The code below generates a graphs for 01/07, 02/07 and 04/07. If you see in the graph there is a value, which are the values I get in my function. If I press Download, I can download a spreadsheet with these values. Everything is OK so far. What I want to try to adjust is the following, if I press Download, I noticed that the graphs corresponding to the dates are also plotted, since the returns are in the if and else condiction. As this is a small base, it goes fast, however, if I have a large base, it takes time, because in addition to taking the coefficient value, it generates the plots no need. How can I help this, that is, when I press the Download button, only download the spreadsheet with the coefficients, and the code doesn't need to make the plots.
Executable code below:
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"),
date2 = c("2021-07-01","2021-07-02","2021-07-04"),
Category = c("ABC","ABC","ABC"),
Week= c("Wednesday","Wednesday","Wednesday"),
DR1 = c(4,1,0),
DR01 = c(4,1,0), DR02= c(4,2,0),DR03= c(9,5,0),
DR04 = c(5,4,0),DR05 = c(5,4,0),DR06 = c(5,4,0),DR07 = c(5,4,0),DR08 = c(5,4,0)),
class = "data.frame", row.names = c(NA, -3L))
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 $"), ~.x
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(date1:Category, DR01_DR01_PV:last_col())
SPV<-data.frame(SPV)
mat1 <- df1 %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(starts_with("DR0")) %>%
pivot_longer(cols = everything()) %>%
arrange(desc(row_number())) %>%
mutate(cs = cumsum(value)) %>%
filter(cs == 0) %>%
pull(name)
(dropnames <- paste0(mat1,"_",mat1, "_PV"))
SPV <- SPV %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(-any_of(dropnames))
if(length(grep("DR0", names(SPV))) == 0) {
SPV[head(mat1,10)] <- NA_real_
}
datas <-SPV %>%
filter(date2 == ymd(dmda)) %>%
group_by(Category) %>%
summarize(across(starts_with("DR0"), sum)) %>%
pivot_longer(cols= -Category, names_pattern = "DR0(. )", values_to = "val") %>%
mutate(name = readr::parse_number(name))
colnames(datas)[-1]<-c("Days","Numbers")
datas <- datas %>%
group_by(Category) %>%
slice((as.Date(dmda) - min(as.Date(df1$date1) [
df1$Category == first(Category)])):max(Days) 1) %>%
ungroup
m<-df1 %>%
group_by(Category,Week) %>%
summarize(across(starts_with("DR1"), mean))
m<-subset(m, Week == df1$Week[match(ymd(dmda), ymd(df1$date2))] & Category == CategoryChosse)$DR1
maxrange <- range(min(0, datas$Numbers, na.rm = TRUE), na.rm = TRUE)
maxrange[1] <- maxrange[1] - (maxrange[1] %%10) 35
max<-max(0, datas$Days, na.rm = TRUE) 1
plot(Numbers ~ Days, xlim= c(0,max), ylim= c(0,maxrange[1]),
xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))
if (nrow(datas)<=2){
abline(h=m,lwd=2)
points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
text(.1,m .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")
return (as.numeric(m))
}
else if(any(table(datas$Numbers) >= 3) & length(unique(datas$Numbers)) == 1){
yz <- unique(datas$Numbers)
lines(c(0,datas$Days), c(yz, datas$Numbers), lwd = 2)
points(0, yz, col = "red", pch = 19, cex = 2, xpd = TRUE)
text(.1,yz .5,round(yz,1), cex=1.1,pos=4,offset =1,col="black")
return(as.numeric(yz))}
else{
mod <- nls(Numbers ~ b1*Days^2 b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
new.data <- data.frame(Days = with(datas, seq(min(Days),max(Days),len = 45)))
new.data <- rbind(0, new.data)
lines(new.data$Days,predict(mod,newdata = new.data),lwd=2)
coef<-coef(mod)[1]
points(0, coef, col="red",pch=19,cex = 2,xpd=TRUE)
text(.99,coef 1,max(0, round(coef,1)), cex=1.1,pos=4,offset =1,col="black")
return(as.numeric(coef(mod)[2])) }
}
ui <- fluidPage(
shiny::navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
br(),
tabPanel("",
sidebarLayout(
sidebarPanel(
uiOutput("date"),
uiOutput("mycode"),
uiOutput('daterange'),
br()
),
mainPanel(
#dataTableOutput('table'),
plotOutput("graph",width = "100%", height = "600"),
br(), br(),
downloadButton("dl", "Download")
),
))
))
server <- function(input, output,session) {
data <- reactive(function.test())
output$date <- renderUI({
req(data())
all_dates <- seq(as.Date('2021-01-01'), as.Date('2021-01-15'), by = "day")
disabled <- as.Date(setdiff(all_dates, as.Date(data()$date2)), origin = "1970-01-01")
dateInput(input = "date2",
label = h4("Choose"),
min = min(data()$date2),
max = max(data()$date2),
value = NA,
datesdisabled = disabled)
})
output$mycode <- renderUI({
req(input$date2)
df1 <- data()
df2 <- df1[as.Date(df1$date2) %in% input$date2,]
selectInput("code", label = h4("Category"),choices=unique(df2$Category))
})
output$graph <- renderPlot({
req(input$date2,input$code)
return_coef(data(),as.character(input$date2),as.character(input$code))})
output$table <- renderDataTable({
data_subset()
})
data_subset <- reactive({
req(input$daterange1)
days <- seq(input$daterange1[1], input$daterange1[2], by = 'day')
df1 <- subset(data(), as.Date(date2) %in% days)
df2 <- df1 %>% select(date2,Category)
Test <- cbind(df2, coef = apply(df2, 1, function(x) {return_coef(data(),x[1],x[2])}))
Test
})
output$daterange <- renderUI({
dateRangeInput("daterange1", "Period you want to see:",
start = min(data()$date2),
end = max(data()$date2))
})
output$dl <- downloadHandler(
filename = function() { "data.xlsx"},
content = function(file) {
writexl::write_xlsx(data_subset(), path = file)
}
)
}
shinyApp(ui = ui, server = server)
Example: I pressed Download, but graphs are plotted.
CodePudding user response：
Try this
library(shiny)
library(shinythemes)
library(dplyr)
library(writexl)
library(tidyverse)
library(lubridate)
library(shinyWidgets)
function.test<-function(){
df1 <- structure(
list(date1= c("2021-06-28","2021-06-28","2021-06-28"),
date2 = c("2021-07-01","2021-07-02","2021-07-04"),
Category = c("ABC","ABC","ABC"),
Week= c("Wednesday","Wednesday","Wednesday"),
DR1 = c(4,1,0),
DR01 = c(4,1,0), DR02= c(4,2,0),DR03= c(9,5,0),
DR04 = c(5,4,0),DR05 = c(5,4,0),DR06 = c(5,4,0),DR07 = c(5,4,0),DR08 = c(5,4,0)),
class = "data.frame", row.names = c(NA, -3L))
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 $"), ~.x
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(date1:Category, DR01_DR01_PV:last_col())
SPV<-data.frame(SPV)
mat1 <- df1 %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(starts_with("DR0")) %>%
pivot_longer(cols = everything()) %>%
arrange(desc(row_number())) %>%
mutate(cs = cumsum(value)) %>%
filter(cs == 0) %>%
pull(name)
(dropnames <- paste0(mat1,"_",mat1, "_PV"))
SPV <- SPV %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(-any_of(dropnames))
if(length(grep("DR0", names(SPV))) == 0) {
SPV[head(mat1,10)] <- NA_real_
}
datas <-SPV %>%
filter(date2 == ymd(dmda)) %>%
group_by(Category) %>%
summarize(across(starts_with("DR0"), sum)) %>%
pivot_longer(cols= -Category, names_pattern = "DR0(. )", values_to = "val") %>%
mutate(name = readr::parse_number(name))
colnames(datas)[-1]<-c("Days","Numbers")
datas <- datas %>%
group_by(Category) %>%
slice((as.Date(dmda) - min(as.Date(df1$date1) [
df1$Category == first(Category)])):max(Days) 1) %>%
ungroup
m<-df1 %>%
group_by(Category,Week) %>%
summarize(across(starts_with("DR1"), mean))
m<-subset(m, Week == df1$Week[match(ymd(dmda), ymd(df1$date2))] & Category == CategoryChosse)$DR1
maxrange <- range(min(0, datas$Numbers, na.rm = TRUE), na.rm = TRUE)
maxrange[1] <- maxrange[1] - (maxrange[1] %%10) 35
max<-max(0, datas$Days, na.rm = TRUE) 1
plot(Numbers ~ Days, xlim= c(0,max), ylim= c(0,maxrange[1]),
xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))
if (nrow(datas)<=2){
abline(h=m,lwd=2)
points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
text(.1,m .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")
return (as.numeric(m))
}
else if(any(table(datas$Numbers) >= 3) & length(unique(datas$Numbers)) == 1){
yz <- unique(datas$Numbers)
lines(c(0,datas$Days), c(yz, datas$Numbers), lwd = 2)
points(0, yz, col = "red", pch = 19, cex = 2, xpd = TRUE)
text(.1,yz .5,round(yz,1), cex=1.1,pos=4,offset =1,col="black")
return(as.numeric(yz))}
else{
mod <- nls(Numbers ~ b1*Days^2 b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
new.data <- data.frame(Days = with(datas, seq(min(Days),max(Days),len = 45)))
new.data <- rbind(0, new.data)
lines(new.data$Days,predict(mod,newdata = new.data),lwd=2)
coef<-coef(mod)[1]
points(0, coef, col="red",pch=19,cex = 2,xpd=TRUE)
text(.99,coef 1,max(0, round(coef,1)), cex=1.1,pos=4,offset =1,col="black")
return(as.numeric(coef(mod)[2])) }
}
ui <- fluidPage(
shiny::navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
br(),
tabPanel("",
sidebarLayout(
sidebarPanel(
uiOutput("date"),
uiOutput("mycode"),
uiOutput('daterange'),
tags$style(".well {background-color:transparent;}"),
br(),br(),br(),
conditionalPanel(condition = "output.mycode",
actionBttn(inputId="plot",
label="PLOT",
style = "simple",
color = "success",
size = "md",
block = FALSE,
no_outline = TRUE
),
div(style="display: inline-block; width: 10px;",HTML("<br>")),
downloadButton("dl", "Download")
)
),
mainPanel(
#dataTableOutput('table'),
plotOutput("graph",width = "100%", height = "600"),
br()
),
))
))
server <- function(input, output,session) {
data <- reactive(function.test())
output$date <- renderUI({
req(data())
all_dates <- seq(as.Date('2021-01-01'), as.Date('2021-01-15'), by = "day")
disabled <- as.Date(setdiff(all_dates, as.Date(data()$date2)), origin = "1970-01-01")
dateInput(input = "date2",
label = h4("Choose"),
min = min(data()$date2),
max = max(data()$date2),
value = NA,
datesdisabled = disabled)
})
output$mycode <- renderUI({
req(input$date2)
df1 <- data()
df2 <- df1[as.Date(df1$date2) %in% input$date2,]
selectInput("code", label = h4("Category"),choices=unique(df2$Category))
})
observeEvent(input$plot, {
output$graph <- renderPlot({
req(input$date2,input$code)
return_coef(data(),as.character(input$date2),as.character(input$code))
})
}, ignoreInit = TRUE)
output$table <- renderDataTable({
data_subset()
})
data_subset <- reactive({
req(input$daterange1)
days <- seq(input$daterange1[1], input$daterange1[2], by = 'day')
df1 <- subset(data(), as.Date(date2) %in% days)
df2 <- df1 %>% select(date2,Category)
Test <- cbind(df2, coef = apply(df2, 1, function(x) {return_coef(data(),x[1],x[2])}))
Test
})
output$daterange <- renderUI({
dateRangeInput("daterange1", "Period you want to see:",
start = min(data()$date2),
end = max(data()$date2))
})
output$dl <- downloadHandler(
filename = function() { "data.xlsx"},
content = function(file) {
writexl::write_xlsx(data_subset(), path = file)
}
)
}
shinyApp(ui = ui, server = server)