Solved – Modern successor to Exploratory Data Analysis by Tukey

data visualizationdescriptive statisticsexploratory-data-analysisreferences

I've been reading Tukey's book "Exploratory Data Analysis". Being written in 1977, the book emphasizes paper/pencil methods. Is there a more 'modern' successor which takes into account that we can now instantaneosly plot large data sets?

Best Answer

The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic.

Related Solutions

Solved – Tukey’s Hinges: Grouping Data

You are going to have problems dividing a set of data into four equal parts if the number of pieces of data is not a multiple of $4$.

One approach might be to duplicate some of the data. So if you have $4n$ data points, just divide into four sets of $n$ points by rank. If you have $4n-1$ points, duplicate the median, including it in both the second and third sets, so again you have four sets of $n$ points by rank. If you have $4n-2$ points, duplicate the first and third quartile points, including the first quartile in both the first and second sets and including the third quartile in the third and fourth sets, so again you have four sets of $n$ points by rank. And if you have $4n-3$ points, duplicate the median and the first and third quartile points, including them in the relevant sets and again you have four sets of $n$ points by rank. There are other approaches.

In your example with $18$ data points, that would give four equally sized subsets sets of

1st group: $1, 2, 2.5, 2.5, 2.5$
2nd group: $2.5, 3, 3, 4, 5$
3rd group: $5, 6, 7, 7.5, 7.5$
4th group: $7.5, 8, 9, 9, 10$

Quantiles (note the change from r to n) are difficult to define easily. Wikipedia gives 10 estimate types while Eric Langford gives 15 methods in the Journal of Statistics Education.

Solved – Panel-data exploratory data analysis

I always start by doing a PCA (Principal Component Analysis) in R because it takes almost no writing. Say you have all this in a data.frame that we call data.

pca <- prcomp(data)
# Screeplot.
plot(pca)
# Biplot.
biplot(pca)

For R users, there is also the ggplot2 library. I know that it can do wonders for data representation, but I don't know how to use it. Maybe someone will suggest something with it?

Best Answer

Related Solutions

Solved – Tukey’s Hinges: Grouping Data

Solved – Panel-data exploratory data analysis

Related Question