Getting started with the qacBase package

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), r-squared, 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 (Kruskal-Wallis) test. Summary statistics, pair-wise group differences (post-hoc 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 two-way frequency table from two categorical variables. There are many options including cell, row, and column percents, plotting options, and a chi-square test of independence. See the vignette for examples.