| #Script to scrape NARS website | |
| library(rvest) | |
| library(dplyr) | |
| nars <- read_html("https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys") | |
| links <- nars %>% | |
| html_nodes(".file-link, a") %>% | |
| html_attr("href") %>% | |
| tbl_df() %>% | |
| filter(grepl("files",value)) %>% |
| library(dplyr) | |
| # create a dummy dataframe with 100,000 groups and 1,000,000 rows | |
| # and partition by group_id | |
| df <- data.frame(group_id=sample(1:1e5, 1e6, replace=T), | |
| val=sample(1:100, 1e6, replace=T)) %>% | |
| group_by(group_id) | |
| # filter rows with a value of 1 naively | |
| system.time(df %>% filter(val == 1)) |
If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?
I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:
| library(pipeR) # https://github.com/renkun-ken/pipeR | |
| library(rlist) # https://github.com/renkun-ken/rlist | |
| library(rvest) # https://github.com/hadley/rvest | |
| library(stringr) # https://github.com/hadley/stringr | |
| # please ensure rvest is the latest dev version | |
| Pipe("http://www.r-project.org/foundation/memberlist.html")$ | |
| html()$ # use xpath to scrape the name list | |
| html_nodes(xpath = "//table[2]//td//text() | //table[3]//td//text()")$ | |
| html_text(trim = TRUE)$ |
| library(httr) | |
| library(XML) | |
| library(selectr) | |
| xpath <- function(x) structure(x, class = "xpath") | |
| sel <- function(x) xpath(css_to_xpath(x, prefix = "//")) | |
| url <- "http://www.boxofficemojo.com/movies/?id=ateam.htm" | |
| html <- content(GET(url), "parsed") |
| library(httr) | |
| library(digest) | |
| library(XML) | |
| s3_request <- function(verb, bucket, path = "/", query = NULL, | |
| content = NULL, date = NULL) { | |
| list( | |
| verb = verb, | |
| bucket = bucket, | |
| path = path, |
| license: gpl-3.0 | |
| redirect: https://beta.observablehq.com/@mbostock/d3-bar-chart |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
create different ssh key according the article Mac Set-Up Git
$ ssh-keygen -t rsa -C "your_email@youremail.com"