Last updated: 2019-03-19

workflowr checks: (Click a bullet for more information)

  • ✔ R Markdown file: up-to-date

    Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

  • ✔ 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(20190319)

    The command set.seed(20190319) 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

    Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

  • ✔ Repository version: 64ac134

    Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

    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:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    vignettes/data/sst_ALL_clim_only.Rdata
    Ignored:    vignettes/data/sst_ALL_event_aov_tukey.Rdata
    Ignored:    vignettes/data/sst_ALL_flat.Rdata
    Ignored:    vignettes/data/sst_ALL_miss.Rdata
    Ignored:    vignettes/data/sst_ALL_miss_cat_chi.Rdata
    Ignored:    vignettes/data/sst_ALL_miss_clim_event_cat.Rdata
    Ignored:    vignettes/data/sst_ALL_miss_clim_only.Rdata
    Ignored:    vignettes/data/sst_ALL_repl.Rdata
    Ignored:    vignettes/data/sst_ALL_smooth.Rdata
    Ignored:    vignettes/data/sst_ALL_smooth_aov_tukey.Rdata
    Ignored:    vignettes/data/sst_ALL_smooth_event.Rdata
    Ignored:    vignettes/data/sst_ALL_trend.Rdata
    Ignored:    vignettes/data/sst_ALL_trend_clim_event_cat.Rdata

Untracked files:
    Untracked:  analysis/bibliography.bib
    Untracked:  analysis/data/
    Untracked:  code/functions.R
    Untracked:  code/workflow.R
    Untracked:  data/sst_WA.csv
    Untracked:  docs/figure/

Unstaged changes:
    Modified:   NEWS.md
    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 64ac134 robwschlegel 2019-03-19 Publish analysis files

Overview

In this vignette the results from the main three vignettes are combined in order to visualise them simultaneously. Following this are individual sections on how to address the challenges presented by length, missing data, and decadal trends respectively.

Overview of reference time series

# A multi-panel summary figure with images and tables
load("~/MHWdetection/analysis/data/sst_ALL.Rdata")

# sst_ALL_summary <- sst_ALL %>% 

# Poential panels
# 1) The base time series
#   1.1) Rug pplot showing events
#   1.2) Events filled in with flames
#   1.3) Overlay thresholds
#   1.4) Overlay category thresholds
# 2) Mini map showing pixel location
# 3) Stats table (showing relevant stats for/from best practices)
# 4) The climatolgies/categories as there own doy x-axis panel
# 5) Lolli plot showing events

# ggplot(sst_ALL_summary)

Overview of results

The following figures show the combined results from time series length, missing data, and decadal trends side by side for each of the three components of the results. This helps to create a more complete pictures of the importance of the different variables.

Climatologies

Event metrics

Category counts

Session information

sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

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

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.16    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.3      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.3   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1