README.md
In Echisholm21/multivaR: Multivariate Analysis Tools for Maritime DFO
multivaR
The goal of multivaR is to organize and simplify the process of creating
a multivariate analysis. This package can format data as well as
caluclate anomalies from raw data, once data is formatted the package
can be used to run Principal Components Analysis on multiple variables.
Installation
You can install the development version from
GitHub with:
# install.packages("devtools")
devtools::install_github("EOGrady21/multivaR")
multivaR is designed to work with other packages developed by the Data
Access team at Bedford Institute of Oceanogrpahy (BIO). Specifically,
this package is designed to work with data which is supplied through the
azmpdata package. This supplementary package can be installed by
using:
devtools::install_github("casaultb/azmpdata")
Example
This is a basic example which shows you how to solve a common problem:
library(multivaR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
library(devtools)
#> Loading required package: usethis
## basic example code
# step 1: assemble data
# search for azmpdata
# if azmpdata is not downloaded:
if(!'azmpdata' %in% installed.packages()){devtools::install_github("casaultb/azmpdata")}
library(azmpdata)
#>
#> casaultb/azmpdata status:
#> (Package ver: 0.2019.0.9100) Up to date
#> (Data ver:2021-01-14 ) is up to date
query(search_key = '')
#> [1] "Derived_Annual_Broadscale" "Derived_Annual_Sections"
#> [3] "Derived_Annual_Stations" "Derived_Monthly_Broadscale"
#> [5] "Derived_Monthly_Stations" "Derived_Occupations_Sections"
#> [7] "Derived_Occupations_Stations" "Discrete_Annual_Broadscale"
#> [9] "Discrete_Occupations_Sections" "Discrete_Occupations_Stations"
#> [11] "Phytoplankton_Annual_Stations" "Phytoplankton_Occupations_Stations"
#> [13] "RemoteSensing_Annual_Broadscale" "RemoteSensing_Weekly_Broadscale"
#> [15] "Zooplankton_Annual_Sections" "Zooplankton_Annual_Stations"
#> [17] "Zooplankton_Occupations_Broadscale" "Zooplankton_Occupations_Sections"
#> [19] "Zooplankton_Occupations_Stations" "Zooplankton_Seasonal_Broadscale"
#> [21] "Zooplankton_Seasonal_Sections"
# load in desired data frames
data("Derived_Occupations_Sections")
data("Zooplankton_Occupations_Sections")
# collate data frames
datfr <- right_join(Derived_Occupations_Sections, Zooplankton_Occupations_Sections)
#> Joining, by = c("section", "station", "latitude", "longitude", "year", "month", "day")
If anomalies need to be calculated, multivaR has options for calculating
monthly, annual or seasonal anomalies with or without normalization.
Anomaly calculations use a reference period to calculate a climatology
(climatologyYears). This reference period is typically standardized,
please check that you are using the current standard climatology period
that is relevant to your data.
# monthly anomalies must be caluclated before annual anomalies (annual anomaly function simply combines monthly anomalies)
danomalies <- monthlyAnomaly(datfr, climatologyYears = c(1999, 2010), var = 'integrated_chlorophyll_0_100')
# annual anomalies function creates new data frame with just year and variable
chl_annual_anomalies <- annualAnomaly(danomalies, anomaly = 'integrated_chlorophyll_0_100_anomaly')
# if you are unsure which anomaly calculation to use you can use the wrapper function which will select the appropriate method
anom_1 <- calculate_anomaly(data = datfr, anomalyType = 'monthly', climatologyYears = c(1999, 2010), var = 'integrated_chlorophyll_0_100', normalizedAnomaly = TRUE)
head(anom_1)
#> section station latitude longitude year month day event_id
#> 1 CSL CSL1 46.95 -60.21 1999 4 15 18HU99005_99003155
#> 2 CSL CSL2 47.02 -60.11 1999 4 15 18HU99005_99003161
#> 3 LL LL2 45.65 -59.70 1999 4 15 18HU99005_99003146
#> 4 LL LL6 44.47 -58.50 1999 4 14 18HU99005_99003115
#> 5 LL LL7 44.13 -58.17 1999 4 14 18HU99005_99003109
#> 6 HL HL5 43.18 -62.10 1999 4 12 18HU99005_99003091
#> integrated_chlorophyll_0_100 integrated_nitrate_0_50
#> 1 671.480 88.2700
#> 2 494.475 139.3200
#> 3 270.295 135.9600
#> 4 52.305 36.8075
#> 5 65.820 116.7955
#> 6 79.950 201.3410
#> integrated_nitrate_50_150 integrated_phosphate_0_50
#> 1 129.750 21.4750
#> 2 1055.200 16.5260
#> 3 882.540 16.9040
#> 4 19.305 15.8745
#> 5 1325.300 14.9350
#> 6 655.405 17.8340
#> integrated_phosphate_50_150 integrated_silicate_0_50
#> 1 11.6575 382.905
#> 2 57.1350 160.415
#> 3 52.2885 141.743
#> 4 5.3650 83.653
#> 5 44.4650 89.151
#> 6 28.1420 137.347
#> integrated_silicate_50_150 season sample_id
#> 1 152.705 Spring 99003157_99003102027002
#> 2 1318.007 Spring 99003163_99003102028002
#> 3 1014.163 Spring 99003142_99003102025002
#> 4 42.676 Spring 99003117_99003102021002
#> 5 1077.957 Spring 99003111_99003102020002
#> 6 552.340 Spring 99003093_99003102017002
#> Calanus_finmarchicus_abundance Pseudocalanus_abundance copepods non_copepods
#> 1 1417.526 19845.36 174355.7 175780.000
#> 2 16743.756 45306.63 256081.0 55155.901
#> 3 38207.547 29716.98 237735.8 72196.981
#> 4 22727.273 26223.78 192307.7 164335.664
#> 5 42459.239 55197.01 362318.8 141621.377
#> 6 49029.271 23416.97 172700.1 7317.802
#> Arctic_Calanus_species warm_offshore_copepods warm_shelf_copepods
#> 1 8505.155 0.000 0.0000
#> 2 26593.024 0.000 0.0000
#> 3 26886.792 0.000 0.0000
#> 4 1748.252 0.000 0.0000
#> 5 15568.388 1415.308 0.0000
#> 6 0.000 0.000 731.7802
#> zooplankton_meso_dry_weight zooplankton_macro_dry_weight
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 NA NA
#> zooplankton_total_dry_weight zooplankton_meso_wet_weight
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA 3.606364
#> 5 NA 28.001359
#> 6 NA 11.201718
#> zooplankton_macro_wet_weight zooplankton_total_wet_weight
#> 1 0.09276289 NA
#> 2 NA NA
#> 3 1.13366038 NA
#> 4 NA 3.604545
#> 5 4.51675725 32.518116
#> 6 NA 10.857130
#> integrated_chlorophyll_0_100_normalizedAnomaly
#> 1 2.0232446
#> 2 1.2685040
#> 3 0.3126115
#> 4 -0.6168871
#> 5 -0.5592598
#> 6 -0.4990101
#> integrated_chlorophyll_0_100_anomaly
#> 1 474.500
#> 2 297.495
#> 3 73.315
#> 4 -144.675
#> 5 -131.160
#> 6 -117.030
anom_2 <- calculate_anomaly(data = datfr, anomalyType = 'seasonal', climatologyYears = c(1999, 2010))
head(anom_2)
#> type_name year season variable value
#> 1 CSL 1999 Spring Calanus_finmarchicus_abundance -1.22078967
#> 2 CSL 1999 Spring Calanus_finmarchicus_abundance 0.77077700
#> 3 LL 1999 Spring Calanus_finmarchicus_abundance 0.39701826
#> 4 LL 1999 Spring Calanus_finmarchicus_abundance -0.06118276
#> 5 LL 1999 Spring Calanus_finmarchicus_abundance 0.52286420
#> 6 HL 1999 Spring Calanus_finmarchicus_abundance 0.02582455
Once data is assembled, PCA can be run
PCA(datfr[,9:15]) # subset dataframe to exclude metadata columns
#> Standard deviations (1, .., p=7):
#> [1] 1.6848330 1.4258073 1.0268635 0.8831113 0.3845731 0.3160633 0.2151381
#>
#> Rotation (n x k) = (7 x 7):
#> PC1 PC2 PC3 PC4
#> integrated_chlorophyll_0_100 -0.04458037 -0.4233590 0.74692663 -0.10672075
#> integrated_nitrate_0_50 -0.34682790 -0.2429350 -0.26962152 0.75247504
#> integrated_nitrate_50_150 -0.54580652 0.2260193 0.09348122 0.11407522
#> integrated_phosphate_0_50 -0.17981267 -0.4813154 -0.50649442 -0.42394932
#> integrated_phosphate_50_150 -0.50517512 0.1272963 -0.12146469 -0.47696671
#> integrated_silicate_0_50 -0.12684758 -0.6604202 0.06991854 0.04443296
#> integrated_silicate_50_150 -0.52554109 0.1632217 0.29066689 -0.01320110
#> PC5 PC6 PC7
#> integrated_chlorophyll_0_100 0.41206803 0.2742135 -0.06706635
#> integrated_nitrate_0_50 0.31874123 0.1718770 0.22503531
#> integrated_nitrate_50_150 0.03315179 -0.1881391 -0.76991120
#> integrated_phosphate_0_50 -0.06599948 0.4784064 -0.25788486
#> integrated_phosphate_50_150 0.45829981 -0.3287557 0.41015096
#> integrated_silicate_0_50 -0.38319425 -0.6259318 0.04757690
#> integrated_silicate_50_150 -0.60520447 0.3621123 0.33927273
PCA results can be presented, plotted and analyzed as usual, using
packages outside of multivaR.
Echisholm21/multivaR documentation built on March 30, 2022, 5:55 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
multivaR
The goal of multivaR is to organize and simplify the process of creating a multivariate analysis. This package can format data as well as caluclate anomalies from raw data, once data is formatted the package can be used to run Principal Components Analysis on multiple variables.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("EOGrady21/multivaR")
multivaR is designed to work with other packages developed by the Data
Access team at Bedford Institute of Oceanogrpahy (BIO). Specifically,
this package is designed to work with data which is supplied through the
azmpdata package. This supplementary package can be installed by
using:
devtools::install_github("casaultb/azmpdata")
Example
This is a basic example which shows you how to solve a common problem:
library(multivaR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(data.table)
#>
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
library(devtools)
#> Loading required package: usethis
## basic example code
# step 1: assemble data
# search for azmpdata
# if azmpdata is not downloaded:
if(!'azmpdata' %in% installed.packages()){devtools::install_github("casaultb/azmpdata")}
library(azmpdata)
#>
#> casaultb/azmpdata status:
#> (Package ver: 0.2019.0.9100) Up to date
#> (Data ver:2021-01-14 ) is up to date
query(search_key = '')
#> [1] "Derived_Annual_Broadscale" "Derived_Annual_Sections"
#> [3] "Derived_Annual_Stations" "Derived_Monthly_Broadscale"
#> [5] "Derived_Monthly_Stations" "Derived_Occupations_Sections"
#> [7] "Derived_Occupations_Stations" "Discrete_Annual_Broadscale"
#> [9] "Discrete_Occupations_Sections" "Discrete_Occupations_Stations"
#> [11] "Phytoplankton_Annual_Stations" "Phytoplankton_Occupations_Stations"
#> [13] "RemoteSensing_Annual_Broadscale" "RemoteSensing_Weekly_Broadscale"
#> [15] "Zooplankton_Annual_Sections" "Zooplankton_Annual_Stations"
#> [17] "Zooplankton_Occupations_Broadscale" "Zooplankton_Occupations_Sections"
#> [19] "Zooplankton_Occupations_Stations" "Zooplankton_Seasonal_Broadscale"
#> [21] "Zooplankton_Seasonal_Sections"
# load in desired data frames
data("Derived_Occupations_Sections")
data("Zooplankton_Occupations_Sections")
# collate data frames
datfr <- right_join(Derived_Occupations_Sections, Zooplankton_Occupations_Sections)
#> Joining, by = c("section", "station", "latitude", "longitude", "year", "month", "day")
If anomalies need to be calculated, multivaR has options for calculating
monthly, annual or seasonal anomalies with or without normalization.
Anomaly calculations use a reference period to calculate a climatology
(climatologyYears). This reference period is typically standardized,
please check that you are using the current standard climatology period
that is relevant to your data.
# monthly anomalies must be caluclated before annual anomalies (annual anomaly function simply combines monthly anomalies)
danomalies <- monthlyAnomaly(datfr, climatologyYears = c(1999, 2010), var = 'integrated_chlorophyll_0_100')
# annual anomalies function creates new data frame with just year and variable
chl_annual_anomalies <- annualAnomaly(danomalies, anomaly = 'integrated_chlorophyll_0_100_anomaly')
# if you are unsure which anomaly calculation to use you can use the wrapper function which will select the appropriate method
anom_1 <- calculate_anomaly(data = datfr, anomalyType = 'monthly', climatologyYears = c(1999, 2010), var = 'integrated_chlorophyll_0_100', normalizedAnomaly = TRUE)
head(anom_1)
#> section station latitude longitude year month day event_id
#> 1 CSL CSL1 46.95 -60.21 1999 4 15 18HU99005_99003155
#> 2 CSL CSL2 47.02 -60.11 1999 4 15 18HU99005_99003161
#> 3 LL LL2 45.65 -59.70 1999 4 15 18HU99005_99003146
#> 4 LL LL6 44.47 -58.50 1999 4 14 18HU99005_99003115
#> 5 LL LL7 44.13 -58.17 1999 4 14 18HU99005_99003109
#> 6 HL HL5 43.18 -62.10 1999 4 12 18HU99005_99003091
#> integrated_chlorophyll_0_100 integrated_nitrate_0_50
#> 1 671.480 88.2700
#> 2 494.475 139.3200
#> 3 270.295 135.9600
#> 4 52.305 36.8075
#> 5 65.820 116.7955
#> 6 79.950 201.3410
#> integrated_nitrate_50_150 integrated_phosphate_0_50
#> 1 129.750 21.4750
#> 2 1055.200 16.5260
#> 3 882.540 16.9040
#> 4 19.305 15.8745
#> 5 1325.300 14.9350
#> 6 655.405 17.8340
#> integrated_phosphate_50_150 integrated_silicate_0_50
#> 1 11.6575 382.905
#> 2 57.1350 160.415
#> 3 52.2885 141.743
#> 4 5.3650 83.653
#> 5 44.4650 89.151
#> 6 28.1420 137.347
#> integrated_silicate_50_150 season sample_id
#> 1 152.705 Spring 99003157_99003102027002
#> 2 1318.007 Spring 99003163_99003102028002
#> 3 1014.163 Spring 99003142_99003102025002
#> 4 42.676 Spring 99003117_99003102021002
#> 5 1077.957 Spring 99003111_99003102020002
#> 6 552.340 Spring 99003093_99003102017002
#> Calanus_finmarchicus_abundance Pseudocalanus_abundance copepods non_copepods
#> 1 1417.526 19845.36 174355.7 175780.000
#> 2 16743.756 45306.63 256081.0 55155.901
#> 3 38207.547 29716.98 237735.8 72196.981
#> 4 22727.273 26223.78 192307.7 164335.664
#> 5 42459.239 55197.01 362318.8 141621.377
#> 6 49029.271 23416.97 172700.1 7317.802
#> Arctic_Calanus_species warm_offshore_copepods warm_shelf_copepods
#> 1 8505.155 0.000 0.0000
#> 2 26593.024 0.000 0.0000
#> 3 26886.792 0.000 0.0000
#> 4 1748.252 0.000 0.0000
#> 5 15568.388 1415.308 0.0000
#> 6 0.000 0.000 731.7802
#> zooplankton_meso_dry_weight zooplankton_macro_dry_weight
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 NA NA
#> zooplankton_total_dry_weight zooplankton_meso_wet_weight
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA 3.606364
#> 5 NA 28.001359
#> 6 NA 11.201718
#> zooplankton_macro_wet_weight zooplankton_total_wet_weight
#> 1 0.09276289 NA
#> 2 NA NA
#> 3 1.13366038 NA
#> 4 NA 3.604545
#> 5 4.51675725 32.518116
#> 6 NA 10.857130
#> integrated_chlorophyll_0_100_normalizedAnomaly
#> 1 2.0232446
#> 2 1.2685040
#> 3 0.3126115
#> 4 -0.6168871
#> 5 -0.5592598
#> 6 -0.4990101
#> integrated_chlorophyll_0_100_anomaly
#> 1 474.500
#> 2 297.495
#> 3 73.315
#> 4 -144.675
#> 5 -131.160
#> 6 -117.030
anom_2 <- calculate_anomaly(data = datfr, anomalyType = 'seasonal', climatologyYears = c(1999, 2010))
head(anom_2)
#> type_name year season variable value
#> 1 CSL 1999 Spring Calanus_finmarchicus_abundance -1.22078967
#> 2 CSL 1999 Spring Calanus_finmarchicus_abundance 0.77077700
#> 3 LL 1999 Spring Calanus_finmarchicus_abundance 0.39701826
#> 4 LL 1999 Spring Calanus_finmarchicus_abundance -0.06118276
#> 5 LL 1999 Spring Calanus_finmarchicus_abundance 0.52286420
#> 6 HL 1999 Spring Calanus_finmarchicus_abundance 0.02582455
Once data is assembled, PCA can be run
PCA(datfr[,9:15]) # subset dataframe to exclude metadata columns
#> Standard deviations (1, .., p=7):
#> [1] 1.6848330 1.4258073 1.0268635 0.8831113 0.3845731 0.3160633 0.2151381
#>
#> Rotation (n x k) = (7 x 7):
#> PC1 PC2 PC3 PC4
#> integrated_chlorophyll_0_100 -0.04458037 -0.4233590 0.74692663 -0.10672075
#> integrated_nitrate_0_50 -0.34682790 -0.2429350 -0.26962152 0.75247504
#> integrated_nitrate_50_150 -0.54580652 0.2260193 0.09348122 0.11407522
#> integrated_phosphate_0_50 -0.17981267 -0.4813154 -0.50649442 -0.42394932
#> integrated_phosphate_50_150 -0.50517512 0.1272963 -0.12146469 -0.47696671
#> integrated_silicate_0_50 -0.12684758 -0.6604202 0.06991854 0.04443296
#> integrated_silicate_50_150 -0.52554109 0.1632217 0.29066689 -0.01320110
#> PC5 PC6 PC7
#> integrated_chlorophyll_0_100 0.41206803 0.2742135 -0.06706635
#> integrated_nitrate_0_50 0.31874123 0.1718770 0.22503531
#> integrated_nitrate_50_150 0.03315179 -0.1881391 -0.76991120
#> integrated_phosphate_0_50 -0.06599948 0.4784064 -0.25788486
#> integrated_phosphate_50_150 0.45829981 -0.3287557 0.41015096
#> integrated_silicate_0_50 -0.38319425 -0.6259318 0.04757690
#> integrated_silicate_50_150 -0.60520447 0.3621123 0.33927273
PCA results can be presented, plotted and analyzed as usual, using
packages outside of multivaR.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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