In andrewstiegler/telemetRy: Functions for Importing and Manipulating DSI Telemetry Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(telemetRy)
library(magrittr)
library(ggplot2)

This vignette will show an example of importing data collected with a DSI telemetry system running Ponemah software into R. The first step is to export data from the DSI telemetry system.

Importing DSI data

First data must be exported from DSI's Ponemah software.

To export data: with an experiment open, navigate to Experiment > Export Data. In this pop-up, select subjects to export, a timerange to export, and make sure to un-check "Pivot Compatible Sheets"

Select "Export" and an Excel file will be generated. To import that Excel file into R, utilize the DSI_export_to_dataframe function:

exported_data <- DSI_export_to_dataframe('path_to_DSI_export')

A progress message will be displayed showing the current import status.

The output of the file is a dataframe:

# Sample dataframe is included as sample_BP_data:
head(sample_BP_data) %>% print(digits = 3)

Where the .id column indicates the subject serial number, and the Time column is the real time of data acquisition. Several useful time columns are generated, including a column for time-of-day only, and elapsed time since acquisition started in several units. The rest of the telemetry parameters are named.

After importing data

Several functions are available for data processing after import. The functions typical_day and circadian_avg both take the newly created dataframe as input.

To calculate a typical day:

# In our sample dataset, room lights turn on at 6AM
sample_typical_day <- typical_day(data = sample_BP_data, lights_on = 6)
head(sample_typical_day) %>% print(digits = 3)

To calculate circadian averages:

# In our sample dataset, room lights turn on at 6AM
sample_circadian_avg <- circadian_avg(data = sample_BP_data, lights_on = 6)
head(sample_circadian_avg[[1]])

Output from typical_day or circadian_avg can be plotted using ggplot:

# To plot typical SBP. first isolate SBP
sample_typical_sbp <- isolate_typical(data = sample_typical_day, 
                                      parameter = "SBP")
# Create a column for mean SBP
sample_typical_sbp$mean <- sample_typical_sbp %>% dplyr::select(-Time) %>% 
  rowMeans()

# Plot the mean against time in hours
ggplot(data = sample_typical_sbp)+
  geom_point(aes(x=Time/3600, y=mean))+
  ylab("Mean SBP (mmHg)")+
  xlab("Time (h)")+
  theme_classic()

# The second list element contains values when room is dark
sample_circadian_dark <- sample_circadian_avg[[2]]
# Isolate SBP
sample_circadian_dark_sbp <- isolate_typical(data = sample_circadian_dark, 
                                             parameter = "SBP")
# Create a column for mean SBP
sample_circadian_dark_sbp$mean <- sample_circadian_dark %>% 
  dplyr::select(-Time) %>% rowMeans()

# Plot the mean against time in days
ggplot(data = sample_circadian_dark_sbp)+
  geom_line(aes(x=Time, y=mean))+
  ylab("Mean SBP 12H-Dark (mmHg)")+
  xlab("Time (d)")+
  theme_classic()

andrewstiegler/telemetRy documentation built on May 1, 2021, 4:56 p.m.