Search Results for "ldap-to-csv"

readr is an R package that provides a fast and friendly way to read rectangular data, such as CSV and TSV files. Part of the Tidyverse, it simplifies data import and parsing tasks in R.​

...It collects and preprocesses large-scale trip datasets (fares, pickup/dropoff, timestamps, locations, passenger counts) to enable data analysis, modeling, and visualization efforts. The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. The repository is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities.

vitae is an R package that streamlines resume and CV creation via R Markdown. It includes a collection of LaTeX and HTML templates along with helper functions to dynamically populate content from data sources such as ORCID or spreadsheets.

sparklyr is an R package that provides seamless interfacing with Apache Spark clusters—either local or remote—while letting users write code in familiar R paradigms. It supplies a dplyr-compatible backend, Spark machine learning pipelines, SQL integration, and I/O utilities to manipulate and analyze large datasets distributed across cluster environments.

activAnalyzer is a Shiny app that has been developed to analyze daily physical behavior data recorded at the hip in adults using an ActiGraph accelerometer (.agd file from a GT3X, GT3X+, wGT3X+ or wGT3X-BT device). Once analysis is completed, the app allows exporting results to .csv files and generating a report of the measurement (in either an .html format or a .pdf format). All the configured inputs relevant for interpreting the results are recorded in the report. Be sure that the inputs that are configured when generating the report correspond to the analysis that was actually performed (in other words, avoid modifying the inputs after generating satisfactory results). ...

Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS

...Each render uses a seed to introduce controlled randomness, so every image is unique while remaining reproducible when the same seed and formula are reused. The package logs the seed, formula, and file name to a CSV, which makes it easy to catalog outputs, re-generate favorites, and track experiments. A small helper sets up a simple directory scaffold for “everything” versus “handpicked” images and a logfile folder, encouraging a tidy, iterative workflow. Rendering is performed with ggplot2, and users can select coordinate systems (Cartesian or polar), foreground/background colors, number of images to generate, and output format (PNG by default with other devices available).