README.md

In SrivastavaLab/bwgtools: Access data from the bromeliad working group

Andrew MacDonald, Dézerald Olivier, Nicholas Marino, A.D. Letaw, & Ethan White. (2017, December 20). SrivastavaLab/bwgtools: First release of bwgtools (Version 1.0.0). Zenodo. http://doi.org/10.5281/zenodo.1120419

Introduction

This is an R package for the bromeliad working group rainfall experiment. Our intention is to create a set of tools that facilitate all steps of the process:

• loading data into R
• combining data from different replicates
• performing calculations

At present the package allows each group of authors to obtain the datasets they will need to begin their analysis.

Please open an issue if there is a feature you would like to see, or if you discover an error or bug!

Installation

bwgtools can be installed directly from Github using devtools:

install.packages("devtools") # if you don't have devtools
library(devtools)
install_github("SrivastavaLab/bwgtools", dependencies = TRUE)

Accessing Dropbox

bwgtools uses rdrop2 to access your Dropbox account, then uses readxl to download the data and read it directly into R. When you first run library(bwgtools) you will see the following message:

library(bwgtools)

## Welcome to the bwg R package! in order to obtain data from the BWG dropbox folder, you need to authorize R to access your dropbox. run the following commands:
##   library(rdrop2)
##   drop_auth(cache = FALSE)
## Then enter your username and password. This should only need to be done when downloading the data.

Important Note: Protect your account!

In Dropbox: by default, drop_auth() saves your Dropbox login to a file called .httr-oauth. Of course, we don't want this shared with everyone in our Dropbox folder, as they would then be able to access your personal Dropbox account! Therefore, we set cache=FALSE. This will require us to re-authenticate every time we want to download fresh data. This should be a quick and painless process, especially if you are already logged in on your computer. However, bwgtools should work from any computer connected to the internet, provided that you have your Dropbox username and password.

Working outside of Dropbox: running drop_acc() or drop_auth() will create a file called .httr-oauth in your directory, which will contain your login credentials for Dropbox. Remember to add this file to your .gitignore if you are using git. For more information, see ?rdrop2::drop_auth.

Reading a single sheet

Once you have authenticated with Dropbox, you can read data directly into R. To obtain a single tab (for example, the "leaf.waterdepths" tab) for a single site (for example, Macae), use the function read_sheet_site:

macae <- read_site_sheet("Macae", "leaf.waterdepths")

## [1] "fetching from dropbox"

## 
##  /tmp/Rtmpcs0nbf/Drought_data_Macae.xlsx on disk 311.868 KB

knitr::kable(head(macae))

site trt.name bromeliad.id date depth.centre.measure.first depth.leafa.measure.first depth.leafb.measure.first depth.centre.water.first depth.leafa.water.first depth.leafb.water.first macae mu0.1k0.5 B24 2013-03-15 NA NA NA 147.0 0.0 60.0 macae mu0.1k0.5 B24 2013-03-16 NA NA NA 133.3 31.6 53.3 macae mu0.1k0.5 B24 2013-03-17 NA NA NA 111.0 5.3 65.1 macae mu0.1k0.5 B24 2013-03-18 NA NA NA 107.2 12.6 67.2 macae mu0.1k0.5 B24 2013-03-19 NA NA NA 104.8 21.5 53.4 macae mu0.1k0.5 B24 2013-03-20 NA NA NA 94.3 20.2 74.3

Working offline

The first argument to this function can either be the name of a site ("Macae", in the example above), or a path to the location of the file on your hard drive. For example, on my (Andrew's) computer, the path to Dropbox is:

../../../Dropbox

So I could read in my local copy of the data like this:

macae <- read_site_sheet("../../../Dropbox/BWG Drought Experiment/raw data/Drought_data_Macae.xlsx", "leaf.waterdepths")

## you downloaded that file already! reading from disk

This is an example of a relative path; the symbol .. means "one directory above my current location". This is the relative path from the directory where I am developing bwgtools to the directory where the Macae data is stored, on my machine. There is more information about relative paths here.

To save typing, I've made a convenience function that fills in the relative path for you. It requires the name of the sheet you want, and the path from your current working directory to the full Dropbox folder:

macae <- read_site_sheet(offline("Macae", default.path = "../../../Dropbox/"), "leaf.waterdepths")

## you downloaded that file already! reading from disk

## or, equivalently:
library(magrittr)

macae <- "Macae" %>% 
  offline(default.path = "../../../Dropbox/") %>%  ## use your own path
  read_site_sheet(sheetname = "leaf.waterdepths")

## you downloaded that file already! reading from disk

PLEASE NOTE: the major disadvantage of this approach is that your local version of the data may be behind the official version on the Dropbox website. To be safe, use the online version whenever you can.

Reading multiple sheets

For all our analyses, we will need to collect data from all sites. ; we can also load and combine the same tab across all sites, with a single function: combine_tab(). This function has two arguments: the first is a vector of all the site names (as spelt in the file names in the raw data/ folder). The second is the name of the sheet to read (as above). For example, to obtain the "site.info" tab for all sites, use the following command:

library(dplyr)
library(knitr)
c("Argentina", "French_Guiana", "Colombia",
  "Macae", "PuertoRico","CostaRica") %>%
  combine_tab(sheetname = "site.info") %>% 
  head %>% 
  kable

[1] "fetching from dropbox" [1] "fetching from dropbox" [1] "fetching from dropbox" [1] "fetching from dropbox" [1] "fetching from dropbox"

site.name combined.correction.factor mean.catchment.area effective.catchment.area identity.bromeliad.species natural.detrital.species.1.15n natural.detrital.species.2.15n fertilized.detrital.species.1.15n fertilized.detrital.species.2.15n natural.bromeliad.15n natural.bromeliad.percentn natural.bromeliad.percentc identity.15n.detrital.species.1 identity.15n.detrital.species.2 identity.leafpack.species.1 identity.leafpack.species.2 start.water.addition last.day.sample ibutton.frequency argentina 0.3570 1039.533 371.1133 aechmea.distichantha NA NA NA NA NA NA NA NA NA myrcianthes.cisplatensis NA 2013-10-10 2013-12-16 hour frenchguiana 0.3800 1060.000 402.8000 vriesea.splendens 0.460000 NA 1148.70000 NA 1.860000 1.0400000 50.62000 Melastomataceae NA duguetia.pycnastera eperua.grandiflora 2012-11-01 2013-01-10 hour colombia 0.3613 2297.898 830.2305 Guzmania.spp -1.093333 -1.296667 0.36653 0.36701 -3.768889 0.5749411 45.09735 alnus.acuminata melastomatacea alnus.acuminata melastomatacea 2013-04-03 2013-06-09 hour macae 0.2700 1218.000 328.8600 neoregelia.cruenta NA NA 6984.80000 NA NA NA NA eugenia.uniflora NA eugenia.uniflora NA 2013-03-15 2013-05-21 hour puertorico 0.3877 1110.140 43.4013 guzmania NA NA NA NA -2.447934 1.0181657 48.83634 melostomataceae NA dacryodes.excelsa dendropanax.arboreus 2014-03-23 2014-05-29 hour costarica 0.3970 1622.000 643.9340 guzmania.spp NA NA NA NA -1.660000 0.4500000 43.78000 conostegia.xalapensis NA conostegia.xalapensis NA 2012-10-06 2012-12-14 hour

combine_tab() does a little bit more than simply combine the output of read_site_sheet(). It also checks the names of the datasets before combining, creates unique bromeliad id numbers, and reshapes the invertebrate data from "wide" to "long" format. Here is a quick explanation of each of these modifications in turn:

bromeliad ids

Many sites have simply numbered their bromeliads, meaning that there are several bromeliads labelled "1", each in a different site. This is not an error on the part of researchers, however it is a potential danger when combining datasets. To make the bromeliad identification numbers unambiguous, I have combined the site name and the bromeliad name into a new variable, "site_brom.id":

phys_data <- c("Argentina", "French_Guiana", "Colombia",
  "Macae", "PuertoRico","CostaRica") %>%
  combine_tab(sheetname = "bromeliad.physical")

## you downloaded that file already! reading from disk
## you downloaded that file already! reading from disk
## you downloaded that file already! reading from disk
## you downloaded that file already! reading from disk
## you downloaded that file already! reading from disk
## you downloaded that file already! reading from disk

kable(phys_data[1:6, 1:3])

site_brom.id site trt.name argentina_1 argentina mu0.1k0.5 argentina_26 argentina mu0.2k0.5 argentina_30 argentina mu0.4k0.5 argentina_20 argentina mu0.6k0.5 argentina_29 argentina mu0.8k0.5 argentina_15 argentina mu1k0.5

"wide" to "long" format

Most of the tabs have a standard shape -- i.e. a fixed number of columns and rows. The exception is two tabs that contain insect data: bromeliad.initial.inverts and bromeliad.final.inverts. In order to combine data from these datasets, it is necessary to convert them to "long" format: i.e., to "gather" all insect columns into only three: one for the name of the species (the former column header) one for abundance, and another for biomass:

invert_data <- c("Argentina", "French_Guiana", "Colombia",
  "Macae", "PuertoRico","CostaRica") %>%
  combine_tab(sheetname = "bromeliad.final.inverts")

kable(invert_data[1:6, ])

site_brom.id site mu k species abundance biomass argentina_1 argentina 0.1 0.5 Coleoptera.33 14 NA argentina_1 argentina 0.1 0.5 Diptera.276 2 NA argentina_1 argentina 0.1 0.5 Diptera.61 7 NA argentina_10 argentina 2.5 1.0 Diptera.250 1 NA argentina_10 argentina 2.5 1.0 Diptera.276 2 NA argentina_10 argentina 2.5 1.0 Diptera.61 6 NA

functional groups -- abundance and biomass.

Now that we can obtain data on all the invertebrates found at the end of the experiment, we can calculate the abundance and biomass of different functional groups by combining species that are in the same category. There are two levels of groups into which we can group species:

pred_prey func.group predator engulfer piercer prey scraper gatherer piercer shredder

Accessing functional traits

In order to do this, we will need to access data about all the species ever observed in bromeliads, combining their trait data with their BWG nicknames (the column headers from bromeliad.final.invert, now in the column species in the output of combine_tab()). Formerly, this data was kept in an excel sheet called "Distributions.organisms.correct.xls". Now, several of us have embarked on a project to improve this data and fill in blanks. The result of these efforts (which are still ongoing, though mostly complete) is this .tsv file. Eventually, it will be added to the master database that the BWG is constructing. In the meantime, it can be downloaded directly from github using get_bwg_names() :

bwg_names <- get_bwg_names()

## this function thinks there are 23 character columns followed by 54 numeric columns

kable(head(bwg_names))

key Name nickname Domain Kingdom Phyllum Sub.Phyllum Class Sub.Class Order Sub.Order Family Sub.family Genus Species Taxonomic.resolution name1 name2 name3 aquatic.terrestrial func.group macro.micro pred_prey BS1 BS2 BS3 BS4 BS5 BS1.1 BS2.1 BS3.1 BS4.1 BS5.1 AS1 AS2 AS3 AS4 RE1 RE2 RE3 RE4 RE5 RE6 RE7 RE8 DM1 DM2 RF1 RF2 RF3 RF4 RM1 RM2 RM3 RM4 RM5 LO1 LO2 LO3 LO4 LO5 LO6 LO7 FD1 FD2 FD3 FD4 FD5 FD6 FD7 FD8 FG1 FG2 FG3 FG4 FG5 FG6 k1 Bryocamptus sp Copepoda.1 Eukaryota Animalia Arthropoda Crustacea Maxillopoda Copepoda Harpacticoida NA Canthocamptidae NA Bryocamptus NA G aquatic filter.feeder micro prey 3 0 0 0 0 3 0 0 0 0 0 0 0 3 0 3 0 0 0 0 0 0 3 0 3 0 3 0 3 0 0 0 0 0 1 0 2 3 3 0 0 3 2 0 0 0 0 0 0 0 3 3 0 0 k2 Hydracarina Acari.2 Eukaryota Animalia Arthropoda Chelicerata Arachnida Acari Trombidiformes Hydracarina NA NA NA NA SO aquatic gatherer micro prey 3 3 0 0 0 0 3 0 0 0 1 2 0 2 0 0 0 3 0 0 0 0 3 0 3 0 3 0 3 0 0 0 0 0 2 3 3 0 0 0 0 3 0 0 0 0 0 0 3 0 0 0 0 0 k3 Acari sp Acari.1 Eukaryota Animalia Arthropoda Chelicerata Arachnida Acari NA NA NA NA NA NA O NA piercer micro predator 3 0 0 0 0 3 0 0 0 0 1 2 0 2 0 0 0 3 0 0 0 0 3 0 3 0 3 0 3 0 0 3 0 0 2 3 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 3 3 k4 Mite sp. 1 Acari.3 Eukaryota Animalia Arthropoda Chelicerata Arachnida Acari NA NA NA NA NA NA O NA piercer micro predator 3 0 0 0 0 3 0 0 0 0 1 2 0 2 0 0 0 3 0 0 0 0 3 0 3 0 3 0 3 0 0 3 0 0 2 3 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 3 3 k5 Mite sp. 2 Acari.4 Eukaryota Animalia Arthropoda Chelicerata Arachnida Acari NA NA NA NA NA NA O NA piercer micro predator 3 0 0 0 0 3 0 0 0 0 1 2 0 2 0 0 0 3 0 0 0 0 3 0 3 0 3 0 3 0 0 3 0 0 2 3 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 3 3 k6 Mite sp. 3 Acari.5 Eukaryota Animalia Arthropoda Chelicerata Arachnida Acari NA NA NA NA NA NA O NA piercer micro predator 3 0 0 0 0 3 0 0 0 0 1 2 0 2 0 0 0 3 0 0 0 0 3 0 3 0 3 0 3 0 0 3 0 0 2 3 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 3 3

Merging and plotting functional trait data

The first step in combining the functional traits and the observed insect data is to match insect names in both datasets. This is easily accomplished by the base function merge() or by dplyr::left_join(). For convenience, bwgtools::merge_func() does the latter for you:

### merge with functional groups
invert_traits <- merge_func(invert_data, bwg_names)

kable(head(invert_traits))

site_brom.id site mu k species abundance biomass key Name Domain Kingdom Phyllum Sub.Phyllum Class Sub.Class Order Sub.Order Family Sub.family Genus Species Taxonomic.resolution name1 name2 name3 aquatic.terrestrial func.group macro.micro pred_prey BS1 BS2 BS3 BS4 BS5 BS1.1 BS2.1 BS3.1 BS4.1 BS5.1 AS1 AS2 AS3 AS4 RE1 RE2 RE3 RE4 RE5 RE6 RE7 RE8 DM1 DM2 RF1 RF2 RF3 RF4 RM1 RM2 RM3 RM4 RM5 LO1 LO2 LO3 LO4 LO5 LO6 LO7 FD1 FD2 FD3 FD4 FD5 FD6 FD7 FD8 FG1 FG2 FG3 FG4 FG5 FG6 argentina_1 argentina 0.1 0.5 Coleoptera.33 14 NA k59 Scirtidae sp. 5545 Eukaryota Animalia Arthropoda NA Insecta Neoptera Coleoptera Polyphaga Scirtidae NA NA NA F aquatic scraper macro prey 0 0 3 3 0 0 0 3 3 0 3 3 0 0 0 0.00 0 3.00 1 0.00 3 0.0 0 3 0.00 0 0.0 3 1 0 3 3 0 1 0 0.0 3 0 1.00 0.00 3.00 3.0 3.00 0.0 0 0.00 0.00 0.00 0.00 2.0 3 0 0.00 0.00 argentina_1 argentina 0.1 0.5 Diptera.276 2 NA k539 Tipulidae sp. 5552 Eukaryota Animalia Arthropoda NA Insecta Neoptera Diptera Nematocera Tipulidae NA NA NA F aquatic shredder macro prey 0 3 3 3 0 0 0 0 3 0 3 3 1 0 0 0.00 0 3.00 3 0.00 0 0.0 0 3 0.00 0 0.0 3 1 3 0 3 0 0 0 0.0 0 3 0.00 0.00 1.00 1.0 3.00 0.0 1 0.00 0.00 1.00 1.00 3.0 0 0 0.00 1.00 argentina_1 argentina 0.1 0.5 Diptera.61 7 NA k188 Chironomidae sp. 5551 Eukaryota Animalia Arthropoda NA Insecta Neoptera Diptera Nematocera Chironomidae NA NA NA F aquatic gatherer macro prey 0 1 3 0 0 0 0 3 0 0 3 3 3 0 0 0.25 0 2.25 2 0.25 0 0.5 3 1 0.25 0 0.5 0 3 1 0 0 0 0 0 0.5 3 1 0.25 1.75 2.25 2.5 0.75 2.5 0 0.25 0.25 0.75 1.25 0.5 1 2 0.75 0.75 argentina_10 argentina 2.5 1.0 Diptera.250 1 NA k515 Tabanidae sp. 5576 Eukaryota Animalia Arthropoda NA Insecta Neoptera Diptera Brachycera Tabanidae NA NA NA F aquatic piercer macro predator 0 0 0 3 3 0 0 0 3 3 1 2 0 0 0 0.00 0 3.00 0 0.00 2 0.0 0 3 0.00 0 1.0 3 0 0 0 3 0 0 1 0.0 3 1 0.00 0.00 0.00 1.0 1.00 0.0 0 0.00 0.00 3.00 1.00 0.0 0 0 3.00 0.00 argentina_10 argentina 2.5 1.0 Diptera.276 2 NA k539 Tipulidae sp. 5552 Eukaryota Animalia Arthropoda NA Insecta Neoptera Diptera Nematocera Tipulidae NA NA NA F aquatic shredder macro prey 0 3 3 3 0 0 0 0 3 0 3 3 1 0 0 0.00 0 3.00 3 0.00 0 0.0 0 3 0.00 0 0.0 3 1 3 0 3 0 0 0 0.0 0 3 0.00 0.00 1.00 1.0 3.00 0.0 1 0.00 0.00 1.00 1.00 3.0 0 0 0.00 1.00 argentina_10 argentina 2.5 1.0 Diptera.61 6 NA k188 Chironomidae sp. 5551 Eukaryota Animalia Arthropoda NA Insecta Neoptera Diptera Nematocera Chironomidae NA NA NA F aquatic gatherer macro prey 0 1 3 0 0 0 0 3 0 0 3 3 3 0 0 0.25 0 2.25 2 0.25 0 0.5 3 1 0.25 0 0.5 0 3 1 0 0 0 0 0 0.5 3 1 0.25 1.75 2.25 2.5 0.75 2.5 0 0.25 0.25 0.75 1.25 0.5 1 2 0.75 0.75

Now, we need to calculate the total abundance and biomass for every group defined by site, site_brom.id, and either pred_prey or func.group. We can write a function that does this for us, and allows us to control the groups that will be summed together. Using dplyr's non-standard evaluation we can define the groups.

REQUEST for feedback: the syntax here is admittedly a bit strange. Should I just write little convenience functions that perform these tasks? sum_functional() and sum_trophic() for example?

For example, to summarize by functional group:

#4 summarize by functional group
func_groups <- sum_func_groups(invert_traits,
                               grps = list(~site,
                                           ~site_brom.id,
                                           ~func.group))
kable(head(func_groups))

site site_brom.id func.group total_abundance total_biomass total_taxa argentina argentina_1 gatherer 7 NA 1 argentina argentina_1 scraper 14 NA 1 argentina argentina_1 shredder 2 NA 1 argentina argentina_10 gatherer 6 NA 1 argentina argentina_10 piercer 1 NA 1 argentina argentina_10 shredder 2 NA 1

This is a convenient format for plotting:

library(ggplot2)
## functional group abundance
func_groups %>%
  ggplot(aes(x = as.factor(func.group), y = total_abundance)) +
  geom_point(position = position_jitter(width = 0.25), alpha = 0.5) +
  stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 0.6) +
  facet_wrap(~site, scales = "free_y") +
  ggtitle("Functional group abundance")

To summarize by trophic level group, simply switch ~func.group to ~pred_prey:

predprey <- sum_func_groups(invert_traits,
                               grps = list(~site,
                                           ~site_brom.id,
                                           ~pred_prey))

predprey %>%
  ggplot(aes(x = as.factor(pred_prey), y = total_abundance)) +
  geom_point(position = position_jitter(width = 0.25), alpha = 0.5) +
  stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 0.6) +
  facet_wrap(~site, scales = "free_y") +
  ggtitle("Trophic level abundance")

Hydrology -- based on rainfall schedule

under construction right now the only measure I'm working on is derived from the number of consecutive dry days. We can use the metric "longest period of consecutive dry days", and/or quantify their distribution in some other way

Hydrology -- based on water depths

We have daily water measurements for some sites, and from these we will be able to calculate several measures of "hydrological stability". First, we obtain the leaf.waterdepths data in the usual way:

leafwater <- c("Argentina", "French_Guiana", "Colombia",
               "Macae", "PuertoRico","CostaRica") %>%
  combine_tab("leaf.waterdepths")

We also need two other tabs: site.info and bromeliad.physical. The former states the beginning and end of the experiment, and the latter the block for each bromeliad. These are necessary pieces of information to calculate the date at which each bromeliad was placed in the field and removed from the field. We need this information because some groups did not measure water at the beginning (Costa Rica) or did so irregularly (Costa Rica, Argentina). Thus we need to fill in missing days.

sites <- c("Argentina", "French_Guiana", "Colombia",
           "Macae", "PuertoRico","CostaRica") %>%
  combine_tab("site.info")

phys <- c("Argentina", "French_Guiana", "Colombia",
          "Macae", "PuertoRico","CostaRica") %>%
  combine_tab("bromeliad.physical")

We can obtain all the hydro variables with one compound function: hydro_variables():

hydro <- hydro_variables(waterdata = leafwater,
                         sitedata = sites,
                         physicaldata = phys)

## Removing all NA groups: data is grouped by site, watered_first
## Joining by: c("site", "trt.name")
## Joining by: c("site_brom.id", "site", "trt.name", "leaf", "watered_first", "date")

kable(head(hydro))

site trt.name leaf len.depth n.depth max.depth min.depth mean.depth var.depth sd.depth net_fluct total_fluct cv.depth amplitude wetness prop.overflow.days prop.driedout.days time.since.minimum argentina mu0.1k0.5 leafa 66 4 64.55 0.00 32.67500 934.6975 30.57282 0 0 93.56641 64.55 0.5061967 0.0151515 0.0151515 NA argentina mu0.1k0.5 leafb 66 4 42.75 0.00 15.10000 409.0150 20.22412 0 0 133.93456 42.75 0.3532164 0.0151515 0.0303030 NA argentina mu0.1k1 leafa 66 6 55.50 0.00 13.65000 465.8800 21.58425 0 0 158.12640 55.50 0.2459459 0.0151515 0.0454545 NA argentina mu0.1k1 leafb 66 6 93.70 18.75 61.08333 735.9147 27.12775 0 0 44.41105 74.95 0.6519032 0.0303030 0.0000000 NA argentina mu0.1k2 leafa 66 10 73.50 0.00 27.51000 893.4821 29.89117 0 0 108.65566 73.50 0.3742857 0.0151515 0.0757576 NA argentina mu0.1k2 leafb 66 10 56.10 0.00 17.93000 565.3640 23.77738 0 0 132.61229 56.10 0.3196078 0.0151515 0.0909091 NA

This function should "just work". As you can see, it is performing one check (removing any groups which have all NA records) and performs two joins (to correct the errors discussed above).

It seems that the centre leaf is not like the others. Therefore by default the fucntion removes it. If you want it anyway, set rm_centre = TRUE (Note the Canadian spelling).

hydro2 <- hydro_variables(waterdata = leafwater,
                         sitedata = sites,
                         physicaldata = phys, rm_centre = FALSE)

## Removing all NA groups: data is grouped by site, watered_first
## Joining by: c("site", "trt.name")
## Joining by: c("site_brom.id", "site", "trt.name", "leaf", "watered_first", "date")
## Removing all NA groups: data is grouped by site_brom.id, site, trt.name, leaf, watered_first, temporal.block

kable(head(hydro2))

site trt.name leaf len.depth n.depth max.depth min.depth mean.depth var.depth sd.depth net_fluct total_fluct cv.depth amplitude wetness prop.overflow.days prop.driedout.days time.since.minimum argentina mu0.1k0.5 centre 66 4 102.50 70.70 82.33750 209.5990 14.47753 0 0 17.58316 31.80 0.8032927 0.0151515 0.0000000 NA argentina mu0.1k0.5 leafa 66 4 64.55 0.00 32.67500 934.6975 30.57282 0 0 93.56641 64.55 0.5061967 0.0151515 0.0151515 NA argentina mu0.1k0.5 leafb 66 4 42.75 0.00 15.10000 409.0150 20.22412 0 0 133.93456 42.75 0.3532164 0.0151515 0.0303030 NA argentina mu0.1k1 centre 66 6 118.20 87.05 106.38333 121.2147 11.00975 0 0 10.34913 31.15 0.9000282 0.0454545 0.0000000 NA argentina mu0.1k1 leafa 66 6 55.50 0.00 13.65000 465.8800 21.58425 0 0 158.12640 55.50 0.2459459 0.0151515 0.0454545 NA argentina mu0.1k1 leafb 66 6 93.70 18.75 61.08333 735.9147 27.12775 0 0 44.41105 74.95 0.6519032 0.0303030 0.0000000 NA

The default behaviour is to calculate all these metrics at the level of the leaf well, the same scale at which the measurements were taken. If you want you can obtain these same calculations AFTER first averaging water depths across all leaves of a plant, within each day. That is, we can make these same calculations at the scale of the bromeliad:

hydro3 <- hydro_variables(waterdata = leafwater,
                         sitedata = sites,
                         physicaldata = phys, aggregate_leaves = TRUE)

## Removing all NA groups: data is grouped by site, watered_first
## Joining by: c("site", "trt.name")
## Joining by: c("site_brom.id", "site", "trt.name", "watered_first", "date")

kable(head(hydro3))

site trt.name len.depth n.depth max.depth min.depth mean.depth var.depth sd.depth net_fluct total_fluct cv.depth amplitude wetness prop.overflow.days prop.driedout.days time.since.minimum argentina mu0.1k0.5 132 8 53.650 0.000 23.88750 435.7680 20.87506 0.00 0.00 87.38904 53.650 0.4452470 0.0151515 0.0151515 NA argentina mu0.1k1 132 12 74.600 9.375 37.36667 433.9820 20.83223 0.00 0.00 55.75085 65.225 0.5008937 0.0151515 0.0000000 NA argentina mu0.1k2 132 20 54.575 0.000 22.72000 560.7702 23.68059 0.00 0.00 104.22794 54.575 0.4163078 0.0454545 0.0757576 NA argentina mu0.2k0.5 132 8 57.625 0.000 14.40625 711.5658 26.67519 0.00 0.00 185.16402 57.625 0.2500000 0.0151515 0.0454545 NA argentina mu0.2k1 132 14 62.150 0.000 27.78236 681.6890 26.10917 0.00 0.00 93.97753 62.150 0.4470210 0.0303030 0.0454545 NA argentina mu0.2k2 132 22 65.500 0.000 38.94705 349.9938 18.70812 3.45 3.45 48.03477 65.500 0.5946114 0.0303030 0.0151515 NA

Licence

We release the code in this repository under the MIT software license. see text of license here

SrivastavaLab/bwgtools documentation built on May 9, 2019, 1:54 p.m.