2018-03-06

bioinformatics.md

@@ -102,6 +102,10 @@ Programming skills
 -  [Rosalind](http://rosalind.info/) (Rosalind is a platform for learning bioinformatics through problem solving)
 -  [**Teaching Materials of Langmead-lab**](http://www.langmead-lab.org/teaching-materials/)
 
+## Book
+
+- [A Primer for Computational Biology](http://library.open.oregonstate.edu/computationalbiology/)
+
 ## Comprehensive packages
 
 - [python] [Biopython](http://biopython.org/)

data-science.md

@@ -5,6 +5,7 @@
 **Table of Contents**
 
 - [Doc](#doc)
+- [Open data](#open-data)
 - [Data storage](#data-storage)
 - [Markup Language](#markup-language)
 - [Languages](#languages)
@@ -26,6 +27,11 @@
 
 -  [Three Things About Data Science You Won't Find In the Books](http://blog.mikiobraun.de/2015/03/three-things-about-data-science.html)
 
+## Open data
+
+- [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets) - A topic-centric list of high-quality open datasets in public domains. By everyone, for everyone!
+- [fivethirtyeight/data](https://github.com/fivethirtyeight/data) - Data and code behind the articles and graphics at FiveThirtyEight https://data.fivethirtyeight.com/
+
 ## Data storage
 
 - [Ten Simple Rulesfor Digital Data Storage](http://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1005097.PDF)

golang.md

@@ -36,6 +36,7 @@
 - [Figure and Chart](#figure%C2%A0and-chart)
 - [Image](#image)
 - [Graphics](#graphics)
+- [PDF](#pdf)
 - [HPC](#hpc)
 - [Distributed systems](#distributed-systems)
 - [Flow / piple](#flow--piple)
@@ -47,6 +48,7 @@
 
 ## doc
 
+-  [A curated selection of blog posts on Go](https://github.com/enocom/gopher-reading-list)
 -  [Ten Useful Techniques in Go](http://arslan.io/ten-useful-techniques-in-go)
 -  [Effective Go in chinese](http://www.hellogcc.org/effective_go.html)
 -  [(Draft) Traps, Gotchas, and Common Mistakes in Go (golang)](http://devs.cloudimmunity.com/gotchas-and-common-mistakes-in-go-golang/)
@@ -202,6 +204,7 @@ Applications:
 - [concurrent-map](https://github.com/streamrail/concurrent-map) - provides a high-performance solution to this by sharding the map with minimal time spent waiting for locks.
 - [lane](https://github.com/oleiade/lane) - A golang queues, stacks and deques implementation library
 - [btree](https://github.com/google/btree) - by google
+- [btree](https://github.com/tidwall/btree) - tidwall's folk
 - [bloom](https://github.com/willf/bloom) - Go package implementing Bloom filters
 - [cfilter](https://github.com/irfansharif/cfilter) - Cuckoo Filter implementation in Go, better than Bloom Filters
 - [bitset](https://github.com/willf/bitset) - Go package implementing bitsets
@@ -396,6 +399,7 @@ packages
 - [gomail](https://github.com/go-gomail/gomail) - The best way to send emails in Go.
 - [go-i18n](https://github.com/nicksnyder/go-i18n) - Translate your Go program into multiple languages with templates and CLDR plural support.
 - [gorequest](https://github.com/parnurzeal/gorequest) - GoRequest -- Simplified HTTP client ( inspired by nodejs SuperAgent ) http://parnurzeal.github.io/gorequest/
+- [melody](https://github.com/olahol/melody) - Minimalist websocket framework for Go
 
 ## Cache
 
@@ -412,6 +416,8 @@ KV
 
 In-memory
 
+- [buntdb](https://github.com/tidwall/buntdb) - BuntDB is an embeddable, in-memory key/value database for Go with custom indexing and geospatial support
+- [summitdb](https://github.com/tidwall/summitdb) - In-memory NoSQL database with ACID transactions, Raft consensus, and Redis API
 - [go-memdb](https://github.com/hashicorp/go-memdb) - Golang in-memory database built on immutable radix trees
 - [yakdb](https://github.com/needcaffeine/yakdb) - yakdb (yet another key-value database) is a highly-performant in-memory key-value store written in Go. http://vicvijayakumar.com/yakdb-a-nosql-database-in-go.html
 
@@ -461,6 +467,10 @@ graph
 
 -  [ln](https://github.com/fogleman/ln) - 3D line art engine.
 
+## PDF
+
+- [signintech/gopdf](https://github.com/signintech/gopdf) - A simple library for generating PDF written in Go lang [Chinese supported]
+- [gofpdf](https://github.com/jung-kurt/gofpdf) - A PDF document generator with high level support for text, drawing and images [Chinese not supported]
 
 ## HPC
 

machine-learning.md

@@ -7,6 +7,7 @@
 - [Doc](#doc)
 - [Random Forest](#random-forest)
 - [unclassified](#unclassified)
+- [Dimensionality reduction](#dimensionality-reduction)
 - [Clustering](#clustering)
 - [benchmark](#benchmark)
 - [Neural Network](#neural-network)
@@ -43,6 +44,12 @@ Learning
 - [machine-learning-for-software-engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
 - [What If I Am Not Good At Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
 
+## Courses
+
+- [机器学习速成课程 from Google](https://developers.google.cn/machine-learning/crash-course/)
+- [深度学习入门系列 - 网易云课堂](https://study.163.com/course/courseMain.htm?courseId=1004111045)
+- [《动手学深度学习》第一季课程汇总](https://discuss.gluon.ai/t/topic/753)
+
 ## Random Forest
 
 - [Powerful Guide to learn Random Forest (with codes in R & Python)](http://www.analyticsvidhya.com/blog/2015/09/random-forest-algorithm-multiple-challenges/), [Tuning the parameters of your Random Forest model](https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/)
@@ -53,6 +60,10 @@ Learning
 
 - [A Guide to Gradient Boosted Trees with XGBoost in Python](https://jessesw.com/XG-Boost/)
 
+## Dimensionality reduction
+
+- [Comprehensive Guide on t-SNE algorithm with implementation in R & Python](https://www.analyticsvidhya.com/blog/2017/01/t-sne-implementation-r-python/)
+
 ## Clustering
 
 - [annoy](https://github.com/spotify/annoy) - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

misc.md

@@ -59,6 +59,7 @@
 
 ## Work && Career
 
+-  [程序员的好日子什么时候才到头？](https://zhuanlan.zhihu.com/p/30771677)
 -  [同工不同酬，年薪 50 万美金的工程师到底有什么神本事？](http://news.cnblogs.com/n/207596/)
 -  [精益技术简历之道——改善技术简历的47条原则](http://zh.lucida.me/blog/lean-technical-resume/) （来个漂亮的在线简历[JJ Ying](http://iconmoon.com/about/)）
 

python.md

@@ -55,6 +55,7 @@
 - [Python Practice Book](http://anandology.com/python-practice-book/index.html)
 - [pymbook](http://pymbook.readthedocs.org/en/latest/)
 - [Full Stack Python](http://www.fullstackpython.com/)
+- [Migrating to Python 3 with pleasure](https://github.com/arogozhnikov/python3_with_pleasure) / [在Python 2.7即将停止支持时，我们为你准备了一份3.x迁移指南](https://mp.weixin.qq.com/s/Q3cqLuOZDSMOdAjVrCDrtQ)
 - [Writing Python 2-3 compatible code](http://python-future.org/compatible_idioms.html) | [编写兼容 Python 2 和 Python 3 的代码](http://blog.segmentfault.com/yexiaobai/1190000000637180)
 - [Python Cookbook](http://chimera.labs.oreilly.com/books/1230000000393/index.html)
 - [more books](http://importpython.com/books/)

r.md

@@ -39,6 +39,7 @@ update all R packages
 - [DataPyR](https://datapyr.zeef.com/kranthi.kumar)
 - [http://www.sthda.com/english/](http://www.sthda.com/english/)
 - [http://www.r-bloggers.com/search/ggplot2](http://www.r-bloggers.com/search/ggplot2)
+- [**Mastering Software Development in R**](https://bookdown.org/rdpeng/RProgDA/)
 
 ## Packages
 
@@ -75,9 +76,12 @@ tools
 - [tidyr](https://github.com/hadley/tidyr) tidyr is a reframing of reshape2 designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis.
 - [dplyr-and-plyr](http://www.slideshare.net/Sheffield_R_/dplyr-and-plyr), [When I use plyr/dplyr](http://educate-r.org//2014/01/24/usePlyr/)，[Using dplyr, and a comparison with plyr.]http://scicomp2014.edc.uri.edu/posts/2014-04-14-Smith.html
 - [dplyr: How to do data manipulation with R](http://www.sharpsightlabs.com/dplyr-intro-data-manipulation-with-r/)
+    - [Data Wrangling Part 1: Basic to Advanced Ways to Select Columns](https://suzan.rbind.io/2018/01/dplyr-tutorial-1/)
+    - [Data Wrangling Part 2: Transforming your columns into the right shape](https://suzan.rbind.io/2018/02/dplyr-tutorial-2/)
 - [Programming with dplyr](http://dplyr.tidyverse.org/articles/programming.html) 
 - [normalising-data-within-groups](http://www.r-bloggers.com/normalising-data-within-groups/), [script](https://dl.dropboxusercontent.com/u/67041874/normalise.R)
 - [**broom**](https://github.com/tidyverse/broom) - Convert Statistical Analysis Objects into Tidy Data Frames
+- [widyr](https://github.com/dgrtwo/widyr) - Widen, process, and re-tidy a dataset
 
 ## CLI
 
@@ -177,7 +181,7 @@ Extended packages. More: [RStartHere](https://github.com/rstudio/RStartHere#visu
 - [ggfittext](https://github.com/wilkox/ggfittext) - ggplot2 geoms to fit text into boxes
 - [**ggbeeswarm**](https://github.com/eclarke/ggbeeswarm) - Provides methods for beeswarm plots in ggplot2
 - [gghighlight](https://github.com/yutannihilation/gghighlight/) - Highlight points and lines in ggplot2
-- [waffle](https://github.com/hrbrmstr/waffle) - Make waffle (square pie) charts in R
+- [**waffle**](https://github.com/hrbrmstr/waffle) - Make waffle (square pie) charts in R
   [introduction](https://nsaunders.wordpress.com/2017/09/08/infographic-style-charts-using-the-r-waffle-package/)
 
 Tutorial
@@ -199,9 +203,18 @@ Tutorial
 
 ## Statistics
 
+book
+
+- [Summary and Analysis of Extension Program Evaluation in R](http://rcompanion.org/handbook/)
+
+misc
+
 - [Quick Multivariate data analysis (PCA, CA, MCA) and visualization](http://www.sthda.com/english/wiki/factoextra-r-package-quick-multivariate-data-analysis-pca-ca-mca-and-visualization-r-software-and-data-mining)
 - [The R Stats Package](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html) ([Adjust P-values for Multiple Comparisons](http://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html), [多重检验中的FDR错误控制方法与p-value的校正及Bonferroni校正](http://fhqdddddd.blog.163.com/blog/static/18699154201093171158444/))
-- [How can I do post-hoc pairwise comparisons in R?](http://statistics.ats.ucla.edu/stat/r/faq/posthoc.htm)
+- [How can I do post-hoc pairwise comparisons in R?](https://stats.idre.ucla.edu/r/faq/how-can-i-do-post-hoc-pairwise-comparisons-in-r/)
+
+- [Kruskal–Wallis Test](http://rcompanion.org/handbook/F_08.html)
+- [Multiple Comparison Procedures](http://www.jerrydallal.com/lhsp/mc.htm)
 
 ## Cluster