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
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!
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)
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.
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.
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 |
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.
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:
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 |
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 |
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 |
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 |
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")
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
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 |
We release the code in this repository under the MIT software license. see text of license here