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update plotly. No repeated use of plots
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@kwlyu
kwlyu committed Aug 22, 2024
1 parent d4574c4 commit 824da2b
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Showing 3 changed files with 45 additions and 64 deletions.
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11 changes: 0 additions & 11 deletions Event-Data-Analysis.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -231,17 +231,6 @@ plotly_chart <- ggplotly(gg_plot)
plotly_chart
```

```{r}
gg_plot <- ggplot(event_summary, aes(x = year, y = term_total, fill = term_category)) +
geom_area() +
labs(x = "Year", y = "Total Events", fill = "Term") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plotly_chart <- ggplotly(gg_plot)
plotly_chart
```



# Breakdown of Events by Support Level
```{r}
Expand Down
98 changes: 45 additions & 53 deletions PAC-data-analyzer/app.R
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Original file line number Diff line number Diff line change
Expand Up @@ -326,7 +326,7 @@ analysisTab <- tabItem(
fluidRow(
column(
width = 8,
shinycssloaders::withSpinner(plotlyOutput("Plot2"))
shinycssloaders::withSpinner(reactableOutput("stand_in"))
),
column(
width = 4,
Expand Down Expand Up @@ -637,9 +637,8 @@ server <- function(input, output, session) {
) %>%
mutate(term_category = factor(term_category, levels = c("Fall", "Winter", "Spring"), ordered = TRUE))


# Create the stacked bar chart
ggplot_event_summary <- ggplot(event_summary, aes(x = year, y = term_total, fill = term_category)) +
ggplot(event_summary, aes(x = year, y = term_total, fill = term_category)) +
geom_bar(stat = "identity") +
geom_text(aes(label = term_total),
position = position_stack(vjust = 0.5),
Expand All @@ -648,14 +647,11 @@ server <- function(input, output, session) {
scale_fill_manual(values = c("Fall" = "#FF9999", "Winter" = "#99CCFF", "Spring" = "#99FF99")) +
labs(x = "Year", y = "Total Events", fill = "Term") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Convert the ggplot object to a plotly object for interactivity
plotly_event_summary <- ggplotly(ggplot_event_summary)
theme(axis.text.x = element_text(angle = 45, hjust = 1)) -> gg_plot

# Initial rendering of the countries plot
output$Plot1 <- renderPlotly({
plotly_event_summary
ggplotly(gg_plot)
})

# Updating plot based on selected countries
Expand All @@ -677,57 +673,37 @@ server <- function(input, output, session) {
})

############################### Plot 2 Overview ##############################
# Calculate total support and average audience count
support_summary <- combined_data_filtered %>%
filter(support_level != "NA") %>%
group_by(year, support_level) %>%
summarize(
total_support = n(),
average_audience_count = mean(as.numeric(audience_count[!is.na(audience_count)]), na.rm = TRUE),
.groups = 'drop'
)

# Function to summarize and pivot data for each year
summarize_and_pivot <- function(current_year) {
combined_data_filtered %>%
filter(year == current_year) %>%
group_by(term_category, support_level) %>%
summarize(Support = n(), .groups = 'drop') %>%
pivot_wider(names_from = support_level, values_from = Support, values_fill = list(Support = 0)) %>%
mutate(year = current_year) # Add a year column to identify the table
}

# List of years to iterate over
years <- unique(combined_data_filtered$year)

# Use purrr::map to apply summarize_and_pivot function for each year
support_tables <- map_dfr(years, summarize_and_pivot) # Combine into a single data frame

# Melt the data for ggplot
support_tables_melted <- support_tables %>%
pivot_longer(cols = -c(term_category, year), names_to = "support_level", values_to = "Support")

# Ensure the term_category and support_level are factors with the correct order
support_tables_melted <- support_tables_melted %>%
filter(support_level != "NA") %>%
mutate(term_category = factor(term_category, levels = c("Fall", "Winter", "Spring"), ordered = TRUE),
# Convert year to a factor with ordered levels if needed
support_summary <- support_summary %>%
mutate(year = factor(year, levels = unique(year), ordered = TRUE),
support_level = factor(support_level, levels = c("NA", "H", "M", "L"), ordered = TRUE))

# Calculate percentages for each segment
support_tables_melted <- support_tables_melted %>%
group_by(year, term_category) %>%
mutate(total_support = sum(Support),
percentage = (Support / total_support) * 100)

# Create the ggplot chart
ggplot_support_summary <- ggplot(support_tables_melted, aes(x = term_category, y = Support, fill = support_level)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(aes(label = paste0(Support, " (", round(percentage, 1), "%)")),
position = position_stack(vjust = 0.5), size = 2, color = "black") +
facet_wrap(~ year) +
labs(x = "Term", y = "Support Count", fill = "Support Level") +
scale_fill_manual(values = c("H" = "#FF9999", "M" = "#99CCFF", "L" = "#99FF99", "NA" = "black")) +
# Create the bubble chart with ggplot2
ggplot_bubble_chart <- ggplot(support_summary, aes(x = year, y = average_audience_count, size = total_support, color = support_level)) +
geom_point(alpha = 0.7) +
scale_size_continuous(range = c(3, 20)) + # Adjust the range for bubble sizes
scale_color_manual(values = c("H" = "#FF9999", "M" = "#99CCFF", "L" = "#99FF99", "NA" = "black")) +
labs(x = "Year", y = "Average Audience Count", size = "Total Support", color = "Support Level") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Convert to a plotly object
plotly_support_summary <- ggplotly(ggplot_support_summary)
plotly_bubble_chart <- ggplotly(ggplot_bubble_chart)

# Initial rendering of the plot
output$Plot2 <- renderPlotly({
# Display the plotly chart
plotly_support_summary
# Render the plotly object (in Shiny, you would use `renderPlotly`)
plotly_bubble_chart
})

# Update plot based on selected variable
Expand Down Expand Up @@ -809,7 +785,7 @@ server <- function(input, output, session) {
# Update plot based on selected variable
observeEvent(input$updatePlot3, {
req(input$compare_var, input$log_scale)
output$Plot2 <- renderPlotly({
output$Plot3 <- renderPlotly({
isolate({
renderContinentPlot(input$compare_var, input$log_scale)
})
Expand All @@ -820,7 +796,7 @@ server <- function(input, output, session) {
observeEvent(input$resetPlot3, {
updateSelectInput(session, "compare_var", selected = "gdp_pcap")
updatePrettyToggle(session, "log_scale", value = TRUE)
output$Plot2 <- renderPlotly({
output$Plot3 <- renderPlotly({
renderContinentPlot(compare = "gdp_pcap", TRUE)
})
})
Expand Down Expand Up @@ -893,7 +869,7 @@ server <- function(input, output, session) {
# Update plot based on selected variable
observeEvent(input$updatePlot4, {
req(input$compare_var, input$log_scale)
output$Plot2 <- renderPlotly({
output$Plot4 <- renderPlotly({
isolate({
renderContinentPlot(input$compare_var, input$log_scale)
})
Expand All @@ -904,7 +880,7 @@ server <- function(input, output, session) {
observeEvent(input$resetPlot4, {
updateSelectInput(session, "compare_var", selected = "gdp_pcap")
updatePrettyToggle(session, "log_scale", value = TRUE)
output$Plot2 <- renderPlotly({
output$Plot4 <- renderPlotly({
renderContinentPlot(compare = "gdp_pcap", TRUE)
})
})
Expand All @@ -925,6 +901,22 @@ server <- function(input, output, session) {
pagination = TRUE
)
})

############################## TEMP 2 ##########################################
output$stand_in <- renderReactable({
## Combine multiple elements together
iris %>%
group_by(Species) %>%
summarize(petal_width = list(Petal.Width)) %>%
reactable(.,
columns = list(petal_width = colDef(cell = react_sparkline(.,
height = 80,
decimals = 1,
statline = "mean",
statline_color = "red",
bandline = "innerquartiles",
bandline_color = "darkgreen"))))
})

observeEvent(input$submitNewTerm, {

Expand Down

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