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covidpercountry's Introduction

Project Info

This project explains the pandemic of our times Covid-19 with the most accurate data. In order to understand better what countries have the most victims , deaths and active cases a database will be created where i can present all of the above. Also, i will use the R programming language, Power-Bi and MS Excel for the visualizations.

Continents with most cases

Deaths by country on Treemap

Deaths of global cases

Data Provider

The data for the project are provided by

Setup

  1. Start Microsoft SQL Server Management Studio
  2. Click Databases
  3. New Database 
  4. Give your new database a name 
  5. Start designing

  1. Start R Studio 
  2. Setting the working directory
  3. Import the dataframe 
  4. Load the libraries

Environment

RStudio
SQL Server Management Studio

Languages Used

 1. SQL
 2. R

Data Visualization tools

1. MS Power-Bi
2. MS Excel

covidpercountry's People

Contributors

Watchers

covidpercountry's Issues

Libraries

What are the necessary libraries for this project

install.packages('tidyverse')
install.packages('ggplot2')

Or download from github with remotes package

remotes::install_github('r/tidyverse')

Data Visualization tools

Tableau
Power-Bi
MS Excel

IDE's used

What IDE's used for this project

1. SSMS
2. RStudio

Starting Setup(SQL , R)

Writing the starting setup both in SQL and R so:

For SQL setup

  1. Start Microsoft SQL Server Management Studio
  2. Click Databases
  3. New Database 
  4. Give your new database a name 
  5. Start designing

For R setup

  1. Start R Studio 
  2. Setting the working directory
  3. Import the dataframe 
  4. Load the libraries
  5. View the uploaded dataframe

Checking for NA values

Fixing Data Provider's link

World Health Oranization webpage
World Health Organization
European Centre for Disease Prevention and Control
ecdc

Project goal

What is the project's goal

Languages

What languages used for this project

Shields

Adding shields on the project
Shields

Visuals

Adding visuals on README

Check for NA values and how to deal with them

Checking for NA values

boxplot(df)

OR
hist(df)

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