I have some data in a table format which looks like:
capRates 1100000 1050000 1000000 950000 900000
1 0.088 0.0812 0.0928 0.1052 0.1185 0.1327
2 0.084 0.0892 0.1009 0.1134 0.1267 0.1409
3 0.080 0.0977 0.1095 0.1220 0.1354 0.1497
4 0.076 0.1068 0.1187 0.1313 0.1447 0.1591
5 0.072 0.1166 0.1285 0.1412 0.1547 0.1692
The second table looks like:
# A tibble: 6 × 25
11000…¹ 11000…² 11000…³ 11000…⁴ 11000…⁵ 10500…⁶ 10500…⁷ 10500…⁸ 10500…⁹ 10500…˟ 10000…˟ 10000…˟ 10000…˟ 10000…˟ 10000…˟ 95000…˟ 95000…˟ 95000…˟ 95000…˟ 95000…˟ 90000…˟ 90000…˟
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.1 e6 -1.1 e6 -1.1 e6 -1.1 e6 -1.1 e6 -1.05e6 -1.05e6 -1.05e6 -1.05e6 -1.05e6 -1 e6 -1 e6 -1 e6 -1 e6 -1 e6 -9.5 e5 -9.5 e5 -9.5 e5 -9.5 e5 -9.5 e5 -9 e5 -9 e5
2 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4 8 e4
3 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4 8.34e4
4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4 8.69e4
5 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4 9.06e4
6 1.21e6 1.26e6 1.32e6 1.39e6 1.46e6 1.21e6 1.26e6 1.32e6 1.39e6 1.46e6 1.21e6 1.26e6 1.32e6 1.39e6 1.46e6 1.21e6 1.26e6 1.32e6 1.39e6 1.46e6 1.21e6 1.26e6
# … with 3 more variables: `900000-0.08` <dbl>, `900000-0.076` <dbl>, `900000-0.072` <dbl>, and abbreviated variable names ¹`1100000-0.088`, ²`1100000-0.084`, ³`1100000-0.08`,
# ⁴`1100000-0.076`, ⁵`1100000-0.072`, ⁶`1050000-0.088`, ⁷`1050000-0.084`, ⁸`1050000-0.08`, ⁹`1050000-0.076`, ˟`1050000-0.072`, ˟`1000000-0.088`, ˟`1000000-0.084`,
# ˟`1000000-0.08`, ˟`1000000-0.076`, ˟`1000000-0.072`, ˟`950000-0.088`, ˟`950000-0.084`, ˟`950000-0.08`, ˟`950000-0.076`, ˟`950000-0.072`, ˟`900000-0.088`, ˟`900000-0.084`
The column names of the second table correspond to a paste of the colnames and rownames of the first table. i.e. the column: 1100000-0.088 corresponds to the first row and first column in table1 with a value of 0.0812. Another example is, 950000-0.076 which corresponds to row 4 column 5 in table1 - or under the column 950000 and row 0.076.
I want to create a "filter" or "select" based on a square in table 1. That is, if the user (in a Shiny App) clicks on the number under 950000 and across from column 0.076 I want to select in Table 2 just this column 950000-0.076 - or ... %>% select(c("950000-0.076")). Which would return the following result:
# A tibble: 6 × 1
`950000-0.076`
<dbl>
1 -950000
2 80000
3 83400
4 86922
5 90570.
6 1387260.
How can I make a table "clickable" and reactive in Shiny?
Data:
table1 = structure(list(capRates = c(0.088, 0.084, 0.08, 0.076, 0.072),
`1100000` = c(0.0812, 0.0892, 0.0977, 0.1068, 0.1166), `1050000` = c(0.0928,
0.1009, 0.1095, 0.1187, 0.1285), `1000000` = c(0.1052, 0.1134,
0.122, 0.1313, 0.1412), `950000` = c(0.1185, 0.1267, 0.1354,
0.1447, 0.1547), `900000` = c(0.1327, 0.1409, 0.1497, 0.1591,
0.1692)), row.names = c(NA, -5L), class = "data.frame")
I also have the following table:
table2 = structure(list(`1100000-0.088` = c(-1100000, 80000, 83400, 86922,
90570.06, 1210953.51814091), `1100000-0.084` = c(-1100000, 80000,
83400, 86922, 90570.06, 1264125.19187143), `1100000-0.08` = c(-1100000,
80000, 83400, 86922, 90570.06, 1322614.032975), `1100000-0.076` = c(-1100000,
80000, 83400, 86922, 90570.06, 1387259.59419474), `1100000-0.072` = c(-1100000,
80000, 83400, 86922, 90570.06, 1459087.99555), `1050000-0.088` = c(-1050000,
80000, 83400, 86922, 90570.06, 1210953.51814091), `1050000-0.084` = c(-1050000,
80000, 83400, 86922, 90570.06, 1264125.19187143), `1050000-0.08` = c(-1050000,
80000, 83400, 86922, 90570.06, 1322614.032975), `1050000-0.076` = c(-1050000,
80000, 83400, 86922, 90570.06, 1387259.59419474), `1050000-0.072` = c(-1050000,
80000, 83400, 86922, 90570.06, 1459087.99555), `1000000-0.088` = c(-1000000,
80000, 83400, 86922, 90570.06, 1210953.51814091), `1000000-0.084` = c(-1000000,
80000, 83400, 86922, 90570.06, 1264125.19187143), `1000000-0.08` = c(-1000000,
80000, 83400, 86922, 90570.06, 1322614.032975), `1000000-0.076` = c(-1000000,
80000, 83400, 86922, 90570.06, 1387259.59419474), `1000000-0.072` = c(-1000000,
80000, 83400, 86922, 90570.06, 1459087.99555), `950000-0.088` = c(-950000,
80000, 83400, 86922, 90570.06, 1210953.51814091), `950000-0.084` = c(-950000,
80000, 83400, 86922, 90570.06, 1264125.19187143), `950000-0.08` = c(-950000,
80000, 83400, 86922, 90570.06, 1322614.032975), `950000-0.076` = c(-950000,
80000, 83400, 86922, 90570.06, 1387259.59419474), `950000-0.072` = c(-950000,
80000, 83400, 86922, 90570.06, 1459087.99555), `900000-0.088` = c(-900000,
80000, 83400, 86922, 90570.06, 1210953.51814091), `900000-0.084` = c(-900000,
80000, 83400, 86922, 90570.06, 1264125.19187143), `900000-0.08` = c(-900000,
80000, 83400, 86922, 90570.06, 1322614.032975), `900000-0.076` = c(-900000,
80000, 83400, 86922, 90570.06, 1387259.59419474), `900000-0.072` = c(-900000,
80000, 83400, 86922, 90570.06, 1459087.99555)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -6L))
Shiny App code:
library(shiny)
library(tidyverse)
library(reactable)
ui <- fluidPage(
titlePanel("Old Faithful Geyser Data"),
navbarPage("United",
theme = shinythemes::shinytheme("cyborg"),
tabPanel("IRR",
fluidRow(
column(6,
tags$h3("Table results"),
# tableOutput("TABLE1"),
reactableOutput("TABLE1")
),
column(6,
tableOutput("TABLE2")
)
)
)
)
)
server <- function(input, output) {
output$TABLE1 = renderReactable({
reactable(table1, selection = "multiple", onClick = "select")
})
# output$TABLE1 = renderTable(
# table1
# )
# TABLE2 = reactive(
# table2 %>%
# select(input$...)
# )
}
shinyApp(ui = ui, server = server)
CodePudding user response：
I'd suggest adding a JavaScript function for onClick in order to get the column name and capRate value for the clicked cell as an input. So, table1's reactable would look like this:
reactable(table1,
onClick = JS("
function(rowInfo, colInfo) {
Shiny.setInputValue('cell_data', colInfo.id '-' rowInfo.row.capRates, { priority: 'event' })
}")
)
Practically, with colInfo.id we get the column name and with rowInfo.row.capRates the capRates value for the selected cell.
Then, table2 can be filtered by the value we passed on cell_data with the JS function.
output$TABLE2 = renderReactable({
req(input$cell_data)
reactable(table2 %>% select(sym(input$cell_data)))
})
So, the app altogether would look like this:
library(shiny)
library(tidyverse)
library(reactable)
ui <- fluidPage(
titlePanel("Old Faithful Geyser Data"),
navbarPage("United",
tabPanel("IRR",
fluidRow(
column(6,
tags$h3("Table results"),
reactableOutput("TABLE1")
),
column(6,
reactableOutput("TABLE2")
)
)
)
)
)
server <- function(input, output) {
output$TABLE1 = renderReactable({
reactable(table1,
onClick = JS("
function(rowInfo, colInfo) {
Shiny.setInputValue('cell_data', colInfo.id '-' rowInfo.row.capRates, { priority: 'event' })
}
")
)
})
output$TABLE2 = renderReactable({
req(input$cell_data)
reactable(table2 %>% select(sym(input$cell_data)))
})
}
shinyApp(ui = ui, server = server)