1.1 “Virallinen” dokumentaatio

Virallinen dokumentaatio on täsmällisin, mutta alussa melko vaikeaselkoista. Siihen on hyvä aina palata kun tiedot lisääntyvät.

• Getting Help with R: https://www.r-project.org/help.html - tutustu tähän huolella
• Official FAQ and HOWTO Documents: https://cran.r-project.org/faqs.html
• The R Manuals https://cran.r-project.org/manuals.html
  • An Introduction to R html, pdf
• sähköpostilistat https://www.r-project.org/mail.html - yleisiä ja tiettyihin teemoihin keskittyviä sähköpostilistoja. R-help listalle tulee kymmeniä viestejä päivässä.

Yksi hyvä tapa tutustua R-projektin rakenteeseen on käydä läpi temaattiset “Task View”:t https://cran.r-project.org/web/views/ kuten CRAN Task View: Statistics for the Social Sciences

Selaamalla R-journaalia pysyt ajan tasalla uusista vakavasti otettavista paketeista, jotka ovat päässeet CRAN:iin, mutta joita kuvaavat paperit ovat myös läpäisseet vertaisarvioinnin.

1.2 Stack Overflow

SO on vilkas ohjelmointiin keskittyvä kysymys-vastaus -sivusto, jossa käydään paljon R:n liittyvää keskustelua. Katso:

• yleinen johdanto/linkkilista R-resursseihin http://stackoverflow.com/tags/r/info
• uusimmat tagilla r olevat kysymykset: http://stackoverflow.com/questions/tagged/r
• syksyn 2016 uutuus Documentations http://stackoverflow.com/documentation/r/topics - suosittelen!

1.3 Rstudion lunttilaput (Cheat-sheets)

• Linkit kaikkiin https://www.rstudio.com/resources/cheatsheets/
  • Base-R https://www.rstudio.com/wp-content/uploads/2016/10/r-cheat-sheet-3.pdf
  • Rstudio IDE https://www.rstudio.com/wp-content/uploads/2016/01/rstudio-IDE-cheatsheet.pdf
  • Data Wrangling with tidyr and dplyr https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
  • Data Visualization with ggplot2 https://www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf
  • Regular expressions https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
  • R Markdown https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf
  • R Markdown Reference Guide https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf
• olennaisimmat yhdessä pdf:ssä

2.1 Lue/tutustu

• An introduction to computational data analysis. Stat 133 - UC Berkeley http://gastonsanchez.com/stat133/
  • http://www.stat.berkeley.edu/~s133/all2011.pdf
• American Statistical Association Undergraduate Guidelines Workgroup. 2014. 2014 curriculum guidelines for undergraduate programs in statistical science. Alexandria, VA: American Statistical Association
• http://www.stat.berkeley.edu/~s133/all2011.pdf
• Data wrangling, exploration, and analysis with R - UBC STAT 545A and 547M http://stat545.com/
• Software carpenty http://software-carpentry.org/
• Data carpenty http://www.datacarpentry.org/
• http://happygitwithr.com
• https://beta.rstudioconnect.com/jennybc/happy-git-with-r/
• https://bookdown.org/connect/#/documents I never see any evidence that I’ve done anything here. Why is that?
• https://bookdown.org/jennybc/happy-git-with-r/ I hope nothing is here!
• https://bookdown.org
• Bookdown: Authoring Books with R Markdown (example and documentation)
  • rendered: https://bookdown.org/yihui/bookdown/
  • source: https://github.com/rstudio/bookdown/tree/master/inst/examples
• Official bookdown minimal example demo
  • rendered: https://bookdown.org/yihui/bookdown-demo/
  • source: https://github.com/rstudio/bookdown-demo
• R for data science
  • rendered: http://r4ds.had.co.nz
  • source: https://github.com/hadley/r4ds
• Efficient R
  • rendered: https://csgillespie.github.io/efficientR/
  • source: https://github.com/csgillespie/efficientR
• Statistical Programming Methods Textbook for STAT 385 at UIUC
  • rendered, custom domain?: http://spm.thecoatlessprofessor.com
  • autodeploys to gh pages via travis: https://coatless.github.io/statprogramming
  • source: https://github.com/coatless/statprogramming
• Time Series is Great!
  • source: https://github.com/coatless/timeseriesisgreat
  • autodeploys to gh pages via travis: https://coatless.github.io/timeseriesisgreat
    
• Applied Statistics Course Textbook
  • source: https://github.com/daviddalpiaz/appliedstats
  • autodeploys to gh pages via travis: https://daviddalpiaz.github.io/appliedstats
  • gh-pages branch: https://github.com/daviddalpiaz/appliedstats/commits/gh-pages
  • Auto-output commit example: https://github.com/daviddalpiaz/appliedstats/commit/0cfdc52e896f719a93d833e16dc5a1ba9095b563
  • Code commit example: https://github.com/daviddalpiaz/appliedstats/commit/a240bd6151d6533b2676833a2bf8231fd5fd3e41
• Mastering DFS Analytics
  • source: https://github.com/znmeb/mastering-dfs-analytics-bookdown
  • rendered, custom domain: https://www.masteringdfsanalytics.com
• Open research methods in computational social sciences and humanities: introducing R (Kainu 2014)

2.2 Katso

• Deborah Nolan: Statistical Thinking in a Data Science Course. UseR2016 https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/Statistical-Thinking-in-a-Data-Science-Course
• Mine Cetinkaya-Rundel: A first-year undergraduate data science course. UseR2016 https://channel9.msdn.com/events/useR-international-R-User-conference/useR2016/A-first-year-undergraduate-data-science-course
• Michael Andrew Levy: Teaching R to 200 people in a week. UseR2016 https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/Teaching-R-to-200-people-in-a-week

2.3 Opettele

• https://www.rstudio.com/online-learning/

3.1 Lue

• R Data Import/Export https://cran.r-project.org/doc/manuals/r-devel/R-data.html This is a guide to importing and exporting data to and from R.
• • R for Data Science: Data import http://r4ds.had.co.nz/data-import.html

3.2 Katso

• Getting Data into R https://vimeo.com/130548869 0.00 ->

3.3 Paketteja datan tuomiseen

These packages help you import data into R and save data.

• feather - a fast, lightweight file format used by both R and Python
• readr - reads tabular data
• readxl - reads Microsoft Excel spreadsheets
• openxlsx - reads Microsoft Excel spreadsheets
• googlesheets - reads Google spreadsheets
• haven - reads SAS, SPSS, and Stata files
• httr - reads data from web APIs
• rvest - scrapes data from web pages
• xml2 - reads HTML and XML data
• webreadr - reads common web log formats
• DBI - a universal interface to database management systems (DBMS)
  • RMySQL - MySQL driver for DBI
  • RPostgres - Postgres driver for DBI
  • RSQLite - SQlite driver for DBI
  • bigrquery - Google BigQuery driver for DBI
• PivotalR - reads data from and interfaces with Postgres, Greenplum, and HAWQ
• dplyr - contains an interface to common databases
• data.table - fread() for fast table reading
• git2r - tools to access git repositories

3.4 Datapaketteja

These packages contain data sets to use as training data or toy examples.

• babynames - Names given to US babies 1880-2014
• neiss - sample of all accidents reported to US emergency rooms 2009-2014
• yrbss - Youth Risk Behaviour Surveillance System data from 1991 to 2013
• nycflights13 - all out-bound flights from NYC in 2013
• hflights - flights departing Houston in 2011
• USAboundaries - Historical and Contemporary Boundaries of the United States of America
• rworldmap - country border data
• usdanutrients - USDA nutrient database
• fueleconomy - EPA fuel economy data
• nasaweather - geographic and atmospheric measures on a very coarse 24 by 24 grid covering Central America
• mexico-mortality - deaths in Mexico
• data-movies and ggplotmovies - data from the Internet Movie Database (IMDB)
• pop-flows - Population flows around the USA in 2008
• data-housing-crisis - Clean data related to the 2008 US housing crisis
• gun-sales - Statistical analysis of monthly background checks of gun purchases from NY times
• stationaRy - hourly meteorological data from one of thousands of global stations
• gapminder - Excerpt from the Gapminder data
• janeaustenr - Jane Austen’s Complete Novels

4.1 Lukemista

• Wickham, Hadley. 2014. ‘Tidy Data’. Journal of Statistical Software 59 (10). doi:10.18637/jss.v059.i10.
• R for Data Science: Tidy Data http://r4ds.had.co.nz/tidy-data.html
• tidyr-paketin vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
• Rstudio blog: Introducing tidyr https://blog.rstudio.org/2014/07/22/introducing-tidyr/
• Data Wrangling Cheat Sheet - RStudio https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

4.2 Paketteja

These packages help you wrangle your data into a form that is easy to analyze in R.

• tidyr - tools for tidying layout of tabular data
• dplyr - tools for joining multiple tables into a tidy data set
• purrr - tools for applying R functions to data structures, very useful when tidying
• broom - tools for tidying statistical models into data frames
• zoo - data structures for time series data
• PivotalR - R wrappers for in-database SQL operations (i.e. join, group by)

5.1 Paketteja

These packages help you transform your data into new types of data.

• dplyr - a grammar of data transformation
• magrittr - a concise syntax for calling sequences of functions
• tibble - efficient display structure for tabular data
• stringr - tools for working with strings and regular expressions
• lubridate - tools for working with dates and times
• xts - tools for time series based data
• data.table - fast data manipulation
• vtreat - tools for pre-processing variables for predictive modeling
• stringi - fast string processing facilities.
• Matrix - LAPACK methods for dense and sparse matrix operations

6.1 Paketteja

These packages help you visualize your data.

• ggplot2 with extensions - a versatile system for making plots
  • ggthemes - plot style themes
  • ggmap - maps with Google Maps, Open Street Maps, etc.
  • ggiraph - interactive ggplots
  • ggstance - horizontal versions of common plots
  • GGally - scatterplot matrices
  • ggalt - additional coordinate systems, geoms, etc.
  • ggforce - additional geoms, etc.
  • ggrepel - prevent plot labels from overlapping
  • ggraph - graphs, networks, trees and more
  • ggpmisc - photo-biology related extensions
  • geomnet - network visualization
  • ggExtra - marginal histograms for a plot
  • gganimate - animations
  • plotROC - interactive ROC plots
  • ggspectra - tools for plotting light spectra
  • ggnetwork - geoms to plot networks
  • ggtech - style themes for plots
  • ggradar - radar charts
  • ggTimeSeries - time series visualizations
  • ggtree - tree visualizations
  • ggseas - seasonal adjustment tools
• lattice - Trellis graphics
• rgl - interactive 3D plots
• ggvis - versatile system for interactive graphs
• htmlwidgets - framework for creating JavaScript widgets with R
  • leaflet - Interactive maps
  • dygraphs - Interactive time series plots
  • plotly - Interactive plots
  • rbokeh - Interactive Bokeh plots
  • Highcharter - Interactive Highcharts plots
  • visNetwork - Interactive network graphs
  • networkD3 - Interative d3 network graphs
  • d3heatmap - Interactive d3 heatmaps
  • DT - Interactive tables
  • threejs - Interactive 3d plots and globes
  • rglwidget - Interactive 3d plot
  • DiagrammeR - Interactive diagrams
  • MetricsGraphics - Interactive MetricsGraphics plots
• rCharts - many interactive JavaScript visualizations
• coefplot - visualizes model statistics
• quantmod - candlestick financial charts
• colorspace - HSL based color palettes
• viridis - Matplotlib viridis color pallete for R
• munsell - Munsell color palettes for R.
• RColorBrewer - color palettes for plots. No manual or website.
• dichromat - color-blind friendly palettes. No manual or website.
• igraph - Network Analysis and Visualization
• latticeExtra - Extensions for lattice graphics
• sp - tools for spatial data

7.1 Lue

• Frank E Harrell Jr: Regression Modeling Strategies (Joint Statistical Meetings Chicago 2016-07-31) http://biostat.mc.vanderbilt.edu/tmp/course.pdf

7.2 Katso

• UseR2016: broom: Converting statistical models to tidy data frames https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/broom-Converting-statistical-models-to-tidy-data-frames

7.3 Paketteja

These packages help you build models and make inferences. Often the same packages will focus on both topics.

• car - functions from An R Companion to Applied Regression
• Hmisc - miscellaneous functions for data analysis
• multcomp - Simultaneous Inference in General Parametric Models
• pbkrtest - parametric bootstrap test for linear mixed effects models
• mvtnorm - Multivariate Normal and t Distributions
• MatrixModels - Modelling with Sparse And Dense Matrices
• SparseM - linear algebra for sparse matrices
• lme4 - Linear Mixed-Effects Models using Eigen C++ library
• broom - tools for tidying statistical models into data frames
• caret - tools for Classification And REgression Training
• glmnet - generalized linear models via penalized maximum likelihood
• mosaic - Tools for teaching mathematics, statistics, computation and modeling
• gbm - gradient boosted regression models
• xgboost - Extreme Gradient Boosting
• randomForest - Random Forests for Classification and Regression
• ranger - a fast implementation of Random Forests
• h2o - parallel distributed machine learning algorithms
• ROCR - plots to visualize classifier performance
• pROC - Tools for visualizing, smoothing and comparing ROC curves
• PivotalR - R wrappers for MADlib’s parallel distributed machine learning algorithms

8.1 Paketteja

These packages help you communicate the results of data science to your audiences.

• rmarkdown - easy-to-use format for reproducible reports and dynamic documents in R
• knitr - embed R code within pdf and html reports
• flexdashboard - easy-to-create dashboards based on rmarkdown
• bookdown - books and long documents built on R Markdown
• rticles - ready to use R Markdown templates
• tufte - Tufte handout R Markdown template
• DT - Interactive data tables
• pixiedust - Customized tables
• xtable - Customized tables
• highr - Syntax Highlighting for R Source Code
• formatR - tidy_source() to format R source code
• yaml - Methods to convert R data to YAML and back
• pander - renders R objects into Pandoc markdown.