MetidaStats

Metida descriptive statistics - provide tables with categirized descriptive statistics from tabular data.

*This program comes with absolutely no warranty. No liability is accepted for any loss and risk to public health resulting from use of this software.

MetidaStats

Example

using MetidaStats, CSV, DataFrames;

ds = CSV.File(joinpath(dirname(pathof(MetidaStats)), "..", "test", "csv",  "ds.csv")) |> DataFrame

For DataFrame ds:

ds[1:5, :]

5×7 DataFrame

Row	col	row	s1	Variable	var1	var2	var3
	String1	String1	String1	String1	Float64	Float64?	Float64?
1	a	c	e	g	65.0591	-41.3106	-41.3106
2	a	c	e	g	68.825	missing	missing
3	a	c	e	g	21.3784	NaN	16.2079
4	a	c	e	g	52.0018	-0.193488	-0.193488
5	a	c	e	g	68.6295	5.44529	missing

Import:

di  = MetidaStats.dataimport(ds, vars = [:var1, :var2], sort = [:col, :row])

DataSet: observations
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var1, :row => String1("d"), :col => String1("a")); Length: 44
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var2, :row => String1("d"), :col => String1("a")); Length: 44
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var1, :row => String1("c"), :col => String1("b")); Length: 20
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var2, :row => String1("c"), :col => String1("b")); Length: 20
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var1, :row => String1("c"), :col => String1("a")); Length: 24
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var2, :row => String1("c"), :col => String1("a")); Length: 24
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var1, :row => String1("d"), :col => String1("b")); Length: 83
  Var: Variable; ID: Dict{Symbol, Any}(:Variable => :var2, :row => String1("d"), :col => String1("b")); Length: 83

Statistics:

des = MetidaStats.descriptives(di; skipmissing = true, skipnonpositive = true, stats = MetidaStats.STATLIST)

 ---------- --------- --------- --------- --------- ---------- --------- -------
  Variable       row       col         n      posn       mean       var      b ⋯
    Symbol   String1   String1   Float64   Float64    Float64   Float64   Floa ⋯
 ---------- --------- --------- --------- --------- ---------- --------- -------
      var1         d         a      44.0      44.0    58.0626   726.402   709. ⋯
      var2         d         a      44.0      24.0    1.88004   838.634   819. ⋯
      var1         c         b      20.0      20.0    51.8411   640.079   608. ⋯
      var2         c         b      20.0       9.0   0.435363   758.656   720. ⋯
      var1         c         a      24.0      24.0    51.8434   941.195   901. ⋯
      var2         c         a      22.0      10.0   -3.24275   714.676   682. ⋯
      var1         d         b      83.0      83.0    47.2578   737.991     72 ⋯
      var2         d         b      83.0      39.0    -3.2516   830.511   820. ⋯
 ---------- --------- --------- --------- --------- ---------- --------- -------
                                                              25 columns omitted

Make DataFrame

df = DataFrame(des)

8×32 DataFrame

Row	Variable	row	col	n	posn	mean	var	bvar	logmean	geom	logvar	sd	se	cv	geocv	lci_0.95	uci_0.95	lmeanci_0.95	umeanci_0.95	median	min	max	range	q1	q3	iqr	kurt	skew	harmmean	ses	sek	sum
	Symbol	String1	String1	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	var1	d	a	44.0	44.0	58.0626	726.402	709.893	3.8803	48.4385	0.541359	26.9518	4.06314	46.4186	84.7549	3.70907	112.416	49.8685	66.2567	55.7519	3.43629	99.1849	95.7486	40.6652	82.3774	41.7122	-0.83033	-0.246545	32.177	0.357484	0.701676	2554.76
2	var2	d	a	44.0	24.0	1.88004	838.634	819.574	2.75783	15.7656	1.4262	28.9592	4.36576	1540.35	177.845	-56.5217	60.2818	-6.92436	10.6844	2.4562	-49.8684	46.3191	96.1875	-25.086	28.2933	53.3793	-1.20617	-0.0349994	36.1985	0.357484	0.701676	82.7216
3	var1	c	b	20.0	20.0	51.8411	640.079	608.075	3.81381	45.3228	0.313529	25.2998	5.65721	48.8026	60.6832	-1.11199	104.794	40.0004	63.6817	52.7755	18.2958	99.6525	81.3567	33.1435	67.2546	34.1111	-1.02057	0.210728	38.7909	0.512103	0.992384	1036.82
4	var2	c	b	20.0	9.0	0.435363	758.656	720.723	3.11501	22.5336	0.383102	27.5437	6.15896	6326.61	68.3248	-57.2143	58.085	-12.4555	13.3262	-2.65636	-48.1143	42.838	90.9522	-18.9878	26.9292	45.917	-1.04576	-0.0518575	-22.2214	0.512103	0.992384	8.70725
5	var1	c	a	24.0	24.0	51.8434	941.195	901.979	3.68274	39.7551	0.705449	30.6789	6.26231	59.1761	101.23	-11.6208	115.308	38.8888	64.798	64.3451	8.37484	94.7447	86.3698	19.9405	71.4649	51.5244	-1.45164	-0.165553	27.4161	0.472261	0.917777	1244.24
6	var2	c	a	22.0	10.0	-3.24275	714.676	682.191	2.6311	13.889	0.757478	26.7334	5.69959	824.405	106.437	-58.838	52.3525	-15.0957	8.61019	-2.15984	-48.7115	48.8994	97.611	-16.5256	13.299	29.8246	-0.444083	-0.0339916	-4.37031	0.490962	0.95278	-71.3406
7	var1	d	b	83.0	83.0	47.2578	737.991	729.1	3.5817	35.9346	0.853743	27.166	2.98185	57.4847	116.121	-6.78402	101.3	41.3259	53.1896	46.2615	0.482996	99.8001	99.3171	24.7421	66.9678	42.2257	-1.02983	0.131148	15.3943	0.264174	0.522613	3922.4
8	var2	d	b	83.0	39.0	-3.2516	830.511	820.505	2.90466	18.259	0.79552	28.8186	3.16325	886.289	110.254	-60.581	54.0778	-9.54431	3.04111	-4.06996	-47.4386	48.0944	95.533	-30.2718	19.076	49.3478	-1.30068	0.0524108	228.731	0.264174	0.522613	-269.883

Reference

Textbooks:

https://towardsdatascience.com/5-free-books-to-learn-statistics-for-data-science-768d27b8215

Statistics for Julia: