AnupJoseph

<script>
  import { json } from "d3";

  import Marks from "./Marks.svelte";

  let dataset = [];
  json(
    "https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/world.geojson"
  ).then((data) => {
    dataset = data.features;

AnupJoseph

<script>
  import { select, selectAll } from "d3-selection";
  import { axisBottom, axisLeft } from "d3-axis";
  import { timeFormat } from "d3-time-format";

  export let innerHeight;
  export let margin;
  export let position;
  export let scale;

AnupJoseph

Gadfly.with_theme(:dark) do
	set_default_plot_size(30cm ,15cm)
	size_col,income_col = "What is the size of the company where you are employed?","What is your current yearly compensation (approximate \$USD)?"
	size_df = select(dataset,[size_col,income_col])

	filter!(size_col => x->!ismissing(x),size_df)
	filter!(income_col => x->!ismissing(x),size_df)

	
	size_df[!,:median_income] = build_median_income.(size_df[!,income_col])

AnupJoseph

Gadfly.with_theme(:dark) do
	income_col,exp_col = 
	"What is your current yearly compensation (approximate \$USD)?","For how many years have you been writing code and/or programming?"
	income_subset = select(dataset,[income_col,exp_col])

	# Clean out all the missing entries as they are way too hard to deal here
	filter!(exp_col => x->!ismissing(x),income_subset)
	filter!(income_col => x->!ismissing(x),income_subset)
	income_exp_counts = combine(
		groupby(income_subset

AnupJoseph

function build_median_income(x)
	x =strip(x,['>','\$','<'])
	if occursin("-",x)
		low,high = split(x,"-")
		low = parse(Int,replace(low,","=>""))
		high = parse(Int,replace(high,","=>""))
		
		return (low+high)/2
	else
		x = parse(Int,replace(x,","=>""))

AnupJoseph

Gadfly.with_theme(:dark) do
	programming_exp = "For how many years have you been writing code and/or programming?"
	experience_subset = select(dataset,programming_exp)

	# Filter missing variables and count the number of respondents per each group
	filter!(programming_exp => x->!ismissing(x),experience_subset)
	experience_counts = combine(
		groupby(experience_subset,programming_exp),
		nrow=>:num_counts
	)

AnupJoseph

Gadfly.with_theme(:dark) do

	# Subset the data with only rows I need
	gender,age = names(dataset)[3],names(dataset)[2]
	age_with_gender_data = dataset[!,2:3]

	# grouby age and gender and then count
	age_and_gender_counts = combine(
		groupby(
			age_with_gender_data,[gender,age])

AnupJoseph

Gadfly.with_theme(:dark) do
	age_counts = countmap(dataset[!,2])

	
	custom_order = ["18-21","22-24","25-29","30-34", "35-39","40-44","45-49","50-54" ,"55-59","60-69" , "70+"]                
	age_order = OrderedDict()

	# Using the above custom order to make a custom dictionary
	for item in custom_order
		age_order[item] = age_counts[item]

AnupJoseph

Gadfly.with_theme(:dark) do   # Dark theme cause dark theme looks great with pluto
	set_default_plot_size(25cm ,15cm)

	# Think if this as pandas value_counts function
	value_counts = sort(countmap(dataset[!,4]),byvalue=true)
	states = collect(keys(value_counts))


	# Going to use a dotplot for the visual
	# Using a log scale here as the graph's boring otherwise

AnupJoseph

import time
from collections import namedtuple

import gi
import re
import numpy
from enum import Enum
gi.require_version("Gst", "1.0")
gi.require_version("Tcam", "1.0")