I am creating a Flexdashboard in R. I want the dashboard to contains both a table and a series of visualizations, that would be filtered through inputs.
As I need to deliver a dashboard locally (without a server running in the background), I am unable to use Shiny, hence I rely on crosstalk.
I know that the crosstalk package provides limited functionality in the front-end. For instance, the documentation says that you can't aggregate the SharedData object.
Nonetheless, I am not clear if I can use the same inputs to filter two different dataframes.
For example, lets say I have:
Dataframe One: Contains original data
df1 <- structure(list(owner = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("John",
"Mark"), class = "factor"), hp = c(250, 120, 250, 100, 110),
car = structure(c(2L, 2L, 2L, 1L, 1L), .Label = c("benz",
"bmw"), class = "factor"), id = structure(1:5, .Label = c("car1",
"car2", "car3", "car4", "car5"), class = "factor")), .Names = c("owner",
"hp", "car", "id"), row.names = c(NA, -5L), class = "data.frame")
Dataframe Two: Contains aggregated data
df2 <- structure(list(car = structure(c(1L, 2L, 1L, 2L), .Label = c("benz",
+ "bmw"), class = "factor"), owner = structure(c(1L, 1L, 2L, 2L
+ ), .Label = c("John", "Mark"), class = "factor"), freq = c(0L,
+ 1L, 2L, 2L)), .Names = c("car", "owner", "freq"), row.names = c(NA,
+ -4L), class = "data.frame")
These two dataframes contain columns with identical values - car and owner. As well as, additional columns too.
I could create two different objects:
library(crosstalk)
shared_df1 <- SharedData$new(df1)
shared_df2 <- SharedData$new(df2)
and than:
filter_select("owner", "Car owner:", shared_df1, ~ owner)
filter_select("owner", "Car owner:", shared_df2, ~ owner)
However, that would mean that the user will need to fill inputs that are essentially identical, twice. Also, if the table is large, this would double the size of the memory needed to use the dashboard.
Is it possible to work around this problem in crosstalk?
Ah I recently ran into this too, there is another argument to SharedData$new(..., group = )! The group argument seems to do the trick. I found out by accident when I had two dataframes and used the group =.
If you make a sharedData object, it will include
What I think happens is that crosstalk filters the sharedData by the key - for all sharedData objects in the same group! So as long as two dataframes use the same key, you should be able to filter them together in one group.
This should work for your example.
---
title: "blabla"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
theme: cerulean
---
```{r}
library(plotly)
library(crosstalk)
library(tidyverse)
```
```{r Make dataset}
df1 <- structure(list(owner = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("John", "Mark"), class = "factor"), hp = c(250, 120, 250, 100, 110), car = structure(c(2L, 2L, 2L, 1L, 1L), .Label = c("benz", "bmw"), class = "factor"), id = structure(1:5, .Label = c("car1", "car2", "car3", "car4", "car5"), class = "factor")), .Names = c("owner", "hp", "car", "id"), row.names = c(NA, -5L), class = "data.frame")
df2 <- structure(list(car = structure(c(1L, 2L, 1L, 2L), .Label = c("benz",
"bmw"), class = "factor"), owner = structure(c(1L, 1L, 2L, 2L
), .Label = c("John", "Mark"), class = "factor"), freq = c(0L,
1L, 2L, 2L)), .Names = c("car", "owner", "freq"), row.names = c(NA,
-4L), class = "data.frame")
```
#
##
### Filters
```{r}
library(crosstalk)
# Notice the 'group = ' argument - this does the trick!
shared_df1 <- SharedData$new(df1, ~owner, group = "Choose owner")
shared_df2 <- SharedData$new(df2, ~owner, group = "Choose owner")
filter_select("owner", "Car owner:", shared_df1, ~owner)
# You don't need this second filter now
# filter_select("owner", "Car owner:", shared_df2, ~ owner)
```
### Plot1 with plotly
```{r}
plot_ly(shared_df1, x = ~id, y = ~hp, color = ~owner) %>% add_markers() %>% highlight("plotly_click")
```
### Plots with plotly
```{r}
plot_ly(shared_df2, x = ~owner, y = ~freq, color = ~car) %>% group_by(owner) %>% add_bars()
```
##
### Dataframe 1
```{r}
DT::datatable(shared_df1)
```
### Dataframe 2
```{r}
DT::datatable(shared_df2)
```
I spent some time on this by trying to extract data from plot_ly() using plotly_data() without luck until I figured out the answer. That's why there's some very simple plots with plotly.