Prevalence of depression by age in D3 Scatter Plot
This scatter plot visualizes the prevalence of depression among people aged 20–24 for a subset of countries, with each year on the x-axis and the percentage of affected individuals on the y-axis. Ten countries are distinguished by unique colors, such as Afghanistan in red and Albania in black. The visualization uses D3 v6 with SVG rendering, employing scales like `scaleLinear` for axes and circle marks to represent data points. Data is loaded from a CSV file on GitHub Gist using `d3.csv`, with axis labels and a title added programmatically.
AI-generated descriptionPrevalence of depression by age in D3 Scatter Plot in D3 Scatter Plot
The X-axis represents the year, and the y-axis represents the prevalence of depression from 20 to 24 years old for each country. I used a different color to describe each country, such as Albania is black color and American Samoa is gold color.
This question one can get from the data viz.
which year is the prevalence of depression age (20 to 24 years old). We are more concerned about young people suicide rates in this question.
Original data set source is here
Original blog is here
Some informative youtube videos about depression 1 is here
Some informative youtube videos about depression 2 is here
This dataset contain :
1)prevalence_by_mental_and_substa
2)depression_by_level_of_educatio
3)prevalence_of_depression_by_age *
4)prevalence_of_depression_males
5)suicide_rates_vs_prevalence_of
6)number_with_depression_by_count
This file is prevalence_of_depression_by_age , and it's contain the following :
1)entity is categorical attributes 2)code is categorical attributes 3)year is categorical attributes 4)20_24_years_old is quantitative 5)10_14_years_old is quantitative 6)all_ages is quantitative 7)70_years_old is quantitative 8)30_34_years_old is quantitative 9)15_19_years_old is quantitative 10)25_29_years_old is quantitative 11)50_69_years_old is quantitative 12)age_standardized is quantitative 13)15_49_years_old is quantitative
I'm intrestin in categorical attributes. I remove the rows with missing values
==========================
The orginal dataset has some issues in thus type of visulazation , so I had to take subset of the orignal .Modification version of the dataset: This is a small subset of the dataset on gist
============================
- X-axis: Year
- Y-axis: 20 to 24 years old % who have depression
- Color: country code as follow :AFG is red, ALB is black, DZA is slateblue, ASM is gold, AND is darkgreen, ATG is green, AGO is blue, ARM is brown, AUS is darkgreen, AUT is blue. These country code shows the intresect between year and the % of people who have depression from 20 to 24 years old.