From c58ad3b7c0fe7be8754ae31d82de4bc95572a573 Mon Sep 17 00:00:00 2001 From: Niall Keleher Date: Sun, 11 Aug 2024 14:49:26 -0700 Subject: [PATCH] refactor site --- .github/workflows/publish.yml | 5 +- README.md | 19 +- .../software/python/execute-results/html.json | 16 -- .../python/figure-html/cell-5-output-1.png | Bin 160355 -> 0 bytes .../python/figure-html/cell-6-output-1.png | Bin 160355 -> 0 bytes .../python/figure-html/cell-9-output-1.png | Bin 64310 -> 0 bytes .../software/sql/execute-results/html.json | 16 -- .../sql/figure-html/cell-8-output-2.png | Bin 36794 -> 0 bytes .../software/stata/execute-results/html.json | 12 - _freeze/site_libs/quarto-listing/list.min.js | 2 - .../quarto-listing/quarto-listing.js | 243 ------------------ .../software/python/execute-results/html.json | 16 ++ .../python/figure-html/cell-7-output-1.png | Bin 358171 -> 358177 bytes .../python/figure-html/cell-8-output-1.png | Bin 174480 -> 174486 bytes .../software/sql/execute-results/html.json | 16 ++ .../sql/figure-html/cell-8-output-2.png | Bin 0 -> 36777 bytes .../software/stata/execute-results/html.json | 12 + .../stata/figure-html/cell-6-output-1.svg | 0 _quarto.yml | 83 +++--- {docs/about => about}/foundations.qmd | 0 {docs/about => about}/ipa_researchers.qmd | 0 {docs/about => about}/ipa_staff.qmd | 0 {docs/about => about}/mmds.qmd | 0 docs/contributing.qmd => contributing.qmd | 8 +- docs/index.qmd | 5 - 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name: Check out repository uses: actions/checkout@v4 - - uses: r-lib/actions/setup-r@v2 - - uses: r-lib/actions/setup-renv@v2 - with: - cache-version: 1 + - uses: extractions/setup-just@v1 - uses: prefix-dev/setup-pixi@v0.8.0 with: diff --git a/README.md b/README.md index 08c692b..c0a1c91 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,23 @@ -# IPA Research Resources +# IPA Data Science, Engineering, and Technology Handbook -Welcome to IPA's Research Resources! +Welcome to IPA's Handbook for Data Science, Engineering, and Technology! > [!WARNING] > This handbook is an early work in progress. Feedback, suggestions and > contributions are appreciated! Please create an Issue or submit a pull request if > there is anything you'd like to see changed or added. + +## About the Handbook + +This handbook is intended as a living document to assist IPA staff and collaborators +with common practices for writing code and working with data. The handbook will never be +comprehensive of every possible software, data object, or shared practice. However, IPA +staff working with data and technology are encouraged to use this handbook as a +reference and a shared project to give tips and advice for working collaboratively with +data analysis and software development. + +This repository is also intended as a reference for how to develop code that aligns with +best practices for software development. While the repository does not cover all +possible applications, software, or scenarios, the nature of this repository is such +that it should grow and improve to address the common work patterns that IPA uses for +writing source code for data science, engineering, and infrastructure management. diff --git a/_freeze/docs/software/python/execute-results/html.json b/_freeze/docs/software/python/execute-results/html.json deleted file mode 100644 index 3721c27..0000000 --- a/_freeze/docs/software/python/execute-results/html.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "hash": "c09c2f1ec6ef532984c8530835b78811", - "result": { - "engine": "jupyter", - "markdown": "---\ntitle: Python\n# Editors are the \"Maintainers\" of the page\neditors:\n - \"[Niall Keleher](https://poverty-action.org/people/niall-keleher)\"\n\n# Authors are the \"Contributors\" of the page\n# Add your name to the list if you have contributed to this page\n# author:\n# - Niall Keleher\nexecute:\n eval: true\njupyter:\n jupytext:\n text_representation:\n extension: .qmd\n format_name: quarto\n format_version: '1.0'\n jupytext_version: 1.16.2\n kernelspec:\n display_name: Python 3 (ipykernel)\n language: python\n name: python3\n---\n\n\n\n\n## What is Python?\n\nPython is a high-level, general-purpose programming language that is widely used in data\nscience, machine learning, and web development. It has a large standard library and a\nvibrant community that provides a wide range of libraries and tools for various\napplications. As such, Python provides a general-purpose ecosystem that can be used for\na wide range of applications.\n\n## How to install Python?\n\nThere are many ways to install Python. We recommend using Python in a virtual\nenvironment to avoid conflicts with other Python installations on your system.\n\nWe recommend using a [rye](https://rye.astral.sh/) or [pixi](https://pixi.sh/latest/).\nBoth of these tools provide a simple way to create and manage Python virtual\nenvironments.\n\nIn both cases, you can manage the python packages that are installed in the virtual\nenvironment using a `pyproject.toml` file. See the pyproject.toml example in this\nrepository for an example of how to manage Python packages. To add package dependencies\nto the virtual environment, using `rye`, you can run:\n\nWe'll use `rye` to demonstrate how to manage a Python virtual environment. Watch the\nfollowing video for a quick introduction to `rye`:\n\n{{\\< video https://youtu.be/q99TYA7LnuA >}}\n\nFirst, install `rye` using `winget` (Windows) or `brew` (MacOS/Linux):\n\n| Platform | Commands |\n| -------- | -------------------- |\n| Windows | `winget install rye` |\n| MacOS | `brew install rye` |\n| Linux | `brew install rye` |\n\nAdd libraries to the virtual environment using `rye add ...`:\n\n::: {#0b9678c2 .cell execution_count=1}\n``` {.python .cell-code}\n> rye add jupyterlab pandas matplotlib seaborn\n```\n:::\n\n\n## Coding Conventions\n\nWe highly recommend working with a [virtual environment](../guides/venv.md) to manage\nPython dependencies. The `pyproject.toml` is the preferred way to keep track of python\ndependencies as well as project-specific python conventions.\n\nWe recommend using [Ruff](https://docs.astral.sh/ruff/) to enforce\n[linting]() and formatting rules. In most\ncases you can use the default linting and formatting rules provided by `ruff`. However,\nyou can customize the rules by modifying the `[tool.ruff]` section of the\n`pyproject.toml` file in the root of your project. for more about the configuration\noptions, see the [Ruff documentation](https://docs.astral.sh/ruff/configuration/).\n\nIf you are working in a virtual environment created by `rye`, you automatically have\naccess to `Ruff via `rye lint`and`rye fmt\\` commands to lint and format your code.\n\nFor more inspiration, see the\n[GitLab Data Team's Python Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/python-guide/)\nand [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html).\n\n## Example Usage\n\nLet's load an example World Bank data via [Gapminder](https://www.gapminder.org/) using\nthe [causaldata](https://github.com/NickCH-K/causaldata) package.\n\n::: {#23edac86 .cell execution_count=2}\n``` {.python .cell-code}\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport statsmodels.formula.api as sm\nfrom causaldata import gapminder\n```\n:::\n\n\nLoad the Gapminder data as a pandas DataFrame:\n\n::: {#ec9292c0 .cell execution_count=3}\n``` {.python .cell-code}\ndf = gapminder.load_pandas().data\n```\n:::\n\n\nWe can check the dimensions of the DataFrame using `df.info()`:\n\n::: {#de42a732 .cell execution_count=4}\n``` {.python .cell-code}\ndf.info()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nRangeIndex: 1704 entries, 0 to 1703\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 country 1704 non-null object \n 1 continent 1704 non-null object \n 2 year 1704 non-null int64 \n 3 lifeExp 1704 non-null float64\n 4 pop 1704 non-null int64 \n 5 gdpPercap 1704 non-null float64\ndtypes: float64(2), int64(2), object(2)\nmemory usage: 80.0+ KB\n```\n:::\n:::\n\n\nLet's take a look at the first few rows of the DataFrame using `df.head()`:\n\n::: {#c5abbb6d .cell execution_count=5}\n``` {.python .cell-code}\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countrycontinentyearlifeExppopgdpPercap
0AfghanistanAsia195228.8018425333779.445314
1AfghanistanAsia195730.3329240934820.853030
2AfghanistanAsia196231.99710267083853.100710
3AfghanistanAsia196734.02011537966836.197138
4AfghanistanAsia197236.08813079460739.981106
\n
\n```\n:::\n:::\n\n\nTake a look at the relationship between GDP per Capita and Life Expectancy:\n\n::: {#abd5bd29 .cell execution_count=6}\n``` {.python .cell-code}\nsns.scatterplot(x=\"gdpPercap\", y=\"lifeExp\", hue=\"continent\", data=df).set(\n xscale=\"log\", ylabel=\"Life Expectancy\", xlabel=\"GDP per Capita\"\n)\n```\n\n::: {.cell-output .cell-output-display}\n![](python_files/figure-html/cell-7-output-1.png){width=585 height=431}\n:::\n:::\n\n\nSeparate the data by year, focusing on 1957 and 2007:\n\n::: {#0f7aec06 .cell execution_count=7}\n``` {.python .cell-code}\nsns.relplot(\n data=df.where(df[\"year\"].isin([1957, 2007])),\n x=\"gdpPercap\",\n y=\"lifeExp\",\n col=\"year\",\n hue=\"continent\",\n col_wrap=1,\n kind=\"scatter\",\n palette=\"muted\",\n).set(xscale=\"log\", ylabel=\"Life Expectancy\", xlabel=\"GDP per Capita\")\n```\n\n::: {.cell-output .cell-output-display}\n![](python_files/figure-html/cell-8-output-1.png){width=575 height=952}\n:::\n:::\n\n\n## Learning Resources\n\n- [The Python Tutorial](https://docs.python.org/3.12/tutorial/index.html)\n- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)\n- [Efficient Python for Data Scientists](https://khuyentran1401.github.io/Efficient_Python_tricks_and_tools_for_data_scientists/README.html)\n- [The Hitchhiker's Guide to Python](https://docs.python-guide.org/)\n\n", - "supporting": [ - "python_files\\figure-html" - ], - "filters": [], - "includes": { - "include-in-header": [ - "\n\n\n" - ] - } - } -} \ No newline at end of file diff --git a/_freeze/docs/software/python/figure-html/cell-5-output-1.png b/_freeze/docs/software/python/figure-html/cell-5-output-1.png deleted file mode 100644 index 2a312393521ce67815dbde34c2cdcb0399d9d8fc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 160355 zcmd43by$^K`z^fa5RjB^L<9sx>4rs#bc1wCcbC#70)ij{0xI3YqC=37R=N?S8xiR< 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1.16.2\n kernelspec:\n display_name: Python 3 (ipykernel)\n language: python\n name: python3\n---\n\n\n\n\n## What is SQL?\n\nSQL (pronounced \"sequel\") stands for Structured Query Language. It is a standard\nlanguage for accessing and manipulating databases. SQL is used to organize data in a\ndatabase as well as as questions of that data.\n\nThe traditional use of SQL is in relational databases, but it can also be used in other\ntypes of databases. In the examples below, we use [duckdb](https://duckdb.org/) which\nhas become a leading option for analytical workflows across a diverse set of data\nstorage types (e.g. relational databases, csv, parquet, json, etc.).\n\n## How to install SQL?\n\nTo use DuckDB, you can add it to your python virtual environment using `rye`:\n\n```{.bash}\nrye add duckdb duckdb-engine\n```\n\n## Using SQL\n\nConnect to a database.\n\n::: {#dc2cd34f .cell execution_count=1}\n``` {.python .cell-code}\n%load_ext sql\n%sql duckdb://\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nThere's a new jupysql version available (0.10.12), you're running 0.10.10. To upgrade: pip install jupysql --upgrade\nDeploy Dash apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\nLoading configurations from C:\\Users\\NKeleher\\code\\ipa-data-tech-handbook\\pyproject.toml.\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\nSettings changed:\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Configvalue
feedback0
autopandasFalse
autopolarsFalse
autolimit0
displaylimit10
\n```\n:::\n:::\n\n\nCreate a table from a CSV file.\n\n::: {#ea2e3f91 .cell execution_count=2}\n``` {.python .cell-code}\n%%sql\nCREATE TABLE penguins AS SELECT * FROM '../assets/data/penguins.csv'\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n\n \n \n \n \n \n \n \n
Count
\n```\n:::\n:::\n\n\nInspect the columns of the table.\n\n::: {#19cd6f90 .cell execution_count=3}\n``` {.python .cell-code}\n%sqlcmd columns -t penguins\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nametypenullabledefaultautoincrementcomment
speciesVARCHARTrueNoneFalseNone
islandVARCHARTrueNoneFalseNone
bill_length_mmFLOATTrueNoneFalseNone
bill_depth_mmFLOATTrueNoneFalseNone
flipper_length_mmBIGINTTrueNoneFalseNone
body_mass_gBIGINTTrueNoneFalseNone
sexVARCHARTrueNoneFalseNone
\n```\n:::\n:::\n\n\nQuery the table using a `SELECT` statement.\n\n::: {#0867770b .cell execution_count=4}\n``` {.python .cell-code}\n%%sql\nSELECT *\nFROM '../assets/data/penguins.csv'\nLIMIT 3\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
AdelieTorgersen39.118.71813750MALE
AdelieTorgersen39.517.41863800FEMALE
AdelieTorgersen40.318.01953250FEMALE
\n```\n:::\n:::\n\n\nRun analytical operations on the data.\n\n::: {#d3daa36f .cell execution_count=5}\n``` {.python .cell-code}\n%%sql\nSELECT species, COUNT(*) AS count\nFROM '../assets/data/penguins.csv'\nGROUP BY species\nORDER BY count DESC\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
speciescount
Adelie152
Gentoo124
Chinstrap68
\n```\n:::\n:::\n\n\nUsing `jupysql` store SQL query results as an object in a Jupyter notebook.\n\n::: {#793e7633 .cell execution_count=6}\n``` {.python .cell-code}\n%%sql species_count <<\nSELECT species, COUNT(*) AS count\nFROM '../assets/data/penguins.csv'\nGROUP BY species\nORDER BY count DESC\n```\n:::\n\n\nPlot the results of the query.\n\n::: {#a13026f9 .cell execution_count=7}\n``` {.python .cell-code}\nax = %sqlplot bar --table '../assets/data/penguins.csv' --column species\n\n# customize plot (this is a matplotlib Axes object)\n_ = ax.set_title(\"Count of penguins by species\")\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\nRemoving NULLs, if there exists any from species\n```\n:::\n\n::: {.cell-output .cell-output-display}\n![](sql_files/figure-html/cell-8-output-2.png){width=593 height=449}\n:::\n:::\n\n\n## Coding Conventions\n\nUse [SQLFluff](https://docs.sqlfluff.com/en/stable/) to lint SQL code.\n\nAdd SQLFluff to your virtual environment using `rye`:\n\n```{.bash}\nrye add sqlfluff\n```\n\nAssuming SQLFluff is installed in your virtual environment using `rye`, you can run the\nfollowing command to lint SQL code:\n\n```{.bash}\nrye run sqlfluff fix --dialect duckdb\n```\n\nSee the\n[GitLab Data Team's SQL Style Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/sql-style-guide/)\n\n## Learning Resources\n\n- [SELECT Star SQL](https://selectstarsql.com/)\n- [SQL Murder Mystery](https://mystery.knightlab.com/walkthrough.html)\n- [sql-workbench](https://sql-workbench.com/)\n- [DuckDB SQL Introduction](https://duckdb.org/docs/sql/introduction)\n\n", - "supporting": [ - "sql_files\\figure-html" - ], - "filters": [], - "includes": { - "include-in-header": [ - "\n\n\n" - ] - } - } -} \ No newline at end of file diff --git a/_freeze/docs/software/sql/figure-html/cell-8-output-2.png b/_freeze/docs/software/sql/figure-html/cell-8-output-2.png deleted file mode 100644 index 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It offers a comprehensive\nlibrary of methods for data cleaning, descriptive statistics, and econometric analysis.\nStata is very well suited for research data workflows and research design tasks,\nincluding power calculations, sample design adjustments, panel data analysis,\ntime series analysis, etc. See [Stata Features](https://www.stata.com/features/)\nfor a full list of what Stata makes available.\n\n## How to install Stata?\n\nIPA staff can download and install the relevant version (`.exe` for Windows,\n`.dmg` for MacOS, or `.tar.gz` for Linux) from IPA on the Box\n[installation packages](https://ipastorage.app.box.com/folder/129276324764?v=install-stata).\n\n## Coding Conventions\n\nSee the following resources for coding conventions in Stata:\n\n- [DIME Analytics Stata Style Guide](https://worldbank.github.io/dime-data-handbook/coding.html#the-dime-analytics-stata-style-guide)\n- [Sean Higgins's Stata guide](https://github.com/skhiggins/Stata_guide)\non GitHub.\n- [Coding and Data for the Social Sciences](https://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml#magicparlabel-1248),\nby Matthew Gentzkow and Jesse Shapiro\n\n## Using Stata from Python\n\nWithin a Python script or Jupyter Notebook, you can call Stata using [pystata](https://www.stata.com/python/pystata18/notebook/Quick%20Start0.html).\n\n::: {#711d00a7 .cell execution_count=1}\n``` {.python .cell-code}\nimport stata_setup\n\n# set configuration to the path where Stata is installed and the flavor of Stata\n# in the case below, we're using Stata 18 SE\nstata_setup.config(\"C:/Program Files/Stata18/\", \"se\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n ___ ____ ____ ____ ____ ®\n /__ / ____/ / ____/ StataNow 18.5\n___/ / /___/ / /___/ SE—Standard Edition\n\n Statistics and Data Science Copyright 1985-2023 StataCorp LLC\n StataCorp\n 4905 Lakeway Drive\n College Station, Texas 77845 USA\n 800-782-8272 https://www.stata.com\n 979-696-4600 service@stata.com\n\nStata license: Unlimited-user network, expiring 22 Jan 2025\nSerial number: 401809300803\n Licensed to: Niall Keleher\n Innovations for Poverty Action\n\nNotes:\n 1. Unicode is supported; see help unicode_advice.\n 2. Maximum number of variables is set to 5,000 but can be increased;\n see help set_maxvar.\n```\n:::\n:::\n\n\n### Call Stata using pystata API functions\n\n::: {#ae4323d7 .cell execution_count=2}\n``` {.python .cell-code}\nfrom pystata import stata\n```\n:::\n\n\n::: {#da83d622 .cell execution_count=3}\n``` {.python .cell-code}\nstata.run(\n \"\"\"\n sysuse auto, clear\n reg mpg price i.foreign\n \"\"\"\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n. \n. sysuse auto, clear\n(1978 automobile data)\n\n. reg mpg price i.foreign\n\n Source | SS df MS Number of obs = 74\n-------------+---------------------------------- F(2, 71) = 23.01\n Model | 960.866305 2 480.433152 Prob > F = 0.0000\n Residual | 1482.59315 71 20.8815937 R-squared = 0.3932\n-------------+---------------------------------- Adj R-squared = 0.3761\n Total | 2443.45946 73 33.4720474 Root MSE = 4.5696\n\n------------------------------------------------------------------------------\n mpg | Coefficient Std. err. t P>|t| [95% conf. interval]\n-------------+----------------------------------------------------------------\n price | -.000959 .0001815 -5.28 0.000 -.001321 -.000597\n |\n foreign |\n Foreign | 5.245271 1.163592 4.51 0.000 2.925135 7.565407\n _cons | 25.65058 1.271581 20.17 0.000 23.11512 28.18605\n------------------------------------------------------------------------------\n\n. \n. \n```\n:::\n:::\n\n\nOr use IPython magic commands to run Stata code in a Jupyter Notebook.\n\n::: {#985e8119 .cell execution_count=4}\n``` {.python .cell-code}\n%%stata\nsysuse auto, clear\ndescribe\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n. sysuse auto, clear\n(1978 automobile data)\n\n. describe\n\nContains data from C:\\Program Files\\Stata18/ado\\base/a/auto.dta\n Observations: 74 1978 automobile data\n Variables: 12 13 Apr 2022 17:45\n (_dta has notes)\n-------------------------------------------------------------------------------\nVariable Storage Display Value\n name type format label Variable label\n-------------------------------------------------------------------------------\nmake str18 %-18s Make and model\nprice int %8.0gc Price\nmpg int %8.0g Mileage (mpg)\nrep78 int %8.0g Repair record 1978\nheadroom float %6.1f Headroom (in.)\ntrunk int %8.0g Trunk space (cu. ft.)\nweight int %8.0gc Weight (lbs.)\nlength int %8.0g Length (in.)\nturn int %8.0g Turn circle (ft.)\ndisplacement int %8.0g Displacement (cu. in.)\ngear_ratio float %6.2f Gear ratio\nforeign byte %8.0g origin Car origin\n-------------------------------------------------------------------------------\nSorted by: foreign\n\n. \n```\n:::\n:::\n\n\n::: {#60f876a3 .cell execution_count=5}\n``` {.python .cell-code}\n%stata scatter mpg price\n```\n\n::: {.cell-output .cell-output-display}\n![](stata_files/figure-html/cell-6-output-1.svg){}\n:::\n:::\n\n\n## Data Visualization\n\nConsider installing the [ipaplots](https://github.com/PovertyAction/ipaplots) for the IPA graph schema in Stata.\n\n## Learning References\n\nFor more information on learning and using Stata, see the\n[IPA-Stata-Trainings](https://github.com/PovertyAction/IPA-Stata-Trainings) repository on GitHub.\n\n- [Stata Video Tutorials](https://www.stata.com/links/video-tutorials/)\n\n", - 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- if (categoryEl) { - categoryEl.classList.add("active"); - } - - // Filter the listings to this category - filterListingCategory(category); -} - -function filterListingCategory(category) { - const listingIds = Object.keys(window["quarto-listings"]); - for (const listingId of listingIds) { - const list = window["quarto-listings"][listingId]; - if (list) { - if (category === "") { - // resets the filter - list.filter(); - } else { - // filter to this category - list.filter(function (item) { - const itemValues = item.values(); - if (itemValues.categories !== null) { - const categories = itemValues.categories.split(","); - return categories.includes(category); - } else { - return false; - } - }); - } - } - } -} diff --git a/_freeze/software/python/execute-results/html.json b/_freeze/software/python/execute-results/html.json new file mode 100644 index 0000000..95b4fe3 --- /dev/null +++ b/_freeze/software/python/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "3643190b1b3f8760b30f7522cde9e85d", + "result": { + "engine": "jupyter", + "markdown": "---\ntitle: Python\nexecute:\n eval: true\njupyter:\n jupytext:\n text_representation:\n extension: .qmd\n format_name: quarto\n format_version: '1.0'\n jupytext_version: 1.16.2\n kernelspec:\n display_name: Python 3 (ipykernel)\n language: python\n name: python3\n---\n\n\n\n\n## What is Python?\n\nPython is a high-level, general-purpose programming language that is widely used in data\nscience, machine learning, and web development. It has a large standard library and a\nvibrant community that provides a wide range of libraries and tools for various\napplications. As such, Python provides a general-purpose ecosystem that can be used for\na wide range of applications.\n\n## How to install Python?\n\nThere are many ways to install Python. We recommend using Python in a virtual\nenvironment to avoid conflicts with other Python installations on your system.\n\nWe recommend using [pixi](https://pixi.sh/latest/) as a a simple way to create and\nmanage Python virtual environments.\n\nYou can manage the python packages that are installed in the virtual\nenvironment using a `pyproject.toml` file. See the pyproject.toml example in this\nrepository for an example of how to manage Python packages. To add package dependencies\nto the virtual environment, using `pixi`, you can run:\n\nFirst, install `pixi` using `winget` (Windows) or `brew` (MacOS/Linux):\n\n| Platform | Commands |\n| -------- | -------------------- |\n| Windows | `winget install prefix-dev.pixi` |\n| MacOS | `brew install pixi` |\n| Linux | `brew install pixi` |\n\nAdd libraries to the virtual environment using `pixi add ...`:\n\n::: {#a7e20e28 .cell execution_count=1}\n``` {.python .cell-code}\n> pixi add jupyterlab pandas matplotlib seaborn --pypi\n```\n:::\n\n\n## Coding Conventions\n\nWe highly recommend working with a [virtual environment](../guides/venv.qmd) to manage\nPython dependencies. The `pyproject.toml` is the preferred way to keep track of python\ndependencies as well as project-specific python conventions.\n\nWe recommend using [Ruff](https://docs.astral.sh/ruff/) to enforce\n[linting]() and formatting rules. In most\ncases you can use the default linting and formatting rules provided by `ruff`. However,\nyou can customize the rules by modifying the `[tool.ruff]` section of the\n`pyproject.toml` file in the root of your project. for more about the configuration\noptions, see the [Ruff documentation](https://docs.astral.sh/ruff/configuration/).\n\nIf you are working in a virtual environment created in this repository`, you automatically have\naccess to `Ruff via `just lint-py` and `just fmt-python` commands to lint and format your code.\n\nFor more inspiration, see the\n[GitLab Data Team's Python Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/python-guide/)\nand [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html).\n\n## Example Usage\n\nLet's load an example World Bank data via [Gapminder](https://www.gapminder.org/) using\nthe [causaldata](https://github.com/NickCH-K/causaldata) package.\n\n::: {#7edc64e0 .cell execution_count=2}\n``` {.python .cell-code}\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport statsmodels.formula.api as sm\nfrom causaldata import gapminder\n```\n:::\n\n\nLoad the Gapminder data as a pandas DataFrame:\n\n::: {#cfa82fec .cell execution_count=3}\n``` {.python .cell-code}\ndf = gapminder.load_pandas().data\n```\n:::\n\n\nWe can check the dimensions of the DataFrame using `df.info()`:\n\n::: {#d36c0824 .cell execution_count=4}\n``` {.python .cell-code}\ndf.info()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nRangeIndex: 1704 entries, 0 to 1703\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 country 1704 non-null object \n 1 continent 1704 non-null object \n 2 year 1704 non-null int64 \n 3 lifeExp 1704 non-null float64\n 4 pop 1704 non-null int64 \n 5 gdpPercap 1704 non-null float64\ndtypes: float64(2), int64(2), object(2)\nmemory usage: 80.0+ KB\n```\n:::\n:::\n\n\nLet's take a look at the first few rows of the DataFrame using `df.head()`:\n\n::: {#ed4450e7 .cell execution_count=5}\n``` {.python .cell-code}\ndf.head()\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n
    \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    countrycontinentyearlifeExppopgdpPercap
    0AfghanistanAsia195228.8018425333779.445314
    1AfghanistanAsia195730.3329240934820.853030
    2AfghanistanAsia196231.99710267083853.100710
    3AfghanistanAsia196734.02011537966836.197138
    4AfghanistanAsia197236.08813079460739.981106
    \n
    \n```\n:::\n:::\n\n\nTake a look at the relationship between GDP per Capita and Life Expectancy:\n\n::: {#fa1711dd .cell execution_count=6}\n``` {.python .cell-code}\nsns.scatterplot(x=\"gdpPercap\", y=\"lifeExp\", hue=\"continent\", data=df).set(\n xscale=\"log\", ylabel=\"Life Expectancy\", xlabel=\"GDP per Capita\"\n)\n```\n\n::: {.cell-output .cell-output-display}\n![](python_files/figure-html/cell-7-output-1.png){width=585 height=431}\n:::\n:::\n\n\nSeparate the data by year, focusing on 1957 and 2007:\n\n::: {#410097cc .cell execution_count=7}\n``` {.python .cell-code}\nsns.relplot(\n data=df.where(df[\"year\"].isin([1957, 2007])),\n x=\"gdpPercap\",\n y=\"lifeExp\",\n col=\"year\",\n hue=\"continent\",\n col_wrap=1,\n kind=\"scatter\",\n palette=\"muted\",\n).set(xscale=\"log\", ylabel=\"Life Expectancy\", xlabel=\"GDP per Capita\")\n```\n\n::: {.cell-output .cell-output-display}\n![](python_files/figure-html/cell-8-output-1.png){width=575 height=952}\n:::\n:::\n\n\n## Learning Resources\n\n- [The Python Tutorial](https://docs.python.org/3.12/tutorial/index.html)\n- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)\n- [Efficient Python for Data Scientists](https://khuyentran1401.github.io/Efficient_Python_tricks_and_tools_for_data_scientists/README.html)\n- [The Hitchhiker's Guide to Python](https://docs.python-guide.org/)\n\n", + "supporting": [ + "python_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/_freeze/docs/software/python/figure-html/cell-7-output-1.png b/_freeze/software/python/figure-html/cell-7-output-1.png similarity index 99% rename from _freeze/docs/software/python/figure-html/cell-7-output-1.png rename to _freeze/software/python/figure-html/cell-7-output-1.png index 407eaef56cede57cbd070694e47297bd5d3c8901..99818b8dd9112a35adbde391e5660f9f76c8a13a 100644 GIT binary patch delta 69 zcmbRJPITcr(FrPy_7hc=RSfkC@{3Chbrdp6N(zdt^!0NSOA2!GOL8)k^zw_+^;NnB Z>>JZs(->RRm|D}AThmy!rm=1<0{~!V7-Rqd delta 42 ycmZ4ZPIUG=(FrPymJ?N#84V`-OR|`-cx-FTY0Y75&0%WIVQ$S~*_y+;wG03}5e|9) diff --git a/_freeze/docs/software/python/figure-html/cell-8-output-1.png b/_freeze/software/python/figure-html/cell-8-output-1.png similarity index 99% rename from _freeze/docs/software/python/figure-html/cell-8-output-1.png rename to _freeze/software/python/figure-html/cell-8-output-1.png index 6e263c6c5b0385f039832add57cb2b9e18a3da3e..5eefcb81573982ba8e84763e4980810632b8c674 100644 GIT binary patch delta 60 zcmbPmnQPi*t_doP_7hc=RSfkC@{3Chbrdp6N(zdt^!0NSOA2!GOL8)k^zw_+^;NnB Q>>JZs(-^m=F=?#?0De6b-2eap delta 33 ncmbPsnQHLoading configurations from C:\\Users\\NKeleher\\code\\ipa-data-tech-handbook\\pyproject.toml.\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\nSettings changed:\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    Configvalue
    feedback0
    autopandasFalse
    autopolarsFalse
    autolimit0
    displaylimit10
    \n```\n:::\n:::\n\n\nCreate a table from a CSV file.\n\n::: {#01f5f5db .cell execution_count=2}\n``` {.python .cell-code}\n%%sql\nCREATE TABLE penguins AS SELECT * FROM '../assets/data/penguins.csv'\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n\n \n \n \n \n \n \n \n
    Count
    \n```\n:::\n:::\n\n\nInspect the columns of the table.\n\n::: {#9c804ae7 .cell execution_count=3}\n``` {.python .cell-code}\n%sqlcmd columns -t penguins\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    nametypenullabledefaultautoincrementcomment
    speciesVARCHARTrueNoneFalseNone
    islandVARCHARTrueNoneFalseNone
    bill_length_mmFLOATTrueNoneFalseNone
    bill_depth_mmFLOATTrueNoneFalseNone
    flipper_length_mmBIGINTTrueNoneFalseNone
    body_mass_gBIGINTTrueNoneFalseNone
    sexVARCHARTrueNoneFalseNone
    \n```\n:::\n:::\n\n\nQuery the table using a `SELECT` statement.\n\n::: {#353a5c12 .cell execution_count=4}\n``` {.python .cell-code}\n%%sql\nSELECT *\nFROM '../assets/data/penguins.csv'\nLIMIT 3\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
    AdelieTorgersen39.118.71813750MALE
    AdelieTorgersen39.517.41863800FEMALE
    AdelieTorgersen40.318.01953250FEMALE
    \n```\n:::\n:::\n\n\nRun analytical operations on the data.\n\n::: {#781dadc0 .cell execution_count=5}\n``` {.python .cell-code}\n%%sql\nSELECT species, COUNT(*) AS count\nFROM '../assets/data/penguins.csv'\nGROUP BY species\nORDER BY count DESC\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
    speciescount
    Adelie152
    Gentoo124
    Chinstrap68
    \n```\n:::\n:::\n\n\nUsing `jupysql` store SQL query results as an object in a Jupyter notebook.\n\n::: {#ba400740 .cell execution_count=6}\n``` {.python .cell-code}\n%%sql species_count <<\nSELECT species, COUNT(*) AS count\nFROM '../assets/data/penguins.csv'\nGROUP BY species\nORDER BY count DESC\n```\n:::\n\n\nPlot the results of the query.\n\n::: {#f4e96aaf .cell execution_count=7}\n``` {.python .cell-code}\nax = %sqlplot bar --table '../assets/data/penguins.csv' --column species\n\n# customize plot (this is a matplotlib Axes object)\n_ = ax.set_title(\"Count of penguins by species\")\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\nRemoving NULLs, if there exists any from species\n```\n:::\n\n::: {.cell-output .cell-output-display}\n![](sql_files/figure-html/cell-8-output-2.png){width=593 height=449}\n:::\n:::\n\n\n## Coding Conventions\n\nUse [SQLFluff](https://docs.sqlfluff.com/en/stable/) to lint SQL code.\n\nAdd SQLFluff to your virtual environment using `pixi`:\n\n```{.bash}\npixi add sqlfluff --pypi\n```\n\nAssuming SQLFluff is installed in your virtual environment using `pixi`, you can run the\nfollowing command to lint SQL code:\n\n```{.bash}\npixi run sqlfluff fix --dialect duckdb\n```\n\nSee the\n[GitLab Data Team's SQL Style Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/sql-style-guide/)\n\n## Learning Resources\n\n- [SELECT Star SQL](https://selectstarsql.com/)\n- [SQL Murder Mystery](https://mystery.knightlab.com/walkthrough.html)\n- [sql-workbench](https://sql-workbench.com/)\n- [DuckDB SQL Introduction](https://duckdb.org/docs/sql/introduction)\n\n", + "supporting": [ + "sql_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/_freeze/software/sql/figure-html/cell-8-output-2.png b/_freeze/software/sql/figure-html/cell-8-output-2.png new file mode 100644 index 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zt@ZZyLKtRIU7SBf>b2^;pqyz)B{VcNFd%p~B8Wn>41vHR1jmUt z#mvG2dM2bf(*voVHiS(&0<#D-+}vaa1_#MZWqkZNnW-RQ+J%Vc1|&tPBWLmkRaR~f zWdr4W21a0}?5N3X@0NY@BvbJs)P91{(=pP%t$8r`AA@BUCjbBd literal 0 HcmV?d00001 diff --git a/_freeze/software/stata/execute-results/html.json b/_freeze/software/stata/execute-results/html.json new file mode 100644 index 0000000..9c89cf2 --- /dev/null +++ b/_freeze/software/stata/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "e92c3bcd63ae179a46270d317aa56ac3", + "result": { + "engine": "jupyter", + "markdown": "---\ntitle: Stata\nexecute:\n eval: true\n# Editors are the \"Maintainers\" of the page\n# editors:\n# - \"[Niall Keleher](https://poverty-action.org/people/niall-keleher)\"\n\n# Authors are the \"Contributors\" of the page\n# Add your name to the list if you have contributed to this page\n# author:\n# - Niall Keleher\n---\n\n\n\n\n## What is Stata?\n\nStata is a statistical software package that is commonly used in the social sciences and economics.\nIt is widely used at IPA for data analysis and management. It offers a comprehensive\nlibrary of methods for data cleaning, descriptive statistics, and econometric analysis.\nStata is very well suited for research data workflows and research design tasks,\nincluding power calculations, sample design adjustments, panel data analysis,\ntime series analysis, etc. See [Stata Features](https://www.stata.com/features/)\nfor a full list of what Stata makes available.\n\n## How to install Stata?\n\nIPA staff can download and install the relevant version (`.exe` for Windows,\n`.dmg` for MacOS, or `.tar.gz` for Linux) from IPA on the Box\n[installation packages](https://ipastorage.app.box.com/folder/129276324764?v=install-stata).\n\n## Coding Conventions\n\nSee the following resources for coding conventions in Stata:\n\n- [DIME Analytics Stata Style Guide](https://worldbank.github.io/dime-data-handbook/coding.html#the-dime-analytics-stata-style-guide)\n- [Sean Higgins's Stata guide](https://github.com/skhiggins/Stata_guide)\non GitHub.\n- [Coding and Data for the Social Sciences](https://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml#magicparlabel-1248),\nby Matthew Gentzkow and Jesse Shapiro\n\n## Using Stata from Python\n\nWithin a Python script or Jupyter Notebook, you can call Stata using [pystata](https://www.stata.com/python/pystata18/notebook/Quick%20Start0.html).\n\n::: {#a5649908 .cell execution_count=1}\n``` {.python .cell-code}\nimport stata_setup\n\n# set configuration to the path where Stata is installed and the flavor of Stata\n# in the case below, we're using Stata 18 SE\nstata_setup.config(\"C:/Program Files/Stata18/\", \"se\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n ___ ____ ____ ____ ____ ®\n /__ / ____/ / ____/ StataNow 18.5\n___/ / /___/ / /___/ SE—Standard Edition\n\n Statistics and Data Science Copyright 1985-2023 StataCorp LLC\n StataCorp\n 4905 Lakeway Drive\n College Station, Texas 77845 USA\n 800-782-8272 https://www.stata.com\n 979-696-4600 service@stata.com\n\nStata license: Unlimited-user network, expiring 22 Jan 2025\nSerial number: 401809300803\n Licensed to: Niall Keleher\n Innovations for Poverty Action\n\nNotes:\n 1. Unicode is supported; see help unicode_advice.\n 2. Maximum number of variables is set to 5,000 but can be increased;\n see help set_maxvar.\n```\n:::\n:::\n\n\n### Call Stata using pystata API functions\n\n::: {#e51619fa .cell execution_count=2}\n``` {.python .cell-code}\nfrom pystata import stata\n```\n:::\n\n\n::: {#48e22c5c .cell execution_count=3}\n``` {.python .cell-code}\nstata.run(\n \"\"\"\n sysuse auto, clear\n reg mpg price i.foreign\n \"\"\"\n)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n. \n. sysuse auto, clear\n(1978 automobile data)\n\n. reg mpg price i.foreign\n\n Source | SS df MS Number of obs = 74\n-------------+---------------------------------- F(2, 71) = 23.01\n Model | 960.866305 2 480.433152 Prob > F = 0.0000\n Residual | 1482.59315 71 20.8815937 R-squared = 0.3932\n-------------+---------------------------------- Adj R-squared = 0.3761\n Total | 2443.45946 73 33.4720474 Root MSE = 4.5696\n\n------------------------------------------------------------------------------\n mpg | Coefficient Std. err. t P>|t| [95% conf. interval]\n-------------+----------------------------------------------------------------\n price | -.000959 .0001815 -5.28 0.000 -.001321 -.000597\n |\n foreign |\n Foreign | 5.245271 1.163592 4.51 0.000 2.925135 7.565407\n _cons | 25.65058 1.271581 20.17 0.000 23.11512 28.18605\n------------------------------------------------------------------------------\n\n. \n. \n```\n:::\n:::\n\n\nOr use IPython magic commands to run Stata code in a Jupyter Notebook.\n\n::: {#d41eee25 .cell execution_count=4}\n``` {.python .cell-code}\n%%stata\nsysuse auto, clear\ndescribe\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n. sysuse auto, clear\n(1978 automobile data)\n\n. describe\n\nContains data from C:\\Program Files\\Stata18/ado\\base/a/auto.dta\n Observations: 74 1978 automobile data\n Variables: 12 13 Apr 2022 17:45\n (_dta has notes)\n-------------------------------------------------------------------------------\nVariable Storage Display Value\n name type format label Variable label\n-------------------------------------------------------------------------------\nmake str18 %-18s Make and model\nprice int %8.0gc Price\nmpg int %8.0g Mileage (mpg)\nrep78 int %8.0g Repair record 1978\nheadroom float %6.1f Headroom (in.)\ntrunk int %8.0g Trunk space (cu. ft.)\nweight int %8.0gc Weight (lbs.)\nlength int %8.0g Length (in.)\nturn int %8.0g Turn circle (ft.)\ndisplacement int %8.0g Displacement (cu. in.)\ngear_ratio float %6.2f Gear ratio\nforeign byte %8.0g origin Car origin\n-------------------------------------------------------------------------------\nSorted by: foreign\n\n. \n```\n:::\n:::\n\n\n::: {#9b991005 .cell execution_count=5}\n``` {.python .cell-code}\n%stata scatter mpg price\n```\n\n::: {.cell-output .cell-output-display}\n![](stata_files/figure-html/cell-6-output-1.svg){}\n:::\n:::\n\n\n## Data Visualization\n\nConsider installing the [ipaplots](https://github.com/PovertyAction/ipaplots) for the IPA graph schema in Stata.\n\n## Learning References\n\nFor more information on learning and using Stata, see the\n[IPA-Stata-Trainings](https://github.com/PovertyAction/IPA-Stata-Trainings) repository on GitHub.\n\n- [Stata Video Tutorials](https://www.stata.com/links/video-tutorials/)\n\n", + "supporting": [ + "stata_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/docs/software/stata/figure-html/cell-6-output-1.svg b/_freeze/software/stata/figure-html/cell-6-output-1.svg similarity index 100% rename from _freeze/docs/software/stata/figure-html/cell-6-output-1.svg rename to _freeze/software/stata/figure-html/cell-6-output-1.svg diff --git a/_quarto.yml b/_quarto.yml index eed8303..3544d56 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -1,13 +1,10 @@ project: type: website - output-dir: _site preview: port: 3434 browser: true - execute: freeze: auto - website: title: "Data and Tech Handbook" announcement: @@ -58,53 +55,55 @@ website: search: true style: "docked" contents: - - href: ./docs/index.qmd + - href: index.qmd text: Home - - section: About + - section: Guides contents: - - href: ./docs/about/foundations.qmd - text: Foundations - - href: ./docs/about/mmds.qmd - text: Minimum Must Dos - - href: ./docs/about/ipa_researchers.qmd - text: For IPA Researchers - - href: ./docs/about/ipa_staff.qmd - text: For IPA staff + - href: ./guides/computer_setup.qmd + text: Computer Setup + - href: ./guides/venv.qmd + text: Virtual Environments + - href: ./guides/code_review.qmd + text: Code Review + - href: ./guides/pre_commit_hooks.qmd + text: pre-commit - - section: Software Guides + - section: Software contents: - - href: ./docs/software/coding_best_practices.qmd - text: Coding Best Practices - - href: ./docs/software/surveycto.qmd - section: SurveyCTO - contents: - - href: ./docs/software/surveycto/scto_best_practices.qmd - text: "SurveyCTO: Best Practices" - - href: ./docs/software/surveycto/scto_tools.qmd - text: "SurveyCTO: IPA Tools" - - - href: ./docs/software/stata.qmd + - href: ./software/git.qmd + text: Git + - href: ./software/github.qmd + text: GitHub + - href: ./software/vscode.qmd + text: VS Code + - href: ./software/quarto.qmd + text: Quarto + - href: ./software/python.qmd + text: Python + - href: ./software/stata.qmd text: Stata - - href: ./docs/software/ml.qmd - text: Machine Learning + - href: ./software/sql.qmd + text: SQL -# right: - # - text: Blog - # href: ./docs/blog/index.qmd - - text: Contributing - href: ./docs/contributing.qmd + - section: Methods + contents: + - href: ./methods/references.qmd + text: References - # tools: - # - icon: github - # href: https://github.com/povertyaction - # aria-label: IPA GitHub - # - icon: twitter - # href: https://twitter.com/poverty_action - # aria-label: IPA Twitter - # - icon: rss - # href: https://povertyaction.github.io/ipa-data-tech-handbook/docs/blog/index.xml - # aria-label: IPA Blog RSS + - section: Teams + contents: + - href: ./teams/dse.qmd + text: DSE + - href: ./teams/grds.qmd + text: GRDS + - href: ./teams/mist.qmd + text: MIST + - href: ./teams/ppi.qmd + text: PPI + + - href: ./contributing.qmd + text: Contributing # bibliography: ./assets/references.bib diff --git a/docs/about/foundations.qmd b/about/foundations.qmd similarity index 100% rename from docs/about/foundations.qmd rename to about/foundations.qmd diff --git a/docs/about/ipa_researchers.qmd b/about/ipa_researchers.qmd similarity index 100% rename from docs/about/ipa_researchers.qmd rename to about/ipa_researchers.qmd diff --git a/docs/about/ipa_staff.qmd b/about/ipa_staff.qmd similarity index 100% rename from docs/about/ipa_staff.qmd rename to about/ipa_staff.qmd diff --git a/docs/about/mmds.qmd b/about/mmds.qmd similarity index 100% rename from docs/about/mmds.qmd rename to about/mmds.qmd diff --git a/docs/contributing.qmd b/contributing.qmd similarity index 96% rename from docs/contributing.qmd rename to contributing.qmd index e5f83f1..2578532 100644 --- a/docs/contributing.qmd +++ b/contributing.qmd @@ -106,7 +106,7 @@ information about software installation): - `winget` (Windows) or `homebrew` (MacOS/Linux) for package management and installation - `git` for source control management - `just` for running common command line patterns -- `rye` for installing Python and managing virtual environments +- `pixi` for installing Python and managing virtual environments First, clone this repository to your local computer either via GitHub Desktop: @@ -131,11 +131,11 @@ following from the command line: | Platform | Commands | | --------- | ------------------------------------------------------------------- | -| Windows | `winget install Git.Git Casey.Just Rye.Rye GitHub.cli Posit.Quarto` | -| Mac/Linux | `brew install just rye gh` | +| Windows | `winget install Git.Git Casey.Just prefix-dev.pixi GitHub.cli Posit.Quarto` | +| Mac/Linux | `brew install just pixi gh` | This will make sure that you have the latest version of `Just`, as well as -[Rye](https://rye.astral.sh/guide/) (installer for Python) and +[Pixi]([https://prefix.d/](https://pixi.sh/latest/)) (installer for Python) and [Quarto](https://quarto.org/docs/guide/) (for writing and compiling scientific and technical documents). diff --git a/docs/index.qmd b/docs/index.qmd deleted file mode 100644 index e3acea0..0000000 --- a/docs/index.qmd +++ /dev/null @@ -1,5 +0,0 @@ ---- -title: IPA Research Resources ---- - -Welcome to IPA's Research Resources! diff --git a/docs/software/stata.qmd b/docs/software/stata.qmd deleted file mode 100644 index bc06fe3..0000000 --- a/docs/software/stata.qmd +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Stata -# Editors are the "Maintainers" of the page -# editors: -# - "[Niall Keleher](https://poverty-action.org/people/niall-keleher)" - -# Authors are the "Contributors" of the page -# Add your name to the list if you have contributed to this page -# author: -# - Niall Keleher ---- - -To be launched... \ No newline at end of file diff --git a/docs/guides/code_review.qmd b/guides/code_review.qmd similarity index 100% rename from docs/guides/code_review.qmd rename to guides/code_review.qmd diff --git a/docs/guides/computer_setup.qmd b/guides/computer_setup.qmd similarity index 98% rename from docs/guides/computer_setup.qmd rename to guides/computer_setup.qmd index d0a1a64..5afe367 100644 --- a/docs/guides/computer_setup.qmd +++ b/guides/computer_setup.qmd @@ -59,8 +59,8 @@ Install packages on Windows # Install a single program (e.g. GitHub for command line) winget install GitHub.cli -# Install multiple programs (Just, Rye for Python) -winget install Casey.Just Rye.Rye +# Install multiple programs (Just, pixi for Python) +winget install Casey.Just prefix-dev.pixi ``` ### MacOS/Linux @@ -75,7 +75,7 @@ relevant if you are working in the brew install gh # Install multiple programs (Just, Rye for Python) -brew install just rye +brew install just pixi ``` See the [Virtual Environment](./venv.qmd) section of the handbook for more information on diff --git a/docs/guides/pre_commit_hooks.qmd b/guides/pre_commit_hooks.qmd similarity index 97% rename from docs/guides/pre_commit_hooks.qmd rename to guides/pre_commit_hooks.qmd index 5fc07d2..df882b6 100644 --- a/docs/guides/pre_commit_hooks.qmd +++ b/guides/pre_commit_hooks.qmd @@ -29,7 +29,7 @@ bugs, improve code quality, and make it easier to collaborate with other develop Add `pre-commit` to your project by running the following command: ```bash -rye install pre-commit +pixi install pre-commit ``` ## Example pre-commit hooks @@ -49,7 +49,7 @@ is made: To run these hooks on all files, run the following command: ```bash -rye run pre-commit run --all-files +pixi run pre-commit run --all-files ``` Alternatively, if you have installed `pre-commit` every time you run `git commit`, the diff --git a/docs/guides/venv.qmd b/guides/venv.qmd similarity index 84% rename from docs/guides/venv.qmd rename to guides/venv.qmd index 5503957..4211d08 100644 --- a/docs/guides/venv.qmd +++ b/guides/venv.qmd @@ -17,12 +17,10 @@ the same package. ## Python Virtual Environments -We recommend using `rye` to manage Python virtual environments as it provides an +We recommend using `pixi` to manage Python virtual environments as it provides an overarching framework for managing Python installations and virtual environments. See -information on `rye` in the [Rye documentation](https://rye.astral.sh/guide/). +information on `pixi` in the [pixi documentation](https://pixi.sh/latest/). -Another option for managing virtual environments is to use `pixi`. See information on -`pixi` in the [Pixi documentation](https://pixi.sh/latest/). ## R Virtual Environments diff --git a/index.qmd b/index.qmd new file mode 100644 index 0000000..d528f7f --- /dev/null +++ b/index.qmd @@ -0,0 +1,53 @@ +--- +title: IPA Data Science, Engineering, and Technology Handbook +--- + +## About the Handbook + +Welcome to IPA's Handbook for Data Science, Engineering, and Technology! + +This handbook is intended as a living document to assist IPA staff and collaborators +with common practices for writing code and working with data. The handbook will never be +comprehensive of every possible software, data object, or shared practice. However, IPA +staff working with data and technology are encouraged to use this handbook as a +reference and a shared project to give tips and advice for working collaboratively with +data analysis and software development. + +### Getting Started + +To get started with software covered in this handbook, read +[Computer Setup](./guides/computer_setup.qmd) to learn how to set up your computer for +data analysis and software development. + +### Contributing + +For instructions on how to contribute to this handbook, see +[Contributing](contributing.qmd) to the Handbook. + +Also, see the [Code Review](./guides/code_review.qmd) guide for information on how to +conduct code reviews. + +### Using Software Tools + +For information about specific software, see the respective pages under the `Software` +directory. + +Here are some specific software that this guide covers: + +- [Git](./software/git.qmd) +- [Github](./software/github.qmd) +- [VS Code](./software/vscode.qmd) +- [Quarto](./software/quarto.qmd) +- [Python](./software/python.qmd) +- and more! + +### Acknowledgements + +This handbook is a collaboration between multiple IPA Teams including +[Data Science and Engineering (DSE)](./teams/dse.qmd), +[Management and Information Systems Team (MIST)](./teams/mist.qmd), +[Global Research and Data Support](./teams/grds.qmd), and +[Poverty Probability Index (PPI®)](./teams/ppi.qmd). + +The handbook is inspired by the +[Gitlab Data Team Handbook](https://handbook.gitlab.com/handbook/business-technology/data-team/). diff --git a/docs/methods/references.md b/methods/references.qmd similarity index 100% rename from docs/methods/references.md rename to methods/references.qmd diff --git a/docs/software/coding_best_practices.qmd b/software/coding_best_practices.qmd similarity index 100% rename from docs/software/coding_best_practices.qmd rename to software/coding_best_practices.qmd diff --git a/docs/software/git.qmd b/software/git.qmd similarity index 100% rename from docs/software/git.qmd rename to software/git.qmd diff --git a/docs/software/github.qmd b/software/github.qmd similarity index 100% rename from docs/software/github.qmd rename to software/github.qmd diff --git a/docs/software/justfile.qmd b/software/justfile.qmd similarity index 100% rename from docs/software/justfile.qmd rename to software/justfile.qmd diff --git a/docs/software/ml.qmd b/software/ml.qmd similarity index 100% rename from docs/software/ml.qmd rename to software/ml.qmd diff --git a/software/python.qmd b/software/python.qmd new file mode 100644 index 0000000..1d69cc8 --- /dev/null +++ b/software/python.qmd @@ -0,0 +1,137 @@ +--- +title: Python +execute: + eval: true +jupyter: + jupytext: + text_representation: + extension: .qmd + format_name: quarto + format_version: '1.0' + jupytext_version: 1.16.2 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +## What is Python? + +Python is a high-level, general-purpose programming language that is widely used in data +science, machine learning, and web development. It has a large standard library and a +vibrant community that provides a wide range of libraries and tools for various +applications. As such, Python provides a general-purpose ecosystem that can be used for +a wide range of applications. + +## How to install Python? + +There are many ways to install Python. We recommend using Python in a virtual +environment to avoid conflicts with other Python installations on your system. + +We recommend using [pixi](https://pixi.sh/latest/) as a a simple way to create and +manage Python virtual environments. + +You can manage the python packages that are installed in the virtual +environment using a `pyproject.toml` file. See the pyproject.toml example in this +repository for an example of how to manage Python packages. To add package dependencies +to the virtual environment, using `pixi`, you can run: + +First, install `pixi` using `winget` (Windows) or `brew` (MacOS/Linux): + +| Platform | Commands | +| -------- | -------------------- | +| Windows | `winget install prefix-dev.pixi` | +| MacOS | `brew install pixi` | +| Linux | `brew install pixi` | + +Add libraries to the virtual environment using `pixi add ...`: + +```{python} +# | eval: false +> pixi add jupyterlab pandas matplotlib seaborn --pypi +``` + +## Coding Conventions + +We highly recommend working with a [virtual environment](../guides/venv.qmd) to manage +Python dependencies. The `pyproject.toml` is the preferred way to keep track of python +dependencies as well as project-specific python conventions. + +We recommend using [Ruff](https://docs.astral.sh/ruff/) to enforce +[linting]() and formatting rules. In most +cases you can use the default linting and formatting rules provided by `ruff`. However, +you can customize the rules by modifying the `[tool.ruff]` section of the +`pyproject.toml` file in the root of your project. for more about the configuration +options, see the [Ruff documentation](https://docs.astral.sh/ruff/configuration/). + +If you are working in a virtual environment created in this repository`, you automatically have +access to `Ruff via `just lint-py` and `just fmt-python` commands to lint and format your code. + +For more inspiration, see the +[GitLab Data Team's Python Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/python-guide/) +and [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html). + +## Example Usage + +Let's load an example World Bank data via [Gapminder](https://www.gapminder.org/) using +the [causaldata](https://github.com/NickCH-K/causaldata) package. + +```{python} +# | eval: true +# | echo: true +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import statsmodels.formula.api as sm +from causaldata import gapminder +``` + +Load the Gapminder data as a pandas DataFrame: + +```{python} +# | echo: true +df = gapminder.load_pandas().data +``` + +We can check the dimensions of the DataFrame using `df.info()`: + +```{python} +# | echo: true +df.info() +``` + +Let's take a look at the first few rows of the DataFrame using `df.head()`: + +```{python} +df.head() +``` + +Take a look at the relationship between GDP per Capita and Life Expectancy: + +```{python} +sns.scatterplot(x="gdpPercap", y="lifeExp", hue="continent", data=df).set( + xscale="log", ylabel="Life Expectancy", xlabel="GDP per Capita" +) +``` + +Separate the data by year, focusing on 1957 and 2007: + +```{python} +sns.relplot( + data=df.where(df["year"].isin([1957, 2007])), + x="gdpPercap", + y="lifeExp", + col="year", + hue="continent", + col_wrap=1, + kind="scatter", + palette="muted", +).set(xscale="log", ylabel="Life Expectancy", xlabel="GDP per Capita") +``` + +## Learning Resources + +- [The Python Tutorial](https://docs.python.org/3.12/tutorial/index.html) +- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) +- [Efficient Python for Data Scientists](https://khuyentran1401.github.io/Efficient_Python_tricks_and_tools_for_data_scientists/README.html) +- [The Hitchhiker's Guide to Python](https://docs.python-guide.org/) diff --git a/docs/software/quarto.qmd b/software/quarto.qmd similarity index 100% rename from docs/software/quarto.qmd rename to software/quarto.qmd diff --git a/software/sql.qmd b/software/sql.qmd new file mode 100644 index 0000000..a34a306 --- /dev/null +++ b/software/sql.qmd @@ -0,0 +1,97 @@ +--- +title: SQL +jupyter: + jupytext: + text_representation: + extension: .qmd + format_name: quarto + format_version: '1.0' + jupytext_version: 1.16.4 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +```{python} +%load_ext sql +%sql duckdb:// +``` + +Create a table from a CSV file. + +```{python} +%%sql +CREATE TABLE penguins AS SELECT * FROM '../assets/data/penguins.csv' +``` + +Inspect the columns of the table. + +```{python} +%sqlcmd columns -t penguins +``` + +Query the table using a `SELECT` statement. + +```{python} +%%sql +SELECT * +FROM '../assets/data/penguins.csv' +LIMIT 3 +``` + +Run analytical operations on the data. + +```{python} +%%sql +SELECT species, COUNT(*) AS count +FROM '../assets/data/penguins.csv' +GROUP BY species +ORDER BY count DESC +``` + +Using `jupysql` store SQL query results as an object in a Jupyter notebook. + +```{python} +%%sql species_count << +SELECT species, COUNT(*) AS count +FROM '../assets/data/penguins.csv' +GROUP BY species +ORDER BY count DESC +``` + +Plot the results of the query. + +```{python} +ax = %sqlplot bar --table '../assets/data/penguins.csv' --column species + +# customize plot (this is a matplotlib Axes object) +_ = ax.set_title("Count of penguins by species") +``` + +## Coding Conventions + +Use [SQLFluff](https://docs.sqlfluff.com/en/stable/) to lint SQL code. + +Add SQLFluff to your virtual environment using `pixi`: + +```{.bash} +pixi add sqlfluff --pypi +``` + +Assuming SQLFluff is installed in your virtual environment using `pixi`, you can run the +following command to lint SQL code: + +```{.bash} +pixi run sqlfluff fix --dialect duckdb +``` + +See the +[GitLab Data Team's SQL Style Guide](https://handbook.gitlab.com/handbook/business-technology/data-team/platform/sql-style-guide/) + +## Learning Resources + +- [SELECT Star SQL](https://selectstarsql.com/) +- [SQL Murder Mystery](https://mystery.knightlab.com/walkthrough.html) +- [sql-workbench](https://sql-workbench.com/) +- [DuckDB SQL Introduction](https://duckdb.org/docs/sql/introduction) diff --git a/software/stata.qmd b/software/stata.qmd new file mode 100644 index 0000000..41d07a9 --- /dev/null +++ b/software/stata.qmd @@ -0,0 +1,93 @@ +--- +title: Stata +execute: + eval: true +# Editors are the "Maintainers" of the page +# editors: +# - "[Niall Keleher](https://poverty-action.org/people/niall-keleher)" + +# Authors are the "Contributors" of the page +# Add your name to the list if you have contributed to this page +# author: +# - Niall Keleher +--- + +## What is Stata? + +Stata is a statistical software package that is commonly used in the social sciences and economics. +It is widely used at IPA for data analysis and management. It offers a comprehensive +library of methods for data cleaning, descriptive statistics, and econometric analysis. +Stata is very well suited for research data workflows and research design tasks, +including power calculations, sample design adjustments, panel data analysis, +time series analysis, etc. See [Stata Features](https://www.stata.com/features/) +for a full list of what Stata makes available. + +## How to install Stata? + +IPA staff can download and install the relevant version (`.exe` for Windows, +`.dmg` for MacOS, or `.tar.gz` for Linux) from IPA on the Box +[installation packages](https://ipastorage.app.box.com/folder/129276324764?v=install-stata). + +## Coding Conventions + +See the following resources for coding conventions in Stata: + +- [DIME Analytics Stata Style Guide](https://worldbank.github.io/dime-data-handbook/coding.html#the-dime-analytics-stata-style-guide) +- [Sean Higgins's Stata guide](https://github.com/skhiggins/Stata_guide) +on GitHub. +- [Coding and Data for the Social Sciences](https://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml#magicparlabel-1248), +by Matthew Gentzkow and Jesse Shapiro + +## Using Stata from Python + +Within a Python script or Jupyter Notebook, you can call Stata using [pystata](https://www.stata.com/python/pystata18/notebook/Quick%20Start0.html). + +```{python} +import stata_setup + +# set configuration to the path where Stata is installed and the flavor of Stata +# in the case below, we're using Stata 18 SE +stata_setup.config("C:/Program Files/Stata18/", "se") +``` + + +### Call Stata using pystata API functions + +```{python} +from pystata import stata +``` + +```{python} +stata.run( + """ + sysuse auto, clear + reg mpg price i.foreign + """ +) +``` + + +Or use IPython magic commands to run Stata code in a Jupyter Notebook. + +```{python} +%%stata +sysuse auto, clear +describe +``` + + +```{python} +%stata scatter mpg price +``` + + +## Data Visualization + +Consider installing the [ipaplots](https://github.com/PovertyAction/ipaplots) for the IPA graph schema in Stata. + +## Learning References + +For more information on learning and using Stata, see the +[IPA-Stata-Trainings](https://github.com/PovertyAction/IPA-Stata-Trainings) repository on GitHub. + +- [Stata Video Tutorials](https://www.stata.com/links/video-tutorials/) diff --git a/docs/software/surveycto.qmd b/software/surveycto.qmd similarity index 100% rename from docs/software/surveycto.qmd rename to software/surveycto.qmd diff --git a/docs/software/surveycto/scto_best_practices.qmd b/software/surveycto/scto_best_practices.qmd similarity index 100% rename from docs/software/surveycto/scto_best_practices.qmd rename to software/surveycto/scto_best_practices.qmd diff --git a/docs/software/surveycto/scto_tools.qmd b/software/surveycto/scto_tools.qmd similarity index 100% rename from docs/software/surveycto/scto_tools.qmd rename to software/surveycto/scto_tools.qmd diff --git a/docs/software/vscode.qmd b/software/vscode.qmd similarity index 100% rename from docs/software/vscode.qmd rename to software/vscode.qmd diff --git a/docs/teams/dse.qmd b/teams/dse.qmd similarity index 100% rename from docs/teams/dse.qmd rename to teams/dse.qmd diff --git a/docs/teams/grds.qmd b/teams/grds.qmd similarity index 100% rename from docs/teams/grds.qmd rename to teams/grds.qmd diff --git a/docs/teams/mist.qmd b/teams/mist.qmd similarity index 100% rename from docs/teams/mist.qmd rename to teams/mist.qmd diff --git a/docs/teams/ppi.qmd b/teams/ppi.qmd similarity index 100% rename from docs/teams/ppi.qmd rename to teams/ppi.qmd