Scatterplot Matrix in R markdown

[user124578]

Is there a way to produce high-quality scatterplot matric in R markdown. I would like to be able to understand the density of the plot more. Perhaps something like resizing.

It seems okay outside of the R markdown.

pairs(airpollution)

[mattwarkentin]

Hi @user124578,

Personally, I recommend using ggplot2 for your graphing purposes. Another package named ggforce has provided a extra ggplot2 functionality that does precisely what you are looking for (and a whole lot more). Try this out and let me know what you think:

library(ggplot2)
library(ggforce)

airquality %>% 
  ggplot() +
  geom_point(aes(x = .panel_x, y = .panel_y)) +
  facet_matrix(vars(everything()))

Basically you can replace everything() with the specific variables you want to plot if you do not want all of them plotted at once.

[user124578]

@mattwarkentin

ggforce

Thanks for this. I am still having the issue where the plot isn't very clear when i am knitting as PDF. It doesn't really show the specfic details for each facet. It's very hard to compare as reader. if i run the code in the R console it fine and I am able to get insights of the data. I think it something to do with r markdown on how it render the file.

[mattwarkentin]

Ah I understand, try playing with some of the chunk options for figure resolution, like this:

```{r dpi=500}
airquality %>% 
  ggplot() +
  geom_point(aes(x = .panel_x, y = .panel_y)) +
  facet_matrix(vars(everything()))
```

[user124578]

Unfortunately, this didn't work. I think i might go down the root of adding as an image to the R Markdown file.

[andresrcs]

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue so we can take a look for ourselves? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…