Last updated: 2023-07-26

Checks: 7 0

Knit directory: ms_mariposas_pheno/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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(20230601)

The command set.seed(20230601) 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.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: fe2a4eb

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version fe2a4eb. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    data/raw_data/.DS_Store

Untracked files:
    Untracked:  data/best_models_prec.csv
    Untracked:  data/best_models_prec.xlsx
    Untracked:  data/best_models_temp.csv
    Untracked:  data/best_models_temp.xlsx
    Untracked:  data/doy_medio_sp.csv
    Untracked:  data/doy_medio_sp.xlsx
    Untracked:  data/models_prec_scaled.csv
    Untracked:  data/models_prec_scaled.xlsx
    Untracked:  data/models_tmed_scaled.csv
    Untracked:  data/models_tmed_scaled.xlsx

Unstaged changes:
    Modified:   analysis/climate_sensibility.Rmd
    Modified:   analysis/index.Rmd
    Modified:   data/models_prec.csv
    Modified:   data/models_prec.xlsx
    Modified:   data/models_tmed.csv
    Modified:   data/models_tmed.xlsx
    Modified:   data/selected_species.csv
    Modified:   data/transectos_climate.csv

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.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/prepare_peak.Rmd) and HTML (docs/prepare_peak.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd fe2a4eb ajpelu 2023-07-26 peak data
html 124ca91 ajpelu 2023-06-01 Build site.
Rmd bb25c0d ajpelu 2023-06-01 remove msg
html 7b1115c ajpelu 2023-06-01 Build site.
Rmd 4e306f7 ajpelu 2023-06-01 prepare peak by species

Introduction

library(tidyverse)
library(here)
library(janitor) 
library(lubridate)
library(ggpubr)
# Read the transect data 
transectos <- read_csv(here::here("data/transectos_metadata.csv"))

Get the peak day of the phenology by year and site for each species

  • JMBA give the a folder with files for each species of the curve fitted by year and site (/raw_data/pheno_sp)

  • We prepare the data: read the data, join the transects metadata and then compute the day of year with the maximum fitted valued (peak_day)

  • There are problems with the files: Glaucopsyche_alexis_BMSSN_pheno.xlsx and Libythea_celtis_BMSSN_pheno.xlsx. We removed them into folder (/raw_data/pheno_sp_novalid)

  • I wrote a custom function to read the data, unite year, month and day and convert it into date. Then compute the day of year (doy), generate a dataframe (pheno) and export the data as /data/pheno.csv

# List the files 
pheno_files <- list.files(here::here("data/raw_data/pheno_sp/"), pattern = "*pheno.xlsx", full.names = TRUE)


prepare_pheno <- function(file) { 
  df_pheno <- readxl::read_excel(file) |>
    janitor::clean_names() |> 
    mutate(date = lubridate::make_date(year, month, day)) |>
    mutate(doy = lubridate::yday(date)) 
  
  return(df_pheno)
  }


pheno <- purrr::map_df(pheno_files, ~ prepare_pheno(.)) 

write_csv(pheno, here::here("data/pheno.csv"))

  • The dataframe pheno has 203134 records.

  • We extract the doy (day of the year) for each species, sites and year with the maximum peak of the fitted valued. For this purpose we write a custom function get_peak_day. The dataframe pheno contains some records with NA value in site_id and also some data with NA values in fitted variable. So we remove them using the option na.rm in the function dplyr::slice_max. The information about transect was joined. The output results is exported as data/doy_peak_sps.csv)

get_peak_day <- function(df_pheno) { 
  peak_day <- df_pheno |> 
    group_by(species, site_id, year) |>
    slice_max(fitted, na_rm = TRUE) |> 
    dplyr::select(species, site_id, year,
                month, day, week,
                fitted, date, doy) |> 
  ungroup()
  
  return(peak_day)
  }
 
doy_peak_sps <- get_peak_day(pheno) |> 
  inner_join(transectos)

write_csv(doy_peak_sps, here::here("data/doy_peak_sps.csv"))

sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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     

other attached packages:
 [1] ggpubr_0.4.0    janitor_2.1.0   here_1.0.1      lubridate_1.9.2
 [5] forcats_1.0.0   stringr_1.5.0   dplyr_1.1.0     purrr_1.0.1    
 [9] readr_2.1.4     tidyr_1.3.0     tibble_3.1.8    ggplot2_3.4.1  
[13] tidyverse_2.0.0 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10      getPass_0.2-2    ps_1.7.1         rprojroot_2.0.3 
 [5] digest_0.6.31    utf8_1.2.2       cellranger_1.1.0 R6_2.5.1        
 [9] backports_1.4.1  evaluate_0.19    httr_1.4.4       pillar_1.8.1    
[13] rlang_1.1.0      readxl_1.4.2     rstudioapi_0.14  whisker_0.4     
[17] car_3.1-0        callr_3.7.3      jquerylib_0.1.4  rmarkdown_2.19  
[21] bit_4.0.4        munsell_0.5.0    broom_1.0.4      compiler_4.2.3  
[25] httpuv_1.6.8     xfun_0.39        pkgconfig_2.0.3  htmltools_0.5.4 
[29] tidyselect_1.2.0 fansi_1.0.3      crayon_1.5.2     tzdb_0.3.0      
[33] withr_2.5.0      later_1.3.0      grid_4.2.3       jsonlite_1.8.4  
[37] gtable_0.3.1     lifecycle_1.0.3  git2r_0.30.1     magrittr_2.0.3  
[41] scales_1.2.1     vroom_1.6.3      carData_3.0-5    cli_3.6.0       
[45] stringi_1.7.8    cachem_1.0.6     ggsignif_0.6.3   fs_1.6.2        
[49] promises_1.2.0.1 snakecase_0.11.0 bslib_0.4.2      ellipsis_0.3.2  
[53] generics_0.1.3   vctrs_0.6.0      tools_4.2.3      bit64_4.0.5     
[57] glue_1.6.2       hms_1.1.2        parallel_4.2.3   abind_1.4-5     
[61] processx_3.7.0   fastmap_1.1.0    yaml_2.3.7       timechange_0.1.1
[65] colorspace_2.0-3 rstatix_0.7.0    knitr_1.41       sass_0.4.5