Open the link http://www.espn.com/nba/team/schedule/_/name/BKN/seasontype/2. Display the source
code and copy and paste this code into a text editor. Then save the file as NetsSchedule1718 using a .html
extension. Once the file is saved, check that you can open the file and it displays the 2017-2018 Brooklyn
Nets Regular Season Schedule.
ii.
setwd(“~/Desktop/Data”)
nets1718 <- readLines("NetsSchedule1718.html")
## Warning in readLines("NetsSchedule1718.html"): incomplete final line found
## on 'NetsSchedule1718.html'
The number of lines in the file corresponds to the length of the vector nets1617.
length(nets1718)
## [1] 828
I can find the number of characters in each line of the file by running nchar(nets1617) since nchar() vectorizes.
This will return a vector of length 9306 with each element telling the number of characters in the corresponding
line of the file. Then we can take a sum of these values to give the total number of characters.
sum(nchar(nets1718))
## [1] 129185
Finally, I can use the max() command, with nchar(nets1617) as its input, to find the maximum number of
characters in any line of the code.
max(nchar(nets1718))
## [1] 9736
iii. In the first game of the regular season, the Nets are playing the Indiana Pacers in Indianapolous
Wednesday, October 18 at 7:00PM. In the last game of the season, the Nets are playing the Boston
Celtics in Boston on Wednesday, April 11 at 8:00PM.
iv. The 321th line corresponds to the first game of the regular season and the 402rd line corresponds to the
last game of the regular season.
v. I use a regular expression to search for a capital letter, followed by two lowercase letters, a comma,
a space, a capital letter, two lowercase letters, a space, and then one or more digits. This regular
expression is found in date_exp. Then I use grep() to search nets1718 for lines with dates in them.
These lines are stored in game.lines. Looking at the first and last values of game.lines I see information
on the first and last games.
1
http://www.espn.com/nba/team/schedule/_/name/BKN/seasontype/2
date_exp <- "[A-Z][a-z]{2},\\s[A-Z][a-z]{2}\\s[0-9]+"
game.lines <- grep(date_exp, nets1718)
game.lines[1]
## [1] 321
nets1718[game.lines[1]]
## [1] "\t\t\t\t
v. gregexpr() returns the starting locations and the lengths of each of the game dates, then we can actually
extract the information using regmatches(). Since the output of regmatches() is a list, we use the unlist()
command to turn it into a vector.
date.locations <- gregexpr(date_exp, nets1718[game.lines])
date <- regmatches(nets1718[game.lines], date.locations)
date <- unlist(date)
vi. Extracting the game times is similar to extracting the dates, but now my regular expression searches
for one or more digits followed by a colon, 2 digits, a space, and then either AM or PM.
time_exp <- "[0-9]+:[0-9]{2} (PM|AM)"
time.locations <- gregexpr(time_exp, nets1718[game.lines])
time <- regmatches(nets1718[game.lines], time.locations)
time <- unlist(time)
vii. In my solution, I use the fact that in each line, the string
appears
before the home or away information. So my regular expression searches for
followed by ‘@’ or
followed by ‘vs’. As in part (v) and (vi) I use
gregexpr() and regmatches() to actually extract the strings which match the regular expression. Since
these strings include
before ‘@’ or ‘vs’, I then use the substr() command
just the ‘@’ or the ‘vs’. Finally, I create the home vector from this information.
away_exp <- "
@|
vs”
away.locations <- gregexpr(away_exp, nets1718[game.lines])
away <- regmatches(nets1718[game.lines], away.locations)
away <- substr(away, 25, nchar(away))
home <- rep(1, length(away))
home[away == "@"] <- 0
2
viii. In my solution, I use the fact that in each line, the string
̈ appears before the
opponent and afterwards. So my regular expression searches for
̈ followed
by anything inside ‘<’ and ‘>’, letters or space, and . gregexpr() and regmatches() are used to
actually extract the strings which match the regular expression. Since these strings include extra
information, I use another regular expression to search for the opponent’s name recognizing that this
will take the form of letters or spaces coming after ‘>’ and before ‘<’. Finally, we extract just the
opponent names using substr().
opponent_exp <- "
<.+>([a-zA-Z]|\\s)+”
opponent.locations <- gregexpr(opponent_exp, nets1718[game.lines])
opponent <- regmatches(nets1718[game.lines], opponent.locations)
opponent <- unlist(opponent)
name_exp <- ">([a-zA-Z]|\\s)+<"
name.locations <- gregexpr(name_exp, opponent)
name <- regmatches(opponent, name.locations)
opponent <- substr(name, 2, nchar(name)-1)
ix.
schedule <- data.frame(date, time, opponent, home)
schedule[1:10,]
## date time opponent home
## 1 Wed, Oct 18 7:00 PM Indiana 0
## 2 Fri, Oct 20 7:30 PM Orlando 1
## 3 Sun, Oct 22 3:30 PM Atlanta 1
## 4 Tue, Oct 24 7:00 PM Orlando 0
## 5 Wed, Oct 25 7:30 PM Cleveland 1
## 6 Fri, Oct 27 7:30 PM NY Knicks 0
## 7 Sun, Oct 29 6:00 PM Denver 1
## 8 Tue, Oct 31 7:30 PM Phoenix 1
## 9 Fri, Nov 3 10:30 PM Los Angeles 0
## 10 Mon, Nov 6 9:00 PM Phoenix 0
3
![](\"http://a.espncdn.com/combiner/i?img=/i/teamlogos/nba/500/scoreboard/Ind.png&h=80&w=80\")
![](\"http://a.espncdn.com/combiner/i?img=/i/teamlogos/nba/500/scoreboard/Bos.png&h=80&w=80\")