In gmcginnis/AirVizR: Visualization Tools for Air Quality Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

library(AirVizR)
library(leaflet)

Inputs

Many functions rely on the same inputs, therefore default to the same defined objects in the environment. It is highly recommended to specify inputs upfront in their own chunk to simplify functionality. Various environment cleans are present in this document to showcase where and when these objects can be discarded.

# The folder in which the data set(s) are stored:
input_oafolder <- "data-raw/oa_moving"

# Timezone which the data is stamped with:
input_timezone <- "America/Los_Angeles"
# If you are unsure, view your OA's Settings data, however keep in mind that the functions work best if you provide a named value rather than a difference from a GMT value

Loading Data

This function works best if your data folder includes a Settings file.

raw_oa <- read_oa_folder(input_oafolder, input_timezone)

Wrangle

# Creating the full data set
oa_full <- wrangle_oa(raw_oa)
# Note that despite the ability to truncate a certain amount of starter times for each data set,
# unusual values can still occur.
# It is recommended to arrange your data set by numeric values and remove any obviously miscalculated values:
oa_full <- dplyr::filter(oa_full, longitude < 0)

# Creating a data set of location data
# Note that if you are measuring spatial variation with your monitor, the lat/long will NOT be accurate in the meta data
oa_meta <- wrangle_oa_meta(oa_full, input_timezone, single = TRUE)

oa_daily <- group_stad(oa_full)
oa_hourly <- group_stad(oa_full, by_day = TRUE, by_hour = TRUE)
oa_diurnal <- group_stad(oa_full, by_day = FALSE, by_hour = TRUE)

# Optional environment clean
remove(input_oafolder, input_timezone)

Next steps

Now we're ready to visualize! See the visualize-data vignette for instructions.
You may wish to first combine the data sets with other STADs, which you can learn about in the combine-data vignette.

Because OA data is continuous, you can view it in the form of a map using map_oa():