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The update process for the projection_sources.rda file should be as follows:

1. Update R/source_classes.R if needed. If not, skip step 3.
2. Build package
3. Run data-raw/source_configs.R (this script will update data/projection_sources.rda)
4. Build package

The standard.rda file is generated using the use_data function in the usethis package https://usethis.r-lib.org/reference/use_data.html

To update the standard.rda (or sysdata.rds?) file, you will have to update the ffanalytics:::player_ids object and then rewrite it into the package with the use_data function. This is a bit labor intensive but allows for robust mapping of players to a common id. Add MFL IDs of the rookies that were added by MFL in the beginning of the season and then find the respective ids for the sources and add them in. (This can be done in a spreadsheet).

If you look at the results from running scrape_data you will see that some players will have values in the src_id column but no value in the id column. This would be the players that would need to be added/updated in the ffanalytics::::player_ids object.

This player_directories script will parse player directories for CBS, FFToday, Fantasypros and NFL. Make sure you load the ffanalytics package first to have access to tidyverse and rvest. The command to create the sysdata.rda file is usethis::use_data(player_ids, overwrite = TRUE, internal = TRUE). This command needs to be executed from the package root folder.

If you want to do a quick check to see if there are new players that needs to be added to the ffanalytics:::player_ids table then you can run player_table %>% filter(!(id %in% ffanalytics:::player_ids$id))

Randal Morris noted, "Below is what I use to get majority of the missing Ids left. Pretty much gets anyone with any relevance."

player_table1 <<- httr::GET("https://api.myfantasyleague.com/2020/export?TYPE=players&L=&APIKEY=&DETAILS=1&SINCE=&PLAYERS=&JSON=1") %>%
httr::content() %>% [[("players") %>% [[("player") %>%
purrr::map(tibble::as_tibble) %>%
dplyr::bind_rows() %>%
tidyr::extract(name, c("last_name", "first_name"), "(.+),\s(.+)") %>%
mutate_all(funs(gsub("(?![.-])[[:punct:]]", "", ., perl = T))) %>%
dplyr::mutate(name = paste0(first_name," ",last_name)) %>%
dplyr::select(id,name)

for (p in list("QB","RB","WR","TE","K")) {
for (j in 1:length(my_scrape[[p]][["id"]])){
if (is.na(my_scrape[[p]][["id"]][[j]])) {
my_scrape[[p]][["id"]][[j]] <- as.character(player_table1[match(my_scrape[[p]][["player"]][[j]], player_table1$name),1])
}
}
}
rm(player_table1)