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library(RJSONIO) | |
library(httr) | |
library(ggplot2) | |
api_key <- scan("~/.meetup.apikey", character(), quiet = TRUE) | |
group_id <- 1414043 ## LA R meetup | |
req_url <- paste0("http://www.meetup.com/2/events?key=", api_key, | |
"&group_id=",group_id,"&status=past") | |
events_data <- fromJSON(rawToChar(GET(req_url)$content))$results |
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--- | |
runtime: shiny | |
output: html_document | |
--- | |
```{r, echo=FALSE} | |
inputPanel( | |
selectInput("n", label = "n:", choices = c(10,50)), | |
submitButton("Update") |
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library(dplyr) | |
read.csv("members_export.csv") %>% | |
mutate(pcrnk = percent_rank(CONFIRM_TIME), wdbl = ifelse(LinkedIn!="",2,1), | |
w = (3-2*pcrnk)*wdbl) %>% | |
sample_n(30, weight = w) %>% | |
mutate(name = paste(First.Name,Last.Name)) %>% select(name) |
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## Minimal example of R's data.table vs pandas aggregation and join benchmark | |
## ( more detailed but still basic benchmark here: | |
## http://datascience.la/dplyr-and-a-very-basic-benchmark/ ) | |
## Just copy paste into R and Ipython, respectively | |
## Timings on a decent server with data.table 1.9.4 & pandas 0.15.1 (Nov 2014) | |
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abbr <- input$inp_abbr | |
dbGetQuery(con, paste0("select * from states where abbr = '",abbr,"'")) |
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abbr <- validinp_character(input$inp_abbr) | |
## or: | |
abbr <- validinp_character(input$inp_abbr, pattern="^((CA)|(NY))$") |
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library(randomForest) | |
library(parallel) | |
genr_data <- function(n,p) { | |
X <- matrix(rnorm(n*p),n,p) | |
y <- as.factor(apply(X,1, function(x) | |
ifelse(sum(x^2)>qchisq(0.5,p),"+","-"))) | |
## Hastie etal 10.2 | |
data.frame(X,y) |
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import numpy as np | |
from scipy.stats import chi2 | |
from sklearn.ensemble import RandomForestClassifier | |
n = 1000 | |
p = 100 | |
def genr_data(n,p): | |
X = np.random.randn(n,p) | |
y = np.zeros(n) |
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## get the data | |
for yr in 2005 2006 2007; do | |
wget http://stat-computing.org/dataexpo/2009/$yr.csv.bz2 | |
bunzip2 $yr.csv.bz2 | |
done | |
## install R and data.table |
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spark-1.3.0-bin-hadoop2.4/bin/spark-shell --driver-memory 100G --executor-memory 100G | |
import org.apache.spark.mllib.regression.LabeledPoint | |
import org.apache.spark.mllib.linalg.Vectors | |
import org.apache.spark.mllib.tree.RandomForest | |
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics | |
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