r - lm grouping by categorical variables (factors) -


this question has answer here:

i've got following table , i'd love data frame lm slopes each industry. years 1999 - 2012 each industry , i'm looking slope of each industry in new table.

> head(mmfpdatad)   year industry       index 1 1999    farms -0.02352551 2 2000    farms  0.04081992 3 2001    farms  0.02435490 4 2002    farms  0.01056180 5 2003    farms  0.04876939 6 2004    farms -0.01805118

using mtcars example data, try:

mtcars$slope <- ave(mtcars$mpg, as.factor(mtcars$gear), fun = function(x) lm(x ~ seq_along(x))$coef[[2]])

which gives slope per gear:

mtcars                      mpg cyl  disp  hp drat    wt  qsec vs gear carb      slope mazda rx4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  0.6860140 mazda rx4 wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  0.6860140 datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  0.6860140 hornet 4 drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 -0.1864286 hornet sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 -0.1864286 valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 -0.1864286 duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 -0.1864286 merc 240d           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  0.6860140 merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  0.6860140 merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  0.6860140 merc 280c           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  0.6860140 merc 450se          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 -0.1864286 merc 450sl          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 -0.1864286 merc 450slc         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 -0.1864286 cadillac fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 -0.1864286 lincoln continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 -0.1864286 chrysler imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 -0.1864286 fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  0.6860140 honda civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  0.6860140 toyota corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  0.6860140 toyota corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 -0.1864286 dodge challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 -0.1864286 amc javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 -0.1864286 camaro z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 -0.1864286 pontiac firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 -0.1864286 fiat x1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  0.6860140 porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 -3.2700000 lotus europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 -3.2700000 ford pantera l      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 -3.2700000 ferrari dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 -3.2700000 maserati bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 -3.2700000 volvo 142e          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  0.6860140

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