Turning kindle notes into a tidy data
This article is originally published at /cleancode/
It is my dream to do everything with R. And we aRe almost there. We can write blogs in blogdown or bookdown, write reports in RMarkdown (thank you Yihui Xie!) create interactive webpages with Shiny (thank you Winston Chang). Control our lifx lights with lifxr (great work Carl!) and use emoticons everywhere with the emo package.
There is even a novel of my vision! I recently found chapter 40 of A Dr. Primestein Adventure™ The Day the Priming Stopped. There is a scene in there which says:
“This Fortress is a monumental technological achievement,” explained Professor Power. “Every aspect of the Fortress’s security is run by R.” As they arrived at the metal doors, the Professor pressed a small button on the wall to the right. “This is an elevatoR, run by its own R package.” They waited for the doors to open, but nothing happened. After a few minutes of alternately waiting and then mashing the elevatoR button, Professor Power called someone on his mobile phone. “The elevatoR is not working…what? Why would they do that?…call Hadley Wickham!…doesn’t anyone around here check packages against the development version of R before upgrading?…yes, we’ll wait.” “Someone upgraded R without permission. Should be fixed soon,” Professor Power explained.
But enough about jokeRs and jesteRs. As it is my life long mission to do everything in R and preferably in the tidyverse, I found something that wasn’t tidy ? !!! Kindle notes!
kindle notes and highlights.
I have a 2010 kindle to read E-books on and once in a while I write a note or highlight some text in the book. If you connect your kindle to the computer you can extract the highlights by copying the file `My Clippings.txt’ to your computer.
This is great, it’s a text file which means you can open it on every computer and search throug the contents. However…
It’s not tidy.
Let’s change that. The general procedure is thus:
- Create a new project in Rstudio
- Create a new folder called
data(or don’t but really this is neat isn’t it?) - Copy the
My Clippings.txtfile to thatdata-folder - Load the tidyverse `library(tidyverse)’
- Hammer away untill the txt file is a data frame.
- profit?
What is in this text file?
First we do some exploratory work on the file. I’ve found that the text file is structured in a particular way:
title (author)
- Highlight on Page 128 | Loc. 1962-68 | Added on Sunday, December 27, 2015, 03:09 PM
<empty line>
highlighted text
==========
title of the next highlighted book (author)
etc.
So how do we force this into a data frame?
Recognize the structure ( we will create functions for that)
- Chunks end with the ten ===== signs, we can split on that
- first line is the title and (author)
- we can seperate the author and the title
- next line of information is devided by ‘|’ signs.
- type, page, location, added date and time (in american time of course…)
- highlighted text (or if it is a bookmark, nothing)
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
raw_text <- read_file("data/My Clippings.txt") # read in the text file
per_chunk <- unlist(strsplit(raw_text, "==========")) # seperate into chunks
per_chunk[4]## [1] "\r\nThe Clean Coder_ A Code of Conduct For Professional Programmers - Robert C. Martin (Robert C. Martin)\r\n- Highlight on Page 90 | Added on Monday, January 25, 2016, 04:06 PM\r\n\r\nK ATA In martial arts, a kata is a precise set of choreographed movements that simulates one side of a combat. The goal, which is asymptotically approached, is perfection. The artist strives to teach his body to make each movement perfectly and to assemble those movements into fluid enactment. Well-executed kata are beautiful to watch. Beautiful though they are, the purpose of learning a kata is not to perform it on stage. The purpose is to train your mind and body how to react in a particular combat situation. The goal is to make the perfected movements automatic and instinctive so that they are there when you need them. A programming kata is a precise set of choreographed keystrokes and mouse movements that simulates the solving of some programming problem. You arent actually solving the problem because you already know the solution. Rather, you are practicing the movements and decisions involved in solving the problem.\r\n"
Above I have created seperate chunks that represent seperate highlights. And a example so you can see what I see.
Now for extracting the seperate elements. I create functions that do one thing.
# This function takes a chunk of character information
# and seperates it into lines.
seperate_into_lines <- function(chunk){
result <- stringr::str_split(chunk, "\r\n")
unlist(result)
}
# result <- seperate_into_lines(per_chunk[100]) # testing if this works
## you should put this into formal test frameworks such as testhat if you
## build a package.
# Extract title sentance and remove author
# This function presumes that you already extracted the raw data into
# character chunks.
extract_title <- function(linechunk){
# search for second line
titleline <- linechunk[2]
return <- gsub("\\(.*\\)", "", titleline) # it took me some
#time to work this regular expression out.
stringr::str_trim(return, side = "both") # remove whitespace at ends
}
#extract_title(result) # testcase to see if it works for me.
# Extract the author from chunk, this function looks
# very much like the one above, it uses the same logic.
extract_author <- function(linechunk){
# search for second line
titleline <- linechunk[2] # identical
author <- stringr::str_extract(titleline, "\\(.*\\)") # extract piece
return <- gsub("\\(|\\)", "", author) #
stringr::str_trim(return, side = "both")
}
# extract_author(result)Let’s see if this works on a subset of the data. I usually take multiple notes in one book before I open another, so in this case the first 20 notes are really boring and all from the same book. To spice this up I take a random subset of rows. I will use a simple for-loop here, but I will use functional programming in the end-result. It works kind of the same, but is more explicit.
Some people will tell you that for-loops are slow in R, or that ‘loops are bad’ but they don’t know what they are talking about.[1]
I first create a data_frame [2] and pre-populate it.
# testset <- per_chunk[1:20] # You would use this if you want the first 20 pieces.
set.seed(4579) # if you do random stuff, it is wise to
# set the seed so that others can reproduce your work.
testset <- per_chunk[base::sample(x =1:length(per_chunk),size = 20)]
# unfortunately dplyr also has a function called sample. to specify that
# we want the 'normal' one I specify the name of the package followed by
# two ':'.
testingframe <- data_frame(
author = character(length = length(testset)),
title = character(length(testset)))
for( i in seq_along(testset)){
hold <- testset[i] %>% seperate_into_lines()
testingframe$author[i] <- hold %>% extract_author()
testingframe$title <- hold %>% extract_title()
}
testingframe## # A tibble: 20 × 2
## author
## <chr>
## 1 Andrew Hunt and David Thomas
## 2 Andrew Hunt and David Thomas
## 3 Alex Reinhart
## 4 David Price
## 5 City Watch #1 Terry Pratchett
## 6 City Watch #1 Terry Pratchett
## 7 Mark Manson
## 8 Kim Stanley Robinson
## 9 Kim Stanley Robinson
## 10 Kim Stanley Robinson
## 11 Douglas DeCarlo, James P. Lewis
## 12 David Price
## 13 City Watch #2 Terry Pratchett
## 14 Kenneth Knoblauch & Laurence T. Maloney
## 15 Alex Reinhart
## 16 Andrew Hunt and David Thomas
## 17 Robert C. Martin
## 18 Andrew Hunt and David Thomas
## 19 Andrew Hunt and David Thomas
## 20 David Price
## # ... with 1 more variables: title <chr>
The author and title functions seem to work, let’s extract some more information. The third row contained multiple pieces of information
example:
- Highlight on Page 132 | Loc. 2017-20 | Added on Saturday, August 20, 2016, 09:37 AM
Like the first functions we first select the correct row [3] and than apply some magic.
# this function extracts all the pieces
# and subsequent functions will deal with the seperate stuff.
extract_type_location_date <- function(linechunk){
meta_row <- linechunk[3]
pieces <- stringr::str_split(meta_row, "\\|") # the literal character,
# the '|' has a special meaning in regexp.
unlist(pieces)
}
# extract_type_location_date(result) # test function
# extract type from combined result.
# Here the use of the pipe `%>%` operator
# makes the steps clear.
extract_type <- function(pieces){
pieces[1] %>% # select the first row
stringr::str_extract( "- [[:alnum:]]{1,} ") %>% # extract at least one character.
gsub("-", "", .) %>% # replace - with nothing, removing it
stringr::str_trim( side = "both") # remove whitespace at both sides
}
# extract_type_location_date(result) %>%
# extract_type()
# extract page number by selecting first piece,
# trimming off of whitespace
# selecting a number, at least 1 times, followed by end of line.
extract_pagenumber <- function(pieces){
pieces[1] %>%
stringr::str_trim( side = "right") %>% # remove right end
stringr::str_extract("[0-9]{1,}$") %>%
as.numeric()
}
# extract_type_location_date(result) %>%
# extract_pagenumber()
# Extract locations. Just like above.
extract_locations <- function(pieces){
pieces[2] %>%
stringr::str_trim( side = "both") %>%
stringr::str_extract("[0-9]{1,}-[0-9]{1,}$")
}
# extract_type_location_date(result) %>%
# extract_locations()
# Extract date and convert to standard time, not US centric.
# I use the strptime from the base package here. The time is
# US-centric, but structured, so we can use the formatting from strptime.
# For example: %B is Full month name in the current locale
# and %I:%M %p means hours, minutes, am/pm.
extract_date <- function(pieces){
pieces[3] %>%
stringr::str_trim( side = "both") %>%
stringr::str_extract("[A-z]{3,} [0-9]{1,2}, [0-9]{4}, [0-9]{2}:[0-9]{2} [A-Z]{2}") %>%
strptime(format = "%B %e, %Y, %I:%M %p")
}
# Extract the highlight part.
extract_highlights <- function(linechunk){
linechunk[5]
}
# extract_highlights(result)In general:
- Split into chunks (already did that: per_chunk)
- Create a data frame
Apply extractors per chunk into data_frame
I would really love it if someone showed me how to do this with purrr
finalframe <- data_frame(
author = character(length = length(testset)),
title = character(length(testset)),
location = character(length(testset)),
pagenr = numeric(length(testset)),
type = character(length(testset)),
highlight = character(length(testset))
)
# loop through all values
for( i in seq_along(testset)){
hold <- testset[i] %>% seperate_into_lines()
finalframe$author[i] <- hold %>% extract_author()
finalframe$title[i] <- hold %>% extract_title()
finalframe$location[i] <- hold %>% extract_type_location_date() %>% extract_locations()
finalframe$pagenr[i] <- hold %>% extract_type_location_date() %>% extract_pagenumber()
finalframe$type[i] <- hold %>% extract_type_location_date() %>% extract_type()
finalframe$highlight[i] <- hold %>% extract_highlights()
}
finalframe## # A tibble: 20 × 6
## author
## <chr>
## 1 Andrew Hunt and David Thomas
## 2 Andrew Hunt and David Thomas
## 3 Alex Reinhart
## 4 David Price
## 5 City Watch #1 Terry Pratchett
## 6 City Watch #1 Terry Pratchett
## 7 Mark Manson
## 8 Kim Stanley Robinson
## 9 Kim Stanley Robinson
## 10 Kim Stanley Robinson
## 11 Douglas DeCarlo, James P. Lewis
## 12 David Price
## 13 City Watch #2 Terry Pratchett
## 14 Kenneth Knoblauch & Laurence T. Maloney
## 15 Alex Reinhart
## 16 Andrew Hunt and David Thomas
## 17 Robert C. Martin
## 18 Andrew Hunt and David Thomas
## 19 Andrew Hunt and David Thomas
## 20 David Price
## # ... with 5 more variables: title <chr>, location <chr>, pagenr <dbl>,
## # type <chr>, highlight <chr>
Cool right? Find this specific project on my github page. (I will also add a script only version shortly)
state of machine
click to expand to see machine info
sessioninfo::session_info()## - Session info ----------------------------------------------------------
## setting value
## version R version 3.3.3 (2017-03-06)
## os Windows 10 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate Dutch_Netherlands.1252
## tz Europe/Berlin
## date 2017-05-08
##
## - Packages --------------------------------------------------------------
## package * version date source
## assertthat 0.1 2013-12-06 CRAN (R 3.3.0)
## backports 1.0.5 2017-01-18 CRAN (R 3.3.2)
## broom 0.4.2 2017-02-13 CRAN (R 3.3.2)
## clisymbols 1.1.0 2017-01-27 CRAN (R 3.3.3)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.3.2)
## DBI 0.6-1 2017-04-01 CRAN (R 3.3.3)
## digest 0.6.12 2017-01-27 CRAN (R 3.3.3)
## dplyr * 0.5.0 2016-06-24 CRAN (R 3.3.1)
## emo 0.0.0.9000 2017-04-27 Github (hadley/emo@97754fd)
## evaluate 0.10 2016-10-11 CRAN (R 3.3.3)
## forcats 0.2.0 2017-01-23 CRAN (R 3.3.2)
## foreign 0.8-67 2016-09-13 CRAN (R 3.3.3)
## ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.3.2)
## gtable 0.2.0 2016-02-26 CRAN (R 3.3.0)
## haven 1.0.0 2016-09-23 CRAN (R 3.3.1)
## hms 0.3 2016-11-22 CRAN (R 3.3.2)
## htmltools 0.3.5 2016-03-21 CRAN (R 3.3.0)
## httr 1.2.1 2016-07-03 CRAN (R 3.3.1)
## jsonlite 1.3 2017-02-28 CRAN (R 3.3.3)
## knitr 1.15.1 2016-11-22 CRAN (R 3.3.2)
## lattice 0.20-35 2017-03-25 CRAN (R 3.3.3)
## lazyeval 0.2.0 2016-06-12 CRAN (R 3.3.0)
## lubridate 1.6.0 2016-09-13 CRAN (R 3.3.1)
## magrittr 1.5 2014-11-22 CRAN (R 3.3.0)
## mnormt 1.5-5 2016-10-15 CRAN (R 3.3.2)
## modelr 0.1.0 2016-08-31 CRAN (R 3.3.2)
## munsell 0.4.3 2016-02-13 CRAN (R 3.3.0)
## nlme 3.1-131 2017-02-06 CRAN (R 3.3.3)
## plyr 1.8.4 2016-06-08 CRAN (R 3.3.0)
## psych 1.7.3.21 2017-03-22 CRAN (R 3.3.3)
## purrr * 0.2.2 2016-06-18 CRAN (R 3.3.1)
## R6 2.2.0 2016-10-05 CRAN (R 3.3.1)
## Rcpp 0.12.10 2017-03-19 CRAN (R 3.3.3)
## readr * 1.1.0 2017-03-22 CRAN (R 3.3.3)
## readxl 0.1.1 2016-03-28 CRAN (R 3.3.0)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.3.2)
## rmarkdown 1.4 2017-03-24 CRAN (R 3.3.3)
## rprojroot 1.2 2017-01-16 CRAN (R 3.3.2)
## rvest 0.3.2 2016-06-17 CRAN (R 3.3.1)
## scales 0.4.1 2016-11-09 CRAN (R 3.3.2)
## sessioninfo 0.0.0.9000 2017-04-25 Github (r-pkgs/sessioninfo@0a5b58f)
## stringi 1.1.5 2017-04-07 CRAN (R 3.3.3)
## stringr 1.2.0 2017-02-18 CRAN (R 3.3.3)
## tibble * 1.3.0 2017-04-01 CRAN (R 3.3.3)
## tidyr * 0.6.1 2017-01-10 CRAN (R 3.3.2)
## tidyverse * 1.1.1 2017-01-27 CRAN (R 3.3.2)
## withr 1.0.2 2016-06-20 CRAN (R 3.3.1)
## xml2 1.1.1 2017-01-24 CRAN (R 3.3.2)
## yaml 2.1.14 2016-11-12 CRAN (R 3.3.2)
Notes
[1] ^1
[2] I use the tidyverse form of a data.frame called tibble or data_frame, it is like a data.frame but it never converts character to factor and never adds rownames . See more at ?tibble::tibble.
[3] This is absolutely not a robust way of programming, if the format ever changes, all my functions are screwed.
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This article is originally published at /cleancode/
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