make {autoshiny}

Create a Shiny app (object or files) from an R function

Description

Create a Shiny app (object or files) from an R function

Usage

makeApp(fun, withGoButton = FALSE)

makeFiles(fun, withGoButton = FALSE, directory)

Arguments

fun

A function (preferably a symbol – a long self-explanatory name – pointing to a pre-defined function object, rather than an anonymous function) with zero or more arguments/parameters. Every argument must have a default value, which will be used to define each argument's:

• type/class,

• allowed values,

• pre-selected/start-up value.

withGoButton

Either TRUE or FALSE (default: FALSE). It indicates if the (re)evaluation of fun in the Shiny app should be immediately triggered by every change in the value of any argument/parameter (withGoButton = FALSE) or if the (re)calculation should be started only when a specific button is pressed (withGoButton = TRUE). The latter is preferred if the (re)evaluation of fun is significantly time-consuming or if fun has no arguments (because then, without the button, only refreshing the web page would trigger the (re)evaluation).

directory

Path to a directory/folder where makeFiles should save the compiled server.R and ui.R files.

Value

makeApp

A Shiny app object as returned by as.shiny.appobj.

makeFiles

NULL. This function saves two plain text files: ui.R and server.R with the R code of function fun translated into a Shiny app. If these files need further manual changes, it is recommended that they are first re-formatted e.g. in RStudio (top menu -> Code -> Reformat Code or Ctrl+Shift+A) or programmatically (e.g. https://github.com/google/rfmt).

Examples

## Not run: 
library(shiny)

### Example 1: Trivial anonymous function
makeApp(function(x=1:3, y=5:9) x+y)

### Example 2: Nicer function and argument names
`Histogram for normal distribution` <-
    function(`Number of observations` =
             # as.integer => the argument interpreted as categorical:
             as.integer(c(100,10,1000)))
        # Generic R plots as "return values" are supported:
        plot(hist(rnorm(`Number of observations`)))
makeApp(`Histogram for normal distribution`)

### Example 3: Data frame in (upload CSV), data frame out (displayed and downloadable as CSV)
`Table of sin and cos values` <-
    function(`Upload CSV file with column "x"` =
                 data.frame(x = seq(0, 2*pi, .25))) {
        dta <- `Upload CSV file with column "x"`
        data.frame(X = dta$x,
                   `Sin of X` = sin(dta$x),
                   `Cos of X` = cos(dta$x),
                   check.names = FALSE)
    }
makeApp(`Table of sin and cos values`)

### Example 4: Arbitrary input and output files
openxlsx::write.xlsx(data.frame(x=1:5,
                                y=11:15),
                     'my_test_file.xlsx')
`Excel file in and out` <-
    function(`Input Excel file` =
                 File('my_test_file.xlsx')) { # File() obligatory here!
        my.data <- openxlsx::read.xlsx(`Input Excel file`)
        my.data2 <- within(my.data,
                           z <- x + y)
        openxlsx::write.xlsx(my.data2,
                             'my_test_file_2.xlsx')
        File('my_test_file_2.xlsx') # File() obligatory here too!
    }
makeApp(`Excel file in and out`)

### Example 5: Using a button as a (re-)evaluation trigger
### Use this option if:
### - the evaluation of your functon takes time, so it should not be re-evaluated with every
###   minor change of the value of inputs/arguments/parameter;
### - the function is impure e.g. depends on some external data fetched internally and takes no
###   arguments/parameters -- in such a case the function would be re-evaluated only through
###   page refresh of the browser; the button is a faster and a more elegant solution.
`Get "GDP and main components" from Eurostat` <-
    function() {
        # Getting data from
        # http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing ...
        # ... ?sort=1&file=data%2Fnama_10_gdp.tsv.gz
        x <- eurodata::importData('nama_10_gdp')
        head(x, 10)
    }
makeApp(`Get "GDP and main components" from Eurostat`,
        withGoButton = TRUE)

### Example 6: Lists of inputs (arguments) and the output list (composite return value)
### are always decomposed
`A function with lists everywhere` <-
    function(`First argument group,` = list(`number one` = 1:3,
                                            `number two` = letters[1:3]),
             `2nd arg group,` = list(`1st argument` = 11:14,
                                     `second arg.` = LETTERS[1:5]))
        list(`Some text` =
                 as.character(c(`First argument group,`$`number two`,
                                `2nd arg group,`$`second arg.`)),
             `Some numbers` =
                 `First argument group,`$`number one` +
                 `2nd arg group,`$`1st argument`,
             `Even a ggplot2 chart` =
                 ggplot2::qplot(a,b,data=data.frame(a=1:20,b=log(1:20))))
makeApp(`A function with lists everywhere`)

## End(Not run)