Part of advanced analytics course.

1. Advanced Data Analytics:
Getting Started with R
Jeffrey Stanton
School of Information Studies
Syracuse University

2. Analytics: Key Steps
• Learn the application domain
• Locate or develop a data source or data set
• Clean and preprocess data: May take 60% of effort!
• Data reduction and transformation
– Find useful pieces, squeeze out redundancies
• Choose analytical approaches
– summarize, visualize, organize, describe, explore, find
patterns, predict, test, infer
• Communicate the results and implications to data users
• Deploy discovered knowledge in a system
• Monitor and evaluate the effectiveness of the system
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3. First Example: Ice Cream Consumption
• We all know the domain, we have all eaten ice cream
• Public data set obtained from supplement to Verbeek’s text:
http://eu.wiley.com/legacy/wileychi/verbeek2ed/datasets.html
• Let’s read the data into R and summarize it:
ICECREAM=read.csv("[pathname]/icecream.csv",header=T)
summary(ICECREAM)
• What do these two R commands do? Did you get a mean of
84.6 for Income? What are “Min,” “1st Qu.” and all of those
other things?
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4. Metadata
• There is a text file that goes with the CSV dataset:
“icecream.txt”
• This describes the meaning of the variables provided in the
dataset; essential if we are to make sense of these data:
Variable labels:
cons: consumption of ice cream per head (in pints);
income: average family income per week (in US Dollars);
price: price of ice cream (per pint);
temp: average temperature (in Fahrenheit);
Time: index from 1 to 30
• We also learn from the metadata that these are time series
data with monthly observations from 18 March 1951 to 11
July 1953
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5. “Sanity Check” Using Histograms and Boxplots
• Cleaning, screening, and preprocessing is essential to ensure
that you understand what your data set contains and that it
does not contain garbage; it is impractical to look at every
data point so we use histograms and boxplots to overview
our data:
hist(ICECREAM$income)
boxplot(ICECREAM$income)
• What is the purpose of the “$” notation in the commands
above? Is there any other way of referring to these
variables?
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6. Interpret These Graphics
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7. Explore
• Perhaps a family with greater income can afford to purchase
more ice cream:
plot(ICECREAM$income,ICECREAM$cons)
• How do you interpret a
scatterplot?
• Is there a pattern here?
• Does our intuitive hypothesis
fit the scatterplot?
• What else could scatterplots
show?
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8. More Tools to Support Exploration
results=lm(ICECREAM$cons~ICECREAM$temp)
# This is a comment line
# The previous command calculates a line
# that best fits the scatterplot with temp
# on the X axis and cons on the Y axis
plot(ICECREAM$temp,ICECREAM$cons)
abline(results) # Plots the best fit line
# The new data structure “results” has
# lots of information about the analysis.
# What does this list contain:
results$residuals
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9. What is the effect of time on these data?
plot(ICECREAM$time,ICECREAM$temp)
plot(ICECREAM$time,ICECREAM$cons)
• What do these plots show? Can you explain why these are
shaped the way they are?
• Based on your answer to the previous question, how does
the situation affect your strategies for understanding ice
cream consumption?
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10. Demonstrating Mastery
• Find a small numeric dataset; try starting at the Journal of
Statistical Education data website:
http://www.amstat.org/publications/jse/jse_data_archive.htm
• Read the dataset into R
• Summarize the variables in that dataset
• Use histograms and boxplots to check and understand your
data; use the metadata description that came with the dataset
to make sure that you know the variables
• Explore the data using plot; look for something interesting
• Put your findings in a slide and communicate them to me or
someone else
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