Package 'tab'

Title: Create Summary Tables for Statistical Reports
Description: Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Authors: Dane R. Van Domelen
Maintainer: Dane R. Van Domelen <[email protected]>
License: GPL (>=3)
Version: 5.1.1
Built: 2024-11-04 02:47:51 UTC
Source: https://github.com/vandomed/tab

Help Index

Format P-values for Functions in the tab Package

Description

Formats p-values for tables generated by the functions in the tab package. Handles rounding and presentation of p-values.

Usage

formatp(
  p,
  decimals = c(2, 3),
  cuts = 0.01,
  lowerbound = 0.001,
  leading0 = TRUE,
  avoid1 = FALSE
)

Arguments

p

Numeric vector of p-values.
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 cuts input.
cuts

Cut-point(s) to control number of decimal places used for p-values. For example, by default cuts = 0.1 and 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.
lowerbound

Controls cut-point at which p-values are no longer printed as their value, but rather <lowerbound. For example, by default lowerbound = 0.001. Under this setting, p-values less than 0.001 are printed as <0.001.
leading0

If TRUE, p-values are printed with 0 before decimal place; if FALSE, the leading 0 is omitted.
avoid1

If TRUE, p-values rounded to 1 are not printed as 1, but as >0.99 (or similarly depending on decimals and cuts).

Value

Character vector.

Examples

# Generate vector of numeric p-values
set.seed(123)
p <- c(runif(n = 5, min = 0, max = 1), 1, 0, 4e-7, 0.009)

# Round to nearest 2 decimals for p in (0.01, 1] and 3 decimals for p < 0.01
pvals <- formatp(p = p)

# Use 2 decimal places, a lower bound of 0.01, and omit the leading 0
pvals <- formatp(p = p, decimals = 2, lowerbound = 0.01, leading0 = FALSE)

Print a GLM Summary Table to the RStudio Viewer

Description

You can call this function as you would glm or pass a previously fitted glm object. Either way, the result is a summary table printed to the Viewer.

Usage

glm_v(...)

Arguments

...

Arguments to pass to glm.

Value

kable

Examples

# Fit and view
glm_v(death_1yr ~ Age + Sex + Race, data = tabdata, family = "binomial")

# Fit then view
fit <- glm(death_1yr ~ Age + Sex + Race, data = tabdata, family = "binomial")
glm_v(fit)

# Piping is OMG so cool Hashtag HexStickerz
fit %>% glm_v()

Create Summary Tables for Statistical Reports

Description

Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

Details

Package: tab
Type: Package
Version: 5.1.1
Date: 2021-08-01
License: GPL-3

See CRAN documentation for full list of functions.

Author(s)

Dane R. Van Domelen
[email protected]

References

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

Create Summary Table for Fitted Cox Proportional Hazards Model

Description

Creates a table summarizing a GEE fit using the coxph function.

Usage

tabcoxph(
  fit,
  columns = c("beta.se", "hr.ci", "p"),
  var.labels = NULL,
  factor.compression = 1,
  sep.char = ", ",
  decimals = 2,
  formatp.list = NULL
)

Arguments

fit

Fitted coxph object.
columns

Character vector specifying what columns to include. Choies for each element are "events", "beta", "se", "beta.se", "beta.betaci", "betaci", "hr", "hr.hrci", "hrci", "z", and "p".
var.labels

Named list specifying labels to use for certain predictors. For example, if fit includes a predictor named "race" that you want to label "Race/ethnicity" and a predictor named "age_yrs" that you want to label "Age (years)", use var.labels = list(race = "Race/ethnicity", age_yrs = "Age (years)".
factor.compression

Integer value from 1 to 5 controlling how much compression is applied to factor predictors (higher value = more compression). If 1, rows are Variable, Level 1 (ref), Level 2, ...; if 2, rows are Variable (ref = Level 1), Level 2, ...; if 3, rows are Level 1 (ref), Level 2, ...; if 4, rows are Level 2 (ref = Level 1), ...; if 5, rows are Level 2, ...
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.

Value

kable.

References

1. Therneau, T. (2015). A Package for Survival Analysis in S. R package version 2.38. https://cran.r-project.org/package=survival.

2. Therneau, T.M. and Grambsch, P.M. (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3.

Examples

# Cox PH model with age, sex, race, and treatment
library("survival")
fit <- coxph(
  Surv(time = time, event = delta) ~ Age + Sex + Race + Group,
  data = tabdata
)
tabcoxph(fit)

# Can also use piping
fit %>% tabcoxph()

# Same as previous, but with custom labels for Age and Race and factors
# displayed in slightly more compressed format
fit %>%
  tabcoxph(
    var.labels = list(Age = "Age (years)", Race = "Race/ethnicity"),
    factor.compression = 2
  )

# Cox PH model with some higher-order terms
fit <- coxph(
  Surv(time = time, event = delta) ~
  poly(Age, 2, raw = TRUE) + Sex + Race + Group + Race*Group,
  data = tabdata
)
fit %>% tabcoxph()

Sample Dataset for tab Package

Description

Data frame with 15 variables, used to illustrate certain functions.

Source

Simulated data in R

Create Frequency Table

Description

Creates an I-by-J frequency table comparing the distribution of y across levels of x.

Usage

tabfreq(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  columns = c("xgroups", "p"),
  cell = "counts",
  parenth = "col.percent",
  sep.char = ", ",
  test = "chi.fisher",
  xlevels = NULL,
  yname = NULL,
  ylevels = NULL,
  compress.binary = FALSE,
  yname.row = TRUE,
  text.label = NULL,
  quantiles = NULL,
  quantile.vals = FALSE,
  decimals = 1,
  formatp.list = NULL,
  n.headings = FALSE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. Sex ~ Group.
data

Data frame containing variables named in formula.
x

Vector indicating group membership for columns of I-by-J table.
y

Vector indicating group membership for rows of I-by-J table.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall distribution of y, "xgroups" for distributions of y for each x group, "test" for test statistic, and "p" for p-value.
cell

Character string specifying what statistic to display in cells. Choices are "counts", "tot.percent", "col.percent", and "row.percent".
parenth

Character string specifying what statistic to display in parentheses. Choices are "none", "se", "ci", "counts", "tot.percent", "col.percent", and "row.percent".
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
test

Character string specifying which test for association between x and y should be used. Choices are "chi.fisher" for Pearson's chi-squared test if its assumptions are met, otherwise Fisher's exact test; "chi"; "fisher"; "z" for z test without continuity correction; and "z.continuity" for z test with continuity correction. The last two only work if both x and y are binary.
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
ylevels

Character vector with labels for the levels of y. Note that levels of y are listed in the order that they appear when you run table(y, x).
compress.binary

Logical value for whether to compress binary y variable to a single row, excluding the first level rather than showing both.
yname.row

Logical value for whether to include a row displaying the name of the y variable and indent the factor levels.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent.
quantiles

Numeric value. If specified, table compares y across quantiles of x created on the fly.
quantile.vals

Logical value for whether labels for x quantiles should show quantile number and corresponding range, e.g. Q1 [0.00, 0.25), rather than just the quantile number.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Value

kable.

Examples

# Compare sex distribution by group
(freqtable1 <- tabfreq(Sex ~ Group, data = tabdata))

# Same as previous, but showing male row only and % (SE) rather than n (%)
(freqtable2 <- tabfreq(Sex ~ Group, data = tabdata,
                       cell = "col.percent", parenth = "se",
                       compress.binary = TRUE))

Create Frequency Table (for Complex Survey Data)

Description

Creates an I-by-J frequency table comparing the distribution of y across levels of x.

Usage

tabfreq.svy(
  formula,
  design,
  columns = c("xgroups", "p"),
  cell = "col.percent",
  parenth = "se",
  sep.char = ", ",
  xlevels = NULL,
  yname = NULL,
  ylevels = NULL,
  compress.binary = FALSE,
  yname.row = TRUE,
  text.label = NULL,
  decimals = 1,
  svychisq.list = NULL,
  formatp.list = NULL,
  n.headings = FALSE,
  N.headings = FALSE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. Race ~ Sex.
design

Survey design object from svydesign.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total unweighted sample size, "N" for total weighted sample size, "overall" for overall distribution of y, "xgroups" for distributions of y for each x group, and "p" for Chi-square p-value.
cell

Character string specifying what statistic to display in cells. Choices are "n", "N", and "col.percent".
parenth

Character string specifying what statistic to display in parentheses. Choices are "none", "n", "N", "col.percent", "se", and "ci".
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
ylevels

Character vector with labels for the levels of y. Note that levels of y are listed in the order that they appear when you run table(y, x).
compress.binary

Logical value for whether to compress binary y variable to a single row, excluding the first level rather than showing both.
yname.row

Logical value for whether to include a row displaying the name of the y variable.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
svychisq.list

List of arguments to pass to svychisq.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display unweighted sample sizes in parentheses in column headings.
N.headings

Logical value for whether to display weighted sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Details

Basically tabmedians for complex survey data. Relies heavily on the survey package.

Value

kable or character matrix.

Examples

# Create survey design object
library("survey")
design <- svydesign(
  data = tabsvydata,
  ids = ~sdmvpsu,
  strata = ~sdmvstra,
  weights = ~wtmec2yr,
  nest = TRUE
)

# Compare race distribution by sex
tabfreq.svy(Race ~ Sex, design)

Create Summary Table for Fitted Generalized Estimating Equation Model

Description

Creates a table summarizing a GEE fit using the gee function.

Usage

tabgee(
  fit,
  data = NULL,
  columns = NULL,
  robust = TRUE,
  var.labels = NULL,
  factor.compression = 1,
  sep.char = ", ",
  decimals = 2,
  formatp.list = NULL
)

Arguments

fit

Fitted gee object.
data

Data frame that served as 'data' in function call to gee. Only needs to be specified if one or more of the predictors is a factor and factor.compression is 1, 2, 3, or 4.
columns

Character vector specifying what columns to include. Choices for each element are "beta", "se", "betaci" for 95% CI for Beta, "beta.se" for Beta (SE), "beta.ci" for Beta (95% CI), "or", "orci" for 95% CI for OR, "or.ci" for OR (95% CI), "hr", "hrci" for 95% CI for HR, "hr.ci" for HR (95% CI), "z" for z statistic, and "p". If OR's or HR's are requested, the function will trust that exponentiated betas correspond to these quantities.
robust

Logical value for whether to use robust standard errors.
var.labels

Named list specifying labels to use for certain predictors. For example, if fit includes a predictor named "race" that you want to label "Race/ethnicity" and a predictor named "age_yrs" that you want to label "Age (years)", use var.labels = list(race = "Race/ethnicity", age_yrs = "Age (years)".
factor.compression

Integer value from 1 to 5 controlling how much compression is applied to factor predictors (higher value = more compression). If 1, rows are Variable, Level 1 (ref), Level 2, ...; if 2, rows are Variable (ref = Level 1), Level 2, ...; if 3, rows are Level 1 (ref), Level 2, ...; if 4, rows are Level 2 (ref = Level 1), ...; if 5, rows are Level 2, ...
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.

Value

kable.

Examples

# Load in sample dataset and convert to long format
tabdata2 <- reshape(data = tabdata,
                    varying = c("bp.1", "bp.2", "bp.3", "highbp.1",
                                "highbp.2", "highbp.3"),
                    timevar = "bp.visit", direction = "long")
tabdata2 <- tabdata2[order(tabdata2$id), ]

# Blood pressure at 1, 2, and 3 months vs. age, sex, race, and treatment
library("gee")
fit <- gee(bp ~ Age + Sex + Race + Group, id = id, data = tabdata2,
           corstr = "unstructured")
tabgee(fit)

# Can also use piping
fit %>% tabgee(data = tabdata2)

# Same as previous, but with custom labels for Age and Race and factors
# displayed in slightly more compressed format
fit %>%
  tabgee(
    data = tabdata2,
    var.labels = list(Age = "Age (years)", Race = "Race/ethnicity"),
    factor.compression = 2
  )

# GEE with some higher-order terms
# higher-order terms
fit <- gee(
  highbp ~ poly(Age, 2, raw = TRUE) + Sex + Race + Group + Race*Group,
  id = id,
  data = tabdata2,
  family = "binomial",
  corstr = "unstructured"
)
fit %>% tabgee(data = tabdata2)

Create Summary Table for Fitted Generalized Linear Model

Description

Creates a table summarizing a GLM fit using glm.

Usage

tabglm(
  fit,
  columns = NULL,
  xvarlabels = NULL,
  factor.compression = 1,
  sep.char = ", ",
  decimals = 2,
  formatp.list = NULL
)

Arguments

fit

Fitted glm object.
columns

Character vector specifying what columns to include. Choices for each element are "beta", "se", "betaci" for 95% CI for Beta, "beta.se" for Beta (SE), "beta.ci" for Beta (95% CI), "or", "orci" for 95% CI for OR, "or.ci" for OR (95% CI), "hr", "hrci" for 95% CI for HR, "hr.ci" for HR (95% CI), "test" for z/t statistic, and "p". If OR's or HR's are requested, the function will trust that exponentiated betas correspond to these quantities.
xvarlabels

Named list specifying labels to use for certain predictors. For example, if fit includes a predictor named "race" that you want to label "Race/ethnicity" and a predictor named "age_yrs" that you want to label "Age (years)", use xvarlabels = list(race = "Race/ethnicity", age_yrs = "Age (years)".
factor.compression

Integer value from 1 to 5 controlling how much compression is applied to factor predictors (higher value = more compression). If 1, rows are Variable, Level 1 (ref), Level 2, ...; if 2, rows are Variable (ref = Level 1), Level 2, ...; if 3, rows are Level 1 (ref), Level 2, ...; if 4, rows are Level 2 (ref = Level 1), ...; if 5, rows are Level 2, ...
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ". kable.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.

Value

kable.

Examples

# Linear regression: BMI vs. age, sex, race, and treatment
fit <- glm(BMI ~ Age + Sex + Race + Group, data = tabdata)
tabglm(fit)

# Can also use piping
fit %>% tabglm()

# Logistic regression: 1-year mortality vs. age, sex, race, and treatment
fit <- glm(
  death_1yr ~ Age + Sex + Race + Group,
  data = tabdata,
  family = binomial
)
fit %>% tabglm()

# Same as previous, but with custom labels for Age and Race and factors
# displayed in slightly more compressed format
fit %>%
  tabglm(
    xvarlabels = list(Age = "Age (years)", Race = "Race/ethnicity"),
    factor.compression = 2
  )

# Logistic regression model with some higher-order terms
fit <- glm(
  death_1yr ~ poly(Age, 2, raw = TRUE) + Sex + BMI + Sex * BMI,
  data = tabdata,
  family = "binomial"
)
fit %>% tabglm()

Create Table Comparing Group Means

Description

Creates a table comparing the mean of y across levels of x.

Usage

tabmeans(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  columns = c("xgroups", "p"),
  parenth = "sd",
  sep.char = ", ",
  variance = "unequal",
  xlevels = NULL,
  yname = NULL,
  text.label = NULL,
  quantiles = NULL,
  quantile.vals = FALSE,
  decimals = NULL,
  formatp.list = NULL,
  n.headings = TRUE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. BMI ~ Group.
data

Data frame containing variables named in formula.
x

Vector of values for the categorical x variable.
y

Vector of values for the continuous y variable.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall mean, "xgroups" for x group means, "diff" for difference in x group means (this one and the next two are only available for binary x), "diffci" for 95 x group means, "diff.ci" for difference in group means and 95 confidence interval, "test" for test statistic, and "p" for p-value.
parenth

Character string specifying what statistic to display in parentheses after the means. Choices are "none", "sd", "se", "t.ci", "z.ci", "range", and "minmax".
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
variance

Character string specifying which version of the two-sample t-test to use if x has 2 levels. Choices are "equal" for equal variance t-test, "unequal" for unequal variance t-test, and "f" for F test to determine which to use.
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent.
quantiles

Numeric value. If specified, table compares y across quantiles of x created on the fly.
quantile.vals

Logical value for whether labels for x quantiles should show quantile number and corresponding range, e.g. Q1 [0.00, 0.25), rather than just the quantile number.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Details

A t-test is used to compare means if x has two levels, and a one-way analysis of variance is used if x has more than two levels. Observations with missing values for x and/or y are dropped.

Value

kable or character matrix.

Examples

# Compare mean BMI in control vs. treatment group in sample dataset
(meanstable1 <- tabmeans(BMI ~ Group, data = tabdata))

# Compare mean baseline systolic BP across tertiles of BMI
(meanstable2 <- tabmeans(bp.1 ~ BMI, data = tabdata,
                         quantiles = 3, yname = "Systolic BP"))

Create Table Comparing Group Means (for Complex Survey Data)

Description

Creates a table comparing the mean of y across levels of x.

Usage

tabmeans.svy(
  formula,
  design,
  columns = c("xgroups", "p"),
  parenth = "sd",
  sep.char = ", ",
  xlevels = NULL,
  yname = NULL,
  text.label = NULL,
  decimals = 1,
  anova.svyglm.list = NULL,
  formatp.list = NULL,
  n.headings = FALSE,
  N.headings = FALSE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. BMI ~ Sex.
design

Survey design object from svydesign.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall mean, "xgroups" for x group means, "diff" for difference in x group means (this one and the next two are only available for binary x), "diffci" for 95 x group means, "diff.ci" for difference in group means and 95 confidence interval, and "p" for p-value.
parenth

Character string specifying what statistic to display in parentheses after the means. Choices are "none", "sd", "se", "t.ci", "z.ci", "range", and "minmax".
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
anova.svyglm.list

List of arguments to pass to anova.svyglm. Only used if x has three or more levels.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
N.headings

Logical value for whether to display weighted sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Details

Basically tabmeans for complex survey data. Relies heavily on the survey package.

Value

kable or character matrix.

Examples

# Create survey design object
library("survey")
design <- svydesign(
  data = tabsvydata,
  ids = ~sdmvpsu,
  strata = ~sdmvstra,
  weights = ~wtmec2yr,
  nest = TRUE
)

# Compare mean BMI by sex
(meanstable <- tabmeans.svy(BMI ~ Sex, design = design))

Create Table Comparing Group Medians

Description

Creates a table comparing the median of y across levels of x.

Usage

tabmedians(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  columns = c("xgroups", "p"),
  parenth = "iqr",
  sep.char = ", ",
  xlevels = NULL,
  yname = NULL,
  text.label = NULL,
  quantiles = NULL,
  quantile.vals = FALSE,
  decimals = NULL,
  formatp.list = NULL,
  n.headings = TRUE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. BMI ~ Group.
data

Data frame containing variables named in formula.
x

Vector of values for the categorical x variable.
y

Vector of values for the continuous y variable.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall median, "xgroups" for x group medians, "diff" for difference in x group medians (only available for binary x), "test" for test statistic, and "p" for p-value.
parenth

Character string specifying what values are shown in parentheses after the medians in each cell. Choices are "none", "iqr", "q1q3" for first and third quartiles, "range", "minmax", and "ci" for 95% confidence interval for the medians based on normal approximation to binomial.
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent.
quantiles

Numeric value. If specified, table compares y across quantiles of x created on the fly.
quantile.vals

Logical value for whether labels for x quantiles should show quantile number and corresponding range, e.g. Q1 [0.00, 0.25), rather than just the quantile number.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Details

If x has 2 levels, a Mann-Whitney U (also known as Wilcoxon rank-sum) test is used to test whether the distribution of y differs in the two groups; if x has more than 2 levels, a Kruskal-Wallis test is used to test whether the distribution of y differs across at least two of the groups. Observations with missing values for x and/or y are dropped.

Value

kable.

Examples

# Compare median BMI in control group vs. treatment group in sample dataset
(medtable1 <- tabmedians(BMI ~ Group, data = tabdata))

# Compare median baseline systolic BP across tertiles of BMI
(medtable2 <- tabmedians(bp.1 ~ BMI, data = tabdata,
                         quantiles = 3, yname = "Systolic BP"))

Create Table Comparing Group Medians (for Complex Survey Data)

Description

Creates a table comparing the median of y across levels of x.

Usage

tabmedians.svy(
  formula,
  design,
  columns = c("xgroups", "p"),
  parenth = "iqr",
  sep.char = ", ",
  xlevels = NULL,
  yname = NULL,
  text.label = NULL,
  decimals = NULL,
  svyranktest.list = NULL,
  formatp.list = NULL,
  n.headings = FALSE,
  N.headings = FALSE,
  kable = TRUE
)

Arguments

formula

Formula, e.g. BMI ~ Sex.
design

Survey design object from svydesign.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall median, "xgroups" for x group medians, "diff" for difference in x group medians (only available for binary x), and "p" for p-value.
parenth

Character string specifying what values are shown in parentheses after the medians in each cell. Choices are "none", "iqr", "q1q3" for first and third quartiles, "range", "minmax", and "ci" for 95% confidence interval for the median.
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yname

Character string with a label for the y variable.
text.label

Character string with text to put after the y variable name, identifying what cell values and parentheses represent.
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
svyranktest.list

List of arguments to pass to svyranktest.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
N.headings

Logical value for whether to display weighted sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.

Details

Basically tabmedians for complex survey data. Relies heavily on the survey package.

Value

kable or character matrix.

Examples

# Create survey design object
library("survey")
design <- svydesign(
  data = tabsvydata,
  ids = ~sdmvpsu,
  strata = ~sdmvstra,
  weights = ~wtmec2yr,
  nest = TRUE
)

# Compare median BMI by sex
(medtable1 <- tabmedians.svy(BMI ~ Sex, design = design))

Create Table Comparing Characteristics Across Levels of a Categorical Variable

Description

Creates a table comparing multiple characteristics (e.g. median age, mean BMI, and race/ethnicity distribution) across levels of x.

Usage

tabmulti(
  formula = NULL,
  data,
  xvarname = NULL,
  yvarnames = NULL,
  ymeasures = NULL,
  columns = c("xgroups", "p"),
  listwise.deletion = FALSE,
  sep.char = ", ",
  xlevels = NULL,
  yvarlabels = NULL,
  ylevels = NULL,
  quantiles = NULL,
  quantile.vals = FALSE,
  decimals = NULL,
  formatp.list = NULL,
  n.headings = FALSE,
  tabmeans.list = NULL,
  tabmedians.list = NULL,
  tabfreq.list = NULL,
  kable = TRUE
)

Arguments

formula

Formula, e.g. Age + Sex + Race + BMI ~ Group.
data

Data frame containing variables named in formula.
xvarname

Character string with name of column variable. Should be one of names(data).
yvarnames

Character vector with names of row variables. Each element should be one of names(data).
ymeasures

Character vector specifying whether each y variable should be summarized by mean, median, or frequency. For example, if you want to compare 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 compares means for numeric variables and frequencies for factor and character variables.
columns

Character vector specifying what columns to include. Choices for each element are "n" for total sample size, "overall" for overall statistics, "xgroups" for x group statistics, "test" for test statistic, and "p" for p-value.
listwise.deletion

Logical value for whether observations with missing values for any y variable should be excluded entirely (as opposed to using all available data for each comparison).
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yvarlabels

Named list specifying labels for certain y variables. For example, if you want variables named "race" and "age_yrs" to print as "Race/ethnicity" and "Age (years)", use \codeyvarlabels = list(race = "Race/ethnicity", age_yrs = "Age (years)").
ylevels

Character vector (if only 1 frequency comparison) or list of character vectors with labels for the levels of each categorical y variable.
quantiles

Numeric value. If specified, function compares y variables across quantiles of x. For example, if x contains BMI values and yvarnames includes HDL and race, setting quantiles = 3 compares mean BMI and distribution of race across BMI tertiles.
quantile.vals

Logical value for whether labels for x quantiles should show quantile number and corresponding range, e.g. Q1 [0.00, 0.25), rather than just the quantile number.
decimals

Numeric vector specifying number of decimal places for numbers other than p-values for each y variable. Can be a single value to use for all y variables.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display group sample sizes in parentheses in column headings.
tabmeans.list

List of arguments to pass to tabmeans.
tabmedians.list

List of arguments to pass to tabmedians.
tabfreq.list

List of arguments to pass to tabfreq.
kable

Logical value for whether to return a kable.

Value

kable or character matrix.

Examples

# Compare age, sex, race, and BMI in control vs. treatment group
tabmulti(Age + Sex + Race + BMI ~ Group, data = tabdata)

# Same as previous, but compare medians rather than means for BMI
tabmulti(Age + Sex + Race + BMI ~ Group, data = tabdata,
         ymeasures = c("mean", "freq", "freq", "median"))

Create Table Comparing Characteristics Across Levels of a Categorical Variable (for Complex Survey Data)

Description

Creates a table comparing multiple characteristics (e.g. median age, mean BMI, and race/ethnicity distribution) across levels of x.

Usage

tabmulti.svy(
  formula = NULL,
  design,
  xvarname = NULL,
  yvarnames = NULL,
  ymeasures = NULL,
  columns = c("xgroups", "p"),
  listwise.deletion = FALSE,
  sep.char = ", ",
  xlevels = NULL,
  yvarlabels = NULL,
  ylevels = NULL,
  decimals = NULL,
  formatp.list = NULL,
  n.headings = FALSE,
  N.headings = FALSE,
  kable = TRUE,
  tabmeans.svy.list = NULL,
  tabmedians.svy.list = NULL,
  tabfreq.svy.list = NULL
)

Arguments

formula

Formula, e.g. Age + Race + BMI ~ Sex.
design

Survey design object from svydesign.
xvarname

Character string with name of column variable. Should be one of names(design$variables).
yvarnames

Character vector with names of row variables. Each element should be one of names(design$variables).
ymeasures

Character vector specifying whether each y variable should be summarized by mean, median, or frequency. For example, if you want to compare 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 compares means for numeric variables and frequencies for factor and character variables.
columns

Character vector specifying what columns to include. Choices for each element are "n" for unweighted sample size, "N" for weighted sample size, "overall" for overall statistics, "xgroups" for x group statistics, and "p" for p-value.
listwise.deletion

Logical value for whether observations with missing values for any y variable should be excluded entirely (as opposed to using all available data for each comparison).
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
xlevels

Character vector with labels for the levels of x, used in column headings.
yvarlabels

Named list specifying labels for certain y variables. For example, if you want variables named "race" and "age_yrs" to print as "Race/ethnicity" and "Age (years)", use \codeyvarlabels = list(race = "Race/ethnicity", age_yrs = "Age (years)").
ylevels

Character vector (if only 1 frequency comparison) or list of character vectors with labels for the levels of each categorical y variable.
decimals

Numeric vector specifying number of decimal places for numbers other than p-values for each y variable. Can be a single value to use for all y variables.
formatp.list

List of arguments to pass to formatp.
n.headings

Logical value for whether to display unweighted sample sizes in parentheses in column headings.
N.headings

Logical value for whether to display weighted sample sizes in parentheses in column headings.
kable

Logical value for whether to return a kable.
tabmeans.svy.list

List of arguments to pass to tabmeans.svy.
tabmedians.svy.list

List of arguments to pass to tabmedians.svy.
tabfreq.svy.list

List of arguments to pass to tabfreq.svy.

Details

Basically tabmulti for complex survey data. Relies heavily on the survey package.

Value

kable or character matrix.

Examples

# Create survey design object
library("survey")
design <- svydesign(
  data = tabsvydata,
  ids = ~sdmvpsu,
  strata = ~sdmvstra,
  weights = ~wtmec2yr,
  nest = TRUE
)

# Compare age, race, and BMI by sex
tabmulti.svy(Age + Race + BMI ~ Sex, design)

Create Regression Table from Betas and Standard Errors

Description

Useful for quickly creating a summary table.

Usage

tabreg(
  betas,
  ses = NULL,
  varcov = NULL,
  columns = c("beta.se", "p"),
  sep.char = ", ",
  decimals = NULL,
  formatp.list = NULL,
  labels = NULL
)

Arguments

betas

Numeric vector.
ses

Numeric vector.
varcov

Numeric matrix.
columns

Character vector specifying what columns to include. Choices are "beta", "se", "betaci", "beta.se", "beta.ci", "or", "orci", "or.ci", and "p".
sep.char

Character string with separator to place between lower and upper bound of confidence intervals. Typically "-" or ", ".
decimals

Numeric value specifying number of decimal places for numbers other than p-values.
formatp.list

List of arguments to pass to formatp.
labels

Character vector.

Value

kable.

Examples

# Create summary table for mtcars regression
fit <- lm(mpg ~ wt + hp + drat, data = mtcars)
tabreg(
  betas = fit$coef,
  varcov = vcov(fit),
  labels = c("Intercept", "Weight", "HP", "Rear axle ratio")
)

Sample Survey Dataset for tab Package

Description

Data frame with with 9 variables, used to illustrate certain functions. Data are derived from the National Health and Nutrition Examination Survey, years 2003-2004, although the variables 'time' and 'event' are simulated (fake).

Source

https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/DEMO_C.htm

References

Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2003-2004. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2003. Accessed June 8, 2019.

Output a Table to the RStudio Viewer

Description

Does some basic formatting and then calls kable and kable_styling to print table to Viewer.

Usage

toviewer(x)

Arguments

x

Character matrix