COVID-19 Data Acquisition in R

Built with R, available in any language, COVID-19 Data Hub provides a worldwide, fine-grained, unified dataset helpful for a better understanding of COVID-19. The user can instantly download up-to-date, structured, historical daily data across several official sources. The data are hourly crunched and made available in csv format on a cloud storage, so to be easily accessible from Excel, R, Python… and any other software. All sources are properly documented, along with their citation.

In this tutorial we explore the R Package COVID19: R Interface to COVID-19 Data Hub.

Quickstart

# install the package
install.packages("COVID19")

# load the package
library("COVID19")

# additional packages to replicate the examples
library("ggplot2")
library("directlabels")

Data

The data are retrieved with the covid19 function. By default, it downloads worldwide data by country, and prints the corresponding data sources.

x <- covid19()

To hide the data sources use verbose = FALSE

x <- covid19(verbose = FALSE)

A table with several columns is returned: cumulative number of confirmed cases, tests, recovered, deaths, daily number of hospitalized, intensive therapy, patients requiring ventilation, policy measures, geographic information, population, and external identifiers to easily extend the dataset with additional sources. Refer to the documentation for further details.

Clean data

By default, the raw data are cleaned by filling missing dates with NA values. This ensures that all locations share the same grid of dates and no single day is skipped. Then, NA values are replaced with the previous non-NA value or 0.

Example: plot confirmed cases by country.

ggplot(data = x, aes(x = date, y = confirmed)) +
  geom_line(aes(color = id)) +
  geom_dl(aes(label = administrative_area_level_1), method = list("last.points", cex = .75, hjust = 1, vjust = 0)) +
  scale_y_continuous(trans = 'log10') +
  theme(legend.position = "none") +
  ggtitle("Confirmed cases (log scale)")

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Example: plot confirmed cases by country as fraction of total population.

ggplot(data = x, aes(x = date, y = confirmed/population)) +
  geom_line(aes(color = id)) +
  geom_dl(aes(label = administrative_area_level_1), method = list("last.points", cex = .75, hjust = 1, vjust = 0)) +
  scale_y_continuous(trans = 'log10') +
  theme(legend.position = "none") +
  ggtitle("Confirmed cases - Fraction of total population (log scale)")

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Raw Data

Filling the data with the previous non-missing data is not always recommended, especially when computing ratios or dealing with more sophisticated analysis other than data visualization. The raw argument allows to skip data cleaning and retrieve the raw data as-is, without any preprocessing.

#data #covid19 #r #data analysis

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COVID-19 Data Acquisition in R
Siphiwe  Nair

Siphiwe Nair

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Osiki  Douglas

Osiki Douglas

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Data Scientist Creates Python Script To Track Available Slots For Covid Vaccinations

Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.

The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.

We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”

Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.

#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script

Cyrus  Kreiger

Cyrus Kreiger

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How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Gerhard  Brink

Gerhard Brink

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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Is There An Upswing In Data Science Jobs in India

With the world starting to open amidst the COVID-19 pandemic, the number of jobs available in data science sees an upward trend in India as we inch closer to providing vaccines for everyone.

The number of vacancies for data science jobs on the top job portals in India increased by 53% from when India eased the lockdown restrictions on June 8 to Nov 30, according to the data collated by AIMResearch. Although it is difficult to ascertain the exact number of open jobs, the top job portals in India, Naukri, LinkedIn, and MonsterIndia together showed almost 125,000 vacancies on Nov 30.

However, the pandemic did result in a decrease in the number of open data science jobs at the start as vacancies reduced from 101,562 from Dec 17 last year to 81,704 on June 8. Despite this decrease, India’s share of open data science jobs in the world increased from 7.2% in January to 9.8% in August.

#data for covid #data science after covid #data science jobs #data science jobs after covid #rising number of data science jobs #upswing in data science jobs