dplyr-tutorial.ipynb

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    "---\n",
    "title: 'Introduction to R: Fundamentals, Visualization and Data Manipulation'\n",
    "author: \"Ali Zaidi, Machine Learning and Data Science Education Team\"\n",
    "date: \"June 6th, 2016\"\n",
    "output:\n",
    "  ioslides_presentation:\n",
    "    logo: images/clark-logo.png\n",
    "    runtime: shiny\n",
    "    smaller: yes\n",
    "    widescreen: yes\n",
    "  html_notebook:\n",
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    "    toc: yes\n",
    "    keep_md: true\n",
    "params: \n",
    "  prestype:\n",
    "    label: \"Presentation Type:\"\n",
    "    value: interactive\n",
    "    input: select\n",
    "    choices: [interactive, static]\n",
    "---\n",
    "\n",
    "# Overview of the R Project\n",
    "\n",
    "## Lesson Plan \n",
    "### R U Ready?\n",
    "\n",
    "> - What the R Language for Statistical Computing is\n",
    "> - R's capabilities and it's limitations\n",
    "> - What types of problems you might want to use R with\n",
    "> - How to manage data with the exceptionally popular open source package dplyr\n",
    "> - How to develop models and write functions in R\n",
    "\n",
    "## What is R? \n",
    "### Why should I care?\n",
    "\n",
    "* R is successor to the S Language, originated at Bell Labs AT&T\n",
    "* It is based on the Scheme interpreter\n",
    "* Originally designed by two University of Auckland Professors for their introductory statistics course\n",
    "\n",
    "![Robert Gentleman and Ross Ihaka](http://revolution-computing.typepad.com/.a/6a010534b1db25970b016766fdae38970b-800wi)\n",
    "\n",
    "## R Philosophy \n",
    "### What R Thou?\n",
    "\n",
    "R follows the [Unix philosophy](http://en.wikipedia.org/wiki/Unix_philosophy)\n",
    "\n",
    "* Write programs that do one thing and do it well. Write programs to work together.\n",
    "* R is extensible with more than 9,000 packages available at CRAN (http://crantastic.org/packages)\n",
    "* R, like it's inspiration, Scheme, is a functional programming language\n",
    "* R evaluates lazily, allowing for a very flexible synta\n",
    "* R is a highly interpreted dynamically typed language, allowing you to mutate variables and analyze datasets quickly, but is significantly slower than low-level, statically typed languages like C or Java\n",
    "* R has a high memory footprint, and can easily lead to crashes if you aren't careful\n",
    "\n",
    "## Development Environments \n",
    "### Where to Write R Code\n",
    "\n",
    "* The most popular integrated development environment for R is [RStudio](https://www.rstudio.com/)\n",
    "* The RStudio IDE is entirely html/javascript based, so completely cross-platform\n",
    "* RStudio Server for cloud instances\n",
    "* Developers of RStudio have also written a plethora of useful R packages\n",
    "* For Windows machines, we have recently announced the general availability of [R Tools for Visual Studio, RTVS](https://www.visualstudio.com/en-us/features/rtvs-vs.aspx)\n",
    "* RTVS will support connectivity to Azure and SQL Server in the future\n",
    "* RTVS has great debugging support\n",
    "\n",
    "# Quick Tour of Your IDE\n",
    "\n",
    "## Strengths of R \n",
    "### Where R Succeeds\n",
    "\n",
    "* Expressive\n",
    "* Open source \n",
    "* Extendable -- nearly 10,000 packages with functions to use, and that list continues to grow\n",
    "* Focused on statistics and machine learning -- utilized by academics and practitioners\n",
    "* Advanced data structures and graphical capabilities\n",
    "* Large user community, academics and industry\n",
    "* It is designed by statisticians \n",
    "\n",
    "## Weaknesses of R \n",
    "### Where R Falls Short\n",
    "\n",
    "* It is designed by statisticians\n",
    "* Inefficient at element-by-element computations\n",
    "* May make large demands on system resources, namely memory\n",
    "* Data capacity limited by memory\n",
    "* Single-threaded\n",
    "\n",
    "## Some Essential Open Source Packages\n",
    "\n",
    "* There are over 10,000 R packages to choose from, what do I start with?\n",
    "* Data Management: `dplyr`, `tidyr`, `data.table`\n",
    "* Visualization: `ggplot2`, `ggvis`, `htmlwidgets`, `shiny`\n",
    "* Data Importing: `haven`, `RODBC`, `readr`, `foreign`\n",
    "* Other favorites: `magrittr`, `rmarkdown`, `caret`\n",
    "\n",
    "# R Foundations\n",
    "\n",
    "## Command line prompts\n",
    "\n",
    "Symbol | Meaning\n",
    "------ | -------\n",
    "  `>`   | ready for a new command\n",
    "  `+`   | awaiting the completion of an existing command \n",
    "  \n",
    "Can change these options either permanently at startup (see `?Startup`) or manually at each session with the `options` function, `options(continue = \" \")` for example.\n",
    "\n",
    "## I'm Lost! | Getting Help with R\n",
    "\n",
    "* [Stack Overflow](http://stackoverflow.com/questions/tagged/r)\n",
    "* [Cross Validated, R](http://stats.stackexchange.com/)\n",
    "* [R Reference Card](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)\n",
    "* [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)\n",
    "* [R help mailing list and archives](https://stat.ethz.ch/mailman/listinfo/r-help)\n",
    "* [CRAN Task Views](https://cran.r-project.org/web/views/)\n",
    "* [Crantastic](http://crantastic.org/)\n",
    "* [Revolutions Blog](http://blog.revolutionanalytics.com/)\n",
    "* [RSeek](rseek.org)\n",
    "* [R-Bloggers](http://www.r-bloggers.com/)\n",
    "\n",
    "## Quick Tour of Things You Need to Know | Data Structures\n",
    "\n",
    "* R's data structures can be described by their dimensionality, and their type.\n",
    "\n",
    "\n",
    "|    | Homogeneous   | Heterogeneous |\n",
    "|----|---------------|---------------|\n",
    "| 1d | Atomic vector | List          |\n",
    "| 2d | Matrix        | Data frame    |\n",
    "| nd | Array         |               |\n",
    "\n",
    "## Quick Tour of Things You Need to Know | Data Types\n",
    "\n",
    "> - Atomic vectors come in one of four types\n",
    "> - `logical` (boolean). Values: `TRUE` | `FALSE`\n",
    "> - `integer`\n",
    "> - `double` (often called numeric)\n",
    "> - `character`\n",
    "> - Rare types:\n",
    "> - `complex` \n",
    "> - `raw`\n",
    "\n",
    "# Lab 1: R Data Types\n",
    "\n",
    "# Data Manipulation with the dplyr Package\n",
    "\n",
    "## Overview\n",
    "\n",
    "Rather than describing the nitty gritty details of writing R code, I'd like you to get started at immediately writing R code.\n",
    "\n",
    "As most of you are data scientists/data enthusiasts, I will showcase one of the most useful data manipulation packages in R, `dplyr`.\n",
    "At the end of this session, you will have learned:\n",
    "\n",
    "* How to manipulate data quickly with `dplyr` using a very intuitive 'grammar'\n",
    "* How to use `dplyr` to perform common exploratory and manipulation procedures\n",
    "* How to apply your own custom functions to group manipulations `dplyr` with `mutate()`, `summarise()` and `do()`\n",
    "* Connect to remote databases to work with larger than memory datasets\n",
    "\n",
    "## Why use dplyr? \n",
    "### The Grammar of Data Manipulation\n",
    "\n",
    "* R comes with a plethora of base functions for data manipulation, so why use `dplyr`?\n",
    "* `dplyr` makes data manipulation easier by providing a few functions for the most common tasks and procedures\n",
    "* `dplyr` achieves remarkable speed-up gains by using a C++ backend\n",
    "* `dplyr` has multiple backends for working with data stored in various sources: SQLite, MySQL, bigquery, SQL Server, and many more\n",
    "* `dplyr` was inspired to give data manipulation a simple, cohesive grammar (similar philosophy to `ggplot` - grammar of graphics)\n",
    "* `dplyr` has inspired many new packages, which now adopt it's easy to understand syntax. \n",
    "* The recent package `dplyrXdf` brings much of the same functionality of `dplyr` to `XDF` data, and `SparkR`, `SparkRext` and finally, `sparklyr` provides the same for manipulating Spark `DataFrames`\n",
    "\n",
    "\n",
    "## Tidy Data and Happier Coding\n",
    "### Premature Optimization \n",
    "\n",
    "![](https://imgs.xkcd.com/comics/the_general_problem.png)\n",
    "\n",
    "+ The most important parameter to optimize in a data science development cycle is YOUR time\n",
    "+ It is therefore important to be able to write efficient code, quickly\n",
    "+ The code should be easy to understand, debug, port, and deploy\n",
    "+ Goals: writing fast code that is: portable, platform invariant, easy to understand, and easy to debug\n",
    "    - __I'm serious about CReUse__!\n",
    "\n",
    "## Manipulation verbs\n",
    "\n",
    "`filter`\n",
    "\n",
    ":    select rows based on matching criteria\n",
    "\n",
    "`slice`\n",
    "\n",
    ":    select rows by number\n",
    "\n",
    "`select`\n",
    "\n",
    ":    select columns by column names\n",
    "\n",
    "`arrange`\n",
    "\n",
    ":    reorder rows by column values\n",
    "\n",
    "`mutate`\n",
    "\n",
    ":    add new variables based on transformations of existing variables\n",
    "\n",
    "`transmute`\n",
    "\n",
    ":    transform and drop other variables\n",
    "\n",
    "\n",
    "\n",
    "## Aggregation verbs\n",
    "\n",
    "`group_by`\n",
    "\n",
    ":    identify grouping variables for calculating groupwise summary statistics\n",
    "\n",
    "\n",
    "`count`\n",
    "\n",
    ":    count the number of records per group\n",
    "\n",
    "\n",
    "`summarise` | `summarize`\n",
    "\n",
    ":    calculate one or more summary functions per group, returning one row of results per group (or one for the entire dataset)\n",
    "\n",
    "## Viewing Data\n",
    "\n",
    "* `dplyr` includes a wrapper called `tbl_df` makes df into a 'local df' that improves the printing of dataframes in the console (there's now a dedicated package [`tibble`](www.github.com/hadley/tibble) for this wrapper)\n",
    "* if you want to see more of the data you can still coerce to `data.frame`"
   ]
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     "text": [
      "Source: local data frame [3,770,319 x 16]\n",
      "\n",
      "   VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag\n",
      "      (chr)           (int)         (dbl)      (chr)              (chr)\n",
      "1         1               1          1.80          1                  N\n",
      "2         1               2          0.90          1                  N\n",
      "3         1               1          0.90          1                  N\n",
      "4         1               1          0.30          1                  N\n",
      "5         2               1          0.96          1                  N\n",
      "6         2               1          2.01          1                  N\n",
      "7         2               3          3.14          1                  N\n",
      "8         1               1          0.50          1                  N\n",
      "9         2               1          0.67          1                  N\n",
      "10        2               1         15.20          2                  N\n",
      "..      ...             ...           ...        ...                ...\n",
      "Variables not shown: payment_type (chr), fare_amount (dbl), tip_amount (dbl),\n",
      "  tolls_amount (dbl), pickup_hour (chr), pickup_dow (chr), dropoff_hour (chr),\n",
      "  dropoff_dow (chr), pickup_nhood (chr), dropoff_nhood (chr), kSplits (chr)\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)\n",
    "library(stringr)\n",
    "# taxi_df <- as.data.frame(data.table::fread('../data/sample_taxi.csv'))\n",
    "load(url(\"https://alizaidi.blob.core.windows.net/training/taxi_df.RData\"))\n",
    "print(taxi_df <- tbl_df(taxi_df))"
   ]
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    "# Filtering and Reordering Data\n",
    "\n",
    "## Subsetting Data\n",
    "\n",
    "* `dplyr` makes subsetting by rows very easy\n",
    "* The `filter` verb takes conditions for filtering rows based on conditions\n",
    "* **every** `dplyr` function uses a data.frame/tbl as it's first argument\n",
    "* Additional conditions are passed as new arguments (no need to make an insanely complicated expression, split em up!)"
   ]
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     "text": [
      "Source: local data frame [859,165 x 16]\n",
      "\n",
      "   VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag\n",
      "      (chr)           (int)         (dbl)      (chr)              (chr)\n",
      "1         1               2          0.90          1                  N\n",
      "2         1               1          0.90          1                  N\n",
      "3         2               1          2.01          1                  N\n",
      "4         2               3          3.14          1                  N\n",
      "5         2               1          6.08          1                  N\n",
      "6         2               1          1.96          1                  N\n",
      "7         1               1          0.40          1                  N\n",
      "8         1               2          1.70          1                  N\n",
      "9         1               2          0.60          1                  N\n",
      "10        1               2          9.30          1                  N\n",
      "..      ...             ...           ...        ...                ...\n",
      "Variables not shown: payment_type (chr), fare_amount (dbl), tip_amount (dbl),\n",
      "  tolls_amount (dbl), pickup_hour (chr), pickup_dow (chr), dropoff_hour (chr),\n",
      "  dropoff_dow (chr), pickup_nhood (chr), dropoff_nhood (chr), kSplits (chr)\n"
     ]
    }
   ],
   "source": [
    "print(filter(taxi_df,\n",
    "       dropoff_dow %in% c(\"Fri\", \"Sat\", \"Sun\"),\n",
    "       tip_amount > 1))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Your turn: \n",
    "\n",
    "* How many observations started in Harlem?\n",
    "  - pick both sides of Harlem, including east harlem\n",
    "* How many observations that started in Harlem ended in the Financial District?\n",
    "\n",
    "## Solution"
   ]
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     "text": [
      "Source: local data frame [32,025 x 16]\n",
      "\n",
      "   VendorID passenger_count trip_distance RateCodeID store_and_fwd_flag\n",
      "      (chr)           (int)         (dbl)      (chr)              (chr)\n",
      "1         1               1          0.70          1                  N\n",
      "2         1               1          1.10          1                  N\n",
      "3         2               1         18.79          2                  N\n",
      "4         2               1          4.66          1                  N\n",
      "5         2               5          0.67          1                  N\n",
      "6         2               3          0.76          1                  N\n",
      "7         1               1          2.70          1                  N\n",
      "8         2               1          3.19          1                  N\n",
      "9         2               1          0.85          1                  N\n",
      "10        2               2          6.06          1                  N\n",
      "..      ...             ...           ...        ...                ...\n",
      "Variables not shown: payment_type (chr), fare_amount (dbl), tip_amount (dbl),\n",
      "  tolls_amount (dbl), pickup_hour (chr), pickup_dow (chr), dropoff_hour (chr),\n",
      "  dropoff_dow (chr), pickup_nhood (chr), dropoff_nhood (chr), kSplits (chr)\n"
     ]
    }
   ],
   "source": [
    "library(stringr)\n",
    "harlem_pickups <- filter(taxi_df, str_detect(pickup_nhood, \"Harlem\"))\n",
    "print(harlem_pickups)"
   ]
  },
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   "source": [
    "## Select a set of columns\n",
    "\n",
    "* You can use the `select()` verb to specify which columns of a dataset you want\n",
    "* This is similar to the `keep` option in SAS's data step.\n",
    "* Use a colon `:` to select all the columns between two variables (inclusive)\n",
    "* Use `contains` to take any columns containing a certain word/phrase/character\n",
    "\n",
    "## Select Example"
   ]
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     "text": [
      "Source: local data frame [3,770,319 x 5]\n",
      "\n",
      "          pickup_nhood      dropoff_nhood fare_amount dropoff_hour\n",
      "                 (chr)              (chr)       (dbl)        (chr)\n",
      "1  Morningside Heights   Hamilton Heights         9.5         6-10\n",
      "2              Midtown            Midtown         6.5         6-10\n",
      "3      Lower East Side               Soho         7.0         6-10\n",
      "4   Financial District Financial District         3.0         6-10\n",
      "5              Chelsea       West Village         5.5         6-10\n",
      "6      Upper East Side             Harlem         9.5         10-5\n",
      "7           Fort Green               Soho        12.5         12-4\n",
      "8      Upper East Side    Upper East Side         4.0         12-4\n",
      "9      Upper West Side    Upper West Side         5.0         12-4\n",
      "10                  NA            Clinton        52.0         12-4\n",
      "..                 ...                ...         ...          ...\n",
      "Variables not shown: trip_distance (dbl)\n"
     ]
    }
   ],
   "source": [
    "print(select(taxi_df, pickup_nhood, dropoff_nhood, fare_amount, dropoff_hour, trip_distance))"
   ]
  },
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    "## Select: Other Options\n",
    "\n",
    "starts_with(x, ignore.case = FALSE)\n",
    "\n",
    ":    name starts with `x`\n",
    "\n",
    "ends_with(x, ignore.case = FALSE)\n",
    "\n",
    ":    name ends with `x`\n",
    "\n",
    "matches(x, ignore.case = FALSE)\n",
    "\n",
    ":    selects all variables whose name matches the regular expression `x`\n",
    "\n",
    "num_range(\"V\", 1:5, width = 1)\n",
    "\n",
    ":    selects all variables (numerically) from `V1` to `V5`.\n",
    "\n",
    "You can also use a `-` to drop variables.\n",
    "\n",
    "## Reordering Data\n",
    "\n",
    "* You can reorder your dataset based on conditions using the `arrange()` verb\n",
    "* Use the `desc` function to sort in descending order rather than ascending order (default)"
   ]
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     "text": [
      "Source: local data frame [3,770,319 x 2]\n",
      "\n",
      "   fare_amount    pickup_nhood\n",
      "         (dbl)           (chr)\n",
      "1      3130.30    Borough Park\n",
      "2      3130.30 Upper West Side\n",
      "3       990.00              NA\n",
      "4       900.00    Little Italy\n",
      "5       900.00              NA\n",
      "6       630.01         Chelsea\n",
      "7       600.00    Throggs Neck\n",
      "8       500.00       Chinatown\n",
      "9       500.00       Chinatown\n",
      "10      500.00         Clinton\n",
      "..         ...             ...\n"
     ]
    }
   ],
   "source": [
    "print(select(arrange(taxi_df, desc(fare_amount), pickup_nhood), fare_amount, pickup_nhood))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Use `arrange()` to  sort on the basis of `tip_amount`, `dropoff_nhood`, and `pickup_dow`, with descending order for tip amount\n",
    "\n",
    "## Solution"
   ]
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   "source": [
    "## Summary\n",
    "\n",
    "filter\n",
    "\n",
    ":    Extract subsets of rows. See also `slice()`\n",
    "\n",
    "select\n",
    "\n",
    ":    Extract subsets of columns. See also `rename()`\n",
    "\n",
    "arrange\n",
    "\n",
    ":    Sort your data\n",
    "\n",
    "# Data Aggregations and Transformations\n",
    "\n",
    "## Transformations\n",
    "\n",
    "* The `mutate()` verb can be used to make new columns"
   ]
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     "text": [
      "Source: local data frame [3,770,319 x 3]\n",
      "\n",
      "     tip_pct fare_amount tip_amount\n",
      "       (dbl)       (dbl)      (dbl)\n",
      "1  0.0000000         9.5       0.00\n",
      "2  0.2384615         6.5       1.55\n",
      "3  0.2371429         7.0       1.66\n",
      "4  0.0000000         3.0       0.00\n",
      "5  0.2363636         5.5       1.30\n",
      "6  0.2273684         9.5       2.16\n",
      "7  0.2000000        12.5       2.50\n",
      "8  0.2500000         4.0       1.00\n",
      "9  0.2000000         5.0       1.00\n",
      "10 0.2755769        52.0      14.33\n",
      "..       ...         ...        ...\n"
     ]
    }
   ],
   "source": [
    "taxi_df <- mutate(taxi_df, tip_pct = tip_amount/fare_amount)\n",
    "print(select(taxi_df, tip_pct, fare_amount, tip_amount))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarise Data by Groups\n",
    "\n",
    "* The `group_by` verb creates a grouping by a categorical variable\n",
    "* Functions can be placed inside `summarise` to create summary functions"
   ]
  },
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     "text": [
      "Source: local data frame [122 x 3]\n",
      "\n",
      "              dropoff_nhood   Num ave_tip_pct\n",
      "                      (chr) (int)       (dbl)\n",
      "1                 Annandale    16  0.16486695\n",
      "2             Ardon Heights    16  0.15149972\n",
      "3  Astoria-Long Island City 34728  0.11710247\n",
      "4                Auburndale   424  0.09363095\n",
      "5              Battery Park 32618  0.14849849\n",
      "6                 Bay Ridge  3171  0.14507851\n",
      "7                Baychester   168  0.06078747\n",
      "8        Bedford-Stuyvesant 17382  0.12722678\n",
      "9              Bedford Park  1028  0.08187483\n",
      "10              Bensonhurst  1081  0.12771086\n",
      "..                      ...   ...         ...\n"
     ]
    }
   ],
   "source": [
    "print(summarise(group_by(taxi_df, dropoff_nhood), Num = n(), ave_tip_pct = mean(tip_pct)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group By Neighborhoods Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source: local data frame [5,224 x 4]\n",
      "Groups: pickup_nhood [?]\n",
      "\n",
      "               pickup_nhood            dropoff_nhood   Num ave_tip_pct\n",
      "                      (chr)                    (chr) (int)       (dbl)\n",
      "1             Ardon Heights            Ardon Heights     1  0.00000000\n",
      "2  Astoria-Long Island City Astoria-Long Island City  6714  0.09712743\n",
      "3  Astoria-Long Island City               Auburndale    13  0.06520367\n",
      "4  Astoria-Long Island City             Battery Park    14  0.10140865\n",
      "5  Astoria-Long Island City                Bay Ridge     5  0.15388657\n",
      "6  Astoria-Long Island City       Bedford-Stuyvesant    68  0.09819024\n",
      "7  Astoria-Long Island City             Bedford Park     3  0.05550679\n",
      "8  Astoria-Long Island City              Bensonhurst     2  0.00000000\n",
      "9  Astoria-Long Island City              Boerum Hill    12  0.09941085\n",
      "10 Astoria-Long Island City             Borough Park     9  0.08205749\n",
      "..                      ...                      ...   ...         ...\n"
     ]
    }
   ],
   "source": [
    "print(summarise(group_by(taxi_df, pickup_nhood, dropoff_nhood), Num = n(), ave_tip_pct = mean(tip_pct)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chaining/Piping\n",
    "\n",
    "* A `dplyr` installation includes the `magrittr` package as a dependency \n",
    "* The `magrittr` package includes a pipe operator that allows you to pass the current dataset to another function\n",
    "* This makes interpreting a nested sequence of operations much easier to understand\n",
    "\n",
    "## Standard Code\n",
    "\n",
    "* Code is executed inside-out.\n",
    "* Let's arrange the above average tips in descending order, and only look at the locations that had at least 10 dropoffs and pickups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>pickup_nhood</th><th scope=col>dropoff_nhood</th><th scope=col>Num</th><th scope=col>ave_tip_pct</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Astoria-Long Island City</td><td>Carroll Gardens</td><td>19</td><td>0.153588261136031</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Astoria-Long Island City</td><td>East Village</td><td>171</td><td>0.143118548207709</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Astoria-Long Island City</td><td>Downtown</td><td>22</td><td>0.133530903723109</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Astoria-Long Island City</td><td>Financial District</td><td>117</td><td>0.132408676626068</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Astoria-Long Island City</td><td>Morningside Heights</td><td>32</td><td>0.129766573181761</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Astoria-Long Island City</td><td>Soho</td><td>58</td><td>0.129501289148457</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>Astoria-Long Island City</td><td>Greenwich Village</td><td>146</td><td>0.12778519776767</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>Astoria-Long Island City</td><td>Central Park</td><td>72</td><td>0.127555754598017</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>Astoria-Long Island City</td><td>Fort Green</td><td>62</td><td>0.122389249656247</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>Astoria-Long Island City</td><td>Park Slope</td><td>25</td><td>0.121939736056366</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>Astoria-Long Island City</td><td>High Bridge</td><td>11</td><td>0.11733532001271</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>Astoria-Long Island City</td><td>Garment District</td><td>283</td><td>0.115972094070329</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>Astoria-Long Island City</td><td>Midtown</td><td>1177</td><td>0.115828501416449</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>Astoria-Long Island City</td><td>Washington Heights</td><td>77</td><td>0.115825310558963</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>Astoria-Long Island City</td><td>Murray Hill</td><td>215</td><td>0.114010766438754</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>Astoria-Long Island City</td><td>Clinton</td><td>163</td><td>0.113484036144098</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>Astoria-Long Island City</td><td>Chelsea</td><td>224</td><td>0.113278651216362</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>Astoria-Long Island City</td><td>Gramercy</td><td>496</td><td>0.112826146099133</td></tr>\n",
       "\t<tr><th scope=row>19</th><td>Astoria-Long Island City</td><td>Williamsburg</td><td>148</td><td>0.112713645644494</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>Astoria-Long Island City</td><td>West Village</td><td>60</td><td>0.112152349243157</td></tr>\n",
       "\t<tr><th scope=row>21</th><td>Astoria-Long Island City</td><td>Lower East Side</td><td>123</td><td>0.10927212382603</td></tr>\n",
       "\t<tr><th scope=row>22</th><td>Astoria-Long Island City</td><td>Upper West Side</td><td>296</td><td>0.107894325111354</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>Astoria-Long Island City</td><td>Carnegie Hill</td><td>29</td><td>0.107606788471196</td></tr>\n",
       "\t<tr><th scope=row>24</th><td>Astoria-Long Island City</td><td>Upper East Side</td><td>1330</td><td>0.105999082693197</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>Astoria-Long Island City</td><td>NA</td><td>2566</td><td>0.103575792024331</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>Astoria-Long Island City</td><td>Forest Hills</td><td>92</td><td>0.103404239734775</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>Astoria-Long Island City</td><td>Battery Park</td><td>14</td><td>0.101408651346081</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>Astoria-Long Island City</td><td>Tribeca</td><td>35</td><td>0.0997829570494477</td></tr>\n",
       "\t<tr><th scope=row>29</th><td>Astoria-Long Island City</td><td>Boerum Hill</td><td>12</td><td>0.0994108465584319</td></tr>\n",
       "\t<tr><th scope=row>30</th><td>Astoria-Long Island City</td><td>North Sutton Area</td><td>147</td><td>0.0989528786884557</td></tr>\n",
       "\t<tr><th scope=row>31</th><td><e2><8b><ae></td><td><e2><8b><ae></td><td><e2><8b><ae></td><td><e2><8b><ae></td></tr>\n",
       "\t<tr><th scope=row>32</th><td>NA</td><td>Utopia</td><td>677</td><td>0.0639512879781143</td></tr>\n",
       "\t<tr><th scope=row>33</th><td>NA</td><td>Parkchester</td><td>173</td><td>0.0629886311634546</td></tr>\n",
       "\t<tr><th scope=row>34</th><td>NA</td><td>Williams Bridge</td><td>65</td><td>0.0627934901192277</td></tr>\n",
       "\t<tr><th scope=row>35</th><td>NA</td><td>Corona</td><td>10</td><td>0.0627017543859649</td></tr>\n",
       "\t<tr><th scope=row>36</th><td>NA</td><td>Hunts Point</td><td>55</td><td>0.061708308248174</td></tr>\n",
       "\t<tr><th scope=row>37</th><td>NA</td><td>Springfield Gardens</td><td>655</td><td>0.0610987827185282</td></tr>\n",
       "\t<tr><th scope=row>38</th><td>NA</td><td>Flushing</td><td>790</td><td>0.060758507229995</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>NA</td><td>South Bronx</td><td>236</td><td>0.0592333323611171</td></tr>\n",
       "\t<tr><th scope=row>40</th><td>NA</td><td>Midland Beach</td><td>12</td><td>0.0587931371958439</td></tr>\n",
       "\t<tr><th scope=row>41</th><td>NA</td><td>Maspeth</td><td>337</td><td>0.0584611776713095</td></tr>\n",
       "\t<tr><th scope=row>42</th><td>NA</td><td>Bedford Park</td><td>156</td><td>0.0584275821553012</td></tr>\n",
       "\t<tr><th scope=row>43</th><td>NA</td><td>Union Port</td><td>64</td><td>0.0480624086824837</td></tr>\n",
       "\t<tr><th scope=row>44</th><td>NA</td><td>Jamaica</td><td>965</td><td>0.0476935412001119</td></tr>\n",
       "\t<tr><th scope=row>45</th><td>NA</td><td>Wakefield-Williamsbridge</td><td>107</td><td>0.0460906740309743</td></tr>\n",
       "\t<tr><th scope=row>46</th><td>NA</td><td>Laurelton</td><td>328</td><td>0.0431768166258215</td></tr>\n",
       "\t<tr><th scope=row>47</th><td>NA</td><td>Country Club</td><td>68</td><td>0.0423540505717144</td></tr>\n",
       "\t<tr><th scope=row>48</th><td>NA</td><td>Westerleigh-Castleton</td><td>11</td><td>0.0419077325552146</td></tr>\n",
       "\t<tr><th scope=row>49</th><td>NA</td><td>Queens Village</td><td>176</td><td>0.0403016424588993</td></tr>\n",
       "\t<tr><th scope=row>50</th><td>NA</td><td>Canarsie</td><td>537</td><td>0.0398018308144379</td></tr>\n",
       "\t<tr><th scope=row>51</th><td>NA</td><td>Todt Hill</td><td>12</td><td>0.0373984501937258</td></tr>\n",
       "\t<tr><th scope=row>52</th><td>NA</td><td>University Heights</td><td>82</td><td>0.0371791664221414</td></tr>\n",
       "\t<tr><th scope=row>53</th><td>NA</td><td>Baychester</td><td>41</td><td>0.0362048845283501</td></tr>\n",
       "\t<tr><th scope=row>54</th><td>NA</td><td>Woodhaven-Richmond Hill</td><td>480</td><td>0.035369359805022</td></tr>\n",
       "\t<tr><th scope=row>55</th><td>NA</td><td>Brownsville</td><td>292</td><td>0.0315687154734968</td></tr>\n",
       "\t<tr><th scope=row>56</th><td>NA</td><td>Saintalbans</td><td>270</td><td>0.030997185901772</td></tr>\n",
       "\t<tr><th scope=row>57</th><td>NA</td><td>East Brooklyn</td><td>478</td><td>0.0292042542515927</td></tr>\n",
       "\t<tr><th scope=row>58</th><td>NA</td><td>Morris Heights</td><td>75</td><td>0.0291305418306146</td></tr>\n",
       "\t<tr><th scope=row>59</th><td>NA</td><td>Soundview</td><td>114</td><td>0.0275407684642633</td></tr>\n",
       "\t<tr><th scope=row>60</th><td>NA</td><td>Eastchester</td><td>19</td><td>0.0261503227289429</td></tr>\n",
       "\t<tr><th scope=row>61</th><td>NA</td><td>Tremont</td><td>119</td><td>0.0253545038097539</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & pickup_nhood & dropoff_nhood & Num & ave_tip_pct\\\\\n",
       "\\hline\n",
       "\t1 & Astoria-Long Island City & Carroll Gardens & 19 & 0.153588261136031\\\\\n",
       "\t2 & Astoria-Long Island City & East Village & 171 & 0.143118548207709\\\\\n",
       "\t3 & Astoria-Long Island City & Downtown & 22 & 0.133530903723109\\\\\n",
       "\t4 & Astoria-Long Island City & Financial District & 117 & 0.132408676626068\\\\\n",
       "\t5 & Astoria-Long Island City & Morningside Heights & 32 & 0.129766573181761\\\\\n",
       "\t6 & Astoria-Long Island City & Soho & 58 & 0.129501289148457\\\\\n",
       "\t7 & Astoria-Long Island City & Greenwich Village & 146 & 0.12778519776767\\\\\n",
       "\t8 & Astoria-Long Island City & Central Park & 72 & 0.127555754598017\\\\\n",
       "\t9 & Astoria-Long Island City & Fort Green & 62 & 0.122389249656247\\\\\n",
       "\t10 & Astoria-Long Island City & Park Slope & 25 & 0.121939736056366\\\\\n",
       "\t11 & Astoria-Long Island City & High Bridge & 11 & 0.11733532001271\\\\\n",
       "\t12 & Astoria-Long Island City & Garment District & 283 & 0.115972094070329\\\\\n",
       "\t13 & Astoria-Long Island City & Midtown & 1177 & 0.115828501416449\\\\\n",
       "\t14 & Astoria-Long Island City & Washington Heights & 77 & 0.115825310558963\\\\\n",
       "\t15 & Astoria-Long Island City & Murray Hill & 215 & 0.114010766438754\\\\\n",
       "\t16 & Astoria-Long Island City & Clinton & 163 & 0.113484036144098\\\\\n",
       "\t17 & Astoria-Long Island City & Chelsea & 224 & 0.113278651216362\\\\\n",
       "\t18 & Astoria-Long Island City & Gramercy & 496 & 0.112826146099133\\\\\n",
       "\t19 & Astoria-Long Island City & Williamsburg & 148 & 0.112713645644494\\\\\n",
       "\t20 & Astoria-Long Island City & West Village & 60 & 0.112152349243157\\\\\n",
       "\t21 & Astoria-Long Island City & Lower East Side & 123 & 0.10927212382603\\\\\n",
       "\t22 & Astoria-Long Island City & Upper West Side & 296 & 0.107894325111354\\\\\n",
       "\t23 & Astoria-Long Island City & Carnegie Hill & 29 & 0.107606788471196\\\\\n",
       "\t24 & Astoria-Long Island City & Upper East Side & 1330 & 0.105999082693197\\\\\n",
       "\t25 & Astoria-Long Island City & NA & 2566 & 0.103575792024331\\\\\n",
       "\t26 & Astoria-Long Island City & Forest Hills & 92 & 0.103404239734775\\\\\n",
       "\t27 & Astoria-Long Island City & Battery Park & 14 & 0.101408651346081\\\\\n",
       "\t28 & Astoria-Long Island City & Tribeca & 35 & 0.0997829570494477\\\\\n",
       "\t29 & Astoria-Long Island City & Boerum Hill & 12 & 0.0994108465584319\\\\\n",
       "\t30 & Astoria-Long Island City & North Sutton Area & 147 & 0.0989528786884557\\\\\n",
       "\t31 & <e2><8b><ae> & <e2><8b><ae> & <e2><8b><ae> & <e2><8b><ae>\\\\\n",
       "\t32 & NA & Utopia & 677 & 0.0639512879781143\\\\\n",
       "\t33 & NA & Parkchester & 173 & 0.0629886311634546\\\\\n",
       "\t34 & NA & Williams Bridge & 65 & 0.0627934901192277\\\\\n",
       "\t35 & NA & Corona & 10 & 0.0627017543859649\\\\\n",
       "\t36 & NA & Hunts Point & 55 & 0.061708308248174\\\\\n",
       "\t37 & NA & Springfield Gardens & 655 & 0.0610987827185282\\\\\n",
       "\t38 & NA & Flushing & 790 & 0.060758507229995\\\\\n",
       "\t39 & NA & South Bronx & 236 & 0.0592333323611171\\\\\n",
       "\t40 & NA & Midland Beach & 12 & 0.0587931371958439\\\\\n",
       "\t41 & NA & Maspeth & 337 & 0.0584611776713095\\\\\n",
       "\t42 & NA & Bedford Park & 156 & 0.0584275821553012\\\\\n",
       "\t43 & NA & Union Port & 64 & 0.0480624086824837\\\\\n",
       "\t44 & NA & Jamaica & 965 & 0.0476935412001119\\\\\n",
       "\t45 & NA & Wakefield-Williamsbridge & 107 & 0.0460906740309743\\\\\n",
       "\t46 & NA & Laurelton & 328 & 0.0431768166258215\\\\\n",
       "\t47 & NA & Country Club & 68 & 0.0423540505717144\\\\\n",
       "\t48 & NA & Westerleigh-Castleton & 11 & 0.0419077325552146\\\\\n",
       "\t49 & NA & Queens Village & 176 & 0.0403016424588993\\\\\n",
       "\t50 & NA & Canarsie & 537 & 0.0398018308144379\\\\\n",
       "\t51 & NA & Todt Hill & 12 & 0.0373984501937258\\\\\n",
       "\t52 & NA & University Heights & 82 & 0.0371791664221414\\\\\n",
       "\t53 & NA & Baychester & 41 & 0.0362048845283501\\\\\n",
       "\t54 & NA & Woodhaven-Richmond Hill & 480 & 0.035369359805022\\\\\n",
       "\t55 & NA & Brownsville & 292 & 0.0315687154734968\\\\\n",
       "\t56 & NA & Saintalbans & 270 & 0.030997185901772\\\\\n",
       "\t57 & NA & East Brooklyn & 478 & 0.0292042542515927\\\\\n",
       "\t58 & NA & Morris Heights & 75 & 0.0291305418306146\\\\\n",
       "\t59 & NA & Soundview & 114 & 0.0275407684642633\\\\\n",
       "\t60 & NA & Eastchester & 19 & 0.0261503227289429\\\\\n",
       "\t61 & NA & Tremont & 119 & 0.0253545038097539\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2,620 x 4]\n",
       "Groups: pickup_nhood [86]\n",
       "\n",
       "               pickup_nhood       dropoff_nhood   Num ave_tip_pct\n",
       "                      (chr)               (chr) (int)       (dbl)\n",
       "1  Astoria-Long Island City     Carroll Gardens    19   0.1535883\n",
       "2  Astoria-Long Island City        East Village   171   0.1431185\n",
       "3  Astoria-Long Island City            Downtown    22   0.1335309\n",
       "4  Astoria-Long Island City  Financial District   117   0.1324087\n",
       "5  Astoria-Long Island City Morningside Heights    32   0.1297666\n",
       "6  Astoria-Long Island City                Soho    58   0.1295013\n",
       "7  Astoria-Long Island City   Greenwich Village   146   0.1277852\n",
       "8  Astoria-Long Island City        Central Park    72   0.1275558\n",
       "9  Astoria-Long Island City          Fort Green    62   0.1223892\n",
       "10 Astoria-Long Island City          Park Slope    25   0.1219397\n",
       "..                      ...                 ...   ...         ..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter(arrange(summarise(group_by(taxi_df, pickup_nhood, dropoff_nhood), Num = n(), ave_tip_pct = mean(tip_pct)), desc(ave_tip_pct)), Num >= 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![damn](http://www.ohmagif.com/wp-content/uploads/2015/01/lemme-go-out-for-a-walk-oh-no-shit.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reformatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>pickup_nhood</th><th scope=col>dropoff_nhood</th><th scope=col>Num</th><th scope=col>ave_tip_pct</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Astoria-Long Island City</td><td>Carroll Gardens</td><td>19</td><td>0.153588261136031</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Astoria-Long Island City</td><td>East Village</td><td>171</td><td>0.143118548207709</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Astoria-Long Island City</td><td>Downtown</td><td>22</td><td>0.133530903723109</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Astoria-Long Island City</td><td>Financial District</td><td>117</td><td>0.132408676626068</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Astoria-Long Island City</td><td>Morningside Heights</td><td>32</td><td>0.129766573181761</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Astoria-Long Island City</td><td>Soho</td><td>58</td><td>0.129501289148457</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>Astoria-Long Island City</td><td>Greenwich Village</td><td>146</td><td>0.12778519776767</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>Astoria-Long Island City</td><td>Central Park</td><td>72</td><td>0.127555754598017</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>Astoria-Long Island City</td><td>Fort Green</td><td>62</td><td>0.122389249656247</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>Astoria-Long Island City</td><td>Park Slope</td><td>25</td><td>0.121939736056366</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>Astoria-Long Island City</td><td>High Bridge</td><td>11</td><td>0.11733532001271</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>Astoria-Long Island City</td><td>Garment District</td><td>283</td><td>0.115972094070329</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>Astoria-Long Island City</td><td>Midtown</td><td>1177</td><td>0.115828501416449</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>Astoria-Long Island City</td><td>Washington Heights</td><td>77</td><td>0.115825310558963</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>Astoria-Long Island City</td><td>Murray Hill</td><td>215</td><td>0.114010766438754</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>Astoria-Long Island City</td><td>Clinton</td><td>163</td><td>0.113484036144098</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>Astoria-Long Island City</td><td>Chelsea</td><td>224</td><td>0.113278651216362</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>Astoria-Long Island City</td><td>Gramercy</td><td>496</td><td>0.112826146099133</td></tr>\n",
       "\t<tr><th scope=row>19</th><td>Astoria-Long Island City</td><td>Williamsburg</td><td>148</td><td>0.112713645644494</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>Astoria-Long Island City</td><td>West Village</td><td>60</td><td>0.112152349243157</td></tr>\n",
       "\t<tr><th scope=row>21</th><td>Astoria-Long Island City</td><td>Lower East Side</td><td>123</td><td>0.10927212382603</td></tr>\n",
       "\t<tr><th scope=row>22</th><td>Astoria-Long Island City</td><td>Upper West Side</td><td>296</td><td>0.107894325111354</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>Astoria-Long Island City</td><td>Carnegie Hill</td><td>29</td><td>0.107606788471196</td></tr>\n",
       "\t<tr><th scope=row>24</th><td>Astoria-Long Island City</td><td>Upper East Side</td><td>1330</td><td>0.105999082693197</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>Astoria-Long Island City</td><td>NA</td><td>2566</td><td>0.103575792024331</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>Astoria-Long Island City</td><td>Forest Hills</td><td>92</td><td>0.103404239734775</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>Astoria-Long Island City</td><td>Battery Park</td><td>14</td><td>0.101408651346081</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>Astoria-Long Island City</td><td>Tribeca</td><td>35</td><td>0.0997829570494477</td></tr>\n",
       "\t<tr><th scope=row>29</th><td>Astoria-Long Island City</td><td>Boerum Hill</td><td>12</td><td>0.0994108465584319</td></tr>\n",
       "\t<tr><th scope=row>30</th><td>Astoria-Long Island City</td><td>North Sutton Area</td><td>147</td><td>0.0989528786884557</td></tr>\n",
       "\t<tr><th scope=row>31</th><td><e2><8b><ae></td><td><e2><8b><ae></td><td><e2><8b><ae></td><td><e2><8b><ae></td></tr>\n",
       "\t<tr><th scope=row>32</th><td>NA</td><td>Utopia</td><td>677</td><td>0.0639512879781143</td></tr>\n",
       "\t<tr><th scope=row>33</th><td>NA</td><td>Parkchester</td><td>173</td><td>0.0629886311634546</td></tr>\n",
       "\t<tr><th scope=row>34</th><td>NA</td><td>Williams Bridge</td><td>65</td><td>0.0627934901192277</td></tr>\n",
       "\t<tr><th scope=row>35</th><td>NA</td><td>Corona</td><td>10</td><td>0.0627017543859649</td></tr>\n",
       "\t<tr><th scope=row>36</th><td>NA</td><td>Hunts Point</td><td>55</td><td>0.061708308248174</td></tr>\n",
       "\t<tr><th scope=row>37</th><td>NA</td><td>Springfield Gardens</td><td>655</td><td>0.0610987827185282</td></tr>\n",
       "\t<tr><th scope=row>38</th><td>NA</td><td>Flushing</td><td>790</td><td>0.060758507229995</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>NA</td><td>South Bronx</td><td>236</td><td>0.0592333323611171</td></tr>\n",
       "\t<tr><th scope=row>40</th><td>NA</td><td>Midland Beach</td><td>12</td><td>0.0587931371958439</td></tr>\n",
       "\t<tr><th scope=row>41</th><td>NA</td><td>Maspeth</td><td>337</td><td>0.0584611776713095</td></tr>\n",
       "\t<tr><th scope=row>42</th><td>NA</td><td>Bedford Park</td><td>156</td><td>0.0584275821553012</td></tr>\n",
       "\t<tr><th scope=row>43</th><td>NA</td><td>Union Port</td><td>64</td><td>0.0480624086824837</td></tr>\n",
       "\t<tr><th scope=row>44</th><td>NA</td><td>Jamaica</td><td>965</td><td>0.0476935412001119</td></tr>\n",
       "\t<tr><th scope=row>45</th><td>NA</td><td>Wakefield-Williamsbridge</td><td>107</td><td>0.0460906740309743</td></tr>\n",
       "\t<tr><th scope=row>46</th><td>NA</td><td>Laurelton</td><td>328</td><td>0.0431768166258215</td></tr>\n",
       "\t<tr><th scope=row>47</th><td>NA</td><td>Country Club</td><td>68</td><td>0.0423540505717144</td></tr>\n",
       "\t<tr><th scope=row>48</th><td>NA</td><td>Westerleigh-Castleton</td><td>11</td><td>0.0419077325552146</td></tr>\n",
       "\t<tr><th scope=row>49</th><td>NA</td><td>Queens Village</td><td>176</td><td>0.0403016424588993</td></tr>\n",
       "\t<tr><th scope=row>50</th><td>NA</td><td>Canarsie</td><td>537</td><td>0.0398018308144379</td></tr>\n",
       "\t<tr><th scope=row>51</th><td>NA</td><td>Todt Hill</td><td>12</td><td>0.0373984501937258</td></tr>\n",
       "\t<tr><th scope=row>52</th><td>NA</td><td>University Heights</td><td>82</td><td>0.0371791664221414</td></tr>\n",
       "\t<tr><th scope=row>53</th><td>NA</td><td>Baychester</td><td>41</td><td>0.0362048845283501</td></tr>\n",
       "\t<tr><th scope=row>54</th><td>NA</td><td>Woodhaven-Richmond Hill</td><td>480</td><td>0.035369359805022</td></tr>\n",
       "\t<tr><th scope=row>55</th><td>NA</td><td>Brownsville</td><td>292</td><td>0.0315687154734968</td></tr>\n",
       "\t<tr><th scope=row>56</th><td>NA</td><td>Saintalbans</td><td>270</td><td>0.030997185901772</td></tr>\n",
       "\t<tr><th scope=row>57</th><td>NA</td><td>East Brooklyn</td><td>478</td><td>0.0292042542515927</td></tr>\n",
       "\t<tr><th scope=row>58</th><td>NA</td><td>Morris Heights</td><td>75</td><td>0.0291305418306146</td></tr>\n",
       "\t<tr><th scope=row>59</th><td>NA</td><td>Soundview</td><td>114</td><td>0.0275407684642633</td></tr>\n",
       "\t<tr><th scope=row>60</th><td>NA</td><td>Eastchester</td><td>19</td><td>0.0261503227289429</td></tr>\n",
       "\t<tr><th scope=row>61</th><td>NA</td><td>Tremont</td><td>119</td><td>0.0253545038097539</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & pickup_nhood & dropoff_nhood & Num & ave_tip_pct\\\\\n",
       "\\hline\n",
       "\t1 & Astoria-Long Island City & Carroll Gardens & 19 & 0.153588261136031\\\\\n",
       "\t2 & Astoria-Long Island City & East Village & 171 & 0.143118548207709\\\\\n",
       "\t3 & Astoria-Long Island City & Downtown & 22 & 0.133530903723109\\\\\n",
       "\t4 & Astoria-Long Island City & Financial District & 117 & 0.132408676626068\\\\\n",
       "\t5 & Astoria-Long Island City & Morningside Heights & 32 & 0.129766573181761\\\\\n",
       "\t6 & Astoria-Long Island City & Soho & 58 & 0.129501289148457\\\\\n",
       "\t7 & Astoria-Long Island City & Greenwich Village & 146 & 0.12778519776767\\\\\n",
       "\t8 & Astoria-Long Island City & Central Park & 72 & 0.127555754598017\\\\\n",
       "\t9 & Astoria-Long Island City & Fort Green & 62 & 0.122389249656247\\\\\n",
       "\t10 & Astoria-Long Island City & Park Slope & 25 & 0.121939736056366\\\\\n",
       "\t11 & Astoria-Long Island City & High Bridge & 11 & 0.11733532001271\\\\\n",
       "\t12 & Astoria-Long Island City & Garment District & 283 & 0.115972094070329\\\\\n",
       "\t13 & Astoria-Long Island City & Midtown & 1177 & 0.115828501416449\\\\\n",
       "\t14 & Astoria-Long Island City & Washington Heights & 77 & 0.115825310558963\\\\\n",
       "\t15 & Astoria-Long Island City & Murray Hill & 215 & 0.114010766438754\\\\\n",
       "\t16 & Astoria-Long Island City & Clinton & 163 & 0.113484036144098\\\\\n",
       "\t17 & Astoria-Long Island City & Chelsea & 224 & 0.113278651216362\\\\\n",
       "\t18 & Astoria-Long Island City & Gramercy & 496 & 0.112826146099133\\\\\n",
       "\t19 & Astoria-Long Island City & Williamsburg & 148 & 0.112713645644494\\\\\n",
       "\t20 & Astoria-Long Island City & West Village & 60 & 0.112152349243157\\\\\n",
       "\t21 & Astoria-Long Island City & Lower East Side & 123 & 0.10927212382603\\\\\n",
       "\t22 & Astoria-Long Island City & Upper West Side & 296 & 0.107894325111354\\\\\n",
       "\t23 & Astoria-Long Island City & Carnegie Hill & 29 & 0.107606788471196\\\\\n",
       "\t24 & Astoria-Long Island City & Upper East Side & 1330 & 0.105999082693197\\\\\n",
       "\t25 & Astoria-Long Island City & NA & 2566 & 0.103575792024331\\\\\n",
       "\t26 & Astoria-Long Island City & Forest Hills & 92 & 0.103404239734775\\\\\n",
       "\t27 & Astoria-Long Island City & Battery Park & 14 & 0.101408651346081\\\\\n",
       "\t28 & Astoria-Long Island City & Tribeca & 35 & 0.0997829570494477\\\\\n",
       "\t29 & Astoria-Long Island City & Boerum Hill & 12 & 0.0994108465584319\\\\\n",
       "\t30 & Astoria-Long Island City & North Sutton Area & 147 & 0.0989528786884557\\\\\n",
       "\t31 & <e2><8b><ae> & <e2><8b><ae> & <e2><8b><ae> & <e2><8b><ae>\\\\\n",
       "\t32 & NA & Utopia & 677 & 0.0639512879781143\\\\\n",
       "\t33 & NA & Parkchester & 173 & 0.0629886311634546\\\\\n",
       "\t34 & NA & Williams Bridge & 65 & 0.0627934901192277\\\\\n",
       "\t35 & NA & Corona & 10 & 0.0627017543859649\\\\\n",
       "\t36 & NA & Hunts Point & 55 & 0.061708308248174\\\\\n",
       "\t37 & NA & Springfield Gardens & 655 & 0.0610987827185282\\\\\n",
       "\t38 & NA & Flushing & 790 & 0.060758507229995\\\\\n",
       "\t39 & NA & South Bronx & 236 & 0.0592333323611171\\\\\n",
       "\t40 & NA & Midland Beach & 12 & 0.0587931371958439\\\\\n",
       "\t41 & NA & Maspeth & 337 & 0.0584611776713095\\\\\n",
       "\t42 & NA & Bedford Park & 156 & 0.0584275821553012\\\\\n",
       "\t43 & NA & Union Port & 64 & 0.0480624086824837\\\\\n",
       "\t44 & NA & Jamaica & 965 & 0.0476935412001119\\\\\n",
       "\t45 & NA & Wakefield-Williamsbridge & 107 & 0.0460906740309743\\\\\n",
       "\t46 & NA & Laurelton & 328 & 0.0431768166258215\\\\\n",
       "\t47 & NA & Country Club & 68 & 0.0423540505717144\\\\\n",
       "\t48 & NA & Westerleigh-Castleton & 11 & 0.0419077325552146\\\\\n",
       "\t49 & NA & Queens Village & 176 & 0.0403016424588993\\\\\n",
       "\t50 & NA & Canarsie & 537 & 0.0398018308144379\\\\\n",
       "\t51 & NA & Todt Hill & 12 & 0.0373984501937258\\\\\n",
       "\t52 & NA & University Heights & 82 & 0.0371791664221414\\\\\n",
       "\t53 & NA & Baychester & 41 & 0.0362048845283501\\\\\n",
       "\t54 & NA & Woodhaven-Richmond Hill & 480 & 0.035369359805022\\\\\n",
       "\t55 & NA & Brownsville & 292 & 0.0315687154734968\\\\\n",
       "\t56 & NA & Saintalbans & 270 & 0.030997185901772\\\\\n",
       "\t57 & NA & East Brooklyn & 478 & 0.0292042542515927\\\\\n",
       "\t58 & NA & Morris Heights & 75 & 0.0291305418306146\\\\\n",
       "\t59 & NA & Soundview & 114 & 0.0275407684642633\\\\\n",
       "\t60 & NA & Eastchester & 19 & 0.0261503227289429\\\\\n",
       "\t61 & NA & Tremont & 119 & 0.0253545038097539\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2,620 x 4]\n",
       "Groups: pickup_nhood [86]\n",
       "\n",
       "               pickup_nhood       dropoff_nhood   Num ave_tip_pct\n",
       "                      (chr)               (chr) (int)       (dbl)\n",
       "1  Astoria-Long Island City     Carroll Gardens    19   0.1535883\n",
       "2  Astoria-Long Island City        East Village   171   0.1431185\n",
       "3  Astoria-Long Island City            Downtown    22   0.1335309\n",
       "4  Astoria-Long Island City  Financial District   117   0.1324087\n",
       "5  Astoria-Long Island City Morningside Heights    32   0.1297666\n",
       "6  Astoria-Long Island City                Soho    58   0.1295013\n",
       "7  Astoria-Long Island City   Greenwich Village   146   0.1277852\n",
       "8  Astoria-Long Island City        Central Park    72   0.1275558\n",
       "9  Astoria-Long Island City          Fort Green    62   0.1223892\n",
       "10 Astoria-Long Island City          Park Slope    25   0.1219397\n",
       "..                      ...                 ...   ...         ..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter(\n",
    "  arrange(\n",
    "    summarise(\n",
    "      group_by(taxi_df, \n",
    "               pickup_nhood, dropoff_nhood), \n",
    "      Num = n(), \n",
    "      ave_tip_pct = mean(tip_pct)), \n",
    "    desc(ave_tip_pct)), \n",
    "  Num >= 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magrittr\n",
    "\n",
    "![](https://github.com/smbache/magrittr/raw/master/inst/logo.png)\n",
    "\n",
    "* Inspired by unix `|`, and F# forward pipe `|>`, `magrittr` introduces the funny character (`%>%`, the _then_ operator)\n",
    "* `%>%` pipes the object on the left hand side to the first argument of the function on the right hand side\n",
    "* Every function in `dplyr` has a slot for `data.frame/tbl` as it's first argument, so this works beautifully!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source: local data frame [2,620 x 4]\n",
      "Groups: pickup_nhood [86]\n",
      "\n",
      "               pickup_nhood       dropoff_nhood   Num ave_tip_pct\n",
      "                      (chr)               (chr) (int)       (dbl)\n",
      "1  Astoria-Long Island City     Carroll Gardens    19   0.1535883\n",
      "2  Astoria-Long Island City        East Village   171   0.1431185\n",
      "3  Astoria-Long Island City            Downtown    22   0.1335309\n",
      "4  Astoria-Long Island City  Financial District   117   0.1324087\n",
      "5  Astoria-Long Island City Morningside Heights    32   0.1297666\n",
      "6  Astoria-Long Island City                Soho    58   0.1295013\n",
      "7  Astoria-Long Island City   Greenwich Village   146   0.1277852\n",
      "8  Astoria-Long Island City        Central Park    72   0.1275558\n",
      "9  Astoria-Long Island City          Fort Green    62   0.1223892\n",
      "10 Astoria-Long Island City          Park Slope    25   0.1219397\n",
      "..                      ...                 ...   ...         ...\n"
     ]
    }
   ],
   "source": [
    "taxi_df %>% \n",
    "  group_by(pickup_nhood, dropoff_nhood) %>% \n",
    "  summarize(Num = n(),\n",
    "            ave_tip_pct = mean(tip_pct)) %>% \n",
    "  arrange(desc(ave_tip_pct)) %>% \n",
    "  filter(Num >= 10) %>% print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![hellyeah](http://i.giphy.com/lF1XZv45kIwMw.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipe + group_by()\n",
    "\n",
    "* The pipe operator is very helpful for group by summaries\n",
    "* Let's calculate average tip amount, and average trip distance, controlling for dropoff day of the week and dropoff location\n",
    "* First filter with the vector `manhattan_hoods`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source: local data frame [398 x 4]\n",
      "Groups: dropoff_nhood [27]\n",
      "\n",
      "   dropoff_nhood     pickup_nhood    ave_tip  ave_dist\n",
      "           (chr)            (chr)      (dbl)     (dbl)\n",
      "1   Battery Park     Central Park 0.12694563  6.149281\n",
      "2   Battery Park          Clinton 0.11996579  4.016902\n",
      "3   Battery Park      East Harlem 0.07116177 10.124000\n",
      "4   Battery Park     East Village 0.14717019  3.537367\n",
      "5   Battery Park Garment District 0.13463903  3.965532\n",
      "6   Battery Park         Gramercy 0.14396885  4.153174\n",
      "7   Battery Park Hamilton Heights 0.06770436  8.843571\n",
      "8   Battery Park           Harlem 0.13829591  9.039286\n",
      "9   Battery Park           Inwood 0.18235294 11.950000\n",
      "10  Battery Park          Midtown 0.13428280  5.496734\n",
      "..           ...              ...        ...       ...\n"
     ]
    }
   ],
   "source": [
    "load(url(\"https://alizaidi.blob.core.windows.net/training/manhattan.RData\"))\n",
    "taxi_df %>% \n",
    "  filter(pickup_nhood %in% manhattan_hoods,\n",
    "         dropoff_nhood %in% manhattan_hoods) %>% \n",
    "  group_by(dropoff_nhood, pickup_nhood) %>% \n",
    "  summarize(ave_tip = mean(tip_pct), \n",
    "            ave_dist = mean(trip_distance)) %>% \n",
    "  filter(ave_dist > 3, ave_tip > 0.05) %>% print\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipe and Plot\n",
    "\n",
    "Piping is not limited to dplyr functions, can be used everywhere!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      ": package 'ggplot2' was built under R version 3.2.5"
     ]
    },
    {
     "data": {
      "image/png": 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iRj2T+myhxiXqHKvV+XVat+JfAe9y9UuS/VZc4/+A2qzCYwzPrJ8Wb3v1Q5gkSQ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\nJoM8ByN6yr1KxUgSoaC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      ],
      "text/plain": [
       "plot without title"
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      "image/svg+xml": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "taxi_df %>% \n",
    "  filter(pickup_nhood %in% manhattan_hoods,\n",
    "         dropoff_nhood %in% manhattan_hoods) %>% \n",
    "  group_by(dropoff_nhood, pickup_nhood) %>% \n",
    "  summarize(ave_tip = mean(tip_pct), \n",
    "            ave_dist = mean(trip_distance)) %>% \n",
    "  filter(ave_dist > 3, ave_tip > 0.05) %>% \n",
    "  ggplot(aes(x = pickup_nhood, y = dropoff_nhood)) + \n",
    "    geom_tile(aes(fill = ave_tip), colour = \"white\") + \n",
    "    theme_bw() + \n",
    "    theme(axis.text.x = element_text(angle = 45, hjust = 1),\n",
    "          legend.position = 'bottom') +\n",
    "    scale_fill_gradient(low = \"white\", high = \"steelblue\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Piping to other arguments\n",
    "\n",
    "* Although `dplyr` takes great care to make it particularly amenable to piping, other functions may not reserve the first argument to the object you are passing into it.\n",
    "* You can use the special `.` placeholder to specify where the object should enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = ave_tip ~ ave_dist, data = .)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.112258 -0.010882  0.002727  0.014168  0.140976 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.1324307  0.0016071  82.402  < 2e-16 ***\n",
       "ave_dist    -0.0017345  0.0003004  -5.773 1.15e-08 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.02468 on 724 degrees of freedom\n",
       "Multiple R-squared:  0.04401,\tAdjusted R-squared:  0.04269 \n",
       "F-statistic: 33.33 on 1 and 724 DF,  p-value: 1.153e-08\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_df %>% \n",
    "  filter(pickup_nhood %in% manhattan_hoods,\n",
    "         dropoff_nhood %in% manhattan_hoods) %>% \n",
    "  group_by(dropoff_nhood, pickup_nhood) %>% \n",
    "  summarize(ave_tip = mean(tip_pct), \n",
    "            ave_dist = mean(trip_distance)) %>% \n",
    "  lm(ave_tip ~ ave_dist, data = .) -> taxi_model\n",
    "summary(taxi_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "  \n",
    "Your turn: \n",
    "\n",
    "* Use the pipe operator to group by day of week and dropoff neighborhood\n",
    "* Filter to Manhattan neighborhoods \n",
    "* Make tile plot with average fare amount in dollars as the fill\n",
    "\n",
    "## Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional Programming\n",
    "\n",
    "## Creating Functional Pipelines \n",
    "### Reusable code\n",
    "\n",
    "* The examples above create a rather messy pipeline operation\n",
    "* Can be very hard to debug\n",
    "![whoaaaaaaaaahhhhh](http://www.ohmagif.com/wp-content/uploads/2015/02/the-scariest-electrical-repair-ever.gif)\n",
    "* The operation is pretty readable, but lacks reusability\n",
    "* Since R is a functional language, we benefit by splitting these operations into functions and calling them separately\n",
    "* This allows resuability; don't write the same code twice!\n",
    "\n",
    "## Functional Pipelines \n",
    "### Summarization\n",
    "\n",
    "* Let's create a function that takes an argument for the data, and applies the summarization by neighborhood to calculate average tip and trip distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "taxi_hood_sum <- function(taxi_data = taxi_df) {\n",
    "  \n",
    "  load(url(\"https://alizaidi.blob.core.windows.net/training/manhattan.RData\"))\n",
    "  taxi_data %>% \n",
    "    filter(pickup_nhood %in% manhattan_hoods,\n",
    "           dropoff_nhood %in% manhattan_hoods) %>% \n",
    "    group_by(dropoff_nhood, pickup_nhood) %>% \n",
    "    summarize(ave_tip = mean(tip_pct), \n",
    "              ave_dist = mean(trip_distance)) %>% \n",
    "    filter(ave_dist > 3, ave_tip > 0.05) -> sum_df\n",
    "  \n",
    "  return(sum_df)\n",
    "  \n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functional Pipelines | Plotting Function\n",
    "\n",
    "* We can create a second function for the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tile_plot_hood <- function(df = taxi_hood_sum()) {\n",
    "  \n",
    "  library(ggplot2)\n",
    "  \n",
    "  ggplot(data = df, aes(x = pickup_nhood, y = dropoff_nhood)) + \n",
    "    geom_tile(aes(fill = ave_tip), colour = \"white\") + \n",
    "    theme_bw() + \n",
    "    theme(axis.text.x = element_text(angle = 45, hjust = 1),\n",
    "          legend.position = 'bottom') +\n",
    "    scale_fill_gradient(low = \"white\", high = \"steelblue\") -> gplot\n",
    "  \n",
    "  return(gplot)\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calling Our Pipeline\n",
    "\n",
    "* Now we can create our plot by simply calling our two functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4xyEdrQZ9oSSMZETEItXZo+P5M5QdOb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\nJoM8ByN6yr1KxUgSoaC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      ],
      "text/plain": [
       "plot without title"
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     },
     "metadata": {
      "image/svg+xml": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_hood_sum(taxi_df) %>% tile_plot_hood\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make that baby interactive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Complex Pipelines with do\n",
    "\n",
    "* The `summarize` function is fun, can summarize many numeric/scalar quantities\n",
    "* But what if you want multiple values/rows back, not just a scalar summary?\n",
    "* Meet the `do` verb -- arbitrary `tbl` operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "taxi_df %>% group_by(dropoff_dow) %>%\n",
    "  filter(!is.na(dropoff_nhood), !is.na(pickup_nhood)) %>%\n",
    "  arrange(desc(tip_pct)) %>% \n",
    "  do(slice(., 1:2)) %>% \n",
    "  select(dropoff_dow, tip_amount, tip_pct, \n",
    "         fare_amount, dropoff_nhood, pickup_nhood)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating Multiple Models with do\n",
    "\n",
    "* A common use of `do` is to calculate many different models by a grouping variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "taxi_df %>% sample_n(10^4) %>% \n",
    "  group_by(dropoff_dow) %>% \n",
    "  do(lm_tip = lm(tip_pct ~ pickup_nhood + passenger_count + pickup_hour,\n",
    "     data = .))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where is it? \n",
    "![digging](http://i.giphy.com/oEnTTI3ZdK6ic.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Output\n",
    "\n",
    "* By design, every function in `dplyr` returns a `data.frame`\n",
    "* In the example above, we get back a spooky `data.frame` with a column of `S3` `lm` objects\n",
    "* You can still modify each element as you would normally, or pass it to a `mutate` function to extract intercept or statistics\n",
    "* But there's also a very handy `broom` package for cleaning up such objects into `data.frames`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "library(broom)\n",
    "taxi_df %>% sample_n(10^5) %>%  \n",
    "  group_by(dropoff_dow) %>% \n",
    "  do(glance(lm(tip_pct ~ pickup_nhood + passenger_count + pickup_hour,\n",
    "     data = .)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "mutate\n",
    "\n",
    ":    Create transformations\n",
    "\n",
    "summarise\n",
    "\n",
    ":    Aggregate\n",
    "\n",
    "group_by\n",
    "\n",
    ":    Group your dataset by levels\n",
    "\n",
    "do\n",
    "\n",
    ":    Evaluate complex operations on a tbl\n",
    "\n",
    "Chaining with the `%>%` operator can result in more readable code.\n",
    "\n",
    "## What We Didn't Cover\n",
    "\n",
    "* There are many additional topics that fit well into the `dplyr` and functional programming landscape\n",
    "* There are too many to cover in one session. Fortunately, most are well documented. The most notable omissions:\n",
    "  1. Connecting to remote databases, see `vignette('databases', package = 'dplyr')`\n",
    "  2. Merging and Joins, see `vignette('two-table', package = 'dplyr')`\n",
    "  3. Programming with `dplyr`,`vignette('nse', package = 'dplyr')`\n",
    "  4. `summarize_each` and `mutate_each`\n",
    "\n",
    "## Thanks for Attending!\n",
    "\n",
    "- Any questions?\n",
    "- [alizaidi@microsoft.com](mailto:alizaidi@microsoft.com)"
   ]
  }
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