World Census

Posted on Aug 30, 2016

In Shiny project, I downloaded the data of total population, mortality rate, birth rate, life expectancy, infant mortality rate, population aged 65 and more that provided from United Nation. I separated the world data by continents so that viewers not only can see the where the rate is high or low around the world, but also can see which country has the high or low rate in different continent.

The purpose of this shiny project is by doing this, I hope I can provide help for people in United Nations to see which country is the worst on certain problem, so United Nations can locate their resource on those nations which needs the most.

First I will show you what's the general picture look like when you enter the shiny.

wc

In the left side, it has the selection of region and type, people can select five different continents from this region bar and also the what type of data they want to look at from the type bar. In the region bar, I created five continents plus the world map, so people can select either the different continent or the world. above is when people select the world view.

below is when people select by clicking into different continent.

as

In above Asia graph, we can see that they select the population. The more blue means that the population is more. the darker means less people. So as we can see from above, we know that in Asia, China and India has the most population, and the rest countries in Asia has relative low population

I will show another example.

bith rate

above I select the Africa as the region and birth rate as the type, the more lighter the blue means those countries has more birth rate per 1000 people, so it's not hard to see that the mid Africa has the highest birth rate. But from the below graph, we may compare with the infant mortality rate.

infant mor rate

In the above graph, we also found out that the infant mortality rate is also very high in the Mid-Africa, so from the above two graph, we can see that even though in central Africa, the birth rate is very high, the whole population may be not increasing too much because the high infant mortality rate.  We may guess it is because of the low medical care or health policy. So United Nations can put their resources on those problems to change the infant health condition in the place where they most needed.

 

 

About Author

Le Wei

Le is a data scientist enthusiast, he brought his passion for data science to this bootcamp. He was majored in marketing when he was reading bachelor's degree. He learned from college that how to make the right decision...
View all posts by Le Wei >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI