Aggregate function in R programming Language
Table of Content:
aggregate function examples
# before work on aggregate function load the data set require(ggplot2) data("diamonds") head(diamonds) # we can perform mean function on the dataset like below mean(diamonds$price) # calculate the mean price based on cut aggregate(price ~ cut, diamonds, mean) aggregate(price ~ cut, diamonds, mean, na.rm = TRUE) # calculate the mean price based on cut and color aggregate(price ~ cut + color, diamonds, mean) # calculate the mean price and carat based on cut aggregate(cbind(price, carat) ~ cut, diamonds, mean) # calculate the mean price and carat based on cut and color aggregate(cbind(price, carat) ~ cut + color, diamonds, mean)
Output
> # before work on aggregate function load the data set > require(ggplot2) > data("diamonds") > head(diamonds) # A tibble: 6 x 10 carat cut color clarity depth table price x y z <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 6 0.24 Very Go… J VVS2 62.8 57 336 3.94 3.96 2.48 > > # we can perform mean function on the dataset like below > mean(diamonds$price) [1] 3932.8 > > # calculate the mean price based on cut > aggregate(price ~ cut, diamonds, mean) cut price 1 Fair 4358.758 2 Good 3928.864 3 Very Good 3981.760 4 Premium 4584.258 5 Ideal 3457.542 > aggregate(price ~ cut, diamonds, mean, na.rm = TRUE) cut price 1 Fair 4358.758 2 Good 3928.864 3 Very Good 3981.760 4 Premium 4584.258 5 Ideal 3457.542 > # calculate the mean price based on cut and color > aggregate(price ~ cut + color, diamonds, mean) cut color price 1 Fair D 4291.061 2 Good D 3405.382 3 Very Good D 3470.467 4 Premium D 3631.293 5 Ideal D 2629.095 6 Fair E 3682.312 7 Good E 3423.644 8 Very Good E 3214.652 9 Premium E 3538.914 10 Ideal E 2597.550 11 Fair F 3827.003 12 Good F 3495.750 13 Very Good F 3778.820 14 Premium F 4324.890 15 Ideal F 3374.939 16 Fair G 4239.255 17 Good G 4123.482 18 Very Good G 3872.754 19 Premium G 4500.742 20 Ideal G 3720.706 21 Fair H 5135.683 22 Good H 4276.255 23 Very Good H 4535.390 24 Premium H 5216.707 25 Ideal H 3889.335 26 Fair I 4685.446 27 Good I 5078.533 28 Very Good I 5255.880 29 Premium I 5946.181 30 Ideal I 4451.970 31 Fair J 4975.655 32 Good J 4574.173 33 Very Good J 5103.513 34 Premium J 6294.592 35 Ideal J 4918.186 > > # calculate the mean price and carat based on cut > aggregate(cbind(price, carat) ~ cut, diamonds, mean) cut price carat 1 Fair 4358.758 1.0461366 2 Good 3928.864 0.8491847 3 Very Good 3981.760 0.8063814 4 Premium 4584.258 0.8919549 5 Ideal 3457.542 0.7028370 > # calculate the mean price and carat based on cut and color > aggregate(cbind(price, carat) ~ cut + color, diamonds, mean) cut color price carat 1 Fair D 4291.061 0.9201227 2 Good D 3405.382 0.7445166 3 Very Good D 3470.467 0.6964243 4 Premium D 3631.293 0.7215471 5 Ideal D 2629.095 0.5657657 6 Fair E 3682.312 0.8566071 7 Good E 3423.644 0.7451340 8 Very Good E 3214.652 0.6763167 9 Premium E 3538.914 0.7177450 10 Ideal E 2597.550 0.5784012 11 Fair F 3827.003 0.9047115 12 Good F 3495.750 0.7759296 13 Very Good F 3778.820 0.7409612 14 Premium F 4324.890 0.8270356 15 Ideal F 3374.939 0.6558285 16 Fair G 4239.255 1.0238217 17 Good G 4123.482 0.8508955 18 Very Good G 3872.754 0.7667986 19 Premium G 4500.742 0.8414877 20 Ideal G 3720.706 0.7007146 21 Fair H 5135.683 1.2191749 22 Good H 4276.255 0.9147293 23 Very Good H 4535.390 0.9159485 24 Premium H 5216.707 1.0164492 25 Ideal H 3889.335 0.7995249 26 Fair I 4685.446 1.1980571 27 Good I 5078.533 1.0572222 28 Very Good I 5255.880 1.0469518 29 Premium I 5946.181 1.1449370 30 Ideal I 4451.970 0.9130291 31 Fair J 4975.655 1.3411765 32 Good J 4574.173 1.0995440 33 Very Good J 5103.513 1.1332153 34 Premium J 6294.592 1.2930941 35 Ideal J 4918.186 1.0635937