This document summarizes Kelli-Jean Chun's presentation on using Python and R for data science. It discusses data science roles, provides an overview of Python and R, compares them for different use cases, and outlines a plan to predict whether NYC dogs are spayed/neutered using both languages. R will be used for exploratory data analysis and visualization, while Python with Scikit-learn, Pandas and NumPy will be used to build and evaluate a predictive model. The languages will be connected using rpy2 to load data from R into Python and reticulate to run Python code in RMarkdown.

1. Turbocharge your data science
with Python or R
Kelli-Jean Chun
North Bay Python
Nov 4, 2018

2. Turbocharge your data science
with Python or AND R
Kelli-Jean Chun
North Bay Python
Nov 4, 2018

3. What the heck is a data scientist?
It depends on the company, here are a few example roles:
- Data science analysts: aka data analysts or business analysts
- Product data scientists: Partner with product managers &
engineers to focus on product initiatives
- Experimentation data scientists
- Growth/marketing data scientists
Leverage data
to gain insight
and solve
problems

4. What is R?
Python R
Indexing starts at 0 1 :)
Loops for i in range(3):
print(i)
for (i in 0:2){
print(i)
}
List/Vector [0, 1, 2, 3] c(0, 1, 2, 3)
Data Frames import pandas as pd
pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
data.frame('A' = c(1,2), 'B' = c(3,4))
When typical people say
this, they usually refer to
Type of snake Letter in the alphabet
“R is a language and environment for statistical computing and graphics.”
Source: https://www.r-project.org/

5. The Great Debate: Python or R

6. A brief comparison of some Python & R packages
used in Data Science
Use Case Python R
Data frame + manipulation Pandas + Numpy Base R + dplyr
Plotting matplotlib, seaborn, bokeh Base R, ggplot2, highcharter
Statistics statsmodels Base R
ML scikit-learn caret + glm + xgboost + ...
Deep Learning TensorFlow TensorFlow
Connecting to the other
language
rpy2, pyRserve, RPython reticulate, PythonInR,
rPython, rJython,
SnakeCharmR

7. So, Python or R?
As a data scientist, I’ll have both!

8. Predicting whether or not a NYC dog is spayed/neutered
There is a publicly available NYC dataset that has
information on licensed dogs, such as the:
- Dog name
- Gender
- Breed
- Birth month & year
- Coloring
- Borough (e.g. Manhattan, Bronx)
- Zip Code
- Whether or not guard or trained
- Whether or not spayed/neutered
Using this dataset, let’s build a model to predict
whether or not a NYC dog is spayed/neutered.
https://project.wnyc.org/dogs-of-nyc/

9. What is my typical data scientific method when
building a model?
- ETLs
- Pre-learning: Explore the data, feature engineering, visualizations
- Learning: Model the data
- Post-learning:
- Evaluate the model
- Document and present the final model in a consumable format for product, engineering, and
other data scientists
- Deployment: Data science as a service / microservice to call the model in production

10. Plan of action
Goal: Using the other features (dog name, gender, etc)
provide a prediction for whether or not we believe a dog is
spayed/neutered
1. Pre-learning: Process the data and explore in R
2. Learning: Develop a predictive model in Python
3. Post-learning: Evaluate the model in Python

11. Pre-Learning
Exploratory data analysis can be quickly done in R and a summary of the
exploration can be easily shared with RMarkdown.
Similar to Jupyter notebooks:
- Allows for reproducible analysis
- Quickly provide a report & visuals for others
- Organize code chunks
- Embed code in report
As a bonus, R provides fast and easy functions (once you understand some of the
strange syntax) to produce clean visuals.

12. RMarkdown HTML (or PDF)

13. Learning & Post-Learning
Python + Sklearn + Pandas + Numpy = 100%
- Sklearn (aka Scikit-learn): provides a wide variety of Machine Learning and
Statistical models. As well as allows for easier splitting of data into training
and testing and model evaluation.
- Pandas: provides the DataFrame type that makes working with data easier.
- NumPy provides broadcasting functions that make it easier to work with
arrays (specifically columns in a pandas DataFrame)

15. How do we connect the two languages?

16. R in Python with rpy2
Loading the data frame of NYC dogs that was processed in R into Python
can be done with rpy2
import rpy2.robjects as robjects
from rpy2.robjects import pandas2ri
# Read in data from R
pandas2ri.activate()
readRDS = robjects.r['readRDS']
df = readRDS('data/dogs_proc.RDS')
df = pandas2ri.ri2py(df)
R function to read R’s
RDS files

17. Python in RMarkdown with reticulate
```{r}
library("reticulate")
```
```{python}
print('Python in R')
for i in range(3):
print(i)
# execute Jupyter notebooks
import papermill as pm
pm.execute_notebook("example_notebook.ipynb",
"executed_notebook/example_notebook.ipynb")
```
Instead of specifying r code
(e.g. with {r}), specify python

19. Thanks!