Introduction

This package aims at supporting the analysis of PM2.5 measures made with RTI MicroPEM. RTI MicroPEM are personal monitoring devices (PM2.5 and PM10) developed by RTI international.

The goal of the package functions is to help in two main tasks:

Checking individual MicroPEM output files after, say, one day of data collection.
Building a data base based on output files, and clean and transform the data for further analysis.

For the examination of individual files, the package provides a function for transforming the output of a RTI MicroPEM into an object of a R6 class called micropem, functions for examining this information in order to look for possible problems in the data. The package moreover provides a Shiny app used for the field work of the CHAI project, but that could easily be adapted to other contexts.

This document aims at providing an overview of the functionalities of the package.

Checking individual files: from input data to `micropem` objects

The MicroPEM device outputs a csv file with all the information about the measures, depending on the options chosen in RTI MicroPEM software when uploading data, for instance:

the measures themselves (relative humidity corrected nephelometer),
other measures that can help interpret them or check that no problem occurred (temperature, relative humidity, battery, orifice pressure, inlet pressure, flow, accelerometer variables, reasons for shutdown, and variables related to user compliance),
a reminder of parameters set by the user (calibration parameters, frequency of measures)
and information about the device (filter ID, version of the software, etc). This is a lot of information, compiled in a handy csv format that is optimal for not losing any data along the way, but not practical for analysis.

Therefore, the micropem package offers a R6 class called micropem for storing the information, that will be easier to use by other functions. The class has fields with measures over time and a field that is a list containing all the information located at the top of the MicroPEM output file, called settings. Here is a picture of a RTI MicroPEM output file showing how the information is stored in the R6 class.

alt text

Fields

`settings` field

This field is a data.frame (dplyr tbl_df) that includes 41 variables.

`measures` field

This field is a data.frame (dplyr tbl_df) with the time-varying variables.

`filename` field

This field contains the full filename of the file that was used to generate the micropem object.

The `convert_output` function.

The convert_output only takes one arguments as input: the path to the output file. The result of a call to this function is an object of the class micropem. Below is a example of a call to convert_output followed by a call to the print method.

library("rtimicropem")
micropem_example <- convert_output(system.file("extdata", "CHAI.csv", package = "rtimicropem"))
class(micropem_example)

## [1] "micropem" "R6"

print(micropem_example)

## An object of class micropem (R6 class)
## A summary of measures is:
## 
## measure                      no._of_not_missing_values   median         mean   minimum   maximum    variance
## --------------------------  --------------------------  -------  -----------  --------  --------  ----------
## rh_corrected_nephelometer                         8634    49.00   49.3745657     45.00     93.00   1.6780557
## temp                                              2878    84.50   84.6830438     82.30     87.60   1.7180023
## rh                                                8634    54.60   55.0061733     46.20     64.90   7.6665285
## battery                                           1464     4.10    4.0872268      3.90      4.30   0.0078272
## inlet_press                                       2878     0.11    0.1111015      0.10      0.13   0.0000538
## orifice_press                                     2878     0.15    0.1505455      0.14      0.16   0.0000072
## flow                                              2878     0.77    0.7703023      0.77      0.78   0.0000029
## 
##  Settings were:
## 
##                                         value                  
## --------------------------------------  -----------------------
## download_date                           2015-04-07             
## total_download_time                     18                     
## device_serial                           MP1411                 
## datetime_hardware                       2013-02-15             
## datetime_software                       2014-01-24             
## version                                 v2.0.5136.37657        
## participantID                                                  
## filterID                                CM1411                 
## participant_weight                      NA                     
## inlet_aerosol_size                      PM2.5                  
## laser_cycling_variables_delay           1                      
## laser_cycling_variables_sampling_time   1                      
## laser_cycling_variables_off_time        8                      
## system_times                            No cycling - Always On 
## nephelometer_slope                      10                     
## nephelometer_offset                     0                      
## nephelometer_log_interval               10                     
## temperature_slope                       10                     
## temperature_offset                      0                      
## temperature_log                         30                     
## humidity_slope                          10                     
## humidity_offset                         0                      
## humidity_log                            10                     
## inlet_pressure_slope                    40.950.000             
## inlet_pressure_offset                   0                      
## inlet_pressure_log                      30                     
## inlet_pressure_high_target              1280                   
## inlet_pressure_low_target               768                    
## orifice_pressure_slope                  40.950.000             
## orifice_pressure_offset                 0                      
## orifice_pressure_log                    30                     
## orifice_pressure_high_target            2167                   
## orifice_pressure_low_target             1592                   
## flow_log                                30                     
## flow_high_target                        900                    
## flow_low_target                         200                    
## flow_rate                               0.5                    
## accelerometer_log                       5                      
## battery_log                             60                     
## ventilation_slope                       NA                     
## ventilation_offset                      NA

Visualizing information contained in a `micropem` object

Plot method

The R6 micropem class has its own plot method. It allows to draw a plot of all time-varying measures against the timeDate field. It takes two arguments: the micropem object to be plotted, and the type of plots to be produced, either a “plain” ggplot2 plot with 6 facets, or its interactive version produced with the rbokeh package – the corresponding values of type are respectively “plain” and “interactive”.

Below we show to examples of uses of the plot method on a micropem object.

This is a “plain” plot.

data("micropemChai")
micropemChai$plot()

The code below would produce an interactive representation: the y-value is displayed when mouse is over each point. It is intended to be used as quick visualization tool as well, not as a plot method for putting a nice figure in a paper.

library("rbokeh")
p <- micropemChai$plot(type = "interactive")
p

`summary` method

Plotting the micropem object is already a good way to notice any problem. Another methods aims at providing more compact information about the time-varying measures. It is called summary and outputs a table with summary statistics for each time-varying measures, except time.

Below is an example of use of this method.

library("xtable")
data("micropemChai")
results <- micropemChai$summary()
results %>% knitr::kable()

measure	no._of_not_missing_values	median	mean	minimum	maximum	variance
rh_corrected_nephelometer	8634	49.00	49.3745657	45.00	93.00	1.6780557
temp	2878	84.50	84.6830438	82.30	87.60	1.7180023
rh	8634	54.60	55.0061733	46.20	64.90	7.6665285
battery	1464	4.10	4.0872268	3.90	4.30	0.0078272
inlet_press	2878	0.11	0.1111015	0.10	0.13	0.0000538
orifice_press	2878	0.15	0.1505455	0.14	0.16	0.0000072
flow	2878	0.77	0.7703023	0.77	0.78	0.0000029

Shiny app developed for the CHAI project

In the context of the CHAI project, we developed a Shiny app based on the previous functions, that allows to explore a MicroPEM output file. The app is called by the function run_shiny_app with no argument. There is one side panel where one can choose the file to analyse. There are four tabs:

One with the output of a call to the summary method of the micropem object created,
One with the output of a call to the chai_alarm function that performs a few checks specific to the CHAI project,
One with the output of a call to the plot method,
One with the output of a call to summarySettings.

This app allows the exploration of a MicroPEM output file with no R experience.

Below we show screenshots of the app.

From a bunch of output files to data ready for further analysis

The batch_convert function allows to convert a set of MicroPEM output files into two csv files, one containing the settings, one containing the measures, in each case with a filename column representing the full filename of the original file.

These csv files can then be used for transformation by device ID for instance, or for analysis by date, or by participant ID of a study if the participant ID is contained in the filename or in the initial MicroPEM output files. The csv file with all the settings of all MicroPEM output files can help checking whether the different monitoring sessions were done in similar conditions.

Introduction to `rtimicropem`

M. Salmon and other CHAI project members

2019-05-15

Introduction

Checking individual files: from input data to `micropem` objects

Fields

`settings` field

`measures` field

`filename` field

The `convert_output` function.

Visualizing information contained in a `micropem` object

Plot method

`summary` method

Shiny app developed for the CHAI project

From a bunch of output files to data ready for further analysis

Introduction to rtimicropem

M. Salmon and other CHAI project members

2019-05-15

Introduction

Checking individual files: from input data to micropem objects

Fields

settings field

measures field

filename field

The convert_output function.

Visualizing information contained in a micropem object

Plot method

summary method

Shiny app developed for the CHAI project

From a bunch of output files to data ready for further analysis

Introduction to `rtimicropem`

Checking individual files: from input data to `micropem` objects

`settings` field

`measures` field

`filename` field

The `convert_output` function.

Visualizing information contained in a `micropem` object

`summary` method