# Server functions

{spsComps} has some useful functions for exception catch, expression validation, and more. Even though we say they are Shiny server functions, but in fact most of them can be run without a Shiny server. We have designed the functions to detect whether there is a Shiny server, if not, they will work only in R console as well.

library(spsComps)

## Loading required package: shiny

## Loading required package: spsUtil

library(magrittr)


## Server components

### shinyCatch

#### basic

The shinyCatch function is useful to capture exception. What we mean exception can be message, warning or error. For example

shinyCatch({
message("This is a message")
warning("This is a warning")
stop("This is an error")
})

## [SPS-INFO] 2021-03-02 23:04:38 This is a message
##
## [SPS-WARNING] 2021-03-02 23:04:38 This is a warning
## [SPS-ERROR] 2021-03-02 23:04:38 This is an error

## NULL


You can see all 3 levels are captured inside the [SPS-XX] log on your console. If you run this in your Shiny app, a pop-up message with the corresponding log level message will be displayed in in app, like following:

So the message on both UI and console is called dual-end logging in SPS.

#### Shiny off

Of course, if you do not want users to see the message, you can hide it by shiny = FALSE, but the message will be still logged on R console. Run the following on your own computer and watch the difference.

library(shiny)

ui <- fluidPage(
spsDepend("toastr")
)

server <- function(input, output, session) {
shinyCatch({
stop("This is an error")
}, shiny = FALSE)
}

shinyApp(ui, server)


#### get return

shinyCatch is able to return you values if your expression has any. Imagine we have a function addNum that gives message, warning or error depeend on the input.

addNum <- function(num){
if (num > 0) {message(num)}
else if (num == 0) {warning("Num is 0")}
else {stop("less than 0")}
return(num + num)
}

value_a <- shinyCatch({
})

## [SPS-INFO] 2021-03-02 23:04:38 1

value_a

## [1] 2

value_b <- shinyCatch({
})

## [SPS-WARNING] 2021-03-02 23:04:38 Num is 0

value_b

## [1] 0

value_c <- shinyCatch({
})

## [SPS-ERROR] 2021-03-02 23:04:38 less than 0

value_c

## NULL


You can see at message and warning level, the expected value returned, and at error level, the return is NULL. So the following code value_c still runs and is not blocked by the error occurred in shinyCatch.

#### Blocking level

More often, if there is an error, we do want take the log in R console, inform the Shinyapp user and then stop the following code. In this case, we need to specify the blocking_level. So, default is "none", do not block and return NULL if there is an error, and you can choose "error", "warning" or "message".

• error: block downstream if the first error detected in shinyCatch
• warning: block downstream if the first error or warning detected in shinyCatch
• message: block downstream if the first error, warning or message detected in shinyCatch

You can see the stringency becomes tighter: message > warning > error

Blocking code will generate error, in order to have the Rmd rendered, we wrap the expression in try

try({
shinyCatch({
stop("error level is the most commonly used level")
}, blocking_level = "error")
print("This will not be evaluated")
})

## [SPS-ERROR] 2021-03-02 23:04:38 error level is the most commonly used level
## Error :

try({
shinyCatch({
message("error level is the most commonly used level")
}, blocking_level = "message")
print("This will not be evaluated either")
})

## [SPS-INFO] 2021-03-02 23:04:38 error level is the most commonly used level
##
## Error :


You can see the following print in both cases are not got evaluated.

#### Block reative

The most useful case for shinyCatch is to use it in the Shiny reactive context. Most errors in shiny::reactive, shiny::observer, shiny::observeEvent, or shiny::renderXXX series function will crash the app. With shinyCatch, it will not. It “dual-logs” the error and stop downstream code.

The following example use shiny::reactiveConsole() to mock a Shiny server session

shiny::reactiveConsole(TRUE)
y <- observe({
stop("an error from a function")
print("some other process")
})

## Warning: Error in <observer>: The value of x is
##   38: stop
##   37: <observer> [#2]
##   35: contextFunc
##   34: env$runWith ## 27: ctx$run
##   26: run
##    7: flushCallback
##    6: FUN
##    5: lapply
##    4: ctx$executeFlushCallbacks ## 3: .getReactiveEnvironment()$flush
##    2: flushReact
##    1: <Anonymous>


It crashes the app. However, if you use shinyCatch

shiny::reactiveConsole(TRUE)
y <- observe({
shinyCatch({
stop("an error from a function")
}, blocking_level = "error")
print("some other process")
})

## [SPS-ERROR] 2021-03-02 22:13:05 an error from a function


It only logs the error and prevent the downstream print to run. Now try following real Shiny apps and watch the difference:

# with shinyCatch
library(shiny)
server <- function(input, output, session) {
observe({
shinyCatch({
stop("an error from a function")
}, blocking_level = "error")
print("some other process")
})
}
shinyApp(fluidPage(spsDepend("toastr")), server)

# without shinyCatch
library(shiny)
server <- function(input, output, session) {
observe({
stop("an error from a function")
print("some other process")
})
}
shinyApp(fluidPage(spsDepend("toastr")), server)


### spsValidate

In data analysis, it is important we do some validations before the downstream process, like make a plot. It is epecially the case in Shiny apps. We cannot predict what the user inputs will be, like what kind of data they will use. Similar to shinyCatch, spsValidate is able to catch exceptions but more useful to handle validations. In addtion to shinyCatch functionalities, it will give users a success message if the expression goes through and return TRUE (shinyCatch returns the final expression value).

shiny::reactiveConsole(TRUE)
x <- reactiveVal(10)

y <- observe({
spsValidate({
# have multiple validations in one expression
if (x() == 1) stop("cannot be 1")
if (x() == 0) stop("cannot be 0")
if (x() < 0) stop("less than 0")
})
message("The value of x is ", x())
})
x(0)
x(-10)

## The value of x is 10
## [ ERROR] 2021-03-02 22:36:16 cannot be 0
## [ ERROR] 2021-03-02 22:36:16 less than 0


Try this real Shiny app:

library(shiny)
ui <- fluidPage(
spsDepend("toastr"),
shiny::sliderInput(
"num", "change number",
min = -1, max = 2, value = 2, step = 1
)
)
server <- function(input, output, session) {
x <- reactive(as.numeric(input$num)) y <- observe({ spsValidate(vd_name = "check numbers", verbose = TRUE, { # have multiple validations in one expression if (x() == 1) stop("cannot be 1") if (x() == 0) stop("cannot be 0") if (x() < 0) stop("less than 0") }) message("The value of x is ", x()) }) } shinyApp(ui, server)  You should see the success message like this: ### shinyCheckPkg Sometimes we want the app behave differently if users have certain packages installed. For example, if some packages are installed, we open up additional tabs on UI to allow more features. This can be done with the shinyCheckPkg function. This function has to run inside Shiny server, an alternative version to use without Shiny is from the spsUtil package, spsUtil::checkNameSpace Use it in Shiny server, specify the packages you want to check by different sources, like CRAN, Bioconductor, or github. shinyCheckPkg(session, cran_pkg = c("pkg1", "pkg2"), bioc_pkg = "bioxxx", github = "user1/pkg1")  It will return TRUE if all packages are installed, otherwise FALSE Now try this real example. We check if the ggplot99 package is installed, if yes we make a plot. It also combines the spsValidate function. You can have a better idea how these functions work. library(shiny) ui <- fluidPage( tags$label('Check if package "pkg1", "pkg2", "bioxxx",
github package "user1/pkg1" are installed'), br(),
actionButton("check_random_pkg", "check random_pkg"),
br(), spsHr(),
tags$label('We can combine spsValidate to block server code to prevent crash if some packages are not installed.'), br(), tags$label('If "shiny" is installed, make a plot.'), br(),
actionButton("check_shiny", "check shiny"), br(),
tags$label('If "ggplot99" is installed, make a plot.'), br(), actionButton("check_gg99", "check ggplot99"), br(), plotOutput("plot_pkg") ) server <- function(input, output, session) { observeEvent(input$check_random_pkg, {
shinyCheckPkg(session, cran_pkg = c("pkg1", "pkg2"), bioc_pkg = "bioxxx", github = "user1/pkg1")
})
observeEvent(input$check_shiny, { spsValidate(verbose = FALSE, { if(!shinyCheckPkg(session, cran_pkg = c("shiny"))) stop("Install packages") }) output$plot_pkg <- renderPlot(plot(1))
})
observeEvent(input$check_gg99, { spsValidate({ if(!shinyCheckPkg(session, cran_pkg = c("ggplot99"))) stop("Install packages") }) output$plot_pkg <- renderPlot(plot(99))
})
}

shinyApp(ui, server)


You should see something like this if there is any missing package: