Asking Questions of Kaggle survey with Julia

Eh, still think that "State of Kaggle" is a better title

Recently, Kaggle published their annual "Machine Learning and Data Science Survey" data. Their stated objective is to:

conduct an industry-wide survey that presents a truly comprehensive view of the state of data science and machine learning.

More likely, its representative of active Kaggle members with the time and inclination to fill out a fairly long survey. Still I think it would be fun to explore this data and see how the Kaggle community shapes up. Plus, Kaggle (still) does not support Julia in its kernels so maybe this post with push them down that path. It certainly won't but one can hope.
The survey consisted of 43 questions containing both multiple choice question with only one selection allowed and multiple selection questions. It generated almost 24,000 responses, with each line representing the choices of an individual respondent. The dataset is available under a Creative Commons licence here.

I am going to use the DataFrames.jl and Gadfly.jl libraries for most of the analysis and plots in this plot. Would have used Makie.jl but my laptop has a love-hate relationship with Makie. Also, Gadfly's defaults look prettier. Let's bring in those and read in the dataset as a dataframe:

using CSV
using DataFrames
using Gadfly
using StatsBase
using Colors
using DataStructures
using ColorSchemes
dataset = CSV.read("kaggle_survey_2022_responses.csv", DataFrame, header=2)
# The first row is just the list of Questions from Q1 to Q47 and not particularly interesting

Let's quickly gain some idea of the shape and composition of the dataset.

size(dataset)

Interesting the dataset has 296 columns for 47 responses. The survey details say that , Responses to multiple selection questions
(multiple choices can be selected) were split into multiple columns (with one column per answer choice). Likely this is what caused those extra columns to appear in the dataset:

Demographic Information

We will first look at the number of survey responsdents per country. I think I know which country will finish at top, but we'll see.

Next let's check out the distribution of age of the survey respondents.

The trends in this graph accurately capture my own experience, i.e. my Data Science and AI teachers were overwhelmingly men, while my own colleagues and peers aren't.

While we are on the topic of experience and age groups lets take a look at the experience levels of the repondents. Interestingly there are two variables we can look at while checking experience. We have For how many years have you been writing code and/or programming? and For how many years have you used machine learning models? I am going to take in For how many years have you been writing code and/or programming? and not just specific ML experience as that question is a bit too specific to me, e.g. would a scientist who has been using Data Science tools for some-time, not fall under the 2nd question because they have never used a ML model? I don't know. Also, the next variable we are going to look at income will more strongly correlate with years of experience rather than just ML experience, I think.

We need some preprocessing so ensure that we can actually sort the incomes and ages. There's mulitple ways we could go about it, but my plan is to simply create a median value for both income and age columns.

Yes, I know, I know these functions don't build an actual median but its the intention which is the point.

I think a heatmap is a good choice here since we need to count a lot of categories. We can use the rectangular binning from Gafly, I think.