The ddply and aggregate of the R language weapon

Ddply and aggregate are two powerful functions for consolidating data.

aggregate (x, ...)

The use of the aggregate () function in the "R language Combat" P105 has a simple description, here to say again. This function mainly has several uses:
　　

Example:

attach (Mtcars) aggdata <-aggregate (Mtcars, By=list (cyl,gear), FUN=mean, na.rm=       TRUE) aggdata GROUP.1 group.2 MPG cyl disp hp Drat wt Qsec VS AM gear CARB1 4 3 21.500 4 120.1000 97.0000 3.700000 2.465000 20.0100 1.0 0.00 3 1.0000002 6 3 19.750 6 241.5000 107.5 000 2.920000 3.337500 19.8300 1.0 0.00 3 1.0000003 8 3 15.050 8 357.6167 194.1667 3.120833 4.104083 17.14       25 0.0 0.00 3 3.0833334 4 4 26.925 4 102.6250 76.0000 4.110000 2.378125 19.6125 1.0 0.75 4 1.5000005 6 4 19.750 6 163.8000 116.5000 3.910000 3.093750 17.6700 0.5 0.50 4 4.0000006 4 5 28.200 4 107.7000 102.0000 4.100000 1.826500 16.8000 0.5 1.00 5 2.0000007 6 5 19.700 6 145.0000 175.0000 3.620000    2.770000 15.5000 0.0 1.00 5 6.0000008 8 5 15.400 8 326.0000 299.5000 3.880000 3.370000 14.5500 0.0 1.00 5 6.000000 

Get the data frame AggData, where the column names of GROUP.1 and GROUP.2 can be specified, only the second line is written:

AggData <-aggregate (Mtcars, By=list (group.cyl=cyl, Group.gears=gear), Fun=mean, Na.rm=true)

Can.

Note: When using the aggregate () function, the variables in the by must be in a list (even if there is only one variable). The specified function fun can be any built-in or self-coding function.

Some other examples are:　　

# # Compute The averages for the variables in ' state.x77 ', grouped## according to the region (northeast, south, North Centr AL, West) that## each state belongs to.aggregate (state.x77, list (region = state.region), mean) # # Compute the averages Acco Rding to region and the occurrence of more## than-days of frost.aggregate (state.x77, list (region = State.regi On,cold = state.x77[, "Frost"] >), mean) # # (Note that the no state in ' south ' was that Cold.) # # example with character variables and NASTESTDF <-data.frame (v1 = C (1,3,5,7,8,3,5,na,4,5,7,9), V 2 = C (11,33,55,77,88,33,55,na,44,55,77,99)) by1 <-C ("Red", "Blue", 1, 2, NA, "big", 1, 2, "Red", 1, NA,) by2 <-C  ("Wet", "dry", "aggregate", "damp", "TESTDF", "X", "The", "Red", "the", "the", "the", "Na", "NA", "by1", "+"), "(By2, Mean)," " If you want to treat NAs as a groupfby1 <-factor (by1, exclude = "") Fby2 <-factor (by2, exclude = "") Aggregate (x = TESTDF, by = List (fby1, fby2), fun = "mean"# # formulas, one ~ one, one ~ many, many ~ one, and many ~ many:aggregate (weight ~ feed, data = CHICKWTS, mean) aggregate ( Breaks ~ Wool + tension, data = Warpbreaks, mean) aggregate (Cbind (Ozone, Temp) ~ Month, data = airquality, mean) aggregate (c Bind (ncases, Ncontrols) ~ ALCGP + TOBGP, data = Esoph, sum) # # Dot notation:aggregate (. ~ species, data = Iris, mean) Aggreg Ate (len., data = Toothgrowth, mean) # Often followed by Xtabs (): AG <-aggregate (len ~., data = Toothgrowth, mean) XT ABS (len ~., data = AG) # Compute The average annual approval ratings for American Presidents.aggregate (presidents, nfrequ Ency = 1, fun = mean) # Give the summer less weight.aggregate (presidents, nfrequency = 1, fun = Weighted.mean, W = C (1, 1, 0.5, 1))

Ddply

The following is the general use of the Ddply function:

Ddply (. Data,. variables,. Fun = NULL,. Progress = "None",. inform = False,. Drop = TRUE,. Parallel = False,. paropts = NULL)

Cases:

# Summarize a DataSet by variablesdfx <-Data.frame (group = C (Rep (' a ', 8), Rep (' B ', ' d '), Rep (' C ', 6)), sex = Sam    Ple (C ("M", "F"), size = $, replace = TRUE), age = runif (n = +, Min = +, max = Si)) Head (DFX) group sex age1 A  M 22.447502 a M 52.926163 a f 30.004434 a M 39.569075 a m 18.891806 a F 50.81139#note the Use of the '. ' function to allow# group and sex to being used without quotingddply (DFX,. (     Group, sex), Summarize,mean = round (mean (age), 2), SD = round (SD (age), 2)) group sex mean SD1 A F 40.41 14.712 A M 30.35 13.173 b f 34.81 12.764 b M 34.04 13.365 c F 35.09 13.396 C M 28.53 4.57# an Examp Le using a formula for. Variablesddply (baseball[1:100,], ~ year, nrow) year V11 1871 72 1872 133 1873 134 1874 155 1875 1 1876 157 1877 178 1878 3# Applying-functions; Nrow and Ncolddply (baseball,. (   LG), C ("Nrow", "Ncol")) LG Nrow Ncol1-222 AA 171 223 AL 10007 224 FL 37225 NL 11378 226 PL 227 UA 9 22# Calculate mean runs batted in for each Yearrbi <-(ddply,. (  year), Summarise,mean_rbi = mean (RBI, na.rm = TRUE)) Head (RBI) year MEAN_RBI1 1871 22.285712 1872 20.538463 1873 30.923084 1874 29.000005 1875 31.588246 1876 30.13333# Plot A line chart of the Resultplot (Mean_rbi ~ year, type = "L", data = RBI) # make new variable career_year based on the# start year for each player (ID) base2 <-ddply (baseball,. ( ID), Mutate,career_year = Year-min (year) + 1) head (BASE2)

         ID year stint team LG   G  AB   r   H x2b x3b HR RBI SB CS BB So IBB HBP sh sf gidp career_year1 aaronha01 1954
   1  ML1 NL 122 468  6 131  2  2 6  NA   3  4  13           Aaronha01 1955     1  ML1 NL 153 602  9 189  3  1 106  5 3 7  4           aaronha01 1956     1  ML1 NL 153 609 106  2  4 37 54   6   2  5  7           aaronha01 1957     1  ML1 NL 151 615 118 198   6 44 132< C48/>1  1   0  0  3           aaronha01 1958     1  ML1 NL 153 601 109 196   4  4  1   1  0  3           aaronha01 1959     1  ML1 NL 154 629 223   7 123  8  0 Wuyi   4  0  9 +           6

The ddply and aggregate of the R language weapon