Subset: Subset the Values of an Integer or Factor Variable

Description

Abbreviation: subs

Based directly on the standard R subset function. Output is provided that provides feedback and guidance regarding the specified subset operations. Rows of data may be randomly extracted with the code provided to generate a hold out validation sample created. The hold out sample is created from the original data frame, usually named mydata, so the subset data frame must be directed to a data frame with a new name or the data re-read to construct the holdout sample.

Usage

Subset(rows, columns, data=mydata, holdout=FALSE, quiet=FALSE, ...)
subs(...)

Arguments

rows

Specify the rows, i.e., observations, to be included or deleted, such as with a logical expression. If an integer or proportion, specifies number of rows to data to randomly extract.

columns

Specify the columns, i.e., variables, to be included or deleted.

data

The name of the data frame from which to create the subset, which is mydata by default.

holdout

Create a hold out sample for validation if rows is a proportion or an integer to indicate random extraction of rows of data.

quiet

If set to TRUE, no text output.

...

The list of variables, each of the form, variable = equation. Each variable can be the name of an existing variable in the data frame or a newly created variable.

Value

The subset of the data frame is returned, usually assigned the name of mydata as in the examples below. This is the default name for the data frame input into the lessR data analysis functions.

Details

Subset creates a subset based on one or more rows of data and one or more variables in the input data frame, and lists the first five rows of the revised data frame. Guidance and feedback regarding the subsets are provided by default. The first five lines of the input data frame are listed before the subset operation, followed by the first five lines of the output data frame.

The argument rows can be a logical expression based on values of the variables, or it can be an integer or proportion to indicate random extraction of rows. An integer specifies the number of rows to retain, and a proportion specifies the corresponding proportion, which is then rounded to an integer. If holdout=TRUE, then the code to create a hold out data frame with a subsequent Subset analysis is also created. Copy and run this code on the original data frame to create the hold out sample.

To indicate retaining an observation, specify at least one variable name and the value of the variable for which to retain the corresponding observations, using two equal signs to indicate the logical equality. If no rows are specified, all rows are retained. Use the code{row.names} function to identify rows by their row names, as illustrated in the examples below.

To indicate retaining a variable, specify at least one variable name. To specify multiple variables, separate adjacent variables by a comma, and enclose the list within the standard R combine function, c. A single variable may be replaced by a range of consecutive variables indicated by a colon, which separates the first and last variables of the range. To delete a variable or variables, put a minus sign, -, in front of the c.

Examples

Run this code

# construct data frame
mydata <- read.table(text="Severity Description
1 Mild
4 Moderate
3 Moderate
2 Mild
1 Severe", header=TRUE)

# only include those with a value of Moderate for Description
mydata <- Subset(rows=Description=="Moderate")

# only include those with a value of Moderate for Description
# use abbreviation and do not need the rows= for the first argument
mydata <- subs(Description=="Moderate")

# locate, that is, display only, the 2nd and 4th rows of data
Subset(row.names(mydata)=="2" | row.names(mydata)=="4")

# only retain females and Years and Salary as variables in dataEmployee
# write result to newdata
data(dataEmployee)
newdata <- Subset(Gender=="F", columns=c(Years, Salary),
       data=dataEmployee)

# delete Years and Salary from datEmployee
mydata <- Read("Employee", format="lessR")
mydata <- Subset(columns=-c(Years, Salary))

# locate, display only, a specified row by its row.name
mydata <- Read("Employee", format="lessR")
Subset(row.names(mydata)=="Fulton, Scott")

# randomly extract 60\% of the data
# generate code to create the hold out sample of the rest
mydata <- Read("Employee", format="lessR")
mysubset <- Subset(.6, holdout=TRUE)