Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----example, eval = FALSE----------------------------------------------------
#
# # First, you need to "attach" the package. You can think of this as loading it.
# # This step is technically optional, but to use the package functions without
# # it, you need to write "Observation::" before each command, e.g.
# # "Observation::data_collection_program()"
#
# library(Observation)
#
# # Now it's time to run the Observation program, which will guide you through the
# # data collection process described by Hibbing et al. (2018).
#
# # data_collection_program()
# # ^This only runs the program, but does not store the data.
# # You will want to define an object that stores the data you collect.
# # To do so, you provide the name ("my_data") and use the "<-" operator
# # to assign the results of data_collection_program() to an object of
# # that name.
#
# my_data <- data_collection_program()
#
# # You can view your work with
#
# View(my_data)
#
# # There is also a sample data set you can examine with
#
# data(example_data, package = "Observation")
# View(example_data)
#
# # The format of "my_data" and "example_data" (and any other data
# # collected with data_collection_program()) will be the same. Information
# # about what each column represents is available with
#
# help(example_data, package = "Observation")
#
# # Once you are finished collecting data, you should save it to an external file.
# # There are a lot of options both for saving in different formats, and for
# # managing data from multiple participants. However, this vignette is not
# # intended as a tutorial for those types of tasks, and you probably already
# # have a system you would rather use at that level. Thus, a minimal example is
# # provided here, and the work of determining the appropriate management scheme
# # for a given study is left to the reader.
#
# write.csv(my_data, file = "My Example Observation Data.csv", row.names = FALSE)
#
# # Naturally, you should change the filename in the above code to suit your
# # needs, and be careful to change the filename each time you run your code, to
# # avoid overwriting previously-collected data files. You can easily automate the
# # data saving process to avoid hazards, but again, that is beyond the scope of
# # this vignette.
#
# # Next, it is time to process the data, again via the scheme described by
# # Hibbing et al. (2018), in reference to the Compendium of Physical Activities.
# # As before, you need to assign the processed data to an object via "<-",
# # which has been named "my_data_processed" below.
#
# my_data_processed <- compendium_reference(my_data)
#
# # You can save this processed data with similar code as given above.
#
# write.csv(my_data_processed, file = "My Example_Processed.csv", row.names = FALSE)
#
## ----development, eval = FALSE------------------------------------------------
#
# if (!"devtools" %in% installed.packages()) install.packages("devtools")
#
# devtools::install_github("SciViews/svDialogs")
# # ^ This installs the official development version, which has accepted some
# # specific changes I made to make using `Observation` more pleasant. As a
# # development version, it may be changing continually in ways that could
# # potentially affect `Observation`. If you're not pleased with the behavior
# # you're getting, you can try installing my personal copy, since I'm not
# # planning to continue contributing to development for `svDialogs`.
#
# # devtools::install_github("paulhibbing/svDialogs")
#
## ----customize, eval = FALSE--------------------------------------------------
#
# library(Observation)
# data(example_data, package = "Observation")
# compendium_reference(example_data, rstudio = FALSE)
#
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