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search.json

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     "href": "plot_trait_evolution.html",
     "title": "Evolutionary lottery of skull and beak morphology",
     "section": "",
-    "text": "Beak and skull shapes in birds of prey (“raptors”) are strongly coupled and largely controlled by size.\n\ngif provided by the awesome Jen Bright @MorphobeakGeek!\n\nIn this exercise we will use a github repo to collaboratively collate and simulate evolutionary trajectories for each participants’ species body size using a simple brownian motion evolutionary model. This assumes evolutionary steps to progress comletely at random. You could say:\n\n\n\nEach participant has created and contributed a file specifying the parameters required to simulate and plot their species evolutionary trajectory. We’ve collect all participants’ files in the master repo. Next we need to simulate species trajectories plot them up.\nParticipants will then get to see the skull and beak shape corresponding to their species relative body size!\n\n\n\n\nFirst we load the required packages and create some objects to compile data on trait evolution for each species.\n\nlibrary(dplyr)\nlibrary(ggplot2) #3.5.1\nlibrary(plotly) #4.10.4\nset.seed(1)\n\nt <- 0:100  # generate time vector\ndt <- NULL # generate object to compile time-series data\ncols <- NULL # generate object to compile trendline colours\n\n\n\n\n\nWe’ll use the parameters supplied in your scripts to generate brownian trait evolution trendline for each species.\n\n#getting the file names for everything except the template that has undefined values\nspp.files <- dir(\"params/\")[dir(\"params/\") != \"params_tmpl.R\"]\n\nfor(spp in spp.files){\n  # source parameters for each species\n  source(file.path(\"params\", spp))\n  \n  # generate trait evolution time-series and compile plotting data\n  dt <-  rbind(dt, data.frame(t, \n                              trait = c(0, rnorm(n = length(t) - 1, sd = sqrt(sig2)) |> cumsum()),\n                              species = species.name))\n  cols <- c(cols, color)\n}\n\nInstalling package into '/home/runner/work/_temp/Library'\n(as 'lib' is unspecified)\n\n\nalso installing the dependencies 'miniUI', 'shinyjs'\n\n\nInstalling package into '/home/runner/work/_temp/Library'\n(as 'lib' is unspecified)\n\n\n\n\n\nUse the data generated to plot all species.\n\n# Specify the order of species based on the order of colors in cols to stop a mismatch in colours\ndt$species <- factor(dt$species, levels = unique(dt$species))\n\n# Create the ggplot object\np <- ggplot(data = dt, aes(x = t, y = trait, group = species, colour = species)) + \n  geom_line() + \n  scale_colour_manual(values = cols) \n\n# Plot the results\nggplotly(p)\n\n\n\n\n\n\n\n\n\n\n\nSkulls are organised from largest to smallest. The largest skulls are vulture-like, (e.g. no. 50, the Andean condor Vultur gryphus) and the smallest are falconet-like, (e.g. no. 1 Collared falconet Microhierax caerulescens)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nsessioninfo::session_info()\n\n─ Session info ───────────────────────────────────────────────────────────────\n setting  value\n version  R version 4.4.0 (2024-04-24)\n os       Ubuntu 22.04.4 LTS\n system   x86_64, linux-gnu\n ui       X11\n language (EN)\n collate  C.UTF-8\n ctype    C.UTF-8\n tz       UTC\n date     2024-09-11\n pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)\n\n─ Packages ───────────────────────────────────────────────────────────────────\n package     * version date (UTC) lib source\n cli           3.6.3   2024-06-21 [1] CRAN (R 4.4.0)\n colorspace    2.1-1   2024-07-26 [1] CRAN (R 4.4.0)\n crosstalk     1.2.1   2023-11-23 [1] CRAN (R 4.4.0)\n data.table    1.16.0  2024-08-27 [1] CRAN (R 4.4.0)\n digest        0.6.37  2024-08-19 [1] CRAN (R 4.4.0)\n dplyr       * 1.1.4   2023-11-17 [1] any (@1.1.4)\n evaluate      0.24.0  2024-06-10 [1] CRAN (R 4.4.0)\n fansi         1.0.6   2023-12-08 [1] CRAN (R 4.4.0)\n fastmap       1.2.0   2024-05-15 [1] CRAN (R 4.4.0)\n generics      0.1.3   2022-07-05 [1] CRAN (R 4.4.0)\n ggplot2     * 3.5.1   2024-04-23 [1] CRAN (R 4.4.0)\n glue          1.7.0   2024-01-09 [1] CRAN (R 4.4.0)\n gtable        0.3.5   2024-04-22 [1] CRAN (R 4.4.0)\n htmltools     0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)\n htmlwidgets   1.6.4   2023-12-06 [1] CRAN (R 4.4.0)\n httr          1.4.7   2023-08-15 [1] CRAN (R 4.4.0)\n jsonlite      1.8.8   2023-12-04 [1] CRAN (R 4.4.0)\n knitr         1.48    2024-07-07 [1] CRAN (R 4.4.0)\n labeling      0.4.3   2023-08-29 [1] CRAN (R 4.4.0)\n lazyeval      0.2.2   2019-03-15 [1] CRAN (R 4.4.0)\n lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.4.0)\n magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.4.0)\n munsell       0.5.1   2024-04-01 [1] CRAN (R 4.4.0)\n pillar        1.9.0   2023-03-22 [1] CRAN (R 4.4.0)\n pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.4.0)\n plotly      * 4.10.4  2024-01-13 [1] CRAN (R 4.4.0)\n purrr         1.0.2   2023-08-10 [1] CRAN (R 4.4.0)\n R6            2.5.1   2021-08-19 [1] CRAN (R 4.4.0)\n rlang         1.1.4   2024-06-04 [1] CRAN (R 4.4.0)\n rmarkdown     2.28    2024-08-17 [1] CRAN (R 4.4.0)\n scales        1.3.0   2023-11-28 [1] CRAN (R 4.4.0)\n sessioninfo   1.2.2   2021-12-06 [1] any (@1.2.2)\n tibble        3.2.1   2023-03-20 [1] CRAN (R 4.4.0)\n tidyr         1.3.1   2024-01-24 [1] CRAN (R 4.4.0)\n tidyselect    1.2.1   2024-03-11 [1] CRAN (R 4.4.0)\n utf8          1.2.4   2023-10-22 [1] CRAN (R 4.4.0)\n vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.4.0)\n viridisLite   0.4.2   2023-05-02 [1] CRAN (R 4.4.0)\n withr         3.0.1   2024-07-31 [1] CRAN (R 4.4.0)\n xfun          0.47    2024-08-17 [1] CRAN (R 4.4.0)\n yaml          2.3.10  2024-07-26 [1] CRAN (R 4.4.0)\n\n [1] /home/runner/work/_temp/Library\n [2] /opt/R/4.4.0/lib/R/site-library\n [3] /opt/R/4.4.0/lib/R/library\n\n──────────────────────────────────────────────────────────────────────────────"
+    "text": "Beak and skull shapes in birds of prey (“raptors”) are strongly coupled and largely controlled by size.\n\ngif provided by the awesome Jen Bright @MorphobeakGeek!\n\nIn this exercise we will use a github repo to collaboratively collate and simulate evolutionary trajectories for each participants’ species body size using a simple brownian motion evolutionary model. This assumes evolutionary steps to progress comletely at random. You could say:\n\n\n\nEach participant has created and contributed a file specifying the parameters required to simulate and plot their species evolutionary trajectory. We’ve collect all participants’ files in the master repo. Next we need to simulate species trajectories plot them up.\nParticipants will then get to see the skull and beak shape corresponding to their species relative body size!\n\n\n\n\nFirst we load the required packages and create some objects to compile data on trait evolution for each species.\n\nlibrary(dplyr)\nlibrary(ggplot2) #3.5.1\nlibrary(plotly) #4.10.4\nset.seed(1)\n\nt <- 0:100  # generate time vector\ndt <- NULL # generate object to compile time-series data\ncols <- NULL # generate object to compile trendline colours\n\n\n\n\n\nWe’ll use the parameters supplied in your scripts to generate brownian trait evolution trendline for each species.\n\n#getting the file names for everything except the template that has undefined values\nspp.files <- dir(\"params/\")[dir(\"params/\") != \"params_tmpl.R\"]\n\nfor(spp in spp.files){\n  # source parameters for each species\n  source(file.path(\"params\", spp))\n  \n  # generate trait evolution time-series and compile plotting data\n  dt <-  rbind(dt, data.frame(t, \n                              trait = c(0, rnorm(n = length(t) - 1, sd = sqrt(sig2)) |> cumsum()),\n                              species = species.name))\n  cols <- c(cols, color)\n}\n\n\n\n\nUse the data generated to plot all species.\n\n# Specify the order of species based on the order of colors in cols to stop a mismatch in colours\ndt$species <- factor(dt$species, levels = unique(dt$species))\n\n# Create the ggplot object\np <- ggplot(data = dt, aes(x = t, y = trait, group = species, colour = species)) + \n  geom_line() + \n  scale_colour_manual(values = cols) \n\n# Plot the results\nggplotly(p)\n\n\n\n\n\n\n\n\n\n\n\nSkulls are organised from largest to smallest. The largest skulls are vulture-like, (e.g. no. 50, the Andean condor Vultur gryphus) and the smallest are falconet-like, (e.g. no. 1 Collared falconet Microhierax caerulescens)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nsessioninfo::session_info()\n\n─ Session info ───────────────────────────────────────────────────────────────\n setting  value\n version  R version 4.4.0 (2024-04-24)\n os       Ubuntu 22.04.4 LTS\n system   x86_64, linux-gnu\n ui       X11\n language (EN)\n collate  C.UTF-8\n ctype    C.UTF-8\n tz       UTC\n date     2024-09-11\n pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)\n\n─ Packages ───────────────────────────────────────────────────────────────────\n package     * version date (UTC) lib source\n cli           3.6.3   2024-06-21 [1] CRAN (R 4.4.0)\n colorspace    2.1-1   2024-07-26 [1] CRAN (R 4.4.0)\n crosstalk     1.2.1   2023-11-23 [1] CRAN (R 4.4.0)\n data.table    1.16.0  2024-08-27 [1] CRAN (R 4.4.0)\n digest        0.6.37  2024-08-19 [1] CRAN (R 4.4.0)\n dplyr       * 1.1.4   2023-11-17 [1] any (@1.1.4)\n evaluate      0.24.0  2024-06-10 [1] CRAN (R 4.4.0)\n fansi         1.0.6   2023-12-08 [1] CRAN (R 4.4.0)\n fastmap       1.2.0   2024-05-15 [1] CRAN (R 4.4.0)\n generics      0.1.3   2022-07-05 [1] CRAN (R 4.4.0)\n ggplot2     * 3.5.1   2024-04-23 [1] CRAN (R 4.4.0)\n glue          1.7.0   2024-01-09 [1] CRAN (R 4.4.0)\n gtable        0.3.5   2024-04-22 [1] CRAN (R 4.4.0)\n htmltools     0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)\n htmlwidgets   1.6.4   2023-12-06 [1] CRAN (R 4.4.0)\n httr          1.4.7   2023-08-15 [1] CRAN (R 4.4.0)\n jsonlite      1.8.8   2023-12-04 [1] CRAN (R 4.4.0)\n knitr         1.48    2024-07-07 [1] CRAN (R 4.4.0)\n labeling      0.4.3   2023-08-29 [1] CRAN (R 4.4.0)\n lazyeval      0.2.2   2019-03-15 [1] CRAN (R 4.4.0)\n lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.4.0)\n magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.4.0)\n munsell       0.5.1   2024-04-01 [1] CRAN (R 4.4.0)\n pillar        1.9.0   2023-03-22 [1] CRAN (R 4.4.0)\n pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.4.0)\n plotly      * 4.10.4  2024-01-13 [1] CRAN (R 4.4.0)\n purrr         1.0.2   2023-08-10 [1] CRAN (R 4.4.0)\n R6            2.5.1   2021-08-19 [1] CRAN (R 4.4.0)\n rlang         1.1.4   2024-06-04 [1] CRAN (R 4.4.0)\n rmarkdown     2.28    2024-08-17 [1] CRAN (R 4.4.0)\n scales        1.3.0   2023-11-28 [1] CRAN (R 4.4.0)\n sessioninfo   1.2.2   2021-12-06 [1] any (@1.2.2)\n tibble        3.2.1   2023-03-20 [1] CRAN (R 4.4.0)\n tidyr         1.3.1   2024-01-24 [1] CRAN (R 4.4.0)\n tidyselect    1.2.1   2024-03-11 [1] CRAN (R 4.4.0)\n utf8          1.2.4   2023-10-22 [1] CRAN (R 4.4.0)\n vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.4.0)\n viridisLite   0.4.2   2023-05-02 [1] CRAN (R 4.4.0)\n withr         3.0.1   2024-07-31 [1] CRAN (R 4.4.0)\n xfun          0.47    2024-08-17 [1] CRAN (R 4.4.0)\n yaml          2.3.10  2024-07-26 [1] CRAN (R 4.4.0)\n\n [1] /home/runner/work/_temp/Library\n [2] /opt/R/4.4.0/lib/R/site-library\n [3] /opt/R/4.4.0/lib/R/library\n\n──────────────────────────────────────────────────────────────────────────────"
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