lmullen / dh-r

Computational Historical Thinking: With Applications in R

Computational Historical Thinking: With Applications in R

This is a textbook in progress on using computational methods for historical research. The book is available online.

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

dh-r's Issues

Structuring a data analysis project

Test

• test

Unclear where historydata package comes from in "Data Manipulation"

Since readers might not approach the book linearly (and chapters may be excerpted), it may be helpful to link back to that part of the introduction.

Data cleaning package: janitor

https://github.com/sfirke/janitor

Section 3.1 Years missing a step

Following along with this section, it wasn't clear how to make the dijon_prices object read out to the console. I found that I needed to load it in first using data("dijon_prices") before it could be read out as the book shows. Maybe something to that effect can be added to this section for clarity.

Databases and out of memory data

add a section on working with PostgreSQL and maybe SQLite for out of memory data.

Bug report

When I ran the codes placed here:
I got the following error:

places <- texts_annotated %>%

• lapply(entities, kind = "location")
  Show Traceback

Rerun with Debug
Error in substring(x, i$start, i$end) : invalid substring arguments
Error in match.fun(FUN) : argument "FUN" is missing, with no default

Here is my sessionInfo()
sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] magrittr_1.5 RWeka_0.4-24 openNLP_0.2-5 NLP_0.1-8 rJava_0.9-8

loaded via a namespace (and not attached):
[1] openNLPdata_1.5.3-2 tools_3.2.3 grid_3.2.3
[4] RWekajars_3.7.12-1

Investigate quantedata package

https://github.com/kbenoit/quanteda

Explain machine learning/classification algorithms

• Decision trees
• Linear regression
• k-means
• Affinity propagation

Link to Jenny Bryan's ggplot examples

Add paragraph about why Python is good too