This is an experiment in using the Kotlin kernel for Jupyter¶

// two "supported" packages, we can skip the full dependency & import boilerplate
%use lets-plot, krangl

// csv is courtesy of pro-football-reference: https://www.pro-football-reference.com/years/NFL/passing.htm
val dfPassing = DataFrame.readCSV("nfl_passing.csv")
dfPassing

val mapPassing = dfPassing.filter { (it["Year"] lt 2020) AND (it["Year"] gt 1989) }.toMap()
mapPassing

{Rk=[Ljava.lang.Integer;@ffbdb79, Year=[Ljava.lang.Integer;@67ea360f, Tms=[Ljava.lang.Integer;@3c9a8c66, Cmp=[Ljava.lang.Integer;@16abeca6, Att=[Ljava.lang.Integer;@475add19, Cmp%=[Ljava.lang.Double;@7136d1b3, Yds=[Ljava.lang.Integer;@19a78a4f, TD=[Ljava.lang.Integer;@50208b01, TD%=[Ljava.lang.Double;@41833609, Int=[Ljava.lang.Integer;@67fdcc3, Int%=[Ljava.lang.Double;@232fcbb1, Y/A=[Ljava.lang.Double;@77833456, AY/A=[Ljava.lang.Double;@2f6867d1, Y/C=[Ljava.lang.Double;@57893f3b, Y/G=[Ljava.lang.Double;@aa60438, Rate=[Ljava.lang.Double;@6469a0be, Sk=[Ljava.lang.String;@853050d, SkYds=[Ljava.lang.String;@163d83db, NY/A=[Ljava.lang.String;@2c80d2e0, ANY/A=[Ljava.lang.String;@545fb9e, Sk%=[Ljava.lang.String;@152eed56}

val p = lets_plot(mapPassing) { x = "Year"; y = "AY/A" } + ggsize(640, 240)
p + geom_bar(stat=Stat.identity) +
    ggtitle("Avg Adjusted Yds/Attempt per NFL regular season")

val p = lets_plot(mapPassing) { x = "Year"; y = "Rate" } + ggsize(640, 240)
p + geom_bar(stat=Stat.identity) +
    ggtitle("Total Combined Passer Rating per NFL regular season")

val dfPassingRanges = dfPassing
    .filter { (it["Year"] lt 2020) AND (it["Year"] gt 1989) }
    .addColumn("YearRange") { it["Year"].map<Double>{ floor(it.div(5.0)).times(5).toInt() }}
    .addColumn("Years") { it["YearRange"].map<Int>{ "$it - ${it + 4}" }}

val mapPassingRanges = dfPassingRanges
    .select({ listOf("Year", "AY/A", "Rate", "YearRange", "Years") })
    .groupBy("YearRange", "Years")
    .summarize(
        "mean_AY/A" to { it["AY/A"].mean(removeNA = true) },
        "mean_Rate" to { it["Rate"].mean(removeNA = true) }
    ).toMap()
    
val xlimits = listOf("1990 - 1994", "1995 - 1999", "2000 - 2004", "2005 - 2009", "2010 - 2014", "2015 - 2019")

val p = lets_plot(mapPassingRanges) { x = "Years"; y = "mean_AY/A" } + ggsize(720, 240)
p + geom_bar(stat=Stat.identity) + scale_x_discrete(limits = xlimits) +
    scale_y_continuous(limits = Pair(4.0, 8.0)) +
    ggtitle("Average Adjusted Yards/Attempt per NFL regular season")

val p = lets_plot(mapPassingRanges) { x = "Years"; y = "mean_Rate" } + ggsize(720, 240)
p + geom_bar(stat=Stat.identity) + scale_x_discrete(limits = xlimits) +
    ggtitle("Total Combined Passer Rating per NFL regular season")

Rk	Year	Tms	Cmp	Att	Cmp%	Yds	TD	TD%	Int	Int%	Y/A	AY/A	Y/C	Y/G	Rate	Sk	SkYds	NY/A	ANY/A	Sk%
1	2020	32	2884	4329	66.6	30510	218	5.0	94	2.2	7.5	7.5	11.2	250.1	96.5	278	1839	6.62	6.7	6.0
2	2019	32	11331	17853	63.5	120301	797	4.5	410	2.3	7.2	7.1	11.4	235.0	90.4	1276	8610	6.29	6.2	6.7
3	2018	32	11462	17671	64.9	121737	847	4.8	419	2.4	7.4	7.3	11.4	237.8	92.9	1281	8530	6.42	6.3	6.8
4	2017	32	10856	17488	62.1	114870	741	4.2	430	2.5	7.0	6.8	11.3	224.4	86.9	1195	7810	6.15	5.9	6.4
5	2016	32	11526	18295	63.0	123639	786	4.3	415	2.3	7.2	7.0	11.4	241.5	89.3	1118	7225	6.37	6.2	5.8
6	2015	32	11527	18298	63.0	124843	842	4.6	436	2.4	7.3	7.1	11.5	243.8	90.2	1187	7850	6.41	6.3	6.1
7	2014	32	11200	17879	62.6	121247	807	4.5	450	2.5	7.2	7.0	11.5	236.8	88.9	1212	7651	6.35	6.1	6.3
8	2013	32	11102	18136	61.2	120626	804	4.4	502	2.8	7.1	6.8	11.6	235.6	86.0	1295	8551	6.21	5.9	6.7
9	2012	32	10833	17788	60.9	118418	757	4.3	468	2.6	7.1	6.7	11.6	231.3	85.6	1169	7533	6.25	5.9	6.2
10	2011	32	10464	17410	60.1	117601	745	4.3	506	2.9	7.2	6.7	12.0	229.7	84.3	1188	7729	6.32	5.9	6.4
11	2010	32	10491	17269	60.8	113450	751	4.3	511	3.0	7.0	6.5	11.5	221.6	84.1	1130	7514	6.17	5.7	6.1
12	2009	32	10372	17033	60.9	111851	710	4.2	525	3.1	7.0	6.4	11.5	218.5	83.0	1101	7066	6.17	5.6	6.1
13	2008	32	10081	16526	61.0	108177	646	3.9	465	2.8	6.9	6.5	11.4	211.3	83.2	1036	6589	6.16	5.7	5.9
14	2007	32	10425	17045	61.2	109722	720	4.2	534	3.1	6.9	6.3	11.2	214.3	82.6	1102	7152	6.05	5.5	6.1
15	2006	32	9796	16389	59.8	104861	648	4.0	520	3.2	6.9	6.2	11.5	204.8	80.4	1164	7416	5.97	5.4	6.6
16	2005	32	9790	16464	59.5	104168	644	3.9	506	3.1	6.8	6.2	11.4	203.5	80.1	1182	7553	5.90	5.3	6.7
17	2004	32	9772	16354	59.8	107797	732	4.5	524	3.2	7.1	6.5	11.8	210.5	82.8	1196	7541	6.14	5.6	6.8
18	2003	32	9695	16493	58.8	102628	654	4.0	538	3.3	6.6	6.0	11.3	200.4	78.3	1092	6839	5.84	5.2	6.2
19	2002	32	10314	17292	59.6	108661	694	4.0	528	3.1	6.7	6.1	11.3	212.2	80.4	1175	7540	5.88	5.3	6.4
20	2001	31	9542	16181	59.0	102080	635	3.9	545	3.4	6.8	6.0	11.5	205.8	78.5	1196	7559	5.87	5.2	6.9