knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
To start, load the package.
library(JWileymisc) library(ggplot2) library(data.table)
The egltable()
function calculates basic descriptive statistics.
egltable(c("mpg", "hp", "qsec", "wt", "vs"), data = mtcars)
pander::pandoc.table( egltable(c("mpg", "hp", "qsec", "wt", "vs"), data = mtcars), caption = "Example descriptive statistics table.", justify = "left")
The strict
argument can be used if variables are categorical but are
not coded as factors. In this case, vs
has two levels: 0 and 1 and
the frequency and percentage of each are shown instead of the mean and
standard deviation.
egltable(c("mpg", "hp", "qsec", "wt", "vs"), data = mtcars, strict=FALSE)
pander::pandoc.table( egltable(c("mpg", "hp", "qsec", "wt", "vs"), data = mtcars, strict=FALSE), caption = "Example descriptive statistics table with automatic categorical variables.", justify = "left")
egltable()
also allows descriptive statistics to be broken down by
another variable by using the g
argument. This not only separates
results by group but also calculates bivariate tests of the
differences between groups and effect sizes. For example, t-tests for
continuous variables and two groups or chi-square tests for
categorical variables. For more than two groups, ANOVAs are used.
egltable(c("mpg", "hp", "qsec", "wt", "vs"), g = "am", data = mtcars, strict = FALSE)
pander::pandoc.table( egltable(c("mpg", "hp", "qsec", "wt", "vs"), g = "am", data = mtcars, strict = FALSE), caption = "Example descriptive statistics table by group.", justify = "left")
For very skewed continuous variables, non-parametric statistics and
tests may be more appropriate. These can be generated using the
parametric
argument. For chi-square tests with small cell sizes,
simulated p-values also can be generated.
egltable(c("mpg", "hp", "qsec", "wt", "vs"), g = "am", data = mtcars, strict = FALSE, parametric = FALSE)
pander::pandoc.table( egltable(c("mpg", "hp", "qsec", "wt", "vs"), g = "am", data = mtcars, strict = FALSE, parametric = FALSE), caption = "Example descriptive statistics table by group.", justify = "left")
We have already seen how to compare descriptives across groups when
the groups were independent. egltable()
also supports using groups
to test paired samples. To use this, the variable passed to the
grouping argument, g
must have exactly two levels and you must also
pass a variable that is a unique ID per unit and specify paired =
TRUE
.
By default for continuous, paired data, mean and standard deviations are presented and a paired samples t-test is used. A pseudo Cohen's d effect size is calculated as the mean of the change score divided by the standard deviation of the change score. If there are missing data, its possible that the mean difference will be different than the difference in means as the means are calculated on all available data, but the effect size can only be calculated on complete cases.
## example with paired data egltable( vars = "extra", g = "group", data = sleep, idvar = "ID", paired = TRUE)
pander::pandoc.table( egltable( vars = "extra", g = "group", data = sleep, idvar = "ID", paired = TRUE), caption = "Example parametric descriptive statistics for paired data.", justify = "left")
If we do not want to make parametric assumptions with continuous
variables, we can set parametric = FALSE
. In this case the
descriptives are medians and a paired Wilcoxon test is used. In this
dataset there are ties and a warning is generated about ties and
zeroes. This warning is generally ignorable, but if these were central
hypothesis tests, it may warrant further testing using, for example,
simulations which are more precise in the case of ties.
egltable( vars = "extra", g = "group", data = sleep, idvar = "ID", paired = TRUE, parametric = FALSE)
pander::pandoc.table( egltable( vars = "extra", g = "group", data = sleep, idvar = "ID", paired = TRUE, parametric = FALSE), caption = "Example non parametric descriptive statistics for paired data.", justify = "left")
We can also work with categorical paired data. The following code creates a categorical variable, the tertiles of chick weights measured over time. The chick weight dataset has many time points, but we will just use two.
## paired categorical data example ## using data on chick weights to create categorical data tmp <- subset(ChickWeight, Time %in% c(0, 20)) tmp$WeightTertile <- cut(tmp$weight, breaks = quantile(tmp$weight, c(0, 1/3, 2/3, 1), na.rm = TRUE), include.lowest = TRUE)
No special code is needed to work with categorical
variables. egltable()
recognises categorical variables and uses
McNemar's test, which is a chi-square of the off diagonals, which
tests whether people (or chicks in this case) change groups equally
over time or preferentially move one direction. In this case, a
significant result suggests that over time chicks' weights change
preferentially one way and the descriptive statistics show us that
there is an increase in weight tertile from time 0 to time 20.
egltable(c("weight", "WeightTertile"), g = "Time", data = tmp, idvar = "Chick", paired = TRUE)
pander::pandoc.table( egltable(c("weight", "WeightTertile"), g = "Time", data = tmp, idvar = "Chick", paired = TRUE), caption = "Continuous and categorical paired data.", justify = "left")
For continuous variables, correlation matrices are commonly examined. This is especially true for structural equation models or path analyses.
The SEMSummary()
function provides a simple way to generate these
under various options. There is a formula interface, similar to lm()
or other regression models. Missing data can be handled using listwise
deletion, pairwise present data, or full information maximum
likelihood (FIML). When assumptions are met, FIML is less biased and
uses all available data, and is the default.
m <- SEMSummary(~ mpg + hp + qsec + wt, data = mtcars) corTab <- APAStyler(m, type = "cor", stars = TRUE)
These correlations can be nicely formatted into a table.
pander::pandoc.table( corTab$table, caption = "Example correlation table.", justify = "left")
Plot methods exist for SEMSummary()
objects.
By default, above the diagonal are correlations and below the diagonal
are p-values. However, the type argument can be set (see ?corplot
)
to get all values to be either correlations or p-values.
By default, another useful feature is that
hierarchical clustering is used to group similar variables together in
clusters, provided a more useful sorting of the data than many
"default" correlation matrices. If a specific order is desired, you
can use the order = "asis"
option to keep the variable order the
same as written in SEMSummary()
.
plot(m) + ggtitle("Order by hierarchical clustering") plot(m, order = "asis") + ggtitle("Order as written")
plot(m, type = "p") + ggtitle("Numbers are p-values")
Correlations also can be broken down by group. Here results are separated by species which are automaticaly used as the title of each graph.
mg <- SEMSummary(~ Sepal.Length + Petal.Length + Sepal.Width + Petal.Width | Species, data = iris) plot(mg)
In much of psychological and consumer/market research, likert rating scales are used. For example, rating a question/item from "Strongly DISagree" to "Strongly Agree" or rating satisfaction from "Not at all" to "Very Satisfied" or adjectives that capture mood/affect from "Not at all" to "Extremely". Likert plots aim to show these results clearly and aid interpretation by presenting the anchors as well.
The following code creates some simulated data, summarizes it, adds the necessary labels/anchors, and creates a nice plot.
## simulate some likert style data set.seed(1234) d <- data.table( Happy = sample(1:5, 200, TRUE, c(.1, .2, .4, .2, .1)), Cheerful = sample(1:5, 200, TRUE, c(.1, .2, .2, .4, .1)), Peaceful = sample(1:5, 200, TRUE, c(.1, .1, .2, .4, .2)), Sad = sample(1:5, 200, TRUE, c(.1, .3, .3, .2, .1)), Hopeless = sample(1:5, 200, TRUE, c(.3, .3, .2, .2, 0)), Angry = sample(1:5, 200, TRUE, c(.4, .3, .2, .08, .02))) dmeans <- melt(d, measure.vars = names(d))[, .(Mean = mean(value, na.rm = TRUE)), by = variable] dmeans[, Low := paste0(variable, "\nNot at all")] dmeans[, High := paste0(variable, "\nExtremely")] dmeans[, variable := as.integer(factor(variable))] ## view the summarised data print(dmeans) gglikert("Mean", "variable", "Low", "High", data = dmeans, xlim = c(1, 5), title = "Average Affect Ratings")
## create a grouping variable dg <- cbind(d, Group = ifelse( d$Happy > mean(d$Happy, na.rm = TRUE), "General Population", "Depressed")) dgmeans <- melt(dg, measure.vars = names(d), id.vars = "Group")[, .(Mean = mean(value, na.rm = TRUE)), by = .(variable, Group)] dgmeans[, Low := paste0(variable, "\nNot at all")] dgmeans[, High := paste0(variable, "\nExtremely")] dgmeans[, variable := as.integer(factor(variable))] ## view the summarised data print(dgmeans) gglikert("Mean", "variable", "Low", "High", colour = "Group", data = dgmeans, xlim = c(1, 5), title = "Average Affect Ratings") + scale_colour_manual( values = c("Depressed" = "black", "General Population" = "grey70"))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.