rOpenSci package or resource used*
URL or code snippet for your use case*
Goal: extract certain block of data from different sections of the .pdf
Strategy: use split-apply-combine approach via locate_areas() (to get box coordinates of sections of interest) and then extract_tables() to get the data with the section.
locate_areas() demo
Code snippet
# |- fxn ----
# data munge function for map() later
CleanHeader <- function(tbl) {
tbl %>%
tidyr::pivot_longer(
cols = -idx
) %>%
dplyr::group_by(name) %>%
dplyr::filter(value != "") %>%
dplyr::summarise(value = paste0(value, collapse = ":")) %>%
tidyr::separate(value, c("cat", "data"), sep = ":", extra = "merge") %>%
dplyr::mutate_at(vars(data), stringr::str_remove, ":") %>%
dplyr::select(cat, data)
}
# |- data ----
file <- "./ropensci_white.pdf"
# get the box coordinates via interactive selection; this info is then used in extract_tables() area args
locate_areas(file, pages = 1)
# extract data
header_raw <- extract_tables(f, pages = 1,
area = list(c(74, 88, 128, 522)),
guess = FALSE)
# data munge; data/application specific
header_raw[[1]] %>%
tibble::as_tibble() %>%
janitor::remove_empty("cols") %>%
dplyr::mutate(idx = cumsum(stringr::str_detect(V1, "Section Marker"))) %>%
dlpyr::group_split(idx) %>%
purrr::map_dfr(CleanHeader)
Image
Sample .pdf where coloured boxes represent sections of interest to extract data from.
See follow up post below for picture (new users may only post 1 image/post 
)
Sector
Energy.
Field(s) of application
Energy.
What did you do?
Use {tabulizer} from rOpenSci to extract data of interest from .pdf to save time and avoid data quality issue that may be introduced if it was done manually.
Comments
I 
{tabulizer}
Twitter handle
@urganmax
