ERROR: there is no package called ‘dplyr’ - shinyapps.io

[Sergio Cruz]

Hello,

I am trying to upload my first Shiny App in shinyapps.io. However, it doesn't work. I have two separates files ui.R and Server.R. I am running on Windows 10, using the newest version of RStudio Version 1.0.143 and R x64 version 3.4.1.

I get the following messages when trying to Publish from the App running locally.

Preparing to deploy application...DONE

Uploading bundle for application: 197041...Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/src/contrib:

  no fue posible abrir la URL 'http://www.stats.ox.ac.uk/pub/RWin/src/contrib/PACKAGES'

DONE

Deploying bundle: 904656 for application: 197041 ...

Waiting for task: 453247494

  building: Processing bundle: 904656

  building: Fetching packages

  building: Installing packages

  building: Installing files

  building: Pushing image: 897653

It opens a html web page that says:

ERROR: there is no package called ‘dplyr’

Just in case is needed my ui.R code is:

.libPaths(c( "C:/Users/Sergio/Documents/R/win-library/3.4", 

             "C:/Program Files/R/R-3.4.1/library"))

## Libraries needed 

library(shiny)

library(dplyr)

library(googleVis)

library(downloader)

library(openxlsx)

# Download the file

url = "https://www.servel.cl/wp-content/uploads/2016/01/resultados_elecciones_presidenciales_1989_al_2013_1_.xlsx"

download(url, "resultados_elecciones_presidenciales_1989_al_2013_1_.xlsx", mode = "wb")

# Reading the file

datos1 = read.xlsx ("resultados_elecciones_presidenciales_1989_al_2013_1_.xlsx")

# Selecting specific columns and manipulating data

datos2 = datos1 %>% select(3,8,10,12,15,17,22)

################## Simplifying column names

datos2 = datos2 %>% rename(Year = AÑO.ELECCION,

                           Round = VOTACION.PRESIDENCIAL,

                           County = NOMBRE.COMUNA,

                           Region = NOMBRE.REGION,

                           Sex = SEXO.MESA,

                           Candidate = NOMBRE.COMPLETO,

                           Votes = VOTACION.VALIDA)

########### Manipulating data: years of election ##################

## This is because it is the same election

datos4 =  datos2 %>% mutate(Year = replace(Year, 

                                           Year==2000, 1999))

datos5 =  datos4 %>% mutate(Year = replace(Year, 

                                           Year==2006, 2005))

datos6 =  datos5 %>% mutate(Year = replace(Year, 

                                           Year==2010, 2009))

datos7 =  datos6 %>% mutate(Round = replace(Round, 

                                            Round=="UNICA VOTACION", "Unique"))

datos8 =  datos7 %>% mutate(Round = replace(Round, 

                                            Round=="PRIMERA VOTACION", "First"))

datos9 =  datos8 %>% mutate(Round = replace(Round, 

                                            Round=="SEGUNDA VOTACION", "Ballotage"))

###### Year as a factor to facilitate manipulation in Shiny 

datos9 = datos9 %>%

    mutate(Year = as.factor(Year)

    )

### Adjusting region names, as later I want to use a map

##

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "METROPOLITANA DE SANTIAGO", "Metropolitana"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DEL BIOBIO", "Bío-Bío"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE VALPARAISO", "Valparaíso"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE LA ARAUCANIA", "Araucanía"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DEL MAULE", "Maule"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE LOS LAGOS", "Los Lagos"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE COQUIMBO", "Coquimbo"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE ANTOFAGASTA", "Antofagasta"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE TARAPACA", "Tarapacá"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE ATACAMA", "Atacama"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE MAGALLANES Y ANTARTICA CH.", "Magallanes"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "AISEN DEL GRAL. CARLOS IBAÑEZ", "Aisén"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "ARICA Y PARINACOTA", "Arica y Parinacota"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE LOS RIOS", "Los Ríos"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DEL LIBERTADOR BDO. O'HIGGINS", "O'Higgins"))

## I used regions codes because GoogleVis did not recognized all the names

## but it did recognized all the codes

data_CL = data.frame(Region_ID = c("CL-RM","CL-BI","CL-VS","CL-AR","CL-ML","CL-LI","CL-LL"

                                   ,'CL-CO','CL-LR','CL-AN','CL-TA','CL-AT', 'CL-AP','CL-MA',

                                   'CL-AI'), Region = c( "Metropolitana","Bío-Bío","Valparaíso","Araucanía",

                                                         "Maule","O'Higgins","Los Lagos", "Coquimbo","Los Ríos",

                                                         "Antofagasta", "Tarapacá","Atacama","Arica y Parinacota",

                                                         "Magallanes", "Aisén"))

#### I merge the data as the original file did not have the codes

datos10 = merge(datos9,data_CL)

## Change capital names

datos10$County = gsub("(?<=\\b)([a-z])", "\\U\\1", tolower(datos10$County), perl=TRUE)

datos10$Candidate = gsub("(?<=\\b)([a-z])", "\\U\\1", tolower(datos10$Candidate), perl=TRUE)

### Added extra information about tournout (participation) in different

## years

Year = as.integer(c(1989, 1993,1999,1999,2005,2005,2009,2009,2013,2013))

Round = c("Unique","Unique","First","Ballotage","First","Ballotage",

          "First","Ballotage","First","Ballotage")

VAP =as.integer(c(8239545,9052632,10126098, 10126098,11344218,

                  11344218,12268311,12268311,13153415,13153415))

Turnout = c(86.9,81.6,71.8,72.4,63.5,63.1,59.2,58.7,50.9,43.3)

Par =data.frame(Year,Round,VAP,Turnout)

Par

########### As all the data is ready, I start Shiny Server

# In the first part of this code, the program goes to the website of 

# the Chilean Electoral Service and download a specific file which has

# data about the Chilean Presidential elections since 1989 to 2013

# Define UI for random distribution application 

shinyUI(pageWithSidebar(

    

    # Application title

    headerPanel("Results of Chilean Presidential Elections 1989-2013"),

    

    # Sidebar with controls to select the random distribution type

    # and number of observations to generate. Note the use of the br()

    # element to introduce extra vertical spacing

    sidebarPanel(

        selectInput("Year",'Step 1: Select year of election:', choices = levels(datos10$Year)),

        submitButton("Update View"),

        br(),  

        uiOutput("Round"),

        submitButton("Update View"),

        br(),

        selectInput("Region", 'Step 3: Select a region:', choices = unique(datos10$Region)),

        submitButton("Update View"),

        br(),

        uiOutput("County"),

        br(),

        submitButton("Update View"),

        width = 2 ),

      

      # Show a tabset that includes a plot, summary, and table view

      # of the generated distribution

      mainPanel(

          tabsetPanel(

            tabPanel("National level", htmlOutput("view1"), h5("Since 2012 the vote

                    is not mandatory but people can choose whether to vote or not."),

                     h5("Voting age population (VAP)"),tableOutput("tab1"),

                    htmlOutput("pieN")

                    ),

            tabPanel("Regional votes comparison",h5("The columns are colored red and blue to indicate the number 

                        of votes for the two leading candidates in the round"),

                     h5(textOutput("textR1")),h5(textOutput("textR2")),

                     htmlOutput("view4"), htmlOutput("view5")

                     ),

            tabPanel("Maps", h5("Coloured Maps by votes in regions"),htmlOutput("maps"),

                     h5("The regions are colored red and blue to indicate the number of votes

                    for the leading candidates in the round"),

                    h5(textOutput("cand1")),h5(textOutput("cand2")), 

                    h5("Note 1: Before 2007 Arica y Parinacota Region was included in Tarapaca Region"),

                    h5("Note 2: Before 2007 Los Rios Region was included in Los Lagos Region")),

            tabPanel("Regional level",h5("Please, select Region and press Update View"), h5(textOutput("textR4")), htmlOutput("reg4")

                     ),

            tabPanel("Table at county level",h5("Please, select County and press Update View"),tableOutput("table")

                     ),

            tabPanel("County level",h5("Please, select County and press Update View"), htmlOutput("view3")

                     )

           

          )

      )

))

#########################

My Server.R code is:

.libPaths(c( "C:/Users/Sergio/Documents/R/win-library/3.4", 

             "C:/Program Files/R/R-3.4.1/library"))

#### Libraries needed 

library(shiny)

library(dplyr)

library(googleVis)

library(downloader)

library(openxlsx)

# Reading the file

datos1 = read.xlsx ("resultados_elecciones_presidenciales_1989_al_2013_1_.xlsx")

# Selecting specific columns and manipulating data

datos2 = datos1 %>% select(3,8,10,12,15,17,22)

################## Simplifying column names

datos2 = datos2 %>% rename(Year = AÑO.ELECCION,

                           Round = VOTACION.PRESIDENCIAL,

                           County = NOMBRE.COMUNA,

                           Region = NOMBRE.REGION,

                           Sex = SEXO.MESA,

                           Candidate = NOMBRE.COMPLETO,

                           Votes = VOTACION.VALIDA)

########### Manipulating data: years of election ##################

## This is because it is the same election

datos4 =  datos2 %>% mutate(Year = replace(Year, 

                                           Year==2000, 1999))

datos5 =  datos4 %>% mutate(Year = replace(Year, 

                                           Year==2006, 2005))

datos6 =  datos5 %>% mutate(Year = replace(Year, 

                                           Year==2010, 2009))

datos7 =  datos6 %>% mutate(Round = replace(Round, 

                                            Round=="UNICA VOTACION", "Unique"))

datos8 =  datos7 %>% mutate(Round = replace(Round, 

                                            Round=="PRIMERA VOTACION", "First"))

datos9 =  datos8 %>% mutate(Round = replace(Round, 

                                            Round=="SEGUNDA VOTACION", "Ballotage"))

###### Year as a factor to facilitate manipulation in Shiny 

datos9 = datos9 %>%

    mutate(Year = as.factor(Year)

    )

### Adjusting region names, as later I want to use a map

##

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "METROPOLITANA DE SANTIAGO", "Metropolitana"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DEL BIOBIO", "Bío-Bío"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE VALPARAISO", "Valparaíso"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE LA ARAUCANIA", "Araucanía"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DEL MAULE", "Maule"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE LOS LAGOS", "Los Lagos"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE COQUIMBO", "Coquimbo"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE ANTOFAGASTA", "Antofagasta"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE TARAPACA", "Tarapacá"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "DE ATACAMA", "Atacama"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE MAGALLANES Y ANTARTICA CH.", "Magallanes"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "AISEN DEL GRAL. CARLOS IBAÑEZ", "Aisén"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region == "ARICA Y PARINACOTA", "Arica y Parinacota"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DE LOS RIOS", "Los Ríos"))

datos9 =  datos9 %>% mutate(Region = replace(Region, 

                                             Region =="DEL LIBERTADOR BDO. O'HIGGINS", "O'Higgins"))

## I used regions codes because GoogleVis did not recognized all the names

## but it did recognized all the codes

data_CL = data.frame(Region_ID = c("CL-RM","CL-BI","CL-VS","CL-AR","CL-ML","CL-LI","CL-LL"

                                   ,'CL-CO','CL-LR','CL-AN','CL-TA','CL-AT', 'CL-AP','CL-MA',

                                   'CL-AI'), Region = c( "Metropolitana","Bío-Bío","Valparaíso","Araucanía",

                                                         "Maule","O'Higgins","Los Lagos", "Coquimbo","Los Ríos",

                                                         "Antofagasta", "Tarapacá","Atacama","Arica y Parinacota",

                                                         "Magallanes", "Aisén"))

#### I merge the data as the original file did not have the codes

datos10 = merge(datos9,data_CL)

## Change capital names

datos10$County = gsub("(?<=\\b)([a-z])", "\\U\\1", tolower(datos10$County), perl=TRUE)

datos10$Candidate = gsub("(?<=\\b)([a-z])", "\\U\\1", tolower(datos10$Candidate), perl=TRUE)

### Added extra information about tournout (participation) in different

## years

Year = as.integer(c(1989, 1993,1999,1999,2005,2005,2009,2009,2013,2013))

Round = c("Unique","Unique","First","Ballotage","First","Ballotage",

          "First","Ballotage","First","Ballotage")

VAP =as.integer(c(8239545,9052632,10126098, 10126098,11344218,

                  11344218,12268311,12268311,13153415,13153415))

Turnout = c(86.9,81.6,71.8,72.4,63.5,63.1,59.2,58.7,50.9,43.3)

Par =data.frame(Year,Round,VAP,Turnout)

Par

########### As all the data is ready, I start Shiny Server

# Define server logic for random distribution application

shinyServer(function(input, output) 

{

    # One thing I needed was to incorporate a dynamic code because

    # some years had first and second round, but others not

    # I used render UI

    ##  Render UI - Getting Round depending on Year

    output$Round = renderUI({    

        

        round = ifelse( input$Year == 1989 | input$Year == 1993, "Unique",

                        as.data.frame(c("First","Ballotage")))

        round = as.data.frame(round)

        selectInput("Round","Step 2: Select a round:", round[,1])

    }) 

    

    # I also used Render UI because I did want to display Counties

    # associated to their regions

    # Render UI showing Counties depending on Regions

    

    output$County <- renderUI({

        

        A = ifelse(input$Year >= 2008 & input$Region %in% "Arica y Parinacota",

                   as.data.frame(c("Arica","Putre","General Lagos","Camarones")),

                   ifelse(input$Year < 2008 & input$Region %in% "Arica y Parinacota",

                          c("Change year because this region was created in 2007"),

                          ifelse(input$Year>= 2008 & input$Region %in% "Los Ríos",

                                 as.data.frame(c("Corral","Lanco","Los Lagos","Máfil",

                                                 "Mariquina","Paillaco","Panguipulli","Valdivia",

                                                 "Futrono","La Unión","Lago Ranco","Río Bueno")),

                                 ifelse(input$Year< 2008 & input$Region %in% "Los Ríos",

                                        c("Change year because this region was created in 2007"),

                                        datos10 %>%

                                            filter(Year %in% input$Year)  %>%

                                            filter(Round %in% input$Round) %>%

                                            filter(Region %in% input$Region) %>%

                                            group_by(County) %>%

                                            summarise(Votes = sum(Votes)) %>%

                                            as.data.frame()

                                 ))))  

        counties = as.data.frame(A)

        selectInput("County", "Step 4: Select a county:", counties[,1])

    })

    

    

    

    ### I start preparing the data for the different tabs

    # National level calculations, depending on Year and Round

    # I get the National results

    

    datos1N = reactive({ 

    datos88 = datos10 %>%

        filter(Year %in% input$Year) %>%

        filter(Round %in% input$Round)

    datosN1 = datos88 %>%

        group_by(Candidate) %>%

        summarise(Votes = sum(Votes)) %>%

        arrange(-Votes)

    datosN1 = as.data.frame(datosN1)

        })

    

    # Depending on Year, Round and show regional results for all the regions

    # Regional level calculations

    datos1R = reactive({ 

    datos77 = datos10 %>%

        filter(Year %in% input$Year) %>%

        filter(Round %in% input$Round)%>%

        filter(Region %in% input$Region)

    datosR1 = datos77 %>%

        group_by(Candidate) %>%

        summarise(Votes = sum(Votes)) %>%

        arrange(-Votes)

    datosR1 = as.data.frame(datosR1)

    

    })

    

    # I need to get County first

    # County level calculations

    datos1C = reactive({  

                datos33 = datos10 %>%

                filter(Year %in% input$Year) %>%

                filter(County %in% input$County) %>%

                filter(Round %in% input$Round)

                datos51 = datos33 %>%

                    group_by(Candidate) %>%

                    summarise(Votes = sum(Votes)) %>%

                    arrange(-Votes)

                datos52 = as.data.frame(datos51)

                n = nrow(datos52)

                datos45 = datos33[1:n,]

                datos46 = datos45 %>%

                select(1:4)

                datos48 = cbind(datos46, datos52)

        

    })

    # Calculating the votes for the two leading candidates of each Round

    # and each Year

    datosTR1 = reactive({  

            datosA = datos10 %>%

            filter(Year %in% input$Year) %>%

            filter(Round %in% input$Round)

            datosA1 = datosA %>%

            group_by(Candidate) %>%

            summarise(Votes=sum(Votes)) %>%

            arrange(-Votes)

            datosA1 = as.data.frame(datosA1)

            datosB = datosA %>%

            group_by(Candidate, Region,Region_ID) %>%

            summarise(Votes=sum(Votes)) %>%

            arrange(-Votes)

            datosB = as.data.frame(datosB)

            datosC = datosB %>%

            filter(Candidate %in% datosA1[1,1])

        })

    datosTR2 = reactive({  

            datosA = datos10 %>%

            filter(Year %in% input$Year) %>%

            filter(Round %in% input$Round)

            datosA1 = datosA %>%

            group_by(Candidate) %>%

            summarise(Votes=sum(Votes)) %>%

            arrange(-Votes)

            datosA1 = as.data.frame(datosA1)

            datosB = datosA %>%

            group_by(Candidate, Region,Region_ID) %>%

            summarise(Votes=sum(Votes)) %>%

            arrange(-Votes)

            datosB = as.data.frame(datosB)

            datosC = datosB %>%

            filter(Candidate %in% datosA1[2,1])

    })

    

    ###### This code prepare the data for the tournout details

    dat2N = reactive({  

       

        

        tab3 = Par %>%

            filter(Year %in% input$Year) %>%

            filter(Round %in% input$Round)

        tab3

    })

    ######################################

    dat3N = reactive({  

        tab1 = datos10 %>%

            filter(Year %in% input$Year) %>%

            filter(Round %in% input$Round)

        datosA1 = tab1 %>%

            group_by(Candidate) %>%

            summarise(Votes=sum(Votes))

        datosA1 = as.data.frame(datosA1)

        votes1 = sum(datosA1$Votes)

        Vot1 =c(votes1)

        Population = Par %>%

            filter(Year %in% input$Year)%>%

            filter(Round %in% input$Round)

        pie2 = data.frame(Vot1,Population)

        Item = c("Tournout","Not voting")

        Total =c(Vot1,pie2$VAP-Vot1)

        pie3 = data.frame(Item,Total)

        pie3

    })

    

#######################################################################   

    

    # Generate outputs

    ########################### Tab 1: National level ##

    

        output$view1 = renderGvis({

            validate(

                need(input$Round != '', 'Please choose a Year, Round, Region and County !')

            )          

             bar1 = gvisBarChart (datos1N(), 

                        xvar="Candidate", yvar="Votes")

                                })

        

        output$tab1 =  renderTable({

            validate(

                need(input$Round != "" , 'Please choose a Year, Round, Region and County !')

            )

            data.frame(dat2N())

        })

        

        output$pieN <- renderGvis({

            validate(

                need(input$Round != '', 'Please choose a Year, Round, Region and County !')

            )

            

            pie = gvisPieChart(dat3N(),labelvar = dat3N()$Item,

                               numvar = dat3N()$Total, 

                               options=list(width=400, height=225))

        })   

        

        

    ##################################  Tab 2 Regional ####################

       

         output$textR1 <- renderText({

           

            a1 = paste("Votes for candidate (in red):", 

                       datosTR1()[1,1])

            

        })

        output$textR2 <- renderText({

            validate(

                need(input$Year != "", 'Please choose a Year and a Round!'),

                need(input$Round != "", 'Please choose a Year and a Round!')

            ) 

            a2= paste("Votes for candidate (in blue):", 

                      datosTR2()[1,1])

            

        })

        

        output$view4 = renderGvis({

            validate(

                need(input$Region != "", "Please choose a Region!")

            )             

            gvisColumnChart (datosTR1(), 

                             xvar="Region", yvar="Votes",

                             options=list(

                                 legend="none",

                                 lineWidth=2, pointSize=0,

                                 title="Votes by Region", vAxis="{title:'Votes'}",

                                 hAxis="{title:'Regions'}",

                                 colors= "['#cd3333']",

                                 width=1000, height=300))

        }) 

        

        output$view5 = renderGvis({

                      

            gvisColumnChart (datosTR2(), 

                             xvar="Region", yvar="Votes",

                             options=list(

                                 legend="none",

                                 lineWidth=2, pointSize=0,

                                 title="Votes by Region", vAxis="{title:'Votes'}",

                                 hAxis="{title:'Regions'}",

                                 colors= "['#0000FF']",

                                 width=1000, height=300))

        })         

        

########################## Tab 3 Maps ###################################################        

    

        output$cand1 <- renderText({

         

            a1 = paste("Votes for candidate (in red):", 

            datosTR1()[1,1])

   

     })

    output$cand2 <- renderText({

         

           a2= paste("Votes for candidate (in blue):", 

              datosTR2()[1,1])

    

    })

        output$maps <- renderGvis({

     

                map1 = gvisGeoChart(datosTR1(),

                 locationvar="Region_ID", colorvar="Votes", hovervar = "Region",

                 options=list(region="CL", displayMode="regions", 

                              resolution="provinces",keepAspectRatio = TRUE,

                              datalessRegionColor = "#fcfcfc",

                              colorAxis="{colors:['#ffebcd', '#cd3333']}"

                                ))     

                map2 = gvisGeoChart(datosTR2(),

                 locationvar="Region_ID", colorvar="Votes", hovervar = "Region",

                 options=list(region="CL", displayMode="regions", 

                              resolution="provinces",keepAspectRatio = TRUE,

                              datalessRegionColor = "#fcfcfc",

                              colorAxis="{colors:['#FFFFFF', '#0000FF']}"

                               ))

                merge = gvisMerge(map1,map2,horizontal=TRUE,tableOptions="cellspacing=10")

                

                return(merge)

                

                })

        ########################## Tab 4 - pie Regional

        

        

        output$textR4 <- renderText({

            validate(

                need(input$Region != '', 'Please choose a Year, Round, Region and County !')

            )     

            a4 = paste("The following is the pie of the electoral results in the Region of:", 

                       datos1C()[1,1])

            

        }) 

        

        output$reg4 <- renderGvis({

            validate(

                need(input$Region != '', 'Please choose a Year, Round, Region and County !')

            )            

            pie = gvisPieChart(datos1R(),labelvar = datos1R()$Candidate,

                               numvar = datos1R()$Votes, 

                               options=list(width=800, height=450))

        })    

        

    ######## Tab 5 County details a Table with the results

    # Generate an HTML table view of the data

        output$table <- renderTable({

            data.frame(datos1C())

                    })

    ########## Tab 6 County details a Coulmn Chart

        output$view3 = renderGvis({

  

        gvisColumnChart (datos1C(), 

                     xvar="Candidate", yvar="Votes")

    

    })     

    ######## This is the end, thanks for your attention

})

[Sergio Cruz]

In addition, just in case is needed:

> rsconnect::appDependencies()

Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/src/contrib:

  cannot open URL 'http://www.stats.ox.ac.uk/pub/RWin/src/contrib/PACKAGES'

       package version source

1           R6   2.2.2   CRAN

2         Rcpp 0.12.11   CRAN

3       digest  0.6.12   CRAN

4    htmltools   0.3.6   CRAN

5       httpuv   1.3.3   CRAN

6     jsonlite     1.5   CRAN

7         mime     0.5   CRAN

8      packrat 0.4.8-1   CRAN

9        shiny   1.0.3   CRAN

10 sourcetools   0.1.6   CRAN

11      xtable   1.8-2   CRAN

Warning messages:

1: In value[[3L]](cond) :

  Failed to parse C:/Users/Sergio/AppData/Local/Temp/Rtmpa0MfDX/file33851cf6936/server.R ; dependencies in this file will not be discovered.

2: In value[[3L]](cond) :

  Failed to parse C:/Users/Sergio/AppData/Local/Temp/Rtmpa0MfDX/file33851cf6936/ui.R ; dependencies in this file will not be discovered.

3: In readLines(serverPath) :

  incomplete final line found on 'C:/Users/Sergio/AppData/Local/Temp/Rtmpa0MfDX/file33851cf6936/server.R'

[Sergio Cruz]

Dear all,

Just to share my solution.

Checking different sites I found that it did not present the same error if I changed in the code "library(dplyr)" for "require(dplyr)". Then dplyr was recognized. 

However, it resulted then that there were more errors and it did not recognize some functions. So I changed them. For example, it did not recognize the function read.xlsx so I installed readxl. After several hours, it finally it is up and running.

Hope this is useful for someone who finds the same problem. 

Good luck !

On Saturday, July 15, 2017 at 4:09:30 PM UTC-4, Sergio Cruz wrote: