Variable Table (vtable)

Nick Huntington-Klein

2023-04-28

The vtable package serves the purpose of outputting automatic variable documentation that can be easily viewed while continuing to work with data.

vtable contains four main functions: vtable() (or vt()), sumtable() (or st()), labeltable(), and dftoHTML()/dftoLaTeX(). This vignette focuses on vtable().

vtable() takes a dataset and outputs a formatted variable documentation file. This serves several purposes.

First, it allows for an easy generation of a variable documentation file, without requiring that one has already been created and made accessible through help(data), or dealing with creating and finding R help documentation files.

Second, it produces a list of variables (and, if provided, their labels) that can be easily viewed while working with the data, preventing repeated calls to head(), and making it much easier to work with confusingly-named variables.

Third, the variable documentation file can be opened in a browser (with option out='browser', saving to file and opening directly, or by opening in the RStudio Viewer pane and clicking ‘Show in New Window’) where it can be easily searched with standard Find-in-Page functions like Ctrl/Cmd-F, allowsing you to search for the variable or variable label you want.

The vtable() function

vtable() (or vt() for short) syntax follows the following outline:

vtable(data,
       out=NA,
       file=NA,
       labels=NA,
       class=TRUE,
       values=TRUE,
       missing=FALSE,
       index=FALSE,
       factor.limit=5,
       char.values=FALSE,
       data.title=NA,
       desc=NA,
       note=NA,
       anchor=NA,
       col.width=NA,
       col.align=NA,
       align=NA,
       note.align='l',
       fit.page=NA,
       summ=NA,
       lush=FALSE,
       opts=list())

The goal of vtable() is to take a data set data and output a usually-HTML (but data.frame, kable, csv, and latex options are there too) file with documentation concerning each of the variables in data. There are several options as to what will be included in the documentation file, and each of these options are explained below. Throughout, the output will be built as kables since this is an RMarkdown document. However, generally you can leave out at its default and it will publish an HTML table to Viewer (in RStudio) or the browser (otherwise). This will also include some additional information about your data that can’t be demonstrated in this vignette:

data

The data argument can take any data.frame, data.table, tibble, or matrix, as long as it has a valid set of variable names stored in the colnames() attribute. The goal of vtable() is to produce documentation of each of the variables in this data set and display that documentation, one variable per row on the output vtable.

If data has embedded variable or value labels, as the data set efc does below, vtable() will extract and use them automatically.

library(vtable)

#Example 1, using base data LifeCycleSavings
data(LifeCycleSavings)
vtable(LifeCycleSavings, out='kable')
LifeCycleSavings
Name Class Values
sr numeric Num: 0.6 to 21.1
pop15 numeric Num: 21.44 to 47.64
pop75 numeric Num: 0.56 to 4.7
dpi numeric Num: 88.94 to 4001.89
ddpi numeric Num: 0.22 to 16.71 
#Example 2, using efc data with embedded variable labels
library(sjlabelled)
data(efc)
#Don't forget the handy shortcut vt()!
vt(efc)
efc
Name Class Label Values
c12hour numeric average number of hours of care per week Num: 4 to 168
e15relat numeric relationship to elder ‘1: spouse/partner’ ‘2: child’ ‘3: sibling’ ‘4: daughter or son -in-law’ ‘5: ancle/aunt’ and 3 more
e16sex numeric elder’s gender ‘1: male’ ‘2: female’
e17age numeric elder’ age Num: 65 to 103
e42dep numeric elder’s dependency ‘1: independent’ ‘2: slightly dependent’ ‘3: moderately dependent’ ‘4: severely dependent’
c82cop1 numeric do you feel you cope well as caregiver? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c83cop2 numeric do you find caregiving too demanding? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c84cop3 numeric does caregiving cause difficulties in your relationship with your friends? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c85cop4 numeric does caregiving have negative effect on your physical health? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c86cop5 numeric does caregiving cause difficulties in your relationship with your family? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c87cop6 numeric does caregiving cause financial difficulties? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c88cop7 numeric do you feel trapped in your role as caregiver? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c89cop8 numeric do you feel supported by friends/neighbours? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c90cop9 numeric do you feel caregiving worthwhile? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c160age numeric carer’ age Num: 18 to 89
c161sex numeric carer’s gender ‘1: Male’ ‘2: Female’
c172code numeric carer’s level of education ‘1: low level of education’ ‘2: intermediate level of education’ ‘3: high level of education’
c175empl numeric are you currently employed? ‘0: no’ ‘1: yes’
barthtot numeric Total score BARTHEL INDEX Num: 0 to 100
neg_c_7 numeric Negative impact with 7 items Num: 7 to 28
pos_v_4 numeric Positive value with 4 items Num: 5 to 16
quol_5 numeric Quality of life 5 items Num: 0 to 25
resttotn numeric Job restrictions Num: 0 to 4
tot_sc_e numeric Services for elderly Num: 0 to 9
n4pstu numeric Care level ‘0: No Care Level’ ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3’ ‘4: Care Level 3+’
nur_pst numeric Care level ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3/3+’

out

The out option determines what will be done with the resulting variable documentation file. There are several options for out:

Option Result
browser Loads variable documentation in web browser.
viewer Loads variable documentation in Viewer pane (RStudio only).
htmlreturn Returns HTML code for variable documentation file.
return Returns variable documentation table in data frame format.
csv Returns variable documentatoin in data.frame format and, with a file option, saves that to CSV.
kable Returns a knitr::kable()
latex Returns a LaTeX table.
latexpage Returns an independently-buildable LaTeX document.

By default, vtable will select ‘viewer’ if running in RStudio, and ‘browser’ otherwise. If it’s being built in an RMarkdown document with knitr, it will default to ‘kable’. Note that an RMarkdown default to ‘kable’ will also include some nice formatting, where out = 'kable' directly will give you a more basic kable you can format yourself.

data(LifeCycleSavings)
vtable(LifeCycleSavings)
vtable(LifeCycleSavings,out='browser')
vtable(LifeCycleSavings,out='viewer')
htmlcode <- vtable(LifeCycleSavings,out='htmlreturn')
vartable <- vtable(LifeCycleSavings,out='return')

#I can easily \input this into my LaTeX doc:
vt(LifeCycleSavings,out='latex',file='mytable1.tex')

file

The file argument will write the variable documentation file to an HTML or LaTeX file and save it. Will automatically append ‘html’ or ‘tex’ filetype if the filename does not include a period.

data(LifeCycleSavings)
vt(LifeCycleSavings,file='lifecycle_variabledocumentation')

labels

The labels argument will attach variable labels to the variables in data. If variable labels are embedded in data and those labels are what you want, the labels argument is unnecessary. Set labels='omit' if there are embedded labels but you do not want them in the table.

labels can be used in any one of three formats.

labels as a vector

labels can be set to be a vector of equal length to the number of variables in data, and in the same order. NA values can be used for padding if some variables do not have labels.

#Note that LifeCycleSavings has five variables
data(LifeCycleSavings)
#These variable labels are taken from help(LifeCycleSavings)
labs <- c('numeric aggregate personal savings',
    'numeric % of population under 15',
    'numeric % of population over 75',
    'numeric real per-capita disposable income',
    'numeric % growth rate of dpi')
vtable(LifeCycleSavings,labels=labs)
LifeCycleSavings
Name Class Label Values
sr numeric numeric aggregate personal savings Num: 0.6 to 21.1
pop15 numeric numeric % of population under 15 Num: 21.44 to 47.64
pop75 numeric numeric % of population over 75 Num: 0.56 to 4.7
dpi numeric numeric real per-capita disposable income Num: 88.94 to 4001.89
ddpi numeric numeric % growth rate of dpi Num: 0.22 to 16.71 
labs <- c('numeric aggregate personal savings',NA,NA,NA,NA)
vtable(LifeCycleSavings,labels=labs)
LifeCycleSavings
Name Class Label Values
sr numeric numeric aggregate personal savings Num: 0.6 to 21.1
pop15 numeric NA Num: 21.44 to 47.64
pop75 numeric NA Num: 0.56 to 4.7
dpi numeric NA Num: 88.94 to 4001.89
ddpi numeric NA Num: 0.22 to 16.71

labels as a two-column data set

labels can be set to a two-column data set (any type will do) where the first column has the variable names, and the second column has the labels. The column names don’t matter.

This approach does not require that every variable name in data has a matching label.

#Note that LifeCycleSavings has five variables
#with names 'sr', 'pop15', 'pop75', 'dpi', and 'ddpi'
data(LifeCycleSavings)
#These variable labels are taken from help(LifeCycleSavings)
labs <- data.frame(nonsensename1 = c('sr', 'pop15', 'pop75'),
nonsensename2 = c('numeric aggregate personal savings',
    'numeric % of population under 15',
    'numeric % of population over 75'))
vt(LifeCycleSavings,labels=labs)
LifeCycleSavings
Name Class Label Values
sr numeric numeric aggregate personal savings Num: 0.6 to 21.1
pop15 numeric numeric % of population under 15 Num: 21.44 to 47.64
pop75 numeric numeric % of population over 75 Num: 0.56 to 4.7
dpi numeric NA Num: 88.94 to 4001.89
ddpi numeric NA Num: 0.22 to 16.71

labels as a one-row data set

labels can be set to a one-row data set in which the column names are the variable names in data and the first row is the variable names. The labels argument can take any data type including data frame, data table, tibble, or matrix, as long as it has a valid set of variable names stored in the colnames() attribute.

This approach does not require that every variable name in data has a matching label.

#Note that LifeCycleSavings has five variables
#with names 'sr', 'pop15', 'pop75', 'dpi', and 'ddpi'
data(LifeCycleSavings)
#These variable labels are taken from help(LifeCycleSavings)
labs <- data.frame(sr = 'numeric aggregate personal savings',
    pop15 = 'numeric % of population under 15',
    pop75 = 'numeric % of population over 75')
vtable(LifeCycleSavings,labels=labs)
LifeCycleSavings
Name Class Label Values
sr numeric numeric aggregate personal savings Num: 0.6 to 21.1
pop15 numeric numeric % of population under 15 Num: 21.44 to 47.64
pop75 numeric numeric % of population over 75 Num: 0.56 to 4.7
dpi numeric NA Num: 88.94 to 4001.89
ddpi numeric NA Num: 0.22 to 16.71

class

The class flag will either report or not report the class of each variable in the resulting variable table. By default this is set to TRUE.

values

The values flag will either report or not report the values that each variable takes. Numeric variables will report a range, logicals will report ‘TRUE FALSE’, and factor variables will report the first factor.limit (default 5) factors listed.

If the variable is numeric but has value labels applied by the sjlabelled package, vtable() will find them and report the numeric-label crosswalk.

data(LifeCycleSavings)
vtable(LifeCycleSavings,values=FALSE)
LifeCycleSavings
Name Class
sr numeric
pop15 numeric
pop75 numeric
dpi numeric
ddpi numeric 
vtable(LifeCycleSavings)
LifeCycleSavings
Name Class Values
sr numeric Num: 0.6 to 21.1
pop15 numeric Num: 21.44 to 47.64
pop75 numeric Num: 0.56 to 4.7
dpi numeric Num: 88.94 to 4001.89
ddpi numeric Num: 0.22 to 16.71 
#CO2 contains factor variables
data(CO2)
vtable(CO2)
CO2
Name Class Values
Plant ordered ‘Qn1’ ‘Qn2’ ‘Qn3’ ‘Qc1’ ‘Qc2’ and 7 more
Type factor ‘Quebec’ ‘Mississippi’
Treatment factor ‘nonchilled’ ‘chilled’
conc numeric Num: 95 to 1000
uptake numeric Num: 7.7 to 45.5 
#efc contains labeled values
#Note that the original value labels do not easily tell you what numerical
#value each label maps to, but vtable() does.
library(sjlabelled)
data(efc)
vtable(efc)
efc
Name Class Label Values
c12hour numeric average number of hours of care per week Num: 4 to 168
e15relat numeric relationship to elder ‘1: spouse/partner’ ‘2: child’ ‘3: sibling’ ‘4: daughter or son -in-law’ ‘5: ancle/aunt’ and 3 more
e16sex numeric elder’s gender ‘1: male’ ‘2: female’
e17age numeric elder’ age Num: 65 to 103
e42dep numeric elder’s dependency ‘1: independent’ ‘2: slightly dependent’ ‘3: moderately dependent’ ‘4: severely dependent’
c82cop1 numeric do you feel you cope well as caregiver? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c83cop2 numeric do you find caregiving too demanding? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c84cop3 numeric does caregiving cause difficulties in your relationship with your friends? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c85cop4 numeric does caregiving have negative effect on your physical health? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c86cop5 numeric does caregiving cause difficulties in your relationship with your family? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c87cop6 numeric does caregiving cause financial difficulties? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c88cop7 numeric do you feel trapped in your role as caregiver? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’
c89cop8 numeric do you feel supported by friends/neighbours? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c90cop9 numeric do you feel caregiving worthwhile? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’
c160age numeric carer’ age Num: 18 to 89
c161sex numeric carer’s gender ‘1: Male’ ‘2: Female’
c172code numeric carer’s level of education ‘1: low level of education’ ‘2: intermediate level of education’ ‘3: high level of education’
c175empl numeric are you currently employed? ‘0: no’ ‘1: yes’
barthtot numeric Total score BARTHEL INDEX Num: 0 to 100
neg_c_7 numeric Negative impact with 7 items Num: 7 to 28
pos_v_4 numeric Positive value with 4 items Num: 5 to 16
quol_5 numeric Quality of life 5 items Num: 0 to 25
resttotn numeric Job restrictions Num: 0 to 4
tot_sc_e numeric Services for elderly Num: 0 to 9
n4pstu numeric Care level ‘0: No Care Level’ ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3’ ‘4: Care Level 3+’
nur_pst numeric Care level ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3/3+’

missing

The missing flag, set to TRUE, will report the number of missing values in each variable. Defaults to FALSE.

index

The index flag will either report or not report the index number of each variable. Defaults to FALSE.

factor.limit

If values is set to TRUE, then factor.limit limits the number of factors displayed on the variable table. factor.limit is by default 5, to cut down on clutter. The table will include the phrase “and more” to indicate that some factors have been cut off.

Setting factor.limit=0 will include all factors. If values=FALSE, factor.limit does nothing.

char.values

If values is set to TRUE, then char.values = TRUE instructs vtable to list the values that character variables take, as though they were factors. If you only want some of the character variables to have their values listed, use a character vector to indicate which variables.

data(USJudgeRatings)
USJudgeRatings$Judge <- row.names(USJudgeRatings)
USJudgeRatings$SecondCharacter <- 'Less Interesting'
USJudgeRatings$ThirdCharacter <- 'Less Interesting Still!'

#Show values for all character variables
vtable(USJudgeRatings,char.values=TRUE)
#Or just for a subset
vtable(USJudgeRatings,char.values=c('Judge','SecondCharacter'))

data.title, desc, note, and anchor

data.title will include a data title in the variable documentation file. If not set manually, this will default to the variable name for data.

desc will include a description of the data set in the variable documentation file. This will by default include information on the number of observations and the number of columns. To remove this, set desc='omit', or include any description and then include ‘omit’ as the last four characters.

note will add a table note in the last row. note.align determines its left/right/center alignment, but is only used with LaTeX (see below).

anchor will add an anchor ID (<a name = in HTML or \label{} in LaTeX) to allow other parts of your document to link to it, if you are including your vtable in a larger document.

data.title and desc will only show up in full-page vtables. That is, you won’t get them with out = 'return', out = 'csv', or out = 'latex' (although out = 'latexpage' works). note and anchor will only show up in formats that support multi-column cells and anchoring, so they won’t work with out = 'return' or out = 'csv'.

out = 'kable' is a half-exception in that it will use data.title as the caption for the kable, and will use the note as a footnote, but won’t use desc or anchor.

library(vtable)

data(LifeCycleSavings)
vtable(LifeCycleSavings)
vtable(LifeCycleSavings,data.title='Intercountry Life-Cycle Savings Data',
    desc='omit')
vtable(LifeCycleSavings,data.title='Intercountry Life-Cycle Savings Data',
    desc='Data on the savings ratio 1960–1970. omit')
vtable(LifeCycleSavings,data.title='Intercountry Life-Cycle Savings Data',
    desc='Data on the savings ratio 1960–1970',
    note='Data from Belsley, Kuh, and Welsch (1980)')

col.width

vtable() will select default column widths for the variable table depending on which measures (name, class, label, values, summ) are included. col.width, as a vector of percentage column widths on the 0-100 scale, will override these defaults.

library(sjlabelled)
data(efc)
#The variable names in this data set are pretty short, and the value labels are
#a little cramped, so let's move that over.
vtable(efc,col.width=c(10,10,40,40))

col.align

col.align can be used to adjust text alignment in HTML output. Set to ‘left’, ‘right’, or ‘center’ to align all columns, or give a vector of column alignments to do each column separately.

If you want to get tricky, you can add a semicolon afterwards and keep putting in whatever CSS attributes you want. They will be applied to the whole column.

This option is only for HTML output and will only work with out values of ‘browser’, ‘viewer’, or ‘htmlreturn’.

library(sjlabelled)
data(efc)
vtable(efc,col.align = 'right')

align, note.align, and fit.page

These options are used only with LaTeX output (out is ‘latex’ or ‘latexpage’).

align and note.align are single strings used for alignment. align will be used as column alignment in standard LaTeX syntax, for example ‘lccc’ for the left column left-aligned and the other three centered. note.align is an alignment note specifically for any table notes set with note (or significance stars), which enters as part of a \multicolumn argument. These both accept ‘p{}’ and other LaTeX column types.

Defaults to left-aligned ‘Variable’ columns and right-aligned everything else. If col.widths is specified, align defaults to ‘p{}’ columns, with widths set by col.width.

fit.page can be used to ensure that the table is a certain width, and will be used as an entry to a \resizebox{}. Set to \\textwidth to set the table to text width, or .9\\textwidth for 90% of the page, and so on, or any recognized width value in LaTeX.

For all of these, be sure to escape special characters, in particular backslashes.

library(sjlabelled)
data(efc)
vtable(efc,align = 'p{.3\\textwidth}cc', fit.page = '\\textwidth', out = 'latex')

summ

summ will calculate summary statistics for all variables that report valid output on the given summary statistics functions. summ is very flexible. It takes a character vector in which each element is of the form function(x), where function(x) is any function that takes a vector and returns a single numeric value. For example, summ=c('mean(x)','median(x)','mean(log(x))') would calculate the mean, median, and mean of the log for each variable.

summ treats as special two vtable functions: propNA(x) and countNA(x), which give the proportion and count of NA values, and the count of non-NA values in the variable, respectively. These two functions are always reported first, and are the only functions that include NA values in their calculations.

library(sjlabelled)
data(efc)

vtable(efc,summ=c('mean(x)','countNA(x)'))
efc
Name Class Label Values Summary
c12hour numeric average number of hours of care per week Num: 4 to 168 countNA: 6, mean: 42.399
e15relat numeric relationship to elder ‘1: spouse/partner’ ‘2: child’ ‘3: sibling’ ‘4: daughter or son -in-law’ ‘5: ancle/aunt’ and 3 more countNA: 7
e16sex numeric elder’s gender ‘1: male’ ‘2: female’ countNA: 7
e17age numeric elder’ age Num: 65 to 103 countNA: 17, mean: 79.121
e42dep numeric elder’s dependency ‘1: independent’ ‘2: slightly dependent’ ‘3: moderately dependent’ ‘4: severely dependent’ countNA: 7
c82cop1 numeric do you feel you cope well as caregiver? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’ countNA: 7
c83cop2 numeric do you find caregiving too demanding? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 6
c84cop3 numeric does caregiving cause difficulties in your relationship with your friends? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 6
c85cop4 numeric does caregiving have negative effect on your physical health? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 10
c86cop5 numeric does caregiving cause difficulties in your relationship with your family? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 6
c87cop6 numeric does caregiving cause financial difficulties? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 8
c88cop7 numeric do you feel trapped in your role as caregiver? ‘1: Never’ ‘2: Sometimes’ ‘3: Often’ ‘4: Always’ countNA: 8
c89cop8 numeric do you feel supported by friends/neighbours? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’ countNA: 7
c90cop9 numeric do you feel caregiving worthwhile? ‘1: never’ ‘2: sometimes’ ‘3: often’ ‘4: always’ countNA: 20
c160age numeric carer’ age Num: 18 to 89 countNA: 7, mean: 53.463
c161sex numeric carer’s gender ‘1: Male’ ‘2: Female’ countNA: 7
c172code numeric carer’s level of education ‘1: low level of education’ ‘2: intermediate level of education’ ‘3: high level of education’ countNA: 66
c175empl numeric are you currently employed? ‘0: no’ ‘1: yes’ countNA: 6
barthtot numeric Total score BARTHEL INDEX Num: 0 to 100 countNA: 25, mean: 64.547
neg_c_7 numeric Negative impact with 7 items Num: 7 to 28 countNA: 16, mean: 11.85
pos_v_4 numeric Positive value with 4 items Num: 5 to 16 countNA: 27, mean: 12.477
quol_5 numeric Quality of life 5 items Num: 0 to 25 countNA: 11, mean: 14.369
resttotn numeric Job restrictions Num: 0 to 4 countNA: 0, mean: 0.329
tot_sc_e numeric Services for elderly Num: 0 to 9 countNA: 0, mean: 1.014
n4pstu numeric Care level ‘0: No Care Level’ ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3’ ‘4: Care Level 3+’ countNA: 9
nur_pst numeric Care level ‘1: Care Level 1’ ‘2: Care Level 2’ ‘3: Care Level 3/3+’ countNA: 419

lush

The default vtable settings may not be to your liking, and in particular you may prefer more information. Setting lush = TRUE is an easy way to get more information. It will force char.values and missing to TRUE, and will also set a default summ value of c('mean(x)', 'sd(x)', 'nuniq(x)').

opts

You can create a named list where the names are the above options and the values are the settings for those options, and input it into vtable using opts=. This is an easy way to set the same options for many vtables.