id_outlier: Identify outliers via point and click on temp vs salinity...

In BLE-LTER/insitu: Process BLE-LTER's insitu deployment data

Description

Identify outliers via point and click on temp vs salinity plot

Usage

id_outlier(data, datecol = "date_time", tempcol = "temperature",
  salcol = "salinity", rowseq = seq(1:nrow(data)),
  plot_type = "sal/time",
  plot_title = "Manually identify anomalous data points by clicking on them. \n Click stop on the upper left and exit for the function to return row numbers of ID'ed points")

Arguments

data	(data.frame) Data frame with columns for date or temperature, salinity.
datecol	(character) Name of colume for date time. Defaults to "date_time".
tempcol	(character) Name of column for temperature. Defaults to "temperature".
salcol	(character) Name of column for salinity. Defaults to "salinity".
rowseq	(numeric) Numeric vector of row numbers starting from one. Defaults to seq(1:nrow(data)).
plot_type	(character) Either "sal/time" for plotting salinity over time, or "temp/sal" for plotting temperature vs salinity. Defaults to "sal/time".
plot_title	(character) Give a plot title. Defaults to "Manually identify anomalous data points by clicking on them. Click stop on the upper left and exit for the function to return row numbers of ID'ed points". This is useful only if you'd like to save the plot as image.

Value

Numeric vector of row numbers manually ID'ed by user. Note that this returns an error if user did not click stop before exiting the plot application.

BLE-LTER/insitu documentation built on Feb. 5, 2021, 5:28 p.m.