r - 调整闪亮的代码以生成与第一个代码一样的结果
问题描述
你能帮我调整下面的第二个代码吗？第一个代码正常工作。首先，我使用了一个名为的数据库Test，它完全符合我的要求。在第二个代码中，请注意我有一个df1数据库、一个函数，然后它会生成一个Test数据库。第二个代码的这个Test数据库生成的结果与第一个代码的数据库完全相同Test，不同之处在于第一个我指定了Test的值，而另一个我使用了一个函数来生成。但是，在我运行闪亮的第二个代码中，它没有显示结果，就像在第一个代码中一样，我想调整它。
第一个代码
library(shiny)
library(shinythemes)
library(dplyr)
library(writexl)
library(tidyverse)
library(lubridate)
Test <- structure(list(date2 = structure(c(18808, 18808, 18809, 18810
), class = "Date"), Category = c("FDE", "ABC", "FDE", "ABC"),
coef = c(4, 1, 6, 1)), row.names = c(NA, 4L), class = "data.frame")
ui <- fluidPage(
shiny::navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
br(),
tabPanel("",
sidebarLayout(
sidebarPanel(
uiOutput('daterange'),
br()
),
mainPanel(
dataTableOutput('table'),
br(), br(),
downloadButton("dl", "Download")
),
))
))
server <- function(input, output,session) {
data <- reactive(Test)
data_subset <- reactive({
req(input$daterange1)
days <- seq(input$daterange1[1], input$daterange1[2], by = 'day')
subset(data(), date2 %in% days)
})
output$daterange <- renderUI({
dateRangeInput("daterange1", "Period you want to see:",
start = min(data()$date2),
end = max(data()$date2))
})
output$table <- renderDataTable({
data_subset()
})
output$dl <- downloadHandler(
filename = function() { "data.xlsx"},
content = function(file) {
writexl::write_xlsx(data_subset(), path = file)
}
)
}
shinyApp(ui = ui, server = server)
第二个代码
library(shiny)
library(shinythemes)
library(dplyr)
library(writexl)
library(tidyverse)
library(lubridate)
function.test<-function(){
df1 <- structure(
list(date1= c("2021-06-28","2021-06-28","2021-06-28","2021-06-28"),
date2 = c("2021-06-30","2021-06-30","2021-07-01","2021-07-02"),
Category = c("FDE","ABC","FDE","ABC"),
Week= c("Wednesday","Wednesday","Friday","Friday"),
DR1 = c(4,1,6,1),
DR01 = c(4,1,4,4), DR02= c(4,2,6,0),DR03= c(9,5,4,0),
DR04 = c(5,4,3,5),DR05 = c(5,4,5,0),
DR06 = c(2,4,3,5),DR07 = c(2,5,4,0),
DR08 = c(3,4,5,0),DR09 = c(2,3,4,0)),
class = "data.frame", row.names = c(NA, -4L))
return(df1)
}
return_coef <- function(df1, dmda, CategoryChosse) {
x<-df1 %>% select(starts_with("DR0"))
x<-cbind(df1, setNames(df1$DR1 - x, paste0(names(x), "_PV")))
PV<-select(x, date2,Week, Category, DR1, ends_with("PV"))
med<-PV %>%
group_by(Category,Week) %>%
summarize(across(ends_with("PV"), median))
SPV<-df1%>%
inner_join(med, by = c('Category', 'Week')) %>%
mutate(across(matches("^DR0\\d+$"), ~.x +
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(date1:Category, DR01_DR01_PV:last_col())
SPV<-data.frame(SPV)
mat1 <- df1 %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(starts_with("DR0")) %>%
pivot_longer(cols = everything()) %>%
arrange(desc(row_number())) %>%
mutate(cs = cumsum(value)) %>%
filter(cs == 0) %>%
pull(name)
(dropnames <- paste0(mat1,"_",mat1, "_PV"))
SPV <- SPV %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(-any_of(dropnames))
datas<-SPV %>%
filter(date2 == ymd(dmda)) %>%
group_by(Category) %>%
summarize(across(starts_with("DR0"), sum)) %>%
pivot_longer(cols= -Category, names_pattern = "DR0(.+)", values_to = "val") %>%
mutate(name = readr::parse_number(name))
colnames(datas)[-1]<-c("Days","Numbers")
datas <- datas %>%
group_by(Category) %>%
slice((as.Date(dmda) - min(as.Date(df1$date1) [
df1$Category == first(Category)])):max(Days)+1) %>%
ungroup
mod <- nls(Numbers ~ b1*Days^2+b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
as.numeric(coef(mod)[2])
Test<-cbind(df1 %>% select(date2,Category), coef = mapply(return_coef, df1$date2, df1$Category))
}
ui <- fluidPage(
shiny::navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
br(),
tabPanel("",
sidebarLayout(
sidebarPanel(
uiOutput('daterange'),
br()
),
mainPanel(
dataTableOutput('table'),
br(), br(),
downloadButton("dl", "Download")
),
))
))
server <- function(input, output,session) {
data <- reactive(function.test())
data_subset <- reactive({
req(input$daterange1)
days <- seq(input$daterange1[1], input$daterange1[2], by = 'day')
subset(data(), date2 %in% days)
})
output$daterange <- renderUI({
dateRangeInput("daterange1", "Period you want to see:",
start = min(data()$date2),
end = max(data()$date2))
})
output$table <- renderDataTable({
data_subset()
})
output$dl <- downloadHandler(
filename = function() { "data.xlsx"},
content = function(file) {
writexl::write_xlsx(data_subset(), path = file)
}
)
}
shinyApp(ui = ui, server = server)
解决方案
问题出在您生成的数据中，日期是字符串，而不是日期。如果将function.test()定义更改为以下内容，它应该可以工作：
function.test<-function(){
df1 <- structure(
list(date1= as.Date(c("2021-06-28","2021-06-28","2021-06-28","2021-06-28"), format="%Y-%m-%d"),
date2 = as.Date(c("2021-06-30","2021-06-30","2021-07-01","2021-07-02"), format="%Y-%m-%d"),
Category = c("FDE","ABC","FDE","ABC"),
Week= c("Wednesday","Wednesday","Friday","Friday"),
DR1 = c(4,1,6,1),
DR01 = c(4,1,4,4), DR02= c(4,2,6,0),DR03= c(9,5,4,0),
DR04 = c(5,4,3,5),DR05 = c(5,4,5,0),
DR06 = c(2,4,3,5),DR07 = c(2,5,4,0),
DR08 = c(3,4,5,0),DR09 = c(2,3,4,0)),
class = "data.frame", row.names = c(NA, -4L))
return(df1)
}
browse()也许您已经知道这一点，所以如果这个附加部分没有帮助，我深表歉意，但我总是发现使用该功能进入应用程序很有用。如果您在 UI 中添加以下内容：
actionButton("browser", "browser"),
以及您的server功能中的以下内容：
observeEvent(input$browser,{
browser()
})
它将创建一个按钮，允许您查看应用程序中的反应元素。使用原始公式，您可以查看生成的数据及其属性：
Browse[1]> data()
# date1 date2 Category Week DR1 DR01 DR02 DR03 DR04 DR05 DR06 DR07 DR08 DR09
# 1 2021-06-28 2021-06-30 FDE Wednesday 4 4 4 9 5 5 2 2 3 2
# 2 2021-06-28 2021-06-30 ABC Wednesday 1 1 2 5 4 4 4 5 4 3
# 3 2021-06-28 2021-07-01 FDE Friday 6 4 6 4 3 5 3 4 5 4
# 4 2021-06-28 2021-07-02 ABC Friday 1 4 0 0 5 0 5 0 0 0
Browse[1]> str(data())
# 'data.frame': 4 obs. of 14 variables:
# $ date1 : chr "2021-06-28" "2021-06-28" "2021-06-28" "2021-06-28"
# $ date2 : chr "2021-06-30" "2021-06-30" "2021-07-01" "2021-07-02"
# $ Category: chr "FDE" "ABC" "FDE" "ABC"
# $ Week : chr "Wednesday" "Wednesday" "Friday" "Friday"
# $ DR1 : num 4 1 6 1
# $ DR01 : num 4 1 4 4
# $ DR02 : num 4 2 6 0
# $ DR03 : num 9 5 4 0
# $ DR04 : num 5 4 3 5
# $ DR05 : num 5 4 5 0
# $ DR06 : num 2 4 3 5
# $ DR07 : num 2 5 4 0
# $ DR08 : num 3 4 5 0
# $ DR09 : num 2 3 4 0
这清楚地表明日期变量是字符串。您还可以查看data_subset()以确认它没有任何数据：
Browse[1]> data_subset()
# [1] date1 date2 Category Week DR1 DR01 DR02 DR03 DR04 DR05 DR06 DR07 DR08 DR09
# <0 rows> (or 0-length row.names)
这将允许您进行一些挖掘：
Browse[1]> days <- seq(input$daterange1[1], input$daterange1[2], by = 'day')
Browse[1]> days
# [1] "2021-06-30" "2021-07-01" "2021-07-02"
Browse[1]> data()$date2 %in% days
# [1] FALSE FALSE FALSE FALSE
Browse[1]> class(days)
# [1] "Date"
Browse[1]> class(data()$date2)
# [1] "character"
这使您可以确定该变量与作为问题症结的向量date2不同的类。days
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