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 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 sasdict option as shown in the
example below. cleanup.import can also transform character or
factor variables to dates.
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
"labels" and "units" attributes can be provided.
Observations can be subsetted. 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 list notation of the standard merge.levels
function. Unless force.single is set to FALSE,
upData also converts double precision vectors to integer if no
fractional values are present in
a vector. upData is also used to process R workspace objects
created by StatTransfer, which puts variable and value labels as attributes on
the data frame rather than on each variable. If such attributes are
present, they are used to define all the labels and value labels
(through conversion to factor variables) before any label changes
take place, and force.single is set to a default of
FALSE, as StatTransfer already does conversion to integer.
Variables having labels but not classed "labelled" (e.g., data
imported using the haven package) have that class added to them
by upData.
The 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.
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, …,
subset, rename, drop, keep, labels, units, levels, force.single=TRUE,
lowernames=FALSE, caplabels=FALSE, moveUnits=FALSE,
charfactor=FALSE, print=TRUE, html=FALSE)
dataframeReduce(data, fracmiss=1, maxlevels=NULL, minprev=0, print=TRUE)
a data frame or list
a data frame or list
a data frame
By default, double precision variables are converted to single precision
(in S-Plus only) unless force.single=FALSE.
force.single=TRUE will also convert vectors having only integer
values to have a storage mode of integer, in R or S-Plus.
Sometimes importing will cause a numeric variable to be
changed to a factor vector. By default, cleanup.import will check
each factor variable to see if the levels contain only numeric values
and "". In that case, the variable will be converted to numeric,
with "" converted to NA. Set force.numeric=FALSE to prevent
this behavior.
set to `F' to not have `cleanup.import' remove `names' or `.Names' attributes from variables
a character vector the same length as the number of variables in
obj. These character values are taken to be variable labels in the
same order of variables in obj.
For upData, labels is a named list or named vector
with variables in no specific order.
set this to TRUE to change variable names to lower case.
upData does this before applying any other changes, so variable
names given inside arguments to upData need to be lower case if
lowernames==TRUE.
a value such that values larger than this in absolute value are set to
missing by cleanup.import
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.
set to TRUE or 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 obj
exceeds 500,000. For dataframeReduce set print to
FALSE to suppress printing information about dropped or
modified variables. Similar for upData.
character vector of names (after lowernames is
applied) of variables to consider as a factor or character vector
containing dates in a format matching dateformat. The
default is "%F" which uses the yyyy-mm-dd format.
character vector of names (after lowernames
is applied) of variables to consider to be date-time variables, with
date formats as described under datevars followed by a space
followed by time in hh:mm:ss format. 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.
for cleanup.import is the input format (see
strptime)
for any of the variables listed in datevars
that have a dateformat that cleanup.import understands,
specifying fixdates allows corrections of certain formatting
inconsistencies before the fields are attempted to be converted to
dates (the default is to assume that the dateformat is followed
for all observation for datevars). Currently
fixdates='year' is implemented, which will cause 2-digit or
4-digit years to be shifted to the alternate number of digits when
dateform is the default "%F" or is "%y-%m-%d",
"%m/%d/%y", or "%m/%d/%Y". Two-digits years are padded with 20
on the left. Set dateformat to the desired format, not the
exceptional format.
set to TRUE to change character variables to
factors if they have fewer than n/2 unique values. Null strings and
blanks are converted to NAs.
for upData, one or more expressions of the form
variable=expression, to derive new variables or change old ones.
an expression that evaluates to a logical vector
specifying which rows of object should be retained. The
expressions should use the original variable names, i.e., before any
variables are renamed but after lowernames takes effect.
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 age and sex to respectively Age and
gender, specify rename=list(age="Age", sex="gender") or
rename=c(age=…).
a vector of variable names to remove from the data frame
a vector of variable names to keep, with all other variables dropped
a named vector or list defining "units" attributes of
variables, in no specific order
a named list defining "levels" attributes for factor variables, in
no specific order. The values in this list may be character vectors
redefining levels (in order) or another list (see
merge.levels if using S-Plus).
set to TRUE to capitalize the first letter of each word in
each variable label
set to TRUE to look for units of measurements in variable
labels and move them to a "units" attribute. If an expression
in a label is enclosed in parentheses or brackets it is assumed to be
units if moveUnits=TRUE.
set to TRUE to print conversion information as html
vertabim at 0.6 size. The user will need to put
results='asis' in a knitr chunk header to properly
render this output.
the maximum permissable proportion of NAs for a
variable to be kept. Default is to keep all variables no matter how
many NAs are present.
the maximum number of levels of a character or categorical or factor variable before the variable is dropped
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
a new data frame
sas.get, data.frame, describe,
label, read.csv, strptime,
POSIXct,Date
# NOT RUN {
dat <- read.table('myfile.asc')
dat <- cleanup.import(dat)
# }
# NOT RUN {
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)
dat3 <- upData(dat, X=X^2, subset = x < (3/7)^2 - 5, rename=c(x='X'))
# 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
# }
# NOT RUN {
mydata2 <- cleanup.import(mydata2, lowernames=TRUE, sasdict=datadict)
# }
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