Filter two tables with crosstalk

Question

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 de

Answer 1

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

• a dataframe
• a key to select rows by - preferably unique, but not necessarily.
• a group name

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.