![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/](https://image.slidesharecdn.com/turbochargeyourdatasciencewithpythonandr-181105003815/85/Turbocharge-your-data-science-with-python-and-r-4-320.jpg)
![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](https://image.slidesharecdn.com/turbochargeyourdatasciencewithpythonandr-181105003815/85/Turbocharge-your-data-science-with-python-and-r-16-320.jpg)
Turbocharge your data science with python and r
- 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!