In casaultb/azmpdata: A data package to share derived metrics from AZMP research documents

library(azmpdata)
library(ggplot2)
library(dplyr)

Introduction

The purpose of this vignette is to demonstrate how to plot data which is pulled from the azmpdata package. We show brief examples of various plotting methods including base plot, and ggplot2. We also review how to recreate default plots from the gslea package which has similar functionality to azmpdata but contains Gulf and Quebec region data products.

The Data

We will use sample data from the azmpdata package for each example. Data can be called using

df <- get('Derived_Annual_Stations')
head(df)

Base plot

Using base R to create plots can often be the simplest way for a novice to explore a dataset.

If we wanted to create a simple plot of a variable over time, it might look like this

plot(df$year, df$temperature_in_air, xlab = 'Year', ylab = 'temperature_in_air')

Obviously there are many more advanced plots that can be made using base plot but we leave these up to the individual users to explore.

ggplot2

Using ggplot2 can give great simple exploratory plots, using a different 'grammar'.

If a user wanted to compare different variables over time, ggplot2 has functions which make this very simple.

p <- ggplot(data = df) +
  geom_line(aes(x = year, y = temperature_in_air, colour = 'air_temperature'), show.legend = TRUE) +
  geom_point(aes(x = year, y = sea_surface_temperature_from_moorings, colour = 'sea_surface_temperature_from_moorings'), show.legend = TRUE)+
  labs(y = 'degrees C' ) +
  scale_color_manual(name = "variables",
                     breaks = c('air_temperature', 'sea_surface_temperature_from_moorings'),
                     values = c('air_temperature' = 'red', 'sea_surface_temperature_from_moorings' = 'blue'))


print(p)

The next two examples contain generic code that could be modified to plot any dataframe (of the same time scale).

Another common plotting task would be to plot the annual means of a given dataframe. This method is fairly generic and could be used for any annual dataset.

 df_data <- get('Derived_Annual_Broadscale') # get data
 variable <- 'temperature_at_sea_floor' # select variable to plot

  # check for metadata and seperate
  metanames <- c('year','area', 'section', 'station' )
  meta_df <- names(df_data)[names(df_data) %in% metanames]

  group <- meta_df[meta_df != 'year']

  df_data <- df_data %>%
    dplyr::select(., all_of(meta_df), all_of(variable) ) %>%
    dplyr::rename(., 'value' = all_of(variable) ) %>%
    dplyr::rename(., 'group' = all_of(group))

  # set x-axis
  x_limits <- c(min(df_data$year)-1, max(df_data$year)+1)
  x_breaks <- seq(x_limits[1], x_limits[2], by=1)
  x_labels <- x_breaks

  # set y-axis
  y_limits <- c(min(df_data$value, na.rm=T) - 0.1*mean(df_data$value, na.rm=T),
                max(df_data$value, na.rm=T) + 0.1*mean(df_data$value, na.rm=T))

  # plot data
  p <-  ggplot2::ggplot() +
    # plot data - line
    ggplot2::geom_line(data=df_data,
                       mapping=ggplot2::aes(x=year, y=value, col = group),
                        size=.5) +
    # plot data - dots
    ggplot2::geom_point(data=df_data,
                       mapping=ggplot2::aes(x=year, y=value, col = group),
                        size=1) +
    # set coordinates system and axes
    ggplot2::coord_cartesian() +
    ggplot2::scale_x_continuous(name="Year", limits=x_limits, breaks=x_breaks, labels=x_labels, expand=c(0,0)) +
    ggplot2::scale_y_continuous(name="", limits=y_limits, expand=c(0,0))

  # customize theme
  p <- p +
    ggplot2::theme_bw() +
    ggplot2::ggtitle(paste(group, variable, sep=" : " )) +
    ggplot2::theme(
      text=ggplot2::element_text(size=8),
      axis.text.x=ggplot2::element_text(colour="black", angle=90, hjust=0.5, vjust=0.5),
      plot.title=ggplot2::element_text(colour="black", hjust=0, vjust=0, size=8),
      panel.grid.major=ggplot2::element_blank(),
      panel.border=ggplot2::element_rect(size=0.25, colour="black"),
      plot.margin=grid::unit(c(0.1,0.1,0.1,0.1), "cm"))

print(p)

A user may also want to plot a timeseries. This method is also fairly generic and could be modified to plot any Occupations dataset.

  df_data <- get('Discrete_Occupations_Stations') # get data
  variable <- 'chlorophyll' # choose variable to plot


  # check for metadata
  metanames <- c('year', 'month', 'day', 'area', 'section', 'station' )
  meta_df <- names(df_data)[names(df_data) %in% metanames]

  group <- meta_df[!meta_df %in% c('year', 'month', 'day')]

  df_data <- df_data %>%
    dplyr::select(., all_of(meta_df), all_of(variable) ) %>%
    dplyr::rename(., 'value' = all_of(variable) ) %>%
    dplyr::rename(., 'group' = all_of(group)) #TODO some dataframes do not have groups!?


  # prepare data
  df_data <- df_data %>%
    tidyr::unite(date, year, month, day, sep="-", remove=F) %>%
    dplyr::mutate(year_dec=lubridate::decimal_date(lubridate::ymd(date))) %>%
    dplyr::select(year, year_dec, value, group)

  # set x-axis
  x_limits <- c(min(df_data$year), max(df_data$year)+1)
  x_breaks <- seq(x_limits[1]+.5, x_limits[2]-.5, by=1)
  x_labels <- x_breaks-.5

  # set y-axis
  y_limits <- c(min(df_data$value, na.rm=T) - 0.1*mean(df_data$value, na.rm=T),
                max(df_data$value, na.rm=T) + 0.1*mean(df_data$value, na.rm=T))

  ## set shaded rectangles breaks
  df_rectangles <- tibble::tibble(xmin=seq(x_limits[1], x_limits[2], by=2),
                                  xmax=seq(x_limits[1], x_limits[2], by=2)+1,
                                  ymin=y_limits[1], ymax=y_limits[2])

  # plot data

  p <-  ggplot2::ggplot() +
    # plot shaded rectangles
    ggplot2::geom_rect(data=df_rectangles,
                       mapping=ggplot2::aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax),
                       fill="gray90", alpha=0.8) +
    # plot data - line
    ggplot2::geom_line(data=df_data,
                       mapping=ggplot2::aes(x=year_dec, y=value, col = group),
                        size=.5) +
    # plot data - dots
    ggplot2::geom_point(data=df_data,
                        mapping=ggplot2::aes(x=year_dec, y=value, col = group),
                        size=1) +
    # set coordinates system and axes
    ggplot2::coord_cartesian() +
    ggplot2::scale_x_continuous(name="Year", limits=x_limits, breaks=x_breaks, labels=x_labels, expand=c(0,0)) +
    ggplot2::scale_y_continuous(name="", limits=y_limits, expand=c(0,0))

  # customize theme
  p <- p +
    ggplot2::theme_bw() +
    ggplot2::ggtitle(paste(group, variable,  sep=" : " )) +
    ggplot2::theme(
      text=ggplot2::element_text(size=8),
      axis.text.x=ggplot2::element_text(colour="black", angle=90, hjust=0.5, vjust=0.5),
      plot.title=ggplot2::element_text(colour="black", hjust=0, vjust=0, size=8),
      panel.grid.major=ggplot2::element_blank(),
      panel.border=ggplot2::element_rect(size=0.25, colour="black"),
      plot.margin=grid::unit(c(0.1,0.1,0.1,0.1), "cm"))

print(p)

gslea

gslea was a package developed to support ecosystem approach research in the Gulf Region. It contains a plotting function EA.plot.f() which can be replicated using azmpdata.

Note these plots may appear very small in the notebook format.

dat <- get('Derived_Annual_Stations')

  actual_EARs <- unique(dat$station) # get regions to plot 
  dat_only <- dat[,!names(dat) %in% c('station', 'year', 'cruiseNumber', 'longitude', 'latitude', 'pressure')] # isolate data variables to plot (not metadata)
  no_plots <- length(dat_only)*length(actual_EARs) # calculate number of plots ot be displayed
  # set par info based on number of plots (max 25 per page)
  if(no_plots > 25) {par(mfcol = c(5, 5), mar = c(1.3,2,3.2,1), omi = c(.1,.1,.1,.1), ask = T)}
  if(no_plots <= 25){par(mfcol = c(length(dat_only), length(actual_EARs)), mar = c(1.3,2,3.2,1), omi = c(.1,.1,.1,.1))}

  counter <- 1
  for(i in actual_EARs){ # loop through regions
    ear_dat <- dat[dat$station == i,]
    for(ii in 1:length(dat_only)){ # loop by variables
      var_dat <- data.frame('value' = ear_dat[[names(dat_only)[[ii]]]], 'station' = ear_dat$station, 'year' = ear_dat$year) # get only one variable for one region
      if(!is.na(diff(range(var_dat$value)))){ # if all values are NA skip over plotting
        # plot
      if(nrow(var_dat) < 1) plot(0, 
                                 xlab = "", ylab = "", 
                                 xaxt = "n", yaxt = "n",
                                 main = paste("Station",i,names(dat_only)[[ii]]))
      if(nrow(var_dat) > 0) plot(var_dat$year, var_dat$value, 
                                 xlab = "", ylab = "", 
                                 main = paste("Station",i,names(dat_only)[[ii]]))
      }
    }
    counter <- counter+1
  }
  # set par info
  par(mfcol = c(1,1), omi = c(0,0,0,0), mar = c(5.1, 4.1, 4.1, 2.1), ask = F)

Conclusion

There are many varied plotting methods which can be used once azmpdata is loaded. We hope these examples help you to explore the possibilities. For more help on plotting please use the resources below:

• https://ggplot2.tidyverse.org/
• https://rstudio-pubs-static.s3.amazonaws.com/84527_6b8334fd3d9348579681b24d156e7e9d.html

casaultb/azmpdata documentation built on July 4, 2025, 11:04 a.m.