knitr::opts_chunk$set(fig.width = 7, fig.height = 5)
How can I save data from temporary monitors to a CSV file?
If you have PWFSLSmoke installed, you should be able to copy and paste this code into the RStudio console.
The coding style uses the "pipe" operator,
%>%, which uses the output of the
preceding function as the first argument of the next function. Package functions
are specifically designed to work well in this manner, encouraging readable
and understandable code.
Think of each chunk as a recipe that begins with what you want to make and is followed by the steps needed to make it.
library(PWFSLSmoke) # All of AIRSIS for 2019 airsis <- airsis_loadAnnual(2019) # AIRSIS in California # - start with airsis # - subset for those where stateCode is one of "CA" airsis_ca <- airsis %>% monitor_subset(stateCodes = ("CA")) # Show the siteNames and IDs # - start with airsis_ca # - extract the "meta" dataframe # - select the "siteName column # - (print by default) airsis_ca %>% monitor_extractMeta() %>% dplyr::select(siteName) # Interactive map to pick a monitor monitor_leaflet(airsis_ca) # Select a single monitor by monitorID # - start with airsis_ca # - subset for the "Mariposa-5085 Bullion St" monitorID Mariposa <- airsis_ca %>% monitor_subset(monitorIDs = "lon_.119.968_lat_37.488_mariposa.1000") # Interactive graph to pick some time limits monitor_dygraph(Mariposa) # Trim this time series to October through December # - start with Mariposa # - subset based on time limits Mariposa <- Mariposa %>% monitor_subset(tlim = c(20191001, 20200101)) # A quick plot for October through December monitor_timeseriesPlot(Mariposa) addAQILines() addAQIStackedBar() addAQILegend("topright") title("Mariposa 2019") # Dump out a meta/data combined CSV file for Mariposa monitor_writeCSV( Mariposa, saveFile = file.path(tempdir(), "Mariposa.csv"), metaOnly = FALSE, dataOnly = FALSE, quietly = TRUE ) # Alternatively, View the metadata and data in RStudio: View(Mariposa$meta) View(Mariposa$data) # Trim the all-California dataset to the period that has data airsis_ca <- monitor_trim(airsis_ca) # Dump out airsis_ca metadata to a CSV file monitor_writeCSV( airsis_ca, saveFile = file.path(tempdir(), "airsis_CA_meta.csv"), metaOnly = TRUE, dataOnly = FALSE, quietly = TRUE ) # Dump out airsis_ca data to a CSV file monitor_writeCSV( airsis_ca, saveFile = file.path(tempdir(), "airsis_CA_data.csv"), metaOnly = FALSE, dataOnly = TRUE, quietly = TRUE ) # Alternatively, View() the metadata and data in RStudio: View(airsis_ca$meta) View(airsis_ca$data) # ============================================================================== # Everything above also applies to temporary data from the Western Regional # Climate Center. Just start with: # All of WRCC for 2019 wrcc <- wrcc_loadAnnual(2019)
Finally, to emphasize what can be done with pipelines, the following calculates
NowCast timeseries for all of California and displays the
in the RStudio viewer:
airsis_loadAnnual(2019) %>% monitor_subset(stateCodes = c("CA")) %>% monitor_subset(tlim = c(20191001, 20200101)) %>% monitor_trim() %>% monitor_nowcast(includeShortTerm = TRUE) %>% monitor_extractData() %>% View()
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