R/0a_global_vars_&_imports.R

In accelEE: Predict Energy Expenditure from Accelerometry Data

# Global Variables --------------------------------------------------------


  #* General ####

    if(getRversion() >= "2.15.1") utils::globalVariables(c(
      ".", ".x", "Accelerometer_Z", "AG", "AL_LW_X_pTen", "AL_RW_Z_cov",
      ".age", "bedrest", "Choi_is_NonWear", "Date", "days", "ENMO", "grp",
      "is_NonWear", "is_Sleep", "is_WakeWear", "is_weekend", ".location",
      "mean.vm", "method", "METs", "METs_lm", "METs_rf", ".model", "model",
      ".monitor", "n", "ratio.df", "sd.vm", "Timestamp", "total_minutes",
      "Tracy_is_Sleep", "TS", "valid_status", "vm", "weekday", "where",
      "X", "Y", "Z"
    ))


  #* Hildebrand ####

    .hildebrand <- dplyr::tibble(
      .age = c(
        rep("adult", 4),
        rep("youth", 4)
      ),
      .monitor = rep(c("ActiGraph", "GENEActiv"), 4),
      .location = rep(c("hip", "hip", "wrist", "wrist"), 2),
      intercept = c(
        6.67, 6.86, 7.28, 7.49,
        10.03, 10.39, 10.83, 11.16
      ),
      slope = c(
        0.0554, 0.053, 0.032, 0.0323,
        0.0559, 0.0498, 0.0356, 0.0357
      ),
      cp = c(
        47.4, 46.9, 44.8, 45.8,
        63.3, 64.1, 35.6, 56.3
      )
    )


  #* Crouter 2015 ####

    .crouter15 <- dplyr::tibble(
      .model = c("VA", "VM"),
      cp = c(35, 100),
      intercept = c(1.592, 1.475),
      slope = c(0.0039, 0.0025)
    )


  #* Summarizing ####

    .sum_names <- c(
      "Axis1", "Axis2", "Axis3", "Vector.Magnitude", "Steps",
      # "SB", "LPA", "MPA", "VPA", "MVPA",
      "is_Sleep", "is_NonWear",
      "hildebrand_linear", "hildebrand_nonlinear",
      "hibbing_left", "hibbing_right",
      "montoye_left", "montoye_right",
      "staudenmayer_lm", "staudenmayer_rf"
    )


# Imports -----------------------------------------------------------------

#' @import magrittr nnet randomForest tree
NULL

#' @importFrom rlang :=
NULL