In am2222/nzdggs: Manipulate and Run Analysis on Netezza Using IDEAS Model

knitr::opts_chunk$set(echo = TRUE)
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This document explains the analytic capabilities of the IDEAS data model.

library(DBI)
library(dplyr)
library(sf)
con = dbConnect(RNetezza::Netezza(), dsn='NZSQL')

We connect to a table containing spatial-time series of annual extreme daily climate variables for entire Canada.

data=tbl(con,"ANUSPLINE3")
head(data)

Next we slice the data set for annual maximum daily precipitation.

datap=data%>%filter(KEY=='PRECIPITATION')
head(datap)

We will calculate time-series of spatial average

avgs=datap%>%group_by(TID)%>%arrange(TID)%>%summarise(VALUE=mean(VALUE))
head(avgs)

We will calculate spatial distribution of temporal average

avgt=datap%>%group_by(DGGID)%>%arrange(DGGID)%>%summarise(VALUE=mean(VALUE))
head(avgt)

Let us plot some of these basic variables.

avgs=collect(avgs)
plot(avgs$TID,avgs$VALUE)

To plot the spatial variable we need to attach it with the spatial tabls.

grid=tbl(con,"FINALGRID2")
head(grid)

avgt=avgt%>%inner_join(grid,by=c('DGGID'))%>%mutate(WKT=inza..ST_AsText(GEOM))%>%
  select(DGGID,VALUE,WKT)%>%arrange(DGGID)%>%head(100)%>%collect()

poly=st_as_sf(avgt, wkt='WKT', crs = 4326)
plot(poly['VALUE'])

Lets get a little more complex now. We want to clip the data for one of the eco-zone over Canada say somwhere over BC, Pacific-Maritime (ecozone=13)

ecozone=tbl(con,"ECOZONE_12")%>%filter(VALUE==13)%>%select(DGGID)
head(ecozone)

datape=datap%>%inner_join(ecozone,by=c('DGGID'))
head(datape)

am2222/nzdggs documentation built on Sept. 7, 2020, 6:39 p.m.