Mara Averick batpigandme

Package Review

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As the reviewer I confirm that there are no conflicts of interest for me to review this work (such as being a major contributor to the software).

Documentation

The package includes all the following forms of documentation:

	##################################
	## Getting Team Preseason Stats ##
	##################################

	## set strings as factors to false
	options(stringsAsFactors = FALSE)

	## load libraries
	library(stattleshipR)
	library(dtplyr)

	## install and load the stattleshipR package
	devtools::install_github("stattleship/stattleship-r")
	library(stattleshipR)

	## optional: load dplyr
	library(dplyr)

	## get your API token stattleship.com
	## set API token
	set_token('YOUR API TOKEN')


	│ three_pointers_attempted │ three_pointers_made │ three_pointers_pct │
	├──────────────────────────┼─────────────────────┼────────────────────┤
	│ Min. : 4.00 │ Min. : 1.000 │ Min. :0.1110 │
	│ 1st Qu.: 9.00 │ 1st Qu.: 3.000 │ 1st Qu.:0.3407 │
	│ Median :11.00 │ Median : 5.000 │ Median :0.4550 │
	│ Mean :10.81 │ Mean : 4.926 │ Mean :0.4401 │
	│ 3rd Qu.:13.00 │ 3rd Qu.: 7.000 │ 3rd Qu.:0.5000 │
	│ Max. :16.00 │ Max. :11.000 │ Max. :0.7330 │

	## steph curry's three-point shooting data from
	## stattleship api

	## density plot of three-pt pctg
	d <- density(steph_gls$three_pointers_pct)
	plot(d, type="n", main="three point percentage")
	polygon(d, col="red", border="gray")

	## histograms
	hist(steph_gls$three_pointers_attempted)

	## visualize team point leads and outcomes with small multiples

	## install and load ggplot2
	install.packages("ggplot2")
	library(ggplot2)

	## make separate point plots for each team
	ggplot(teamgame_point_leads, aes(x=final_point_diff, y=points_biggest_lead)) + geom_point() +
	facet_wrap(~team_slug)

	## set keys for the data table on game_slug and opponent_slug
	team_game_logs <- setkey(team_game_logs, game_slug, opponent_slug)

	## get opponent leads by matching on both key columns
	team_game_logs$opp_biggest_lead <- team_game_logs[. (team_game_logs$game_slug, team_game_logs$opponent_slug)]$points_biggest_lead

	## set keys for the data table on game_slug and opponent_slug
	team_game_logs <- setkey(team_game_logs, game_slug, opponent_slug)

	## get opponent leads by matching on both key columns

	## load the stattleshipR and dplyr packages
	library(stattleshipR)
	library(dplyr)

	## set params for team_game_logs
	sport <- "basketball"
	league <- "nba"
	ep <- "team_game_logs"

	## leave q_body empty to get all

	d3pct <- density(steph_gls$three_pointers_pct)
	plot(d3pct)