R/daily_acticity_wrap_plot.R

In nasserdr/digyRhythm: Analyzing Animal's Rhythmicity

#' Plot daily average over a period of time for a specific variable.
#'
#' Takes an activity dataset as input and plot and save the daily average of the
#' specified activity column
#' @param df The dataframe containing the activity data
#' @param activity the name of activity
#' @param activity_alias A string containing the name of the activity to be
#' shown on the graph.
#' @param start The start day (in "%Y-%m-%d" format).
#' @param end The end day (in "%Y-%m-%d" format).
#' @param save if NULL, the image is not saved. Otherwise, this parameter will
#' @param sampling_rate the sampling rate of the data.
#' @param ncols the number of columns to spread the graphs on.
#' be the name of the saved image. it should contain the path and name without
#' the extension.
#'
#' @return A ggplot2 object that contains the daily average activity plot
#'
#' @import dplyr
#' @import ggplot2
#' @import magrittr
#'
#' @export
#'
#' @examples
#' data("df516b_2")
#' df <- df516b_2
#' activity <- names(df)[2]
#' activity_alias <- "Motion Index"
#' start <- "2020-05-01" # year-month-day
#' end <- "2020-05-07" # year-month-day
#' ncols <- 3
#' sampling_rate <- 30
#' my_dwp <- daily_activity_wrap_plot(
#'   df, activity, activity_alias, start, end, sampling_rate,
#'   ncols
#' )
daily_activity_wrap_plot <- function(
    df,
    activity,
    activity_alias,
    start,
    end,
    sampling_rate,
    ncols,
    save = "daily_wrap_plot") {
  start <- lubridate::date(start)
  end <- lubridate::date(end)

  df$date <- lubridate::date(df$datetime)
  data_to_plot <- df %>%
    filter(date >= start) %>%
    filter(date <= end)

  data_to_plot$time <- format(data_to_plot$datetime,
    format = "%H:%M",
    tz = "CET"
  )



  # days <- unique(data_to_plot$date)
  # l <- ceiling(length(days)/ncols)

  # batch1 <- data_to_plot %>% filter(data_to_plot$date <= days[l])
  # batch2 <- data_to_plot %>% filter(data_to_plot$date > days[l])

  time2 <- rep(1:(24 * 60 / sampling_rate), length(unique(data_to_plot$date)))
  data_to_plot$time2 <- time2[1:dim(data_to_plot)[1]]

  avg_plot <- ggplot(data_to_plot, aes(
    x = time2,
    y = !!as.name(activity)
  )) +
    geom_area() +
    facet_wrap(~date, ncol = ncols) +
    ggtitle(paste(
      activity_alias, "(", data_to_plot$date[1], "-",
      last(data_to_plot$date), ")"
    )) +
    xlab("Time") +
    theme(
      axis.text = element_text(color = "black"),
      panel.background = element_rect(fill = "white"),
      legend.key = element_rect(fill = "white"),
      legend.key.width = unit(0.5, "cm"),
      legend.justification = "left",
      legend.key.size = unit(7, "pt"),
      strip.background = element_blank(),
      strip.text.x = element_blank(),
      axis.title.y = element_blank(),
      axis.text.y = element_blank(),
      axis.ticks.y = element_blank(),
      axis.line = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.position = c(0.05, 0.89)
    ) +
    scale_x_continuous(
      breaks = seq(sampling_rate - 2,
        24 * 60 / sampling_rate - sampling_rate + 4,
        length.out = 4
      ),
      labels = c("03:00", "09:00", "15:00", "21:00")
    )


  print(avg_plot)
  return(avg_plot)
}