将数据帧作为参数从 Shiny 应用程序传递到 RMarkdown

Question

我仍在努力解决我正在开发的 Shiny 应用程序的某些方面。目的是用户上传 csv 数据文件，然后对其进行处理以生成报告（来自 .Rmd 模板），然后用户可以将其下载为可编辑的 Word .doc。

如果我在正常的 R 会话中渲染它，.Rmd 工作正常。但是，如果从我的 Shiny 应用程序完成，我会收到以下错误：

Warning: Error in unique: object 'report.data' not found
  [No stack trace available]

report.data 应该是通过读取输入 .csv 文件生成的数据帧。令人困惑的是，该应用程序有时确实可以工作（我认为如果 report.data 已经在全局环境中可用，就会发生这种情况。）。

我尝试在 .Rmd 文件的标头中定义参数（请参阅下面的注释行。） - 如果我这样做，则代码运行时不会出现错误，但生成的 Word 文档是空白的，除了标题。

谁能看到我哪里出错了？一如既往，感谢您花时间阅读本文并回复。

抱歉，我觉得我正在创建很多线程来寻求有关 Shiny 中似乎非常基本的事情的帮助，但我确实搜索了类似的问题，但从未找到完全正确的事情！但一旦我具备了这些基本的东西，我应该能够自己取得进步。

为report.data生成示例输入的.csv文件的代码：

library(dplyr)
set.seed(1234)

product1.parameter1.location1 <- data.frame(
  result = rnorm(25, mean = 2.5, sd = 0.2), 
  product = c("Red Aeroplanes"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:25), 
  location = c("Factory 1")
  )

product1.parameter1.location2 <- data.frame(
  result = rnorm(25, mean = 2.6, sd = 0.1), 
  product = c("Red Aeroplanes"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:25), 
  location = c("Factory 2")
  )

product1 <- rbind(product1.parameter1.location1, product1.parameter1.location2)

product2.parameter1.location1 <- data.frame(
  result = rnorm(25, mean = 10, sd = 2), 
  product = c("Blue Trollies"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:25), 
  location = c("Factory 1")
  )

product2.parameter1.location2 <- data.frame(
  result = rnorm(25, mean = 9.5, sd = 0.75), 
  product = c("Blue Trollies"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:25), 
  location = c("Factory 2"))
product2.parameter1 <- rbind(product2.parameter1.location1, product2.parameter1.location2)

product2.parameter2.location1 <- data.frame(
  result = rnorm(25, mean = 30, sd = 1.8), 
  product = c("Blue Trollies"), 
  parameter = c("Parameter 2"), 
  sample.no = c(1:25), 
  location = c("Factory 1")
  )

product2.parameter2.location2 <- data.frame(
  result = rnorm(25, mean = 25, sd = 0.75), 
  product = c("Blue Trollies"), 
  parameter = c("Parameter 2"), 
  sample.no = c(1:25), 
  location = c("Factory 2"))
product2.parameter2 <- rbind(product2.parameter2.location1, product2.parameter2.location2)

product2 <- rbind(product2.parameter1, product2.parameter2)

product3.parameter1.location1 <- data.frame(
  result = rnorm(35, mean = 2, sd = 0.2), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:35), 
  location = c("Factory 1")
)

product3.parameter1.location2 <- data.frame(
  result = rnorm(35, mean = 1.9, sd = 0.15), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 1"), 
  sample.no = c(1:35), 
  location = c("Factory 2"))
product3.parameter1 <- rbind(product3.parameter1.location1, product3.parameter1.location2)

product3.parameter2.location1 <- data.frame(
  result = rnorm(35, mean = 4, sd = 0.4), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 2"), 
  sample.no = c(1:35), 
  location = c("Factory 1")
)

product3.parameter2.location2 <- data.frame(
  result = rnorm(35, mean = 3.8, sd = 0.5), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 2"), 
  sample.no = c(1:35), 
  location = c("Factory 2"))

product3.parameter2 <- rbind(product3.parameter2.location1, product3.parameter2.location2)

product3.parameter3.location1 <- data.frame(
  result = rnorm(35, mean = 10, sd = 1.8), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 3"), 
  sample.no = c(1:35), 
  location = c("Factory 1")
)

product3.parameter3.location2 <- data.frame(
  result = rnorm(35, mean = 10, sd = 2), 
  product = c("Brown Carriages"), 
  parameter = c("Parameter 3"), 
  sample.no = c(1:35), 
  location = c("Factory 2"))

product3.parameter3 <- rbind(product3.parameter3.location1, product3.parameter3.location2)

product3 <- rbind(product3.parameter1, product3.parameter2, product3.parameter3)

write.csv(product1, "product1.csv", row.names = FALSE)
write.csv(product2, "product2.csv", row.names = FALSE)
write.csv(product3, "product3.csv", row.names = FALSE)

report.data <- rbind(product1, product2, product3) %>% mutate(identifier = paste(product, parameter, sep = " ")) 
write.csv(report.data, "all.data.csv", row.names = FALSE)

app.R代码：

#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

library(shiny)

# Define UI for application that draws a histogram
ui <- fluidPage(

    # Application title
  titlePanel("R Shiny app"),

  # Sidebar with file input
  sidebarLayout(
    sidebarPanel(
      fileInput(
        inputId = "file1",
        label = "Select file(s)",
        multiple = TRUE,
        accept = NULL,
        width = NULL,
        buttonLabel = "Browse...",
        placeholder = "No file(s) selected"
      ),
      downloadButton("report", "Generate report")
    ),

        # Show a plot of the generated distribution
        mainPanel(
           plotOutput("distPlot")
        )
    )
)

# Define server logic required to draw a histogram
server <- function(input, output) {
  
  output$report <- downloadHandler(
    reactive(file <- input$file1),
    # For PDF output, change this to "report.pdf"
    filename = "report.doc",
    content = function(file) {
      # Copy the report file to a temporary directory before processing it, in
      # case we don't have write permissions to the current working dir (which
      # can happen when deployed).
      tempReport <- file.path(tempdir(), "wordreport.Rmd")
      file.copy("wordreport.Rmd", tempReport, overwrite = TRUE)
      # Knit the document, passing in the `params` list, and eval it in a
      # child of the global environment (this isolates the code in the document
      # from the code in this app).
      params <- list(report.data = input$file1)
      rmarkdown::render(tempReport, output_file = "wordreport.doc",
                        params = params,
                        envir = new.env(parent = globalenv()))
      file.copy("wordreport.doc", file)
    }
    )

}

# Run the application 
shinyApp(ui = ui, server = server)

.Rmd文件（与参数声明相关的行被注释掉）：

---
title: "Comparison Report  for [CATEGORY] in [MONTH/YEAR]"
output: word_document
toc: yes
#params:
  #report.data: report.data
---

```{r setup, include=FALSE, comment = "", results = 'asis', echo = FALSE}
library(dplyr)
library(ggplot2)
library(purrr)
knitr::opts_chunk$set(echo = FALSE)
```
#report.data <- params$report.data
```
 my_plot <- function(df) {
    ggplot(df, aes(x = sample.no, y = result)) +
    geom_point(aes(colour = location)) +
    geom_hline(aes(yintercept = mean(result)), colour = "black", linetype = "dotted") +
    geom_hline(aes(yintercept = mean(result) + 1.96 * sd(result)), colour = "red", linetype = "dashed") +
    geom_hline(aes(yintercept = mean(result) - 1.96 * sd(result)), colour = "red", linetype = "dashed") +
    theme_classic() +
    theme(legend.title = element_blank()) +
    labs(
      title = paste0("Comparison for ", unique(df$identifier)),
      x = "Sample number",
      y = "Result") +
      #caption = paste0("Caption here.")) +
    expand_limits(y = 0) +
    coord_cartesian(xlim = c(0, max(df$sample.no) + 2)) +    
    theme(
      plot.caption=element_text(size=12, hjust = 0, margin = margin(t=20)),
      plot.margin = margin(b=50)
    )
}

```

```{r, comment = "", results = 'asis', echo = FALSE}

purrr::map(unique(report.data$identifier),
                           function(x) {
                             #section heading
                             cat("#", (x), "\n")
                             cat("\n\n")
                             # filter data before passing it to the plot function
                             report.data %>% 
                               dplyr::filter(identifier == x) %>%
                               my_plot() %>% print()
                             cat("\n\n")
                             no.outofbounds <- report.data %>% 
                               dplyr::filter(identifier == x) %>%
                               mutate(outofbounds = ifelse(result > mean(result)+1.96*sd(result)|result < mean(result)-1.96*sd(result), TRUE, FALSE)) %>% 
                               dplyr::filter(outofbounds == TRUE) %>% 
                               nrow()
                             ifelse(no.outofbounds > 0, paste(cat(no.outofbounds), " results greater than 1.96 standard deviations away from the mean."), "All results within 1.96 standard deviations of the mean.") %>% 
                               cat()
                             cat("\n\n")
                             CV <- report.data %>% 
                               dplyr::filter(identifier == x) %>%
                               summarise(CV = sd(result)/mean(result) * 100) %>% 
                               round(2)
                             cat("\n\n")
                             paste("The all-site/factor CV for this parameter is ", CV, "%.") %>% 
                               cat()
                             cat("\n\n")
                             cat("APPROVED/REJECTED.")
                             cat("\n\n")
                             
                           }
) -> results
```

Answer 1

您的代码有几个问题。我将一一检查一下

downloadHandler() 中的无效参数

您正在将 reactive 类的对象传递给 downloadHandler()contentType 参数代码>.

downloadHandler(
  reactive(file <- input$file1),     ## <--- here
  filename = "report.doc",
  content = function(file) {
    # ...
  }
)

这似乎搞乱了 downloadHandler() 的整个逻辑，并导致客户端出现“服务器错误”消息，而闪亮的没有错误或警告。

需要删除此行才能成功下载文件

正确引用 Rmd 参数

当您想要从 Rmd 报告访问参数时，您将需要使用 params$report.data。仅使用 report.data 将导致以下错误：object 'report.data' not found。

---
output: word_document
params:
  report.data: NULL
---

```{r}
report.data <- params$report.data
# ...
```

修复生成文件的路径

您正在临时目录中编织 Rmd，这通常是一个好主意。然而，找到正确的路径并不总是那么容易。在您的情况下，我使用了以下版本。

rendered_report <- rmarkdown::render(
  tempReport, output_file = "wordreport.doc",
  params = params,
  envir = new.env(parent = globalenv())
)
file.copy(rendered_report, file)

您的版本不起作用的原因是生成的报告是在临时目录 alogside tmpReport 内创建的。有关更多详细信息，请参阅 ?rmarkdown::render 的参考文档。

我使用 rmarkdown::render() 的返回值来代替，它保存生成文件的绝对路径。这不容易出错，如果您事先不知道生成文件的文件扩展名，则特别有用

使用 read.csv 将上传的文件转换为 data.frame

Shiny 不会自动将上传的 csv 文件转换为 dataframe。您需要定义一个解析逻辑来做到这一点。

params <- list(report.data = read.csv(input$file1$datapath))

最后一句话

尝试让你的编码项目更有条理，并将未来 SO 问题的范围限制为一次一个问题。创建“最小的可重现示例”一开始可能看起来很乏味，但是这样做有几个优点：

• 其他人可以阅读问题和答案并在自己的项目中轻松重用它们，而无需剖析代码墙
• 回答这些问题要容易得多。对于这样的问题，SO 社区通常只提供评论，因为正确回答这些问题需要付出很大的努力。
• 最小化和隔离问题是一项技能，可以帮助您更轻松地找出未来编码项目中的问题