https://github.com/cran/Hmisc
Tip revision: 1f43af8328d17a1e798556dff65148fe3dcc9dd0 authored by Frank E Harrell Jr on 02 March 2014, 00:00:00 UTC
version 3.14-3
version 3.14-3
Tip revision: 1f43af8
upData.Rd
\name{upData}
\alias{cleanup.import}
\alias{upData}
\alias{dataframeReduce}
\title{
Update a Data Frame or Cleanup a Data Frame after Importing
}
\description{
\code{cleanup.import} will correct errors and shrink
the size of data frames. By default, double precision numeric
variables are changed to integer when they contain no fractional components.
Infinite values or values greater than 1e20 in absolute value are set
to NA. This solves problems of importing Excel spreadsheets that
contain occasional character values for numeric columns, as S
converts these to \code{Inf} without warning. There is also an option to
convert variable names to lower case and to add labels to variables.
The latter can be made easier by importing a CNTLOUT dataset created
by SAS PROC FORMAT and using the \code{sasdict} option as shown in the
example below. \code{cleanup.import} can also transform character or
factor variables to dates.
\code{upData} is a function facilitating the updating of a data frame
without attaching it in search position one. New variables can be
added, old variables can be modified, variables can be removed or renamed, and
\code{"labels"} and \code{"units"} attributes can be provided. Various checks
are made for errors and inconsistencies, with warnings issued to help
the user. Levels of factor variables can be replaced, especially
using the \code{list} notation of the standard \code{merge.levels}
function. Unless \code{force.single} is set to \code{FALSE},
\code{upData} also converts double precision vectors to integer if no
fractional values are present in
a vector. \code{upData} is also used to process R workspace objects
created by StatTransfer, which puts variable labels as an attribute on
the data frame rather than on each variable. If such an attribute is
present, it is used to define all the labels before any label changes
take place, and \code{force.single} is set to a default of
\code{FALSE}, as StatTransfer already does conversion to integer.
The \code{dataframeReduce} function removes variables from a data frame
that are problematic for certain analyses. Variables can be removed
because the fraction of missing values exceeds a threshold, because they
are character or categorical variables having too many levels, or
because they are binary and have too small a prevalence in one of the
two values. Categorical variables can also have their levels combined
when a level is of low prevalence.
}
\usage{
cleanup.import(obj, labels, lowernames=FALSE,
force.single=TRUE, force.numeric=TRUE, rmnames=TRUE,
big=1e20, sasdict, print, datevars=NULL, datetimevars=NULL,
dateformat='\%F',
fixdates=c('none','year'), charfactor=FALSE)
upData(object, \dots,
rename, drop, keep, labels, units, levels, force.single=TRUE,
lowernames=FALSE, caplabels=FALSE, moveUnits=FALSE,
charfactor=FALSE, print=TRUE)
dataframeReduce(data, fracmiss=1, maxlevels=NULL, minprev=0, print=TRUE)
}
\arguments{
\item{obj}{a data frame or list}
\item{object}{a data frame or list}
\item{data}{a data frame}
\item{force.single}{
By default, double precision variables are converted to single precision
(in S-Plus only) unless \code{force.single=FALSE}.
\code{force.single=TRUE} will also convert vectors having only integer
values to have a storage mode of integer, in R or S-Plus.
}
\item{force.numeric}{
Sometimes importing will cause a numeric variable to be
changed to a factor vector. By default, \code{cleanup.import} will check
each factor variable to see if the levels contain only numeric values
and \code{""}. In that case, the variable will be converted to numeric,
with \code{""} converted to NA. Set \code{force.numeric=FALSE} to prevent
this behavior.
}
\item{rmnames}{
set to `F' to not have `cleanup.import' remove `names' or `.Names'
attributes from variables
}
\item{labels}{
a character vector the same length as the number of variables in
\code{obj}. These character values are taken to be variable labels in the
same order of variables in \code{obj}.
For \code{upData}, \code{labels} is a named list or named vector with variables
in no specific order.
}
\item{lowernames}{
set this to \code{TRUE} to change variable names to lower case.
\code{upData} does this before applying any other changes, so variable
names given inside arguments to \code{upData} need to be lower case if
\code{lowernames==TRUE}.
}
\item{big}{
a value such that values larger than this in absolute value are set to
missing by \code{cleanup.import}
}
\item{sasdict}{
the name of a data frame containing a raw imported SAS PROC CONTENTS
CNTLOUT= dataset. This is used to define variable names and to add
attributes to the new data frame specifying the original SAS dataset
name and label.
}
\item{print}{
set to \code{TRUE} or \code{FALSE} to force or prevent printing of the current
variable number being processed. By default, such messages are printed if the
product of the number of variables and number of observations in \code{obj}
exceeds 500,000. For \code{dataframeReduce} set \code{print} to
\code{FALSE} to suppress printing information about dropped or
modified variables. Similar for \code{upData}.}
\item{datevars}{character vector of names (after \code{lowernames} is
applied) of variables to consider as a factor or character vector
containing dates in a format matching \code{dateformat}. The
default is \code{"\%F"} which uses the yyyy-mm-dd format.}
\item{datetimevars}{character vector of names (after \code{lowernames}
is applied) of variables to consider to be date-time variables, with
date formats as described under \code{datevars} followed by a space
followed by time in hh:mm:ss format. \code{chron} is used to store
date-time variables. If all times in the variable
are 00:00:00 the variable will be converted to an ordinary date variable.}
\item{dateformat}{for \code{cleanup.import} is the input format (see
\code{\link{strptime}})}
\item{fixdates}{for any of the variables listed in \code{datevars}
that have a \code{dateformat} that \code{cleanup.import} understands,
specifying \code{fixdates} allows corrections of certain formatting
inconsistencies before the fields are attempted to be converted to
dates (the default is to assume that the \code{dateformat} is followed
for all observation for \code{datevars}). Currently
\code{fixdates='year'} is implemented, which will cause 2-digit or
4-digit years to be shifted to the alternate number of digits when
\code{dateform} is the default \code{"\%F"} or is \code{"\%y-\%m-\%d"},
\code{"\%m/\%d/\%y"}, or \code{"\%m/\%d/\%Y"}. Two-digits years are padded with \code{20}
on the left. Set \code{dateformat} to the desired format, not the
exceptional format.
}
\item{charfactor}{set to \code{TRUE} to change character variables to
factors if they have fewer than n/2 unique values. Null strings and
blanks are converted to \code{NA}s.}
\item{\dots}{
for \code{upData}, one or more expressions of the form
\code{variable=expression}, to derive new variables or change old ones.
}
\item{rename}{
list or named vector specifying old and new names for variables. Variables are
renamed before any other operations are done. For example, to rename
variables \code{age} and \code{sex} to respectively \code{Age} and
\code{gender}, specify \code{rename=list(age="Age", sex="gender")} or
\code{rename=c(age=\dots)}.
}
\item{drop}{a vector of variable names to remove from the data frame}
\item{keep}{a vector of variable names to keep, with all other
variables dropped}
\item{units}{
a named vector or list defining \code{"units"} attributes of
variables, in no specific order
}
\item{levels}{
a named list defining \code{"levels"} attributes for factor variables, in
no specific order. The values in this list may be character vectors
redefining \code{levels} (in order) or another list (see
\code{merge.levels} if using S-Plus).
}
\item{caplabels}{
set to \code{TRUE} to capitalize the first letter of each word in
each variable label
}
\item{moveUnits}{
set to \code{TRUE} to look for units of measurements in variable
labels and move them to a \code{"units"} attribute. If an expression
in a label is enclosed in parentheses or brackets it is assumed to be
units if \code{moveUnits=TRUE}.
}
\item{fracmiss}{the maximum permissable proportion of \code{NA}s for a
variable to be kept. Default is to keep all variables no matter how
many \code{NA}s are present.}
\item{maxlevels}{the maximum number of levels of a character or
categorical or factor variable before the variable is dropped}
\item{minprev}{the minimum proportion of non-missing observations in a
category for a binary variable to be retained, and the minimum
relative frequency of a category before it will be combined with other
small categories}
}
\value{a new data frame}
\author{
Frank Harrell, Vanderbilt University
}
\seealso{
\code{\link{sas.get}}, \code{\link{data.frame}}, \code{\link{describe}},
\code{\link{label}}, \code{\link{read.csv}}, \code{\link{strptime}},
\code{\link{POSIXct}},\code{\link{Date}}
}
\examples{
\dontrun{
dat <- read.table('myfile.asc')
dat <- cleanup.import(dat)
}
dat <- data.frame(a=1:3, d=c('01/02/2004',' 1/3/04',''))
cleanup.import(dat, datevars='d', dateformat='\%m/\%d/\%y', fixdates='year')
dat <- data.frame(a=(1:3)/7, y=c('a','b1','b2'), z=1:3)
dat2 <- upData(dat, x=x^2, x=x-5, m=x/10,
rename=c(a='x'), drop='z',
labels=c(x='X', y='test'),
levels=list(y=list(a='a',b=c('b1','b2'))))
dat2
describe(dat2)
dat <- dat2 # copy to original name and delete dat2 if OK
rm(dat2)
# Remove hard to analyze variables from a redundancy analysis of all
# variables in the data frame
d <- dataframeReduce(dat, fracmiss=.1, minprev=.05, maxlevels=5)
# Could run redun(~., data=d) at this point or include dataframeReduce
# arguments in the call to redun
# If you import a SAS dataset created by PROC CONTENTS CNTLOUT=x.datadict,
# the LABELs from this dataset can be added to the data. Let's also
# convert names to lower case for the main data file
\dontrun{
mydata2 <- cleanup.import(mydata2, lowernames=TRUE, sasdict=datadict)
}
}
\keyword{data}
\keyword{manip}