facet_grid() causing crash - r

Question

I can not figure out what I'm missing. I keep crashing r or causing it to give very weird plots.
> head(vData)
vix.Close vstoxx vxfxi.Close Date
2011-03-16 29.40 35.2293 35.84 2011-03-16
2011-03-17 26.37 30.6133 31.77 2011-03-17
2011-03-18 24.44 28.5337 29.31 2011-03-18
2011-03-21 20.61 25.2355 25.95 2011-03-21
2011-03-22 20.21 24.3914 24.52 2011-03-22
2011-03-23 19.17 23.9226 24.03 2011-03-23
The below works:
p1.1<-ggplot(data = vData, aes(x = Date, y = vix.Close)) + geom_line(col= "red")
p1.1
p2<-p1.1 + geom_line(data = vData[!is.na(vData$vstoxx),], aes(x = Date, y = vstoxx), col="blue")
p2
p3<-p2 + geom_line(data = vData[!is.na(vData$vxfxi.Close),], aes(x = Date, y = vxfxi.Close), col="green")
p3
p4<-p3 + labs(title = "Volatility Indexes", x = "Time", y = "Index")
p4
But this is the part that is giving me trouble:
p5<- p4 + facet_grid(Date~., scales = Date)
p5

Answer 1

I echo what baptiste said: what is it you're trying to do? The code you've provided suggests that you're trying to create a separate line chart for each date in the dataset, which doesn't make much sense. For this demonstration, I'll show you how to facet the data by year to see the correlations between the different measurements of volatility over time. If you provide more detail as a comment, I'll revisit the code.
First let's take a look at what you've already done.
library(tidyverse)
library(gridExtra)
library(lubridate)
library(reshape2)
#Generate dummy data
vData <- tibble(
vix.Close = rnorm(1000, mean = 12, sd = 5),
vstoxx = rnorm(1000, mean = 12, sd = 5),
vxfxi.Close = rnorm(1000, mean = 12, sd = 5),
Date = as.Date(1:1000, origin = '2011-01-01')
)
# Generate individual plots per your question
p1.1 <-
ggplot(data = vData, aes(x = Date, y = vix.Close)) + geom_line(col = "red")
p1.1
p2 <-
p1.1 + geom_line(data = vData[!is.na(vData$vstoxx), ], aes(x = Date, y = vstoxx), col =
"blue")
p2
p3 <-
p2 + geom_line(data = vData[!is.na(vData$vxfxi.Close), ], aes(x = Date, y = vxfxi.Close), col =
"green")
p3
p4 <-
p3 + labs(title = "Volatility Indexes", x = "Time", y = "Index")
p4
You're creating four different plots and then layering them on top of each other. This approach works here, but it's cumbersome to make changes to each of the calls to ggplot or if you want to add/remove variables. Let's move your data to a "long" format and simplify the ggplot call.
# Melt the data into three columns and remove NAs
vData <- melt(vData, id = "Date") %>%
filter(!is.na(value)) %>%
tbl_df()
# Create one ggplot for all three indexes
ggplot(data = vData, aes(x = Date, y = value, color = variable)) +
geom_line() +
labs(title = "Volatility Indexes", x = "Time", y = "Index")
Now back to the big problem: you shouldn't be faceting by date because that would give you a huge number of tiny unreadable line charts. There are a number of other facets that might make sense. For example, you could look at the distribution of the three indexes by year.
ggplot(data = vData, aes(x = variable, y = value, color = variable)) +
geom_boxplot() +
labs(title = "Volatility Indexes", x = "", y = "") +
facet_grid(year(Date) ~ .)
So put some thought into what exactly you want to show.