Kindle clippings.txt with R
[This article was first published on Max Humber, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I highlight a lot of junk on my Kindle. Well, it’s not all junk! ? There’s usually some good stuff buried deep within my clippings.txt
file. But it’s hard to manually parse through the file (and the junk).
In the past I’ve relied on online tools to organize my clippings. But this year, I thought I’d try and build my script to manage and clean it all.
Here’s what I ended up with:
library(tidyverse) library(stringr) txt <- read_lines("clippings_archive_2016.txt") parse_clippings <- function(txt) { h1_tibble <- txt %>% as_tibble() h2_prepend <- bind_rows(tibble(value = "=========="), h1_tibble) h3_index <- h2_prepend %>% mutate(index = ifelse( str_detect(pattern = "==========", value), 0, NA)) %>% mutate(row = row_number()) %>% mutate(row = ifelse(index == 0, row, NA)) %>% fill(row, .direction = "down") %>% mutate(entry = row %/% 5 + 1) %>% group_by(entry) %>% mutate(meta = row_number()) meta <- tribble( ~meta, ~type, 1, "start", 2, "book", 3, "location", 4, "blank", 5, "highlight" ) h4_meta <- h3_index %>% left_join(meta, "meta") %>% select(entry, value, type) %>% spread(type, value) %>% ungroup() h5_separate <- h4_meta %>% select(book, location, highlight) %>% separate(location, into = c("page", "location", "date"), sep = "\\s\\|\\s", fill = "right") %>% separate(book, into = c("book", "author"), sep = "\\s\\(", extra = "merge", fill = "right") h6_replace <- h5_separate %>% mutate(author = str_replace_all( author, pattern = "\\)", "")) %>% mutate(page = str_replace_all( page, "\\-\\sYour\\sHighlight\\son\\spage\\s", "")) %>% mutate(location = str_replace_all( location, "Location\\s", "")) %>% mutate(date = str_replace_all( date, "Added\\son\\s", "")) h7_format <- h6_replace %>% drop_na() %>% mutate(date = as.Date(date, format = "%A, %B %d, %Y %I:%M:%S %p")) %>% mutate(page = as.integer(page)) return(h7_format) } clippings <- parse_clippings(txt)
I’ve just pumped my clippings from 2016 through the script and everything seems to be working! I’m sifting through each highlight right now, trying to curate them down my to annual “Favourite Quotes of YYYY” post. Should have it up tomorrow!
To leave a comment for the author, please follow the link and comment on their blog: Max Humber.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.