KEY- visualizing relationships

Author

Your Name

Published

January 28, 2026

Note

This template follows the relationships slides. Please be sure to cross-reference the slides, which contain important information and additional context!

Setup

Data are downloaded from DataOne (find instructions for getting the download link on the {metajam} package README).

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(metajam) 
library(tidyverse)
library(ggExtra)
library(ggdensity)

#...................download data from DataOne...................
# you only need to do this once (then I recommend commenting it out)!
metajam::download_d1_data("https://cn.dataone.org/cn/v2/resolve/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fknb-lter-hbr%2F208%2F14%2F024b6acc5cb2e03a14fff5558bbffc0c",
                 path = here::here("week4")) 

#  ~ NOTE: You should rename the downloaded folder to 'data/' so that it's automatically ignored by your repo's .gitignore! ~

#....................read in downloaded files....................
stream_chem_all <- metajam::read_d1_files(here::here("week4", "data"))

#........................get the data file.......................
#stream_chem_data <- stream_chem_all$data
stream_chem_data <- stream_chem_all$HubbardBrook_weekly_stream_chemistry.csv

Scatter plots

Basic scatter plot

#.......................basic scatter plot.......................
p1 <- stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH)) + 
  geom_point(alpha = 0.5)

p1

Alt 1: add a rug plot

#.......................scatter + rug plot.......................
p1 +
  geom_rug()

Alt 2: marginal plot alternatives using {ggExtra}

#................add marginal plots with {ggExtra}...............
# also try "density", "boxplot"
ggExtra::ggMarginal(p1, type = "histogram")

Alt 3: scatter + marginal plot with 2+ groups

#............scatter plot with points colored by site............
p2 <- stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = site)) + 
  geom_point(alpha = 0.5) +
  theme(legend.position = "bottom")

#..................with marginal density plot....................
ggExtra::ggMarginal(p2, type = "density", groupFill = TRUE, groupColour = TRUE)

Trend lines

Default (<1000 data points): LOESS method

#......................add a smoothed line.......................
stream_chem_data |> 
  dplyr::filter(waterYr %in% c(2014:2024)) |> 
  dplyr::filter(site == "W5") |> 
  ggplot(aes(x = DOC, y = pH)) + 
  geom_point(alpha = 0.5) +
  geom_smooth()

Line of best fit

#........................add a trend line........................
stream_chem_data |> 
  dplyr::filter(waterYr %in% c(2014:2024)) |> 
  dplyr::filter(site == "W5") |>  
  ggplot(aes(x = DOC, y = pH)) + 
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE)

Visualizing a third numeric variable

Bubble charts

  • variation of a scatter plot for visualizing a third numeric variable
  • be mindful: it’s challenging to interpret 3 numeric variables
#..........................bubble chart..........................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = site, size = Al_ICP)) + 
  geom_point(alpha = 0.5) +
  labs(x = "DOC (mg/L)", size = "Al (mg/L)", color = "Site")

  • adjust size range of bubbles
  • IMPORTANT: don’t scale size by radius (can be deceptive)
#.......................scale size by area.......................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = site, size = Al_ICP)) + 
  geom_point(alpha = 0.5) +
  scale_size(range = c(1, 10)) +
  labs(x = "DOC (mg/L)", size = "Al (mg/L)", color = "Site")
#...................DON'T scale size by radius...................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = site, size = Al_ICP)) + 
  geom_point(alpha = 0.5) +
  scale_radius(range = c(1, 10)) +
  labs(x = "DOC (mg/L)", size = "Al (mg/L)", color = "Site")

Use color for third variable

#...................color for a third variable...................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = Al_ICP)) + 
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  labs(x = "DOC (mg/L)", color = "Al (mg/L)")

But oftentimes best to just create two different plots

#......................effect of DOC on pH.......................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = DOC, y = pH, color = site)) + 
  geom_point(alpha = 0.5)
#....................effect of DOC on Al_ICP.....................
stream_chem_data |> 
  dplyr::filter(waterYr == 2024) |> 
  ggplot(aes(x = Al_ICP, y = pH, color = site)) + 
  geom_point(alpha = 0.5)

Overplotting

  • if you have too many points, scatter plots can be ineffective
#.....................wayyyy too many points.....................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) + 
  geom_point()

Initial strategies

#................smaller more transparent points.................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) + 
  geom_point(size = 0.5, alpha = 0.3) 
#..........................add rug plot..........................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) + 
  geom_point(size = 0.5, alpha = 0.3) +
  geom_rug()
#.........................color by group.........................
ggplot(stream_chem_data, aes(x = SO4, y = pH, color = site)) + 
  geom_point(size = 0.5, alpha = 0.3)

Alt 1: 2d heatmaps

  • e.g. like you’re looking down on a histogram
#....................heatmap of 2d bin counts....................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) +
  geom_bin2d() +
  scale_fill_viridis_c()
#..................heatmap with hexagonal bins...................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) +
  geom_hex() +
  scale_fill_viridis_c() +
  guides(fill = guide_colorbar(title = "Count", 
                                barwidth = 1, barheight = 15))

Alt 2: 2d density / contour plots

  • e.g. like you’re looking down on a density plot
  • legend provides an estimate of the proportion of data points fall within a colored region (density of distribution of points sums to 1)
    • interpreting legend: 0-2% of of points fall within a 1x1 square in the darkest blue region, while 24-26% fall within a 1x1 square in the brightest yellow region
#..........................contour plot..........................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) +
  geom_density_2d_filled()

Alt 3: 2d density plots using {ggdensity}

  • can be easier to interpret
  • compute and plot the resulting highest density regions (HDRs)
  • HDRs are computed to be the smallest such regions that bound that level of probability
#................2d density plot with {ggdensity}................
ggplot(stream_chem_data, aes(x = SO4, y = pH)) +
  ggdensity::geom_hdr() # update prob level e.g. `probs = c(0.99, 0.75, 0.5, 0.25)`