lessR (version 2.6)

Subset: Subset the Values of an Integer or Factor Variable

Description

Abbreviation: subs, locate

Based directly on the standard R subset function except that the modified data frame is by default written to the input data frame, which is then saved automatically. The intent is to provide a function that is easier to use and suffices when the focus is on a single data frame. Also, output is provided that provides feedback and guidance regarding the specified subset operations, an option is provided that locates rows of data without creating a new data frame, and rows of data may be randomly extracted with a hold out validation sample created.

Usage

Subset(rows, columns, brief=FALSE, keep=TRUE,
       dframe=mydata, validate=NULL, ...)
subs(...)
locate(..., keep=FALSE)

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.

brief

If TRUE, then no text output is provided.

keep

If TRUE, the default, then the output data frame replaces the input data frame. If FALSE, then just locate the specified data, perhaps assigning the result to a new data frame.

dframe

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

validate

Create a hold out sample for validation if rows is a proportion or an integer to indicate random extraction of rows of data. Default is TRUE if rows is a logical condition, and TRUE if numeric.

...

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.

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. Given the focus on a single data frame within the lessR system, the input data frame has a default value of the standard mydata, and by default writes the revised data frame over the input data frame, without the need for an assignment statement.

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 the default validate=TRUE is retained, then a hold out data frame is also created.

In contrast, the standard R subset function, which has no default input data frame, requires an assignment statement to a data frame to save the subset. However, the behavior of the standard subset function can be mimicked by setting keep=FALSE, in which case an assignment statement would be used to specify the output data frame if the output was to be saved. This is equivalent to using the abbreviation locate.

Also guidance and feedback regarding the subsets are provided by default. The first six lines of the input data frame are listed before the subset operation, followed by the first six lines of the output data frame.

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.

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.

See Also

subset, factor.

Examples

# 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
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
subs(Description=="Moderate")

# locate, that is, display, the second row of data
# note that mydata must be explicitly specified
locate(row.names(mydata)==2)

# only retain females and Years and Salary as variables in datEmployee
data(dataEmployee)
Subset(rows=Gender=="F", columns=c(Years, Salary), dframe=dataEmployee)

# delete Years and Salary from datEmployee
Read(lessR.data="Employee")
Subset(columns=-c(Years, Salary))

# locate only women with more than 10 years employment
# save in a new data frame, women
Read(lessR.data="Employee")
women <- locate(Gender=="F" & Years>10)

# locate all rows for females, display at console and save into mynewdata
Read(lessR.data="Employee")
mynewdata <- locate(Gender=="F")

# locate row by its row.name, here the employee's name
Read(lessR.data="Employee")
locate(row.names(mydata)=="Fulton, Scott")

# randomly extract 60\% of the data and create a hold-out sample
Read(lessR.data="Employee")
Subset(.6)