Install packages

First up is installing the R packages:

```{r}
# Install packages if not already installed
# install.packages(c("tidyverse", "reticulate"))
library(tidyverse)
library(reticulate)
```

In your project, create a folder called “image”. Save the image you would like to convert to a dataset in that folder. To recreate this paper’s dataset, go to this page and download the cartoon gorilla.

The paper mentions the ‘getpixel’ function. With a bit of digging, we find that is from the package pillow (a fork from a package called PIL). Like in R, we need to install the package. In an R Markdown document, I run the functions from {reticulate} below.

```{r}
use_python("/usr/local/bin/python")
# If you haven't installed Python, the line below will prompt you!
py_install("pillow")
```

Since {reticulate} is the package that allows you to call Python, if you do not have Python installed, then you will get a message (which you would reply Y to):

No non-system installation of Python could be found.
Would you like to download and install Miniconda?
Miniconda is an open source environment management system for Python.
See https://docs.conda.io/en/latest/miniconda.html for more details.

Create coordinates

This Stack Overflow thread was very helpful to determine what we need to do with the ‘getpixel’ function in pillow. Again, like in R, we need to call the functions we need but this time, we are doing it in a python chunk.

```{python}
import numpy as np
from PIL import Image
```

Then, we point to the image and create coordinates pixels for the outline of the cartoon. We can set a threshold level for which pixels to keep/discard.

```{python}
# Thanks to Bart Huntley for pointing out a typo previously in this chunk!
im = Image.open("/cloud/project/image/gorilla.jpg")
pixels = np.asarray(im)

# Set threshold level
threshold_level = 50

# Find coordinates of all pixels below threshold
coords = np.column_stack(np.where(pixels < threshold_level))
```

Bring back into R

This results in a NumPy ndarray called coords that contains the coordinates of the pixels of the outline. That’s great but… it’s in Python! How do we bring it back into R?

The {reticulate} Cheat Sheet was very helpful in figuring this out. The section “Python in R Markdown” shows that you can use the py object to access objects created in Python chunks from R chunks.

Knowing that, we can create an R object from coords using py. Next, we follow the steps outlined in the paper for the data preparation.

```{r}
coords <-
  as.data.frame(py$coords) %>%
  sample_n(1768) %>%
  mutate(bmi = V2 * 17 + 15,
         steps = 15000 - V1 * 15000/max(V1)) %>%
  mutate(randvar = rnorm(n(), mean = 0, sd = 10),
         randi = steps * (1 + randvar),
         gender = case_when(randi < median(steps) ~ "Female",
                            TRUE ~ "Male"))
```

If we’d like to see the distribution of data by male/female, we can use count().

```{r}
coords %>%
  count(gender)
```

Create plots

Now for the fun part - visualizing the plots (spoiler: one should always do this before starting an analysis)!

```{r}
coords %>%
  ggplot(aes(x = bmi,
              y = steps)) +
  geom_point() +
  theme_void() +
  xlim(0, 15000)
```

```{r}
coords %>%
  ggplot(aes(x = bmi,
             y = steps,
             color = gender)) +
  geom_point() +
  theme_void() +
  xlim(0, 15000)
```

With that, we’ve seamlessly gone from Python to R and created a dataset leveraging the power of both languages. Thanks to Professors Yanai and Lerche for their publication!