The goal of mermaidreporting is to provide utility functions for
cleaning, summarizing, and visualizing MERMAID
Collect data. It works alongside the
mermaidr package, which
helps you accesses MERMAID data in R.
If you run into any problems working with this package, please open an issue.
You can install the development version of mermaidreporting with:
# install.packages("remotes")
remotes::install_github("data-mermaid/mermaidreporting", upgrade = "never")Data is available from the
mermaidr package. There
are details there on authorization and accessing data.
All functions in mermaidreporting (and mermaidr) are of the form
mermaid_*(), to take advantage of RStudio auto-completion and make
them easier to find when you have other packages loaded.
First, we read in data using mermaidr:
library(mermaidr)
library(dplyr)
my_projects <- mermaid_get_my_projects()
xpdc <- my_projects %>%
filter(name == "XPDC Kei Kecil 2018")
xpdc_fishbelt <- xpdc %>%
mermaid_get_project_data(method = "fishbelt", data = "sampleevents")The data from mermaidr comes in a form that may seem a little
unfriendly to work with!
For example, in xpdc_fishbelt, the mean total biomass data is
contained in a data frame column:
xpdc_fishbelt %>%
select(biomass_kgha_by_trophic_group_avg)
#> # A tibble: 46 x 1
#> biomass_kgha_by… $piscivore $planktivore $`invertivore-m… $`herbivore-det…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 42.3 58.1 392. 20.2 55.9
#> 2 NA 27.1 26.5 NA 1.38
#> 3 19.7 32.4 642. 384. 48.4
#> 4 5.81 31.8 15.2 33.1 22.7
#> 5 7.46 36.6 349. 74.2 105.
#> 6 42.7 21.5 772. 12.1 41.2
#> 7 6.42 21.3 267. 38.1 30.4
#> 8 2.49 NA 159. 23.7 25.1
#> 9 17.3 50.2 780. 72.7 31.6
#> 10 NA 2.52 96.9 1.6 NA
#> # … with 36 more rows, and 3 more variables: $`invertivore-sessile` <dbl>,
#> # $`herbivore-macroalgae` <dbl>, $other <dbl>Use mermaid_clean_columns() to clean this up and split each trophic
group into its own column:
library(mermaidreporting)
xpdc_fishbelt %>%
select(biomass_kgha_by_trophic_group_avg) %>%
mermaid_clean_columns()
#> # A tibble: 46 x 8
#> omnivore piscivore planktivore invertivore_mob… herbivore_detri…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 42.3 58.1 392. 20.2 55.9
#> 2 NA 27.1 26.5 NA 1.38
#> 3 19.7 32.4 642. 384. 48.4
#> 4 5.81 31.8 15.2 33.1 22.7
#> 5 7.46 36.6 349. 74.2 105.
#> 6 42.7 21.5 772. 12.1 41.2
#> 7 6.42 21.3 267. 38.1 30.4
#> 8 2.49 NA 159. 23.7 25.1
#> 9 17.3 50.2 780. 72.7 31.6
#> 10 NA 2.52 96.9 1.6 NA
#> # … with 36 more rows, and 3 more variables: invertivore_sessile <dbl>,
#> # herbivore_macroalgae <dbl>, other <dbl>This works on Benthic PIT data, as well, and auto-detects any columns in this format to clean!
xpdc_benthicpit <- xpdc %>%
mermaid_get_project_data(method = "benthicpit", data = "sampleevents")
xpdc_benthicpit %>%
select(percent_cover_by_benthic_category_avg) %>%
mermaid_clean_columns()
#> # A tibble: 38 x 8
#> sand rubble hard_coral macroalgae soft_coral turf_algae other_invertebr…
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 5 30 32 11.7 18 1.33 3.67
#> 2 5 2.5 52 9 26.7 6.33 3
#> 3 1 31.3 51.3 11.7 1.5 3.5 6
#> 4 33 34 17.3 8.33 5.33 1.5 2
#> 5 NA 15.7 73.7 9.33 NA 2 1
#> 6 25 39.3 20 1.67 2.67 4.67 4.67
#> 7 22.7 21 36.7 13 5.33 NA 1.5
#> 8 50 20.3 13.7 5.67 6.33 3.33 2
#> 9 14.7 34.3 37 8 2.67 1 2.67
#> 10 4.33 17.3 55.3 6 16 NA 1.5
#> # … with 28 more rows, and 1 more variable: bare_substrate <dbl>Default data from mermaidr has specific details about management
rules:
north_sulawesi_fishbelt <- my_projects %>%
filter(name == "North Sulawesi (North Minahasa, Sitaro, Tatoareng MPA) Ecological Survey") %>%
mermaid_get_project_data("fishbelt", "sampleevents")
north_sulawesi_fishbelt %>%
distinct(management_rules)
#> # A tibble: 5 x 1
#> management_rules
#> <chr>
#> 1 "no take"
#> 2 "gear restriction; size limits; species restriction"
#> 3 "open access"
#> 4 "gear restriction; periodic closure; size limits; species restriction"
#> 5 ""If you want to look at management rules more broadly, you can clean up
the data and group together Partial Restrictions using
mermaid_clean_management_rules:
north_sulawesi_fishbelt %>%
distinct(management_rules) %>%
mermaid_clean_management_rules()
#> # A tibble: 5 x 1
#> management_rules
#> <chr>
#> 1 No Take
#> 2 Partial Restrictions
#> 3 Open Access
#> 4 Partial Restrictions
#> 5 <NA>This groups together the Partial Restrictions rules, tidies up the
names, and converts any empty strings to NAs for better missing-value
handling!
If you want to have a different value, instead of NA, you can change
it via missing_value:
north_sulawesi_fishbelt %>%
distinct(management_rules) %>%
mermaid_clean_management_rules(missing_value = "Not Specified")
#> # A tibble: 5 x 1
#> management_rules
#> <chr>
#> 1 No Take
#> 2 Partial Restrictions
#> 3 Open Access
#> 4 Partial Restrictions
#> 5 Not SpecifiedYou can generate map of site locations using
mermaid_map_sites_static() and mermaid_map_sites_interactive(),
which produce static and interactive maps, respectively.
library(mermaidreporting)
xpdc_sites <- xpdc %>%
mermaid_get_project_sites()
mermaid_map_sites_static(xpdc_sites)mermaid_map_sites_interactive(xpdc_sites)Note that this is just a screenshot of the interactive plot, but you can try it out in an interactive R session by running the above code!
You can also map sites by the value of a variable. For example, to map
sites by mean total biomass use mermaid_map_sites_static() with
plot_var set to biomass_kgha_avg:
mermaid_map_sites_static(xpdc_fishbelt, biomass_kgha_avg)Or by management rules:
xpdc_fishbelt %>%
mermaid_clean_management_rules() %>%
mermaid_map_sites_static(management_rules)You can plot Fish Belt biomass, split by a grouping variable (such as trophic group) and compared by another variable (such as reef exposure).
xpdc_fishbelt %>%
mermaid_plot_fish_belt_biomass(biomass_kgha_by_trophic_group_avg, reef_exposure)Or by reef zone:
xpdc_fishbelt %>%
mermaid_plot_fish_belt_biomass(biomass_kgha_by_trophic_group_avg, reef_zone)Similarly, you can plot Benthic PIT percent cover, split by a grouping variable (such as benthic category) and compared by another variable (such as management rules):
xpdc_benthicpit %>%
mermaid_clean_management_rules(missing_value = "Not Specified") %>%
mermaid_plot_benthic_pit_percent_cover(percent_cover_by_benthic_category_avg, management_rules)