Tasnim-Jahan / Analysis-of-Global-COVID-19-Pandemic-Data

install.packages("curl")

library("curl")

install.packages("httr")

library("httr")

install.packages("rvest")

library("rvest")

Call the get_wiki_covid19_page function and print the response

covid19_url <- "https://en.wikipedia.org/w/index.php?title=Template:COVID-19_testing_by_country" response <- GET(covid19_url) response

Get the root html node from the http response in task 1

covid19_root_node <- read_html( "https://en.wikipedia.org/w/index.php?title=Template:COVID-19_testing_by_country") covid19_root_node

Get the table node from the root html node

covid19_table_node <- html_node(covid19_root_node, "table") covid19_table_node

Read the table node and convert it into a data frame, and print the data frame for review

covid19_data_frame <- html_table(covid19_table_node) head(covid19_data_frame)

Print the summary of the data frame

summary(covid19_data_frame)

call preprocess_covid_data_frame function and assign it to a new data frame

wiki_covid19_data_frame <- preprocess_covid_data_frame(covid19_data_frame) wiki_covid19_data_frame

Print the summary of the processed data frame again

summary(wiki_covid19_data_frame)

Export the data frame to a csv file

write.csv(wiki_covid19_data_frame, file = "covid.csv", row.names = FALSE)

Get working directory

wd <- getwd()

Get exported

file_path <- paste(wd, sep="", "/covid.csv")

File path

print(file_path) file.exists(file_path)

Download a sample csv file

#covid_csv_file <- download.file("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-RP0101EN-Coursera/v2/dataset/covid.csv", destfile="covid.csv") #covid_data_frame_csv <- read.csv("covid.csv", header=TRUE, sep=",")

Read covid_data_frame_csv from the csv file

covid_data_frame_csv <- read.csv("covid.csv", header=TRUE, sep=",")

Get the 5th to 10th rows, with two "country" "confirmed" columns

covid_data_frame_csv[ 5:10, c( "country", "confirmed") ]

Get the total confirmed cases worldwide

confirmed_cases <- covid_data_frame_csv[ , 4] confirmed_cases total_confirmed_cases <- sum(confirmed_cases) total_confirmed_cases

Get the total tested cases worldwide

tested_cases <- covid_data_frame_csv[ , 3] tested_cases total_tested_cases <- sum(tested_cases) total_tested_cases

Get the positive ratio (confirmed / tested)

positive_ratio <- total_confirmed_cases/total_tested_cases positive_ratio

Get the country column

country_column <- covid_data_frame_csv[ , 1] country_column

Check its class ( should be Factor)

class(country_column)

Conver the country column into character so that you can easily sort them

as.character ( country_column)

Sort the countries AtoZ

sort(country_column)

Sort the countries ZtoA

Country_ZtoA <- sort(country_column, decreasing = TRUE) Country_ZtoA

Print the sorted ZtoA list

print( Country_ZtoA)

Use a regular expression United.+ to find matches

matches <- regexpr("United.+", covid_data_frame_csv[ ,"country"]) countires_start_with_United<- regmatches(covid_data_frame_csv[ ,"country"], matches) countires_start_with_United

Print the matched country names

print(countires_start_with_United)

Select a subset (should be only one row) of data frame based on a selected country name and columns

wiki_covid19_data_frame[1, c( "country", "confirmed", "confirmed.population.ratio") ]

Select a subset (should be only one row) of data frame based on a selected country name and columns

wiki_covid19_data_frame[ 20, c("country", "confirmed", "confirmed.population.ratio") ]

if (49621 > 1491) { print( "Afghanistan has larger ratio of confirmed cases to population") } else { print( "Bhutan has larger ratio of confirmed cases to population") }

Get a subset of any countries with confirmed.population.ratio less than the threshold

threshold = "lessRisk" if (threshold == "lessRisk"){ subset(wiki_covid19_data_frame, confirmed.population.ratio < .01) } else { subset(wiki_covid19_data_frame, confirmed.population.ratio > .01)
}