The qacBase package contains functions and data sets
designed to simplify data analyses and aid in the instruction of data
science courses. The primary functions are described below.
Preparing Data
Function

Description

recodes

recodes() provides a simple way to recode the values of
numeric, character, or factor variables. See the vignette for examples.

standardize

standardize() transforms all the numeric variables in a
data frame to same mean and standard deviation (mean=0 sd=1, by
default), without modifying character, factor, or dummy coded variables.

normalize

normalize() transforms all the numeric variables in a data
frame to same range of values ([0, 1] by default). Again, character and
factor variables are left unchanged.

Describing a data set
Function

Description

contents

contents() provides a comprehensive description of a data
frame. The output is much more detailed than that
provided by the base summary.data.frame() function, and is
easier to read and understand. This function should be your first stop
when looking at a new dataset.

df_plot

df_plot() helps you visual a data frame. Variable are
grouped by type (numeric, integer, character, factor, date) and color
coded. The percent of missing data for each variable is also displayed,
along with the total number of variables and cases.

barcharts

barcharts() provides bar charts of all the character or
factor variables in a data frame, within a single graph.

histograms

histograms() provides histograms of the all quantitative
variables in a data frame, within a single graph.

densities

densities() provides density charts of all the quantitative
variables in a data frame, within a single graph.

Exploratory data analysis
Numeric variables
Function

Description

qstats

qstats() allows you to easily calculate any number of
descriptive statistics (e.g., n, mean, sd) for a quantiatative variable.
The results can be broken down by the levels of one of more categorical
variables (groups). Any function that produces a single number can be
used. See the vignette for examples.

univariate_plot

univariate_plot() provides a detailed visualization of the
distribution of values in a quantiative variable. The graph contains a
histrogram, jittered dot plot, density curve, and boxplot, Annotations
provide statistics such as n, mean, sd,
median, min, max, skew, and
outliers.

scatter

scatter() generates a scatter plot and line of best fit
with 95% confidence interval displaying the relationship between two
quantiative variables. Annotations include the slope, correlation
coefficient (r), rsquared, and p_value. Oultiers (determinded by
studentized residuals) are flagged. Optionally, marginal distributions
(histograms, boxplots, density curves, violin plots) can be added to the
margins of the plot.

cor_plot

corplot() plots the correlations among numeric variables in
a data frame. Variables can be sorted to place variables with similar
correlation patterns together.

groupdiff

groupdiff() compares groups on a quantitative outcome using
either a parametric (ANOVA) or nonparametric (KruskalWallis) test.
Summary statistics, pairwise group differences (posthoc comparisons),
and plots are provided.

mean_plot

mean_plot() plots means and error bars for each level of a
categorical variable. Interaction with a second categorical variable can
also be added. Error ranges can represent standard deviations, standard
errors, or confidence intervals. Each can be based on standard or robust
statistics.

Categorical Variables
Function

Description

tab

tab() generates a frequency table and bar chart for a
categorical variable. There are many options including sorting
categories by frequency, adding cumulative frequencies and percents, and
combining infrequent categories into an ‘Other’ category. See the vignette for examples.

crosstab

crosstab() generates a twoway frequency table from two
categorical variables. There are many options including cell, row, and
column percents, plotting options, and a chisquare test of
independence. See the vignette for examples.
