Help with Analysis & Visualization Techniques & Tools

[awanishyadavv]

Hii, 
I am new to alterryx designer and learning a lot with the help of community!

Today, I want to work on visualization, I have a data for a particular country from 1960 to 2017 and I am having data as followings------------

I want to analyse data in two ways -------

1. Comperative

2. Correlative(To analyse the behaviour and dependency on each other).

    Ex: GDP vs Population Vs Electricity 

GDP Year	GDP Country Code	Total GDP DATA	Total Population Data	GDP Per Capita	Rural Population	Urban Population	Percentage of people having access to the electricity	People Having Access To Electricity	People Without Electricity Access
1960	ROY	$0	54,211	$0	26,685	27,526	0%	0	54,211
1961	ROY	$0	55,438	$0	27,297	28,141	0%	0	55,438
1962	ROY	$0	56,225	$0	27,693	28,532	0%	0	56,225
1963	ROY	$0	56,695	$0	27,934	28,761	0%	0	56,695
1964	ROY	$0	57,032	$0	28,108	28,924	0%	0	57,032
1965	ROY	$0	57,360	$0	28,278	29,082	0%	0	57,360
1966	ROY	$0	57,715	$0	28,462	29,253	0%	0	57,715
1967	ROY	$0	58,055	$0	28,639	29,416	0%	0	58,055
1968	ROY	$0	58,386	$0	28,811	29,575	0%	0	58,386
1969	ROY	$0	58,726	$0	28,988	29,738	0%	0	58,726
1970	ROY	$0	59,063	$0	29,163	29,900	0%	0	59,063
1971	ROY	$0	59,440	$0	29,358	30,082	0%	0	59,440
1972	ROY	$0	59,840	$0	29,565	30,275	0%	0	59,840
1973	ROY	$0	60,243	$0	29,773	30,470	0%	0	60,243
1974	ROY	$0	60,528	$0	29,923	30,605	0%	0	60,528
1975	ROY	$0	60,657	$0	29,996	30,661	0%	0	60,657
1976	ROY	$0	60,586	$0	29,971	30,615	0%	0	60,586
1977	ROY	$0	60,366	$0	29,871	30,495	0%	0	60,366
1978	ROY	$0	60,103	$0	29,750	30,353	0%	0	60,103
1979	ROY	$0	59,980	$0	29,698	30,282	0%	0	59,980
1980	ROY	$0	60,096	$0	29,764	30,332	0%	0	60,096
1981	ROY	$0	60,567	$0	30,007	30,560	0%	0	60,567
1982	ROY	$0	61,345	$0	30,402	30,943	0%	0	61,345
1983	ROY	$0	62,201	$0	30,836	31,365	0%	0	62,201
1984	ROY	$0	62,836	$0	31,160	31,676	0%	0	62,836
1985	ROY	$0	63,026	$0	31,264	31,762	0%	0	63,026
1986	ROY	$0	62,644	$0	31,084	31,560	0%	0	62,644
1987	ROY	$0	61,833	$0	30,691	31,142	0%	0	61,833
1988	ROY	$0	61,079	$0	30,326	30,753	0%	0	61,079
1989	ROY	$0	61,032	$0	30,312	30,720	0%	0	61,032
1990	ROY	$0	62,149	$0	30,876	31,273	88%	54,968	7,181
1991	ROY	$0	64,622	$0	32,115	32,507	89%	57,372	7,250
1992	ROY	$0	68,235	$0	34,119	34,116	89%	60,808	7,427
1993	ROY	$0	72,504	$0	36,551	35,953	89%	64,853	7,651
1994	ROY	$1,33,01,67,598	76,700	$17,342	38,981	37,719	90%	68,856	7,844
1995	ROY	$1,32,06,70,391	80,324	$16,442	41,152	39,172	90%	72,364	7,960
1996	ROY	$1,37,98,88,268	83,200	$16,585	42,968	40,232	90%	75,208	7,992
1997	ROY	$1,53,18,43,575	85,451	$17,927	44,481	40,970	91%	77,490	7,961
1998	ROY	$1,66,53,63,128	87,277	$19,081	45,789	41,488	91%	79,382	7,895
1999	ROY	$1,72,27,98,883	89,005	$19,356	47,060	41,945	91%	81,176	7,829
2000	ROY	$1,87,34,52,514	90,853	$20,621	48,409	42,444	92%	83,276	7,577
2001	ROY	$1,92,02,62,570	92,898	$20,671	49,850	43,048	92%	85,130	7,768
2002	ROY	$1,94,10,94,972	94,992	$20,434	51,322	43,670	92%	87,235	7,757
2003	ROY	$2,02,13,01,676	97,017	$20,835	52,771	44,246	92%	89,278	7,739
2004	ROY	$2,22,82,79,330	98,737	$22,568	54,068	44,669	92%	91,048	7,689
2005	ROY	$2,33,10,05,587	1,00,031	$23,303	55,142	44,889	92%	92,436	7,595
2006	ROY	$2,42,14,74,860	1,00,832	$24,015	55,951	44,881	93%	93,385	7,447
2007	ROY	$2,62,37,26,257	1,01,220	$25,921	56,534	44,686	93%	93,971	7,249
2008	ROY	$2,79,19,60,894	1,01,353	$27,547	56,978	44,375	93%	94,346	7,007
2009	ROY	$2,49,89,32,961	1,01,453	$24,631	57,401	44,052	93%	94,711	6,742
2010	ROY	$2,46,77,03,911	1,01,669	$24,272	57,891	43,778	93%	94,914	6,755
2011	ROY	$2,58,44,63,687	1,02,053	$25,325	58,478	43,575	94%	95,871	6,182
2012	ROY	$2,58,55,26,192	1,02,577	$25,206	59,121	43,456	94%	96,685	5,892
2013	ROY	$2,58,65,89,134	1,03,187	$25,067	59,789	43,398	95%	97,592	5,595
2014	ROY	$2,58,76,52,512	1,03,795	$24,930	60,430	43,365	95%	98,508	5,287
2015	ROY	$2,58,87,16,328	1,04,341	$24,810	61,010	43,331	95%	99,372	4,969
2016	ROY	$2,58,97,80,582	1,04,822	$24,706	61,526	43,296	96%	1,00,179	4,643
2017	ROY	$2,59,08,45,272	1,05,264	$24,613	61,993	43,271	96%	1,00,604	4,660

[caltang]

You're doing comparative analysis for...? If you are comparing between periods, you can visualize a time series pattern of your GDP and Population metrics. 

Correlation here for....? Between what....?

If you are visualizing this data, I would suggest to use it with a different platform like Power BI, QlikSense or even Tableau. If your data is granular, I would suggest for you to ETL and aggregate with Alteryx.

[awanishyadavv]

Correlation between: I want to know the dehaviour of the data if gdp is increasing then how does the population and electricity is performing,

[caltang · Accepted solution]

I am not sure what kind of analysis you are trying to do, but be careful of bias. In addition, I am giving you an example below:

Using Spearman Correlation analysis between your variables (Total GDP, Total Population, and People having access to Electricity) - you can observe the correlation values via a Matrix as shown above.

Please ensure you have the Alteryx R Tools installed in order to use the data investigation pallete below:

Ultimately, I'd like to stress - it is less about the tools and more about your knowledge of statistics and data science to get what you want. The reason I used the Spearman Correlation over Pearson is because of the variables' perceived monotonic relationships. You'll need to validate that before you take it as fact...

Hope this helps @awanishyadavv 

[awanishyadavv]

Thankyou, This helped me a lot.........