如何将 RShiny reactiveFileReader 与 reactiveUI 和不存在的文件一起使用？
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【中文标题】如何将 RShiny reactiveFileReader 与 reactiveUI 和不存在的文件一起使用？【英文标题】：How to use RShiny reactiveFileReader with reactiveUI and non-existent files? 【发布时间】：2018-12-07 20:05:06 【问题描述】：我将如何构建一个响应式 UI，以响应具有不同数据输入的 reactiveFileReader？
我有兴趣将 reactiveFileReader 集成到一个应用程序中，该应用程序在数据中绘制组图并按组显示选定的点。
挑战：
-
并非我可以通过前缀和后缀识别的每个文件都存在。
每个文件有不同数量的组。
当我
-
尝试打开一个不存在的文件。
更新文件（因此它确实检测到有更改）
可能的解决方案：
reactive() 和req() 修复
isolate() 依赖 UI，因此它仅在第一次加载文件时更改组数。
我包含了模拟数据（及其生成）、一个 UI、损坏的服务器和一个没有响应式文件阅读器的工作服务器。
更新
唯一剩下的就是让renderUI“组”在文件被重新读取时不重置。通常这是一件好事，但在这里我不想要那样。
包
library(tidyr); library(dplyr); library(ggplot2); library(readr); library(stringr)
library(shiny)
#library(DT)
模拟数据
a1 <- structure(list(Group = c("alpha_1", "alpha_1", "alpha_2", "alpha_2", "alpha_3", "alpha_3"), Sample = c("ps_1", "ps_2", "ps_1", "ps_2", "ps_1", "ps_2"), x = c(1, 1.1, 4, 4.1, 6.8, 7), y = c(2.1, 2, 7.3, 7, 10, 9.7)), .Names = c("Group", "Sample", "x", "y"), row.names = c(NA,-6L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(cols = structure(list(Group = structure(list(), class = c("collector_character", "collector")),Sample = structure(list(), class = c("collector_character","collector")), x = structure(list(), class = c("collector_double", "collector")), y = structure(list(), class = c("collector_double", "collector"))), .Names = c("Group", "Sample", "x", "y")), default = structure(list(), class = c("collector_guess", "collector"))), .Names = c("cols", "default"), class = "col_spec"))
a2 <- structure(list(Group = c("alpha_6", "alpha_6", "alpha_7", "alpha_7", "alpha_9", "alpha_9", "alpha_10", "alpha_10"), Sample = c("ps_1", "ps_2", "ps_1", "ps_2", "ps_1", "ps_2", "ps_1", "ps_2"), x = c(3,3.2, 5, 5.1, 1, 1.1, 5, 5.1), y = c(8.1, 7, 3, 4, 14, 15, 4,3)), .Names = c("Group", "Sample", "x", "y"), row.names = c(NA, -8L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(cols = structure(list(Group = structure(list(), class = c("collector_character", "collector")), Sample = structure(list(), class = c("collector_character","collector")), x = structure(list(), class = c("collector_double", "collector")), y = structure(list(), class = c("collector_double", "collector"))), .Names = c("Group", "Sample", "x", "y")), default = structure(list(), class = c("collector_guess", "collector"))), .Names = c("cols", "default"), class = "col_spec"))
b2 <- structure(list(Group = c("beta_3", "beta_3", "beta_4", "beta_4", "beta_6", "beta_6"), Sample = c("ps_1", "ps_2", "ps_1", "ps_2", "ps_1", "ps_2"), x = c(3, 3.2, 5, 5.1, 1, 1.1), y = c(8.1, 7, 3, 4, 14, 15)),.Names = c("Group", "Sample", "x", "y"), row.names = c(NA, -8L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(cols = structure(list(Group = structure(list(), class = c("collector_character", "collector")), Sample = structure(list(), class = c("collector_character","collector")), x = structure(list(), class = c("collector_double", "collector")), y = structure(list(), class = c("collector_double", "collector"))), .Names = c("Group", "Sample", "x", "y")), default = structure(list(), class = c("collector_guess", "collector"))), .Names = c("cols", "default"), class = "col_spec"))
b3 <- structure(list(Group = c("beta_3", "beta_3", "beta_4", "beta_4", "beta_6", "beta_6"), Sample = c("ps_1", "ps_2", "ps_1", "ps_2", "ps_1", "ps_2"), x = c(3, 3.2, 5, 5.1, 1, 1.1), y = c(8.1, 7, 3, 4, 14, 15)), .Names = c("Group", "Sample", "x", "y"), row.names = c(NA, -8L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(cols = structure(list(Group = structure(list(), class = c("collector_character", "collector")), Sample = structure(list(), class = c("collector_character","collector")), x = structure(list(), class = c("collector_double", "collector")), y = structure(list(), class = c("collector_double", "collector"))), .Names = c("Group", "Sample", "x", "y")), default = structure(list(), class = c("collector_guess", "collector"))), .Names = c("cols", "default"), class = "col_spec"))
# Data export to simulate the problem
lz_write <- function(input)
write_csv(input, paste0(substitute(input), ".csv"))
lz_write(a1); lz_write(a2); lz_write(b2); lz_write(b3) # Messed up function for lapply...
# rm(list = ls()) # Clean the environment
用户界面
ui <- fluidPage(
titlePanel("Minimal Example"),
fluidRow(
column(width = 2, class = "well",
# File selection
html(paste("Which file?")),
# Prefix:
selectInput(inputId = "p",
label = "Prefix:",
choices = c("a", "b", "c"),
selected = "a"),
# Suffix:
numericInput(inputId = "s",
label = "Suffix:",
min = 1,
max = 3,
value = 1,
step = 1)),
column(width = 10,
plotOutput(outputId = "scatterplot",
dblclick = "plot_dblclick", # Might not be necessary, but it's not more work to include but more work to exclude
brush = brushOpts(id = "plot_brush", resetOnNew = TRUE)))
),
fluidRow(
column(width = 3,
br(),
uiOutput(outputId = "group_n")),
column(width = 9,
fixedRow(
column(width = 3,
HTML(paste0("Arg 1"))),
column(width = 3,
HTML(paste0("Arg 2"))),
column(width = 3,
uiOutput(outputId = "num_2"))
)
)
),
fluidRow(
br(), br(), br(), #Lets add some gaps or spacing
DT::dataTableOutput(outputId = "Table")) # Summary table
) # Not sure if actually necessary for this example
服务器损坏 现在唯一的问题是用户界面会在重新读取文件时重置...
server_broken <- function(input, output, session) # Broken version
#Larger subset: A Reactive Expression # May be used later...
args <- reactive(
list(input$p, input$s) #which file do we wish to input. This was our tag
)
# Reactive File-reader Subset
path <- reactive(
paste0(input$p, input$s, ".csv")
) # Reactive Filename, kinda like our args...
filereader <- function(input) # The function we pass into a reactive filereader.
suppressWarnings(read_csv(input, col_types = cols(
Group = col_character(),
Sample = col_character(),
x = col_double(),
y = col_double())
))
##BROKEN REACTIVE FILE READER HERE##
data_1 <- reactiveValues() # The function we use for livestream data
observe(
if(file.exists(path()) == TRUE)
fileReaderData <- reactiveFileReader(500, session, path(), filereader)
else
message("This file does not exist")
## OR DO I DO SOMETHING ELSE HERE??##
data_1$df <- reactive(
## STOPS APP CRASHING, BUT NO LONGER REFRESHES CONSTANTLY ##
req(fileReaderData())
fileReaderData()
)
) # Honestly don't understand still
data <- reactive(data_1$df()) # Pulling things out just so the rest of our code can stay the same.
## END OF BROKEN FILE READER##
## Reactive UI HERE##
data_m <- reactive(
req(data())
args()
tmp <- isolate(select(data(), Group))
tmp %>% distinct()
) # number of groups
output$num_2 <- renderUI(
req(data())
numericInput(inputId = "n",
label = "Group:",
min = 1,
max = length(data_m()$Group),
value = 1
)
) #This is our 'reactive' numeric input for groups. This caps the max of our function based on the number of groups there are per file
n <- reactive(input$n) #which marker number we are dealing with.
## End of reactive UI##
data_n <- reactive(
req(data()); req(data_m())
dt <- filter(data(), Group == data_m()[[1]][input$n])
)
# Create scatterplot object the plotOutput function is expecting ----
ranges <- reactiveValues(x = NULL, y = NULL)
output$scatterplot <- renderPlot(
validate(need(data(), "The specified file does not exist. Please try another"))
p <- as.numeric(input$p)
plot <- ggplot(data_n(), aes(x, y)) +
labs(title = paste0("Group ", data_n()$Group[1])) +
labs(x = "X vals", y = "Y vals") +
geom_point() + theme_bw() # I already have customized aesthetics. Removed for minimalism
plot + coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE) # So we see all points more readily. messes up the zoom but oh well
)
# When a double-click happens, check if there's a brush on the plot.
# If so, zoom to the brush bounds; if not, reset the zoom.
observeEvent(input$plot_dblclick,
brush <- input$plot_brush
if (!is.null(brush))
ranges$x <- c(brush$xmin, brush$xmax)
ranges$y <- c(brush$ymin, brush$ymax)
else
ranges$x <- NULL
ranges$y <- NULL
)
#Creating text ----
output$group_n <- renderText(
req(data())
paste0("There are ", length(data_m()$Group), " groups in this file.",
tags$br("This is Group: ", data_m()$Group[n()])
)
)
#Building a table for you to visibly see points. You may need to update the DT to the github version ----
output$Table <- DT::renderDataTable(
req(data())
brushedPoints(data_n(), brush = input$plot_brush) %>%
select(Sample)
)
功能服务器
它已被删除，因为损坏的至少不会崩溃，并且问题很明显。查看之前对原件的修改。
咨询的来源
Interactive file input and reactive reading in shinyapp https://github.com/rstudio/shiny/issues/167 在isolate 和observers 上寻找灵感
https://groups.google.com/forum/#!topic/shiny-discuss/QgdUfWGsuVU
会话信息
R 版本 3.4.2 (2017-09-28) 平台：x86_64-w64-mingw32/x64（64 位） 运行于：Windows 7 x64（内部版本 7601）Service Pack 1更新
在Observe() 中放置一个响应式可以阻止应用程序崩溃，并且它确实会更新文件（忘记删除一些内容）。剩下的就是将依赖的 UI 保存在某个地方......
【问题讨论】：
【参考方案1】：简而言之，问题是由于没有正确理解observers 的逻辑，在反应后缺少()，并且没有调用req 来阻止某些部分重新执行（参见HERE）。
具体的逐行更新可以通过查找下面的##CHANGE: 来找到...最重要的变化（不分先后）是：
-
将
isolate() 用于renderUI
在renderUI 中使用req() 来减慢它的速度，并且在组数有更新之前不运行，但调用args() 使其依赖于文件选择
预计算renderUI 之外的组数
更新的服务器
server_fixed <- function(input, output, session)
#Larger subset: A Reactive Expression # May be used later...
args <- reactive(
list(input$p, input$s) #which file do we wish to input. This was our tag
)
# Reactive File-reader Subset
path <- reactive(
paste0(input$p, input$s, ".csv")
) # Reactive Filename, kinda like our args...
filereader <- function(input) # The function we pass into a reactive filereader.
suppressWarnings(read_csv(input, col_types = cols(
Group = col_character(),
Sample = col_character(),
x = col_double(),
y = col_double())
))
data_1 <- reactiveValues() # The function we use for livestream data
observe(
if(file.exists(path()) == TRUE)
fileReaderData <- reactiveFileReader(500, session, path(), filereader)
else
message("This file does not exist")
data_1$df <- reactive(
# if(exists(fileReaderData()))
# fileReaderData()
# # Crashed from the beginning
req(fileReaderData())
fileReaderData()
)
)
data <- reactive(data_1$df()) ##CHANGE: FORGOT THE ()##
# Group setting...
data_m <- reactive(
req(data())
args()
tmp <- isolate(select(data(), Group))
tmp %>% distinct()
) #number of markers, keeping only the marker name
data_m_length <- reactive( ##CHANGE: TOOK OUT OF output$num_2##
##CHANGE: ADDED AN ISOLATE to fix the # of groups per file ##
isolate(length(data_m()$Group))
)
output$num_2 <- renderUI(
req(data_m_length()) ## CHANGE: ONLY EXECUTE ONCE WE HAVE OUR isolated data_m_length##
args() ## CHANGE: DEPENDENT UPON changing files##
isolate(
numericInput(inputId = "n",
label = "Group:",
min = 1,
max = data_m_length(),
value = 1 # THIS SHOULD BE CACHED!
)) ##CHANGE: ADDED IT IN ISOLATE when testing. NOT SURE IF STILL NEEDED##
) #This is our 'reactive' numeric input for groups. This caps the max of our function based on the number of groups there are per file
n <- reactive(input$n) #which marker number we are dealing with.
data_n <- reactive(
req(data()); req(data_m())
dt <- filter(data(), Group == data_m()[[1]][n()])
)
# Create scatterplot object the plotOutput function is expecting ----
ranges <- reactiveValues(x = NULL, y = NULL)
output$scatterplot <- renderPlot(
validate(need(data(), "The specified file does not exist. Please try another"))
p <- as.numeric(input$p)
plot <- ggplot(data_n(), aes(x, y)) +
labs(title = paste0("Group ", data_n()$Group[1])) +
labs(x = "X vals", y = "Y vals") +
geom_point() + theme_bw() # I already have customized aesthetics. Removed for minimalism
plot + coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE) # So we see all points more readily. messes up the zoom but oh well
)
# When a double-click happens, check if there's a brush on the plot.
# If so, zoom to the brush bounds; if not, reset the zoom.
observeEvent(input$plot_dblclick,
brush <- input$plot_brush
if (!is.null(brush))
ranges$x <- c(brush$xmin, brush$xmax)
ranges$y <- c(brush$ymin, brush$ymax)
else
ranges$x <- NULL
ranges$y <- NULL
)
#Creating text ----
output$group_n <- renderText(
req(data())
paste0("There are ", length(data_m()$Group), " groups in this file.",
tags$br("This is Group: ", data_m()$Group[n()])
)
)
#Building a table for you to visibly see points. You may need to update the DT to the github version ----
output$Table <- DT::renderDataTable(
req(data())
brushedPoints(data_n(), brush = input$plot_brush) %>%
select(Sample)
)
剩下的就是适当地使用suppressError 和validate。
【讨论】：
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