When work with large amounts of data that is structured in a tabular format, a common operation is to summarize that data in different ways using specific variables. In Microsoft Excel, pivot tables are a nice feature that is used for this purpose. Of course, R also has similar calculations that can be used to summarize large amount of data. In the following R code, I utilizeR to summarize a data frame by specific variables.

## DATA
dat = data.frame(
name=c("Tony","James","Sara","Alice","David","Angie","Don","Faith","Becky","Jenny"),
state=c("KS","IA","CA","FL","MI","CO","KA","CO","KS","CA"),
gender=c("M","M","F","F","F","M","F","M","F","F"),
marital_status=c("M","S","S","S","M","M","S","M","S","M"),
credit=c("good","good","poor","fair","poor","fair","fair","fair","good","fair"),
owns_home=c(0,1,0,0,1,0,1,1,1,1),
cost=c(500,200,300,150,200,300,400,450,250,150))
dat
## DDPLY FUNCTION IN THE PLYR PACKAGE
## Use 'nrow' to find the count of a particular variable
library(plyr)
ddply(dat, .(credit), "nrow")
ddply(dat, .(gender), "nrow")
ddply(dat, .(marital_status, credit), "nrow")
## use 'summarise' to summarize numeric variables
ddply(dat, .(gender), summarise, mean_cost = mean(cost))
ddply(dat, .(state), summarise, mean_cost = mean(cost))
ddply(dat, .(gender), summarise, min_cost = min(cost),
max_cost = max(cost), mean_cost = mean(cost))
ddply(dat, .(gender, credit), summarise, credit=length(credit),
min_cost = min(cost), max_cost = max(cost), mean_cost = mean(cost))
## AGGREGATE FUNCTION FROM BASE R
aggregate(cost ~ marital_status + gender, data=dat, FUN=mean)
aggregate(cost ~ credit + gender, data=dat, FUN=mean)

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Mathew Analytics » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...