data exploration tables/ggplot

[jimcasaer]

similar to the geographical data visualization based on deployments, most features used in map_dep() are also interesting to explore data based on timescales (day, week, month, year, period (from - till))
visualization is possible using timescale as x-axis /

possible features:

• effort (different units)
• number of cameras active
• number of deployments
• number of observed species
• number of observations (a subset of species)
• rai

possible timescales
day
week
month
year
period (from - till))

possible output

• table or dataframe
• ggplot

** seems to be logical to make again use of predicates for this data exploration functions

[damianooldoni]

I suppose you have histograms in mind, right? No lines or points...

[jimcasaer]

yep !

although when the timescale is the day the histograms will resemble lines :-)

[damianooldoni]

A little overview with some questions about the list of features you would like to plot.

The following can be indeed plot with time on the x-axis and using different scales:

• number of cameras active
• number of observed species
• number of observations (a subset of species)

This one I don't understand:

• number of deployments

If I well understand a deployment is linked to a camera, so the number of active cameras is equal to the number of active deployments at a certain moment? One of the two is therefore redundant?

The following ones I think I need more explanation:

• effort
• RAI

Do you mean a cumulative effort RAI over all deployments as function of time?

[jimcasaer]

@damianooldoni

• the number of active camera's and the number of deployments do indeed coincide
• effort from xx till yy equals the number of days (24 hours) cameras were active between xx en yy * 24 hours (or start/end hours)
• RAI is based on the number of observations (for a species) between xx and yy divided by the effort between xx and yy

<= the camOps table can play a role as input for the effort calculation
<= the number of observations is calculated directly from the datapackage$observations table or from record.table