Search Results for "clear"

dplyr is an R package that provides a consistent and intuitive grammar for data manipulation, enabling users to filter, arrange, summarize, and transform data efficiently. Part of the tidyverse ecosystem, dplyr simplifies complex data operations through a clear and readable syntax, whether working with data frames, tibbles, or databases. It is widely used in data science and statistical analysis workflows.

magrittr introduces the pipe operator (%>%) and related functional utilities into R. It underlies the powerful piped syntax widely adopted in tidyverse workflows by enabling left-hand argument passing and providing helpers like compound assignment pipes and exposition pipes.

...TinyTeX, is a custom LaTeX distribution based on TeX Live that is small in size but still functions well in most cases. Even if you run into the problem of missing LaTeX packages, it should be super clear to you what you need to do. In fact, if you are an R Markdown user, there is nothing you need to do, because missing packages will just be installed automatically. You may not even know the existence of LaTeX at all since it should rarely bother you. Currently, TinyTeX works best for R users. Other users can use it, too—it is just that missing LaTeX packages won’t be automatically installed, and you need to install them manually. ...

R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods,...

AnomalyDetection is an R package developed by Twitter for detecting anomalies in seasonal univariate time series. It implements the Seasonal Hybrid Extreme Studentized Deviate (S‑H‑ESD) test, which reliably identifies both global and local outliers in data with trends and seasonality—commonly applied to system metrics, engagement data, and business KPIs.

...The data requires careful handling due to its size of more than 2 million rows and coded missing values. By processing the date and time fields into proper formats, it becomes possible to generate clear time-series plots of energy usage. The repository demonstrates effective exploratory data analysis practices in R with a reproducible workflow for transforming raw data into visual insights.