The goal of walking is to provide some algorithms to detect walking in
tri-axial accelerometers.
You can install the development version of walking from GitHub with:
# install.packages("devtools")
devtools::install_github("muschellij2/walking")This is a basic example which shows you how to solve a common problem:
library(walking)
csv_file = system.file("test_data_bout.csv", package = "walking")
x = readr::read_csv(csv_file)
#> Rows: 98 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): accuracy
#> dbl (4): timestamp, x, y, z
#> dttm (1): UTC time
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
colnames(x)[colnames(x) == "UTC time"] = "time"
res = find_walking(data = x)
#> Preprocessing Bout
#> Bout is Preprocessed
#> OAK: Find walking is done
print(res)
#> time steps
#> 1 2020-02-25 18:18:31 1.65
#> 2 2020-02-25 18:18:32 1.60
#> 3 2020-02-25 18:18:33 1.55
#> 4 2020-02-25 18:18:34 1.60
#> 5 2020-02-25 18:18:35 1.55
#> 6 2020-02-25 18:18:36 1.85
#> 7 2020-02-25 18:18:37 1.80
#> 8 2020-02-25 18:18:38 1.75
#> 9 2020-02-25 18:18:39 1.75
#> 10 2020-02-25 18:18:40 1.70Running forest and stepcount.
The two Python modules (forest and stepcount) can be be installed in
the same conda environment, but if they are not, this will lead to an
error message. The options. One solution is to run them in two separate
R sessions (recommended).
Alternatively, you can try to install forest in the stepcount conda
environment, such as:
envname = "stepcount2"
stepcount::conda_create_stepcount(envname = envname)
# if you have RETICULATE_PYTHON set
stepcount::unset_reticulate_python()
stepcount::use_stepcount_condaenv(envname = envname)
walking::install_forest(envname = envname)and then run examples such as:
library(walking)
library(stepcount)
envname = "stepcount2"
# if you have RETICULATE_PYTHON set
stepcount::unset_reticulate_python()
stepcount::use_stepcount_condaenv(envname = envname)
csv_file = system.file("test_data_bout.csv", package = "walking")
x = readr::read_csv(csv_file)
colnames(x)[colnames(x) == "UTC time"] = "time"
res = find_walking(data = x)Remember, however, best practices for Python is “Always create a separate virtual environment for each project” and sometimes one for each “goal”.