Q3.1 Get Ebola data from the CDC.
The CDC provides data on the recent Ebola Outbreak
http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html Read the Ebola outbreak data and store as a nice data frame. Hint: You can try readHTMLTablele
Solution
setwd("C:/Users/Manjari/Desktop/Machine learning/Home Work Solutions")
library(XML)
url_cdc<-"http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html"
Ebola_outbreak<- readHTMLTable(url_cdc)
Ebola_outbreak## $`cases-widespread`
## Country Total Cases (Suspected, Probable, and Confirmed)
## 1 Guinea 3584
## 2 Liberia* 10322
## 3 Sierra Leone 12371
## 4 Total 26277
## Laboratory-Confirmed Cases Total Deaths
## 1 3158 2377
## 2 3151 4608
## 3 8586 3899
## 4 14895 10884
##
## $`cases-travel-associated`
## Country
## 1 No countries currently in this classification
## 2 Total
## Total Cases (Suspected, Probable, and Confirmed)
## 1 0
## 2 0
## Laboratory-Confirmed Cases Total Deaths
## 1 0 0
## 2 0 0
##
## $`cases-localized-transmission`
## Country Total Cases (Suspected, Probable, and Confirmed)
## 1 Nigeria 20
## 2 Senegal 1
## 3 Spain 1
## 4 United States 4
## 5 Mali 8
## 6 United Kingdom 1
## 7 Total 35
## Laboratory-Confirmed Cases Total Deaths
## 1 19 8
## 2 1 0
## 3 1 0
## 4 4 1
## 5 7 6
## 6 1 0
## 7 33 15Q 3.2 Clean up the numbers (no asterisk) Some numbers in the data above have an asterisk. (19). Get rid of those ‘stars’**
Hints: try grep/gsub Hint: When searching for the “*" character (you have to use “\*" because the star is
a special character. Using the double backslash is called “escaping.” Example try this: gsub(“\“,”star“,”1234”) Hint: If a data frame column has odd characters, you can access it using the [row,
column] notation. For example, try df[ ,3] to print the third column
setwd("C:/Users/Manjari/Desktop/Machine learning/Home Work Solutions")
url_cdc<-"http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html"
Ebola_outbreak<- readHTMLTable(url_cdc,header=T,sep=",")[[1]]
Ebola_outbreak1<-readHTMLTable(url_cdc,header=T,sep=",")[[2]]
Ebola_outbreak2<-readHTMLTable(url_cdc,header=T,sep=",")[[3]]
dataframe <-rbind(Ebola_outbreak,Ebola_outbreak1,Ebola_outbreak2)
df <- lapply(dataframe[2:4], FUN = function(x) as.numeric(gsub("\\*", "", x)))
data.frame(df)## Total.Cases..Suspected..Probable..and.Confirmed.
## 1 3584
## 2 10322
## 3 12371
## 4 26277
## 5 0
## 6 0
## 7 20
## 8 1
## 9 1
## 10 4
## 11 8
## 12 1
## 13 35
## Laboratory.Confirmed.Cases Total.Deaths
## 1 3158 2377
## 2 3151 4608
## 3 8586 3899
## 4 14895 10884
## 5 0 0
## 6 0 0
## 7 19 8
## 8 1 0
## 9 1 0
## 10 4 1
## 11 7 6
## 12 1 0
## 13 33 15Q3.3 Combine the 3 tables in the page into one dataframe.
Hint: Use rbind() rbind(table1, table2) You have remove the intermediate total lines. Keep only the rows for each country,
throw out the rest.
library(XML)
url_cdc<-"http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html"
Ebola_outbreak<- readHTMLTable(url_cdc,header=T,sep=",")[[1]]
Ebola_outbreak1<-readHTMLTable(url_cdc,header=T,sep=",")[[2]]
Ebola_outbreak2<-readHTMLTable(url_cdc,header=T,sep=",")[[3]]
dataframe <-rbind(Ebola_outbreak,Ebola_outbreak1,Ebola_outbreak2)
dataframe## Country
## 1 Guinea
## 2 Liberia*
## 3 Sierra Leone
## 4 Total
## 5 No countries currently in this classification
## 6 Total
## 7 Nigeria
## 8 Senegal
## 9 Spain
## 10 United States
## 11 Mali
## 12 United Kingdom
## 13 Total
## Total Cases (Suspected, Probable, and Confirmed)
## 1 3584
## 2 10322
## 3 12371
## 4 26277
## 5 0
## 6 0
## 7 20
## 8 1
## 9 1
## 10 4
## 11 8
## 12 1
## 13 35
## Laboratory-Confirmed Cases Total Deaths
## 1 3158 2377
## 2 3151 4608
## 3 8586 3899
## 4 14895 10884
## 5 0 0
## 6 0 0
## 7 19 8
## 8 1 0
## 9 1 0
## 10 4 1
## 11 7 6
## 12 1 0
## 13 33 15Q3.4 Write an R Function Write an R function that will take the name of a country in West Africa and return
the “Total Cases” of Ebola in that country. Use the Ebola data frame you built for question 3.1.
Input: Country Name, Ebola data frame
Output: The corresponding value for the country for Total Cases of Ebola
library(XML)
url_cdc<-"http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html"
Ebola_outbreak<- readHTMLTable(url_cdc,header=T,sep=",")[[1]]
Ebola_outbreak1<-readHTMLTable(url_cdc,header=T,sep=",")[[2]]
Ebola_outbreak2<-readHTMLTable(url_cdc,header=T,sep=",")[[3]]
dataframe <-rbind(Ebola_outbreak,Ebola_outbreak1,Ebola_outbreak2)
dataframe## Country
## 1 Guinea
## 2 Liberia*
## 3 Sierra Leone
## 4 Total
## 5 No countries currently in this classification
## 6 Total
## 7 Nigeria
## 8 Senegal
## 9 Spain
## 10 United States
## 11 Mali
## 12 United Kingdom
## 13 Total
## Total Cases (Suspected, Probable, and Confirmed)
## 1 3584
## 2 10322
## 3 12371
## 4 26277
## 5 0
## 6 0
## 7 20
## 8 1
## 9 1
## 10 4
## 11 8
## 12 1
## 13 35
## Laboratory-Confirmed Cases Total Deaths
## 1 3158 2377
## 2 3151 4608
## 3 8586 3899
## 4 14895 10884
## 5 0 0
## 6 0 0
## 7 19 8
## 8 1 0
## 9 1 0
## 10 4 1
## 11 7 6
## 12 1 0
## 13 33 15ebola1 <- lapply(dataframe[1:4], FUN = function(x)(gsub("\\*", "", x)))
df <- data.frame(ebola1)
cases_summary <- function(x){
if(x == "United States" || x == "Spain")
{
print("Not a west African Country")
}
else
{
case <-subset(df,Country == x,select =c(Country,Total.Cases) )
return(case)
}
}Q3.5 Reading from Wikipedia From the wiki page:
http://en.wikipedia.org/wiki/List_of_Ebola_outbreaks Find out how many humans have died of Ebola since 1976.
. Hint: Consider using readHTMLTable {XML package}.
. If a page has multiple tables, and you want the second table, you’d do [[2]]. . If a column name has spaces, you put the whole name inside quotes: - Example: df$“Human deaths” Print the total number of Human deaths from this table
Solution
setwd("C:/Users/Manjari/Desktop/Machine learning/Home Work Solutions")
library(XML)
url_wikipage<-"http://en.wikipedia.org/wiki/List_of_Ebola_outbreaks"
wikipedia<-readHTMLTable(url_wikipage)[[2]]
condition <- (wikipedia$Year==1976)
(wikipedia$"Human deaths"[condition])## NULLQ3.6 dplyr Take the readymade dataset airquality Q: Calculate the average Ozone levels for each month in the data.
(Make sure you avoid the NA values!)
setwd("C:/Users/Manjari/Desktop/Machine learning/Home Work Solutions")
sapply(1:length(airquality$Ozone),function(i) mean(airquality[airquality$Month==i, ]$Ozone, na.rm=TRUE))## [1] NaN NaN NaN NaN 23.61538 29.44444 59.11538
## [8] 59.96154 31.44828 NaN NaN NaN NaN NaN
## [15] NaN NaN NaN NaN NaN NaN NaN
## [22] NaN NaN NaN NaN NaN NaN NaN
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## [85] NaN NaN NaN NaN NaN NaN NaN
## [92] NaN NaN NaN NaN NaN NaN NaN
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## [148] NaN NaN NaN NaN NaN NaN