gofigR is the R client for https://gofigr.io, a zero-effort reproducibility engine. It works with any R library which outputs to R graphics devices, with automatic publishing for ggplot.
gofigR integrates with R markdown, both in knitr and in interactive sessions in RStudio. GoFigr also works in scripts. We tested with R 4.3.2 but any reasonably recent version should work.
GoFigr will automatically publish all ggplot output assuming you call gofigR::enable(auto_publish=TRUE). GoFigr will not publish old-style R plots unless you tell it to. See the "Usage" section below.
library(devtools)
devtools::install_github("gofigr/gofigR")
On the R prompt, simply load the gofigR package and call gfconfig(). You only need to do this once.
If you don't have an account, you can register at https://app.gofigr.io/register.
> library(gofigR)
Attaching package: ‘gofigR’
> gfconfig()
-------------------------------------------------------------------
Welcome to GoFigr! This wizard will help you get up and running.
-------------------------------------------------------------------
Username: publicdemo
Testing connection...
=> Success
API key (leave blank to generate a new one):
Key name (e.g. Alyssa's laptop): my API key
Fetching workspaces...
1. Scratchpad - e5249bed-40f0-4336-9bd3-fef30d3ed10d
Please select a default workspace (1-1): 1
Configuration saved to /Users/maciej/.gofigr. Happy analysis!
To enable GoFigr, simply call enable in your setup chunk. You can also optionally specify an analysis_name (it will be created automatically if it doesn't exist).
```{r setup, include=FALSE}
library(gofigR)
gofigR::enable(auto_publish=TRUE)
```
auto_publish is FALSE by default. Set it to TRUE to override plot and print and publish figures automatically.
If auto_publish is on, GoFigr will intercept all calls to plot and print and publish the results if they are from a compatible library. At the moment, we support any graphics format also supported by ggplotify:
ggplot2
ComplexHeatmap
cowplot
patchwork
lattice
To capture output manually, simply call publish:
hm1 <- Heatmap(matrix(rnorm(100), nrow=10, ncol=10)) publish(hm1, "Heatmaps are cool!") # second argument is the figure name
To capture output from base R plotting, call publish_base with a plotting expression:
gofigR::publish_base({
base::plot(pressure, main="Pressure vs temperature")
text(200, 50, "Note the non-linear relationship")
}, data=pressure, figure_name="Pressure vs temperature")
gofigR::publish_base({
# The mtcars dataset:
data <- as.matrix(mtcars)
coul <- colorRampPalette(brewer.pal(8, "PiYG"))(25)
heatmap(data, scale="column", col = coul, main="Visualizing mtcars")
}, data=mtcars, figure_name="Cars")
Note the optional data argument following the expression. It specifies the data which you want to associate with the figure -- it will show up under "files" (as .RDS) once published.
You can replace plotOutput + renderPlot with gfPlot + gfPlotServer and give your users the ability to publish interactive plots to GoFigr. For example:
library(shiny)
library(gofigR)
gofigR::enable()
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("Old Faithful Geyser Data"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),
# Show a plot of the generated distribution
mainPanel(
gfPlot("distPlot"),
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
gfPlotServer("distPlot", {
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white',
xlab = 'Waiting time to next eruption (in mins)',
main = 'Histogram of waiting times')
}, input, figure_name="Old faithful waiting times")
}
# Run the application
shinyApp(ui = ui, server = server)
Note that we pass input to gfPlotServer. This will capture your current Shiny inputs – they will become available under the "Metadata" tab in GoFigr.
We support knitr as well as interactive sessions in RStudio.
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