sensorIMG | R Documentation |
This function plots sensor data as an image. An actogram for instance is a type of sensor image.
sensorIMG( date, sensor_data, tz = "UTC", plotx = TRUE, ploty = TRUE, labelx = TRUE, labely = TRUE, offset = 0, dt = NA, xlab = "Hour", ylab = "Date", cex = 2, col = c("black", viridis::magma(90)), ... )
date |
Date data in POSIXct format, most commonly |
sensor_data |
sensor data, for example look at |
tz |
Time zone for POSIXct, default set to "UTC" |
plotx |
wherether or not to plot the x axis ticks + labels (for instance when compiling multifigures) |
ploty |
wherether or not to plot the y axis ticks + labels (for instance when compiling multifigures) |
labelx |
wherether or not to write the name of the x axis (for instance when compiling multifigures) |
labely |
wherether or not to write the name of the y axis (for instance when compiling multifigures) |
offset |
This parameter determines where the center of the graph is. When |
dt |
the time interval to which the data are resampled (secs). Default is |
xlab |
label for x-axis (as a character string) |
ylab |
label for y axis (as a character string) |
cex |
size of labels |
col |
Colour scheme of plot. Default |
... |
Any additional parameters used by graphics::image |
an image of the sensor data, for instance with activity it would produce an actogram
##specify the data location #data(hoopoe) #start = as.POSIXct("2016-07-01","%Y-%m-%d", tz="UTC") #end = as.POSIXct("2017-06-01","%Y-%m-%d", tz="UTC") #PAM_data = cutPAM(hoopoe,start,end) ## Create plots with 3 together (mfrow) #par( mfrow= c(1,3), oma=c(0,2,0,6)) #par(mar = c(4,2,4,2)) #sensorIMG(PAM_data$acceleration$date, ploty=FALSE, # PAM_data$acceleration$act, main = "Activity", # col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2) #par(mar = c(4,2,4,2)) #sensorIMG(PAM_data$pressure$date, plotx=TRUE, ploty=FALSE, labely=FALSE, # PAM_data$pressure$obs, main="Pressure", # col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2) #par(mar = c(4,2,4,2)) #sensorIMG(PAM_data$temperature$date, labely=FALSE, # PAM_data$temperature$obs, main="Temperature", # col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2) ###################################################### # Look at a classification output ###################################################### ## Classification #classification = classifyFLAP(dta = PAM_data$acceleration, period = 10, toPLOT=FALSE) #par( mfrow= c(1,3), oma=c(0,2,0,6),mar = c(4,2,4,2)) #sensorIMG(PAM_data$pressure$date, c(0,abs(diff(PAM_data$pressure$obs))), # main="Pressure difference", # ploty=FALSE, # col=c("black",viridis::cividis(90)), cex=1.2, cex.main = 2) #sensorIMG(PAM_data$acceleration$date, PAM_data$acceleration$act, main="Activity", # ploty=FALSE, labely=FALSE, # col=c(viridis::cividis(90)), cex=1.2, cex.main = 2) #sensorIMG(PAM_data$acceleration$date, # ifelse(classification$classification == classification$migration, 1,2), # main="Migration Classification", # labely=FALSE, # col = c("orange","black"), # cex=1.2, cex.main = 2) #twilights <- GeoLight::twilightCalc(PAM_data$light$date, # PAM_data$light$obs, # LightThreshold = 2, # ask = FALSE) #addTWL(twilights$tFirst, offset=0, # col= ifelse(twilights$type == 1, # "goldenrod","cornflowerblue"), # pch=16, cex=0.5)
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