我刚开始学习Shiny,我正在尝试做一个简单的项目,以此来了解它是如何作为开发工具.
我的目标:做一个wordcloud应用程序.输入:.txt文件.输出:wordcloud.
我收到“不正确的cex值”错误,我的猜测是我的文件没有正确上传…我是否正确?如果是这样,对于文本文件,read.csv会等同于什么?我已经收集到它是read.table,但我显然是错的,因为我使用read.table收到错误
这是我的代码,大大改编自WordCloud:
* global.r *
library(tm)
library(wordcloud)
library(memoise)
# Using "memoise" to automatically cache the results
getTermMatrix <- function(text) {
# Careful not to let just any name slip in here; a
# malicious user could manipulate this value.
myCorpus = Corpus(VectorSource(text))
myCorpus = tm_map(myCorpus, content_transformer(tolower))
myCorpus = tm_map(myCorpus, removePunctuation)
myCorpus = tm_map(myCorpus, removeNumbers)
myCorpus = tm_map(myCorpus, removeWords,
c(stopwords("SMART"), "thy", "thou", "thee", "the", "and", "but"))
myDTM = TermDocumentMatrix(myCorpus,
control = list(minWordLength = 1))
m = as.matrix(myDTM)
sort(rowSums(m), decreasing = TRUE)
}
server.r
function(input, output, session) {
# Define a reactive expression for the document term matrix
my_data <- reactive({
inFile <- input$files
if (is.null(inFile))
return(NULL)
data <- read.table(inFile, header=T, sep="\t", fileEncoding="UTF-8")
data
})
terms <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing corpus...")
getTermMatrix(input$inFile)
})
})
})
# Make the wordcloud drawing predictable during a session
wordcloud_rep <- repeatable(wordcloud)
output$plot <- renderPlot({
v <- terms()
wordcloud_rep(names(v), v, scale=c(4,0.5),
min.freq = input$freq, max.words=input$max,
colors=brewer.pal(8, "Dark2"))
})
}
ui.r
fluidPage(
# Application title
titlePanel("Word Cloud"),
sidebarLayout(
# Sidebar with a slider and selection inputs
sidebarPanel(
#######
fileInput("selection", "Choose a text:"),
#
actionButton("update", "Change"),
hr(),
sliderInput("freq",
"Minimum Frequency:",
min = 1, max = 50, value = 15),
sliderInput("max",
"Maximum Number of Words:",
min = 1, max = 300, value = 100)
),
# Show Word Cloud
mainPanel(
plotOutput("plot")
)
)
)
**样本输入文件**
按照要求.你可以使用这个.txt(这是莎士比亚):http://www.gutenberg.org/cache/epub/2242/pg2242.txt
最佳答案 要使应用正常运行,需要进行一些更改/修改!你处理文件输入的方式是完全错误的:).您可以直接在getTermMatrix()函数中输入input $selection,然后读取global.R中的文件内容.看一下
this,了解如何上传文件并在Shiny中阅读其内容.
该错误是因为没有文件读取,因此没有数据被输入Corpus()函数.在以下代码中,由于启动应用程序时没有文件输入,因此显示错误,表明没有文件被读取.但是,在上传文件后,错误消失并显示语料库.为了不显示错误,我在ui.R中包含了一个小标签().也许你可以找到一个更好的解决方案.
查看以下工作代码并尝试将其扩展到您的未来目的.
ui.R
shinyUI(
fluidPage(
# Application title
titlePanel("Word Cloud"),
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
),
sidebarLayout(
# Sidebar with a slider and selection inputs
sidebarPanel(
#######
fileInput("selection", "Choose a text:"),
actionButton("update", "Change"),
hr(),
sliderInput("freq",
"Minimum Frequency:",
min = 1, max = 50, value = 15),
sliderInput("max",
"Maximum Number of Words:",
min = 1, max = 300, value = 100)
),
# Show Word Cloud
mainPanel(
plotOutput("plot")
)
)
)
)
server.R
library(shiny)
shinyServer(function(input, output, session) {
# Define a reactive expression for the document term matrix
terms <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing corpus...")
getTermMatrix(input$selection)
})
})
})
# Make the wordcloud drawing predictable during a session
wordcloud_rep <- repeatable(wordcloud)
output$plot <- renderPlot({
v <- terms()
wordcloud_rep(names(v), v, scale=c(4,0.5),
min.freq = input$freq, max.words=input$max,
colors=brewer.pal(8, "Dark2"))
})
})
global.R
library(tm)
library(wordcloud)
library(memoise)
# Using "memoise" to automatically cache the results
getTermMatrix <- function(f) {
# Careful not to let just any name slip in here; a
# malicious user could manipulate this value.
text <- readLines(f$datapath,encoding = "UTF-8")
myCorpus = Corpus(VectorSource(text))
myCorpus = tm_map(myCorpus, content_transformer(tolower))
myCorpus = tm_map(myCorpus, removePunctuation)
myCorpus = tm_map(myCorpus, removeNumbers)
myCorpus = tm_map(myCorpus, removeWords,
c(stopwords("SMART"), "thy", "thou", "thee", "the", "and", "but"))
myDTM = TermDocumentMatrix(myCorpus,
control = list(minWordLength = 1,wordLengths=c(0,Inf)))
m = as.matrix(myDTM)
sort(rowSums(m), decreasing = TRUE)
}