I'm building a dynamic flexdashboard with plotly and I was wondering if there was a way to dynamically resize my dashboard. For example, I have created plots of subjects being tested over time. When I shrink the page down, what I'd like is for it to dynamically adjust to a time-series plot of the average for the group at each test day.
My data looks like this:
library(flexdashboard)
library(knitr)
library(tidyverse)
library(plotly)
subject <- rep(c("A", "B", "C"), each = 8)
testDay <- rep(1:8, times = 3)
variable1 <- rnorm(n = length(subject), mean = 30, sd = 10)
variable2 <- rnorm(n = length(subject), mean = 15, sd = 3)
df <- data.frame(subject, testDay, variable1, variable2)
subject testDay variable1 variable2
1 A 1 21.816831 8.575000
2 A 2 14.947327 17.387903
3 A 3 18.014435 16.734653
4 A 4 33.100524 11.381793
5 A 5 37.105911 13.862776
6 A 6 32.181317 10.722458
7 A 7 41.107293 9.176348
8 A 8 36.674051 17.114815
9 B 1 33.710838 17.508234
10 B 2 23.788428 13.903532
11 B 3 42.846120 17.032208
12 B 4 9.785957 15.275293
13 B 5 32.551619 21.172497
14 B 6 36.912465 18.694263
15 B 7 40.061797 13.759541
16 B 8 41.094825 15.472144
17 C 1 27.663408 17.949291
18 C 2 31.263966 11.546486
19 C 3 39.734050 19.831854
20 C 4 25.461309 19.239821
21 C 5 22.128139 10.837672
22 C 6 31.234339 16.976004
23 C 7 46.273664 19.255745
24 C 8 27.057218 21.086204
My plotly code looks like this (a graph of each subject over time):
Dynamic Chart
===========================
Row
-----------------------------------------------------------------------
```{r}
p1 <- df %>%
ggplot(aes(x = as.factor(testDay), y = variable1, color = subject, group = 1)) +
geom_line() +
theme_bw() +
ggtitle("Variable 1")
ggplotly(p1)
```
```{r}
p2 <- df %>%
ggplot(aes(x = as.factor(testDay), y = variable2, color = subject, group = 1)) +
geom_line() +
theme_bw() +
ggtitle("Variable 2")
ggplotly(p2)
```
Is there a way that when I shrink the website down these plots can dynamically change to a group average plot, like this:
p1_avg <- df %>%
ggplot(aes(x = as.factor(testDay), y = variable1, group = 1)) +
stat_summary(fun.y = "mean", geom = "line") +
theme_bw() +
ggtitle("Variable 1 Avg")
ggplotly(p1_avg)
p2_avg <- df %>%
ggplot(aes(x = as.factor(testDay), y = variable2, group = 1)) +
stat_summary(fun.y = "mean", geom = "line") +
theme_bw() +
ggtitle("Variable 2 Avg")
ggplotly(p2_avg)
You can put your plotly object inside the plotly function renderPlotly() for dynamically resizing to the page. See an example how I used the function in this blog post: https://medium.com/analytics-vidhya/shiny-dashboards-with-flexdashboard-e66aaafac1f2