Create dropdown list in R using plotly?

DORSETRIGS

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02-09-2024

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Creating Interactive Dropdown Filters for Plotly Charts in R

This article will explore how to create interactive dropdown lists for filtering data in plotly charts using R. We will focus on the example provided by the Stack Overflow user and demonstrate how to implement a dropdown to filter by year.

Understanding the Challenge:

The user wants to create a bar chart showing the percentage of TBDR cases by region and state in Brazil for different years. They have a ggplot code for creating the chart for a single year, and they want to add a dropdown menu to allow users to dynamically select the year they want to view.

The Solution:

The key lies in understanding how to combine the functionalities of plotly and ggplot. While ggplot excels in static visualizations, plotly brings interactivity to the table. We'll leverage the ggplotly() function from the plotly package to convert our ggplot object into an interactive plotly object. Then, we will add a dropdown menu that updates the data displayed in the chart.

The Code Breakdown:

library(plotly)
library(ggplot2)
library(dplyr)

# Creating a sample data frame
dadosbr <- data.frame(
  TIPO_DE_RESISTENCIA = sample(c("Primária", "Adquirida", "Ignorado"), 7525, replace = TRUE),
  Regiao = sample(c("Centro-Oeste", "Nordeste", "Norte", "Sudeste", "Sul"), 7525, replace = TRUE),
  ANO_TRAT = sample(2019:2024, 7525, replace = TRUE),
  UF = sample(c("Acre", "Alagoas", "Amapá", "Amazonas", "Bahia", "Ceará", "Distrito Federal", "Espírito Santo", "Goiás", "Maranhão", "Mato Grosso", "Mato Grosso do Sul",
                "Minas Gerais", "Pará", "Paraíba", "Paraná", "Pernambuco", "Piauí", "Rio de Janeiro", "Rio Grande do Norte", "Rio Grande do Sul", "Rondônia", "Roraima",
                "Santa Catarina", "São Paulo", "Sergipe", "Tocantins"), 7525, replace = TRUE)
)

# Preparing the data for plotting
graf9 <- dadosbr %>%
  group_by(ANO_TRAT, Regiao, TIPO_DE_RESISTENCIA, UF) %>%
  tally() %>%
  mutate(TIPO_DE_RESISTENCIA = factor(TIPO_DE_RESISTENCIA, levels = c("Primária", "Adquirida", "Ignorado")))

# Creating the ggplot base chart
graf9_ggplot <- ggplot(graf9, aes(x = UF, y = n, fill = TIPO_DE_RESISTENCIA)) +
  geom_bar(position = "fill", stat = "identity") +
  labs(x = "UF de residência", 
       y = "% casos TBDR",
       fill = "Tipo de resistência") +
  scale_fill_brewer(palette = "Set1", direction = 1) +
  theme_classic() + 
  scale_y_continuous(labels = scales::percent, expand = expansion(mult = c(0, .1))) +
  facet_grid(. ~ Regiao , scales = "free", space = "free") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  theme(
    legend.position = "right",
    axis.line = element_line(colour = "black"),
    axis.text.x = element_text(colour = "black"),
    axis.text.y = element_text(colour = "black"),
    plot.caption = element_text(hjust = 0.5, vjust = 1, margin = margin(t = 10), size = 10),
    plot.title = element_text(hjust = 0.5, vjust = 1, margin = margin(t = 10), size = 12)
  )

# Converting ggplot to interactive plotly
graf9_plotly <- ggplotly(graf9_ggplot)

# Adding the dropdown menu for year filtering
graf9_plotly <- graf9_plotly %>%
  layout(
    updatemenus = list(
      list(
        active = 0,
        buttons = list(
          list(label = "2019", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2019, ], traces = c(1,2))), visible = TRUE),
          list(label = "2020", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2020, ], traces = c(1,2))), visible = TRUE),
          list(label = "2021", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2021, ], traces = c(1,2))), visible = TRUE),
          list(label = "2022", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2022, ], traces = c(1,2))), visible = TRUE),
          list(label = "2023", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2023, ], traces = c(1,2))), visible = TRUE),
          list(label = "2024", method = "update", args = list(list(data = graf9[graf9$ANO_TRAT == 2024, ], traces = c(1,2))), visible = TRUE)
        )
      )
    )
  )

# Display the interactive plot
graf9_plotly

Explanation:

1. We start by importing the necessary libraries: plotly, ggplot2, and dplyr.
2. We create a sample data frame dadosbr containing information about TBDR cases by year, region, state, and type of resistance.
3. We prepare the data by grouping it based on the relevant variables and creating a new variable n for the count of cases.
4. We create the initial ggplot chart (graf9_ggplot) that forms the base of our interactive plot.
5. We convert the ggplot object into a plotly object using ggplotly().
6. The key step is adding the dropdown menu using layout(). We define a list of buttons within an updatemenus list. Each button corresponds to a year, and its args argument specifies the data to update the chart when the button is selected. The data argument is a subset of the graf9 dataframe filtered for the corresponding year, and the traces argument indicates the trace indices to update (1 and 2 in this case as we have two bars).

Additional Value:

This solution provides the user with a dynamic way to analyze the data by year. They can easily explore trends and patterns across different time periods without needing to manually recreate the chart each time. The interactive dropdown makes the chart more user-friendly and engaging, enhancing data exploration.

Key Takeaways:

• Plotly allows you to create interactive visualizations from ggplot charts.
• Using updatemenus within layout enables you to add interactive dropdown menus to control chart elements.
• Carefully define the args for each button in the dropdown to specify the data update when a button is selected.

This approach allows for a seamless integration of ggplot's visualization capabilities with plotly's interactivity, making your R charts more engaging and data-driven.