Package: MazamaTimeSeries 0.3.0

Jonathan Callahan

MazamaTimeSeries: Core Functionality for Environmental Time Series

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". Ephemerides calculations are based on code originally found in NOAA's "Solar Calculator" <https://gml.noaa.gov/grad/solcalc/>.

Authors:Jonathan Callahan [aut, cre], Hans Martin [ctb], Eli Grosman [ctb], Roger Bivand [ctb], Sebastian Luque [ctb]

MazamaTimeSeries_0.3.0.tar.gz
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MazamaTimeSeries_0.3.0.tgz(r-4.4-any)MazamaTimeSeries_0.3.0.tgz(r-4.3-any)
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MazamaTimeSeries.pdf |MazamaTimeSeries.html✨
MazamaTimeSeries/json (API)
NEWS

# Install 'MazamaTimeSeries' in R:
install.packages('MazamaTimeSeries', repos = c('https://mazamascience.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mazamascience/mazamatimeseries/issues

Datasets:

On CRAN:

timeseries

39 exports 1.34 score 111 dependencies 1 dependents 62 scripts 376 downloads

Last updated 6 months agofrom:d9d2491ff4. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 04 2024
R-4.5-winWARNINGSep 04 2024
R-4.5-linuxWARNINGSep 04 2024
R-4.4-winWARNINGSep 04 2024
R-4.4-macWARNINGSep 04 2024
R-4.3-winWARNINGSep 04 2024
R-4.3-macWARNINGSep 04 2024

Exports:%>%mts_arrangemts_checkmts_collapsemts_combinemts_distinctmts_extractDatamts_extractMetamts_filterDatamts_filterDatemts_filterDatetimemts_filterMetamts_getDistancemts_isEmptymts_isValidmts_pullmts_samplemts_selectmts_selectWheremts_setTimeAxismts_slice_headmts_slice_tailmts_summarizemts_trimmts_trimDaterequiredMetaNamessts_checksts_combinests_distinctsts_extractDatasts_extractMetasts_filtersts_filterDatests_filterDatetimests_isEmptysts_isValidsts_summarizests_trimDatetimeInfo

Dependencies:askpassbase64encbrewbriobslibcachemcallrclicliprcommonmarkcpp11crayoncredentialscurldescdevtoolsdiffobjdigestdownlitdplyrellipsisevaluatefansifastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsgeodistgeohashToolsgertghgitcredsgluehighrhtmltoolshtmlwidgetshttpuvhttrhttr2inijquerylibjsonliteknitrlambda.rlaterlifecyclelubridatemagrittrMazamaCoreUtilsMazamaRollUtilsmemoisemimeminiUIopensslpillarpkgbuildpkgconfigpkgdownpkgloadpraiseprettyunitsprocessxprofvispromisespspurrrR6raggrappdirsrcmdcheckRcpprematch2remotesrlangrmarkdownroxygen2rprojrootrstudioapirversionsrvestsassselectrsessioninfoshinysourcetoolsstringistringrsyssystemfontstestthattextshapingtibbletidyselecttimechangetinytexurlcheckerusethisutf8vctrswaldowhiskerwithrxfunxml2xopenxtableyamlzip

Developer Style Guide

Mazama Science

Rendered fromDeveloper_Style_Guide.Rmdusingknitr::rmarkdownon Sep 04 2024.

Last update: 2024-03-08
Started: 2021-03-11

Introduction to MazamaTimeSeries

Mazama Science

Rendered fromMazamaTimeSeries.Rmdusingknitr::rmarkdownon Sep 04 2024.

Last update: 2024-03-08
Started: 2021-10-07

Readme and manuals

Help Manual

Help pageTopics
Camp Fire example datasetCamp_Fire
Carmel Valley example datasetCarmel_Valley
Example _mts_ datasetexample_mts
Example RAWS datasetexample_raws
Example _sts_ datasetexample_sts
Core functionality for environmental time seriesMazamaTimeSeries-package MazamaTimeSeries
Order _mts_ time series by metadata valuesmts_arrange
Check _mts_ object for validitymts_check
Collapse an _mts_ time series object into a single time seriesmts_collapse
Combine multiple _mts_ time series objectsmts_combine
Retain only distinct data records in 'mts$data'mts_distinct
Extract dataframes from _mts_ objectsmts_extractData mts_extractDataFrame mts_extractMeta
General purpose data filtering for _mts_ time series objectsmts_filterData
Date filtering for _mts_ time series objectsmts_filterDate
Datetime filtering for _mts_ time series objectsmts_filterDatetime
General purpose metadata filtering for _mts_ time series objectsmts_filterMeta
Calculate distances from _mts_ time series locations to a location of interestmts_getDistance
Test for an empty _mts_ objectmts_isEmpty
Test _mts_ object for correct structuremts_isValid
Extract a column of metadata or datamts_pull
Sample time series for an _mts_ time series objectmts_sample
Reorder and subset time series within an _mts_ time series objectmts_select
Data-based subsetting of time series within an _mts_ object.mts_selectWhere
Extend/contract _mts_ time series to new start and end timesmts_setTimeAxis
Subset time series based on their positionmts_slice_head mts_slice_tail
Create summary time series for an _mts_ time series objectmts_summarize
Trim _mts_ time series by removing missing valuesmts_trim
Trim _mts_ time series object to full daysmts_trimDate
Required columns for the 'meta' dataframerequiredMetaNames
Check _sts_ object for validitysts_check
Combine multiple _sts_ time series objectssts_combine
Retain only distinct data records in 'sts$data'sts_distinct
Extract dataframes from _sts_ objectssts_extractData sts_extractDataFrame sts_extractMeta
General purpose data filtering for _sts_ time series objectssts_filter
Date filtering for _sts_ time series objectssts_filterDate
Datetime filtering for _sts_ time series objectssts_filterDatetime
Test for empty _sts_ objectsts_isEmpty
Test _sts_ object for correct structurests_isValid
Create summary time series for an _sts_ time series objectsts_summarize
Trim _sts_ time series object to full dayssts_trimDate
Get time related informationtimeInfo