Observation: Collect and Process Physical Activity Direct Observation Data

2.1 A First Critical Note (Don’t miss the second critical note [Section 3.2])

The following sections provide basic instructions for installing software. For those who already have R and RStudio installed, please note that having outdated versions of the applications can affect the functionality of Observation. In particular, updating RStudio can help with certain issues (go to Help -> Check for Updates). The need to update R has not been specifically confirmed, and should presumably not be necessary, perhaps unless you have a fairly old version (e.g. < 3.0). Before updating your application(s), you may wish to try some of the suggestions in Section 3.2 to improve your experience using Observation.

2.2 R and RStudio

To use Observation, you must naturally have R installed, which you can download for free at https://www.r-project.org. It is recommended to also download and install RStudio for free from https://www.rstudio.com.

R is a programming language, whereas RStudio is an “integrated development environment.” That is, RStudio includes additional functionality to streamline the way you use R.

2.3 The Observation Package

To install the Observation package, open Rstudio and type into the console (bottom left pane) install.packages("Observation"). Press enter to execute the command, and the package will be automatically installed.

3.1 Code

# 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)

3.2 A Second Critical Note

When used interactively, Observation depends heavily on the svDialogs package, which generates dialog boxes. svDialogs is focused on providing functionality that works across operating systems and R interfaces, which sometimes means that default settings behave in a less-than-ideal way in a context like Observation. It should be possible to circumvent such issues, but an understanding of how both packages are structured is essential for doing so.

Observation is set up to use the default settings of all the svDialogs functions it uses, which ensures a working outcome for all users. However, Observation also gives users the capability of experimenting with different svDialogs settings. The catch is that, in the current CRAN version of svDialogs (v 1.0.0), there are not really options to customize. To get those, you will have to work with development versions of svDialogs instead of the CRAN version, until the development versions are released. Fortunately this is easy to do. The safest bet is to run the following code.

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")

Once you have a development version of svDialogs installed, you can adapt your usage of Observation code to hopefully improve your experience using the package, especially the compendium_reference function. In particular, I recommend trying the following.

library(Observation)
data(example_data, package = "Observation")
compendium_reference(example_data, rstudio = FALSE)

If you are using RStudio desktop, the above code (loosely) allows you to bypass RStudio’s dialog boxes and access your system’s instead. You can use the same option (rstudio = FALSE) in data_collection_program as well. It is possible that this will make things worse, so you can always go back to the default settings by taking out rstudio = FALSE.

3.3 Additional Comments

• It is not necessary to run compendium_reference immediately after collecting data. If you store the data you collect, you can load it again later (e.g. using my_data <- read.csv("My Example Observation Data.csv", stringsAsFactors = FALSE)) and then run compendium_reference(my_data).

• If you will be performing all your analyses in R, it is recommendable to use save and load, rather than write.csv and read.csv.

• If you are planning to move forward with csv files, and it is taking too long to read/write the files, you should install the data.table package and use the fread and fwrite commands instead. Those commands work essentially the same as read.csv and write.csv, but operate much faster.

• When you are specifying filenames, the safest thing to do is write out the whole file path. For example, instead of writing file = "My Example Observation Data.csv" inside write.csv(), you may wish to write something more like file = "C:/Users/Me/Documents/My Example Observation Data.csv". Otherwise, the command getwd() is helpful for finding files if you’re not sure where you saved them.

• The general approach for the compendium_reference function is to examine the activity descriptions provided during data collection and determine the intensity category (when it was not previously detected) by cross-referencing the 2011 Compendium of Physical Activities.