fed_pubs/make_file.R at master · BrunaLab/fed_pubs · GitHub

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# SCRAPE DATA FROM SCOPUS
#
# `01a_scopus_API_fed.R`
# `01b_scopus_API_uni.R`

# DOWNLOAD DATA FROM USGS PUBLICATIONS WAREHOUSE
# https://pubs.usgs.gov/
# save as usgs_YYYYMMDD.csv in './data_raw/usgs_warehouse_downloads'


###########################################################################
###########################################################################

# to fix: 60002746_2017_04-1

# chose data download date and max month  ---------------------------------


date<-"20260101"
PM_max<-12
PY_max<-2025
PY_min_dataset<-2020
PY_min_analyses<-2020
author_position<-"first"
# author_position<-"anywhere"

# process federal records -------------------------------------------------

# process the usgs publication file downloaded from their website ---------

# api_code<-"---"
# usgs_mostrecent<-"usgs_20260327.csv"
# # Process with `process_usgs.R`
# source("./code/process_usgs.R")
# process_usgs(usgs_mostrecent,api_code)

# within year: bind csvs for the scopus IDs -------------------------------

cat<-"fed"
source("./code/csv_binder_within_year.R")
csv_binder_within_year(cat,date)

cat<-"uni"
source("./code/csv_binder_within_year.R")
csv_binder_within_year(cat,date)

# across years: bind the annual csvs --------------------------------------

cat<-"fed"
source("./code/csv_binder_across_years.R")
csv_binder_across_years(cat,date)

cat<-"uni"
source("./code/csv_binder_across_years.R")
csv_binder_across_years(cat,date)

# clean up the composite files --------------------------------------------

##### Need to update fix_usgs_affils to get most recent usgs


# Fed Files
source("./code/clean_fed.R")
clean_fed(date)

# Uni Files
source("./code/clean_uni.R")
clean_uni(date)


# prep datasets for analyses and make overall summaries

# NOTE THE FOLLOWING ARE EXCLUDING *ONLY* BOOK CHAPTERS
# IF YOU WANT BOOK CHAPTERS NEED TO UNCOMMENT
# AND ALSO
# NOT EXCLUDING ANY BY FLAG WORDS

source("./code/prep_analysis_datasets_fed.R")
prep_analysis_datasets_fed(date, PM_max,PY_min_dataset,PY_max)

source("./code/prep_analysis_datasets_uni.R")
prep_analysis_datasets_uni(date, PM_max, PY_min_dataset, PY_max)


# figures and summaries of results ----------------------------------------

source("./code/make_figs_fed.R")
make_figs_fed(date,PM_max,PY_min_analyses,PY_max,author_position)

source("./code/make_figs_uni.R")
make_figs_uni(date,PM_max,PY_min_analyses,PY_max,author_position)


# bootstrapping -----------------------------------------------------------

source("./code/bootstrap_npubs_fed.R")
bootstrap_npubs_fed(date,PM_max,author_position)

source("./code/bootstrap_npubs_uni.R")
bootstrap_npubs_uni(date,PM_max,author_position)

# render MS ^ Supplementary Information File -------------------------------


# rmarkdown::render('./docs/Bruna_MS.Rmd'),params=list(args = myarg))

# rmarkdown::render('./docs/Bruna_Supporting_Info.Rmd'),params=list(args = myarg))


###########################################################################
###########################################################################


# lag test ----------------------------------------------------------------


# lag test ----------------------------------------------------------------

source("code/lag_test.R")
lag_test_output<-lag_test()
lag_data<-as.data.frame(lag_test_output[1])
lag_fig<-lag_test_output[2]
lag_fig<-lag_fig[[1]]
sum(lag_data$n_lag)
# date1<-"20250901"
# date2<-"20251010"
#
#
# source("./code/lag_test_fed.R")
# lag_test_uni(date1, date2,PM_max)
#
#
# source("./code/lag_test_uni.R")
# lag_test_uni(cat, date,PM_max)