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LeeMendelowitz

@LeeMendelowitz·15 public vizzes

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Excel Macro for exporting all sheets as csv

This example presents a VBA macro that exports every worksheet in an Excel workbook to a separate CSV file. It iterates through each sheet using a `For Each` loop over `Application.ActiveWorkbook.Worksheets`. For each sheet, it copies the sheet to a new workbook and saves it with the sheet’s name using the `SaveAs` method with `FileFormat:=xlCSV`. The macro then closes the temporary workbook, leaving the original file untouched.

Apr 8, 2016
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Time R Function Calls

This example demonstrates two R functions for timing the execution of arbitrary code: `time.call` evaluates an expression using `evalq` and prints the elapsed time via `proc.time`, while `time.call.q` times pre-quoted expressions passed to `eval`. It also shows how to combine `time.call.q` with the `magrittr` pipe operator (`%>%`) to wrap a quoted assignment in a readable, one-line pipeline. The code uses base R functions only, with no external visualization libraries or datasets.

Mar 7, 2015
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Bash one-liners

Two Bash one-liners demonstrate how to process files from the command line. The first uses `find` and `xargs` to locate all `*.aln` files, then pipes each filename into a `sh -c` command that runs `cut` to extract the first column, saving the output as a new file with a `.cut` extension. The second one-liner pipes the output of `file` into a redirect, capturing the file type information for every `*.stderr` file into a text file named `file_types`. The examples use standard Unix tools—`find`, `xargs`, `sh -c`, `cut`, `file`, and shell redirection—to perform file discovery and simple text transformation.

Mar 3, 2015
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Knitr Latex Template

This example demonstrates a LaTeX-based report template for integrating R graphics and analysis into a reproducible document. The code shows how to structure a PDF report using the `article` document class, with customized margins, headers, and float handling via the `float` package. It employs `knitr` to embed R code chunks, including a `ggplot` visualization, and uses `xtable` for potential table output. The template also configures hyperlinks, date formatting, and absolute positioning through `textpos`, providing a complete setup for generating polished, data-driven reports.

Feb 25, 2015
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Take a snapshot of a page with phantom JS

This example captures a static snapshot of a live web page using PhantomJS. It loads the DC Metro Metrics daily service report for February 23, 2015, then waits five seconds before writing the rendered HTML content to standard output, producing a full page dump. The code relies on PhantomJS’s `webpage` and `system` modules, setting a Chrome user-agent and using `page.open` with a callback and `setTimeout` to ensure the page finishes rendering. The result is a saved HTML file that preserves the report’s final visual state.

Feb 25, 2015
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DC Metro Metrics Data Download

This visualization presents a day-by-day summary of escalator and elevator performance for the Washington DC Metro system, drawn from the DC Metro Metrics dataset. It shows daily availability and broken-time percentages for each unit, allowing users to spot patterns of recurring outages and repairs. The code reads the daily unit reports CSV and renders a time series, using d3 scales and axes to plot the data over the calendar year.

Feb 22, 2015
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Using tr

This example demonstrates how to convert CSV files with non-Unix line endings into Unix format using the command-line utility `tr`. It shows two conversion scenarios: replacing carriage returns (`\r`) with line feeds (`\n`) for files with Mac OS 9 endings, and deleting carriage returns to handle Windows-style `\r\n` endings. The code processes the DC Metrorail ridership dataset, which contains daily totals, and verifies the results by comparing output files with `md5sum`. Alternative tools like `mac2unix` and `dos2unix` are also presented for the same task.

Feb 4, 2015
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Rename files in a directory by removing non-alphanumeric characters that shouldn't be in a filename.

This example shows a Python script that recursively renames files and directories by replacing whitespace with underscores and stripping non-alphanumeric characters except periods, underscores, and hyphens. It walks the file system using `os.walk` and applies regular-expression substitutions via the `re` module, then moves each item with `shutil.move`. The script operates on user-specified directories or the current working directory, printing each rename action to standard error as it proceeds.

Oct 31, 2014
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Gist demonstrating an R error

This example demonstrates a subtle ggplot2 error that occurs when mapping the `size` aesthetic in a scatter plot while also adding multiple `geom_path` layers for circles. The code uses the `ggplot()` function with `aes(x, y, size = s)` and `geom_point()`, then attempts to overlay concentric circles via `geom_path()` using a helper function `make.circle()`. The error arises only in `make.plot.1()`, which combines both the size mapping and the circle paths, while `make.plot.2()` (no size mapping) and `make.plot.3()` (no circles) run successfully, isolating the conflict between these features.

Jul 23, 2014
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Predict.lm not working for matrix data

This example shows the pitfalls of using R's `lm` function with matrix data. It demonstrates that `lm(y ~ X, data)` produces predictions of the wrong length when `X` is a matrix, while explicitly naming columns (`lm(y ~ X.1 + X.2 + X.3)`) or building a formula dynamically works correctly. The code uses the `poly` function to create a cubic polynomial regressor matrix and `predict.lm` to generate fitted values plotted with base R's `plot` and `lines`. A custom `make.predict` function highlights the length mismatch and logs warnings for erroneous cases.

Jun 17, 2014
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Wrapping a file function

This example demonstrates a Python decorator that transparently manages file handles for functions expecting file objects. The `wrap_file_function` class wraps a function so it can accept either a file handle or a filename string, automatically opening and closing the file when a string is passed. The code shows usage across writing, reading, and multiple-file scenarios, including error handling when a nonexistent file is read. Using Python's built-in `open`, `isinstance`, and function wrapping, it illustrates how decorators can standardize file I/O patterns.

May 3, 2014
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Dynamic Table

A dynamic table displays randomly generated rows and columns of digits, with each row keyed to a letter. The visualization uses D3 v3’s data join to bind nested arrays to table rows and cells, distinguishing new, updated, and removed elements by color—green, blue, and red respectively. Transitions animate opacity with staggered delays, while key functions and `enter`, `update`, and `exit` selections manage row insertion, column resizing, and deletion.

Apr 29, 2014
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Playing with Enter and Update

This example demonstrates how D3’s enter and update selections behave when binding an array of five numbers to existing and empty paragraph groups. Three sections show different styling strategies: the first inserts all paragraphs as green enter selections, the second merges enter and update selections before applying violet text and color, and the third styles update selections blue and enter selections green separately before setting text. The code uses `d3.select`, `.data()`, `.enter().append()`, and selection merging via `sec2_update`. Data is hardcoded as `[1,2,3,4,5]`, and the script runs after a two-second delay via `setTimeout`.

Apr 29, 2014
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ipython notebook demonstrating Python generators and the any/all functions.

This notebook demonstrates Python generator expressions and the built-in `any` and `all` functions. It first shows how a generator yields boolean comparisons for each character in the string 'python', and how `any` and `all` consume these values, printing `True` or `False` as results. The example then highlights the short-circuiting behavior of these functions, using a list of `t()` and `f()` functions to show that `any` stops after the first `True` and `all` stops after the first `False`, with the printed output revealing exactly how many generator values are consumed.

Jan 31, 2014
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Gist 7650194

This visualization explores how the number of sequence clusters varies with a minimum LCP (longest common prefix) threshold. It plots the count of intervals against the threshold value, using a NumPy-backed Python routine to parse and segment the data from a file containing over a million sequences. The chart uses Matplotlib’s `pyplot` to render a line graph, with the title "Number of jobs vs minimum lcp," demonstrating a trend of decreasing cluster counts as the threshold rises.

Nov 25, 2013