IEC 2018 plots

This notebook takes the data from the ATCHU campaign and generates plots for use in the presentation of the results.

Load Libraries

library(librarian)
shelf(openair,
      reshape2,
      ggplot2,
      readr)

Load data

data.wide <- read_delim("~/data/ATCHU/ATCHU_dataset.csv",delim = ',',
                        col_types = cols(col_datetime(format = ""),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double(),
                                      col_double()),progress = FALSE)

number of columns of result is not a multiple of vector length (arg 1)16635 parsing failures.
row # A tibble: 5 x 5 col     row col   expected   actual     file                             expected   <int> <chr> <chr>      <chr>      <chr>                            actual 1     1 <NA>  16 columns 17 columns '~/data/ATCHU/ATCHU_dataset.csv' file 2     2 <NA>  16 columns 17 columns '~/data/ATCHU/ATCHU_dataset.csv' row 3     3 <NA>  16 columns 17 columns '~/data/ATCHU/ATCHU_dataset.csv' col 4     4 <NA>  16 columns 17 columns '~/data/ATCHU/ATCHU_dataset.csv' expected 5     5 <NA>  16 columns 17 columns '~/data/ATCHU/ATCHU_dataset.csv'
... ................. ... .................................................................... ........ .................................................................... ...... .................................................................... .... .................................................................... ... .................................................................... ... .................................................................... ........ ....................................................................
See problems(...) for more details.

attr(data.wide$date,"tzone") <- "NZST"

Calculate extra vars

data.wide$NOxHS <- data.wide$NOHS + data.wide$NO2HS
data.wide$NOxMP <- data.wide$NOMP + data.wide$NO2MP
data.wide$BC.NOxHS <- data.wide$BCHS / data.wide$NOxHS

Plots

All pollution roses are normalised so that the fraction of measurements of certain level is shown.

Describe Halsley Drive

pollutionRose(data.wide,
              pollutant = 'BCHS',
              ws='wsHS',
              wd='wdHS',
              normalise = TRUE,
              cols = 'heat',
              breaks = c(0,500,1500,3000),
              statistic = 'prop.count')

Some values are below minimum break.

pollutionRose(data.wide,
              pollutant = 'NOHS',
              ws='wsHS',
              wd='wdHS',
              normalise = TRUE,
              cols = 'heat',
              breaks = c(0,2,4,6),
              statistic = 'prop.count')

Describe UoA

pollutionRose(data.wide,
              pollutant = 'BCUoA',
              ws='wsHS',
              wd='wdHS',
              normalise = TRUE,
              cols = 'heat',
              breaks = c(0,500,1500,3000),
              statistic = 'prop.count')

Describe Musick Point

pollutionRose(data.wide,
              pollutant = 'NOMP',
              ws='wsMP',
              wd='wdMP',
              normalise = TRUE,
              cols = 'heat',
              breaks = c(0,2,4,6),
              statistic = 'prop.count')

Some values are below minimum break.