- CRAN package download logs
- Download stats API in R:
- https://cranlogs.r-pkg.org/
- JSON API: https://github.com/r-hub/cranlogs.app
- e.g., https://cranlogs.r-pkg.org/downloads/total/2014-01-01:2014-12-31/ggplot2
- access through R package
canlogs: https://github.com/r-hub/cranlogs
- Python package
cranlogspip install cranlogs- https://cranlogs.readthedocs.io/en/latest/cran_downloads.html
- Pypi data:
- Conda data:
| Purpose | Python package | Python function | R package | R function |
|---|---|---|---|---|
| Data import and export | pandas | read_csv, to_csv | readr | read.csv, write.csv |
| pandas | read_excel | readxl | .xls and .xlsx | |
| pandas | read_json | jsonlite | json | |
| json | json.loads | jsonlite | json | |
| xml | xml.etree.ElementTree.parse | XML | xmlparse | |
| Data processing | pandas | apply, map | purrr | map |
| pandas | apply, map | plyr | apply functions, e.g., aaply | |
| pandas | loc, sort_values, aggregate, groupby, merge | dplyr, data.table | filter, arrange, select, mutate, summarise, groupby, join | |
| pandas | pivot, melt, stack, unstack, groupby | tidyr | gather, spead | |
| pandas | . | magrittr | %>% | |
| pandas, matplotlib | tidyverse | a set of packages that include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, and forcats | ||
| datetime | lubridate, hms | |||
| Models | ||||
| Linear models | sklearn.linear_model, statsmodels | stats | lm() | |
| Generalised linear models | statsmodels | GLM | stats | glm() |
| Generalised additive models | pyGAM, stasmodels.gam | mgcv | gam() | |
| Penalised linear models | sklearn.linear_model | Lasso, ElasticNet | glmnet | glmnet() |
| Penalised linear models | glmnet | glmnet | glmnet() | |
| Robust linear models | statsmodels | RLM | MASS | rlm() |
| Linear Mixed-Effects Models | stasmodels | mixedlm | lme4, lmerTest | |
| Structural eqation modeling | semopy | lavaan | ||
| k-means | sklearn.cluster | KMeans | stats | kmeans |
| PCA | sklearn.decomposition | PCA | stats | prcomp |
| t-SNE | sklearn.manifold | TSNE | Rtsne | Rtsne |
| KNN | sklearn.neighbors | KNeighborsClassifier | class,FNN | KNN |
| Decision Trees | sklearn | tree | rpart | rpart() |
| Random forest | sklearn.ensemble | RandomForestClassifier | randomForest | randomForest() |
| Gradient boosting | xgboost | xgboost | ||
| other | scipy, numpy, sklearn | e1071 | collection of models (Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier,...) | |
| other | sklearn | caret | caret is a set of functions that attempt to streamline the process for creating predictive models | |
| network analysis | networkx | igraph | ||
| Deep learning | keras, tensorflow | keras, tensorflow | ||
| Visualization | matplotlib, bokeh | ggplot2, graphics | ||
| Dashboard | panel, voila | shiny |
I retrieved package download data from Conda, PyPI, and Cran from Janurary 2018 to July 2019. Then I combined Conda and PyPI downloads to represent total downloads of Python packages.
Pandas was downloaded a lot more than dplyr, tidyverse and data.table. And the growth of pandas is substantial over time.
scikit-learn is downloaded a lot more than statsmodels in Python, caret in R, and e1071 in R.
Most people use Keras and Tensorflow in Python, few use them in R.
People use matplotlib a lot. ggplot2 is as popular as Bokeh.
For creating dashboard, most people use Shiny. Few use Panel and Voila.
Except for creating dashboard, people seem to download a lot more Python packages than R packages for data manipulation, visualization, machine learning, and deep learning.
Python package download numbers can be inflated for the following reasons:
- CI systems: A lot of the downloads could be coming from CI systems. And R might have a lot less downloads from CI systems.
- Environments: Python users might be more likely to manage multiple environment and install packages in various environment. However, R users might just use one environment.
- Packaging updates: R users might not update packages as often as Python users.
https://r4ds.had.co.nz/
https://lgatto.github.io/IntroMachineLearningWithR/
https://topepo.github.io/caret/index.html
https://daviddalpiaz.github.io/r4sl/
https://www.tidyverse.org/
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent