This code provides examples of visualizations shown on the MERMAID dashboard that focus on the habitat complexity protocol.
Getting summary sample event data
The following code downloads summary sample event data from MERMAID, using the mermaidr package (documentation can be found at https://data-mermaid.github.io/mermaidr/). The summary sample events contain all surveys (i.e. sample events) that have permissions of “public summary” or “public”.
rm(list = ls()) #remove past stored objects
options(scipen = 999) #turn off scientific notation
#### Load packages and libraries ####
## If this is the first time using mermaidr, install the package through "remotes"
# install.packages("remotes")
# remotes::install_github("data-mermaid/mermaidr")
# install.packages("tidyverse")
# install.packages("plotly")
# install.packages("htmlwidgets")
library(mermaidr) #package to download data from datamermaid.org
library(tidyverse) #package that makes it easier to work with data
library(plotly) #for interactive plotting
library(htmlwidgets) #for saving plots at html files
#### Get data from MERMAID for creating aggregate visualizations ####
allMermaidSampEventsTBL <- mermaidr::mermaid_get_summary_sampleevents()Histogram - habitat complexity scores across surveys
The following code creates a histogram showing the distribution of habitat complexity scores (0-5) across surveys (i.e. summary sample events). It uses the average habitat complexity scores per survey (sample event) but bins those averages into integers, which more closely reflects the fact that habitat complexity is scored as integers from 1-5.
### Get all of the relevant habitat complexity data from summary sample events
habCompSurveySummTBL <- allMermaidSampEventsTBL %>%
filter(!is.na(habitatcomplexity_score_avg_avg))
habitatComplAggHist <-
plot_ly(x = habCompSurveySummTBL$habitatcomplexity_score_avg_avg,
type = 'histogram',
xbins = list(size = 1),
marker = list(color = "#769fca"),
height = 450) %>%
config(displayModeBar = TRUE,
displaylogo = FALSE,
modeBarButtonsToRemove = c('zoom','pan', 'select', 'zoomIn', 'zoomOut',
'autoScale', 'resetScale', 'lasso2d',
'hoverClosestCartesian', 'hoverCompareCartesian')) %>%
layout(bargap = 0.1,
xaxis = list(title = "Score",
linecolor = "black",
linewidth = 2),
yaxis = list(title = "Number of surveys",
linecolor = "black", # Set the y-axis line color to black
linewidth = 2),
annotations = list(
list(x = 0, y = 1.15, text = "HABITAT COMPLEXITY", showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 20)),
list(x = 0, y = 1.08,
text = paste0(length(habCompSurveySummTBL$habitatcomplexity_score_avg_avg),
" Surveys"),
showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 12))
),
margin = list(t = 50, b = 75)) # Increase top margin to create more space for title and subtitle
# Visualize the plot
habitatComplAggHist
Time series - % of surveys with each habitat complexity score over time
Stacked barplots showing the percent of surveys (y-axis) with each habitat complexity score (colors) by year (x-axis). Different colors represent each of the six habitat complexity scores (0, 1, 2, 3, 4, 5).
### Get the relevant habitat complexity information
habCompNumSurveysByYearTBL <- allMermaidSampEventsTBL %>%
filter(!is.na(habitatcomplexity_score_avg_avg)) %>%
mutate(RoundHabitatComplexity = as.factor(round(
habitatcomplexity_score_avg_avg)),
year = year(sample_date)) %>%
group_by(year, RoundHabitatComplexity) %>%
dplyr::summarise(NumSurveysByHabCompYear = length(RoundHabitatComplexity),
.groups = "keep") %>%
ungroup()
#Merge with the total number of surveys per year to get percentage
habCompPercSurveysByYearTBL <- habCompNumSurveysByYearTBL %>%
left_join(habCompNumSurveysByYearTBL %>%
group_by(year) %>%
summarise(NumSurveysByYear = sum(NumSurveysByHabCompYear)) %>%
ungroup(),
by = "year") %>%
mutate(PercSurveysByHabCompYear = NumSurveysByHabCompYear/
NumSurveysByYear * 100)
#### Create color map for habitat complexity
habitatComplexityColorMap <- setNames(
object = c("#eff7ff", "#cbdaea", "#a8bed5", "#85a3c1", "#6288ad", "#3c6e9a"),
nm = levels(habCompPercSurveysByYearTBL$RoundHabitatComplexity))
habitatComplTimeSeriesBarplot <-
plot_ly(data = habCompPercSurveysByYearTBL,
x = ~year,
y = ~PercSurveysByHabCompYear,
type = 'bar',
color = ~RoundHabitatComplexity,
colors = habitatComplexityColorMap,
height = 450) %>%
config(displayModeBar = TRUE,
displaylogo = FALSE,
modeBarButtonsToRemove = c('zoom','pan', 'select', 'zoomIn', 'zoomOut',
'autoScale', 'resetScale', 'lasso2d',
'hoverClosestCartesian',
'hoverCompareCartesian')) %>%
layout(barmode = "stack",
bargap = 0.1,
yaxis = list(title = "% of Surveys",
linecolor = "black",
tickvals = seq(0, 100, by = 10), # Set y-axis tick values
ticktext = seq(0, 100, by = 10),
linewidth = 2),
xaxis = list(title = "Year",
linecolor = "black", # Set the x-axis line color to black
linewidth = 2),
annotations = list(
list(x = 0, y = 1.15,
text = "HABITAT COMPLEXITY",
showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 20)),
list(x = 0, y = 1.08,
text = paste0(
nrow(
allMermaidSampEventsTBL %>%
filter(!is.na(habitatcomplexity_score_avg_avg))
), " Surveys"),
showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 12))
),
legend = list(orientation = "h",
xanchor = "center",
x = 0.5, y = -0.2),
margin = list(t = 50, b = 75)) # Increase top margin to create more space for title and subtitle
# Visualize the plot
habitatComplTimeSeriesBarplot
Simple scale plot - Average habitat complexity score for a single survey
This code creates a very simple representation of a single number for average habitat complexity score for a single survey (i.e. MERMAID sample event), along a gradient of possible scores from 0-5. The horizontal line shows the gradient and the vertical line shows the average habitat complexity score, with a box to show the actual number. Icons were added to represent the gradient from low to high habitat complexity.
### Get relevant average habitat complexity scores
habCompSingleSeTBL <- allMermaidSampEventsTBL %>%
filter(!is.na(habitatcomplexity_score_avg_avg))
#### Get icon images ####
lowHabIcon <- png::readPNG("../icons/LowHabitatComplexity.png")
medHabIcon <- png::readPNG("../icons/MediumHabitatComplexity.png")
highHabIcon <- png::readPNG("../icons/HighHabitatComplexity.png")
habitatComplSingleSeDotLinePlot <-
plot_ly(data = habCompSingleSeTBL[1,],
height = 450) %>%
add_segments(x = 0, xend = 5, y = 0, yend = 0,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 0, xend = 0, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 1, xend = 1, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 2, xend = 2, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 3, xend = 3, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 4, xend = 4, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = 5, xend = 5, y = -0.05, yend = 0.05,
line=list(color="black", linewidth = 4)) %>%
add_segments(x = ~habitatcomplexity_score_avg_avg,
xend = ~habitatcomplexity_score_avg_avg,
y = 0, yend = 0.8,
line=list(color="#70aae6", linewidth = 4)) %>%
add_trace(x = ~habitatcomplexity_score_avg_avg,
y = 0,
type = "scatter",
mode = "markers",
marker = list(color = "#70aae6", size = 20)) %>%
config(displayModeBar = TRUE,
displaylogo = FALSE,
modeBarButtonsToRemove = c('zoom','pan', 'select', 'zoomIn', 'zoomOut',
'autoScale', 'resetScale', 'lasso2d',
'hoverClosestCartesian',
'hoverCompareCartesian')) %>%
layout(xaxis = list(title = "",
range = c(-0.5,5.5),
zeroline = FALSE,
showline = FALSE,
showticklabels = TRUE,
tickvals = c(0,1,2,3,4,5),
showgrid = FALSE),
yaxis = list(title = "",
range = c(-0.1,1.5),
zeroline = FALSE,
showline = FALSE,
showticklabels = FALSE,
showgrid = FALSE),
images = list(
list(source = raster2uri(as.raster(lowHabIcon)),
x = -0.5, y = 0.1,
sizex = 1, sizey = 1,
xref = "x", yref = "y",
layer = "below",
xanchor = "left", yanchor = "bottom"),
list(source = raster2uri(as.raster(medHabIcon)),
x = 2, y = 0.1,
sizex = 1, sizey = 1,
xref = "x", yref = "y",
layer = "below",
xanchor = "left", yanchor = "bottom"),
list(source = raster2uri(as.raster(highHabIcon)),
x = 4.5, y = 0.1,
sizex = 1, sizey = 1,
xref = "x", yref = "y",
layer = "below",
xanchor = "left", yanchor = "bottom")),
showlegend = FALSE,
annotations = list(
list(x = 0, y = 1.15,
text = "HABITAT COMPLEXITY",
showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 20)),
list(x = ~habitatcomplexity_score_avg_avg,
y = 1,
text = paste0(habCompSingleSeTBL$habitatcomplexity_score_avg_avg[1],
"\nAverage Habitat\ncomplexity"),
showarrow = FALSE,
xref = "x", yref = "y",
xanchor = "middle", yanchor = "middle",
bordercolor = "#70aae6",
bgcolor = "white",
borderwidth = 4,
borderpad = 3,
font = list(size = 18)),
list(x = 0, y = 1.08,
text = paste0(length(habCompSingleSeTBL$habitatcomplexity_sample_unit_count[1]),
" Survey"),
showarrow = FALSE,
xref = 'paper', yref = 'paper', xanchor = 'left', yanchor = 'top',
font = list(size = 12))
),
margin = list(t = 50, b = 75)) # Increase top margin to create more space for title and subtitle
# Visualize the plot
habitatComplSingleSeDotLinePlot