---
title: "plotly figures in Rijksoverheid style"
format:
  html:
    tabset: TRUE
vignette: >
  %\VignetteIndexEntry{plotly figures in Rijksoverheid style}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(plotly)
library(ROvis.plotly) # nolint: missing_package_linter.
library(ROvis.utils)
```

# Introduction

This vignette demonstrates how to create clear, publication-ready visualizations in R using the [plotly](https://plotly.com/r/) package, styled according to the Rijksoverheids style The provided functions allow you to quickly apply consistent fonts, colors, grid lines, axes, legends, tooltips, and annotation styling across all your Plotly charts.

# Key Function

- **`ro_ply_standard()`**  


# Code Examples

## Column Chart / Grouped Vertical Bar Chart

This example shows how to create a grouped vertical bar chart (column chart) displaying the number of cases by age group and sex.

```{r example column chart}

# We first make some random 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"))
  )


# Then we make the basic plotly plot
theme_rivm <- ro_ply_theme(barmode = "group")
kleuren <- c(ro_color("hemelblauw"), ro_color("robijnrood"),  ro_color("paars"))

fig <- plot_ly(
  data = data,
  x = ~`Age group`,
  y = ~`Number of Cases`,
  type = "bar",
  color = ~Sex,
  colors = kleuren,
  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"
)

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "bargraph", 
                      barmode = "group",
                      y_data = data$"Age group", 
                      y_label = "Aantal cases", 
                      title = "COVID-19 cases per leeftijdsgroep en geslacht", 
                      x_label = "Leeftijdsgroepen")
fig
```


## Bar Graph

This example demonstrates a simple vertical bar chart / bar graph, showing the number of cases per sex, with the default Rijksoverheid / RIVM theme.

```{r example bar graph}
# We first make some random data
bar_data <- tibble::tibble(
  Sex = c("Man", "Vrouw"),
  n = c(1234, 1675)
)

# Then we make the basic plotly plot
sex_colors <- c(
  "Man" = ro_color("hemelblauw"),
  "Vrouw" = ro_color("robijnrood")
)

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

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "bargraph",
                      y_data = bar_data$n, 
                      y_label = "Aantal cases", 
                      title = "COVID-19 cases per geslacht",  
                      x_label = "Geslacht")
fig
```

## Horizontal Bar Chart

This example demonstrates how to create a horizontal bar chart grouped by age group and sex.  

```{r example horizontal barchart}
# We first make some random data
data <- tibble::tibble(expand.grid(`Age group` = c("A", "B", "C", "D", "E"),
                                   `Sex` = c("Man", "Vrouw")),
                       `Number of Cases` = sample(1:50, 10, TRUE))


# Then we make the basic plotly plot
kleuren <- c(
  "Man" = ro_color("hemelblauw"),
  "Vrouw" = ro_color("robijnrood")
)

fig <- plot_ly() |>
  plotly::add_trace(data = data,
    x = ~`Number of Cases`,
    y = ~`Age group`,
    color = ~`Sex`,
    colors = kleuren,
    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"
  )

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "barchart",
                      y_data = data$"Age group", 
                      y_label = "Groep", 
                      title = "COVID-19 cases per geslacht en groep", 
                      x_label = "Aantal cases",
                      y_axis_spike = TRUE,
                      x_axis_spike = FALSE,
                      y_axis_showline = TRUE,
                      x_axis_showline = FALSE,
                      y_axis_showgrid = FALSE,
                      x_axis_showgrid = TRUE
                      )
fig
```

## Stacked Bar Graph

This example demonstrates how to create a stacked bar chart displays the number of cases per age group and sex.

The theme uses:
`theme_rivm <- ro_ply_theme(barmode = "stack")`
where `barmode = "stack"` stacks the bars by sex within each age group.

```{r example stacked bar graph}
library(dplyr)

# We first make some random data
stacked_data <- tibble::tibble(
  Agegroup = rep(c("0-9", "10-19", "20-29", "30-39"), each = 3),
    Sex = factor(rep(c("Man", "Vrouw", "Onbekend"), times = 4), 
               levels = c("Man", "Vrouw", "Onbekend")),
  n = c(10, 14, 2, 15, 17, 1, 23, 18, 0, 9, 11, 3)
)

# Then we make the basic plotly plot
sex_colors <- c(
  "Man" = ro_color("hemelblauw"),
  "Vrouw" = ro_color("robijnrood"),
  "Onbekend" = ro_color("donkergeel")
)

fig <- plot_ly(
  data = stacked_data,
  x = ~Agegroup,
  y = ~n,
  type = "bar",
  color = ~Sex,
  colors = sex_colors,
  text = ~paste0(
    "Leeftijdsgroep: <b>", Agegroup, "</b>",
    "<br>Geslacht: <b>", Sex, "</b>",
    "<br>Aantal cases: <b>", 
    format(round(n, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
  ),
  hovertext = ~paste0(
    "Leeftijdsgroep: <b>", Agegroup, "</b>",
    "<br>Geslacht: <b>", Sex, "</b>",
    "<br>Aantal cases: <b>", 
    format(round(n, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
  ),
  textposition = "none",
  hoverinfo = "text"
)

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "bargraph", 
                      barmode = "stack",
                      y_data = stacked_data |>  group_by(Agegroup) |>  summarise(n_total = sum(n)) |>  pull(n_total),
                      title = "COVID-19 cases per leeftijdsgroep en geslacht",
                      x_label = "Leeftijdsgroep",
                      y_label = "Aantal cases"
                      )
fig
```
## Proportional Stacked Bar Graph

This example demonstrates how to create aroportional stacked bar chart, displaying  the proportion of cases per age group and sex as a horizontal.

```{r example proportional stacked bar graph}
library(dplyr)

# We first make some random data
prop_data <- tibble::tibble(
  age_cat = rep(c("0-9", "10-19", "20-29", "30-39"), each = 2),
  Sex = rep(c("Man", "Vrouw"), times = 4),
  n = c(12, 8, 15, 12, 20, 18, 10, 7)
) |> 
  group_by(age_cat) |> 
  mutate(
    total = sum(n),
    prop = n / total,
    prop_label = paste0(round(prop * 100, 1), "%"),
    bar_label = age_cat
  ) |> 
  ungroup()

# Then we make the basic plotly plot
sex_colors <- c(
  "Man" = ro_color("hemelblauw"),
  "Vrouw" = ro_color("robijnrood")
)

fig <- plot_ly(
  data = prop_data,
  y = ~bar_label,
  x = ~prop,
  color = ~Sex,
  colors = sex_colors,
  type = "bar",
  orientation = "h",
  text = ~paste0(
    "Leeftijdsgroep: <b>", age_cat, "</b><br>",
    "Geslacht: <b>", Sex, "</b><br>",
    "Percentage: <b>", prop_label, "</b>"
  ),
  textposition = "auto",
  hovertemplate = ~paste0(
    "Leeftijdsgroep: <b>", age_cat, "</b><br>",
    "Geslacht: <b>", Sex, "</b><br>",
    "Percentage: <b>", prop_label, "</b>"
  ),
  hoverinfo = "text"
) |> 
  layout(
    xaxis = list(
      range = c(0, 1)
    )
  )

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "bargraph", 
                      barmode = "stack",
                      y_data = prop_data$bar_label,
                      y_label = "Leeftijdsgroep",
                      title = "Percentage COVID-19 cases per leeftijdsgroep en geslacht",
                      x_label = "Percentage",
                      x_axis_tick_format = ".0%",
                      )
fig

```

## Trendline

This example shows a trendline (with points and a moving average) for daily case counts.

```{r example trendline}
library(dplyr)
library(zoo)
library(stringr)

# We first make some random data
tab_cases_rolling_average <- tibble::tibble(
  Date_statistics = seq(as.Date("2022-11-01"), as.Date("2022-12-31"), by = "day"),
  n = round(seq(100, 400, length.out = 61) + rnorm(61, 0, 20)) # trend + noise
) |> 
  mutate(
    moving_average = zoo::rollmean(n, k = 7, fill = NA, align = "right"),
    text = str_c(
      "Dag: <b>", format(Date_statistics, "%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>"
    )
  )

# Then we make the basic plotly plot
fig <- plot_ly(
  data = tab_cases_rolling_average,
  x = ~Date_statistics
) |> 
  plotly::add_markers(
    y = ~n,
    name = "Aantal cases",
    marker = list(color = ro_color("robijnrood_tint60"), size = 5),
    text = ~text,
    hoverinfo = "text"
  ) |> 
  plotly::add_lines(
    y = ~moving_average,
    name = "7-daags gemiddelde",
    line = list(color = ro_color("robijnrood"), width = 2),
    text = ~text,
    hoverinfo = "text"
  )

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "trendline", 
                      y_data = tab_cases_rolling_average$n,
                      y_label = "Aantal gevallen",
                      title = "COVID-19 gevallen per dag en 7-daags gemiddelde",
                      x_label = "Datum",
                      x_axis_tick_format = "%d %b"
                      )
fig

```

## Grouped Trendline
This example shows a grouped trendline for multiple provinces.

```{r example grouped trendline}
library(dplyr)

# We first make some random data
weeks <- seq(as.Date("2022-11-01"), as.Date("2022-12-31"), by = "week")
tab_cases_week_by_prov <- tibble::tibble(
  week = rep(weeks, 2),
  Province = rep(c("Groningen", "Zeeland"), each = length(weeks)),
  n = c(
    seq(100, 200, length.out = length(weeks)) + rnorm(length(weeks), 0, 10), # Slightly increasing with noise
    seq(100, 200, length.out = length(weeks)) + rnorm(length(weeks), 0, 10) 
  )
)

# Then we make the basic plotly plot
prov_names <- unique(tab_cases_week_by_prov$Province)
prov_colors <- setNames(theme_rivm$colorway[seq_along(prov_names)], prov_names)

fig <- plot_ly()
for (prov in prov_names) {
  prov_data <- filter(tab_cases_week_by_prov, Province == prov)
  fig <- fig |> 
    plotly::add_trace(
      data = prov_data,
      x = ~week,
      y = ~n,
      type = "scatter",
      mode = "lines+markers", 
      name = prov,
      line = list(color = prov_colors[prov], width = 2),
      text = ~paste0(
        "Provincie: <b>", Province, "</b>",
        "<br>Week: <b>", format(week, "%e %b %Y"), "</b>",
        "<br>Aantal cases: <b>", format(round(n, 0), big.mark = ".", decimal.mark = ",", scientific = FALSE), "</b>"
      ),
      hoverinfo = "text"
    )
}


# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "trendline", 
                      y_data = tab_cases_week_by_prov$n,
                      y_label = "Aantal gevallen",
                      title = "COVID-19 gevallen per week voor twee provincies",
                      x_label = "Week",
                      x_axis_tick_format = "%d %b"
                      )
fig
```

## Line chart

This example creates a grouped line chart showing cases by year and sex.

```{r example line chart}
library(dplyr)
library(stringr)

# We first make some random data
data <- tibble::tibble(
  expand.grid(Jaar = 2001:2020, Geslacht = c("Man", "Vrouw")),
  value = sample(1:50, 40, TRUE)
)
data_sum <- data |>
  group_by(Jaar) |>
  summarize(value = sum(value), .groups = "drop") |>
  mutate(Geslacht = "Totaal")
data_all <- bind_rows(data, data_sum) |>
  mutate(Geslacht = factor(Geslacht, levels = c("Man", "Vrouw", "Totaal")))

# Then we make the basic plotly plot
kleuren <- c(
  "Man" = ro_color("hemelblauw"),
  "Vrouw" = ro_color("robijnrood"),
  "Totaal" = ro_color("paars")
)

fig <- plot_ly()
for (geslacht in levels(data_all$Geslacht)) {
  subset <- filter(data_all, Geslacht == geslacht)
  fig <- fig |> 
    plotly::add_trace(
      data = subset,
      x = ~Jaar,
      y = ~value,
      type = "scatter",
      mode = "lines",
      name = geslacht,
      line = list(color = kleuren[geslacht], width = 2),
      text = ~str_c(
        "Geslacht: <b>", Geslacht,
        "</b><br>Jaar: <b>", Jaar,
        "</b><br>Aantal cases: <b>", value
      ),
      hoverinfo = "text"
    )
}

# And apply the Rijksoverheid / RIVM theme, and accesibility features
fig <- ro_ply_standard(fig,
                      type = "linechart", 
                      y_data = data_all$value,
                      y_label = "Aantal cases",
                      title = "COVID-19 cases per geslacht",
                      x_label = "" #For the year, no x-axis title is needed
                      )
fig
```