Save an Image Uploaded Through R Shiny
Building a Shiny app to upload and visualize spatio-temporal data
In this affiliate we bear witness how to build a Shiny web application to upload and visualize spatio-temporal data (Chang et al. 2021). The app allows to upload a shapefile with a map of a region, and a CSV file with the number of disease cases and population in each of the areas in which the region is divided. The app includes a variety of elements for interactive data visualization such as a map congenital with leaflet (Cheng, Karambelkar, and Xie 2021), a tabular array congenital with DT (Xie, Cheng, and Tan 2021), and a time plot built with dygraphs (Vanderkam et al. 2018). The app also allows interactivity by giving the user the possibility to select specific information to be shown. To build the app, we use information of the number of lung cancer cases and population in the 88 counties of Ohio, U.s.a., from 1968 to 1988 (Figure 15.ane).
Effigy 15.1: Snapshot of the Shiny app to upload and visualize spatio-temporal information.
Shiny
Shiny is a spider web application framework for R that enables to build interactive web applications. Chapter 13 provides an introduction to Shiny and examples, and here nosotros review its basic components. A Shiny app can be built past creating a directory (called, for example, appdir) that contains an R file (chosen, for example, app.R) with three components:
- a user interface object (
ui) which controls the layout and appearance of the app, - a
server()function with the instructions to build the objects displayed in theui, and - a call to
shinyApp()that creates the app from theui/serverpair.
Shiny apps contain input and output objects. Inputs permit users collaborate with the app by modifying their values. Outputs are objects that are shown in the app. Outputs are reactive if they are built using input values. The following code shows the content of a generic app.R file.
# load the shiny package library(shiny) # define user interface object ui <- fluidPage( * Input(inputId = myinput, characterization = mylabel, ...) * Output(outputId = myoutput, ...) ) # ascertain server() role server <- function(input, output){ output$myoutput <- return*({ # lawmaking to build the output. # If information technology uses an input value (input$myinput), # the output will exist rebuilt whenever # the input value changes })} # telephone call to shinyApp() which returns the Shiny app shinyApp(ui = ui, server = server) The app.R file is saved inside a directory chosen, for example, appdir. Then, the app can exist launched by typing runApp("appdir_path") where appdir_path is the path of the directory that contains app.R, or by clicking the Run button of RStudio.
Setup
To build the Shiny app of this instance, nosotros need to download the folder appdir from the book webpage and save it in our computer. This folder contains the following subfolders:
-
datawhich contains a file calledinformation.csvwith the data of lung cancer in Ohio, and a binder calledfe_2007_39_countywith the shapefile of Ohio, and -
wwwwith an image of a Shiny logo calledimageShiny.png.
Structure of app.R
We kickoff creating the Shiny app by writing a file chosen app.R with the minimum lawmaking needed to create a Shiny app:
We save this file with the proper noun app.R inside a directory called appdir. Then, we can launch the app by clicking the Run App button at the superlative of the RStudio editor or by executing runApp("appdir_path") where appdir_path is the path of the directory that contains the app.R file. The Shiny app created has a blank user interface. In the following sections, nosotros include the elements and functionality we wish to have in the Shiny app.
Layout
We build a user interface with a sidebar layout. This layout includes a title panel, a sidebar panel for inputs on the left, and a primary panel for outputs on the right. The elements of the user interface are placed within the fluidPage() role and this permits the app to arrange automatically to the dimensions of the browser window. The title of the app is added with titlePanel(). So we write sidebarLayout() to create a sidebar layout with input and output definitions. sidebarLayout() takes the arguments sidebarPanel() and mainPanel(). sidebarPanel() creates a a sidebar console for inputs on the left. mainPanel() creates a main panel for displaying outputs on the correct.
We can add content to the app by passing it equally an argument to titlePanel(), sidebarPanel(), and mainPanel(). Hither we have added texts with the clarification of the panels. Annotation that to include multiple elements in the same panel, we need to separate them with commas.
HTML content
Here we add a title, an image and a website link to the app. First we add the title "Spatial app" to titlePanel(). Nosotros desire to evidence this title in bluish and so we utilize p() to create a paragraph with text and fix the style to the #3474A7 color.
titlePanel(p("Spatial app", manner = "color:#3474A7")), So we add an image with the img() function. The images that we wish to include in the app must exist in a binder named world wide web in the same directory as the app.R file. Nosotros use the paradigm called imageShiny.png and put it in the sidebarPanel() past using the following education.
sidebarPanel(img(src = "imageShiny.png", width = "70px", height = "70px")), Hither src denotes the source of the image, and height and width are the image height and width in pixels, respectively. We also add together text with a link referencing the Shiny website.
p("Made with", a("Shiny", href = "http://shiny.rstudio.com"), "."), Notation that in sidebarPanel() nosotros need to write the function to generate the website link and the function to include the prototype separated with a comma.
sidebarPanel( p("Made with", a("Shiny", href = "http://shiny.rstudio.com"), "."), img(src = "imageShiny.png", width = "70px", top = "70px")), Beneath is the content of app.R we have until now. A snapshot of the Shiny app is shown in Figure 15.2.
library ( shiny ) # ui object ui <- fluidPage ( titlePanel ( p ( "Spatial app", fashion = "colour:#3474A7" ) ), sidebarLayout ( sidebarPanel ( p ( "Fabricated with", a ( "Shiny", href = "http://shiny.rstudio.com" ), "." ), img ( src = "imageShiny.png", width = "70px", height = "70px" ) ), mainPanel ( "main panel for outputs" ) ) ) # server() server <- function ( input, output ) { } # shinyApp() shinyApp (ui = ui, server = server ) 
Effigy 15.2: Snapshot of the Shiny app afterward including a title, an image and a website link.
Read information
Now we import the data we desire to show in the app. The data is in the folder called data in the appdir directory. To read the CSV file information.csv, nosotros use the read.csv() function, and to read the shapefile of Ohio that is in the folder fe_2007_39_county, we employ the readOGR() function of the rgdal packet.
We but need to read the data one time and so we write this code at the beginning of app.R outside the server() office. By doing this, the code is not unnecessarily run more once and the performance of the app is not decreased.
Adding outputs
At present nosotros show the data in the Shiny app by including several outputs for interactive visualization. Specifically, nosotros include HTML widgets created with JavaScript libraries and embedded in Shiny past using the htmlwidgets bundle (Vaidyanathan et al. 2021). The outputs are created using the following packages:
- DT to display the information in an interactive table,
- dygraphs to display a time plot with the data, and
- leaflet to create an interactive map.
Outputs are added in the app by including in ui an *Output() function for the output, and calculation in server() a render*() office to the output that specifies how to build the output. For example, to add a plot, we write in the ui plotOutput() and in server() renderPlot().
Tabular array using DT
We testify the data in data with an interactive table using the DT package. In ui we use DTOutput(), and in server() we utilize renderDT().
Time plot using dygraphs
We evidence a time plot with the information with the dygraphs package. In ui nosotros apply dygraphOutput(), and in server() we utilize renderDygraph(). dygraphs plots an extensible time series object xts. We can create this blazon of object using the xts() role of the xts package (Ryan and Ulrich 2020) specifying the values and the dates. The dates in information are the years of column year. For now we choose to plot the values of the variable cases of data.
We demand to construct a xts object for each county and then put them together in an object chosen dataxts. For each of the counties, nosotros filter the data of the county and assign it to datacounty. Then nosotros construct a xts object with values datacounty$cases and dates as.Date(paste0(data$year, "-01-01")). So nosotros assign the name of the counties to each xts (colnames(dataxts) <- counties) so canton names tin can be shown in the legend.
dataxts <- NULL counties <- unique ( data $ county ) for ( 50 in i : length ( counties ) ) { datacounty <- data [ data $ county == counties [ l ], ] dd <- xts ( datacounty [, "cases" ], as.Date ( paste0 ( datacounty $ year, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } colnames ( dataxts ) <- counties Finally, we plot dataxts with dygraph(), and use dyHighlight() to allow mouse-over highlighting.
dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.2 ) We customize the legend so that merely the name of the highlighted series is shown. To do this, one option is to write a css file with the instructions and pass the css file to the dyCSS() function. Alternatively, nosotros can set the css directly in the lawmaking as follows:
dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.2 ) -> d1 d1 $ x $ css <- " .dygraph-legend > span {brandish:none;} .dygraph-legend > bridge.highlight { display: inline; } " d1 The complete code to build the dygraphs object is the following:
library ( dygraphs ) library ( xts ) # in ui dygraphOutput (outputId = "timetrend" ) # in server() output $ timetrend <- renderDygraph ( { dataxts <- NULL counties <- unique ( data $ canton ) for ( l in 1 : length ( counties ) ) { datacounty <- data [ data $ county == counties [ fifty ], ] dd <- xts ( datacounty [, "cases" ], every bit.Date ( paste0 ( datacounty $ year, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } colnames ( dataxts ) <- counties dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.two ) -> d1 d1 $ 10 $ css <- " .dygraph-fable > span {brandish:none;} .dygraph-legend > span.highlight { display: inline; } " d1 } ) Map using leaflet
We use the leaflet package to build an interactive map. In ui we apply leafletOutput(), and in server() nosotros use renderLeaflet(). Inside renderLeaflet() we write the instructions to return a leaflet map. First, nosotros need to add the data to the shapefile so the values can exist plotted in a map. For at present we cull to plot the values of the variable in 1980. Nosotros create a dataset chosen datafiltered with the data corresponding to that year. And so we add together datafiltered to map@data in an order such that the counties in the data match the counties in the map.
datafiltered <- data [ which ( data $ twelvemonth == 1980 ), ] # this returns positions of map@data$NAME in datafiltered$canton ordercounties <- friction match ( map @ data $ NAME, datafiltered $ county ) map @ data <- datafiltered [ ordercounties, ] Nosotros create the leaflet map with the leaflet() role, create a colour palette with colorBin(), and add a legend with addLegend(). For now we choose to plot the values of variable cases. Nosotros also add labels with the area names and values that are displayed when the mouse is over the map.
library ( leaflet ) # in ui leafletOutput (outputId = "map" ) # in server() output $ map <- renderLeaflet ( { # add information to map datafiltered <- data [ which ( data $ year == 1980 ), ] ordercounties <- match ( map @ data $ Proper noun, datafiltered $ county ) map @ data <- datafiltered [ ordercounties, ] # create leaflet pal <- colorBin ( "YlOrRd", domain = map $ cases, bins = 7 ) labels <- sprintf ( "%s: %g", map $ county, map $ cases ) %>% lapply ( htmltools :: HTML ) l <- leaflet ( map ) %>% addTiles ( ) %>% addPolygons ( fillColor = ~ pal ( cases ), color = "white", dashArray = "3", fillOpacity = 0.vii, label = labels ) %>% leaflet :: addLegend ( pal = pal, values = ~ cases, opacity = 0.vii, title = NULL ) } ) Below is the content of app.R we have until now. A snapshot of the Shiny app is shown in Effigy 15.3.
library ( shiny ) library ( rgdal ) library ( DT ) library ( dygraphs ) library ( xts ) library ( leaflet ) data <- read.csv ( "information/data.csv" ) map <- readOGR ( "data/fe_2007_39_county/fe_2007_39_county.shp" ) # ui object ui <- fluidPage ( titlePanel ( p ( "Spatial app", style = "colour:#3474A7" ) ), sidebarLayout ( sidebarPanel ( p ( "Made with", a ( "Shiny", href = "http://shiny.rstudio.com" ), "." ), img ( src = "imageShiny.png", width = "70px", superlative = "70px" ) ), mainPanel ( leafletOutput (outputId = "map" ), dygraphOutput (outputId = "timetrend" ), DTOutput (outputId = "table" ) ) ) ) # server() server <- role ( input, output ) { output $ table <- renderDT ( information ) output $ timetrend <- renderDygraph ( { dataxts <- NULL counties <- unique ( information $ county ) for ( l in 1 : length ( counties ) ) { datacounty <- data [ data $ county == counties [ fifty ], ] dd <- xts ( datacounty [, "cases" ], every bit.Date ( paste0 ( datacounty $ year, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } colnames ( dataxts ) <- counties dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.2 ) -> d1 d1 $ 10 $ css <- " .dygraph-legend > span {display:none;} .dygraph-fable > span.highlight { display: inline; } " d1 } ) output $ map <- renderLeaflet ( { # Add together data to map datafiltered <- data [ which ( data $ yr == 1980 ), ] ordercounties <- match ( map @ data $ Proper noun, datafiltered $ canton ) map @ data <- datafiltered [ ordercounties, ] # Create leaflet pal <- colorBin ( "YlOrRd", domain = map $ cases, bins = seven ) labels <- sprintf ( "%s: %one thousand", map $ canton, map $ cases ) %>% lapply ( htmltools :: HTML ) l <- leaflet ( map ) %>% addTiles ( ) %>% addPolygons ( fillColor = ~ pal ( cases ), color = "white", dashArray = "three", fillOpacity = 0.7, label = labels ) %>% leaflet :: addLegend ( pal = pal, values = ~ cases, opacity = 0.vii, title = Zilch ) } ) } # shinyApp() shinyApp (ui = ui, server = server ) 
Effigy 15.iii: Snapshot of the Shiny app after including the map, the fourth dimension plot, and the table.
Calculation reactivity
Now we add together functionality that enables the user to select a specific variable and year to be shown. To be able to select a variable, we include an input of a menu containing all the possible variables. So, when the user selects a particular variable, the map and the time plot will be rebuilt. To add an input in a Shiny app, we demand to place an input function *Input() in the ui object. Each input function requires several arguments. The start two are inputId, an id necessary to access the input value, and label which is the text that appears next to the input in the app. We create the input with the bill of fare that contains the possible choices for the variable every bit follows.
# in ui selectInput ( inputId = "variableselected", label = "Select variable", choices = c ( "cases", "population" ) ) In this input, the id is variableselected, characterization is "Select variable" and choices contains the variables "cases" and "population". The value of this input can be accessed with input$variableselected. Nosotros create reactivity by including the value of the input (input$variableselected) in the return*() expressions in server() that build the outputs. Thus, when we select a different variable in the menu, all the outputs that depend on the input volition be rebuilt using the updated input value.
Similarly, we add together a card with id yearselected and with choices equal to all possible years so we can select the year we want to see. When we select a year, the input value input$yearselected changes and all the outputs that depend on it will be rebuilt using the new input value.
# in ui selectInput ( inputId = "yearselected", label = "Select twelvemonth", choices = 1968 : 1988 ) Reactivity in dygraphs
In this section we modify the dygraphs time plot and the leaflet map so that they are built with the input values input$variableselected and input$yearselected. We modify renderDygraph() past writing datacounty[, input$variableselected] instead of datacounty[, "cases"].
# in server() output $ timetrend <- renderDygraph ( { dataxts <- NULL counties <- unique ( data $ canton ) for ( 50 in 1 : length ( counties ) ) { datacounty <- data [ information $ canton == counties [ 50 ], ] # CHANGE "cases" past input$variableselected dd <- xts ( datacounty [, input $ variableselected ], as.Date ( paste0 ( datacounty $ year, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } ... } ) Reactivity in leaflet
We as well modify renderLeaflet() by selecting data corresponding to year input$yearselected and plot variable input$variableselected instead of variable cases. We create a new column in map called variableplot with the values of variable input$variableselected and plot the map with the values in variableplot. In leaflet() we modify colorBin(), addPolygons(), addLegend() and labels to evidence variableplot instead of variable cases.
output $ map <- renderLeaflet ( { # Add data to map # Alter 1980 by input$yearselected datafiltered <- data [ which ( data $ twelvemonth == input $ yearselected ), ] ordercounties <- match ( map @ data $ NAME, datafiltered $ county ) map @ information <- datafiltered [ ordercounties, ] # Create variableplot # ADD this to create variableplot map $ variableplot <- as.numeric ( map @ information [, input $ variableselected ] ) # Create leaflet # CHANGE map$cases past map$variableplot pal <- colorBin ( "YlOrRd", domain = map $ variableplot, bins = 7 ) # CHANGE map$cases by map$variableplot labels <- sprintf ( "%southward: %g", map $ county, map $ variableplot ) %>% lapply ( htmltools :: HTML ) # Modify cases by variableplot l <- leaflet ( map ) %>% addTiles ( ) %>% addPolygons ( fillColor = ~ pal ( variableplot ), color = "white", dashArray = "3", fillOpacity = 0.7, label = labels ) %>% # CHANGE cases by variableplot leaflet :: addLegend ( pal = pal, values = ~ variableplot, opacity = 0.7, championship = NULL ) } ) Note that a better way to alter an existing leaflet map is using the leafletProxy() function. Details on how to use this role are given in the RStudio website. The content of the app.R file is shown below and a snapshot of the Shiny app is shown in Effigy 15.4.
library ( shiny ) library ( rgdal ) library ( DT ) library ( dygraphs ) library ( xts ) library ( leaflet ) data <- read.csv ( "information/data.csv" ) map <- readOGR ( "information/fe_2007_39_county/fe_2007_39_county.shp" ) # ui object ui <- fluidPage ( titlePanel ( p ( "Spatial app", mode = "color:#3474A7" ) ), sidebarLayout ( sidebarPanel ( selectInput ( inputId = "variableselected", label = "Select variable", choices = c ( "cases", "population" ) ), selectInput ( inputId = "yearselected", label = "Select year", choices = 1968 : 1988 ), p ( "Made with", a ( "Shiny", href = "http://shiny.rstudio.com" ), "." ), img ( src = "imageShiny.png", width = "70px", superlative = "70px" ) ), mainPanel ( leafletOutput (outputId = "map" ), dygraphOutput (outputId = "timetrend" ), DTOutput (outputId = "table" ) ) ) ) # server() server <- role ( input, output ) { output $ table <- renderDT ( data ) output $ timetrend <- renderDygraph ( { dataxts <- NULL counties <- unique ( data $ county ) for ( l in ane : length ( counties ) ) { datacounty <- data [ data $ county == counties [ l ], ] dd <- xts ( datacounty [, input $ variableselected ], as.Date ( paste0 ( datacounty $ year, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } colnames ( dataxts ) <- counties dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.2 ) -> d1 d1 $ x $ css <- " .dygraph-legend > span {display:none;} .dygraph-legend > span.highlight { display: inline; } " d1 } ) output $ map <- renderLeaflet ( { # Add data to map # Modify 1980 past input$yearselected datafiltered <- information [ which ( data $ year == input $ yearselected ), ] ordercounties <- friction match ( map @ data $ Proper noun, datafiltered $ county ) map @ information <- datafiltered [ ordercounties, ] # Create variableplot # ADD this to create variableplot map $ variableplot <- equally.numeric ( map @ information [, input $ variableselected ] ) # Create leaflet # CHANGE map$cases past map$variableplot pal <- colorBin ( "YlOrRd", domain = map $ variableplot, bins = 7 ) # CHANGE map$cases by map$variableplot labels <- sprintf ( "%s: %g", map $ county, map $ variableplot ) %>% lapply ( htmltools :: HTML ) # CHANGE cases by variableplot l <- leaflet ( map ) %>% addTiles ( ) %>% addPolygons ( fillColor = ~ pal ( variableplot ), color = "white", dashArray = "3", fillOpacity = 0.7, label = labels ) %>% # Change cases by variableplot leaflet :: addLegend ( pal = pal, values = ~ variableplot, opacity = 0.7, title = Aught ) } ) } # shinyApp() shinyApp (ui = ui, server = server ) 
FIGURE 15.four: Snapshot of the Shiny app later adding reactivity.
Uploading data
Instead of reading the data at the beginning of the app, we may desire to let the user upload his or her ain files. In lodge to exercise that, we delete the code we previously used to read the information, and add together ii inputs that enable to upload a CSV file and a shapefile.
Inputs in ui to upload a CSV file and a shapefile
We create inputs to upload the data with the fileInput() function. fileInput() has a parameter chosen multiple that tin can exist prepare to Truthful to allow the user to select multiple files. It also has a parameter called take that can be set up to a character vector with the type of files the input expects. Hither nosotros write two inputs. One of the inputs is to upload the data. This input has id filedata and the input value tin can exist accessed with input$filedata. This input accepts .csv files.
# in ui fileInput(inputId = "filedata", label = "Upload data. Choose csv file", accept = c(".csv")), The other input is to upload the shapefile. This input has id filemap and the input value tin can be accessed with input$filemap. This input accepts multiple files of type '.shp', '.dbf', '.sbn', '.sbx', '.shx', and '.prj'.
# in ui fileInput(inputId = "filemap", label = "Upload map. Choose shapefile", multiple = True, take = c('.shp','.dbf','.sbn','.sbx','.shx','.prj')), Note that a shapefile consists of different files with extensions .shp, .dbf, .shx etc. When we are uploading the shapefile in the Shiny app, we demand to upload all these files at one time. That is, we need to select all the files and so click upload. Selecting just the file with extension .shp does not upload the shapefile.
Uploading CSV file in server()
Nosotros use the input values to read the CSV file and the shapefile. We do this inside a reactive expression. A reactive expression is an R expression that uses an input value and returns a value. To create a reactive expression we use the reactive() role which takes an R expression surrounded by braces ({}). The reactive expression updates whenever the input value changes.
For example, we read the information with read.csv(input$filedata$datapath) where input$filedata$datapath is the information path contained in the value of the input that uploads the data. We put read.csv(input$filedata$datapath) inside reactive(). In this way, each time input$filedata$datapath is updated, the reactive expression is reexecuted. The output of the reactive expression is assigned to information. In server(), information can be accessed with data(). data() will exist updated each time the reactive expression that builds is reexecuted.
Uploading shapefile in server()
We also write a reactive expression to read the map. We assign the issue of the reactive expression to map. In server(), nosotros access the map with map(). To read the shapefile, we use the readOGR() role of the rgdal package. When files are uploaded with fileInput() they accept unlike names from the ones in the directory. We starting time rename files with the actual names and then read the shapefile with readOGR() passing the proper name of the file with .shp extension.
# in server() map <- reactive ( { # shpdf is a data.frame with the proper noun, size, type and datapath # of the uploaded files shpdf <- input $ filemap # The files are uploaded with names # 0.dbf, one.prj, 2.shp, three.xml, 4.shx # (path/names are in column datapath) # Nosotros need to rename the files with the actual names: # fe_2007_39_county.dbf, etc. # (these are in column proper name) # Proper noun of the temporary directory where files are uploaded tempdirname <- dirname ( shpdf $ datapath [ i ] ) # Rename files for ( i in ane : nrow ( shpdf ) ) { file.rename ( shpdf $ datapath [ i ], paste0 ( tempdirname, "/", shpdf $ proper name [ i ] ) ) } # At present nosotros read the shapefile with readOGR() of rgdal package # passing the proper name of the file with .shp extension. # We employ the role grep() to search the blueprint "*.shp$" # within each element of the graphic symbol vector shpdf$name. # grep(pattern="*.shp$", shpdf$name) # ($ at the end denote files that finish with .shp, # not but that incorporate .shp) map <- readOGR ( paste ( tempdirname, shpdf $ name [ grep (blueprint = "*.shp$", shpdf $ name ) ], sep = "/" ) ) map } ) Treatment missing inputs
After adding the inputs to upload the CSV file and the shapefile, we note that the outputs in the Shiny app render error letters until the files are uploaded (Effigy fifteen.5). Here we modify the Shiny app to eliminate these mistake messages past including lawmaking that avoids to show the outputs until the files are uploaded.
FIGURE 15.5: Snapshot of the Shiny app after adding inputs to upload the data and the map. The Shiny app renders error messages until the files are uploaded.
Requiring input files to be available using req()
First, inside the reactive expressions that read the files, we include req(input$inputId) to require input$inputId to exist bachelor earlier showing the outputs. req() evaluates its arguments one at a time and if these are missing the execution of the reactive expression stops. In this manner, the value returned by the reactive expression will not exist updated, and outputs that use the value returned by the reactive expression will not be reexecuted. Details on how to use req() are in the RStudio website.
We add req(input$filedata) at the beginning of the reactive expression that reads the data. If the information has non been uploaded yet, input$filedata is equal to "". This stops the execution of the reactive expression, and so information() is not updated, and the output depending on data() is not executed.
# in ui. Beginning line in the reactive() that reads the data req ( input $ filedata ) Similarly, we add req(input$filemap) at the start of the reactive expression that reads the map. If the map has not been uploaded all the same, input$filemap is missing, the execution of the reactive expression stops, map() is not updated, and the output depending on map() is not executed.
# in ui. First line in the reactive() that reads the map req ( input $ filemap ) Checking data are uploaded before creating the map
Before constructing the leaflet map, the data has to be added to the shapefile. To do this, we need to make sure that both the data and the map are uploaded. We can do this past writing at the kickoff of renderLeaflet() the following code.
When either information() or map() are updated, the instructions of renderLeaflet() are executed. Then, at the beginning of renderLeaflet() it is checked whether either data() or map() are NULL. If this is TRUE, the execution stops returning Zero. This avoids the fault that we would get when trying to add together the data to the map when either of these ii elements is NULL.
Conclusion
In this chapter, nosotros have shown how to create a Shiny app to upload and visualize spatio-temporal data. We take shown how to upload a shapefile with a map and a CSV file with data, how to create interactive visualizations including a tabular array with DT, a map with leaflet and a time plot with dygraphs, and how to add reactivity that enables the user to show specific information. The complete lawmaking of the Shiny app is given beneath, and a snapshot of the Shiny app created is shown in Figure 15.one. We can improve the advent and functionality of the Shiny app past modifying the layout and adding other inputs and outputs. The website http://shiny.rstudio.com/ contains multiple resources that can be used to better the Shiny app.
library ( shiny ) library ( rgdal ) library ( DT ) library ( dygraphs ) library ( xts ) library ( leaflet ) # ui object ui <- fluidPage ( titlePanel ( p ( "Spatial app", style = "color:#3474A7" ) ), sidebarLayout ( sidebarPanel ( fileInput ( inputId = "filedata", characterization = "Upload data. Cull csv file", accept = c ( ".csv" ) ), fileInput ( inputId = "filemap", label = "Upload map. Choose shapefile", multiple = True, accept = c ( ".shp", ".dbf", ".sbn", ".sbx", ".shx", ".prj" ) ), selectInput ( inputId = "variableselected", label = "Select variable", choices = c ( "cases", "population" ) ), selectInput ( inputId = "yearselected", characterization = "Select year", choices = 1968 : 1988 ), p ( "Made with", a ( "Shiny", href = "http://shiny.rstudio.com" ), "." ), img ( src = "imageShiny.png", width = "70px", height = "70px" ) ), mainPanel ( leafletOutput (outputId = "map" ), dygraphOutput (outputId = "timetrend" ), DTOutput (outputId = "tabular array" ) ) ) ) # server() server <- office ( input, output ) { information <- reactive ( { req ( input $ filedata ) read.csv ( input $ filedata $ datapath ) } ) map <- reactive ( { req ( input $ filemap ) # shpdf is a information.frame with the name, size, type and # datapath of the uploaded files shpdf <- input $ filemap # The files are uploaded with names # 0.dbf, 1.prj, 2.shp, 3.xml, 4.shx # (path/names are in cavalcade datapath) # We need to rename the files with the bodily names: # fe_2007_39_county.dbf, etc. # (these are in column name) # Name of the temporary directory where files are uploaded tempdirname <- dirname ( shpdf $ datapath [ 1 ] ) # Rename files for ( i in 1 : nrow ( shpdf ) ) { file.rename ( shpdf $ datapath [ i ], paste0 ( tempdirname, "/", shpdf $ name [ i ] ) ) } # Now we read the shapefile with readOGR() of rgdal package # passing the name of the file with .shp extension. # We use the function grep() to search the pattern "*.shp$" # within each chemical element of the character vector shpdf$name. # grep(blueprint="*.shp$", shpdf$name) # ($ at the finish announce files that finish with .shp, # not but that contain .shp) map <- readOGR ( paste ( tempdirname, shpdf $ name [ grep (pattern = "*.shp$", shpdf $ name ) ], sep = "/" ) ) map } ) output $ table <- renderDT ( data ( ) ) output $ timetrend <- renderDygraph ( { information <- data ( ) dataxts <- NULL counties <- unique ( data $ county ) for ( l in ane : length ( counties ) ) { datacounty <- data [ information $ canton == counties [ l ], ] dd <- xts ( datacounty [, input $ variableselected ], every bit.Appointment ( paste0 ( datacounty $ twelvemonth, "-01-01" ) ) ) dataxts <- cbind ( dataxts, dd ) } colnames ( dataxts ) <- counties dygraph ( dataxts ) %>% dyHighlight (highlightSeriesBackgroundAlpha = 0.ii ) -> d1 d1 $ x $ css <- " .dygraph-fable > span {display:none;} .dygraph-legend > span.highlight { display: inline; } " d1 } ) output $ map <- renderLeaflet ( { if ( is.null ( information ( ) ) | is.naught ( map ( ) ) ) { return ( NULL ) } map <- map ( ) information <- data ( ) # Add data to map datafiltered <- data [ which ( information $ year == input $ yearselected ), ] ordercounties <- match ( map @ data $ NAME, datafiltered $ county ) map @ data <- datafiltered [ ordercounties, ] # Create variableplot map $ variableplot <- as.numeric ( map @ data [, input $ variableselected ] ) # Create leaflet pal <- colorBin ( "YlOrRd", domain = map $ variableplot, bins = 7 ) labels <- sprintf ( "%southward: %g", map $ county, map $ variableplot ) %>% lapply ( htmltools :: HTML ) 50 <- leaflet ( map ) %>% addTiles ( ) %>% addPolygons ( fillColor = ~ pal ( variableplot ), color = "white", dashArray = "three", fillOpacity = 0.7, label = labels ) %>% leaflet :: addLegend ( pal = pal, values = ~ variableplot, opacity = 0.vii, title = Aught ) } ) } # shinyApp() shinyApp (ui = ui, server = server ) Source: https://www.paulamoraga.com/book-geospatial/sec-shinyexample.html
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