paper.bib

@misc{tidytext-archive,
  author       = {Julia Silge and
                  David Robinson and
                  Jim Hester},
  title        = {tidytext: Text mining using dplyr, ggplot2, and other tidy tools},
  month        = jun,
  year         = 2016,
  doi          = {10.5281/zenodo.56714},
  url          = {http://dx.doi.org/10.5281/zenodo.56714}
}

@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2016},
  url = {https://www.R-project.org/},
}

@Manual{R-dplyr,
  title = {dplyr: A Grammar of Data Manipulation},
  author = {Hadley Wickham and Romain Francois and RStudio},
  year = {2015},
  note = {R package version 0.4.3},
  url = {https://CRAN.R-project.org/package=dplyr},
}

@Manual{R-ggplot2,
  title = {ggplot2: An Implementation of the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and RStudio},
  year = {2016},
  note = {R package version 2.1.0},
  url = {https://CRAN.R-project.org/package=ggplot2},
}

@Manual{R-broom,
  title = {broom: Convert Statistical Analysis Objects into Tidy Data Frames},
  author = {David Robinson and Matthieu Gomez and Boris Demeshev and Dieter Menne and Benjamin Nutter and Luke Johnston and Ben Bolker and Francois Briatte and Hadley Wickham},
  year = {2015},
  note = {R package version 0.4.0},
  url = {https://CRAN.R-project.org/package=broom},
}

@article{tm,
  title = {Text Mining Infrastructure in R},
  author = {Ingo Feinerer, Kurt Hornik, and David Meyer},
  year = {2008},
  journal = {Journal of Statistical Software},
  volume = {25},
  number = {5},
  pages = {1-54},
  url = {http://www.jstatsoft.org/v25/i05/},
}

@Manual{R-quanteda,
  title = {quanteda: Quantitative Analysis of Textual Data},
  author = {Kenneth Benoit and Paul Nulty},
  year = {2016},
  note = {R package version 0.9.4},
  url = {https://CRAN.R-project.org/package=quanteda},
}


@article{tidydata,
   author = {Hadley  Wickham},
   title = {Tidy Data},
   journal = {Journal of Statistical Software},
   volume = {59},
   number = {1},
   year = {2014},
   keywords = {},
   abstract = {A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.},
   issn = {1548-7660},   
   pages = {1--23},
   doi = {10.18637/jss.v059.i10},
   url = {https://www.jstatsoft.org/index.php/jss/article/view/v059i10}
}