I recently wrote a blog on trends and seasonal variation in fruit and veg wholesale prices provided by DEFRA. It was using a beatiful technique called ‘STL’ or seasonal-trend decomposition via loess[^1]. Just now I spotted a dataset from the Office... [Read more...]

I was recently browsing stackoverflow.com (often called SO) for the most voted questions under R tag. To my surprise, many questions on the first page were quite well addressed with the data.table package. I found a few other questions that could benefit from a data.table answer, therefore ... [Read more...]

I recently wrote a blog on trends and seasonal variation in fruit and veg wholesale prices provided by DEFRA. It was using a beatiful technique called ‘STL’ or seasonal-trend decomposition via loess[^1]. Just now I spotted a dataset from the Office for National Statistics on winter mortality. ONS highlight that: ... [Read more...]

I was recently browsing stackoverflow.com (often called SO) for the most voted questions under R tag. To my surprise, many questions on the first page were quite well addressed with the data.table package. I found a few other questions that could benefit from a data.table answer, therefore ... [Read more...]

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There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It’s also a dynamic language and rapidly changing, so [Read more...]

Widespread attention towards the death of black men by police has sparked protests and public outrage in many a city. Recently, the Federal Government has launched an investigation into the conduct of the entire Chicago police department. However, by protests and videos popping up all over the country, the apparent ... [Read more...]

Widespread attention towards the death of black men by police has sparked protests and public outrage in many a city. Recently, the Federal Government has launched an investigation into the conduct of the entire Chicago police department. However, by p... [Read more...]

In case you missed it, Jonas Dalege and his colleagues at the PsychoSystems research group have recently published an article in Psychological Review detailing how attitudes can be represented as network graphs. It is all done using R and a dataset that can be downloaded by registering at the ANES ... [Read more...]

In case you missed it, Jonas Dalege and his colleagues at the PsychoSystems research group have recently published an article in Psychological Review detailing how attitudes can be represented as network graphs. It is all done using R and a dataset tha... [Read more...]

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One of the most common types of datasets people want to plot is one where there is a continuous dependent variable – like age, company profit, or beard length – as a function of a categorical independent variable – like gender, a specific company, or one’s pirate ship. Unfortunately, despite the vast ... [Read more...]

Part 3: Monster vs. Mythology word use Mulder: We’ve both lost so much… but I believe that what we’re looking for is in the X-Files. I’m more certain than ever that the truth is in there. Scully: I’ve heard the truth, Mulder. Now what I want are ... [Read more...]

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by Henrik B Nyberg, Mango Solutions   Tell us a bit about your background in Data Science. During the final year of my master in chemical engineering I steered away from lab work and the large scale industrial applications. Computers … Continue reading → [Read more...]

[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In [1]: # LOAD PACKAGES In [2]: import pandas as pd In [3]: import numpy as np In [4]: from sklearn import preprocessing as pp In [5]: from sklearn import cross_validation as cv In [6]: from neupy.algorithms import GRNN as grnn In [7]: from neupy.functions import mse In [8]: # DATA PROCESSING In [9]: df = pd.read_table("csdata.txt") In [10]: y = df.ix[:, 0] In [11]: y.describe() Out[11]: count 4421.000000 mean 0.090832 std 0.193872 min 0.000000 25% 0.000000 50% 0.000000 75% 0.011689 max 0.998372 Name: LEV_LT3, dtype: float64 In [12]: x = df.ix[:, 1:df.shape[1]] In [13]: st_x = pp.scale(x) In [14]: st_x.mean(axis = 0) Out[14]: array([ 1.88343648e-17, 5.76080438e-17, -1.76540780e-16, -7.71455583e-17, -3.80705294e-17, 3.79409490e-15, 4.99487355e-17, -2.97100804e-15, 3.93261537e-15, -8.70310886e-16, -1.30728071e-15]) In [15]: st_x.std(axis = 0) Out[15]: array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]) In [16]: x_train, x_test, y_train, y_test = cv.train_test_split(st_x, y, train_size = 0.7, random_state = 2015) In [17]: [...] [Read more...]

How many times you have an error in your code, query, etc and you don't have the solution? How many times in these cases you open your favorite browser and search in your favorite search engine and type (I mean copy/paste) that error and you click the first result ... [Read more...]