Last updated: 2018-10-08

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  • ✖ R Markdown file: uncommitted changes The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

  • ✔ Environment: empty

    Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

  • ✔ Seed: set.seed(20180713)

    The command set.seed(20180713) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

  • ✔ Session information: recorded

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  • ✔ Repository version: 0d0b30d

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    Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 
    
Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    Extra feature drafts/
    Ignored:    Shiny App Practice.R
    Ignored:    Shiny2.0/.DS_Store
    Ignored:    analysis/figure/
    Ignored:    data/.DS_Store
    Ignored:    data/big_data/

Untracked files:
    Untracked:  pesticide_explorer-Sara’s MacBook Pro.Rproj

Unstaged changes:
    Modified:   analysis/California.Rmd
    Modified:   analysis/Data.Rmd
    Modified:   analysis/Tox_load_vig.Rmd
    Modified:   analysis/about_tox_load.Rmd
    Modified:   analysis/neonic_vig.Rmd
    Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
Expand here to see past versions:
    File Version Author Date Message
    Rmd 6d69e9e ssoba 2018-08-09 Revised GitHub website for all typos
    html 6d69e9e ssoba 2018-08-09 Revised GitHub website for all typos
    html 9972b21 ssoba 2018-08-09 HTML files for last commit
    Rmd 4cd55fa ssoba 2018-08-08 Added crop key, Cropland Data Layer download, and About tab to shiny. Also did some minor cleanups.
    html 36cbc40 ssoba 2018-08-06 Build site.
    Rmd 15a59b5 ssoba 2018-08-03 Spelling fixes
    html 15a59b5 ssoba 2018-08-03 Spelling fixes
    html 1d48d55 ssoba 2018-08-03 Build site.
    Rmd dd313f8 ssoba 2018-08-01 Fixed all spelling mistakes and some formatting issues
    html dd313f8 ssoba 2018-08-01 Fixed all spelling mistakes and some formatting issues
    html 8b09700 ssoba 2018-08-01 Build site.
    Rmd 4b1a915 ssoba 2018-07-31 Added Vignette tab to nav bar, fixed California vignette to be insecticides not all pesticides. Cleaned up the Home page
    html 4b1a915 ssoba 2018-07-31 Added Vignette tab to nav bar, fixed California vignette to be insecticides not all pesticides. Cleaned up the Home page
    Rmd 9531377 ssoba 2018-07-20 Added some new pages: the California story
    html 9531377 ssoba 2018-07-20 Added some new pages: the California story

Toxic load is a measure of insecticide use that accounts for the different potencies of diverse insecticide products to insects. You could think of it as a measurement of insecticide intensity from a particular organism’s perspective. Historically, insecticide use has been measured using total kg applied. However, this measurement is misleading in determining the status of insecticide use. One kilogram of one type of insecticide does not have the same properties as one kilogram of a different pesticide. Overall amount of pesticides applied to an area doesn’t account for possible differences in how that insecticide will effect plants and/or insects.

Toxic load is a more accurate description of insecticide use than total kg applied because toxic load accounts for the differing toxicities of insecticide active ingredients.

How is Toxic Load measured?

Toxic load is derived by dividing the amount of insecticide by the Ld50 value of the active ingredient (ld50 stands for lethal dose for 50% of the population). The ld50 value of a compound is a measurement of toxicity and it is how many ug (mu-grams) of a chemical compound it takes to kill 50% of a honeybee population (ug compound/bees).

What does this measurement mean?

What the toxic load is telling us is the number of lethal doses to honey bees contained in a given amount of insecticides. However, it is important to note that we are using toxic load as an indicator of insecticide use intensity, NOT as a scientific prediction or measurement of the number of bees that will die in a given area. So, when we see areas with a high toxic load, we would predict that the health risk of insecticides to bees and other insects is higher than an area with a low toxic load. Real-life effects of insecticides also depend on many additional factors such as where, when, and how an insecticide is applied.

How is all of this useful?

Looking at graphs of change in toxic load over time can help us better understand how the insecticide use landscape has been changing from the perspective of insect health.

Now that you’re familiar with Toxic Load, head over to the graphs section to explore changes in insecticide use over time!

Session information

sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.17      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.22.1      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.2.3     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.10    tools_3.5.0      
[16] stringr_1.3.1     yaml_2.1.19       compiler_3.5.0   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1