Full description
This is a repository for codes and datasets for the paper that is in press for open-access publication in Linguistik Indonesia, the flagship journal for the Linguistic Society of Indonesia (Masyarakat Linguistik Indonesia [MLI]) (cf. the link in the references below).To cite the paper:
Rajeg, G. P. W., Denistia, K., & Rajeg, I. M. (2018). Working with a linguistic corpus using R: An introductory note with Indonesian negating construction. Linguistik Indonesia, 36(1), 1–36.
To cite this repository:
Click on the "cite" (dark-pink button on the top-left) and select the citation style through the "datacite" option (right-hand side)
This repository consists of the following files:
1. Source R Markdown file for the writing of the paper, containing the R codes to generate the analyses in the paper.
2. Tutorial to download the Leipzig Corpus file used in the paper. It is freely available on the Leipzig Corpora Collection Download page (cf. the References link below).
3. Accompanying datasets as images and .rds format to run all code-chunks in the R Markdown file.
4. BibLaTeX and .csl files for the referencing and bibliography (with APA 6th style).
5. A snippet of the R session info after running all codes in the R Markdown file.
6. Project file .Rproj for the paper to be open in the RStudio.
7. .docx template following the basic stylesheet for Linguistik Indonesia
7. .docx template following the basic stylesheet for Linguistik Indonesia
Put all these files in the same folder!
To render the R Markdown into MS Word document, we use the "bookdown" R package (Xie, 2018). Make sure this package is installed in R.
Yihui Xie (2018). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.6.
Issued: 2018-04-06
Subjects
Corpus Linguistics |
Distinctive Collexeme Analysis |
Indonesian |
Indonesian language |
Indonesian linguistics |
Leipzig Corpora |
Negating Construction |
Open Access Resource |
R Markdown |
R programming language |
RStudio |
Reproducible research |
bookdown |
collostructional analysis |
data science skills |
quantitative corpus linguistics |
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Identifiers
- DOI : 10.4225/03/5A7EE2AC84303