Author: Ben Marwick
Date: 2022-05-13

# Introduction to the gsloid package

#### Ben Marwick

#### 2022-05-13

## Overview

This package includes commonly used datasets in palaeoecology and archaeology:

- Global sea level curve
- Global oxygen isotope curve
- Boundary ages for Marine Isotope Stages

There are many possible sources for these kinds of data, this package includes data from:

Dataset | Source
--- | ---
lisiecki2005 | Lisiecki, L.E. and M.E. Raymo. 2005. A Pliocene-Pleistocene stack of 57 globally distributed benthic D18O records. Paleoceanography, Vol. 20, PA1003, https://doi.org/10.1029/2004PA001071
spratt2016 | Spratt, Rachel M. and Lorraine E. Lisiecki 2016. A Late Pleistocene sea level stack. Climate of the Past. Vol. 12, 1079-1092, https://doi.org/10.5194/cp-12-1-2016

You must cite those sources in the table above if you use the data in this package.

Here’s the structure of the main data sets:

    library(gsloid)
    str(lisiecki2005)

    ## 'data.frame':    2115 obs. of  3 variables:
    ##  $ Time : num  0 1 2 3 4 5 6 7 8 9 ...
    ##  $ d18O : num  3.23 3.23 3.18 3.29 3.3 3.26 3.33 3.37 3.42 3.38 ...
    ##  $ Error: num  0.03 0.04 0.03 0.03 0.03 0.03 0.04 0.04 0.03 0.04 ...

    str(spratt2016)

    ## 'data.frame':    799 obs. of  9 variables:
    ##  $ age_calkaBP            : num  0 1 2 3 4 5 6 7 8 9 ...
    ##  $ SeaLev_shortPC1        : num  8.49 7.63 4.01 4.35 3.13 ...
    ##  $ SeaLev_shortPC1_err_sig: num  5.23 4.87 4.83 4.72 4.74 4.57 5.04 5.9 6.79 8.3 ...
    ##  $ SeaLev_shortPC1_err_lo : num  -1.72 -2.9 -4.51 -6.93 -10.43 ...
    ##  $ SeaLev_shortPC1_err_up : num  17.93 16.39 13.59 12.08 8.41 ...
    ##  $ SeaLev_longPC1         : num  8.96 7.72 5.96 3.54 1.88 0 -2 -5.38 -7.12 -11.6 ...
    ##  $ SeaLev_longPC1_err_sig : num  5.72 5.13 4.69 4.42 4.39 ...
    ##  $ SeaLev_longPC1_err_lo  : num  -1.21 -2.77 -5.01 -7.28 -10.54 ...
    ##  $ SeaLev_longPC1_err_up  : num  20.38 17.1 14.21 10.9 7.63 ...

    str(LR04_MISboundaries)

    ## 'data.frame':    232 obs. of  7 variables:
    ##  $ MIS_Boundary     : chr  "1/2" "2/3" "3/4" "4/5" ...
    ##  $ start_MIS        : chr  "1" "2" "3" "4" ...
    ##  $ end_MIS          : chr  "2" "3" "4" "5" ...
    ##  $ label_MIS        : chr  "1" "2" "3" "4" ...
    ##  $ LR04_Age_ka_start: num  14 29 57 71 82 87 96 109 123 130 ...
    ##  $ LR04_Age_ka_end  : num  0 14 29 57 71 82 87 96 109 123 ...
    ##  $ LR04_Age_ka_mid  : num  7 21.5 43 64 76.5 ...

Detailed descriptions of the variables are avaliable in the data documentation, run `?lisiecki2005`, `?spratt2016` and `?LR04_MISboundaries` for more information.

## Usage

Atlhough these data are suitable for many kinds of analyses, the primary reason that I made this package is so I can easily make my own plots of these data. Here’s how I typically start with plotting the oxygen isotope data. I have set limits on the x-axis so it only shows 0-250 ka because that’s what I’m interested in:

    library(ggplot2)

    ggplot(lisiecki2005,
           aes(Time,
               d18O)) +
      geom_line() +
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_reverse(name = bquote(delta^18*O)) +
      theme_bw()

And here’s how I start to plot the sea level data:

    ggplot(spratt2016,
           aes(age_calkaBP,
               SeaLev_shortPC1)) +
      geom_line() +
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_continuous(name = "Sea Level, meters above present day") +
      theme_bw()

Often we want to see the Marine isotope stages on these plots also. This package includes the dataset `LR04_MISboundaries` of start and end dates for each stage, so we can draw these stages easily. I obtained these data from Lorraine Lisiecki’s web page: http://lorraine-lisiecki.com/LR04_MISboundaries.txt

Some care is required to get the MIS numbers positions in easy-to-read locations. Here I use `rep()` and `seq()` to help position the numbers.

    # subset the MIS data for the last 250 ka years
    mis_last_250ka <- LR04_MISboundaries[LR04_MISboundaries$LR04_Age_ka_start <= 250, ]

    # plot the oxygen istope data line on top of the MIS lines
    ggplot() +
      geom_vline(data = mis_last_250ka,          # add MIS lines
                 aes(xintercept = LR04_Age_ka_start),
                 colour = "blue") +
      annotate("text",
               label = mis_last_250ka$label_MIS,
               x = mis_last_250ka$LR04_Age_ka_mid,
               y = c(rep(3.0, 4),
                     seq(3.2, 2.7, length.out = 6),
                     rep(3.0, 2)),
               size = 3) +
      geom_line(data = lisiecki2005,                # add d18O line
                aes(Time,
                    d18O)) +
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_reverse(name = bquote(delta^18*O)) +
      theme_bw()

Sometimes we prefer to indicate the MIS by horizontal lines. Once again we have to carefully place the line segments and labels so they are clear to read:

    ggplot() +
      geom_segment(data = mis_last_250ka, # add MIS lines
                   aes(x =    LR04_Age_ka_end,
                       xend = LR04_Age_ka_start,
                       y =    seq(3, 2.5, length.out = nrow(mis_last_250ka)),
                       yend = seq(3, 2.5, length.out = nrow(mis_last_250ka))),
                   colour = "blue",
                   size = 1) +
      annotate("text",
               label = mis_last_250ka$label_MIS,
               x =     mis_last_250ka$LR04_Age_ka_mid,
               y = c(seq(2.9, 2.7, length.out = 4),
                     seq(3.1, 2.8, length.out = 5),
                     seq(2.4, 2.3, length.out = 3)),
               size = 3) +
      geom_line(data = lisiecki2005,                # add d18O line
                aes(Time,
                    d18O)) +
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_reverse(name = bquote(delta^18*O)) +
      theme_bw()

And sometimes we might want shaded rectangles to indicate the MIS:

    ggplot() +
      annotate("rect",
               xmin = mis_last_250ka$LR04_Age_ka_end,
               xmax = mis_last_250ka$LR04_Age_ka_start,
               ymin = -Inf,
               ymax = Inf,
            alpha = .2,
            fill = rep(c("grey70", "white"),
                       nrow(mis_last_250ka)/2)) +
      annotate("text",
               label = mis_last_250ka$label_MIS,
               x =     mis_last_250ka$LR04_Age_ka_mid,
               y = c(rep(3, 4),
                     seq(3.1, 2.8, length.out = 5),
                     rep(3, 3)),
               size = 3) +
      geom_line(data = lisiecki2005,                # add d18O line
                aes(Time,
                    d18O)) +
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_reverse(name = bquote(delta^18*O)) +
      theme_bw()

Maybe we want the MIS regions by themselves so we can plot some other time series besides the ones includes here. We need to adjust the `y` values in the `annotate()` function to ensure that the MIS labels show in a readble way. Here I show an imaginary variable that might have a maximum value of about 10. So I adjust the `y` values in the `annotate()` function to position the MIS labels just below 10 on the y axis.

    ggplot() +
      annotate("rect",
               xmin = mis_last_250ka$LR04_Age_ka_end ,
               xmax = mis_last_250ka$LR04_Age_ka_start,
               ymin = -Inf,
               ymax = Inf,
            alpha = .2,
            fill = rep(c("grey70", "white"),
                       nrow(mis_last_250ka)/2)) +
      annotate("text",
               label = mis_last_250ka$label_MIS,
               x =     mis_last_250ka$LR04_Age_ka_mid,
               y = c(rep(9, 4),
                     seq(8.1, 9.9, length.out = 5),
                     rep(9, 3)),
               size = 3) +
      # add in other geoms here...
      scale_x_continuous(limits = c(0, 250),
                         name = "x 1000 years ago") +
      scale_y_continuous(limits = c(0, 10),
                      name = "Some other variable (units)") +
      theme_bw()