Download the script here
Task Clean the environment and load the packages
rm(list=ls())
library(tidyverse)
library(readr)# Download Data
##CoronaNet Core Data:
download.file("https://raw.githubusercontent.com/saudiwin/corona_tscs/master/data/CoronaNet/coronanet_release.csv", "./data/coronanet_release.csv")
##CoronaNet All Vars Data:
download.file("https://raw.githubusercontent.com/saudiwin/corona_tscs/master/data/CoronaNet/coronanet_release_allvars.csv", "./data/coronanet_release_allvars.csv")
#Load data
coronaNet <- read.csv('data/coronanet_release.csv')
# Worldbank Data
# Datasets can come from a variety of sources.
# Today we will be investigating GDP data from the World Bank and Penn World Tables in particular
## * Getting World Bank Data: *
# i) Go to the https://data.worldbank.org
# ii) Search for the indicator that you want
# iii) Download the corresponding data in .csv format
## How to load .csvs
# First identify where your downloaded .csv is located....
# ..... or probably better, put your downloaded .csv in a specified place and note the file path
# (the file path is the path to your file of interest.)
# then there are any number of ways you can try to access your data
# a) directly type in the file path
wb <- read.csv('data/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_988619.csv',stringsAsFactors = FALSE, header = TRUE, skip = 4)# lets take a look at the first 6 rows
head(coronaNet)
# lets take a look at what classes the different data are stored in and the values they can take on
str(coronaNet)
# lets take a look at the distribution of the countries that have initiated a policy
table(coronaNet$country)%>% sort(decreasing = TRUE)
# lets look at the distribution of different policies
table(coronaNet$type) %>% sort()
# Class question: how could you look at the distribution of different sub-types: type_sub_cat ?
names(coronaNet)coronaNet = coronaNet %>%
mutate(type = ifelse(type == 'Quarantine/Lockdown', 'Lockdown', type)) #We want the variable Quarantine/Lockdown to be called "Lockdown"
# We want to gather the WB data and reshape it in order to have the right merging data
# Gather data:
stocks <- tibble(
time = as.Date('2009-01-01') + 0:9,
X = rnorm(10, 0, 1),
Y = rnorm(10, 0, 2),
Z = rnorm(10, 0, 4)
)
stocks
gather(stocks, "stock", "price", -time)
stocks %>% gather("stock", "price", -time) %>% data.frame()
# Now with the Worldbank Data
wb = wb %>% gather("year", "gdpPPP", -`Country Name`, -`Country Code`, -`Indicator Name`, -`Indicator Code`, -X65)
?gather
wb = wb %>% select( -`Indicator Name`, -`Indicator Code`, -X65)
wb$year = as.numeric(wb$year)
wb$lgdpPPP= log(wb$gdpPPP)# Let´s assume we are only interested in the Business Restrictions
businessRestrict = coronaNet %>% filter(type == "Restriction of Non-Essential Businesses")
# what are the dimensions of this new subsetted data?
dim(businessRestrict)
# which countries have instituted restrictions of non-essential businesses?
businessRestrict$country %>% unique()
unique(businessRestrict$country)
# what are the different sub types for the broader non-essentital businesses policy type?
unique(businessRestrict$type_sub_cat)# We want to merge the information on GDP per Capita to the CoronaNet data
# This means we will have countries as the identifier
names(wb)[1] = 'country'
names(businessRestrict)
data = merge(businessRestrict, wb %>% filter(year == 2018), by= 'country', all.x = TRUE)
# how can we find out what gdp of the Germany is in this 'data'?
data %>% filter(country == 'Germany') %>% select(gdpPPP)
# how can we find out what the gdp of the United States is in this data?
data %>% filter(country == 'United States of America') %>% select(gdpPPP)
# how can we investigate which countries match across different datasets?
intersect(coronaNet$country, wb$country)
# and the countries that don't match?
cbind(setdiff(coronaNet$country, wb$country),
setdiff( wb$country, coronaNet$country)) %>% head()
# The problem: The country names differ between both datasets. We therefore take the country code (ISO3) as an optimal unique identifieer
names(wb)[2] = 'ccode'
names(businessRestrict)[31] = 'ccode'
data = merge(businessRestrict, wb %>% filter(year == 2018), by= 'ccode', all.x = TRUE)
# how can we find out what the gdp of the United States is in this data?
data %>% filter(ccode == 'USA') %>% select(gdpPPP)
# IT WORKED!