Difference between revisions of "R"
Onnowpurbo (talk | contribs) |
Onnowpurbo (talk | contribs) |
||
| Line 92: | Line 92: | ||
* [[R: tidytext: sentiment comparing the three sentiment dictionaries]] | * [[R: tidytext: sentiment comparing the three sentiment dictionaries]] | ||
* [[R: tidytext: sentiment Most common positive and negative words]] | * [[R: tidytext: sentiment Most common positive and negative words]] | ||
| + | * [[R: tidytext: sentiment wordcloud]] | ||
===Bayesian=== | ===Bayesian=== | ||
Revision as of 09:41, 3 December 2019
R adalah bahasa pemrograman dan lingkungan perangkat lunak bebas untuk komputasi statistik dan grafik yang didukung oleh R Foundation for Statistical Computing. Bahasa R banyak digunakan di kalangan ahli statistik dan data miner untuk mengembangkan perangkat lunak statistik dan analisis data. Jajak pendapat, survei penambangan data dan studi basis data literatur ilmiah, menunjukkan peningkatan popularitas yang cukup besar dalam beberapa tahun terakhir. Pada Agustus 2018, R peringkat ke-18 dalam indeks TIOBE, ukuran popularitas bahasa pemrograman.
Paket GNU, source code untuk lingkungan perangkat lunak R ditulis terutama di C, Fortran dan R sendiri dan tersedia secara gratis di bawah GNU General Public License. Versi biner pre-compiled disediakan untuk berbagai sistem operasi. Meskipun R hanya menggunakan command line interface, ada beberapa antarmuka pengguna grafis, seperti RStudio, lingkungan pengembangan terintegrasi.
Referensi
- http://www.rexamples.com/
- http://www.sthda.com/english/articles/32-r-graphics-essentials/
- https://www.tidytextmining.com/
Pranala Menarik
- R: Install di Ubuntu 18.04
- R: Install di Ubuntu
- R: Install java
- R: Menggunakan library CRAN
- R: Matematika Dasar
- R: Tipe Data - Numeric Character Logical
- R: ls dan ls.str untuk melihat daftar variable dan isinya
- R: garbage cleanup
- R: sort
Data
R Studio
- https://gist.github.com/ElToro1966/999f1c8ca51a75648dd587a3170e4335
- http://web.cs.ucla.edu/~gulzar/rstudio/basic-tutorial.html
- RStudio: Install
File Processing
- R: operasi file
- R: readtext
- R: read PDF
- R: read CSV
- R: Read File
- R: multiread file
- R: save dan load data
Text Processing
- R: Package Pendukung Text Processing
- R: twitter persiapan
- R: stopwords
- R: Text Vector
- R: tidy text dataset - tibble
- R: contoh DataframeSource
- R: wordcloud
- R: wordcloud dari dua sumber
- R: evaluasi twit
- R: bigram
- R: bigram 2
- R: trigram
- R: ngram dan frekuensi-nya
- R: term frequency tf dan inverse document frequency idf
- R: ngram word clouds
- R: spam classification
Referensi
TidyText Processing
Ref: https://github.com/dgrtwo/tidy-text-mining
tidy data has a specific structure:
- Each variable is a column
- Each observation is a row
- Each type of observational unit is a table
- R: read multi PDF ke tidytext
- R: tidytext data frame
- R: tidytext Jane Austen Book
- R: sentiments analysis
- R: tidytext RPJP BAPPENAS
- R: tidytext NASA data
- R: tidytext analisa twitter
- R: tidytext: tidytext
- R: tidytext: tidytext hgwells
- R: tidytext: bronte
- R: tidytext: compare text
- R: tidytext: sentiment analysis basic
- R: tidytext: sentiment comparing the three sentiment dictionaries
- R: tidytext: sentiment Most common positive and negative words
- R: tidytext: sentiment wordcloud
Bayesian
- http://www.bnlearn.com/
- https://www.r-bloggers.com/text-message-classification/
- https://www.r-bloggers.com/bayesian-network-in-r-introduction/
- https://cran.r-project.org/web/packages/BayesianNetwork/vignettes/BayesianNetwork.html
- http://www.di.fc.ul.pt/~jpn/r/bayesnets/bayesnets.html
Time Series
- https://a-little-book-of-r-for-time-series.readthedocs.io
- https://www.statmethods.net/advstats/timeseries.html
- https://a-little-book-of-r-for-biomedical-statistics.readthedocs.io
- R: time series
- R: Rserve
- R: ARDUINO dengan RServe
- R: Stock Prices Prediction Using Machine Learning and Deep Learning Techniques with Python codes
Regression Analysis
- R Regression: Dataset
- R Regression: Simple Linear Regression
- R Regression: Linear Regression
- R Regression: Categorical Variables
- R Regression: multicollinearity essentials and vif
- R Regression: stepwise regression essentials
- R Regression: penalized regression essentials ridge lasso elastic.net