Histogram of records over time

This is a useful function for visualising your data and how the number of records change over time. This is key for understanding biases that may be present in your data (Isaac et al, 2014).

Usage

recsOverTime(
  time_period,
  Log = FALSE,
  col = "black",
  xlab = "Year",
  ylab = ifelse(Log, "log(Frequency)", "Frequency"),
  ...
)

Arguments

time_period

A numeric vector of user defined time periods, or a date vector, as long as the number of observations.

Log

Logical, should the y-axis be on a log scale?

col

Passed to barplot, the colour of bars

xlab

Passed to barplot, the x-axis label

ylab

Passed to barplot, the y-axis label

...

other arguements to pass to barplot

Value

A plot

Examples

if (FALSE) {

# Create data
n <- 3000 #size of dataset
nyr <- 10 # number of years in data
nSamples <- 30 # set number of dates
nSites <- 15 # set number of sites

# Create somes dates
first <- as.POSIXct(strptime("2010/01/01", "%Y/%m/%d")) 
last <- as.POSIXct(strptime(paste(2010+(nyr-1),"/12/31", sep=''), "%Y/%m/%d")) 
dt <- last-first 
rDates <- first + (runif(nSamples)*dt)

# taxa are set as random letters
taxa <- sample(letters, size = n, TRUE)

# three sites are visited randomly
site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)

# the date of visit is selected at random from those created earlier
time_period <- sample(rDates, size = n, TRUE)

# combine this to a dataframe (adding a final row of 'bad' data)
df <- data.frame(taxa = c(taxa,'bad'),
                 site = c(site,'A1'),
                 time_period = c(time_period, as.POSIXct(strptime("1200/01/01", "%Y/%m/%d"))))
                 
# This reveals the 'bad data'                  
recsOverTime(df$time_period)

# remove and replot
df <- df[format(df$time_period, '%Y') > 2000, ]
recsOverTime(df$time_period)

# plot with style
recsOverTime(df$time_period, col = 'blue', main = 'Records of Species A',
             ylab = 'log(number of records)', Log = TRUE) 

}