R Programming
https://www.youtube.com/watch?v=K-ss_ag2k9E
https://www.youtube.com/watch?v=9q7gssUP8UA
http://r4ds.had.co.nz/
http://rdpeng.github.io/RProgDA/
https://campus.datacamp.com/courses/importing-data-in-r-part-2/importing-data-from-databases-part-1?ex=8
R有一系列的資料分析pipeline packages 稱作 tidyverse (dplyr、tidyr、ggplot2、readr、tibble、purrr …) Hadley的演講 - “Data Science with R” 解釋了tidy data、Functional programming的理念，以及何以對data analysis有助益： https://www.youtube.com/watch?v=K-ss_ag2k9E – tidyverse https://blog.rstudio.org/2016/09/15/tidyverse-1-0-0/ – The tidy tools manifesto https://mran.microsoft.com/…/tidyv…/vignettes/manifesto.html – R, at its heart, is a functional programming (FP) language http://adv-r.had.co.nz/Functional-programming.html – Data Analysis pipeline: ggplot2, for data visualization. dplyr, for data manipulation. tidyr, for data tidying. readr, for data import. purrr, for functional programming. tibble, for tibbles, a modern re-imagining of data frames. – hms, for times. stringr, for strings. lubridate, for date/times. forcats, for factors. – Data import: DBI, for databases. haven, for SPSS, SAS and Stata files. httr, for web apis. jsonlite for JSON. readxl, for .xls and .xlsx files. rvest, for web scraping. xml2, for XML. – Modelling: modelr, for simple modelling within a pipeline broom, for turning models into tidy data
Logical Indexing
v[v>0]
v[confidence >0]
v[v>confidence]
v[v<0] = c(100, 200) # recycling
v[v<0] = v[v<0] + c(100, 200) # recycling
Matrix A
A[]
works for Matlab too.