actinet

The goal of actinet is to wrap up the https://github.com/OxWearables/actinet algorithm.

Installation

Install actinet Python Module

See https://github.com/OxWearables/actinet?tab=readme-ov-file#install for how to install the actinet python module. In the new reticulate, you can do this via:

Sys.setenv(
  RETICULATE_PYTHON = "managed"
)
reticulate::py_require("actinet==0.7.2", python_version = "3.10")
sc <- reticulate::import("actinet")

This will install actinet via uv every time you run the command a new time.

You can also install a conda environment via:

envname = "actinet"
reticulate::conda_create(envname = envname, packages = c("python=3.10"))
Sys.unsetenv("RETICULATE_PYTHON")
reticulate::use_condaenv(envname)
reticulate::py_install("actinet", envname = envname, method = "conda", pip = TRUE)

Once this is finished, you should be able to check this via:

actinet::have_actinet()

The actinet_check function can determine if the actinet module can be loaded and run:

actinet::actinet_check()
#> [1] TRUE

In some cases, you ay want to set RETICULATE_PYTHON variable:

clist = reticulate::conda_list()
Sys.setenv(RETICULATE_PYTHON = clist$python[clist$name == "actinet"])
reticulate::use_condaenv("actinet")

Usage

Running actinet (file)

The main function is actinet::actinet, which takes can take in a file directly:

library(actinet)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.4.3
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.4.3
library(tidyr)
#> Warning: package 'tidyr' was built under R version 4.4.1
file = system.file("extdata/P30_wrist100.csv.gz", package = "actinet")
if (actinet_check()) {
  out = actinet(file = file)
}
#> Warning in normalizePath(outdir, winslash = "/"):
#> path[1]="/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc":
#> No such file or directory
#> Checking Data

Let’s see inside the output, which is a list of values, namely a data.frame of steps with the time (in 10s increments) and the number of steps in those 10 seconds, a data.frame named walking which has indicators for if there is walking within that 10 second period:

names(out)
#> [1] "outdir_passed" "outdir"        "outfiles"
str(out)
#> List of 3
#>  $ outdir_passed: chr "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc"
#>  $ outdir       : chr "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc/P30_wrist100.csv"
#>  $ outfiles     : chr [1:3] "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc/P30_wrist100.csv/P30_wrist100.cs"| __truncated__ "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc/P30_wrist100.csv/P30_wrist100.cs"| __truncated__ "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpNYuoYa/file1301f1a68ebdc/P30_wrist100.csv/P30_wrist100.csv-Daily.csv.gz"