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# The Grammar of Data Science: Python vs R

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In this post, I will elaborate on my experience switching teams by comparing and contrasting R and Python solutions to some simple data exploration exercises.

The main issue here is that the model fit, not the actual data, controls the scale of the y-axis. The model fits dwarf the actual data! Tufte would not approve. In R, ggplot automatically solves this problem for you, which makes the visualization useful out of the box without further effort. Furthermore, I find the R code easier to write than the Python code because it is composed of a combination of simple and easy to remember elements, e.g., geoms. It reads like English, and allows me to fit arbitrarily complex models to the data using the formula syntax. In Python, I always forget what the more specialized lmplot is called, and how to use it. I find that using R minimizes cognitive load relative to using Python for exploring data to quickly test hypotheses. In Seaborn's defense, it produces more elegant visualizations than vanilla Matplotlib with a simpler API. It is also younger than ggplot2, which means it has had less time to mature.

### Not convinced? dplyr will blow your mind

Let’s consider another example in which dplyr comes into play. Suppose we’re curious about how the cut, price, carat, and volume (a derived feature of the data) of these diamonds all covary with one another. In R, we can create a simple visualization that helps us quickly answer this question using ggpairs. But first, we need to construct a volume feature, select only the subset of the variables we care about, and sample the data to avoid overplotting.

Here’s the R code that does the trick:

``````library(GGally)

diamonds %>%
mutate(volume = x*y*z) %>%
select(cut, carat, price, volume) %>%
sample_frac(0.5, replace=TRUE) %>%
ggpairs(axisLabels="none") +
theme_bw()
``````

Again, the code reads like English. The methods mutate, select, and sample_frac (verbs!) are part of the dplyr data manipulation library, which I found very easy to quickly become proficient with. All I needed was this handy cheat sheet! I wish all libraries were this easy to learn and use.

Here’s the resulting visualization: The function ggpairs plots each variable against the others intelligently. For example, we see scatterplots for continuous vs. continuous data (e.g., volume vs. carat) or grouped histograms for continuous vs. categorical data (e.g., volume vs. cut). On the diagonal we see kernel density estimates for continuous data (e.g., the distribution of volume on the lower right) or histograms for categorical data (e.g., the distribution of cuts on the upper left). On the upper triangle we see correlation coefficients for continuous vs. continuous data (e.g., the correlation between volume and carat is 0.996) or grouped boxplots for continuous vs. categorical data (e.g., volume vs. cut). We can learn so much about the covariance structure of our data with such an informative graphic built from so few lines of code! Get KDnuggets, a leading newsletter on AI, Data Science, and Machine Learning