我在闪亮应用程序中制作交互式绘图时遇到问题。我使用的数据由 6 个变量和 512 个观察值组成：年、月、频道、viewCount、commentCount、likeCount。这是我的可重现代码：
library(shiny)
library(shinyWidgets)
library(shinyjs)
library(plotly)
library(ggplot2)
library(thematic)
library(ragg)
library(showtext)
df <- read.csv(file_path)
thematic_shiny(font = "Pacifico")
ui <- fluidPage(
# Select theme
shinythemes::themeSelector(),
tags$style(HTML("
body {
font-family: 'Pacifico', 15; /*Set up fonts for the page
")),
# Fix widgets
tags$head(
tags$script(HTML('
$(document).ready(function() {
// Get the position of the sidebar
var sidebarPosition = $(".sidebar").offset().top;
// Function to fix or unfix the sidebar based on scrolling
function fixSidebar() {
var scrollTop = $(window).scrollTop();
if (scrollTop > sidebarPosition) {
$(".sidebar").addClass("fixed-sidebar");
} else {
$(".sidebar").removeClass("fixed-sidebar");
}
}
// Attach the function to the scroll event
$(window).scroll(fixSidebar);
// Call the function once to set the initial state
fixSidebar();
});
'))
),
# Application title
titlePanel("Youtube Data science Channels Analytics"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderTextInput(
inputId = "year_slider",
label = "Select Year",
choices = as.character(2017:2024),
selected = "2023",
width = "300px"
)
)
),
mainPanel(
tabsetPanel(
tabPanel('View Count', plotOutput('view_plot')),
tabPanel('Comment Count', plotOutput('comment_plot')),
tabPanel('Like Count', plotOutput('like_plot'))
)
)
)
server<-function(input, output) {
library(dplyr)
library(ggplot2)
library(lubridate)
df$channel <- as.factor(df$channel)
#Filtering
channel_pipeline <- reactive({
df %>%
group_by(channel, Year) %>%
summarize(
viewCount = mean(viewCount),
commentCount = mean(commentCount),
likeCount = mean(likeCount)) %>%
arrange(Year, .by_group = T) %>%
filter(as.numeric(Year) %in% as.numeric(input$year_slider)) %>%
as.data.frame()
})
# View Plot
library(RColorBrewer)
# Define color scale
color_scale <- brewer.pal(length(unique(df$channel)), "Spectral")
output$view_plot <- renderPlotly({
channel_data <- channel_pipeline()
print(head(isolate(channel_data()))) # Add this line for debugging
plot_ly(data = channel_pipeline(), x = ~as.character(channel_pipeline()$channel), y = ~viewCount*1e-3, type = 'bar', color = ~as.character(channel_pipeline()$channel),
colors = color_scale(length(unique(df$channel))))
layout(xaxis = list(title = ''), yaxis = list(title = expresiom(10^3)))
# Second approach to build an interactive plot
# p1 <- ggplot(data = channel_pipeline(), aes(x = channel, y = viewCount*1e-3, fill = channel)) +
# geom_bar(stat = "identity", position = "dodge", width = 0.7, alpha = .8) +
# scale_fill_manual(values = RColorBrewer::brewer.pal(n = length(unique(df$channel)), name = 'Spectral')) +
# labs(title = "Videos performance by channels", x = "Channel") +
# scale_y_continuous(expression(10^3)) +
# theme_minimal() +
# theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1),
# axis.text.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5),
# axis.title.y = element_text(angle = 0)) +
# scale_x_discrete(labels = function(x) {
# if (is.Date(x[1])) {
# format(x, "%Y")
# } else {
# x
# }
# })
# ggplotly(p1)
# p1
})
# Comments plot
output$comment_plot <- renderPlotly({
channel_data <- channel_pipeline()
print(head(isolate(channel_data())))
plotly(data = channel_data, x =~as.charater(channel_data()$channel), y =~commentCount*1e-3, color = as.character(channel_data()$channel), type = 'bar',
colors = color_scale(length(unique(df$channel))))
layout(xaxis = list(title = ''), yaxis = list(title = expresiom(10^3)))
# Second approach to build an interactive plot
# p2 <- ggplot(data = channel_pipeline(), aes(x = channel, y = commentCount*1e-3, fill = channel)) +
# geom_bar(stat = "identity", position = "dodge", width = 0.7, alpha =.8) +
# scale_fill_manual(values = RColorBrewer::brewer.pal(n = length(unique(df$channel)), name = 'Spectral')) +
# labs(title = "Videos performance by channels",
# x = "Channel"
# ) +
# scale_y_continuous(expression(10^3)) +
# theme_minimal() +
# theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1),
# axis.text.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5),
# axis.title.y = element_text(angle = 0)) +
# scale_x_discrete(labels = function(x) {
# if (is.Date(x[1])) {
# format(x, "%Y")
# } else {
# x
# }
# })
#
# # ggplotly(p2)
# p2
})
# Likes plot
output$like_plot <- renderPlotly({
channel_data <- channel_pipeline()
print(head(isolate(channel_data())))
plotly(data = channel_data, x =~as.charater(channel_data()$channel), y =~likeCount*1e-3, color = as.character(channel_data()$channel), type = 'bar',
colors = color_scale(length(unique(df$channel))))
layout(xaxis = list(title = ''), yaxis = list(title = expresiom(10^3)))
# Second approach to build an interactive plot
# p3 <- ggplot(data = channel_pipeline(), aes(x = channel, y = likeCount*1e-3, fill = channel)) +
# geom_bar(stat = "identity", position = "dodge", width = 0.7, alpha =.8) +
# scale_fill_manual(values = RColorBrewer::brewer.pal(n = length(unique(df$channel)), name = 'Spectral')) +
# labs(title = "Videos performance by channels",
# x = "Channel"
# ) +
# scale_y_continuous(expression(10^3)) +
# theme_minimal() +
# theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1),
# axis.text.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5),
# axis.title.y = element_text(angle = 0)) +
# scale_x_discrete(labels = function(x) {
# if (is.Date(x[1])) {
# format(x, "%Y")
# } else {
# x
# }
# })
# ggplotly(p3)
# p3
}
)
})
shinyApp(ui, server)
我尝试使用 ggplotly 将 ggplots 转换为交互式图表，但我只得到简单的静态图。 如果您有时间查看我的问题以构建带有交互式绘图的闪亮应用程序，我将非常感激。
在 Shiny 中使用plotly 时，请确保在 UI 中使用
plotlyOutput()plotOutput()mainPanel()mainPanel(
tabsetPanel(
tabPanel('View Count', plotlyOutput('view_plot')),
tabPanel('Comment Count', plotlyOutput('comment_plot')),
tabPanel('Like Count', plotlyOutput('like_plot'))
)
)
)
希望对你有帮助！