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Users may want to edit the names of the column types, example.
Cat -> Categorías
Num -> Números
Useful for multilingual apps
Point out errors in incongruous edition
i think it could be useful ---especially for the apps-- to overwrite this names so that it can be a guide for the correct content of the column. Moreover, as it establish the correct content of every column there will be no need -- as in the bar chart race app or gender app -- to use selectors so that the user indicates which column has the information needed... Also, it would be good when a user fills the table from scratch
Dejar el menor número de opciones
For example in the gender app: the output is going to be the same table as the input table but with an extra column (the columns which the guessed genders). We want this column to be highlighted or distinguished (styles changes). It can also apply for rows and cells.
Validations should kick in... but what if the users don't know what types are?
How can we train them our custom Data Types?
Users should be able to double click and rename column labels
the table size is nonfunctional in shinypanel
Los datos son los siguientes:
https://docs.google.com/spreadsheets/d/1Ro5sLWe6Dz-8Xqhj2BNlyyWTqKXkPBjyPBNXB636GBI/edit#gid=2139997082
Al cargar esta base el resultado del fringe es NULL
library(shiny)
library(hotr)
library(shinypanels)
library(tidyverse)
ui <- panelsPage(
panel(title = "First Panel", color = "chardonnay", collapsed = FALSE, width = 350,
head = NULL,
body = verbatimTextOutput('test'),
footer = NULL
),
panel(title = "Second panel", color = "chardonnay", collapsed = FALSE, width = 350,
head = NULL,
body = uiOutput("dataTable"),
footer = NULL
)
)
server <- function(input, output, session) {
output$dataTable <- renderUI({
d_mex <- read_csv("Desaparecidos en México. Source_ El Universal - desaparecidos-mex_data.csv")
hotr('hot-table', data = d_mex)
})
output$test <- renderPrint(input$hot-table
)
}
shinyApp(ui,server)
When used as follows, the data in input$data_input
seems to be trimmed at 26 columns even if inputData()
has more columns.
hotr("data_input", data = inputData(), options = list(height = 200))
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