Making QC plots
qcplots.RmdEditable script
Here is a script you can use if you would prefer to have all the code for the QC plot step in a single script rather than an installable package format. Just click the clipboard icon in the top right corner of this code chunk to copy the code, then open a new R script in RStudio and paste it there, and save the script wherever you would like to store it on your computer.
# load necessary libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(plotly)
library(htmlwidgets)
library(lubridate)
# directory containing cropped data to be plotted
cropped_loc <- "data/2022_summer/2_cropped_csv/"
# directory where QC plots will be stored
qc_plots_loc <- "data/2022_summer/3_qc_plots/"
# get a list of all the cropped files
filenames <- list.files(path = cropped_loc, pattern=".csv")
# this is now a list of all filenames; we haven't read in the data yet, but
# make sure this lists all the cropped files you want to plot
filenames
# Loop to read all the data files and combine them into one datafile for easier plotting
Combined <-NULL # Create new data frame to hold combined data
cat("Reading in cropped data files...", fill = TRUE)
i = 0
for (selection in filenames) {
i = i+1
#selection = filenames[1] # for troubleshooting the for-loop
cat(paste0("Reading file ", i, " of ", length(filenames), ": ", selection), fill = TRUE)
# split the filename string everywhere there is an underscore or period
# so that we can get the following metadata from it:
# site name, deployment season and year, and media (air/water)
info_from_filename = unlist(strsplit(selection, split="[_.]"))[1:4]
sitename = info_from_filename[1]
media = info_from_filename[2] # air or water; assumes this info is the third chunk after splitting the filename
deploy_season = info_from_filename[3]
if(deploy_season != "sum" & deploy_season != "fall"){
stop("filename does not say sum or fall.")
}
deploy_year = 2000 + as.integer(info_from_filename[4]) # or we can format as character
oneread <- read.csv(
file = paste0(cropped_loc, selection), as.is=T, skip=1, fill=T, header=F
) %>% ## Reads the selected datafile.
dplyr::select(1:3) %>% #select the first 3 columns (remove air temp from Hoh River sites)
# add the metadata as variables/columns
dplyr::mutate(sitename = sitename,
media = media,
deploy_season = deploy_season,
deploy_year = deploy_year)
Combined <- dplyr::bind_rows(Combined, oneread) ## Adds the datafile's data to the existing combined datafile.
} # filenames loop
cat("Done reading in cropped data.", fill = TRUE)
cat("Computing temperature differences...", fill = TRUE)
# only compute AWMaxDiff and AirRange if there is air data for at least one site
if("air" %in% unique(Combined$media)){
Combined <- Combined %>%
dplyr::rename(row = V1, datetime = V2, temp = V3) %>% # Rename the first three variables that came from the csv file
dplyr::mutate(datetime = lubridate::ymd_hms(datetime)) %>% # change format of datetime column
dplyr::mutate(date = lubridate::date(datetime)) %>%
dplyr::group_by(sitename, date, media) %>% # compute daily max/min by site, date, and media (air/water)
dplyr::summarize(dailymax = max(temp, na.rm = TRUE),
dailymin = min(temp, na.rm = TRUE)) %>%
# pivot dataframe to add air or water to column name; this is necessary to calculate stats
# this puts air and water on the same rows so we can just subtract columns in the next line
# this requires that at least one site has air temperature!
tidyr::pivot_wider(names_from = media,
values_from = c(dailymin, dailymax)) %>%
dplyr::mutate(AWMaxDiff = dailymax_air - dailymax_water,
AirRange = dailymax_air - dailymin_air,
WaterRange = dailymax_water - dailymin_water) %>%
#pivot longer for plotting purposes later; to plot on same graph, need "calc" column ("grouping variable")
tidyr::pivot_longer(cols = dailymin_air:WaterRange, names_to = "calc", values_to = "value")
}else{
Combined <- Combined %>%
dplyr::rename(row = V1, datetime = V2, temp = V3) %>% # Rename the first three variables that came from the csv file
dplyr::mutate(datetime = lubridate::ymd_hms(datetime)) %>% # change format of datetime column
dplyr::mutate(date = lubridate::date(datetime)) %>% # extract just the date
dplyr::group_by(sitename, date, media) %>% # compute daily max/min by site, date, and media (air/water)
dplyr::summarize(dailymax = max(temp, na.rm = TRUE),
dailymin = min(temp, na.rm = TRUE)) %>%
# pivot dataframe to add air or water to column name; this is necessary to calculate stats
# this puts air and water on the same rows so we can just subtract columns in the next line
# this requires that at least one site has air temperature!
tidyr::pivot_wider(names_from = media,
values_from = c(dailymin, dailymax)) %>%
dplyr::mutate(WaterRange = dailymax_water - dailymin_water) %>%
#pivot longer for plotting purposes later; to plot on same graph, need "calc" column ("grouping variable")
tidyr::pivot_longer(cols = dailymin_water:WaterRange, names_to = "calc", values_to = "value")
}
# set some plotting parameters
sites <- unique(Combined$sitename)
range.colors <- c(AirRange = "blue", WaterRange = "black")
maxdiff.colors <- c(dailymax_air = "blue", dailymax_water = "black", AWMaxDiff = "purple")
# loop through sites to plot all graphs for all sites
i = 0
for(s in sites){
i = i+1
cat(paste0("Making QC plots for site ", i, " of ", length(sites), ": ", s), fill = TRUE)
# s = sites[1] # uncomment if you want to troubleshoot this loop
# filter the data to just this site
this.site.combined = dplyr::filter(Combined, sitename == s)
rangeplot <- ggplot2::ggplot(this.site.combined %>%
dplyr::filter(calc %in% c("AirRange","WaterRange")),
ggplot2::aes(x = date, y = value, color = calc)) +
ggplot2::geom_line(na.rm=TRUE) +
ggplot2::geom_point(na.rm=TRUE) +
ggplot2::labs(title = paste0(s," Air and Water Temperature ranges"),
x = "Date", y = "Temperature (C)",
color = "Media") +
ggplot2::scale_color_manual(values = range.colors) +
ggplot2::geom_hline(yintercept = 3, linewidth = 0.3, color = "red")
ggplot2::ggsave(paste0(qc_plots_loc, s, "_AirWaterTempRange.png"), rangeplot,
width = 11, height = 8.5, units = "in")
maxdiffplot <- ggplot2::ggplot(this.site.combined %>%
dplyr::filter(calc %in% c("dailymax_air", "dailymax_water", "AWMaxDiff")),
ggplot2::aes(x = date, y = value, color = calc)) +
ggplot2::geom_line(na.rm=TRUE) +
ggplot2::labs(title = paste0(s," Max Air and Water Temperature and Difference"),
x = "Date", y = "Temperature (C)",
color = "Media") +
ggplot2::scale_color_manual(values = maxdiff.colors) +
ggplot2::geom_hline(yintercept = 20, linewidth = 0.3, color = "red")
ggplot2::ggsave(paste0(qc_plots_loc, s, "_MaxDiffAirWaterTemp.png"),
maxdiffplot, width = 11, height = 8.5, units = "in")
maxdifplotly <- plotly::ggplotly(maxdiffplot)#to create plotly of maxdifplot to trace plot
rangeplotly <- plotly::ggplotly(rangeplot)#to create plotly of rangeplot to trace plot
htmlwidgets::saveWidget(maxdifplotly, paste0(qc_plots_loc, s, "_MaxDiffAirWaterTemp.html"))
htmlwidgets::saveWidget(rangeplotly, paste0(qc_plots_loc, s, "_AirWaterTempRange.html"))
}; cat("Done.", fill = TRUE)