r-Shiny:错误:尝试渲染第二个相关绘图时，quartz()设备大小无效

Question

这里是个十足的新手。我正在尝试使用在第一个ggiraph图中选择的eventID在Shiny中渲染第二个图。我可以获得要渲染的第一个图，但我会自动获得第二个图的错误：无效quartz((设备大小(表2：静态时间序列图(。不确定如何修复此问题。在Mac上使用Rstudio 1.2.5042版本。

Data和Shiny应用程序可在此处找到：https://github.com/dustinkincaid/FluxRegimes-闪亮的应用程序在/viewTimeSeries

# Packages
library('shiny')
library('tidyverse')
library('ggiraph')
library('lubridate')
# Read data
# Stream time series data (created using create_timeSeries_for_viewTimeSeries.R)
ts <- read_csv('Data/streamData_for_viewTimeSeriesApp.csv', col_types = cols(event_start = col_character(),
           rain_start = col_character(),
           falling_inf_pt = col_character(),
           event_end = col_character(),
           event_start_7dB4 = col_character(),
           event_end_7dAF = col_character(),
           eventID = col_character())) %>% 
mutate(timestamp = ymd_hms(timestamp, tz = "Etc/GMT+4")) %>% 
filter(!is.na(eventID))
# Clustered events with linear regression data (created using Results_yields_summary.Rmd)
lr <- read_csv('../Data/results_clusters_withLinearRegressions.csv') %>% 
# Add an event ID
mutate(siteabbrev = ifelse(site == "Hungerford", "HB", "WB"),
eventID = paste(siteabbrev, "-", as.character(event_start), sep = "")) %>% 
select(-siteabbrev)    
# Tab 2 plots
# Plotting specifics
theme1 <- theme_classic() +
theme(axis.text = element_text(size = 11),
axis.title = element_text(size = 12),
axis.title.x = element_text(margin=margin(5,0,0,0)),
axis.title.y = element_text(margin=margin(0,5,0,0)),
legend.title = element_text(size = 9),
legend.text = element_text(size = 9),
strip.text = element_text(size = 12))

# User Interface
in1 <- selectInput(
inputId = 'selected_event',
label = 'Select an event to view',
choices = unique(ts[['eventID']])
)
# Tab 1 outputs
# This will output the event ID
out1 <- textOutput('eventID')
# This will output the plot below the event ID
out2 <- plotOutput('ts_plot')
# This is the first tab/page of the app (may only have 1 tab)
tab1 <- tabPanel(title = 'View event TS',
in1, out1, out2)
# This is the second tab
tab2 <- tabPanel(title = 'Linear regs. vs. TS',
fluidRow(selectInput(
inputId = 'selected_site',
label = 'Select a site',
choices = unique(lr[['site']]))
),
fluidRow(ggiraph::ggiraphOutput('int_reg_plot')),
fluidRow(ggiraph::ggiraphOutput('ts_plot_2'))
)
ui <- navbarPage(title = 'Event Time Series Explorer',
tab1, 
tab2)
# Server
server <- function(input, output) {
# Tab 1: event ID text output above static time series plot      
output[['eventID']] <- renderText({
input[['selected_event']]
})

# Tab 1: static time series plot
output[['ts_plot']] <- renderPlot({
df <- ts %>% 
dplyr::filter(eventID == input[['selected_event']]) %>% 
tidyr::pivot_longer(cols = c(NO3, SRP, turb, q_cms), names_to = "var", values_to = "value") %>% 
dplyr::mutate(var = factor(var, levels = c("q_cms", "NO3", "SRP", "turb"), labels = c("Q (cms)", "NO3 (mg N/L)", "SRP (mg P/L)", "Turbidity (NTU)")))
ggplot(df, aes(x = timestamp, y = value)) +
facet_wrap(~var, scales = "free_y", ncol = 1) +
geom_vline(aes(xintercept = ymd_hms(event_start, tz = "Etc/GMT+4")), linetype = "dashed") +
geom_vline(aes(xintercept = ymd_hms(event_end, tz = "Etc/GMT+4")), linetype = "dashed") +
# geom_point(size = 2, shape = 1) +
geom_path() +
scale_x_datetime(date_breaks = "4 days") +
ylab("Value") +
theme_classic() +
theme(strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12),
strip.text = element_text(size = 14))
})

# Tab 2: establish the selected data point and eventID on interactive linear regression plot as input
selected_eventID <- reactive({
input$plot_selected
})

# Tab 2: interactive linear regression plot
output$int_reg_plot <- renderGirafe({
df <- lr %>% 
dplyr::filter(site == input[['selected_site']]) %>% 
dplyr::mutate(season = factor(season, levels = c("spring", "summer", "fall"), labels = c("Spring", "Summer", "Fall"))) %>% 
dplyr::mutate(var = factor(var, levels = c("NO3_kg_km2", "SRP_kg_km2", "turb_kg_km2"), labels = c(expression(NO[3]^-{}~(kg~N~km^{-2})) , expression(SRP~(kg~P~km^{-2})), expression(Turb.~(Sigma~NTU~km^{-2})))))
gg_linReg <-
ggplot(df, aes(x = q_mm, y = yield)) +
facet_grid(var~season, scales = "free", labeller = label_parsed) +
geom_smooth(method=lm, se=FALSE, color = "black") +
geom_point_interactive(aes(data_id = eventID,
tooltip = eventID,
color = factor(clust_5cl),
shape = factor(year(event_start))),
size = 3, stroke = 0.75, alpha = 0.8) +
scale_shape_manual(name = "Year",
values = c(19, 17, 15)) +
scale_color_manual(name = "Cluster",
values = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#0072B2", "#D55E00", "#CC79A7")) +
ylab(expression(Event~NO[3]^-{}~or~SRP~or~turb.~yield)) +
xlab("Event water yield (mm)") +
theme1 +
theme(strip.background = element_blank(),
axis.title.x = element_blank(),
legend.position = "none") +
ggtitle("Hungerford")
x <- girafe(code = print(gg_linReg),
width_svg = 8, 
height_svg = 6,
options = list(
opts_hover(css = "fill:#FF3333;stroke:black;cursor:pointer;", reactive = TRUE),
opts_selection(
type = "multiple", css = "fill:#FF3333;stroke:black;")
))
x
})

# Tab 2: static time series plot
output$ts_plot_2 <- renderPlot({
df <- ts %>% 
# dplyr::filter(site == input[['selected_eventID']])
dplyr::filter(site %in% selected_eventID()) %>% 
tidyr::pivot_longer(cols = c(NO3, SRP, turb, q_cms), names_to = "var", values_to = "value") %>% 
dplyr::mutate(var = factor(var, levels = c("q_cms", "NO3", "SRP", "turb"), labels = c("Q (cms)", "NO3 (mg N/L)", "SRP (mg P/L)", "Turbidity (NTU)")))
ggplot(df, aes(x = timestamp, y = value)) +
facet_wrap(~var, scales = "free_y", ncol = 1) +
geom_vline(aes(xintercept = ymd_hms(event_start, tz = "Etc/GMT+4")), linetype = "dashed") +
geom_vline(aes(xintercept = ymd_hms(event_end, tz = "Etc/GMT+4")), linetype = "dashed") +
# geom_point(size = 2, shape = 1) +
geom_path() +
scale_x_datetime(date_breaks = "4 days") +
ylab("Value") +
theme_classic() +
theme(strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12),
strip.text = element_text(size = 14))
})  
}
# Create the Shiny App
shinyApp(ui = ui, server = server)

其他人收到这个错误，只是我的Mac上的软件有问题吗？或者我编码错误了？

Answer 1

我想通了！关键是最后一个绘图代码块中的一行代码，它从交互式int_reg_plot绘图中获取eventID输入，以过滤第二个绘图的df：

# Tab 2: static time series plot
output$ts_plot_2 <- renderPlot({
df <- ts %>% 
# This line of code fixed the issue:
dplyr::filter(eventID %in% input$int_reg_plot_selected) %>%
# These two dplyr::filter lines don't work
# dplyr::filter(site == input[['selected_eventID']])
# dplyr::filter(site %in% selected_eventID()) %>% 
tidyr::pivot_longer(cols = c(NO3, SRP, turb, q_cms), names_to = "var", values_to = "value") %>% 
dplyr::mutate(var = factor(var, levels = c("q_cms", "NO3", "SRP", "turb"), labels = c("Q (cms)", "NO3 (mg N/L)", "SRP (mg P/L)", "Turbidity (NTU)")))
ggplot(df, aes(x = timestamp, y = value)) +
facet_wrap(~var, scales = "free_y", ncol = 1) +
geom_vline(aes(xintercept = ymd_hms(event_start, tz = "Etc/GMT+4")), linetype = "dashed") +
geom_vline(aes(xintercept = ymd_hms(event_end, tz = "Etc/GMT+4")), linetype = "dashed") +
# geom_point(size = 2, shape = 1) +
geom_path() +
scale_x_datetime(date_breaks = "4 days") +
ylab("Value") +
theme_classic() +
theme(strip.background = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.y = element_text(size = 12),
strip.text = element_text(size = 14))
})