Importing Data in R - r

Question

I want to import data into R but I am getting a few errors. I download my ".CSV" file to my computer and specify the file path like this setwd("C:/Users/intellipaat/Desktop/BLOG/files") and then I am writing read.data <- read.csv("file1.csv"), but the console returns an error like this.
"read.data<-read.csv(file1.csv)
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
'file1.csv' object not found
What should I do for this? I tried the internet link route, but again I encountered a problem.
I wrote like this:
install.packages("XML")
install.packages("RCurl")
to load the packages, run the following command:
library("XML")
library("RCurl")
url <- "https://en.wikipedia.org/wiki/Ease_of_doing_business_index#Ranking"
tabs <- getURL(url)
and the console wrote me this error;
Error in function (type, msg, asError = TRUE) :
error:1407742E:SSL routines:SSL23_GET_SERVER_HELLO:tlsv1 alert protocol version
I would be glad if you help me in this regard...

Answer 1

The Ease of Doing Business rankings table on Wikipedia is an HTML table, not a comma separated values file.
Loading the HTML table into an R data frame can be handled in a relatively straightforward manner with the rvest package. Instead of downloading the HTML file we can read it directly into R with read_html(), and then use html_table() to extract the tabular data into a data frame.
library(rvest)
wiki_url <- "https://en.wikipedia.org/wiki/Ease_of_doing_business_index#Ranking"
aPage <- read_html(wiki_url)
aTable <- html_table(aPage)[[2]] # second node is table of rankings
head(aTable)
...and the first few rows of output:
> head(aTable)
Classification Jurisdiction 2020 2019 2018 2017 2016 2015 2014 2013 2012
1 Very Easy New Zealand 1 1 1 1 2 2 3 3 3
2 Very Easy Singapore 2 2 2 2 1 1 1 1 1
3 Very Easy Hong Kong 3 4 5 4 5 3 2 2 2
4 Very Easy Denmark 4 3 3 3 3 4 5 5 5
5 Very Easy South Korea 5 5 4 5 4 5 7 8 8
6 Very Easy United States 6 8 6 8 7 7 4 4 4
2011 2010 2009 2008 2007 2006
1 3 2 2 2 2 1
2 1 1 1 1 1 2
3 2 3 4 4 5 7
4 6 6 5 5 7 8
5 16 19 23 30 23 27
6 5 4 3 3 3 3
>
Next, we confirm that the last countries were read correctly: Libya, Yemen, Venezuela, Eritrea, and Somalia.
> tail(aTable,n=5)
Classification Jurisdiction 2020 2019 2018 2017 2016 2015 2014 2013 2012
186 Below Average Libya 186 186 185 188 188 188 187 N/A N/A
187 Below Average Yemen 187 187 186 179 170 137 133 118 99
188 Below Average Venezuela 188 188 188 187 186 182 181 180 177
189 Below Average Eritrea 189 189 189 189 189 189 184 182 180
190 Below Average Somalia 190 190 190 190 N/A N/A N/A N/A N/A
2011 2010 2009 2008 2007 2006
186 N/A N/A N/A N/A N/A N/A
187 105 99 98 113 98 90
188 172 177 174 172 164 120
189 180 175 173 171 170 137
190 N/A N/A N/A N/A N/A N/A
Finally, we use tidyr and dplyr to convert the data to narrow format tidy data for subsequent analysis.
library(dplyr)
library(tidyr)
aTable %>%
# convert years 2017 - 2020 to character because pivot_longer()
# requires all columns to be of same data type
mutate_at(3:6,as.character) %>%
pivot_longer(-c(Classification,Jurisdiction),
names_to="Year",values_to="Rank") %>%
# convert Rank and Year to numeric values (introducing NA values)
mutate_at(c("Rank","Year"),as.numeric) -> rankings
head(rankings)
...and the output:
> head(rankings)
# A tibble: 6 x 4
Classification Jurisdiction Year Rank
<chr> <chr> <dbl> <dbl>
1 Very Easy New Zealand 2020 1
2 Very Easy New Zealand 2019 1
3 Very Easy New Zealand 2018 1
4 Very Easy New Zealand 2017 1
5 Very Easy New Zealand 2016 2
6 Very Easy New Zealand 2015 2
>