebar / toolbar can be distracting or inaccessible for keyboard users.

The ro_ply_disable_toolbar() function removes the toolbar and disables legend popup further improving accessibility.

Example data and plot

The example data is used from the horizontal barchart as shown above.

data <- tidyr::tibble(
  expand.grid(`Age group` = c("A", "B", "C", "D", "E"), `Sex` = c("Man", "Vrouw")),
  `Number of Cases` = sample(1:50, 10, TRUE)
) 
library(ggplot2)
library(plotly)
 
p <- ggplot(data, aes(
  x = `Number of Cases`,
  y = `Age group`,
  fill = Sex,
  text = paste0(
    "Leeftijdsgroep: <b>", `Age group`, "</b><br>",
    "Geslacht: <b>", Sex, "</b><br>",
    "Aantal cases: <b>", `Number of Cases`, "</b>"
  )
)) +
  geom_col(position = "dodge") +
  labs(
    x = "Aantal cases",
    y = "Leeftijdsgroep",
    fill = "Geslacht"
  ) 

# Convert to plotly and add accessibility
fig <- ggplotly(p, tooltip = "text")

# Add the keyboard accessibility in the plot
fig <- ro_ply_keyboard_nav_barchart(fig)
# Add keyboard accessibility to the legend
fig <- ro_ply_keyboard_nav_legend(fig)

Then the toolbar / modbar can be disabled, and the pop-up on filtering can be disabled as well, as this not accessible with the keyboard

# Disable the modbar and filtering pop-up
fig <- ro_ply_disable_toolbar(fig)
fig

ggplotly and plotly keyboard accessibility

Introduction

Keyboard accessibility - ggplotly and plotly plots

Horizontal Barchart

Example data

ggplotly - Horizontal Barchart

plotly - Horizontal Barchart

Grouped Bar Graph / Column Chart

Example data

ggplotly Grouped Bar Graph / Column Chart

plotly Grouped Bar Graph / Column Chart

Bar Graph

Example data

ggplotly Bar Graph

plotly Bar Graph

Stacked Bar Graph

Example data

ggplotly Stacked Bar Graph

plotly Stacked Bar Graph

Proportional Stacked Bar Graph

Example data

plotly Proportional Stacked Bar Graph

Trendline

Example data

ggplotly Trendline

plotly Trendline

Grouped Trendline

Example data

ggplotly Grouped Trendline

plotly Grouped Trendline

Line Chart

Example data

ggplotly Line Chart

plotly Line Chart

Keyboard accessibility - ggplotly and plotly legends

Example data and plot (horizontal barchart from above)

ggplotly and plotly disable toolbar

Example data and plot