Visualizing Cesar Cameratrap Data

Visualizing as a simple interactive map and calendar

a lot of camera trap data from Galictis Biodiversidad
R
calendar
map
Author
Affiliation

Diego J. Lizcano

WildMon

Published

June 15, 2024

As a simple map and calendar

Load packages

Code

# library(ggpmthemes)
library(glue) # Interpreted String Literals
library(curl) # A Modern and Flexible Web Client for R
library(patchwork) # The Composer of Plots
library(readxl) # Read Excel Files
library(sf) # Simple Features for R
library(mapview) # Interactive Viewing of Spatial Data in R
library(ggTimeSeries) # calendar
library(grateful) # Facilitate Citation of R Packages

library(knitr) # A General-Purpose Package for Dynamic Report Generation in R
# options(kableExtra.auto_format = FALSE)
library(kableExtra) # Construct Complex Table with 'kable' and Pipe Syntax
library(tidyverse) # Easily Install and Load the 'Tidyverse'
library(ggforce) # Accelerating 'ggplot2'

source("C:/CodigoR/CameraTrapCesar/R/organiza_datos.R")

Load data

Code

datos <- read_excel("C:/CodigoR/CameraTrapCesar/data/CT_Cesar.xlsx")

Convert to sf

Code

datos_distinct <- datos |> distinct(Longitude, Latitude, CT, Proyecto)

projlatlon <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

datos_sf <-  st_as_sf(x = datos_distinct,
                         coords = c("Longitude", 
                                    "Latitude"),
                         crs = projlatlon)

Plot all

Code
mapview(datos_sf, zcol="Proyecto")
Code


calendario <- function(data=datos, Proyect=Proyecto){
  dtData<- datos |> filter(Proyecto==Proyect) |> #|> filter(Year=="2022")
  # becerril$Date_Time <- as_date(as.character(data$eventDate))
      mutate(Date_Time=as.Date(eventDate, "%d/%m/%Y")) |> 
      count(Date_Time) |> na.omit()
  
  # base plot
  p1 = ggplot_calendar_heatmap(
     dtData,
     'Date_Time',
     'n',
     dayBorderSize = 0.1,
     monthBorderSize = 0.7
  )
  
  # adding some formatting
  p1 +
     xlab(NULL) +
     ylab(NULL) +
     scale_fill_continuous(low = 'cyan', high = 'red') +
     facet_wrap(~Year, ncol = 1) # number of columns
} # end function

See camera calendar per Proyecto

Becerril

Code

calendario(data=datos, Proyect = "Becerril")

Code

# species <- f.det_history.creator(data=becerril_2022)
# 
# 
# min(dmy(becerril_2022$Start))
# max(dmy(becerril_2022$Start))
# 
# min(dmy(becerril_2022$eventDate))

Las camaras en Becerril estuvieron activas año y medio.

LaPaz_Manaure

Code

calendario(data=datos, Proyect = "LaPaz_Manaure")

PCF

Code
calendario(data=datos, Proyect = "PCF")

CL

Code
calendario(data=datos, Proyect = "CL")

EDN

Code
calendario(data=datos, Proyect = "EDN")

PB

Code
calendario(data=datos, Proyect = "PB" )

EDS

Code
calendario(data=datos, Proyect = "EDS" )

CCI

Code
calendario(data=datos, Proyect = "CCI" )

MLJ

Code
calendario(data=datos, Proyect = "MLJ" )

MCAL

Code
calendario(data=datos, Proyect = "MCAL" )

Package Citation

Code
pkgs <- cite_packages(output = "paragraph", out.dir = ".") #knitr::kable(pkgs)
pkgs

We used R version 4.3.2 (R Core Team 2023) and the following R packages: curl v. 5.2.0 (Ooms 2023), devtools v. 2.4.5 (Wickham et al. 2022), ggforce v. 0.4.2 (Pedersen 2024a), ggTimeSeries v. 1.0.2 (Kothari 2022), glue v. 1.7.0 (Hester and Bryan 2024), kableExtra v. 1.4.0 (Zhu 2024), knitr v. 1.46 (Xie 2014, 2015, 2024), mapview v. 2.11.2 (Appelhans et al. 2023), patchwork v. 1.2.0 (Pedersen 2024b), quarto v. 1.4 (Allaire and Dervieux 2024), rmarkdown v. 2.27 (Xie, Allaire, and Grolemund 2018; Xie, Dervieux, and Riederer 2020; Allaire et al. 2024), sf v. 1.0.15 (Pebesma 2018; Pebesma and Bivand 2023), styler v. 1.10.3 (Müller and Walthert 2024), tidyverse v. 2.0.0 (Wickham et al. 2019).

Sesion info

Session info 
#> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31 ucrt)
#>  os       Windows 10 x64 (build 19042)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  Spanish_Colombia.utf8
#>  ctype    Spanish_Colombia.utf8
#>  tz       America/Bogota
#>  date     2024-06-23
#>  pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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References

Allaire, JJ, and Christophe Dervieux. 2024. quarto: R Interface to “Quarto” Markdown Publishing System. https://CRAN.R-project.org/package=quarto.
Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2024. rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown.
Appelhans, Tim, Florian Detsch, Christoph Reudenbach, and Stefan Woellauer. 2023. mapview: Interactive Viewing of Spatial Data in r. https://CRAN.R-project.org/package=mapview.
Hester, Jim, and Jennifer Bryan. 2024. glue: Interpreted String Literals. https://CRAN.R-project.org/package=glue.
Kothari, Aditya. 2022. ggTimeSeries: Time Series Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggTimeSeries.
Müller, Kirill, and Lorenz Walthert. 2024. styler: Non-Invasive Pretty Printing of r Code. https://CRAN.R-project.org/package=styler.
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———. 2024b. patchwork: The Composer of Plots. https://CRAN.R-project.org/package=patchwork.
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Citation

BibTeX citation:
@online{j._lizcano2024,
  author = {J. Lizcano, Diego},
  title = {A Calendar to Visualize Camera Trap Data},
  date = {2024-06-15},
  url = {https://dlizcano.github.io/cametrapcesar/posts/2024-06-15-visualizing-the-data/},
  langid = {en}
}
For attribution, please cite this work as:
J. Lizcano, Diego. 2024. “A Calendar to Visualize Camera Trap Data.” June 15, 2024. https://dlizcano.github.io/cametrapcesar/posts/2024-06-15-visualizing-the-data/.