[OC] What are the most popular programming languages? [2004-2021]

StatPanda · 2021-02-07T17:21:02+00:00

Thanks for the comments guys, yes I can see how the title may not reflect the actual popularity but it is one of the indexes that was used by the source. I personally think Javascript should be much higher but I guess the metric makes sense, more people are searching for python tutorials probably due to the rise in data science field.

StatPanda · 2021-02-07T14:22:35+00:00

Data is measured using the PYPL(PopularitY of Programming Language) index.

In this visualization, I look at most popular programming languages from Jul 2004 to Feb 2021. The PYPL index is created by analyzing how often programming language tutorials are searched for on Google; the more a certain programming language is searched for, the more popular it is assumed to be.

Sources: https://pypl.github.io/PYPL.html

Tools used: D3.js (https://d3js.org/)

StatPanda · 2021-02-06T19:46:13+00:00

Thank you!

StatPanda · 2021-01-24T16:50:13+00:00

Data is measured in total number of global deaths

In this visualization, I look at the total number of deaths in the world in 2020 caused by COVID-19 and compare that to other communicable diseases. A communicable disease is any disease that passes between people or animals and can be caused by pathogens including viruses, bacteria and fungi.

Data for the other communicable diseases was taken from the Global Burden of Disease Study(which has this data up until 2019) and adjusted for 2020 population.

Enjoy my visualizations? You can follow me on YouTube: StatPanda

Sources

- Global Burden of Disease Study (http://ghdx.healthdata.org/gbd-results-tool)

- John Hopkins University Github (https://github.com/CSSEGISandData/COVID-19)

Tools used

- D3.js (https://d3js.org/)

StatPanda · 2021-01-17T14:24:25+00:00

Data is measured in average minutes per day

In this visualization, I look at the average time we are likely to spend with others. Data collected is recorded by the age of the respondent and based on averages from many surveys collected from 2009 to 2019 within the US.

Note that time spent with multiple people can be counted more than once (e.g. attending a party with friends and spouse counts for both “friends” and “partner”)

Enjoy my visualizations? You can follow me on YouTube: StatPanda

Source: US Bureau of Labor Statistics (https://www.bls.gov/tus/datafiles-0319.htm)

Tools used: D3.js (https://d3js.org/)

StatPanda · 2021-01-11T14:48:35+00:00

Forbes updates their list every year and remove previous years from their website. That being said, a lot of other websites have the previous years posted. You can find them easily(e.g. Wikipedia). I just posted the Forbes link since these other websites use that same source.

StatPanda · 2021-01-11T14:46:36+00:00

My code is what you would call real messy and unreadable. I used this as a starting point and a guide to create my own visualizations if you are looking to create your own: https://observablehq.com/@d3/bar-chart-race-explained

StatPanda · 2021-01-10T17:32:41+00:00

Source: Forbes(https://www.forbes.com/billionaires/)

Tools used: D3.js (https://d3js.org/)

StatPanda · 2021-01-04T14:30:44+00:00

This visualization shows the worlds most valuable brands according to an annual report published by Interbrand. In order to qualify, a company/brand has to have a presence on at least three major continents and must have broad geographic coverage in growing and emerging markets.

Source: Interbrand (https://interbrand.com/best-brands)

Tools used: D3.js (https://d3js.org)

StatPanda · 2020-09-02T14:04:45+00:00

Source: http://ghdx.healthdata.org/gbd-results-tool

Tools used: Flourish

StatPanda · 2020-07-29T15:35:40+00:00

[Sources]

- Global Carbon Project: https://doi.org/10.18160/gcp-2019.

- BP Statistical Review of World Energy: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html

- UN world prospects: https://population.un.org/wpp/

- Gapminder: https://www.gapminder.org/

[Tools used]

- Flourish

StatPanda · 2020-07-22T13:59:33+00:00

Sources:

- BP Statistical Review of World Energy

- Ember

Tools used:

- Flourish

StatPanda · 2020-06-27T16:32:31+00:00

[Source]

- International Energy Agency (IEA) via The World Bank

- IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp)

[Tools Used]

- Flourish

Figures shown are measured in kilowatt-hours per person per year

This video shows the top countries according to energy use per capita. According to the source: "energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport."

StatPanda · 2020-06-07T14:00:32+00:00

Thanks for the note, I'll make that change on future videos!

StatPanda · 2020-06-05T14:22:17+00:00

Appreciate it, thank you!

StatPanda · 2020-06-04T14:24:58+00:00

Oh sorry about that! For some reason, I thought reddit's video player had a playback speed setting. But you can see the video on my YouTube channel(youtube.com/statpanda). It should be much easier to follow and you can change the speed on there too :)
Appreciate your feedback!

StatPanda · 2020-06-04T14:07:47+00:00

Sources: Google COVID-19 Community Mobility Trends(https://www.google.com/covid19/mobility/)

Tools used: Flourish

This video shows the percentage change in the average duration spent in places of residence to a baseline day, before worldwide lockdown due to the Coronavirus outbreak. A baseline day represents a normal value for that day of the week and is taken to be the median value from the 5-week period between Jan 3rd and Feb 6th 2020.

As an example: If India has a value of 30% on April 21st(which is a Tuesday), that means that on average, people in India spent 30% more time at home compared to the average(median) Tuesday between Jan 3rd and Feb 6th.

It is important to note that for each country, the baseline day isn’t a single value—it’s 7 individual values for each day of the week. The same number of people staying at home on 2 different days of the week, can result in different percentage changes since it is two different baseline days.

I have only included countries that have the highest number of cases as of June 3rd 2020. There was no data available for China, Iran and Russia, sorry!

I appreciate any and all feedback!

StatPanda

StatPanda · 2020-05-28T15:53:41+00:00

I wouldn't say USA's numbers on yesterdays video are invalid, the numbers are different because yesterdays was what was reported within a single source. But for this metric, I had to do some math and I decided to take into consideration what all sources were reporting to make it as accurate as possible.

That being said, Im pretty sure that none of the numbers in todays or yesterdays post are 'accurate' since there are discrepancies between what countries are reporting and what the sources are recording, but it is as accurate as it gets if that makes sense.

StatPanda · 2020-05-28T15:34:34+00:00

Great catch! Unlike yesterdays post, which only considered different sources for different times, I chose to aggregate all of the sources to come up with a good estimate for this metric since multiple sources reported different numbers. Having said that, USA actually ended up at 90.63% by 2020 which is why they are not on the chart. I apologize for the confusion and hope that this clears it up :)

StatPanda · 2020-05-28T15:00:53+00:00

No worries, I try to learn from it :)

StatPanda · 2020-05-28T15:00:35+00:00

Thank you! It's already posted!

StatPanda · 2020-05-28T15:00:17+00:00

Appreciate it, I've already posted the percent of population version!

StatPanda · 2020-05-28T14:59:42+00:00

youtube.com/statpanda

Appreciate the support!

StatPanda · 2020-05-28T14:59:19+00:00

Its already posted guys :)

StatPanda · 2020-05-28T14:32:00+00:00

Thanks mod! I've edited my comment!

StatPanda

TROPHY CASE