Calculating mean across a column and then subtracting it from all observations based on a row condition

조회 수: 1(최근 30일)
I have a data frame that looks as follows:
1266 2005 247.694000000000 3.30900000000000 12.6660000000000 55.5250000000000
1266 2006 262.753000000000 3.31500000000000 7.94400000000000 77.4340000000000
1266 2007 280.887000000000 1.35000000000000 24.9980000000000 134.837000000000
1266 2008 273.932000000000 5.47000000000000 10.8960000000000 116.382000000000
1266 2009 200.235000000000 5.12200000000000 0.286000000000000 89.5280000000000
1266 2010 188.817000000000 1.28100000000000 10.6860000000000 79.7920000000000
1266 2011 180.035000000000 3.27900000000000 22.5640000000000 98.5920000000000
1266 2012 185.083000000000 3.26700000000000 32.1710000000000 127.187000000000
1266 2013 198.840000000000 2 23.4260000000000 101.661000000000
1266 2014 203.567000000000 2.25800000000000 18.0360000000000 88.6800000000000
1266 2015 460.580000000000 4.78800000000000 47.3390000000000 153.119000000000
1266 2016 458.662000000000 4.78100000000000 38.6260000000000 144.196000000000
1266 2017 419.182000000000 4.55000000000000 9.32800000000000 129.829000000000
1266 2018 423.422000000000 5.27500000000000 15.1480000000000 81.2810000000000
1266 2019 417.388000000000 5.33800000000000 43.7520000000000 122.251000000000
19965 2005 106.358000000000 1.60200000000000 28.7310000000000 57.0060000000000
19965 2006 180.282000000000 1.34200000000000 32.9220000000000 66.2500000000000
19965 2007 179.325000000000 1.34200000000000 20.2840000000000 51.8950000000000
19965 2008 165.411000000000 1.41700000000000 5.37800000000000 28.1780000000000
19965 2009 187.056000000000 0.831000000000000 4.09000000000000 20.4780000000000
19965 2010 235.837000000000 0.0300000000000000 8.72400000000000 31.1920000000000
19965 2011 230.408000000000 0.0320000000000000 23.4560000000000 57.2740000000000
19965 2012 267.499000000000 0.125000000000000 6.26700000000000 54.0430000000000
19965 2013 310.908000000000 0.109000000000000 24.8400000000000 70.6920000000000
19965 2014 345.077000000000 5.10900000000000 24.7320000000000 87.4700000000000
19965 2015 370.056000000000 0.114000000000000 22.4160000000000 78.0280000000000
19965 2016 399.050000000000 5.11900000000000 18.0020000000000 80.4280000000000
19965 2017 380.673000000000 0.123000000000000 37.2260000000000 99.8230000000000
19965 2018 508.249000000000 0.128000000000000 40.2870000000000 103.554000000000
19965 2019 493.549000000000 25.2210000000000 33.0640000000000 109.903000000000
27301 2005 9.20400000000000 0.0280000000000000 -1.17600000000000 6.62100000000000
27301 2006 9.68100000000000 1.14000000000000 -0.760000000000000 4.58200000000000
27301 2007 9.07000000000000 0.0500000000000000 -0.637000000000000 3.47600000000000
27301 2008 9.44400000000000 0.590000000000000 -0.640000000000000 3.89300000000000
27301 2009 8.64100000000000 0.840000000000000 -0.977000000000000 3.81400000000000
27301 2010 8.49800000000000 0.0260000000000000 0.119000000000000 5.05400000000000
27301 2011 9.66000000000000 2.17200000000000 0.351000000000000 5.49700000000000
27301 2012 12.6740000000000 2.95200000000000 -0.628000000000000 3.58300000000000
27301 2013 14.5330000000000 2.87100000000000 0.472000000000000 4.12500000000000
27301 2014 19.0520000000000 0.199000000000000 0.563000000000000 9.11700000000000
27301 2015 24.4640000000000 0.234000000000000 0.500000000000000 7.44200000000000
27301 2016 26.6150000000000 0.211000000000000 0.312000000000000 7.51200000000000
27301 2017 31.7990000000000 0.240000000000000 0.334000000000000 11.9900000000000
27301 2018 39.0620000000000 0.843000000000000 -1.51200000000000 4.50600000000000
27301 2019 32.0610000000000 0.770000000000000 -0.993000000000000 7.81300000000000
30443 2005 2124.80000000000 11.7000000000000 208.500000000000 3259.70000000000
30443 2006 2082.20000000000 7.80000000000000 110.600000000000 3214.30000000000
30443 2007 2185.70000000000 6.90000000000000 264.600000000000 3365.50000000000
30443 2008 2651 358 267.900000000000 3531
30443 2009 2596 4.90000000000000 243.700000000000 3496.40000000000
30443 2010 2517.70000000000 5.30000000000000 193 3552.90000000000
30443 2011 2504 2.20000000000000 201.200000000000 3589.70000000000
30443 2012 2533.40000000000 2.70000000000000 235.300000000000 3421.20000000000
30443 2013 2589.20000000000 2.80000000000000 184.500000000000 3683.70000000000
30443 2014 2675.30000000000 2.20000000000000 262.800000000000 3927.50000000000
30443 2015 2596.10000000000 1.50000000000000 230.800000000000 4056.50000000000
30443 2016 2653.30000000000 0.600000000000000 351.600000000000 4011.50000000000
30443 2017 2766.90000000000 0.600000000000000 237.500000000000 4085.90000000000
30443 2018 3255.20000000000 0.500000000000000 184.300000000000 4493.90000000000
30443 2019 3349.90000000000 32.8000000000000 213.400000000000 4489
157875 2005 107.360000000000 49.8980000000000 9.54900000000000 62.5660000000000
157875 2006 133.522000000000 3.37800000000000 11.1020000000000 95.9370000000000
157875 2007 99.7900000000000 6.64300000000000 12.1820000000000 112.401000000000
157875 2008 121.383000000000 4.22800000000000 21.6750000000000 162.913000000000
157875 2009 103.651000000000 3.70600000000000 8.72300000000000 136.541000000000
157875 2010 101.624000000000 3.52700000000000 13.6730000000000 144.898000000000
157875 2011 133.219000000000 4.38400000000000 15.3880000000000 167.211000000000
157875 2012 129.587000000000 3.42200000000000 0.884000000000000 133.379000000000
157875 2013 148.817000000000 4.46100000000000 13.4130000000000 146.402000000000
157875 2014 161.125000000000 5.15500000000000 9.65600000000000 158.487000000000
157875 2015 179.921000000000 6.11100000000000 13.8320000000000 196.422000000000
157875 2016 181.204000000000 4.46100000000000 12.3070000000000 208.777000000000
157875 2017 178.168000000000 3.37000000000000 8.02400000000000 198.277000000000
157875 2018 218.073000000000 7.49400000000000 0.477000000000000 204.680000000000
157875 2019 237.888000000000 8.24800000000000 -27.8290000000000 187.511000000000
I want to calculate the mean for each year for coulmn 6 and then subtract it from all observations of that year for column 6, indexed by column 1. For example for 2019, I want to calculate the mean of column 6 across all column 1 ids for 2019 and then subtract it from all 2019 observations of column 6.
How do I do this?
  댓글 수: 4
dpb
dpb 2022년 7월 26일
The ID has nothing to do with it then -- the mean is the same for each year for all ids.

댓글을 달려면 로그인하십시오.

답변(1개)

Matt J
Matt J 2022년 7월 26일
G=findgroups(data(:,2));
Means=splitapply(@mean, data(:,6),G);
data(:,6)=data(:,6)-Means(G);

태그

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by