Plots & Graphs

Generating plots in R is supported through Storage file uploads. To upload a plot to Storage, save the file in the output directory for files (out/files/). Each file in that directory will be automatically saved into Storage File Uploads.

To make file handling a bit easier, it is possible to write a manifest, which describes the file. This can be used to set file tags and other file upload options. To write the manifest, use the app$writeFileManifest function with the following signature:

app$writeFileManifest = function(fileName, fileTags = vector(), isPublic = FALSE, isPermanent = TRUE, notify = FALSE)

Remember to use the out/files/ directory for plots. And note that the rDocker tag will be automatically added to all files with manifests.

In the following examples, use the sample graph-source.csv data file. Create a new bucket in Storage and upload the table to it.

Screenshot - Upload table

Example 1 – Output of a single file

Create a new R transformation, and add the graph-source table in the input mapping. There is no output mapping.

Screenshot - Configure transformations

Then use the following R script in the transformation and run it.

data <- read.csv("/data/in/tables/graph-source.csv")

model <- lm(formula = time_spent_in_shop ~ customer_age + I(customer_age^2), data = data)
png("/data/out/files/graph-2x2.png", width = 600, height = 600)
# plot graphs to 2x2 grid

oldPar <- par(mfrow = c(2,2))
plot(model)
# write to file

dev.off()
par(oldPar)

app$writeFileManifest("/data/out/files/graph-2x2.png", c("regression", "all-in-one"))

Once the transformation finishes, a file will be added to File uploads:

Screenshot - File Uploads

Because the lm.plot function produces multiple plots, the above example plots all four graphs to a single image using a 2x2 grid.

Result linear model plots

Example 2 – Output of multiple files

Use the following script the same way as in Example 1. The only difference is that this script produces multiple files.

data <- read.csv("/data/in/tables/graph-source.csv")

model <- lm(formula = time_spent_in_shop ~ customer_age + I(customer_age^2), data = data)
png("/data/out/files/graph-%d.png", width = 800, height = 800, res=120)
plot(model)
dev.off()

app$writeFileManifest("/data/out/files/graph-1.png", c("regression", "residuals vs. fitted"))
app$writeFileManifest("/data/out/files/graph-2.png", c("regression", "QQ plot"))
app$writeFileManifest("/data/out/files/graph-3.png", c("regression", "scale-location"))
app$writeFileManifest("/data/out/files/graph-4.png", c("regression", "residuals vs. leverage"))

If a plot function produces multiple graphs, like the lm.plot in the above example, use %d in the file name in order to generate multiple files. A manifest is written for each file individually.

Example 3 – Using ggplot

If you want to use the ggplot package, use a different function for saving a file in your script. The rest remains the same as in the previous examples.

library(ggplot2)
data <- read.csv("/data/in/tables/graph-source.csv")

plot <- ggplot(data, aes(y = time_spent_in_shop, x = customer_age)) +
    geom_point(shape = 1) +
    stat_smooth(method = "lm", formula = y ~ x + I(x^2), size = 1)
ggsave(filename = "/data/out/files/graph2.png", width = 10, height = 10, units = "cm")

app$writeFileManifest("/data/out/files/graph2.png", c("regression", "ggplot"))

Do not forget to list ggplot2 in the packages section of the transformation.