tabmulti: Generate Multi-row Tables Comparing Means/Medians/Frequencies...

In tab: Functions for Creating Summary Tables for Statistical Reports

Description

This function basically provides an alternative to making multiple calls to tabmeans, tabmedians, and tabfreq, then using rbind to combine the results into a single table.

Usage

tabmulti(dataset, xvarname, yvarnames, ymeasures = NULL,
         listwise.deletion = TRUE, latex = FALSE, xlevels = NULL,
         ynames = yvarnames, ylevels = NULL, quantiles = NULL,
         quantile.vals = FALSE, parenth.sep = "-", cell = "n",
         freq.parenth = NULL, freq.text.label = NULL, freq.tests = "chi",
         means.parenth = "sd", means.text.label = NULL, variance = "unequal",
         medians.parenth = "iqr", medians.text.label = NULL, decimals = NULL,
         p.include = TRUE, p.decimals = c(2, 3), p.cuts = 0.01,
         p.lowerbound = 0.001, p.leading0 = TRUE, p.avoid1 = FALSE,
         overall.column = TRUE, n.column = FALSE, n.headings = TRUE,
         compress = FALSE, bold.colnames = TRUE, bold.varnames = FALSE,
         bold.varlevels = FALSE, variable.colname = "Variable",
         print.html = FALSE, html.filename = "table1.html")

Arguments

dataset	Data frame or matrix containing variables of interest.
xvarname	Character string with name of column variable. Should be one of colnames(dataset).
yvarnames	Character vector with names of row variables. Each element should be one of colnames(dataset).
ymeasures	Character string or vector of character strings indicating whether each y variable should be summarized by mean, median, or frequency. For example, if yvarnames has length three and you wish to display frequencies for the first variable, means for the second, and medians for the third, you would set ymeasures = c("freq", "mean", "median"). If unspecified, function displays frequencies for any factor variable or numeric variable with five or fewer unique values, and means for numeric variables with more than five levels.
listwise.deletion	If TRUE, observations with missing values for any y variable are excluded entirely; if FALSE, all available data is used for each comparison.
latex	If TRUE, object returned is formatted for printing in LaTeX using xtable [1]; if FALSE, formatted for copy-and-pasting from RStudio into a word processor.
xlevels	Optional character vector to label the levels of x, used in the column headings. If unspecified, the function uses the values that x takes on.
ynames	Optional labels for the y variables. If unspecified, yvarnames is used.
ylevels	Character vector or list of character vectors to label the levels of the categorical y variables.
quantiles	If specified, function compares y variables across quantiles of the x variable. For example, if x contains continuous BMI values and y contains continuous HDL and race, setting quantiles = 3 would result in mean HDL and distribution of race being compared across tertiles of BMI.
quantile.vals	If TRUE, labels for x show quantile number and corresponding range of the x variable, e.g. Q1 [0.00, 0.25). If FALSE, labels for quantiles just show quantile number, e.g. Q1. Only used if xlevels is not specified.
parenth.sep	Optional character specifying the separator between first and second numbers in parentheses (e.g. lower and upper bound of confidence intervals, when requested). Usually either "-" or ", " depending on user preference.
cell	Controls what values are placed in cells for frequency comparisons. Possible values are "n" for counts, "tot.percent" for table percentage, "col.percent" for column percentage, "row.percent" for row percentage, "tot.prop" for table proportion, "col.prop" for column proportion, "row.prop" for row proportion, "n/totn" for count/total counts, "n/coln" for count/column count, and "n/rown" for count/row count.
freq.parenth	Controls what values (if any) are placed in parentheses after the values in each cell for frequency comparisons. By default, if cell is "n", "n/totn", "n/coln", or "n/rown" then the corresponding percentage is shown in parentheses; if cell is "tot.percent", "col.percent", "row.percent", "tot.prop", "col.prop", or "row.prop" then a 95% confidence interval for the requested percentage of proportion is shown in parentheses. Possible values are "none", "se" for standard error of requested percentage or proportion, "ci" for 95% confidence interval for requested percentage or proportion, and "tot.percent", "col.percent", "row.percent", "tot.prop", "col.prop", or "row.prop" for various percentages or proportions.
freq.text.label	Optional text to put after the y variable name for frequency comparisons, identifying what cell values and parentheses indicate in the table. If unspecified, function uses default labels based on cell and freq.parenth settings. Set to "none" for no text labels.
freq.tests	Character string or vector of character strings indicating what statistical tests should be used to compare distributions of each categorical y variable across levels of the x variable. Elements can be "chi" for Pearson's chi-squared test, "fisher" for Fisher's exact test, "z" for z test without continuity correction, or "z.continuity" for z test with continuity correction. "z" and "z.continuity" can only be used for binary x and y variables.
means.parenth	Controls what values (if any) are placed in parentheses after the means in each cell for mean comparisons. Possible values are "none", "sd" for standard deviation, "se" for standard error, "t.ci" for 95% confidence interval for population mean based on t distribution, and "z.ci" for 95% confidence interval for population mean based on z distribution.
means.text.label	Optional text to put after the y variable name, identifying what cell values and parentheses indicate in the table. If unspecified, function uses default labels based on means.parenth, e.g. M (SD) if means.parenth = "sd". Set to "none" for no text labels.
variance	Controls whether equal variance t-test or unequal variance t-test is used for mean comparisons when x has two levels. Possible values are "equal" for equal variance, "unequal" for unequal variance, and "ftest" for F test to determine which version of the t-test to use. Note that unequal variance t-test is less restrictive than equal variance t-test, and the F test is only valid when y is normally distributed in both x groups.
medians.parenth	Controls what values (if any) are placed in parentheses after the medians in each cell for median comparisons. Possible values are "none", "iqr" for difference between first and third quartiles, "range" for difference between minimum and maximum, "minmax" for minimum and maximum, and "q1q3" for first and third quartiles.
medians.text.label	Optional text to put after the y variable name for median comparisons, identifying what cell values and parentheses indicate in the table. If unspecified, function uses default labels based on medians.parenth, e.g. Median (IQR) if medians.parenth = "iqr". Set to "none" for no text labels.
decimals	Numeric value or vector of numeric values indicating how many decimal places should be used in reporting statistics for each y variable.
p.include	If FALSE, statistical tests are not performed and p-values are not returned.
p.decimals	Number of decimal places for p-values. If a vector is provided rather than a single value, number of decimal places will depend on what range the p-value lies in. See p.cuts.
p.cuts	Cut-point(s) to control number of decimal places used for p-values. For example, by default p.cuts = 0.1 and p.decimals = c(2, 3). This means that p-values in the range [0.1, 1] will be printed to two decimal places, while p-values in the range [0, 0.1) will be printed to three decimal places.
p.lowerbound	Controls cut-point at which p-values are no longer printed as their value, but rather <lowerbound. For example, by default p.lowerbound = 0.001. Under this setting, p-values less than 0.001 are printed as <0.001.
p.leading0	If TRUE, p-values are printed with 0 before decimal place; if FALSE, the leading 0 is omitted.
p.avoid1	If TRUE, p-values rounded to 1 are not printed as 1, but as >0.99 (or similarly depending on p.decimals and p.cuts).
overall.column	If FALSE, column showing frequencies/means/medians for y in full sample is suppressed.
n.column	If TRUE, the table will have a column for sample size.
n.headings	If TRUE, the table will indicate the sample size overall and in each group in parentheses after the column headings.
compress	If TRUE, categorical y variables with two levels are compressed into a single row.
bold.colnames	If TRUE, column headings are printed in bold font. Only applies if latex = TRUE.
bold.varnames	If TRUE, variable name in the first column of the table is printed in bold font. Only applies if latex = TRUE.
bold.varlevels	If TRUE, levels of categorical y variables are printed in bold font. Only applies if latex = TRUE.
variable.colname	Character string with desired heading for first column of table, which shows the y variable name and levels.
print.html	If TRUE, function prints a .html file to the current working directory.
html.filename	Character string indicating the name of the .html file that gets printed if print.html = TRUE.

Details

See help files for tabmeans, tabmedians, and tabfreq for details on statistical tests.

Value

A character matrix comparing means/medians/frequencies of y variables across levels of the x variable. If latex = TRUE, the character matrix will be formatted for inserting into a Markdown/Sweave/knitr report using the xtable package [1].

Note

If you wish to paste your tables into Word, you can use either of these approaches:

1. Use the write.cb function in the Kmisc package [2]. If your table is stored in a character matrix named table1, use write.cb(table1) to copy the table to your clipboard. Paste the result into Word, then highlight the text and go to Insert - Table - Convert Text to Table... OK.

2. Set print.html = TRUE. This will result in a .html file writing to your current working directory. When you open this file, you will see a nice looking table that you can copy and paste into Word. You can control the name of this file with html.filename.

If you wish to use LaTeX, R Markdown, knitr, Sweave, etc., set latex = TRUE and then use xtable [1]. You may have to set sanitize.text.function = identity when calling print.xtable.

If you have suggestions for additional options or features, or if you would like some help using any function in tab, please e-mail me at [e-mail]. Thanks!

Author(s)

Dane R. Van Domelen

References

1. Dahl DB (2013). xtable: Export tables to LaTeX or HTML. R package version 1.7-1, https://cran.r-project.org/package=xtable.

2. Kevin Ushey (2013). Kmisc: Kevin Miscellaneous. R package version 0.5.0. https://CRAN.R-project.org/package=Kmisc.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

See Also

tabfreq
tabmeans
tabmedians
tabglm
tabcox
tabgee
tabfreq.svy
tabmeans.svy
tabmedians.svy
tabmulti.svy
tabglm.svy

Examples

# Load in sample dataset d
data(d)

# Compare age, sex, race, and BMI in control vs. treatment group
# data for each comparison
table1 <- tabmulti(dataset = d, xvarname = "Group",
                   yvarnames = c("Age", "Sex", "Race", "BMI"))

# Repeat, but use all available data for each comparison (as opposed to listwise
# deletion)
table2 <- tabmulti(dataset = d, xvarname = "Group", n.column = TRUE,
                   n.headings = FALSE,
                   yvarnames = c("Age", "Sex", "Race", "BMI"),
                   listwise.deletion = FALSE)

# Same as table1, but compare medians rather than means for BMI
table3 <- tabmulti(dataset = d, xvarname = "Group",
                   yvarnames = c("Age", "Sex", "Race", "BMI"),
                   ymeasures = c("mean", "freq", "freq", "median"))

Example output

Unequal variance t-test was used to compare mean Age in the two groups.
Pearson's chi-square test was used to test whether the distribution of Sex differed across groups.
Pearson's chi-square test was used to test whether the distribution of Race differed across groups.
Unequal variance t-test was used to compare mean BMI in the two groups.
Unequal variance t-test was used to compare mean Age in the two groups.
Pearson's chi-square test was used to test whether the distribution of Sex differed across groups.
Pearson's chi-square test was used to test whether the distribution of Race differed across groups.
Unequal variance t-test was used to compare mean BMI in the two groups.
Unequal variance t-test was used to compare mean Age in the two groups.
Pearson's chi-square test was used to test whether the distribution of Sex differed across groups.
Pearson's chi-square test was used to test whether the distribution of Race differed across groups.

tab documentation built on May 2, 2019, 6:50 p.m.