- R을 이용한 데이터 분석 실무
- R – 체계적 위험 척도 베타 계수 자동 계산
- Hierarchical Clustering with R (feat. D3.js and Shiny)
- The easiest way to learn R programming and data science
- 김형준
- pubdata.tistory.com/category/Lecture_R
- SAS vs. R (vs. Python) – which tool should I learn?
- http://bcho.tistory.com/category/빅데이타/R
- New York R conference
- Choosing R or Python for data analysis? An infographic
- R vs Python part 1 why R?
- R vs Python part 2 why Python?
- Ending the R vs Python war
- Machine Learning with R: An Irresponsibly Fast Tutorial
- R을 활용한 머신러닝
- Accelerating Analysis with Parallelism
- Parallelism, R, and OpenMP
- GPU-Accelerated R in the Cloud with Teraproc Cluster-as-a-Service
- R and openMP: boosting compiled code on multi-core cpu-s
- Multi-threaded R
- R IN A 64 BIT WORLD
- Hacking “Chutes and Ladders” using R
- R User Conference in Korea 2015
- Speakers 발표자료 링크 있음
- Links to slides from rstudio::conf 2017
- R at Microsoft
- Mortgages Are About Math: Open-Source Loan-Level Analysis of Fannie and Freddie
- Big data analysis with R and Apache Tajo (in Korean)
- Geographic visualization with R's ggmap
- Applied Spatial Data Science with R
- THE VECTOR SPACE OF THE POLISH PARLIAMENT IN PICTURES
- How to perform Twitter analytics in R
- Neural Network for Concrete Strength using R
- Build your own neural network classifier in R
- A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R
- Tufte in R
- Exploring the Demographics of Ferguson, Missouri
- 5 New R Packages for Data Scientists
- Application of PageRank algorithm to analyze packages in R
- 고석범 의사 “통계용 언어 R, 오피스처럼 써볼까요?”
- Build a web scraper for a literature search – from soup to nuts
- ANIMATED LOGISTIC MAPS OF CHAOTIC SYSTEMS IN R
- Coding, Visualizing, and Animating Bootstrap Resampling
- R vs Python: head to head data analysis
- Using Bayes Factors to Get the Most out of Linear Regression: A Practical Guide Using R
- [디블로터] ① “출발, 데이터 저널리즘 스터디”
- [디블로터] ②데이터 정제 실습, ‘공유 많은 시간대’ 찾기
- [디블로터] ③‘ggplot2’로 데이터 시각화하기
- [디블로터] ④‘ggmap’으로 지도를 그려보자
- [디블로터] ⑤데이터 과학 입문 + ‘니터’ 입문
- [디블로터] ⑥‘말끔한 데이터’란?
- [디블로터] ⑦선형회귀와 감자칩, R 마크다운
- [디블로터] ⑨네이버가 좋아하는 뉴스, 분석해보니
- [디블로터] ⑩‘니터’ 활용법 정리
- [디블로터] ⑪인터랙티브 차트도 손쉽게…‘플로틀리’ 패키지
- [디블로터] ⑫어느 인포그래픽이 더 적절할까?
- [디블로터] ⑬시간 처리를 편리하게…‘루브리데이트’ 패키지
- [디블로터] ⑭R에서 만든 반응형 차트를 웹으로, ‘플로틀리’
- A Quine in R
- 데이터 과학의 첫걸음: R 맛보기
- James Bond movies
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- bayesianR
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- 웹에서 하는 R 통계
- Handling Time Data
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- using r from ipython
- datascienceschool/rpython 설치 및 실행
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- R을 활용한 데이터 분석 #1 – R, 그것이 알고 싶다!
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- R 유니코드, 인코딩
- Using TensorFlow with R
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- R’s way for Deep Learning with Keras
- 딥러닝(Deep learning)을 R로 구현하기 – Prediction Model
- Tutorial: Deep Learning with R on Azure with Keras and CNTK
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- TensorFlow with R
- tensorflow.rstudio.com
- tensoRflow
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- sparklyr — R interface for Apache Spark
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- R 노트북을 써보았다: 간단한 PCA
- Announcing RStudio v1.0!
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- fivethirtyeight: Data and Code Behind the Stories and Interactives at 'FiveThirtyEight'
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- Visualization of MRI data in R
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- FLASK ON R (W. RETICULATE)
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- R에서 iteration 별 결과를 손쉽게 저장할 수 있는 replicate() 함수에 대해
- Running Pleasingly Parallel workloads using rxExecBy on Spark, SQL, Local and Localpar compute contexts
- MAC 시에라에서 한글 로케일 문제 해결하기
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- 예제 : 제네릭 함수와 전략 패턴
- Why R is Bad for You
- Introduction to Neurohacking In R
- rOpenSci Text Workshop 참석 후기
- R에서 행렬 간 이루어지는 다양한 곱셈에 대해 알아보기 (행렬의 곱셈, 하다마드 곱셈, 크로네커 곱셈)
- Tutorial: Data Wrangling and Mapping in R
- Docker 를 이용해 Rstudio server 띄워보기 (on Mac OSX)
- 능선회귀분석 with R - 1. 데이터 마련하기
- 능선회귀분석 with R - 2. 패라미터 추정하기
- 능선회귀분석 with R - 3. Coefficient paths 그리기
- 능선회귀분석 with R - 4. 베이지안 관점에서의 능선회귀분석
- 능선회귀분석 with R - 5. Cross Validation을 통한 람다값 정하기
- Optimization with R - 1. Newton’s method를 통한 해찾기
- Optimization with R - 2. Newton’s method를 통한 최대값 찾기
- Optimization with R - 3. 2차 근사로서의 뉴턴 방법에 대하여(테일러 정리의 활용)
- Optimization with R - 4. 다변수 함수 최적화, 뉴턴법(Newton’s method) 적용 예제
- Optimization with R - 5. 뉴턴 방법을 사용한 로지스틱 회귀분석 모수 추정하기
- 감마(Gamma) 분포 최대우도추정 with R - 1. 감마 분포의 pdf에 관하여
- 감마(Gamma) 분포 MLE 추정 with R - 2. 적률법(MOM)을 통한 모수 추정
- 감마(Gamma) 분포 MLE 추정 with R - 3. 최대 가능도 추정량(Maximum Likelihood Estimator) 구하기
- 감마(Gamma) 분포 MLE 추정 with R - 4. MOME vs. MLE 의 비교 Bias와 MSE에 대하여
- Make your own R package! - 1. 준비작업
- Make your own R package! - 2. R 패키지의 구조에 대하여
- Make your own R package! - 3. DESCRIPTION 파일 설정
- Make your own R package! - 4. 나만의 함수 추가하기 with roxygen2
- Make your own R package! - 5. Imports, Suggests, and README.Rmd 에 대하여
- Make your own R package! - 6. 함수 벡터화(Vectorized function)에 대하여
- Make your own R package! - 7. R과 C 언어 연결하기
- Make your own R package! - 8. C언어에서 recycling 구현과 functional programming
- Make your own R package! - 9. C 함수 R 패키지에 넣기 native routines 등록에 대하여
- 베이지안 통계 with R - 1. 베타분포(Beta dist.)의 확률밀도함수(pdf)에 대하여
- 베이지안 통계 with R - (번외편) 이산형 사전분포(descrete prior distribution)에 대하여
- 베이지안 통계 with R - 2. 이항분포와 베타분포의 궁합! 켤레사전분포(Conjugate prior dist.)에 대하여
- 베이지안 통계 with R - 3. 베타-이항분포(beta-binomial dist.)와 사후예측분포(posterior predictive dist.)에 대하여
- Simulation with R - 1. Metropolis-Hastings 난수추출 알고리즘에 대하여
- Simulation with R - 2. 깁스 표집(Gibbs sampling)을 사용한 다변량 정규분포 난수 생성
- R의 회귀분석계수 계산과정에 대하여 - gmp 패키지, 촐레스키(Cholesky) 분해, 그리고 QR 분해
- EM 알고리즘에 대하여(1) - Optimization with R
- EM 알고리즘의 ascent property에 대하여 - Optimization with R
- R 추천시스템
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- r-podcast.org
- 고객 구매주기 및 서비스 이탈 고객 판정
- 예제 : 제네릭 함수와 전략 패턴
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- Stanford Open Policing Project
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- Don't use deep learning your data isn't that big
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- Mac 용 R에서 잘 읽지 못 하는 한글 파일을 잘 읽는 방법
- Concept of Bayesian data analysis with a coin example and rejection sampling
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- 아빠가 들려주는 통계
- www.di.fc.ul.pt/~jpn/r
- R Statistical Programming Using MariaDB as the Background Database
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- Analyzing Baseball Data with R Clevland Indians에서 일하는 Max Marchi의 책
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- Baseball Analytics Proves a Theory
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- baseball_R - Companion to Analyzing Baseball Data with R
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- How to make any plot in ggplot2?
- Data Visualization with ggplot2
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- Top 50 ggplot2 Visualizations - The Master List (With Full R Code)
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- greta
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- KoNLPer - KoNLP 결과를 보내주는 flask with r 서버 dockerize http://konlper.duckdns.org/list
- miniCRAN
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- RcppArmadillo 0.5.200.1.0
- Renjin is a JVM-based interpreter for the R language for statistical computing
- ReporteRs - an R package for creating Microsoft Word and Powerpoint documents
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- revisit: a "Statistical Audit" for Statistical Reproducibility and Alternate Analysis
- RMySQL
- rnaturalearth - an R package to hold and facilitate interaction with natural earth map data
- rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python
- R Tools for Visual Studio preview now available
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- shinyHeatmaply – a shiny app for creating interactive cluster heatmaps
- tidyverse
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