Help for package sjPlot (original) (raw)

Type:	Package
Encoding:	UTF-8
Title:	Data Visualization for Statistics in Social Science
Version:	2.9.0
Maintainer:	Daniel Lüdecke d.luedecke@uke.de
Description:	Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.
License:	GPL-3
Depends:	R (≥ 4.1)
Imports:	graphics, grDevices, stats, utils, bayestestR (≥ 0.16.1), datawizard (≥ 1.1.0), dplyr, ggeffects, ggplot2 (≥ 3.2.0), knitr, insight (≥ 1.3.1), parameters (≥ 0.27.0), performance (≥ 0.15.0), purrr, rlang, scales, sjlabelled (≥ 1.1.2), sjmisc (≥ 2.8.2), sjstats (≥ 0.17.8), tidyr (≥ 1.0.0)
Suggests:	brms, car, clubSandwich, cluster, cowplot, effects, haven, GPArotation, ggrepel, glmmTMB, gridExtra, ggridges, httr, lme4, MASS, nFactors, pscl, psych, rmarkdown, rstanarm, sandwich, splines, survey, TMB, testthat
URL:	https://strengejacke.github.io/sjPlot/
BugReports:	https://github.com/strengejacke/sjPlot/issues
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-10 18:10:34 UTC; DL
Author:	Daniel Lüdecke [aut, cre], Alexander Bartel [ctb], Carsten Schwemmer [ctb], Chuck Powell [ctb], Amir Djalovski [ctb], Johannes Titz [ctb]
Repository:	CRAN
Date/Publication:	2025-07-10 19:00:05 UTC

Data Visualization for Statistics in Social Science

Description

Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA and correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots of interaction terms in regression models, constructing index or score variables and much more.

The package supports labelled data, i.e. value and variable labels from labelled data (like vectors or data frames) are automatically used to label the output. Own labels can be specified as well.

What does this package do?

In short, the functions in this package mostly do two things:

1. compute basic or advanced statistical analyses
2. either plot the results as ggplot-figure or print them as html-table

How does this package help me?

One of the more challenging tasks when working with R is to get nicely formatted output of statistical analyses, either in graphical or table format. The sjPlot-package takes over these tasks and makes it easy to create beautiful figures or tables.

There are many examples for each function in the related help files and a comprehensive online documentation at https://strengejacke.github.io/sjPlot/.

A note on the package functions

The main functions follow specific naming conventions, hence starting with a specific prefix, which indicates what kind of task these functions perform.

• sjc - cluster analysis functions
• sjp - plotting functions
• sjt - (HTML) table output functions

Author(s)

Daniel Lüdecke d.luedecke@uke.de

Plot chi-squared distributions

Description

This function plots a simple chi-squared distribution or a chi-squared distribution with shaded areas that indicate at which chi-squared value a significant p-level is reached.

Usage

dist_chisq(
  chi2 = NULL,
  deg.f = NULL,
  p = NULL,
  xmax = NULL,
  geom.colors = NULL,
  geom.alpha = 0.7
)

Arguments

Examples

# a simple chi-squared distribution
# for 6 degrees of freedom
dist_chisq(deg.f = 6)

# a chi-squared distribution for 6 degrees of freedom,
# and a shaded area starting at chi-squared value of ten.
# With a df of 6, a chi-squared value of 12.59 would be "significant",
# thus the shaded area from 10 to 12.58 is filled as "non-significant",
# while the area starting from chi-squared value 12.59 is filled as
# "significant"
dist_chisq(chi2 = 10, deg.f = 6)

# a chi-squared distribution for 6 degrees of freedom,
# and a shaded area starting at that chi-squared value, which has
# a p-level of about 0.125 (which equals a chi-squared value of about 10).
# With a df of 6, a chi-squared value of 12.59 would be "significant",
# thus the shaded area from 10 to 12.58 (p-level 0.125 to p-level 0.05)
# is filled as "non-significant", while the area starting from chi-squared
# value 12.59 (p-level < 0.05) is filled as "significant".
dist_chisq(p = 0.125, deg.f = 6)

Plot F distributions

Description

This function plots a simple F distribution or an F distribution with shaded areas that indicate at which F value a significant p-level is reached.

Usage

dist_f(
  f = NULL,
  deg.f1 = NULL,
  deg.f2 = NULL,
  p = NULL,
  xmax = NULL,
  geom.colors = NULL,
  geom.alpha = 0.7
)

Arguments

Examples

# a simple F distribution for 6 and 45 degrees of freedom
dist_f(deg.f1 = 6, deg.f2 = 45)

# F distribution for 6 and 45 degrees of freedom,
# and a shaded area starting at F value of two.
# F-values equal or greater than 2.31 are "significant"
dist_f(f = 2, deg.f1 = 6, deg.f2 = 45)

# F distribution for 6 and 45 degrees of freedom,
# and a shaded area starting at a p-level of 0.2
# (F-Value about 1.5).
dist_f(p = 0.2, deg.f1 = 6, deg.f2 = 45)

Plot normal distributions

Description

This function plots a simple normal distribution or a normal distribution with shaded areas that indicate at which value a significant p-level is reached.

Usage

dist_norm(
  norm = NULL,
  mean = 0,
  sd = 1,
  p = NULL,
  xmax = NULL,
  geom.colors = NULL,
  geom.alpha = 0.7
)

Arguments

Examples

# a simple normal distribution
dist_norm()

# a simple normal distribution with different mean and sd.
# note that curve looks similar to above plot, but axis range
# has changed.
dist_norm(mean = 2, sd = 4)

# a simple normal distribution
dist_norm(norm = 1)

# a simple normal distribution
dist_norm(p = 0.2)

Plot t-distributions

Description

This function plots a simple t-distribution or a t-distribution with shaded areas that indicate at which t-value a significant p-level is reached.

Usage

dist_t(
  t = NULL,
  deg.f = NULL,
  p = NULL,
  xmax = NULL,
  geom.colors = NULL,
  geom.alpha = 0.7
)

Arguments

Examples

# a simple t-distribution
# for 6 degrees of freedom
dist_t(deg.f = 6)

# a t-distribution for 6 degrees of freedom,
# and a shaded area starting at t-value of one.
# With a df of 6, a t-value of 1.94 would be "significant".
dist_t(t = 1, deg.f = 6)

# a t-distribution for 6 degrees of freedom,
# and a shaded area starting at p-level of 0.4
# (t-value of about 0.26).
dist_t(p = 0.4, deg.f = 6)

Sample dataset from the EUROFAMCARE project

Description

A SPSS sample data set, imported with the [read_spss](../../sjlabelled/refman/sjlabelled.html#topic+read%5Fspss) function.

Plot frequencies of variables

Description

Plot frequencies of a variable as bar graph, histogram, box plot etc.

Usage

plot_frq(
  data,
  ...,
  title = "",
  weight.by = NULL,
  title.wtd.suffix = NULL,
  sort.frq = c("none", "asc", "desc"),
  type = c("bar", "dot", "histogram", "line", "density", "boxplot", "violin"),
  geom.size = NULL,
  geom.colors = "#336699",
  errorbar.color = "darkred",
  axis.title = NULL,
  axis.labels = NULL,
  xlim = NULL,
  ylim = NULL,
  wrap.title = 50,
  wrap.labels = 20,
  grid.breaks = NULL,
  expand.grid = FALSE,
  show.values = TRUE,
  show.n = TRUE,
  show.prc = TRUE,
  show.axis.values = TRUE,
  show.ci = FALSE,
  show.na = FALSE,
  show.mean = FALSE,
  show.mean.val = TRUE,
  show.sd = TRUE,
  drop.empty = TRUE,
  mean.line.type = 2,
  mean.line.size = 0.5,
  inner.box.width = 0.15,
  inner.box.dotsize = 3,
  normal.curve = FALSE,
  normal.curve.color = "red",
  normal.curve.size = 0.8,
  normal.curve.alpha = 0.4,
  auto.group = NULL,
  coord.flip = FALSE,
  vjust = "bottom",
  hjust = "center",
  y.offset = NULL
)

Arguments

Value

A ggplot-object.

Note

This function only works with variables with integer values (or numeric factor levels), i.e. scales / centered variables with fractional part may result in unexpected behaviour.

Examples

library(sjlabelled)
data(efc)
data(iris)

# simple plots, two different notations
plot_frq(iris, Species)
plot_frq(efc$tot_sc_e)

# boxplot
plot_frq(efc$e17age, type = "box")

if (require("dplyr")) {
  # histogram, pipe-workflow
  efc |>
    dplyr::select(e17age, c160age) |>
    plot_frq(type = "hist", show.mean = TRUE)

  # bar plot(s)
  plot_frq(efc, e42dep, c172code)
}

if (require("dplyr") && require("gridExtra")) {
  # grouped data frame, all panels in one plot
  efc |>
    group_by(e42dep) |>
    plot_frq(c161sex) |>
    plot_grid()
}


library(sjmisc)
# grouped variable
ageGrp <- group_var(efc$e17age)
ageGrpLab <- group_labels(efc$e17age)
plot_frq(ageGrp, title = get_label(efc$e17age), axis.labels = ageGrpLab)

# plotting confidence intervals. expand grid and v/hjust for text labels
plot_frq(
  efc$e15relat, type = "dot", show.ci = TRUE, sort.frq = "desc",
  coord.flip = TRUE, expand.grid = TRUE, vjust = "bottom", hjust = "left"
)

# histogram with overlayed normal curve
plot_frq(efc$c160age, type = "h", show.mean = TRUE, show.mean.val = TRUE,
        normal.curve = TRUE, show.sd = TRUE, normal.curve.color = "blue",
        normal.curve.size = 3, ylim = c(0,50))

Plot grouped proportional tables

Description

Plot grouped proportional crosstables, where the proportion of each level of x for the highest category in yis plotted, for each subgroup of grp.

Usage

plot_gpt(
  data,
  x,
  y,
  grp,
  colors = "metro",
  geom.size = 2.5,
  shape.fill.color = "#f0f0f0",
  shapes = c(15, 16, 17, 18, 21, 22, 23, 24, 25, 7, 8, 9, 10, 12),
  title = NULL,
  axis.labels = NULL,
  axis.titles = NULL,
  legend.title = NULL,
  legend.labels = NULL,
  wrap.title = 50,
  wrap.labels = 15,
  wrap.legend.title = 20,
  wrap.legend.labels = 20,
  axis.lim = NULL,
  grid.breaks = NULL,
  show.total = TRUE,
  annotate.total = TRUE,
  show.p = TRUE,
  show.n = TRUE
)

Arguments

Details

The p-values are based on [chisq.test](../../../../doc/manuals/r-patched/packages/stats/refman/stats.html#topic+chisq.test) of xand y for each grp.

Value

A ggplot-object.

Examples

if (requireNamespace("haven")) {
  data(efc)

  # the proportion of dependency levels in female
  # elderly, for each family carer's relationship
  # to elderly
  plot_gpt(efc, e42dep, e16sex, e15relat)

  # proportion of educational levels in highest
  # dependency category of elderly, for different
  # care levels
  plot_gpt(efc, c172code, e42dep, n4pstu)
}

Arrange list of plots as grid

Description

Plot multiple ggplot-objects as a grid-arranged single plot.

Usage

plot_grid(x, margin = c(1, 1, 1, 1), tags = NULL)

Arguments

Details

This function takes a list of ggplot-objects as argument. Plotting functions of this package that produce multiple plot objects (e.g., when there is an argument facet.grid) usually return multiple plots as list (the return value is named plot.list). To arrange these plots as grid as a single plot, use plot_grid.

Value

An object of class gtable.

Examples

if (require("dplyr") && require("gridExtra")) {
  library(ggeffects)
  data(efc)

  # fit model
  fit <- glm(
    tot_sc_e ~ c12hour + e17age + e42dep + neg_c_7,
    data = efc,
    family = poisson
  )

  # plot marginal effects for each predictor, each as single plot
  p1 <- ggpredict(fit, "c12hour") |>
    plot(show_y_title = FALSE, show_title = FALSE)
  p2 <- ggpredict(fit, "e17age") |>
    plot(show_y_title = FALSE, show_title = FALSE)
  p3 <- ggpredict(fit, "e42dep") |>
    plot(show_y_title = FALSE, show_title = FALSE)
  p4 <- ggpredict(fit, "neg_c_7") |>
    plot(show_y_title = FALSE, show_title = FALSE)

  # plot grid
  plot_grid(list(p1, p2, p3, p4))

  # plot grid
  plot_grid(list(p1, p2, p3, p4), tags = TRUE)
}

Plot grouped or stacked frequencies

Description

Plot grouped or stacked frequencies of variables as bar/dot, box or violin plots, or line plot.

Usage

plot_grpfrq(
  var.cnt,
  var.grp,
  type = c("bar", "dot", "line", "boxplot", "violin"),
  bar.pos = c("dodge", "stack"),
  weight.by = NULL,
  intr.var = NULL,
  title = "",
  title.wtd.suffix = NULL,
  legend.title = NULL,
  axis.titles = NULL,
  axis.labels = NULL,
  legend.labels = NULL,
  intr.var.labels = NULL,
  wrap.title = 50,
  wrap.labels = 15,
  wrap.legend.title = 20,
  wrap.legend.labels = 20,
  geom.size = NULL,
  geom.spacing = 0.15,
  geom.colors = "Paired",
  show.values = TRUE,
  show.n = TRUE,
  show.prc = TRUE,
  show.axis.values = TRUE,
  show.ci = FALSE,
  show.grpcnt = FALSE,
  show.legend = TRUE,
  show.na = FALSE,
  show.summary = FALSE,
  drop.empty = TRUE,
  auto.group = NULL,
  ylim = NULL,
  grid.breaks = NULL,
  expand.grid = FALSE,
  inner.box.width = 0.15,
  inner.box.dotsize = 3,
  smooth.lines = FALSE,
  emph.dots = TRUE,
  summary.pos = "r",
  facet.grid = FALSE,
  coord.flip = FALSE,
  y.offset = NULL,
  vjust = "bottom",
  hjust = "center"
)

Arguments

Details

geom.colors may be a character vector of color values in hex-format, valid color value names (see demo("colors") or a name of a color brewer palette. Following options are valid for the geom.colors argument:

• If not specified, a default color brewer palette will be used, which is suitable for the plot style (i.e. diverging for likert scales, qualitative for grouped bars etc.).
• If "gs", a greyscale will be used.
• If "bw", and plot-type is a line-plot, the plot is black/white and uses different line types to distinguish groups (see this package-vignette).
• If geom.colors is any valid color brewer palette name, the related palette will be used. Use RColorBrewer::display.brewer.all() to view all available palette names.
• Else specify own color values or names as vector (e.g. geom.colors = c("#f00000", "#00ff00")).

Value

A ggplot-object.

Examples

data(efc)
plot_grpfrq(efc$e17age, efc$e16sex, show.values = FALSE)

# boxplot
plot_grpfrq(efc$e17age, efc$e42dep, type = "box")

# grouped bars
plot_grpfrq(efc$e42dep, efc$e16sex, title = NULL)

# box plots with interaction variable
plot_grpfrq(efc$e17age, efc$e42dep, intr.var = efc$e16sex, type = "box")

# Grouped bar plot
plot_grpfrq(efc$neg_c_7, efc$e42dep, show.values = FALSE)

# same data as line plot
plot_grpfrq(efc$neg_c_7, efc$e42dep, type = "line")

# show ony categories where we have data (i.e. drop zero-counts)
library(dplyr)
efc <- dplyr::filter(efc, e42dep %in% c(3,4))
plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = TRUE)

# show all categories, even if not in data
plot_grpfrq(efc$c161sex, efc$e42dep, drop.empty = FALSE)

Plot likert scales as centered stacked bars

Description

Plot likert scales as centered stacked bars.

Usage

plot_likert(
  items,
  groups = NULL,
  groups.titles = "auto",
  title = NULL,
  legend.title = NULL,
  legend.labels = NULL,
  axis.titles = NULL,
  axis.labels = NULL,
  catcount = NULL,
  cat.neutral = NULL,
  sort.frq = NULL,
  weight.by = NULL,
  title.wtd.suffix = NULL,
  wrap.title = 50,
  wrap.labels = 30,
  wrap.legend.title = 30,
  wrap.legend.labels = 28,
  geom.size = 0.6,
  geom.colors = "BrBG",
  cat.neutral.color = "grey70",
  intercept.line.color = "grey50",
  reverse.colors = FALSE,
  values = "show",
  show.n = TRUE,
  show.legend = TRUE,
  show.prc.sign = FALSE,
  grid.range = 1,
  grid.breaks = 0.2,
  expand.grid = TRUE,
  digits = 1,
  reverse.scale = FALSE,
  coord.flip = TRUE,
  sort.groups = TRUE,
  legend.pos = "bottom",
  rel_heights = 1,
  group.legend.options = list(nrow = NULL, byrow = TRUE),
  cowplot.options = list(label_x = 0.01, hjust = 0, align = "v")
)

Arguments

Value

A ggplot-object.

Note

Note that only even numbers of categories are possible to plot, so the "positive" and "negative" values can be splitted into two halfs. A neutral category (like "don't know") can be used, but must be indicated by cat.neutral.

The catcount-argument indicates how many item categories are in the Likert scale. Normally, this argument can be ignored because the amount of valid categories is retrieved automatically. However, sometimes (for instance, if a certain category is missing in all items), auto-detection of the amount of categories fails. In such cases, specify the amount of categories with the catcount-argument.

Examples

if (requireNamespace("ggrepel") && requireNamespace("sjmisc")) {
library(sjmisc)
data(efc)
# find all variables from COPE-Index, which all have a "cop" in their
# variable name, and then plot that subset as likert-plot
mydf <- find_var(efc, pattern = "cop", out = "df")

plot_likert(mydf)

plot_likert(
  mydf,
  grid.range = c(1.2, 1.4),
  expand.grid = FALSE,
  values = "sum.outside",
  show.prc.sign = TRUE
)

# Plot in groups

plot_likert(mydf, c(2,1,1,1,1,2,2,2,1))

if (require("parameters") && require("nFactors")) {
  groups <- parameters::principal_components(mydf)
  plot_likert(mydf, groups = parameters::closest_component(groups))
}

plot_likert(mydf,
            c(rep("B", 4), rep("A", 5)),
            sort.groups = FALSE,
            grid.range = c(0.9, 1.1),
            geom.colors = "RdBu",
            rel_heights = c(6, 8),
            wrap.labels = 40,
            reverse.scale = TRUE)

# control legend items
six_cat_example = data.frame(
  matrix(sample(1:6, 600, replace = TRUE),
  ncol = 6)
)

## Not run: 
six_cat_example <-
  six_cat_example |>
  dplyr::mutate_all(~ordered(.,labels = c("+++","++","+","-","--","---")))

# Old default
plot_likert(
  six_cat_example,
  groups = c(1, 1, 1, 2, 2, 2),
  group.legend.options = list(nrow = 2, byrow = FALSE)
)

# New default
plot_likert(six_cat_example, groups = c(1, 1, 1, 2, 2, 2))

# Single row
plot_likert(
  six_cat_example,
  groups = c(1, 1, 1, 2, 2, 2),
  group.legend.options = list(nrow = 1)
)
## End(Not run)
}

Plot regression models

Description

plot_model() creates plots from regression models, either estimates (as so-called forest or dot whisker plots) or marginal effects.

Usage

plot_model(
  model,
  type = c("est", "re", "eff", "emm", "pred", "int", "std", "std2", "slope", "resid",
    "diag"),
  transform,
  terms = NULL,
  sort.est = NULL,
  rm.terms = NULL,
  group.terms = NULL,
  order.terms = NULL,
  pred.type = c("fe", "re"),
  mdrt.values = c("minmax", "meansd", "zeromax", "quart", "all"),
  ri.nr = NULL,
  title = NULL,
  axis.title = NULL,
  axis.labels = NULL,
  legend.title = NULL,
  wrap.title = 50,
  wrap.labels = 25,
  axis.lim = NULL,
  grid.breaks = NULL,
  ci.lvl = NULL,
  se = NULL,
  vcov.fun = NULL,
  vcov.args = NULL,
  colors = "Set1",
  show.intercept = FALSE,
  show.values = FALSE,
  show.p = TRUE,
  show.data = FALSE,
  show.legend = TRUE,
  show.zeroinf = TRUE,
  value.offset = NULL,
  value.size,
  jitter = NULL,
  digits = 2,
  dot.size = NULL,
  line.size = NULL,
  vline.color = NULL,
  p.threshold = c(0.05, 0.01, 0.001),
  p.val = NULL,
  p.adjust = NULL,
  grid,
  case,
  auto.label = TRUE,
  prefix.labels = c("none", "varname", "label"),
  bpe = "median",
  bpe.style = "line",
  bpe.color = "white",
  ci.style = c("whisker", "bar"),
  std.response = TRUE,
  ...
)

get_model_data(
  model,
  type = c("est", "re", "eff", "pred", "int", "std", "std2", "slope", "resid", "diag"),
  transform,
  terms = NULL,
  sort.est = NULL,
  rm.terms = NULL,
  group.terms = NULL,
  order.terms = NULL,
  pred.type = c("fe", "re"),
  ri.nr = NULL,
  ci.lvl = NULL,
  colors = "Set1",
  grid,
  case = "parsed",
  digits = 2,
  ...
)

Arguments

Details

Different Plot Types

type = "std"

Plots standardized estimates. See details below.

type = "std2"

Plots standardized estimates, however, standardization follows Gelman's (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one. Resulting coefficients are then directly comparable for untransformed binary predictors.

type = "pred"

Plots estimated marginal means (or marginal effects). Simply wraps [ggpredict](../../ggeffects/refman/ggeffects.html#topic+ggpredict). See alsothis package-vignette.

type = "eff"

Plots estimated marginal means (or marginal effects). Simply wraps [ggeffect](../../ggeffects/refman/ggeffects.html#topic+ggeffect). See alsothis package-vignette.

type = "int"

A shortcut for marginal effects plots, where interaction terms are automatically detected and used asterms-argument. Furthermore, if the moderator variable (the second - and third - term in an interaction) is continuous, type = "int"automatically chooses useful values based on the mdrt.values-argument, which are passed to terms. Then, [ggpredict](../../ggeffects/refman/ggeffects.html#topic+ggpredict)is called. type = "int" plots the interaction term that appears first in the formula along the x-axis, while the second (and possibly third) variable in an interaction is used as grouping factor(s) (moderating variable). Use type = "pred" or type = "eff"and specify a certain order in the terms-argument to indicate which variable(s) should be used as moderator. See alsothis package-vignette.

type = "slope" and type = "resid"

Simple diagnostic-plots, where a linear model for each single predictor is plotted against the response variable, or the model's residuals. Additionally, a loess-smoothed line is added to the plot. The main purpose of these plots is to check whether the relationship between outcome (or residuals) and a predictor is roughly linear or not. Since the plots are based on a simple linear regression with only one model predictor at the moment, the slopes (i.e. coefficients) may differ from the coefficients of the complete model.

type = "diag"

For Stan-models, plots the prior versus posterior samples. For linear (mixed) models, plots for multicollinearity-check (Variance Inflation Factors), QQ-plots, checks for normal distribution of residuals and homoscedasticity (constant variance of residuals) are shown. For generalized linear mixed models, returns the QQ-plot for random effects.

Standardized Estimates

Default standardization is done by completely refitting the model on the standardized data. Hence, this approach is equal to standardizing the variables before fitting the model, which is particularly recommended for complex models that include interactions or transformations (e.g., polynomial or spline terms). When type = "std2", standardization of estimates follows Gelman's (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one. Resulting coefficients are then directly comparable for untransformed binary predictors.

Value

Depending on the plot-type, plot_model() returns aggplot-object or a list of such objects. get_model_datareturns the associated data with the plot-object as tidy data frame, or (depending on the plot-type) a list of such data frames.

References

Gelman A (2008) "Scaling regression inputs by dividing by two standard deviations." Statistics in Medicine 27: 2865-2873.

Aiken and West (1991). Multiple Regression: Testing and Interpreting Interactions.

Examples

# prepare data
if (requireNamespace("haven")) {
library(sjmisc)
data(efc)
efc <- to_factor(efc, c161sex, e42dep, c172code)
m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)

# simple forest plot
plot_model(m)

# grouped coefficients
plot_model(m, group.terms = c(1, 2, 3, 3, 3, 4, 4))

# keep only selected terms in the model: pos_v_4, the
# levels 3 and 4 of factor e42dep and levels 2 and 3 for c172code
plot_model(m, terms = c("pos_v_4", "e42dep [3,4]", "c172code [2,3]"))
}

# multiple plots, as returned from "diagnostic"-plot type,
# can be arranged with 'plot_grid()'
## Not run: 
p <- plot_model(m, type = "diag")
plot_grid(p)
## End(Not run)

# plot random effects
if (require("lme4") && require("glmmTMB")) {
  m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
  plot_model(m, type = "re")

  # plot marginal effects
  plot_model(m, type = "pred", terms = "Days")
}
# plot interactions
## Not run: 
m <- glm(
  tot_sc_e ~ c161sex + c172code * neg_c_7,
  data = efc,
  family = poisson()
)
# type = "int" automatically selects groups for continuous moderator
# variables - see argument 'mdrt.values'. The following function call is
# identical to:
# plot_model(m, type = "pred", terms = c("c172code", "neg_c_7 [7,28]"))
plot_model(m, type = "int")

# switch moderator
plot_model(m, type = "pred", terms = c("neg_c_7", "c172code"))
# same as
# ggeffects::ggpredict(m, terms = c("neg_c_7", "c172code"))
## End(Not run)

# plot Stan-model
## Not run: 
if (require("rstanarm")) {
  data(mtcars)
  m <- stan_glm(mpg ~ wt + am + cyl + gear, data = mtcars, chains = 1)
  plot_model(m, bpe.style = "dot")
}
## End(Not run)

Forest plot of multiple regression models

Description

Plot and compare regression coefficients with confidence intervals of multiple regression models in one plot.

Usage

plot_models(
  ...,
  transform = NULL,
  std.est = NULL,
  std.response = TRUE,
  rm.terms = NULL,
  title = NULL,
  m.labels = NULL,
  legend.title = "Dependent Variables",
  legend.pval.title = "p-level",
  axis.labels = NULL,
  axis.title = NULL,
  axis.lim = NULL,
  wrap.title = 50,
  wrap.labels = 25,
  wrap.legend.title = 20,
  grid.breaks = NULL,
  dot.size = 3,
  line.size = NULL,
  value.size = NULL,
  spacing = 0.4,
  colors = "Set1",
  show.values = FALSE,
  show.legend = TRUE,
  show.intercept = FALSE,
  show.p = TRUE,
  p.shape = FALSE,
  p.threshold = c(0.05, 0.01, 0.001),
  p.adjust = NULL,
  ci.lvl = 0.95,
  vcov.fun = NULL,
  vcov.args = NULL,
  vline.color = NULL,
  digits = 2,
  grid = FALSE,
  auto.label = TRUE,
  prefix.labels = c("none", "varname", "label")
)

Arguments

Value

A ggplot-object.

Examples

data(efc)

# fit three models
fit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc)
fit3 <- lm(tot_sc_e ~ c160age + c12hour + c161sex + c172code, data = efc)

# plot multiple models
plot_models(fit1, fit2, fit3, grid = TRUE)

# plot multiple models with legend labels and
# point shapes instead of value labels
plot_models(
  fit1, fit2, fit3,
  axis.labels = c(
    "Carer's Age", "Hours of Care", "Carer's Sex", "Educational Status"
  ),
  m.labels = c("Barthel Index", "Negative Impact", "Services used"),
  show.values = FALSE, show.p = FALSE, p.shape = TRUE
)

## Not run: 
# plot multiple models from nested lists argument
all.models <- list()
all.models[[1]] <- fit1
all.models[[2]] <- fit2
all.models[[3]] <- fit3

plot_models(all.models)

# plot multiple models with different predictors (stepwise inclusion),
# standardized estimates
fit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars)
fit2 <- update(fit1, . ~ . + hp)
fit3 <- update(fit2, . ~ . + am)

plot_models(fit1, fit2, fit3, std.est = "std2")

## End(Not run)

Plot predicted values and their residuals

Description

This function plots observed and predicted values of the response of linear (mixed) models for each coefficient and highlights the observed values according to their distance (residuals) to the predicted values. This allows to investigate how well actual and predicted values of the outcome fit across the predictor variables.

Usage

plot_residuals(
  fit,
  geom.size = 2,
  remove.estimates = NULL,
  show.lines = TRUE,
  show.resid = TRUE,
  show.pred = TRUE,
  show.ci = FALSE
)

Arguments

Value

A ggplot-object.

Note

The actual (observed) values have a coloured fill, while the predicted values have a solid outline without filling.

Examples

data(efc)
# fit model
fit <- lm(neg_c_7 ~ c12hour + e17age + e42dep, data = efc)

# plot residuals for all independent variables
plot_residuals(fit)

# remove some independent variables from output
plot_residuals(fit, remove.estimates = c("e17age", "e42dep"))

Plot (grouped) scatter plots

Description

Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. Furthermore, fitted lines can be added for each group as well as for the overall plot.

Usage

plot_scatter(
  data,
  x,
  y,
  grp,
  title = "",
  legend.title = NULL,
  legend.labels = NULL,
  dot.labels = NULL,
  axis.titles = NULL,
  dot.size = 1.5,
  label.size = 3,
  colors = "metro",
  fit.line = NULL,
  fit.grps = NULL,
  show.rug = FALSE,
  show.legend = TRUE,
  show.ci = FALSE,
  wrap.title = 50,
  wrap.legend.title = 20,
  wrap.legend.labels = 20,
  jitter = 0.05,
  emph.dots = FALSE,
  grid = FALSE
)

Arguments

Value

A ggplot-object. For grouped data frames, a list of ggplot-objects for each group in the data.

Examples

# load sample date
library(sjmisc)
library(sjlabelled)
data(efc)

# simple scatter plot
plot_scatter(efc, e16sex, neg_c_7)

# simple scatter plot, increased jittering
plot_scatter(efc, e16sex, neg_c_7, jitter = .4)

# grouped scatter plot
plot_scatter(efc, c160age, e17age, e42dep)

# grouped scatter plot with marginal rug plot
# and add fitted line for complete data
plot_scatter(
  efc, c12hour, c160age, c172code,
  show.rug = TRUE, fit.line = "lm"
)

# grouped scatter plot with marginal rug plot
# and add fitted line for each group
plot_scatter(
  efc, c12hour, c160age, c172code,
  show.rug = TRUE, fit.grps = "loess",
  grid = TRUE
)

Plot stacked proportional bars

Description

Plot items (variables) of a scale as stacked proportional bars. This function is useful when several items with identical scale/categoroies should be plotted to compare the distribution of answers.

Usage

plot_stackfrq(
  items,
  title = NULL,
  legend.title = NULL,
  legend.labels = NULL,
  axis.titles = NULL,
  axis.labels = NULL,
  weight.by = NULL,
  sort.frq = NULL,
  wrap.title = 50,
  wrap.labels = 30,
  wrap.legend.title = 30,
  wrap.legend.labels = 28,
  geom.size = 0.5,
  geom.colors = "Blues",
  show.prc = TRUE,
  show.n = FALSE,
  show.total = TRUE,
  show.axis.prc = TRUE,
  show.legend = TRUE,
  grid.breaks = 0.2,
  expand.grid = FALSE,
  digits = 1,
  vjust = "center",
  coord.flip = TRUE
)

Arguments

Value

A ggplot-object.

Examples

# Data from the EUROFAMCARE sample dataset
library(sjmisc)
data(efc)
# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive first item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
# auto-detection of labels
plot_stackfrq(efc[, start:end])

# works on grouped data frames as well
library(dplyr)
efc |>
  group_by(c161sex) |>
  select(start:end) |>
  plot_stackfrq()

Plot contingency tables

Description

Plot proportional crosstables (contingency tables) of two variables as ggplot diagram.

Usage

plot_xtab(
  x,
  grp,
  type = c("bar", "line"),
  margin = c("col", "cell", "row"),
  bar.pos = c("dodge", "stack"),
  title = "",
  title.wtd.suffix = NULL,
  axis.titles = NULL,
  axis.labels = NULL,
  legend.title = NULL,
  legend.labels = NULL,
  weight.by = NULL,
  rev.order = FALSE,
  show.values = TRUE,
  show.n = TRUE,
  show.prc = TRUE,
  show.total = TRUE,
  show.legend = TRUE,
  show.summary = FALSE,
  summary.pos = "r",
  drop.empty = TRUE,
  string.total = "Total",
  wrap.title = 50,
  wrap.labels = 15,
  wrap.legend.title = 20,
  wrap.legend.labels = 20,
  geom.size = 0.7,
  geom.spacing = 0.1,
  geom.colors = "Paired",
  dot.size = 3,
  smooth.lines = FALSE,
  grid.breaks = 0.2,
  expand.grid = FALSE,
  ylim = NULL,
  vjust = "bottom",
  hjust = "center",
  y.offset = NULL,
  coord.flip = FALSE
)

Arguments

Value

A ggplot-object.

Examples

# create 4-category-items
grp <- sample(1:4, 100, replace = TRUE)
# create 3-category-items
x <- sample(1:3, 100, replace = TRUE)

# plot "cross tablulation" of x and grp
plot_xtab(x, grp)

# plot "cross tablulation" of x and y, including labels
plot_xtab(x, grp, axis.labels = c("low", "mid", "high"),
         legend.labels = c("Grp 1", "Grp 2", "Grp 3", "Grp 4"))

# plot "cross tablulation" of x and grp
# as stacked proportional bars
plot_xtab(x, grp, margin = "row", bar.pos = "stack",
         show.summary = TRUE, coord.flip = TRUE)

# example with vertical labels
library(sjmisc)
library(sjlabelled)
data(efc)
sjPlot::set_theme(geom.label.angle = 90)
plot_xtab(efc$e42dep, efc$e16sex, vjust = "center", hjust = "bottom")

# grouped bars with EUROFAMCARE sample dataset
# dataset was importet from an SPSS-file,
# see ?sjmisc::read_spss
data(efc)
efc.val <- get_labels(efc)
efc.var <- get_label(efc)

plot_xtab(efc$e42dep, efc$e16sex, title = efc.var['e42dep'],
         axis.labels = efc.val[['e42dep']], legend.title = efc.var['e16sex'],
         legend.labels = efc.val[['e16sex']])

plot_xtab(efc$e16sex, efc$e42dep, title = efc.var['e16sex'],
         axis.labels = efc.val[['e16sex']], legend.title = efc.var['e42dep'],
         legend.labels = efc.val[['e42dep']])

# -------------------------------
# auto-detection of labels works here
# so no need to specify labels. For
# title-auto-detection, use NULL
# -------------------------------
plot_xtab(efc$e16sex, efc$e42dep, title = NULL)

plot_xtab(efc$e16sex, efc$e42dep, margin = "row",
         bar.pos = "stack", coord.flip = TRUE)

Save ggplot-figure for print publication

Description

Convenient function to save the last ggplot-figure in high quality for publication.

Usage

save_plot(
  filename,
  fig = ggplot2::last_plot(),
  width = 12,
  height = 9,
  dpi = 300,
  theme = ggplot2::theme_get(),
  label.color = "black",
  label.size = 2.4,
  axis.textsize = 0.8,
  axis.titlesize = 0.75,
  legend.textsize = 0.6,
  legend.titlesize = 0.65,
  legend.itemsize = 0.5
)

Arguments

Note

This is a convenient function with some default settings that should come close to most of the needs for fontsize and scaling in figures when saving them for printing or publishing. It uses cairographics anti-aliasing (see [png](../../../../doc/manuals/r-patched/packages/grDevices/refman/grDevices.html#topic+png)).

For adjusting plot appearance, see also [sjPlot-themes](#topic+sjPlot-themes).

Set global theme options for sjp-functions

Description

Set global theme options for sjp-functions.

Usage

set_theme(
  base = ggplot2::theme_grey(),
  theme.font = NULL,
  title.color = "black",
  title.size = 1.2,
  title.align = "left",
  title.vjust = NULL,
  geom.outline.color = NULL,
  geom.outline.size = 0,
  geom.boxoutline.size = 0.5,
  geom.boxoutline.color = "black",
  geom.alpha = 1,
  geom.linetype = 1,
  geom.errorbar.size = 0.7,
  geom.errorbar.linetype = 1,
  geom.label.color = NULL,
  geom.label.size = 4,
  geom.label.alpha = 1,
  geom.label.angle = 0,
  axis.title.color = "grey30",
  axis.title.size = 1.1,
  axis.title.x.vjust = NULL,
  axis.title.y.vjust = NULL,
  axis.angle.x = 0,
  axis.angle.y = 0,
  axis.angle = NULL,
  axis.textcolor.x = "grey30",
  axis.textcolor.y = "grey30",
  axis.textcolor = NULL,
  axis.linecolor.x = NULL,
  axis.linecolor.y = NULL,
  axis.linecolor = NULL,
  axis.line.size = 0.5,
  axis.textsize.x = 1,
  axis.textsize.y = 1,
  axis.textsize = NULL,
  axis.tickslen = NULL,
  axis.tickscol = NULL,
  axis.ticksmar = NULL,
  axis.ticksize.x = NULL,
  axis.ticksize.y = NULL,
  panel.backcol = NULL,
  panel.bordercol = NULL,
  panel.col = NULL,
  panel.major.gridcol = NULL,
  panel.minor.gridcol = NULL,
  panel.gridcol = NULL,
  panel.gridcol.x = NULL,
  panel.gridcol.y = NULL,
  panel.major.linetype = 1,
  panel.minor.linetype = 1,
  plot.backcol = NULL,
  plot.bordercol = NULL,
  plot.col = NULL,
  plot.margins = NULL,
  legend.pos = "right",
  legend.just = NULL,
  legend.inside = FALSE,
  legend.size = 1,
  legend.color = "black",
  legend.title.size = 1,
  legend.title.color = "black",
  legend.title.face = "bold",
  legend.backgroundcol = "white",
  legend.bordercol = "white",
  legend.item.size = NULL,
  legend.item.backcol = "grey90",
  legend.item.bordercol = "white"
)

Arguments

Value

The customized theme object, or NULL, if a ggplot-theme was used.

See Also

[sjPlot-themes](#topic+sjPlot-themes)

Examples

## Not run: 
library(sjmisc)
data(efc)
# set sjPlot-defaults, a slightly modification
# of the ggplot base theme
set_theme()

# legends of all plots inside
set_theme(legend.pos = "top left", legend.inside = TRUE)
plot_xtab(efc$e42dep, efc$e16sex)

# Use classic-theme. you may need to
# load the ggplot2-library.
library(ggplot2)
set_theme(base = theme_classic())
plot_frq(efc$e42dep)

# adjust value labels
set_theme(
  geom.label.size = 3.5,
  geom.label.color = "#3366cc",
  geom.label.angle = 90
)

# hjust-aes needs adjustment for this
update_geom_defaults('text', list(hjust = -0.1))
plot_xtab(efc$e42dep, efc$e16sex, vjust = "center", hjust = "center")

# Create own theme based on classic-theme
set_theme(
  base = theme_classic(), axis.linecolor = "grey50",
  axis.textcolor = "#6699cc"
)
plot_frq(efc$e42dep)
## End(Not run)

Modify plot appearance

Description

Set default plot themes, use pre-defined color scales or modify plot or table appearance.

Usage

theme_sjplot(base_size = 12, base_family = "")

theme_sjplot2(base_size = 12, base_family = "")

theme_blank(base_size = 12, base_family = "")

theme_538(base_size = 12, base_family = "")

font_size(
  title,
  axis_title.x,
  axis_title.y,
  labels.x,
  labels.y,
  offset.x,
  offset.y,
  base.theme
)

label_angle(angle.x, angle.y, base.theme)

legend_style(inside, pos, justify, base.theme)

scale_color_sjplot(palette = "metro", discrete = TRUE, reverse = FALSE, ...)

scale_fill_sjplot(palette = "metro", discrete = TRUE, reverse = FALSE, ...)

sjplot_pal(palette = "metro", n = NULL)

show_sjplot_pals()

css_theme(css.theme = "regression")

Arguments

Details

When using the colors argument in function calls (e.g.plot_model()) or when calling one of the predefined scale-functions (e.g. scale_color_sjplot()), there are pre-defined colour palettes in this package. Use show_sjplot_pals() to show all available colour palettes.

Examples

# prepare data
if (requireNamespace("haven")) {
library(sjmisc)
data(efc)
efc <- to_factor(efc, c161sex, e42dep, c172code)
m <- lm(neg_c_7 ~ pos_v_4 + c12hour + e42dep + c172code, data = efc)

# create plot-object
p <- plot_model(m)

# change theme
p + theme_sjplot()

# change font-size
p + font_size(axis_title.x = 30)

# apply color theme
p + scale_color_sjplot()

# show all available colour palettes
show_sjplot_pals()

# get colour values from specific palette
sjplot_pal(pal = "breakfast club")
}

Plot Pearson's Chi2-Test of multiple contingency tables

Description

Plot p-values of Pearson's Chi2-tests for multiple contingency tables as ellipses or tiles. Requires a data frame with dichotomous (dummy) variables. Calculation of Chi2-matrix taken fromTales of R.

Usage

sjp.chi2(
  df,
  title = "Pearson's Chi2-Test of Independence",
  axis.labels = NULL,
  wrap.title = 50,
  wrap.labels = 20,
  show.legend = FALSE,
  legend.title = NULL
)

Arguments

Value

A ggplot-object.

Examples

# create data frame with 5 dichotomous (dummy) variables
mydf <- data.frame(as.factor(sample(1:2, 100, replace=TRUE)),
                   as.factor(sample(1:2, 100, replace=TRUE)),
                   as.factor(sample(1:2, 100, replace=TRUE)),
                   as.factor(sample(1:2, 100, replace=TRUE)),
                   as.factor(sample(1:2, 100, replace=TRUE)))
# create variable labels
items <- list(c("Item 1", "Item 2", "Item 3", "Item 4", "Item 5"))

# plot Chi2-contingency-table
sjp.chi2(mydf, axis.labels = items)

Plot polynomials for (generalized) linear regression

Description

This function plots a scatter plot of a term poly.termagainst a response variable x and adds - depending on the amount of numeric values in poly.degree - multiple polynomial curves. A loess-smoothed line can be added to see which of the polynomial curves fits best to the data.

Usage

sjp.poly(
  x,
  poly.term,
  poly.degree,
  poly.scale = FALSE,
  fun = NULL,
  axis.title = NULL,
  geom.colors = NULL,
  geom.size = 0.8,
  show.loess = TRUE,
  show.loess.ci = TRUE,
  show.p = TRUE,
  show.scatter = TRUE,
  point.alpha = 0.2,
  point.color = "#404040",
  loess.color = "#808080"
)

Arguments

Details

For each polynomial degree, a simple linear regression on x (resp. the extracted response, if x is a fitted model) is performed, where only the polynomial term poly.term is included as independent variable. Thus, lm(y ~ x + I(x^2) + ... + I(x^i)) is repeatedly computed for all values in poly.degree, and the predicted values of the reponse are plotted against the raw values of poly.term. If x is a fitted model, other covariates are ignored when finding the best fitting polynomial.

This function evaluates raw polynomials, not orthogonal polynomials. Polynomials are computed using the [poly](../../../../doc/manuals/r-patched/packages/stats/refman/stats.html#topic+poly) function, with argument raw = TRUE.

To find out which polynomial degree fits best to the data, a loess-smoothed line (in dark grey) can be added (with show.loess = TRUE). The polynomial curves that comes closest to the loess-smoothed line should be the best fit to the data.

Value

A ggplot-object.

Examples

library(sjmisc)
data(efc)
# linear fit. loess-smoothed line indicates a more
# or less cubic curve
sjp.poly(efc$c160age, efc$quol_5, 1)

# quadratic fit
sjp.poly(efc$c160age, efc$quol_5, 2)

# linear to cubic fit
sjp.poly(efc$c160age, efc$quol_5, 1:4, show.scatter = FALSE)


# fit sample model
fit <- lm(tot_sc_e ~ c12hour + e17age + e42dep, data = efc)
# inspect relationship between predictors and response
plot_model(fit, type = "slope")
# "e17age" does not seem to be linear correlated to response
# try to find appropiate polynomial. Grey line (loess smoothed)
# indicates best fit. Looks like x^4 has the best fit,
# however, only x^3 has significant p-values.
sjp.poly(fit, "e17age", 2:4, show.scatter = FALSE)

## Not run: 
# fit new model
fit <- lm(tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),
          data = efc)
# plot marginal effects of polynomial term
plot_model(fit, type = "pred", terms = "e17age")
## End(Not run)

Wrapper to create plots and tables within a pipe-workflow

Description

This function has a pipe-friendly argument-structure, with the first argument always being the data, followed by variables that should be plotted or printed as table. The function then transforms the input and calls the requested sjp.- resp. sjt.-function to create a plot or table.

Both sjplot() and sjtab() support grouped data frames.

Usage

sjplot(data, ..., fun = c("grpfrq", "xtab", "aov1", "likert"))

sjtab(data, ..., fun = c("xtab", "stackfrq"))

Arguments

Details

Following fun-values are currently supported:

"grpfrq"

calls [plot_grpfrq](#topic+plot%5Fgrpfrq). The first two variables in data are used (and required) to create the plot.

"likert"

calls [plot_likert](#topic+plot%5Flikert). datamust be a data frame with items to plot.

"stackfrq"

calls [tab_stackfrq](#topic+tab%5Fstackfrq).data must be a data frame with items to create the table.

"xtab"

calls [plot_xtab](#topic+plot%5Fxtab) or [tab_xtab](#topic+tab%5Fxtab). The first two variables in data are used (and required) to create the plot or table.

Value

See related sjp. and sjt.-functions.

Note

The ...-argument is used, first, to specify the variables from datathat should be plotted, and, second, to name further arguments that are used in the subsequent plotting functions. Refer to the online-help of supported plotting-functions to see valid arguments.

data may also be a grouped data frame (see [group_by](../../dplyr/refman/dplyr.html#topic+group%5Fby)) with up to two grouping variables. Plots are created for each subgroup then.

Examples

library(dplyr)
data(efc)

# Grouped frequencies
efc |> sjplot(e42dep, c172code, fun = "grpfrq")

# Grouped frequencies, as box plots
efc |> sjplot(e17age, c172code, fun = "grpfrq",
               type = "box", geom.colors = "Set1")

## Not run: 
# table output of grouped data frame
efc |>
  group_by(e16sex, c172code) |>
  select(e42dep, n4pstu, e16sex, c172code) |>
  sjtab(fun = "xtab", use.viewer = FALSE) # open all tables in browser

## End(Not run)

Summary of correlations as HTML table

Description

Shows the results of a computed correlation as HTML table. Requires either a [data.frame](../../../../doc/manuals/r-patched/packages/base/refman/base.html#topic+data.frame) or a matrix with correlation coefficients as returned by the [cor](../../../../doc/manuals/r-patched/packages/stats/refman/stats.html#topic+cor)-function.

Usage

tab_corr(
  data,
  na.deletion = c("listwise", "pairwise"),
  corr.method = c("pearson", "spearman", "kendall"),
  title = NULL,
  var.labels = NULL,
  wrap.labels = 40,
  show.p = TRUE,
  p.numeric = FALSE,
  fade.ns = TRUE,
  val.rm = NULL,
  digits = 3,
  triangle = "both",
  string.diag = NULL,
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Note

If data is a matrix with correlation coefficients as returned by the [cor](../../../../doc/manuals/r-patched/packages/stats/refman/stats.html#topic+cor)-function, p-values can't be computed. Thus, show.p, p.numeric and fade.nsonly have an effect if data is a [data.frame](../../../../doc/manuals/r-patched/packages/base/refman/base.html#topic+data.frame).

Examples

## Not run: 
if (interactive()) {
  # Data from the EUROFAMCARE sample dataset
  library(sjmisc)
  data(efc)

  # retrieve variable and value labels
  varlabs <- get_label(efc)

  # recveive first item of COPE-index scale
  start <- which(colnames(efc) == "c83cop2")
  # recveive last item of COPE-index scale
  end <- which(colnames(efc) == "c88cop7")

  # create data frame with COPE-index scale
  mydf <- data.frame(efc[, c(start:end)])
  colnames(mydf) <- varlabs[c(start:end)]

  # we have high correlations here, because all items
  # belong to one factor.
  tab_corr(mydf, p.numeric = TRUE)

  # auto-detection of labels, only lower triangle
  tab_corr(efc[, c(start:end)], triangle = "lower")

  # auto-detection of labels, only lower triangle, all correlation
  # values smaller than 0.3 are not shown in the table
  tab_corr(efc[, c(start:end)], triangle = "lower", val.rm = 0.3)

  # auto-detection of labels, only lower triangle, all correlation
  # values smaller than 0.3 are printed in blue
  tab_corr(efc[, c(start:end)], triangle = "lower",val.rm = 0.3,
           CSS = list(css.valueremove = 'color:blue;'))
}
## End(Not run)

Print data frames as HTML table.

Description

These functions print data frames as HTML-table, showing the results in RStudio's viewer pane or in a web browser.

Usage

tab_df(
  x,
  title = NULL,
  footnote = NULL,
  col.header = NULL,
  show.type = FALSE,
  show.rownames = FALSE,
  show.footnote = FALSE,
  alternate.rows = FALSE,
  sort.column = NULL,
  digits = 2,
  encoding = "UTF-8",
  CSS = NULL,
  file = NULL,
  use.viewer = TRUE,
  ...
)

tab_dfs(
  x,
  titles = NULL,
  footnotes = NULL,
  col.header = NULL,
  show.type = FALSE,
  show.rownames = FALSE,
  show.footnote = FALSE,
  alternate.rows = FALSE,
  sort.column = NULL,
  digits = 2,
  encoding = "UTF-8",
  CSS = NULL,
  file = NULL,
  use.viewer = TRUE,
  rnames = NULL,
  ...
)

Arguments

Details

How do I use CSS-argument?

With the CSS-argument, the visual appearance of the tables can be modified. To get an overview of all style-sheet-classnames that are used in this function, see return value page.style for details. Arguments for this list have following syntax:

1. the class-name as argument name and
2. each style-definition must end with a semicolon

You can add style information to the default styles by using a + (plus-sign) as initial character for the argument attributes. Examples:

• table = 'border:2px solid red;' for a solid 2-pixel table border in red.
• summary = 'font-weight:bold;' for a bold fontweight in the summary row.
• lasttablerow = 'border-bottom: 1px dotted blue;' for a blue dotted border of the last table row.
• colnames = '+color:green' to add green color formatting to column names.
• arc = 'color:blue;' for a blue text color each 2nd row.
• caption = '+color:red;' to add red font-color to the default table caption style.

See further examples in this package-vignette.

Value

A list with following items:

• the web page style sheet (page.style),
• the HTML content of the data frame (page.content),
• the complete HTML page, including header, style sheet and body (page.complete)
• the HTML table with inline-css for use with knitr (knitr)
• the file path, if the HTML page should be saved to disk (file)

Note

The HTML tables can either be saved as file and manually opened (use argument file) or they can be saved as temporary files and will be displayed in the RStudio Viewer pane (if working with RStudio) or opened with the default web browser. Displaying resp. opening a temporary file is the default behaviour.

Examples

## Not run: 
data(iris)
data(mtcars)
tab_df(iris[1:5, ])
tab_dfs(list(iris[1:5, ], mtcars[1:5, 1:5]))

# sort 2nd column ascending
tab_df(iris[1:5, ], sort.column = 2)

# sort 2nd column descending
tab_df(iris[1:5, ], sort.column = -2)
## End(Not run)

Summary of factor analysis as HTML table

Description

Performs a factor analysis on a data frame or matrix and displays the factors as HTML table, or saves them as file.

In case a data frame is used as parameter, the Cronbach's Alpha value for each factor scale will be calculated, i.e. all variables with the highest loading for a factor are taken for the reliability test. The result is an alpha value for each factor dimension.

Usage

tab_fa(
  data,
  rotation = "promax",
  method = c("ml", "minres", "wls", "gls", "pa", "minchi", "minrank"),
  nmbr.fctr = NULL,
  fctr.load.tlrn = 0.1,
  sort = FALSE,
  title = "Factor Analysis",
  var.labels = NULL,
  wrap.labels = 40,
  show.cronb = TRUE,
  show.comm = FALSE,
  alternate.rows = FALSE,
  digits = 2,
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete),
• the html-table with inline-css for use with knitr (knitr),
• the factor.index, i.e. the column index of each variable with the highest factor loading for each factor and
• the removed.items, i.e. which variables have been removed because they were outside of the fctr.load.tlrn's range.

for further use.

Note

This method for factor analysis relies on the functions[fa](../../psych/refman/psych.html#topic+fa) and [fa.parallel](../../psych/refman/psych.html#topic+fa.parallel) from the psych package.

Examples

## Not run: 
# Data from the EUROFAMCARE sample dataset
library(sjmisc)
library(GPArotation)
data(efc)

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
# auto-detection of labels
if (interactive()) {
  tab_fa(efc[, start:end])
}
## End(Not run)

Summary of item analysis of an item scale as HTML table

Description

This function performs an item analysis with certain statistics that are useful for scale or index development. The resulting tables are shown in the viewer pane resp. webbrowser or can be saved as file. Following statistics are computed for each item of a data frame:

• percentage of missing values
• mean value
• standard deviation
• skew
• item difficulty
• item discrimination
• Cronbach's Alpha if item was removed from scale
• mean (or average) inter-item-correlation

Optional, following statistics can be computed as well:

• kurstosis
• Shapiro-Wilk Normality Test

If factor.groups is not NULL, the data frame df will be splitted into groups, assuming that factor.groups indicate those columns of the data frame that belong to a certain factor (see return value of function [tab_pca](#topic+tab%5Fpca)as example for retrieving factor groups for a scale and see examples for more details).

Usage

tab_itemscale(
  df,
  factor.groups = NULL,
  factor.groups.titles = "auto",
  scale = FALSE,
  min.valid.rowmean = 2,
  alternate.rows = TRUE,
  sort.column = NULL,
  show.shapiro = FALSE,
  show.kurtosis = FALSE,
  show.corr.matrix = TRUE,
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

sjt.itemanalysis(
  df,
  factor.groups = NULL,
  factor.groups.titles = "auto",
  scale = FALSE,
  min.valid.rowmean = 2,
  alternate.rows = TRUE,
  sort.column = NULL,
  show.shapiro = FALSE,
  show.kurtosis = FALSE,
  show.corr.matrix = TRUE,
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• df.list: List of data frames with the item analysis for each sub.group (or complete, if factor.groups was NULL)
• index.scores: A data frame with of standardized scale / index scores for each case (mean value of all scale items for each case) for each sub-group.
• ideal.item.diff: List of vectors that indicate the ideal item difficulty for each item in each sub-group. Item difficulty only differs when items have different levels.
• cronbach.values: List of Cronbach's Alpha values for the overall item scale for each sub-group.
• knitr.list: List of html-tables with inline-css for use with knitr for each table (sub-group)
• knitr: html-table of all complete output with inline-css for use with knitr
• complete.page: Complete html-output.

If factor.groups = NULL, each list contains only one elment, since just one table is printed for the complete scale indicated by df. If factor.groupsis a vector of group-index-values, the lists contain elements for each sub-group.

Note

• The Shapiro-Wilk Normality Test (see column W(p)) tests if an item has a distribution that is significantly different from normal.
• Item difficulty should range between 0.2 and 0.8. Ideal value is p+(1-p)/2 (which mostly is between 0.5 and 0.8).
• For item discrimination, acceptable values are 0.20 or higher; the closer to 1.00 the better. See [item_reliability](../../performance/refman/performance.html#topic+item%5Freliability) for more details.
• In case the total Cronbach's Alpha value is below the acceptable cut-off of 0.7 (mostly if an index has few items), the mean inter-item-correlation is an alternative measure to indicate acceptability. Satisfactory range lies between 0.2 and 0.4. See also [item_intercor](../../performance/refman/performance.html#topic+item%5Fintercor).

References

• Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis of the Dynamics Concept Inventory. (web)
• Briggs SR, Cheek JM (1986) The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x
• McLean S et al. (2013) Stigmatizing attitudes and beliefs about bulimia nervosa: Gender, age, education and income variability in a community sample. International Journal of Eating Disorders. doi: 10.1002/eat.22227
• Trochim WMK (2008) Types of Reliability.

Examples

# Data from the EUROFAMCARE sample dataset
library(sjmisc)
library(sjlabelled)
data(efc)

# retrieve variable and value labels
varlabs <- get_label(efc)

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")

# create data frame with COPE-index scale
mydf <- data.frame(efc[, start:end])
colnames(mydf) <- varlabs[start:end]

## Not run: 
if (interactive()) {
  tab_itemscale(mydf)

  # auto-detection of labels
  tab_itemscale(efc[, start:end])

  # Compute PCA on Cope-Index, and perform a
  # item analysis for each extracted factor.
  indices <- tab_pca(mydf)$factor.index
  tab_itemscale(mydf, factor.groups = indices)

  # or, equivalent
  tab_itemscale(mydf, factor.groups = "auto")
}
## End(Not run)

Print regression models as HTML table

Description

tab_model() creates HTML tables from regression models.

Usage

tab_model(
  ...,
  transform,
  show.intercept = TRUE,
  show.est = TRUE,
  show.ci = 0.95,
  show.ci50 = FALSE,
  show.se = NULL,
  show.std = NULL,
  std.response = TRUE,
  show.p = TRUE,
  show.stat = FALSE,
  show.df = FALSE,
  show.zeroinf = TRUE,
  show.r2 = TRUE,
  show.icc = TRUE,
  show.re.var = TRUE,
  show.ngroups = TRUE,
  show.fstat = FALSE,
  show.aic = FALSE,
  show.aicc = FALSE,
  show.dev = FALSE,
  show.loglik = FALSE,
  show.obs = TRUE,
  show.reflvl = FALSE,
  terms = NULL,
  rm.terms = NULL,
  order.terms = NULL,
  keep = NULL,
  drop = NULL,
  title = NULL,
  pred.labels = NULL,
  dv.labels = NULL,
  wrap.labels = 25,
  bootstrap = FALSE,
  iterations = 1000,
  seed = NULL,
  vcov.fun = NULL,
  vcov.args = NULL,
  string.pred = "Predictors",
  string.est = "Estimate",
  string.std = "std. Beta",
  string.ci = "CI",
  string.se = "std. Error",
  string.std_se = "standardized std. Error",
  string.std_ci = "standardized CI",
  string.p = "p",
  string.std.p = "std. p",
  string.df = "df",
  string.stat = "Statistic",
  string.std.stat = "std. Statistic",
  string.resp = "Response",
  string.intercept = "(Intercept)",
  strings = NULL,
  ci.hyphen = " – ",
  minus.sign = "-",
  collapse.ci = FALSE,
  collapse.se = FALSE,
  linebreak = TRUE,
  col.order = c("est", "se", "std.est", "std.se", "ci", "std.ci", "ci.inner", "ci.outer",
    "stat", "std.stat", "p", "std.p", "df.error", "response.level"),
  digits = 2,
  digits.p = 3,
  digits.rsq = 3,
  digits.re = 2,
  emph.p = TRUE,
  p.val = NULL,
  df.method = NULL,
  p.style = c("numeric", "stars", "numeric_stars", "scientific", "scientific_stars"),
  p.threshold = c(0.05, 0.01, 0.001),
  p.adjust = NULL,
  case = "parsed",
  auto.label = TRUE,
  prefix.labels = c("none", "varname", "label"),
  bpe = "median",
  CSS = css_theme("regression"),
  file = NULL,
  use.viewer = TRUE,
  encoding = "UTF-8"
)

Arguments

Details

Standardized Estimates

Default standardization is done by completely refitting the model on the standardized data. Hence, this approach is equal to standardizing the variables before fitting the model, which is particularly recommended for complex models that include interactions or transformations (e.g., polynomial or spline terms). When show.std = "std2", standardization of estimates follows Gelman's (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one. Resulting coefficients are then directly comparable for untransformed binary predictors. For backward compatibility reasons, show.std also may be a logical value; if TRUE, normal standardized estimates are printed (same effect as show.std = "std"). Use show.std = NULL(default) or show.std = FALSE, if no standardization is required.

How do I use CSS-argument?

With the CSS-argument, the visual appearance of the tables can be modified. To get an overview of all style-sheet-classnames that are used in this function, see return value page.style for details. Arguments for this list have following syntax:

1. the class-names with "css."-prefix as argument name and
2. each style-definition must end with a semicolon

You can add style information to the default styles by using a + (plus-sign) as initial character for the argument attributes. Examples:

• css.table = 'border:2px solid red;' for a solid 2-pixel table border in red.
• css.summary = 'font-weight:bold;' for a bold fontweight in the summary row.
• css.lasttablerow = 'border-bottom: 1px dotted blue;' for a blue dotted border of the last table row.
• css.colnames = '+color:green' to add green color formatting to column names.
• css.arc = 'color:blue;' for a blue text color each 2nd row.
• css.caption = '+color:red;' to add red font-color to the default table caption style.

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Note

Summary of principal component analysis as HTML table

Description

Performes a principle component analysis on a data frame or matrix (with varimax or oblimin rotation) and displays the factor solution as HTML table, or saves them as file.

In case a data frame is used as parameter, the Cronbach's Alpha value for each factor scale will be calculated, i.e. all variables with the highest loading for a factor are taken for the reliability test. The result is an alpha value for each factor dimension.

Usage

tab_pca(
  data,
  rotation = c("varimax", "quartimax", "promax", "oblimin", "simplimax", "cluster",
    "none"),
  nmbr.fctr = NULL,
  fctr.load.tlrn = 0.1,
  title = "Principal Component Analysis",
  var.labels = NULL,
  wrap.labels = 40,
  show.cronb = TRUE,
  show.msa = FALSE,
  show.var = FALSE,
  alternate.rows = FALSE,
  digits = 2,
  string.pov = "Proportion of Variance",
  string.cpov = "Cumulative Proportion",
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete),
• the html-table with inline-css for use with knitr (knitr),
• the factor.index, i.e. the column index of each variable with the highest factor loading for each factor and
• the removed.items, i.e. which variables have been removed because they were outside of the fctr.load.tlrn's range.

for further use.

Examples

## Not run: 
# Data from the EUROFAMCARE sample dataset
library(sjmisc)
data(efc)

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
# auto-detection of labels
if (interactive()) {
  tab_pca(efc[, start:end])
}
## End(Not run)

Summary of stacked frequencies as HTML table

Description

Shows the results of stacked frequencies (such as likert scales) as HTML table. This function is useful when several items with identical scale/categories should be printed as table to compare their distributions (e.g. when plotting scales like SF, Barthel-Index, Quality-of-Life-scales etc.).

Usage

tab_stackfrq(
  items,
  weight.by = NULL,
  title = NULL,
  var.labels = NULL,
  value.labels = NULL,
  wrap.labels = 20,
  sort.frq = NULL,
  alternate.rows = FALSE,
  digits = 2,
  string.total = "N",
  string.na = "NA",
  show.n = FALSE,
  show.total = FALSE,
  show.na = FALSE,
  show.skew = FALSE,
  show.kurtosis = FALSE,
  digits.stats = 2,
  file = NULL,
  encoding = NULL,
  CSS = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Examples

# -------------------------------
# random sample
# -------------------------------
# prepare data for 4-category likert scale, 5 items
likert_4 <- data.frame(
  as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.2, 0.3, 0.1, 0.4))),
  as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.5, 0.25, 0.15, 0.1))),
  as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.25, 0.1, 0.4, 0.25))),
  as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.1, 0.4, 0.4, 0.1))),
  as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.35, 0.25, 0.15, 0.25)))
)

# create labels
levels_4 <- c("Independent", "Slightly dependent",
              "Dependent", "Severely dependent")

# create item labels
items <- c("Q1", "Q2", "Q3", "Q4", "Q5")

# plot stacked frequencies of 5 (ordered) item-scales
## Not run: 
if (interactive()) {
  tab_stackfrq(likert_4, value.labels = levels_4, var.labels = items)

  # -------------------------------
  # Data from the EUROFAMCARE sample dataset
  #  Auto-detection of labels
  # -------------------------------
  data(efc)
  # recveive first item of COPE-index scale
  start <- which(colnames(efc) == "c82cop1")
  # recveive first item of COPE-index scale
  end <- which(colnames(efc) == "c90cop9")

  tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE)

  tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,
               show.n = TRUE, show.na = TRUE)

  # --------------------------------
  # User defined style sheet
  # --------------------------------
  tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,
               show.total = TRUE, show.skew = TRUE, show.kurtosis = TRUE,
               CSS = list(css.ncol = "border-left:1px dotted black;",
                          css.summary = "font-style:italic;"))
}

## End(Not run)

Summary of contingency tables as HTML table

Description

Shows contingency tables as HTML file in browser or viewer pane, or saves them as file.

Usage

tab_xtab(
  var.row,
  var.col,
  weight.by = NULL,
  title = NULL,
  var.labels = NULL,
  value.labels = NULL,
  wrap.labels = 20,
  show.obs = TRUE,
  show.cell.prc = FALSE,
  show.row.prc = FALSE,
  show.col.prc = FALSE,
  show.exp = FALSE,
  show.legend = FALSE,
  show.na = FALSE,
  show.summary = TRUE,
  drop.empty = TRUE,
  statistics = c("auto", "cramer", "phi", "spearman", "kendall", "pearson", "fisher"),
  string.total = "Total",
  digits = 1,
  tdcol.n = "black",
  tdcol.expected = "#339999",
  tdcol.cell = "#993333",
  tdcol.row = "#333399",
  tdcol.col = "#339933",
  emph.total = FALSE,
  emph.color = "#f8f8f8",
  prc.sign = " %",
  hundret = "100.0",
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE,
  ...
)

sjt.xtab(
  var.row,
  var.col,
  weight.by = NULL,
  title = NULL,
  var.labels = NULL,
  value.labels = NULL,
  wrap.labels = 20,
  show.obs = TRUE,
  show.cell.prc = FALSE,
  show.row.prc = FALSE,
  show.col.prc = FALSE,
  show.exp = FALSE,
  show.legend = FALSE,
  show.na = FALSE,
  show.summary = TRUE,
  drop.empty = TRUE,
  statistics = c("auto", "cramer", "phi", "spearman", "kendall", "pearson", "fisher"),
  string.total = "Total",
  digits = 1,
  tdcol.n = "black",
  tdcol.expected = "#339999",
  tdcol.cell = "#993333",
  tdcol.row = "#333399",
  tdcol.col = "#339933",
  emph.total = FALSE,
  emph.color = "#f8f8f8",
  prc.sign = " %",
  hundret = "100.0",
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE,
  ...
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Examples

# prepare sample data set
data(efc)

# print simple cross table with labels
## Not run: 
if (interactive()) {
  tab_xtab(efc$e16sex, efc$e42dep)

  # print cross table with manually set
  # labels and expected values
  tab_xtab(
    efc$e16sex,
    efc$e42dep,
    var.labels = c("Elder's gender", "Elder's dependency"),
    show.exp = TRUE
  )

  # print minimal cross table with labels, total col/row highlighted
  tab_xtab(efc$e16sex, efc$e42dep, show.cell.prc = FALSE, emph.total = TRUE)

  # User defined style sheet
  tab_xtab(efc$e16sex, efc$e42dep,
           CSS = list(css.table = "border: 2px solid;",
                      css.tdata = "border: 1px solid;",
                      css.horline = "border-bottom: double blue;"))

  # ordinal data, use Kendall's tau
  tab_xtab(efc$e42dep, efc$quol_5, statistics = "kendall")

  # calculate Spearman's rho, with continuity correction
  tab_xtab(
    efc$e42dep,
    efc$quol_5,
    statistics = "spearman",
    exact = FALSE,
    continuity = TRUE
  )
}

## End(Not run)

View structure of labelled data frames

Description

Save (or show) content of an imported SPSS, SAS or Stata data file, or any similar labelled data.frame, as HTML table. This quick overview shows variable ID number, name, label, type and associated value labels. The result can be considered as "codeplan" of the data frame.

Usage

view_df(
  x,
  weight.by = NULL,
  alternate.rows = TRUE,
  show.id = TRUE,
  show.type = FALSE,
  show.values = TRUE,
  show.string.values = FALSE,
  show.labels = TRUE,
  show.frq = FALSE,
  show.prc = FALSE,
  show.wtd.frq = FALSE,
  show.wtd.prc = FALSE,
  show.na = FALSE,
  max.len = 15,
  sort.by.name = FALSE,
  wrap.labels = 50,
  verbose = FALSE,
  CSS = NULL,
  encoding = NULL,
  file = NULL,
  use.viewer = TRUE,
  remove.spaces = TRUE
)

Arguments

Value

Invisibly returns

• the web page style sheet (page.style),
• the web page content (page.content),
• the complete html-output (page.complete) and
• the html-table with inline-css for use with knitr (knitr)

for further use.

Examples

## Not run: 
# init dataset
data(efc)

# view variables
view_df(efc)

# view variables w/o values and value labels
view_df(efc, show.values = FALSE, show.labels = FALSE)

# view variables including variable typed, orderd by name
view_df(efc, sort.by.name = TRUE, show.type = TRUE)

# User defined style sheet
view_df(efc,
        CSS = list(css.table = "border: 2px solid;",
                   css.tdata = "border: 1px solid;",
                   css.arc = "color:blue;"))
## End(Not run)