Learning is happening increasingly outside of formal educational settings and in unsupervised environments. Technological advantages provide new tools and opportunities for lifetime learners. This reading note is my first step in managing my learning (aka Personal Knowledge Management). This step is called Personal Information Management (PIM). The goals are:

  • Organize information
  • Process the data and understand the information structure
  • Chunking: cluster similar information, filter out some detail, degrade the complexity

The reading notes are according to the book category in my reading list since 2009. For some reason, I lost all reading information before 2009. Many paper notes need to be digitized, and I will keep working on them in my spare time. My comments will start with “Hui:” in blue. Sometimes I summarize the contents in my own words. I wrote these notes with R Markdown and the R package bookdown

# dat <- read_csv("1_KnowledgeStructure/RawData/ReadingListHui.csv")
# library(RCurl)

u <- ""

# tc <- getURL(u, ssl.verifypeer=FALSE, .encoding = "CE_UTF8")
# dat <- read.csv(textConnection(tc), stringsAsFactors =  F)
dat <- read.csv("")
idx <- grep("Novel",dat$Category)
dat$Category[idx] <- "Fiction"

dat$KnowledgeType[which(dat$KnowledgeType == '')] = 'none'

dat %>%
  arrange(KnowledgeType, desc(Category))%>%
  select(Name, ProDetail, Category, Topic) %>%
  datatable(filter = "top", options = list(pageLength = 10))
  • Name: Book name
  • Category: Book category
  • ProDetail: My reading status
    • HaveNotRead: have not started reading
    • RoughReading: just got the structure and key ideas of the book without getting to every glorious detail
    • PartialReading: read carefully for some parts of the book but not the whole
    • ThroughReading: read word by word for the whole book
    • IntensiveReading: read repeatedly and try to understand it very well
  • Topic: the subject of the book. I use the information to classify books to do “syntoptical reading” which is to read two or more books on the same subjects. You may find lots of missing values here as I just began my journey on this. Also, it is not easy to identify books with “same subject”. I will only assign subject to non-fictions.
  • Priority: priority of writing book summary

The categories are:

dat %>%
  group_by(Category) %>%
  summarise( count = n()) %>%
  arrange(desc(count)) %>%
  datatable(filter = "top", options = list(pageLength = 50))