Utility functions for working with environmental time series data from known locations. The compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment".
This package supports data management activities associated with environmental time series collected at fixed locations in space. The motivating fields include both air and water quality monitoring where fixed sensors report at regular time intervals.
The most compact format for time series data collected at fixed
locations is a list including two tables.
MazamaTimeSeries stores time series measurements in a
data table where each row is a synoptic record containing
all measurements associated with a particular UTC time stamp and each
column contains data measured by a single sensor (aka “device”). Any
time invariant metadata associated with a sensor at a known location
(aka a “device-deployment”) is stored in a separate
table. A unique
deviceDeploymentID connects the two tables.
In the language of relational databases, this “normalizes” the database
and can greatly reduce the disk space and memory needed to store and
work with the data.
Time series data from a single environmental sensor typically
consists of multiple parameters measured at successive times. This data
is stored in an R list containing two dataframes. The package refers to
this structure as an
sts object for
sts$meta – 1 row = unique device-deployment; cols =
sts$data – rows = UTC times; cols = measured parameters
(plus an additional
sts objects can support the following types of time
Raw, “engineering data” containing uncalibrated measurements, instrument voltages and QC flags may be stored in this format. This format is also appropriate for processed and QC’ed data whenever multiple parameters are measured by a single device.
sts object time axis
data$datetime reflects device measurement
times and is not required to have uniform spacing. (It may be
regular but it need not be.)
Working with timeseries data from multiple sensors at once is often
challenging because of the amount of memory required to store all the
data from each sensor. However, a common situation is to have time
series that share a common time axis – e.g. hourly
measurements. In this case, it is possible to create single-parameter
data dataframes that contain all data for all sensors for a
single parameter of interest. In air quality applications, common
parameters of interest include PM2.5 and Ozone.
Multi-sensor, single-parameter time series data is stored in an R
list with two dataframes. The package refers to this structure as an
mts object for
mts$meta – N rows = unique device-deployments; cols =
mts$data – rows = UTC times; N cols = device-deployments
(plus an additional
A key feature of
mts objects is the use of the
deviceDeploymentID as a “foreign key” that allows sensor
data columns to be mapped onto the associated spatial and
sensor metadata in a
meta row. The following will always be
identical(names(mts$data), c('datetime', mts$meta$deviceDeploymentID))
mts objects can support the following types of time
Each column of
mts$data represents a timeseries
associated with a particular device-deployment while each row represents
a synoptic snap shot of all measurements made at a particular
In this manner, software can create both timeseries plots and maps
from a single
mts object in memory.
mts object time axis
data$datetime is guaranteed to be a regular
hourly axis with no gaps.
This R package was created with funding from the USFS AirFire Research Team.