Category: R

Joachim Zuckarelli walks us through the xplain package in R:

The above XML produces the following output (don’t worry too much about the call of xplain(), we will discuss later on in more detail how to work with the xplain() function):

library(car)
library(xplain)
xplain(call="lm(education ~ young + income + urban, data=Anscombe)", 
xml="http://www.zuckarelli.de/xplain/example_lm_foreach.xml")

##
## Call:
## lm(formula = education ~ young + income + urban, data = Anscombe)
##
## Coefficients:
## (Intercept) young income urban
## -286.83876 0.81734 0.08065 -0.10581
##
##
## Interpreting the coefficients
## —————————–
## Your coefficient ‘(Intercept)’ is smaller than zero.
##
## Your coefficient ‘young’ is larger than zero. This means that the
## value of your dependent variable ‘education’ changes by 0.82 for
## any increase of 1 in your independent variable ‘young’.
##
## Your coefficient ‘income’ is larger than zero. This means that the
## value of your dependent variable ‘education’ changes by 0.081 for
## any increase of 1 in your independent variable ‘income’.
##
## Your coefficient ‘urban’ is smaller than zero. This means that the
## value of your dependent variable ‘education’ changes by -0.11 for
## any increase of 1 in your independent variable ‘urban’.

I’ll be interested in looking at this in more detail, though my first glance indication is that it’ll be useful mostly in large shops with different teams creating and using models.

Sara Locatelli analyzes the lyrics to Hotel California using tidytext:

Sentiment analysis is a method of natural language processing that involves classifying words in a document based on whether a word is positive or negative, or whether it is related to a set of basic human emotions; the exact results differ based on the sentiment analysis method selected. The tidytext R package has 4 different sentiment analysis methods:

• “AFINN” for Finn Årup Nielsen – which classifies words from -5 to +5 in terms of negative or positive valence
• “bing” for Bing Liu and colleagues – which classifies words as either positive or negative
• “loughran” for Loughran-McDonald – mostly for financial and nonfiction works, which classifies as positive or negative, as well as topics of uncertainty, litigious, modal, and constraining
• “nrc” for the NRC lexicon – which classifies words into eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) as well as positive or negative sentiment

Sentiment analysis works on unigrams – single words – but you can aggregate across multiple words to look at sentiment across a text.

To demonstrate sentiment analysis, I’ll use one of my favorite songs: “Hotel California” by the Eagles.

Read the whole thing, though you can’t check out afterward.