Last updated: 2019-11-06

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The purpose of this vignette is to briefly show the process used to go about quantify the effects that missing data have on the detection of MHWs. Specifically, the relationship between the percentage of missing data and how the MHW metrics may differ from those detected against the same time series with no missing data.

The missing data will be ‘created’ by striking out existing data from the three pre-packaged time series in the heatwaveR package, which themselves have no missing data. These data will first be detrended so that the random missing data removed will not be conflated with any trend in the data.

# The packages used in this analysis

# The custom functions written for the analysis
# The function used to de-trend the time series:
detrend <- function(df){
  resids <- broom::augment(lm(temp ~ t, df))
  res <- df %>%
    mutate(temp = round((temp - resids$.fitted),2))

The effects that missing data have on the results are investigated with the same methodology as the time series length and long-term trend tests, with the difference that data are being randomly removed before the analysis is run.

Random missing data

First up we begin with the random removal of increasing proportions of the data. We are going to use the full 37 year time series for these missing data experiments. We will randomly remove 0 – 50% of the data from each of the three times series in 1% steps. This is not being repeated here so the results will look very jagged. The necessary replication of this study is performed by repeating it on 1000 randomly selected pixels, which may be seen in ‘code/workflow.R’ in the GitHub repository.

control_missing <- function(prop, df){
  # NB: Don't allow sampling of first and last value to ensure
  # all time series are the same length
  ts_length <- nrow(df)
  miss_index <- sample(seq(2, ts_length-1, 1), ts_length*prop, replace = F)
  res <- df %>%
    mutate(row_index = 1:n(),
           temp = replace(temp, which(row_index %in% miss_index), NA),
           test = "missing",
           index_vals = prop) %>%
    dplyr::select(test, index_vals, t, temp)


The full analysis on the results, including the functions shown above, is run for all of the tests (time series length, missing data, and long-term trend) all at once to ensure consistency of methodology across tests. For this reason the exact step-by-step process for the missing data analysis is not laid out below. To investigate the source code one may go to ‘code/workflow.R’ in the GitHub repository. A link to that site may be found in the top right of the navigation bar for this site (the GitHub icon).

sst_ALL <- rbind(mutate(sst_WA, site = "WA"),
                 mutate(sst_NW_Atl, site = "NW_Atl"),
                 mutate(sst_Med, site = "Med"))
  sst_ALL_results <- plyr::ddply(sst_ALL, c("site"), single_analysis, .parallel = T,
                                 full_seq = T, clim_metric = F, count_miss = T, windows = T)
) # 65 seconds


A more thorough explanation of the results may be found in the manuscript. Below we show what the simple results calculated above for the effect of missing data on the count of the focal MHW look like.

sst_ALL_results %>% 
  filter(test == "missing",
         var == "focus_count",
         id == "mean_perc") %>% 
  ggplot(aes(x = index_vals*100, y = val, colour = site)) +
  geom_line() +
  labs(x = "Missing data (%)", y = "Change from control (% count of MHWs)", colour = "Site")


Session information

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/openblas-base/
LAPACK: /usr/lib/

 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

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

other attached packages:
 [1] doParallel_1.0.15    iterators_1.0.10     foreach_1.4.4       
 [4] ncdf4_1.17           lubridate_1.7.4      heatwaveR_0.4.1.9003
 [7] forcats_0.4.0        stringr_1.4.0        dplyr_0.8.3         
[10] purrr_0.3.3          readr_1.3.1          tidyr_1.0.0         
[13] tibble_2.1.3         ggplot2_3.2.1.9000   tidyverse_1.2.1     

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  xfun_0.10         haven_2.1.1      
 [4] lattice_0.20-35   colorspace_1.4-1  vctrs_0.2.0      
 [7] generics_0.0.2    viridisLite_0.3.0 htmltools_0.4.0  
[10] yaml_2.2.0        plotly_4.9.0      rlang_0.4.1      
[13] R.oo_1.22.0       pillar_1.4.2      glue_1.3.1       
[16] withr_2.1.2       R.utils_2.7.0     modelr_0.1.5     
[19] readxl_1.3.1      lifecycle_0.1.0   munsell_0.5.0    
[22] gtable_0.3.0      workflowr_1.1.1   cellranger_1.1.0 
[25] rvest_0.3.4       R.methodsS3_1.7.1 htmlwidgets_1.5.1
[28] codetools_0.2-15  evaluate_0.14     labeling_0.3     
[31] knitr_1.25        broom_0.5.2       Rcpp_1.0.2       
[34] backports_1.1.5   scales_1.0.0      jsonlite_1.6     
[37] hms_0.5.1         digest_0.6.22     stringi_1.4.3    
[40] grid_3.6.1        rprojroot_1.3-2   cli_1.1.0        
[43] tools_3.6.1       maps_3.3.0        magrittr_1.5     
[46] lazyeval_0.2.2    crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.3   zeallot_0.1.0     data.table_1.12.6
[52] xml2_1.2.2        assertthat_0.2.1  rmarkdown_1.16   
[55] httr_1.4.1        rstudioapi_0.10   R6_2.4.0         
[58] nlme_3.1-137      git2r_0.23.0      compiler_3.6.1   

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