How to Webscrape in R, the RVest and pipeR Way

Load the Necessary Packages

c('rvest','dplyr','pipeR', 'knitr') -> packages # you don't need knitr but I do to make this post
#dplyr or pipeR use the install.packages function to install them, install.packages('dplyr') and install.packages('pipeR')
#If you don't have rvest do the following - install devtools, install.packages('devtools')
#Load devtools using library(devtools) and then install rvest by using install_github('hadley/rvest')
lapply(packages, library, character.only = T) #loops through the packages and loads them

##
## Attaching package: 'rvest'
##
## The following object is masked from 'package:utils':
##
##     history
##
##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
##     filter
##
## The following objects are masked from 'package:base':
##
##     intersect, setdiff, setequal, union

## [[1]]
## [1] "rvest"     "stats"     "graphics"  "grDevices" "utils"     "datasets"
## [7] "methods"   "base"
##
## [[2]]
## [1] "dplyr"     "rvest"     "stats"     "graphics"  "grDevices" "utils"
## [7] "datasets"  "methods"   "base"
##
## [[3]]
##  [1] "pipeR"     "dplyr"     "rvest"     "stats"     "graphics"
##  [6] "grDevices" "utils"     "datasets"  "methods"   "base"
##
## [[4]]
##  [1] "knitr"     "pipeR"     "dplyr"     "rvest"     "stats"
##  [6] "graphics"  "grDevices" "utils"     "datasets"  "methods"
## [11] "base"

Start Scraping

Step 1 lets get the table into R. Since we want scale this to other tables we are going to turn off the headers and see if we can find the row with the column names. In Basketball-References all the stats tables usually start with a column called Rk or rank.

It looks like the headers are in the first row [but in some cases its the second]. Let’s extract out that row and place it into a character vector. Next we use that vector to name our Data Frame [what r calls the table with all the data in it]. Since R is a case sensitive language it’s good practice to use lower case headers and we will do that here. Also R hates things likes spaces or characters like % or / in headers so let’s replace any of those with a period. Finally we want to skip the row with the headers and in order to prepare ourselves for the function we will soon write, we want to find the row the headers are in and then take the data from the row after that until the end.

'http://www.basketball-reference.com/leagues/NBA_2014.html' -> url
'#team' -> css_page
url %>>%
    html %>>%
    html_nodes(css_page) %>>%
    html_table(header = F) %>>%
    data.frame() %>>%
    tbl_df() -> total_table
total_table %>>%
    filter(X.1 == 'Rk') %>>% as.character -> names
'Rk' %>>% grep(x = total_table$X.1) -> row_of_header #find where rank is
names %>>% tolower -> names(total_table)
names(total_table) %>>% (gsub('\\%|/','\\.',.)) -> names(total_table)
(row_of_header + 1) %>>% (total_table[.:nrow(total_table),]) -> total_table #skip that row and go to the end row and go to the end
total_table %>>% head

## Source: local data frame [6 x 26]
##
##   rk                    team  g    mp   fg  fga  fg.  3p  3pa  3p.   2p
## 1  1   Los Angeles Clippers* 82 19755 3208 6761 .474 693 1966 .352 2515
## 2  2        Houston Rockets* 82 19830 3118 6603 .472 779 2179 .358 2339
## 3  3  Minnesota Timberwolves 82 19855 3189 7175 .444 600 1757 .341 2589
## 4  4 Portland Trail Blazers* 82 19855 3207 7134 .450 770 2071 .372 2437
## 5  5  Oklahoma City Thunder* 82 19805 3194 6782 .471 664 1839 .361 2530
## 6  6      San Antonio Spurs* 82 19755 3326 6844 .486 698 1757 .397 2628
## Variables not shown: 2pa (chr), 2p. (chr), ft (chr), fta (chr), ft. (chr),
##   orb (chr), drb (chr), trb (chr), ast (chr), stl (chr), blk (chr), tov
##   (chr), pf (chr), pts (chr), pts.g (chr)

Booya It’s In R

Now you will see a Data Frame in your workspace called total_table, we already found and replaced the headers but there are still a few more quick data cleaning chores remaining before you have a clean dataset. It looks like there are asterisks at the end of some of the team’s that indicate whether the team made the playoffs, sorry Knicks fans. Let’s add a logical column for teams that made the playoffs and then remove the astericks. Next let’s remove the rank column and get rid of the row with the league averages since that is not an actual team and we can easily recalculate summary statistics when we need them.

We Did It, Look at That Clean Data Frame

team	g	mp	fg	fga	fg.	3p	3pa	3p.	2p	2pa	2p.	ft	fta	ft.	orb	drb	trb	ast	stl	blk	tov	pf	pts	pts.g	playoff_team
Los Angeles Clippers	82	19755	3208	6761	.474	693	1966	.352	2515	4795	.525	1741	2384	.730	858	2668	3526	2016	703	397	1136	1767	8850	107.9	TRUE
Houston Rockets	82	19830	3118	6603	.472	779	2179	.358	2339	4424	.529	1814	2549	.712	920	2797	3717	1755	621	461	1323	1676	8829	107.7	TRUE
Minnesota Timberwolves	82	19855	3189	7175	.444	600	1757	.341	2589	5418	.478	1790	2301	.778	1024	2644	3668	1963	718	297	1142	1504	8768	106.9	FALSE
Portland Trail Blazers	82	19855	3207	7134	.450	770	2071	.372	2437	5063	.481	1569	1926	.815	1022	2786	3808	1904	454	387	1125	1576	8753	106.7	TRUE
Oklahoma City Thunder	82	19805	3194	6782	.471	664	1839	.361	2530	4943	.512	1653	2052	.806	887	2781	3668	1794	678	501	1256	1858	8705	106.2	TRUE
San Antonio Spurs	82	19755	3326	6844	.486	698	1757	.397	2628	5087	.517	1289	1642	.785	762	2786	3548	2064	604	420	1180	1495	8639	105.4	TRUE
Phoenix Suns	82	19755	3172	6845	.463	765	2055	.372	2407	4790	.503	1520	2004	.758	928	2601	3529	1563	688	374	1258	1798	8629	105.2	FALSE
Dallas Mavericks	82	19830	3249	6858	.474	721	1877	.384	2528	4981	.508	1378	1733	.795	840	2514	3354	1935	704	356	1110	1636	8597	104.8	TRUE
Denver Nuggets	82	19755	3147	7042	.447	702	1959	.358	2445	5083	.481	1563	2154	.726	1009	2717	3726	1838	615	459	1305	1890	8559	104.4	FALSE
Golden State Warriors	82	19830	3236	7005	.462	774	2037	.380	2462	4968	.496	1303	1731	.753	896	2819	3715	1912	642	407	1247	1784	8549	104.3	TRUE
Los Angeles Lakers	82	19705	3139	6980	.450	774	2032	.381	2365	4948	.478	1390	1835	.757	745	2620	3365	2006	611	446	1239	1627	8442	103.0	FALSE
Miami Heat	82	19880	3142	6272	.501	665	1829	.364	2477	4443	.558	1431	1884	.760	627	2397	3024	1847	732	367	1212	1596	8380	102.2	TRUE
Toronto Raptors	82	19955	2992	6718	.445	713	1917	.372	2279	4801	.475	1608	2055	.782	935	2552	3487	1737	577	343	1159	1882	8305	101.3	TRUE
Atlanta Hawks	82	19830	3061	6688	.458	768	2116	.363	2293	4572	.502	1392	1782	.781	713	2565	3278	2041	680	326	1251	1577	8282	101.0	TRUE
Detroit Pistons	82	19780	3182	7124	.447	507	1580	.321	2675	5544	.483	1415	2111	.670	1196	2525	3721	1714	687	395	1193	1666	8286	101.0	FALSE
Washington Wizards	82	20055	3177	6920	.459	647	1704	.380	2530	5216	.485	1253	1715	.731	886	2573	3459	1909	668	377	1204	1675	8254	100.7	TRUE
Sacramento Kings	82	19830	3026	6766	.447	491	1475	.333	2535	5291	.479	1698	2237	.759	990	2656	3646	1547	587	318	1249	1849	8241	100.5	FALSE
New Orleans Pelicans	82	19855	3101	6761	.459	486	1303	.373	2615	5458	.479	1489	1936	.769	933	2486	3419	1745	647	523	1129	1857	8177	99.7	FALSE
Philadelphia 76ers	82	19855	3108	7150	.435	577	1847	.312	2531	5303	.477	1362	1918	.710	949	2556	3505	1791	765	330	1384	1844	8155	99.5	FALSE
New York Knicks	82	19855	3027	6739	.449	759	2038	.372	2268	4701	.482	1271	1670	.761	870	2437	3307	1641	631	367	1063	1815	8084	98.6	FALSE
Brooklyn Nets	82	19880	2931	6391	.459	709	1922	.369	2222	4469	.497	1508	2002	.753	721	2407	3128	1714	705	311	1191	1777	8079	98.5	TRUE
Cleveland Cavaliers	82	19930	3036	6955	.437	584	1640	.356	2452	5315	.461	1398	1861	.751	989	2629	3618	1738	579	304	1163	1640	8054	98.2	FALSE
Charlotte Bobcats	82	19905	2976	6730	.442	516	1471	.351	2460	5259	.468	1474	2000	.737	776	2724	3500	1778	499	421	1010	1493	7942	96.9	TRUE
Indiana Pacers	82	19780	2949	6573	.449	550	1542	.357	2399	5031	.477	1485	1907	.779	834	2831	3665	1651	550	446	1237	1675	7933	96.7	TRUE
Orlando Magic	82	19955	3022	6784	.445	563	1596	.353	2459	5188	.474	1307	1714	.763	794	2654	3448	1726	630	350	1222	1678	7914	96.5	FALSE
Boston Celtics	82	19730	2996	6883	.435	575	1729	.333	2421	5154	.470	1325	1706	.777	980	2505	3485	1726	584	343	1261	1743	7892	96.2	FALSE
Memphis Grizzlies	82	19805	3122	6723	.464	405	1147	.353	2717	5576	.487	1235	1666	.741	950	2526	3476	1792	631	375	1124	1568	7884	96.1	TRUE
Milwaukee Bucks	82	19880	2952	6737	.438	548	1553	.353	2404	5184	.464	1377	1843	.747	971	2399	3370	1760	541	403	1238	1713	7829	95.5	FALSE
Utah Jazz	82	19780	2951	6652	.444	543	1577	.344	2408	5075	.474	1346	1803	.747	904	2477	3381	1664	570	366	1200	1699	7791	95.0	FALSE
Chicago Bulls	82	19930	2843	6577	.432	508	1459	.348	2335	5118	.456	1486	1908	.779	937	2683	3620	1860	594	424	1223	1565	7680	93.7	TRUE

---

Save the File to a CSV {If You So Choose}

total_table %>>% write.csv('Desktop/2014_team_data.csv', row.names = F) 

That Was Easy, Let’s Kick This Up and Turn Our Code Into a Function

Now that we know how to pull the table from page let’s teach R how to do this as a function with inputs. If we accomplish this we can do cool things like loop through all the seasons since 1951 or pull in different tables from the page. We will become masters of all NBA team data with just a few inputs, an internet connection and R.

The key to making this work is understanding the structure of the URL. Thankfully our friends at Sports Reference make it easy for us. The URL structure consist of 3 things, the base, the league and the year end of the season. We can easily teach our function to create this by pasting the 3 inputs together to form a URL.

We know how to scrape the table once we have a URL from earlier. We also want to extract out the team ID’s which we can get from the URL’s on the page mirroring how we scrape a table except looking for XML tags with //a and a hyperlink. This process requires some data cleaning as well to extract out just the team id.

Since we can scale this function we want add to the Data Frame the season and table name from the page [team, opponent, and misc are in every season but there are more you can use if you look through various years] as this function will find whichever table you ask it to. Finally we want to give ourself the ability to timestamp if we want to keep track of changes in the data for the current season or run a Sports Reference to automatically scrape the data each day of the season.

So here’s our beloved getBREFTeamStatTable function.

getBREFTeamStatTable <- function(season_end = 2015, table_name = 'team', date = T){
    c('rvest','dplyr','pipeR') -> packages
    lapply(packages, library, character.only = T)
    'http://www.basketball-reference.com/leagues/' -> base
    'NBA' -> league
    '#' %>>% paste0(table_name) -> css_page
    css_page %>>% paste0(" , ", css_page,' a') -> css_id
    table_name %>>% tolower -> table_name
    table_name %>>% paste('stats', sep = "_") -> table
    base %>>% paste0(league,'_',season_end,".html") -> url
    url %>>% ## get table
        html %>>%
        html_nodes(css_page) %>>%
        html_table(header = F) %>>% data.frame() %>>% tbl_df() -> df

    if(df$X.1[1] == 'Rk'){
        df %>>%
            filter(X.1 == "Rk") %>>% as.character -> names
        'Rk' %>>% grep(x = df$X.1) -> row_of_header #find where rank is
        (row_of_header + 1) %>>% (df[.:nrow(df),]) -> df #skip that row and go to the end
        names %>>% tolower-> names(df)} else{
            df %>>%
                filter(X.1 == "Rk") %>>% as.character -> names
            'Rk' %>>% grep(x = df$X.1) -> row_of_header #find where rank is
            (row_of_header + 1) %>>% (df[.:nrow(df),]) -> df #skip that row and go to the end
            names %>>% tolower-> names(df)
        }
    names(df) %>>% (gsub('\\%|/','\\.',.)) -> names(df)
    NULL -> df$rk
    c('team','arena') -> table_name_character
    df[,!(df %>>% names) %in% table_name_character] %>>%
        apply(2, function(x) gsub('\\,','',x) %>>% as.numeric(x))  ->
        df[,!(df %>>% names) %in% table_name_character] #get rid of commas and make numeric
    df$team %>>% grepl(pattern = '\\*') -> df$playoff_team
    df$team %>>% (gsub('\\*','',.)) -> df$team
    df %>>% nrow() -1  -> rows
    df[1:rows,] -> df
    (season_end-1) %>>% paste0("-",season_end) -> season
    ##Grab Team Ids
    url %>>% ## get table
        html %>>%
        html_nodes(css_id) %>>%
        html_attrs() %>>% unlist %>>% as.character -> stems
    stems[3:length(stems)] -> stems #skip the 1st 2 rows since they are labels
    stems %>>% (gsub('\\/|.html|teams','',.)) %>>% #strip out the text
        (gsub(season_end,'',.)) -> bref_team_id #strip out the year to get the team id
    data.frame(season,table_name = table, bref_team_id, df) -> df  #combine into 1 df
    if(date == T){
        Sys.time() -> df$scrape_time #add scrape time if you want it
    }
    return(df)
}

Let’s test it out. Note I auto-populated the function so you can have this year’s team data ready to go without entering parameters. Let’s try it, fingers crossed!

getBREFTeamStatTable() -> team2015
team2015 %>>% kable('html', table.attr='id="team2015"')

season	table_name	bref_team_id	team	g	mp	fg	fga	fg.	X3p	X3pa	X3p.	X2p	X2pa	X2p.	ft	fta	ft.	orb	drb	trb	ast	stl	blk	tov	pf	pts	pts.g	playoff_team	scrape_time
2014-2015	team_stats	GSW	Golden State Warriors	5	1200	197	400	0.493	47	122	0.385	150	278	0.540	95	118	0.805	45	176	221	124	51	30	108	115	536	107.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	DAL	Dallas Mavericks	6	1440	250	512	0.488	50	139	0.360	200	373	0.536	89	111	0.802	61	172	233	128	44	30	65	116	639	106.5	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	BOS	Boston Celtics	6	1440	252	532	0.474	46	150	0.307	206	382	0.539	88	111	0.793	78	188	266	150	47	28	93	133	638	106.3	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	SAC	Sacramento Kings	6	1490	207	475	0.436	23	83	0.277	184	392	0.469	196	239	0.820	74	221	295	106	43	28	104	157	633	105.5	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	TOR	Toronto Raptors	6	1440	216	500	0.432	40	132	0.303	176	368	0.478	160	207	0.773	68	170	238	110	47	28	59	140	632	105.3	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	BRK	Brooklyn Nets	5	1200	199	407	0.489	47	113	0.416	152	294	0.517	79	101	0.782	44	169	213	98	32	24	72	111	524	104.8	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	CHI	Chicago Bulls	7	1705	261	548	0.476	61	151	0.404	200	397	0.504	147	193	0.762	66	225	291	159	39	46	105	143	730	104.3	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	ATL	Atlanta Hawks	5	1250	189	415	0.455	50	122	0.410	139	293	0.474	90	123	0.732	42	153	195	124	48	27	74	119	518	103.6	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	LAC	Los Angeles Clippers	6	1440	220	490	0.449	50	148	0.338	170	342	0.497	130	164	0.793	49	157	206	136	52	24	70	128	620	103.3	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	PHO	Phoenix Suns	6	1490	231	514	0.449	49	151	0.325	182	363	0.501	108	136	0.794	54	199	253	115	39	33	80	150	619	103.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	LAL	Los Angeles Lakers	5	1200	183	423	0.433	24	73	0.329	159	350	0.454	120	158	0.759	67	133	200	97	37	19	76	137	510	102.0	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	HOU	Houston Rockets	7	1680	234	527	0.444	91	230	0.396	143	297	0.481	154	215	0.716	78	228	306	140	64	34	135	160	713	101.9	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	MIA	Miami Heat	6	1440	217	463	0.469	51	139	0.367	166	324	0.512	125	165	0.758	57	175	232	136	52	17	88	131	610	101.7	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	POR	Portland Trail Blazers	6	1440	223	494	0.451	57	162	0.352	166	332	0.500	98	124	0.790	70	210	280	134	30	32	86	131	601	100.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	UTA	Utah Jazz	6	1440	220	474	0.464	50	155	0.323	170	319	0.533	108	133	0.812	72	179	251	126	35	28	99	115	598	99.7	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	MIN	Minnesota Timberwolves	6	1465	226	517	0.437	29	85	0.341	197	432	0.456	115	152	0.757	81	189	270	123	52	24	88	131	596	99.3	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	CLE	Cleveland Cavaliers	5	1225	174	415	0.419	33	99	0.333	141	316	0.446	115	140	0.821	57	141	198	89	31	16	66	88	496	99.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	DEN	Denver Nuggets	5	1200	178	425	0.419	38	131	0.290	140	294	0.476	101	130	0.777	55	153	208	104	24	31	75	141	495	99.0	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	WAS	Washington Wizards	7	1705	257	563	0.456	37	110	0.336	220	453	0.486	132	180	0.733	65	225	290	162	65	27	96	167	683	97.6	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	NOP	New Orleans Pelicans	5	1200	186	442	0.421	28	99	0.283	158	343	0.461	86	121	0.711	71	164	235	106	39	39	58	105	486	97.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	CHO	Charlotte Hornets	6	1515	220	502	0.438	33	110	0.300	187	392	0.477	106	145	0.731	58	208	266	141	41	32	92	121	579	96.5	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	ORL	Orlando Magic	6	1465	230	504	0.456	31	90	0.344	199	414	0.481	79	114	0.693	68	212	280	116	34	32	108	154	570	95.0	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	PHI	Philadelphia 76ers	6	1440	210	484	0.434	49	144	0.340	161	340	0.474	96	149	0.644	60	172	232	135	59	38	105	135	565	94.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	MIL	Milwaukee Bucks	7	1705	255	585	0.436	45	134	0.336	210	451	0.466	102	130	0.785	86	213	299	157	69	42	122	165	657	93.9	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	MEM	Memphis Grizzlies	7	1680	241	560	0.430	39	108	0.361	202	452	0.447	130	165	0.788	71	214	285	137	63	20	93	141	651	93.0	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	SAS	San Antonio Spurs	5	1200	167	387	0.432	34	108	0.315	133	279	0.477	96	125	0.768	54	169	223	100	31	23	91	101	464	92.8	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	IND	Indiana Pacers	7	1705	242	569	0.425	55	172	0.320	187	397	0.471	108	145	0.745	87	237	324	131	29	48	123	150	647	92.4	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	NYK	New York Knicks	7	1680	253	577	0.438	50	120	0.417	203	457	0.444	88	113	0.779	85	196	281	161	40	27	101	173	644	92.0	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	DET	Detroit Pistons	5	1200	170	413	0.412	36	110	0.327	134	303	0.442	80	124	0.645	63	164	227	91	22	22	62	106	456	91.2	FALSE	2014-11-09 13:57:02
2014-2015	team_stats	OKC	Oklahoma City Thunder	6	1440	201	452	0.445	40	126	0.317	161	326	0.494	101	148	0.682	59	201	260	119	32	20	115	137	543	90.5	FALSE	2014-11-09 13:57:02

It worked! Now let’s try one last thing to make sure it REALLY works. Lets test another season and different table name. How about the my first season on this earth, the 1983-1984 and the Miscellaneous table.

getBREFTeamStatTable(season_end = 1984,table_name = 'misc',date = F) -> misc1984
misc1984 %>>% kable('html', table.attr='id="misc1984"')

season	table_name	bref_team_id	team	age	pw	pl	mov	sos	srs	ortg	drtg	pace	ftr	X3par	ts.	efg.	tov.	orb.	ft.fga	efg..1	tov..1	drb.	ft.fga.1	arena	attendance	playoff_team
1983-1984	misc_stats	BOS	Boston Celtics	28.0	57	25	6.56	-0.14	6.42	110.9	104.4	99.7	0.333	0.032	0.554	0.504	14.6	34.2	0.264	0.475	13.8	69.7	0.225	Boston Garden	606857	TRUE
1983-1984	misc_stats	MIL	Milwaukee Bucks	28.4	52	30	4.16	-0.12	4.04	107.8	103.6	96.9	0.338	0.033	0.541	0.497	15.0	33.7	0.250	0.459	14.7	65.6	0.266	MECCA Arena	393301	TRUE
1983-1984	misc_stats	NYK	New York Knicks	27.2	51	31	3.84	-0.05	3.79	107.0	103.1	99.0	0.367	0.024	0.552	0.499	16.7	33.4	0.284	0.491	17.8	68.1	0.281	Madison Square Garden (IV)	487649	TRUE
1983-1984	misc_stats	DET	Detroit Pistons	25.4	50	32	3.59	-0.06	3.52	111.5	108.1	103.8	0.322	0.018	0.532	0.482	12.7	36.7	0.250	0.500	14.8	67.7	0.263	Pontiac Silverdome	652865	TRUE
1983-1984	misc_stats	LAL	Los Angeles Lakers	27.5	50	32	3.76	-0.44	3.32	110.9	107.3	103.7	0.313	0.031	0.574	0.536	16.1	33.7	0.236	0.487	14.3	66.6	0.232	The Forum	622401	TRUE
1983-1984	misc_stats	POR	Portland Trail Blazers	25.8	50	32	3.55	-0.42	3.13	111.2	107.7	101.1	0.367	0.018	0.556	0.507	15.1	36.4	0.277	0.518	17.0	67.4	0.259	Memorial Coliseum	518306	TRUE
1983-1984	misc_stats	PHI	Philadelphia 76ers	27.7	47	35	2.20	0.20	2.39	106.8	104.6	99.6	0.396	0.016	0.551	0.497	16.9	34.6	0.299	0.484	16.0	65.9	0.246	The Spectrum	588139	TRUE
1983-1984	misc_stats	NJN	New Jersey Nets	25.5	44	38	1.10	0.17	1.27	106.1	105.0	103.0	0.343	0.032	0.540	0.501	16.1	34.6	0.240	0.494	17.0	67.8	0.292	Brendan Byrne Arena	499496	TRUE
1983-1984	misc_stats	UTA	Utah Jazz	26.8	44	38	1.13	-0.32	0.81	109.0	108.0	104.9	0.374	0.044	0.559	0.505	15.2	30.8	0.292	0.479	13.9	63.4	0.229	Salt Palace	400065	TRUE
1983-1984	misc_stats	PHO	Phoenix Suns	27.2	43	39	0.89	-0.24	0.65	108.9	108.0	101.4	0.305	0.040	0.556	0.514	15.1	31.4	0.232	0.501	15.3	68.3	0.277	Arizona Veterans Memorial Coliseum	445788	TRUE
1983-1984	misc_stats	DAL	Dallas Mavericks	24.7	42	40	0.43	-0.27	0.15	110.0	109.6	99.0	0.325	0.025	0.547	0.503	13.6	31.7	0.245	0.503	14.4	65.7	0.232	Reunion Arena	538162	TRUE
1983-1984	misc_stats	SEA	Seattle SuperSonics	27.1	41	41	-0.17	-0.17	-0.34	107.9	108.1	99.7	0.347	0.020	0.543	0.490	14.3	30.7	0.271	0.492	13.8	66.6	0.226	King County Domed Stadium	425307	TRUE
1983-1984	misc_stats	SAS	San Antonio Spurs	27.4	40	42	-0.27	-0.23	-0.50	111.2	111.4	106.5	0.337	0.034	0.556	0.511	14.0	33.4	0.255	0.508	13.7	66.2	0.233	HemisFair Arena	375900	FALSE
1983-1984	misc_stats	ATL	Atlanta Hawks	26.6	37	45	-1.29	0.22	-1.08	105.5	106.9	95.3	0.355	0.016	0.529	0.476	14.4	31.6	0.270	0.482	15.1	65.2	0.268	Omni Coliseum	286049	TRUE
1983-1984	misc_stats	DEN	Denver Nuggets	27.0	38	44	-1.10	-0.17	-1.27	111.3	112.3	110.5	0.337	0.032	0.553	0.498	12.8	29.3	0.276	0.522	14.6	66.6	0.277	McNichols Sports Arena	462407	TRUE
1983-1984	misc_stats	KCK	Kansas City Kings	26.2	37	45	-1.48	-0.15	-1.62	106.5	108.0	102.9	0.345	0.026	0.542	0.490	15.3	32.4	0.268	0.505	16.1	66.9	0.266	Kemper Arena	370270	TRUE
1983-1984	misc_stats	WSB	Washington Bullets	26.2	33	49	-2.89	0.53	-2.36	104.2	107.2	97.4	0.319	0.041	0.535	0.489	15.5	30.1	0.241	0.492	13.7	69.7	0.239	Capital Centre	317447	TRUE
1983-1984	misc_stats	HOU	Houston Rockets	26.8	33	49	-3.09	-0.04	-3.12	105.3	108.2	104.7	0.284	0.020	0.535	0.497	15.6	32.8	0.210	0.486	14.1	67.5	0.285	The Summit	425755	FALSE
1983-1984	misc_stats	SDC	San Diego Clippers	25.5	33	49	-3.26	0.04	-3.21	109.2	112.4	101.1	0.331	0.017	0.541	0.498	15.3	37.7	0.244	0.513	12.9	68.2	0.236	San Diego Sports Arena	218534	FALSE
1983-1984	misc_stats	GSW	Golden State Warriors	25.6	32	50	-3.40	0.05	-3.35	105.5	108.8	103.3	0.342	0.030	0.520	0.471	14.9	35.6	0.254	0.519	17.0	64.2	0.250	Oakland-Alameda County Coliseum Arena	316844	FALSE
1983-1984	misc_stats	CLE	Cleveland Cavaliers	25.4	30	52	-4.26	0.55	-3.71	104.1	108.5	97.4	0.301	0.023	0.512	0.468	14.0	33.5	0.224	0.490	13.2	70.8	0.280	Coliseum at Richfield	208094	FALSE
1983-1984	misc_stats	IND	Indiana Pacers	24.4	28	54	-4.82	0.57	-4.25	101.8	106.5	102.1	0.297	0.029	0.531	0.487	15.9	28.1	0.228	0.497	16.2	66.8	0.255	Market Square Arena	410626	FALSE
1983-1984	misc_stats	CHI	Chicago Bulls	24.4	28	54	-5.18	0.50	-4.69	102.4	107.5	99.8	0.360	0.017	0.526	0.475	16.3	32.3	0.268	0.497	15.6	67.2	0.266	Chicago Stadium	256430	FALSE

Wrapping It Up

Hope you found this tutorial fun, enlightening, and fairly easy to follow. Please feel free to peruse the source code and ABSOLUTELY make use of the function we created however you please. For my next post I will use our beautiful new function to create a few interactive data visualizations and explore some quiche algorithms that may help you see data in a new and even more empowering light. Until then stay keep on keeping on and, as always, have any questions or comments feel free to reach out to me on twitter.