upData {Hmisc} | R Documentation

## Update a Data Frame or Cleanup a Data Frame after Importing

### Description

`cleanup.import` will correct errors and shrink the size of data frames created by the S-Plus `File ... Import` dialog or by other methods such as `scan` and `read.table`. By default, double precision numeric variables are changed to single precision (S-Plus only) or 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-Plus 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. 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 single precision (if not under R), or to integer if no fractional values are present in a vector.

Both `cleanup.import` and `upData` will fix a problem with data frames created under S-Plus before version 5 that are used in S-Plus 5 or later. The problem was caused by use of the `label` function to set a variable's class to `"labelled"`. These classes are removed as the S version 4 language does not support multiple inheritance. Failure to run data frames through one of the two functions when these conditions apply will result in simple numeric variables being set to `factor` in some cases. Extraneous `"AsIs"` classes are also removed.

For S-Plus, a function `exportDataStripped` is provided that allows exporting of data to other systems by removing attributes `label, imputed, format, units`, and `comment`. It calls `exportData` after stripping these attributes. Otherwise `exportData` will fail.

`csv.get` reads comma-separated text data files, allowing optional translation to lower case for variable names after making them valid S names. Original possibly non-legal names are taken to be variable labels. Character or factor variables containing dates can be converted to date variables. `cleanup.import` is invoked to finish the job.

### Usage

    cleanup.import(obj, labels, lowernames=FALSE,
                   force.single=TRUE, force.numeric=TRUE, rmnames=TRUE,
                   big=1e20, sasdict, pr, datevars=NULL, dateformat='

    upData(object, ...,
           rename, drop, labels, units, levels,
           force.single=TRUE, lowernames=FALSE, moveUnits=FALSE)

    exportDataStripped(data, ...)

    csv.get(file, lowernames=FALSE, datevars=NULL, dateformat='%d%b%Y', ...)

### Arguments

`obj` | a data frame or list
`object` | a data frame or list
`data` | a data frame
`force.single` | 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.
`force.numeric` | 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.
`rmnames` | set to `F' to not have `cleanup.import' remove `names' or `.Names' attributes from variables
`labels` | 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.
`lowernames` | 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`.
`big` | a value such that values larger than this in absolute value are set to missing by `cleanup.import`
`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.
`pr` | 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.
`datevars` | 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`
`dateformat` | for `cleanup.import` is the input format (see `strptime`)
`...` | for `upData`, one or more expressions of the form `variable=expression`, to derive new variables or change old ones. For `exportDataStripped`, optional arguments that are passed to `exportData`. For `csv.get`, arguments to pass to `read.csv`.
`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 `age` and `sex` to respectively `Age` and `gender`, specify `rename=list(age="Age", sex="gender")` or `rename=c(age=...)`.
`drop` | a vector of variable names to remove from the data frame
`units` | a named vector or list defining `"units"` attributes of variables, in no specific order
`levels` | 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).
`moveUnits` | 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`.
`file` | a file name to import

### Value

a new data frame

### Author(s)

Frank Harrell, Vanderbilt University

### See Also

`sas.get`, `data.frame`, `describe`, `label`, `read.csv`, `strptime`, `POSIXct`

### Examples

    ## Not run:
    dat <- read.table('myfile.asc')
    dat <- cleanup.import(dat)
    ## End(Not run)
    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)

    # 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)
    ## End(Not run)

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