Stringr Cheat Sheet

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Introduction to Data Manipulation 1 Nicolas Attalides BarcelonaR - Workshop. A picture containing drawing Description automatically generated. Note: The above stringr functions are locale-sensitive –depending on your locale you may get slightly different results. returns the indexes of the sorted vector - sorts a character vector order: sort. Stringr vignette stringr cheat sheet regex guide 2 / 31. Stringr Why stringr? Part of tidyverse Fast and consistent manipulation of string data. Usage All functions in stringr start with str and take a vector of strings as the first argument. Most stringr functions work with regular expressions. Seven main verbs to work with strings. R For Data Science Cheat Sheet Tidyverse for Beginners Learn More R for Data Science Interactively at www.datacamp.com Tidyverse DataCamp Learn R for Data Science Interactively The tidyverse is a powerful collection of R packages that are actually data tools for transforming and visualizing data. All packages of the.

So, kinda the whole point of this “making-a-blog” endeavor (aside from getting to play with Yihui Xie’s super awesome blogdown 📦) was to give a more permanent home to some of the resources I’ve found and/or tweeted. So, without further ado…

`stringr` «aide-mémoire»

This visual guide (or cheat sheet, if you prefer) was made by Lise Vaudor (@LVaudor on twitter), and originally shared in her blog post, Manipuler des strings avec R.

In case you hadn’t caught on, her blog is in French 🇫🇷, and, while Google translate does a pretty solid job of rendering it en anglais, someone else (@suman12029) found it to be so darn helpful that they translated it into English 🇬🇧 and posted it here on GitHub.

Regardless of language, it’s a helpful intro to the stringr 📦. So, go check it out!

The cheat sheet itself needs no translation 🌏:

Just for kicks, I also exported a version as a pdf, but 💯% of the credit belongs to Lise Vaudor.

Fin



Big Data¶

Parallel Computing¶

parallel. PDF.

sparklyr¶

sparklyr. PDF.

Data mining and modeling¶

Data Mining. PDF only.
- association rules, sequential patterns, classification & prediction, regression, clustering, outliers, time series, text mining, socila networks, graph mining, spatial data, statistics, graphics, data manipulation, data access, big data, parallel computing, reports, weka, editors, guis

data.table¶

data.table Intro. PDF only (explanatory article).
data.table. PDF.

dplyr¶

Grammar of dplyr. PDF only (explanatory slides).
dplyr. PDF.

forcats¶

forcats. PDF.

sjmisc¶

sjmisc. PDF.

Import and Tidy up¶

readr and tidyr. PDF.

Machine Learning¶

caret¶

caret. PDF.

estimatr¶

estimatr. PDF.

h2o¶

h2o. PDF.

Keras¶

Keras. PDF.

Machine Learning¶

Machine Learning. PDF only.
- Supervised Learning;
- Unsupervised Learning;
- Deep Learning;
- Machine Learning Tips and Tricks;
- Probabilities and Statistics;
- Linear Algebra and Calculus.
Big Data Machine Learning. PDF only.
- linear regression, logistic regression, regularization (ridge, lasso), neural network, support vector machine, nayesian network and naïve bayes, k-nearest neighbors, decision tree, tree ensembles (bagging or random forest, boosting)
Machine Learning Modelling in R. PDF.

mlr¶

mlr. PDF.

Regressions¶

Regressions. PDF only.
- linear model, variable selection, diagnostics, graphics, tests, variable transformation, ridge, segmented, gls, glm, nls, gnls, loess, splines, robust, structural equation, simultaneous equation, pls, principal components, quantile, linear and nonlinear mixed effects, generalized additive, survival analysis, classification & regression trees, beta

Survival Analysis¶

survminer. PDF only.
- curve, ggplot2, cox model

NLP¶

quanteda¶

quanteda. PDF.

Regex¶

Basic Regular Expressions. PDF.