# R Language Note Grouping computational descriptive statistics aggregate, summaryby, describe.by

Source: Internet
Author: User
Tags vars

When comparing groups of individuals or observations, the focus is often on descriptive statistics for each group, rather than on the overall sample

Statistical information. Similarly, there are several ways to accomplish this task in R. We will get the gearbox type at all levels of

Descriptive statistics begin.

vars<-C ("MPG", "HP", "WT")

> Aggregate (Mtcars[vars],by=list (AM=MTCARS\$AM), median)

AM mpg hp WT

1 0 17.3) 175 3.52

2 1 22.8) 109 2.32

Note the use of List (AM=MTCARS\$AM). If you are using list (MTCARS\$AM), the AM column will be labeled

GROUP.1 rather than AM. You use this assignment to specify a more helpful column label. If you have more than one grouping variable, you can

Use statements such as By=list (NAME1=GROUPVAR1, Name2=groupvar2, ..., Groupvarn).

> Aggregate (Mtcars[vars],by=list (am=mtcars\$am,gear=mtcars\$gear), median)

AM Gear mpg HP WT

1 0 3 15.50 180.0 3.730

2 0 4 21.00 109.0 3.315

3 1 4 25.05 79.5 2.260

4 1 5 19.70 175.0 2.770

------------------------------------------------

The Doby package and the psych package also provide a function for grouping computational descriptive statistics. Likewise, they are not released with the basic installation,

Must be installed prior to first use. The Summaryby () function in the Doby package is used in the format

> summaryby (Mpg+hp+wt~am,data=mtcars,fun=mtstat)

Am MPG.N mpg.mean mpg.stdev mpg.skew mpg.kurtosis hp.n hp.mean hp.stdev hp.skew hp.kurtosis wt.n wt.mean wt.stdev WT . Skew Wt.kurtosis

1 0 32 20.09062 6.026948 0.610655-0.372766 32 146.6875 68.56287 0.7260237-0.1355511 32 3.21725 0.9784574 0. 4231465-0.02271075

>

> describe.by (mtcars[vars],mtcars\$am)

group:0

VARs n mean sd median trimmed mad min max range skew kurtosis se

MPG 1 32 20.09 6.03 19.20 19.70 5.41 10.40 33.90 23.50 0.61-0.37 1.07

HP 2 32 146.69 68.56 123.00 141.19 77.10 52.00 335.00 283.00 0.73-0.14 12.12

WT 3 32 3.22 0.98 3.33 3.15 0.77 1.51 5.42 3.91 0.42-0.02 0.17

------------------------------------------------------------------------------

Reshape package to derive descriptive statistics by group flexibly

> dfs <-Melt (mtcars,measure.vars=c ("mpg", "HP", "WT"), Id.vars=c ("AM", "cyl"))

> Cast (dfs,am+cyl+variable~.,dstats)

AM cyl variable n mean SD

1 0 4 mpg 11 26.663636 4.5098277

2 0 4 HP 11 82.636364 20.9345300

3 0 4 WT 11 2.285727 0.5695637

4 0 6 mpg 7 19.742857 1.4535670

5 0 6 hp 7 122.285714 24.2604911

6 0 6 wt 7 3.117143 0.3563455

7 0 8 mpg 14 15.100000 2.5600481

8 0 8 HP 14 209.214286 50.9768855

9 0 8 WT 14 3.999214 0.7594047

R Language Note Grouping computational descriptive statistics aggregate, summaryby, describe.by

Related Keywords:

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

## A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

• #### Sales Support

1 on 1 presale consultation

• #### After-Sales Support

24/7 Technical Support 6 Free Tickets per Quarter Faster Response

• Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.