This is an R Markdown document of Rongbin Ye, who is pursuing the concentration of data scientist for JHU coursera course.
Question 1: En_Us.blog.txt file is ___ Megabytes? 210.2 MB
size<-file.info("~/Downloads/final/en_US/en_US.blogs.txt")
kb<-size$size/1024
mb<-kb/1024
mb
## [1] 200.4242
Question 2:How many lines of twitter - over 2 million
library(readr)
# Loading Data into the connection
contwitters <- file("~/Downloads/final/en_US/en_US.twitter.txt", "r")
conblogs <- file("~/Downloads/final/en_US/en_US.blogs.txt", "r")
connews <- file("~/Downloads/final/en_US/en_US.news.txt", "r")
twitter <- readLines(con <- contwitters, encoding = "UTF-8", skipNul = TRUE)
blogs <- readLines(con <- conblogs, encoding = "UTF-8", skipNul = TRUE)
news <- readLines(con <- connews, encoding = "UTF-8", skipNul = TRUE)
length(twitter)
## [1] 2360148
Question 3: How many lines for each of them.
summary(nchar(twitter))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 37.00 64.00 68.68 100.00 140.00
summary(nchar(blogs))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 47 156 230 329 40833
summary(nchar(news))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 110.0 185.0 201.2 268.0 11384.0
Question 4: In the en_US twitter data set, if you divide the number of lines where the word “love” (all lowercase) occurs by the number of lines the word “hate” (all lowercase) occurs, about what do you get?
love<-length(grep("love", twitter))
hate<-length(grep("hate", twitter))
love/hate
## [1] 4.108592
Question 5: The one tweet in the en_US twitter data set that matches the word “biostats” says what?
grep("biostats", twitter, value = TRUE)
## [1] "i know how you feel.. i have biostats on tuesday and i have yet to study =/"
Question 6:How many tweets have the exact characters “A computer once beat me at chess, but it was no match for me at kickboxing”. (I.e. the line matches those characters exactly.)
grep("A computer once beat me at chess, but it was no match for me at kickboxing", twitter)
## [1] 519059 835824 2283423