为长而复杂的代码块道歉。
我已经将Rstudio和Shiny更新到了最新版本，不幸的是打破了我漂亮的交互式仪表板，并且在调试时没有任何运气。
代码：
title: "Antibiotic Use"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
navbar:
source_code: embed
runtime: shiny
---
```{r setup, include=FALSE}
##Load R Libraries for Dashboard
## Dashboard
library(flexdashboard)
library(shiny)
## Data import and manipulation
library(tidyverse)
library(rmarkdown)
## Define function to import data from xlsx into DFs, named by xlsx tab
## Also an option to import into items on a list; but can't work out how to dplyr list data properly
importDataFrames <- function(filename){
sheets <- readxl::excel_sheets(filename)
for(x in seq_along(sheets)){
assign(sheets[x], readxl::read_excel(filename, sheet = sheets[x], col_names = TRUE), envir = .GlobalEnv)
}
}
## Define list vectors for sorting
hoslist <- c("hospital1", "hospital2", "hospital3", "hospital4", "hospital5")
abxlist = c("dicloxacillin", "flucloxacillin", "benzylpenicillin", "phenoxymethylpenicillin", "amoxycillin", "ampicillin", "ertapenem", "imipenem", "meropenem", "amoxycillin-clavulanate", "piperacillin-tazobactam", "ticarcillin-clavulanate", "cotrimoxazole", "cephalexin", "cefalotin", "cefazolin", "cefoxitin", "cefuroxime", "ciprofloxacin", "moxifloxacin", "norfloxacin", "cefipime", "teicoplanin", "vancomycin", "metronidazole", "tinidazole", "clindamycin", "lincomycin", "azithromycin", "erythromycin", "clarithromycin", "roxithromycin", "aztreonam", "gentamicin", "tobramycin", "daptomycin", "linezolid", "colisitin", "fusidin", "pristinamycin", "doxycycline", "cefotaxime", "ceftazidime", "ceftriaxone", "trimethoprim")
staphpen <- c("dicloxacillin", "flucloxacillin")
narrowpen <- c("benzylpenicillin", "phenoxymethylpenicillin", "amoxycillin", "ampicillin")
penem <- c("ertapenem", "imipenem", "meropenem")
pseudpen <- c("piperacillin-tazobactam", "ticarcillin-clavulanate")
earlyceph <- c("cephalexin", "cefalotin", "cefazolin", "cefoxitin", "cefuroxime")
quinolone <- c("ciprofloxacin", "norfloxacin", "moxifloxacin")
lateceph <- c("ceftriaxone", "cefotaxime", "ceftazidime", "cefipime")
gpeptide <- c("teicoplanin", "vancomycin")
lincos <- c("clindamycin", "lincomycin")
macrolide <- c("azithromycin", "erythromycin", "clarithromycin", "roxithromycin")
aglycos <- c("gentamicin", "tobramycin", "amikacin")
other <- c("amoxycillin-clavulanate", "metronidazole", "tinidazole", "cotrimoxazole", "aztreonam", "daptomycin", "linezolid", "colisitin", "fusidin", "pristinamycin", "doxycycline", "trimethoprim")
```
```{r}
## Import data from xlsx
## Data source is 5-tabbed spreadsheet, tab hospitals, date rows, antibiotic columns, usage data
importDataFrames("district_abx.xlsx")
## Data output is 5x data frames, one for each tab
## Tidy data; would love to do this with a list and lapply if possible
c1 <- hospital1 %>%
gather(antibiotic, usage, -date) # column "antibiotic" from df column headers
cact <- filter(c1, antibiotic == "activity") # spreadsheet also includes activity data - separate this
cact <- mutate(cact, hospital = "hospital1") # column with hospital name for activity df
c2 <- c1 %>%
filter(antibiotic %in% abxlist) %>% # filter out the extra columns (activity, totals)
droplevels()
hospital1 <- mutate(c2, hospital = "hospital1") # column "hospital" for antibiotic data
a1 <- hospital2 %>%
gather(antibiotic, usage, -date)
aact <- filter(a1, antibiotic == "activity")
aact <- mutate(aact, hospital ="atherton")
a2 <- a1 %>%
filter(antibiotic %in% abxlist) %>%
droplevels()
hospital2 <- mutate(a2, hospital = "hospital2")
i1 <- hospital3 %>%
gather(antibiotic, usage, -date)
iact <- filter(i1, antibiotic == "activity")
iact <- mutate(iact, hospital = "hospital3")
i2 <- i1 %>%
filter(antibiotic %in% abxlist) %>%
droplevels()
hospital3 <- mutate(i2, hospital = "hospital3")
ma1 <- hospital4 %>%
gather(antibiotic, usage, -date)
maact <- filter(ma1, antibiotic == "activity")
maact <- mutate(maact, hospital = "hospital4")
ma2 <- ma1 %>%
filter(antibiotic %in% abxlist) %>%
droplevels()
hospital4 <- mutate(ma2, hospital = "hospital4")
mo1 <- hospital5 %>%
gather(antibiotic, usage, -date)
moact <- filter(mo1, antibiotic == "activity")
moact <- mutate(moact, hospital = "hospital5")
mo2 <- mo1 %>%
filter(antibiotic %in% abxlist) %>%
droplevels()
hospital5 <- mutate(mo2, hospital = "hospital5")
activity <- rbind(cact, aact, iact, maact, moact) # single df for all the activity data
activity$date <- as.Date(activity$date) # make sure activity dates are as.Date
usage <- rbind(hospital1, hospital2, hospital3, hospital4, hospital5) #single df for the usage data
usage$date <- as.Date(usage$date) # make sure usage dates are as.Date
usage$hospital <- ordered(usage$hospital, levels = c("hospital1", "hospital2", "hospital3", "hospital4", "hospital5")) # bar graphs in sensible hospital order
## add column to data for antibiotic class for sorting / graphing
## there *must* be a way to apply this, surely?
usage$class[usage$antibiotic %in% staphpen == TRUE] <- "anti.staph.penicillin"
usage$class[usage$antibiotic %in% narrowpen == TRUE] <- "narrow.penicillin"
usage$class[usage$antibiotic %in% penem == TRUE] <- "carbapenem"
usage$class[usage$antibiotic %in% pseudpen == TRUE] <- "anti.pseud.penicillin"
usage$class[usage$antibiotic %in% earlyceph == TRUE] <- "early.gen.cephalosporin"
usage$class[usage$antibiotic %in% quinolone == TRUE] <- "fluoroquinolone"
usage$class[usage$antibiotic %in% lateceph == TRUE] <- "late.gen.cephalosporin"
usage$class[usage$antibiotic %in% gpeptide == TRUE] <- "glycopeptide"
usage$class[usage$antibiotic %in% lincos == TRUE] <- "lincosamide"
usage$class[usage$antibiotic %in% macrolide == TRUE] <- "macrolide"
usage$class[usage$antibiotic %in% aglycos == TRUE] <- "aminoglycoside"
usage$class[usage$antibiotic %in% other == TRUE] <- "other.antimicrobials"
```
Inputs {.sidebar}
-----------------------------------------------------------------------
```{r}
## Hospital / District
selectInput("hosp",
label = h4("Select hospital:"),
choices = list("District" = "district",
"Hosp 1" = "hospital1",
"Hosp 2" = "hospital2",
"Hosp 3" = "hospital3",
"Hosp 4" = "hospital4",
"Hosp 5" = "hospital5"), selected = "hospital1")
selectInput("ctype",
label = h4("Select Second Chart Type:"),
choices = list("None" = "none",
"By class" = "byclass",
"By agent" = "bydrug"), selected = "byclass")
```
```{r}
conditionalPanel("input.ctype == 'byclass'",
selectInput("abclass",
label = h4("Select Class:"),
choices = list("Anti-Pseud Penicillins" = "anti.pseud.penicillin",
"Late-gen Cephalosporins" = "late.gen.cephalosporin",
"Fluoroquinolones" = "fluoroquinolone",
"Carbapenems" = "carbapenem",
"Lincosamides" = "lincosamide",
"Macrolides" = "macrolide",
"Glycopeptides" = "glycopeptide",
"Early Cephalosporin" = "early.gen.cephalosporin",
"Narrow Penicillins" = "narrow.penicillin",
"Anti-Staph Penicillins" = "anti.staph.penicillin",
"Aminoglycosides" = "aminoglycoside",
"Other Antibiotics" = "other.antimicrobials"), selected = "anti.pseud.penicillin")
)
```
```{r}
conditionalPanel("input.ctype == 'bydrug'",
selectInput("abagent",
label = h4("Select Agent:"),
choices = list("Pip-Tazo" = "piperacillin-tazobactam",
"Ceftriaxone" = "ceftriaxone",
"Meropenem" = "meropenem",
"Ciprofloxacin" = "ciprofloxacin",
"Lincomycin" = "lincomycin",
"Clindamycin" = "clindamycin",
"Azithromycin" = "azithromycin",
"Amoxycillin" = "amoxycillin",
"Co-amoxyclav" = "amoxycillin-clavulanate",
"Cephazolin" = "cefazolin",
"Gentamicin" = "gentamicin",
"Doxycycline" = "doxycycline"), selected = "piperacillin-tazobactam")
)
```
```{r}
sliderInput("dates",
label = h4("Select dates:"),
min = as.Date("2006-01-01"), max = as.Date("2017-09-01"),
value = c(as.Date("2006-01-01"), as.Date("2017-09-01")),
timeFormat = "%b-%Y")
checkboxInput('points', 'Points', value = FALSE)
checkboxInput('smooth', 'Smooth', value = TRUE)
checkboxInput('line', "Line", value = FALSE)
checkboxInput("contct", "Control Limit", value = FALSE)
```
Row
-----------------------------------------------------------------------
### Most Recent data
```{r}
# infobox showing the most recent data point to see completeness of dataset
nicedate <- format(max(usage$date), "%b %Y")
valueBox(max(nicedate), icon = "fa-calendar")
```
### Number of Observations
```{r}
# infobox with number of observations; mainly to pad out the box row; probably not useful
valueBox(count(usage), icon = "fa-calculator")
```
### Mean Service Activity
```{r}
## If district selected, return mean total service activity, else facility mean activity
renderValueBox({
if(input$hosp == "chhhs"){
msact <- mean(summarise(group_by(activity, date), total = sum(usage))$total)
} else {msact <- mean(summarise(group_by(filter(activity, hospital == input$hosp), date), total = sum(usage))$total)}
valueBox(
value = round(msact, digits = 0),
icon = "fa-hospital-o"
)
})
```
### Last Month Service Activity
```{r}
## HHS or Facility service activity for most recent month in dataset
## If activity is < 15% above mean; normal
## If activity 15<30% above mean; warn
## If activity >30%; danger
renderValueBox({
if(input$hosp == "chhhs"){
msact <- mean(summarise(group_by(activity, date), total = sum(usage))$total)
actwk <- summarise(group_by(activity, date), total = sum(usage)) # makes it easier to select!
scact <- actwk$total[actwk$date == max(actwk$date)]
} else {
msact <- mean(summarise(group_by(filter(activity, hospital == input$hosp), date), total = sum(usage))$total)
actwk <- summarise(group_by(filter(activity, hospital == input$hosp),date), total = sum(usage))
scact <- actwk$total[actwk$date == max(actwk$date)]
}
cactr <- scact / msact
if(cactr < 1.15){
valueBox(scact,
icon = "fa-ambulance",
color = "primary")
} else if(cactr > 1.30){
valueBox(scact,
icon = "fa-warning",
color = "danger")
} else {
valueBox(scact,
icon = "fa-ambulance",
color = "warning")
}
})
```
Row
-----------------------------------------------------------------------
### Total Usage
```{r}
renderPlot({
if(input$hosp == "district"){
plotdat <- summarise(group_by(filter(usage, date >= input$dates[1] & date <= input$dates[2]), date, hospital), total = round(sum(usage)))
p <- ggplot(data = plotdat, aes(x = date, y = total)) + geom_bar(stat = "identity", aes(fill = hospital)) + scale_x_date(date_breaks = "1 year", date_labels = "%b-%Y", name = "Month - Year") + scale_y_continuous(name = "DDD / 1000 patient-days") + scale_fill_brewer(palette = "Spectral")
print(p)
}
else if(input$hosp != "district"){
plotdat <- summarise(group_by(filter(usage, hospital == input$hosp, date >= input$dates[1] & date <= input$dates[2]), date), total = round(sum(usage)))
if(input$contct){
plotdat <- mutate(plotdat, contlim =(total + (sd(plotdat$total)*2)))
}
p <- ggplot(data = plotdat, aes(x = date, y = total)) + scale_x_date(date_breaks = "1 year", date_labels = "%b-%Y", name = "Month - Year") + scale_y_continuous(name = "DDD / 1000 patient-days")
if(input$points){
p <- p + geom_point()
}
if(input$smooth){
p <- p + stat_smooth(colour = 'red')
}
if(input$line){
p <- p + geom_line()
}
if(input$contct){
p <- p + geom_smooth(se = FALSE, aes(y = contlim), color = 'orange')
}
print(p)
}
})
```
```{r}
```
Row {.tabset}
-----------------------------------------------------------------------
### Agent / Class
```{r}
renderPlot({
if(input$hosp == "chhhs"){
plotdat <- summarise(group_by(filter(usage, date >= input$dates[1] & date <= input$dates[2]), date), total = round(sum(usage)))
if(input$contct){
plotdat <- mutate(plotdat, contlim =(total + (sd(plotdat$total)*2)))
}
p <- ggplot(data = plotdat, aes(x = date, y = total)) + scale_x_date(date_breaks = "1 year", date_labels = "%b-%Y", name = "Month - Year") + scale_y_continuous(name = "DDD / 1000 patient-days")
if(input$points){
p <- p + geom_point()
}
if(input$smooth){
p <- p + stat_smooth(colour = 'red')
}
if(input$line){
p <- p + geom_line()
}
if(input$contct){
p <- p + geom_smooth(se = FALSE, aes(y = contlim), color = 'orange')
}
print(p)
}
else if(input$ctype == "bydrug"){
plotdat <- summarise(group_by(filter(usage, hospital == input$hosp, antibiotic == input$abagent, date >= input$dates[1] & date <= input$dates[2]), date), total = round(sum(usage)))
}
else if(input$ctype == "byclass"){
plotdat <- summarise(group_by(filter(usage, hospital == input$hosp, class == input$abclass, date >= input$dates[1] & date <= input$dates[2]), date), total = round(sum(usage)))
}
if(input$contct){
plotdat <- mutate(plotdat, contlim =(total + (sd(plotdat$total)*2)))
}
p <- ggplot(data = plotdat, aes(x = date, y = total)) + scale_x_date(date_breaks = "1 year", date_labels = "%b-%Y", name = "Month - Year") + scale_y_continuous(name = "DDD / 1000 patient-days")
if(input$points){
p <- p + geom_point()
}
if(input$smooth){
p <- p + stat_smooth(colour = 'red')
}
if(input$line){
p <- p + geom_line()
}
if(input$contct){
p <- p + geom_smooth(se = FALSE, aes(y = contlim), color = 'orange')
}
print(p)
})
```
### Activity
```{r}
renderPlot({
if(input$hosp == "chhhs"){
plotdat = summarise(group_by(filter(activity, date >= input$dates[1] & date <= input$dates[2]), date), total = sum(usage))
} else {
plotdat = summarise(group_by(filter(activity, hospital == input$hosp, date >= input$dates[1] & date <= input$dates[2]), date), total = sum(usage))
}
p <- ggplot(data = plotdat, aes (x = date, y = total)) + scale_x_date(date_breaks = "1 year", date_labels = "%b-%Y", name = "Month - Year") + scale_y_continuous(name = "Patient-days / month")
if(input$points){
p <- p + geom_point()
}
if(input$smooth){
p <- p + stat_smooth(colour = 'blue')
}
if(input$line){
p <- p + geom_line()
}
print(p)
})
```
### Facility Data
```{r}
DT::renderDataTable({
tab1 <- summarise(group_by(filter(usage, hospital == input$hosp, date >= as.Date(input$dates[1]) & date <= as.Date(input$dates[2])), date), total = round(sum(usage)))
DT::datatable(tab1, options = list(pageLength = 100))
})
```
### Agent Data
```{r}
DT::renderDataTable({
tab2 <- summarise(group_by(filter(usage, antibiotic == input$abagent, hospital == input$hosp, date >= as.Date(input$dates[1]) & date <= as.Date(input$dates[2])), date), total = round(sum(usage)))
DT::datatable(tab2, options = list(pageLength = 100))
})
```
### District Data
```{r}
DT::renderDataTable({
tab3 <- summarise(group_by(filter(usage, date >= as.Date(input$dates[1]) & date <= as.Date(input$dates[2])), date), total = round(sum(usage)))
DT::datatable(tab3, options = list(pageLength = 100))
})
```
其中说： “发生错误。检查您的日志”。
日志：
Listening on http://127.0.0.1:41233
processing file: index.Rmd
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 2.2.1.9000 ✔ purrr 0.2.4
✔ tibble 1.4.2 ✔ dplyr 0.7.4
✔ tidyr 0.8.0 ✔ stringr 1.2.0
✔ readr 1.1.1 ✔ forcats 0.2.0
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':
last_plot
The following object is masked from 'package:stats':
filter
The following object is masked from 'package:graphics':
layout
Warning: Unknown or uninitialised column: 'class'.
output file: /tmp/RtmpdrRzNh/index.knit.md
pandoc: /tmp/RtmpdrRzNh/rmarkdown-str510e40ebd6c3.html: openFile: does not exist (No such file or directory)
Warning: Error in : pandoc document conversion failed with error 1
Stack trace (innermost first):
100: pandoc_convert
99: convert
98: <Anonymous>
97: do.call
96: contextFunc
95: .getReactiveEnvironment()$runWith
94: shiny::maskReactiveContext
93: <reactive>
82: doc
81: shiny::renderUI
80: func
79: origRenderFunc
78: output$__reactivedoc__
3: <Anonymous>
2: do.call
1: rmarkdown::run
processing file: index.Rmd
Warning: Unknown or uninitialised column: 'class'.
output file: /tmp/RtmpdrRzNh/index.knit.md
pandoc: /tmp/RtmpdrRzNh/rmarkdown-str510e3380b7e9.html: openFile: does not exist (No such file or directory)
Warning: Error in : pandoc document conversion failed with error 1
Stack trace (innermost first):
100: pandoc_convert
99: convert
98: <Anonymous>
97: do.call
96: contextFunc
95: .getReactiveEnvironment()$runWith
94: shiny::maskReactiveContext
93: <reactive>
82: doc
81: shiny::renderUI
80: func
79: origRenderFunc
78: output$__reactivedoc__
3: <Anonymous>
2: do.call
1: rmarkdown::run
这是在Ubuntu Linux机器上运行，带有RStudio 1.1.419和R 3.4.3，所有内容都已更新。
我特别希望将错误整理出来，因为我使用破折号进行工作。但更一般地说，如果代码有其他改进，我也会很感激！