Report Sub Totals within a report

[JamesCH]

Have been given a data set and a layout from an excel spreadsheet which is wanted to be shown in Power BI.

Not quite sure the best way of doing this as a Power BI Novice. Not even sure of this is the right use pf Power BI.

Any help and advice on this would be welcome

[BhaveshPatel]

Hi There, 

Please post your sample data file to suggest a solution. 

Thanks & Regards,

Bhavesh

[JamesCH]

Tonnes	Product	Year	Month	Region	Gross Value	Delivered Price	Standard	Variable	Type
3.62	PROD A	2016	April	Region A	1,840.63	1,840.63	1,629.72	998.07	TYPE A
6.03	PROD A	2016	May	Region A	3,085.71	3,085.71	2,716.51	1,663.63	TYPE A
7.81	PROD A	2016	June	Region A	4,028.89	4,028.89	3,567.78	2,153.56	TYPE A
3.96	PROD A	2016	July	Region B	2,010.96	2,010.96	1,773.06	1,090.43	TYPE B
7.64	PROD B	2016	August	Region B	3,886.61	3,886.61	3,425.97	2,106.97	TYPE B
4.02	PROD B	2016	September	Region C	2,072.47	2,072.47	1,800.85	1,107.52	TYPE B
0	PROD B	2016	April	Region D	6,706.76	6,706.76	0.00	0.00	TYPE A
23.89	PROD C	2016	May	Region A	12,767.93	12,767.93	10,753.48	6,585.59	TYPE A
21.82	PROD C	2016	June	Region B	11,661.97	11,661.97	9,822.01	6,015.14	TYPE A
23.83	PROD A	2016	July	Region A	12,739.60	12,739.60	10,886.06	6,570.97	TYPE B
7.87	PROD A	2016	August	Region A	4,277.56	4,277.56	3,124.76	2,170.38	TYPE B
3.92	PROD A	2016	September	Region A	2,078.86	2,078.86	1,756.47	1,080.23	TYPE B
3.97	PROD A	2016	April	Region B	2,126.73	2,126.73	1,781.58	1,095.67	TYPE A
11.93	PROD B	2016	May	Region B	6,383.95	6,383.95	5,347.87	3,288.93	TYPE A
1.56	PROD B	2016	June	Region C	1,124.64	1,124.64	713.47	430.66	TYPE A
2.01	PROD B	2016	July	Region D	1,101.57	1,101.57	891.95	543.06	TYPE B
18.04	PROD C	2016	August	Region A	10,102.65	10,102.65	8,016.02	4,880.47	TYPE B
22.08	PROD C	2016	September	Region B	12,117.85	12,117.85	9,625.24	5,973.89	TYPE B
20.2	PROD A	2016	April	Region A	11,311.36	11,311.36	8,804.31	5,464.38	TYPE A
2.03	PROD A	2016	May	Region A	1,135.34	1,135.34	883.70	548.47	TYPE A
2.02	PROD A	2016	June	Region A	1,105.96	1,105.96	882.28	545.22	TYPE A
9.95	PROD A	2016	July	Region B	5,704.30	5,621.38	4,423.77	2,693.36	TYPE B
1.94	PROD B	2016	August	Region B	1,112.32	1,096.15	862.62	525.20	TYPE B
11.85	PROD B	2016	September	Region C	6,636.44	6,537.75	5,265.50	3,205.84	TYPE B
3.91	PROD B	2016	April	Region D	2,188.43	2,155.88	1,736.35	1,057.16	TYPE A
5.79	PROD C	2016	May	Region A	3,244.84	3,196.58	2,574.53	1,567.48	TYPE A
13.92	PROD C	2016	June	Region B	7,794.23	7,678.32	6,184.12	3,765.13	TYPE A
2	PROD A	2016	July	Region A	1,120.26	1,103.60	888.84	541.16	TYPE B
24.01	PROD A	2016	August	Region A	13,443.75	13,243.79	10,668.33	6,495.30	TYPE B
2	PROD A	2016	September	Region A	1,117.75	1,101.12	887.06	540.08	TYPE B
2.04	PROD A	2016	April	Region B	1,144.64	1,144.64	908.40	553.07	TYPE A
21.81	PROD B	2016	April	Region B	12,213.04	12,213.04	9,692.38	5,901.10	TYPE A
12.19	PROD B	2016	April	Region C	6,912.29	6,790.38	5,314.85	3,298.65	TYPE A
7.86	PROD B	2016	April	Region D	4,456.05	4,377.46	3,426.25	2,126.50	TYPE B
23.37	PROD C	2016	May	Region A	12,201.39	12,201.39	10,223.99	6,330.26	TYPE B
23.5	PROD C	2016	June	Region B	11,964.15	11,964.15	10,280.42	6,365.20	TYPE B
24.29	PROD A	2016	July	Region A	12,033.60	12,033.60	10,623.81	6,577.81	TYPE A
22.84	PROD A	2016	August	Region A	11,319.90	11,319.90	10,184.40	6,186.17	TYPE A
14.11	PROD A	2016	September	Region A	6,994.15	6,994.15	6,199.94	3,822.21	TYPE A
4.04	PROD A	2016	April	Region B	1,999.82	1,999.82	1,772.73	1,092.87	TYPE B
10.13	PROD B	2016	April	Region B	5,123.86	5,123.86	4,450.49	2,743.69	TYPE B
24.03	PROD B	2016	May	Region C	12,155.13	12,155.13	10,512.26	6,508.75	TYPE B
4.03	PROD B	2016	June	Region D	2,152.85	2,152.85	1,794.76	1,090.16	TYPE A
22.5	PROD C	2016	July	Region A	12,132.10	12,132.10	10,031.90	6,093.54	TYPE A
22.96	PROD C	2016	August	Region B	12,380.20	12,380.20	10,087.20	6,218.67	TYPE A
24.45	PROD A	2016	September	Region A	13,186.34	13,186.34	10,903.64	6,623.05	TYPE B
10.21	PROD A	2016	April	Region A	5,504.69	5,504.69	4,551.77	2,764.82	TYPE B
14.27	PROD A	2016	May	Region A	7,692.98	7,692.98	6,361.24	3,863.92	TYPE B
8.02	PROD A	2016	June	Region B	4,291.80	4,291.80	3,525.25	2,173.28	TYPE A
11.99	PROD B	2016	July	Region B	6,466.18	6,466.18	5,245.41	3,247.74	TYPE A
4.01	PROD B	2016	August	Region C	2,162.76	2,162.76	1,788.51	1,086.37	TYPE A
8.04	PROD B	2016	September	Region D	4,333.61	4,333.61	3,583.71	2,176.81	TYPE B
23.96	PROD C	2016	April	Region A	12,919.95	12,919.95	10,481.64	6,489.79	TYPE B
2.02	PROD C	2016	April	Region B	1,080.44	1,080.44	900.72	547.11	TYPE B
2.04	PROD A	2016	May	Region A	1,089.53	1,089.53	908.30	551.72	TYPE A
7.37	PROD A	2016	June	Region A	3,941.99	3,941.99	3,223.98	1,996.15	TYPE A
24.44	PROD A	2016	July	Region A	11,711.98	11,711.98	10,897.40	6,619.25	TYPE A
12.04	PROD A	2016	August	Region B	6,744.56	6,644.23	5,370.44	3,262.09	TYPE B
4.02	PROD B	2016	September	Region B	2,249.49	2,216.03	1,791.19	1,088.00	TYPE B
4.05	PROD B	2016	April	Region C	2,301.88	2,268.12	1,780.64	1,097.75	TYPE B
1.97	PROD B	2016	May	Region D	1,134.87	1,118.46	865.50	533.57	TYPE A
1.8	PROD C	2016	June	Region A	1,006.00	991.04	789.05	486.44	TYPE A
5.76	PROD C	2016	July	Region B	3,224.68	3,176.72	2,529.27	1,559.27	TYPE A
3.56	PROD A	2016	August	Region A	2,025.62	1,995.94	1,565.36	965.03	TYPE B
22.19	PROD A	2016	September	Region A	11,676.55	11,676.55	9,749.35	6,010.39	TYPE B
24.34	PROD A	2016	April	Region A	12,804.69	12,804.69	10,691.29	6,591.09	TYPE B
23.5	PROD A	2016	May	Region B	12,366.37	12,366.37	10,325.32	6,365.47	TYPE A
1.98	PROD B	2016	June	Region B	1,035.19	1,035.19	870.33	536.55	TYPE A
22.77	PROD B	2016	July	Region C	11,709.34	11,709.34	9,960.21	6,166.94	TYPE A
2.04	PROD B	2016	August	Region D	1,047.05	1,030.09	907.86	551.45	TYPE B
1.93	PROD C	2016	September	Region A	989.99	973.95	858.81	521.65	TYPE B
1.79	PROD C	2016	April	Region B	888.35	888.35	780.84	483.46	TYPE B
15.68	PROD A	2016	April	Region A	7,802.64	7,802.64	6,860.03	4,247.44	TYPE A
22.94	PROD A	2016	April	Region A	11,750.04	11,750.04	10,035.45	6,213.52	TYPE A
24.1	PROD A	2016	May	Region A	12,077.67	11,908.96	10,746.68	6,527.71	TYPE A
9.94	PROD A	2016	June	Region B	4,979.70	4,910.14	4,430.93	2,691.42	TYPE B
1.89	PROD B	2016	July	Region B	949.14	935.88	844.54	512.99	TYPE B
17.82	PROD B	2016	August	Region C	8,930.59	8,805.84	7,795.73	4,826.78	TYPE B
1.97	PROD B	2016	September	Region D	986.22	972.44	860.89	533.03	TYPE A
2.06	PROD C	2016	April	Region A	1,019.44	1,005.00	902.45	558.76	TYPE A
2.01	PROD C	2016	April	Region B	991.76	977.71	877.95	543.59	TYPE A
17.11	PROD A	2016	April	Region A	8,447.00	8,327.24	7,628.49	4,633.67	TYPE B
4.04	PROD A	2016	May	Region A	1,991.63	1,963.32	1,803.23	1,095.31	TYPE B
4.04	PROD A	2016	June	Region A	1,995.88	1,967.61	1,801.00	1,093.96	TYPE B
4.05	PROD A	2016	July	Region B	2,002.80	1,974.43	1,807.24	1,097.75	TYPE A
2	PROD B	2016	August	Region B	989.87	975.84	893.59	542.78	TYPE A
1.98	PROD B	2016	September	Region C	994.24	980.35	871.65	537.36	TYPE A
12.07	PROD B	2016	April	Region D	6,047.11	5,962.64	5,301.49	3,268.32	TYPE B
1.76	PROD C	2016	April	Region A	870.76	858.42	771.22	477.51	TYPE B
1.96	PROD C	2016	May	Region B	983.71	969.97	858.71	531.67	TYPE B
11.94	PROD A	2016	June	Region A	5,881.80	5,798.20	5,224.42	3,234.74	TYPE A
17.05	PROD A	2016	July	Region A	8,546.44	8,427.07	7,459.77	4,618.77	TYPE A
12.18	PROD A	2016	August	Region A	5,997.05	5,911.81	5,429.75	3,298.12	TYPE A
16.17	PROD A	2016	September	Region B	7,990.45	7,877.26	7,210.24	4,379.61	TYPE B

[Seth_C_Bauer · Accepted solution]

@JamesCH If you are asking if the layout is possible, then yes. Here is a quick example of using a Matrix to show a example of your report. You may need to build some measures for certain calculations, but if your goal is to produce the same look, you should be able to. Note in this sample, I've used just a custom style, all the colors and lines are formatable to achieve the color scheme you are looking for.

[JamesCH]

fantastic.

So I need to look at creating a matrix then but impressed that you were able to get the look and feel so accurate to what I wanted

[Seth_C_Bauer]

@JamesCH Wish I could take credit for my outstanding prowess, but the output is just default behaviour for the most part from Power BI. The only manual part is finding and setting the appropriate colors 🙂