vignettes/working_with_xts_timeseries.R

In Greensway/BIRDS: Biodiversity Information Review and Decision Support

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----checkProj2, echo=FALSE---------------------------------------------------
projVer <- sf::sf_extSoftVersion()["PROJ"]
doNextChunk <- as.logical(compareVersion("5.1.0", projVer) == -1)

## ----SB, eval = doNextChunk, echo=TRUE, message=FALSE, warning=FALSE----------
library(BIRDS)
library(xts)
# Create a grid for your sample area that will be used to summarise the data:
grid <- makeGrid(gotaland, gridSize = 10)
# The grid can be easily created in different ways. 
# Import the species observation data:
PBD <- bombusObs
# alternatively, you could load a previously downloaded .CSV file 
# PBD <- read.csv(file="path/to/your/file.csv)

# Convert the data from an observation-based to a visit-based format, 
# adding a unique identifier for each visit:
OB <- organizeBirds(PBD, sppCol = "scientificName")

# Summarise the data:
SB <- summariseBirds(OB, grid = grid)

## ----head, eval = doNextChunk-------------------------------------------------
sb.xts <- SB$temporal
head(sb.xts)
dim(sb.xts)

## ----subsetting, eval = doNextChunk, echo=TRUE, message=FALSE, warning=FALSE----
sb.xts["2017-09"] #a specific month
sb.xts["2017-09-07"] #a specific day
sb.xts["2017-01-01/2017-05-01"] #for a period

## ----monthly and plot, eval = doNextChunk, echo=TRUE, fig.height=5, fig.width=9, message=FALSE, warning=FALSE----
obs.m <- to.monthly(sb.xts$nObs) 
obs.m["2017-04"]
sb.xts["2017-04"]

plot(obs.m["2010/2017",2], col = "darkblue", grid.ticks.on = "month",   
     major.ticks = "month", grid.col = "lightgrey",  
     main = "Maximum number of daily observations per month")

## ----completting, eval = doNextChunk, echo=TRUE, message=FALSE, warning=FALSE----
# sb.xts.na is same as sb.xts but with all missing days added (with NAs)
rng <- range(time(sb.xts))
sb.xts.na <- merge(sb.xts, xts(, seq(rng[1], rng[2], by = "day")))

## ----completting2, eval = doNextChunk, echo=TRUE, fig.height=5, fig.width=9, message=FALSE, warning=FALSE----
z.seq <- seq(rng[1], rng[2], by = "month")
YM.sb.xts <- unique(as.Date(as.yearmon(index(sb.xts))))
YM.z.seq <- as.Date(as.yearmon(z.seq))
wTrm <- which(YM.z.seq %in% YM.sb.xts)
z.seq <- z.seq[-wTrm] ## remove the days for which 

z <- xts(, z.seq)

sb.xts.0 <- merge(sb.xts, z, fill = 0)

obs.m.0 <- to.monthly(sb.xts.0$nObs)
plot(obs.m.0["2010/2017",2], col = "darkblue", grid.ticks.on = "month",   
     major.ticks = "month", grid.col = "lightgrey", 
     main = "Maximum number of daily observations per month")