Description Details Usage Table of Contents Loading Data Examples
nsm3data: Designed to add datasets which are used in the textbook
Additional Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition
THIS IS NOT a substitute for the textbook. You will almost certainly not be able to use anything in this package without having the textbook. Rather, this package is supplemental and is only designed to save you time that you would otherwise spend typing data into R or into a spreadsheet.
nsm3data contributes no additional functions, but it does have data and documentation. You need not specify the location of a dataset; simply use the data()
function to read the data into your current environment after you have loaded the library.
All datasets have accompanying descriptions that have varying degrees of helpfulness. To view this description, use the help()
function in R to view the object's documentation. For example, use
help(pokeweed)
to view the pokeweed
dataset documentation.
nsm3data contains its own table of contents for the datasets that it includes. This table of contents may not be up-to-date, and we would appreciate your contributions if you find a dataset that doesn't have an entry. Instructions on how to do so are in CONTRIBUTING.md.
To view the table of contents, use the help()
function in R to view the toc
object documentation:
help(toc)
.
Please note that the toc
object doesn't actually exist in any meaningful way, so trying to reference it will result in just a message.
All datasets in nsm3data are provided as R datasets, so to use a dataset, simply use the data()
function after you have loaded in the library. Consult the table of contents to find the name of a dataset. For example, use
data(pokeweed)
to load the pokeweed
dataset into your environment. The data will be loaded in as a dataframe, so you can use any method to view or manipulate that data as you would any other dataframe, including using tidyr::
functions. If you are new to R, you need not fear overwriting the library datasets, since you are editing data that is in your own environment.
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