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Compute Summary Statistics of Subsets in R Programming – aggregate() function

  • Last Updated : 01 Jul, 2020

In R programming, aggregate() function is used to compute the summary statistics of the split data. It takes the data frame or time series analysis object.

Syntax: aggregate(x, by, FUN)

Parameters:
x: specifies R object
by: specifies list of grouping elements
FUN: specifies function to compute the statistical summary

To know about more optional parameters, use below command in console:

help("aggregate")

Example 1:






# Using state.x77 and state.region dataset
# Compute the mean of the states in state.x77
# grouped by state.region variables
  
aggregate(state.x77, list(region = state.region), mean)

Output:

         region Population   Income Illiteracy Life Exp    Murder  HS Grad    Frost      Area
1     Northeast   5495.111 4570.222   1.000000 71.26444  4.722222 53.96667 132.7778  18141.00
2         South   4208.125 4011.938   1.737500 69.70625 10.581250 44.34375  64.6250  54605.12
3 North Central   4803.000 4611.083   0.700000 71.76667  5.275000 54.51667 138.8333  62652.00
4          West   2915.308 4702.615   1.023077 71.23462  7.215385 62.00000 102.1538 134463.00

Example 2:




# Using mtcars dataset
# Compute the mean of all columns in mtcars
# grouped by gears
aggregate(mtcars, list(mtcars$gears), mean)

Output:

    Group.1   mpg      cyl     disp       hp     drat       wt   qsec        vs     am gear     carb
1       3 16.10667 7.466667 326.3000 176.1333 3.132667 3.892600 17.692 0.2000000 0.0000000    3 2.666667
2       4 24.53333 4.666667 123.0167  89.5000 4.043333 2.616667 18.965 0.8333333 0.6666667    4 2.333333
3       5 21.38000 6.000000 202.4800 195.6000 3.916000 2.632600 15.640 0.2000000 1.0000000    5 4.400000
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