[drake](https://github.com/ropensci/drake) for Workflow Happiness in R

# <code>[drake](https://github.com/ropensci/drake)</code> for Workflow Happiness in R
### <code>Amanda Dobbyn</code>

---

# **Warning**

### This presentation contains less rap than you might have expected.

---

## Quick About Me

**Day job**: ultimate frisbee player

**For fun**: Data Scientist at [Earlybird Software](http://www.earlybird.co/), former co-organizer of [R-Ladies Chicago](https://rladieschicago.org/)

**GitHub**: [@aedobbyn](https://github.com/aedobbyn)

**Website**: https://dobb.ae

**Twitter**: [@dobbleobble](https://twitter.com/dobbleobble)

]

---

## The Plan

1) I'll give an intro to what `drake` is and how it works.

2) We'll switch to a [live coding Rmd](https://github.com/aedobbyn/nyc-fires/blob/master/live_code.md) which hopefully won't totally break 🤞

In that part, we'll use the Twitter and Google Maps geocoding APIs to run a `drake` pipeline.

All code and slides on [GitHub](https://github.com/aedobbyn/nyc-fires).

---

## `drake`'s Main Idea

[`drake`](https://github.com/ropensci/drake) is workflow manager for your R code.

In a complex analysis pipeline, it makes changing your code easier.

`drake` loves changes.

---

## `drake`'s Main Idea

When something changes that makes the most recent results **out-of-date**, `drake` rebuilds *only* things that need to be rebuilt, so that

*what gets done stays done*.

Created and maintained by [Will](https://twitter.com/wmlandau) [Landau](https://github.com/wlandau) and friends.

---

## What's the deal with the name?

**`d`**`ataframes in`

**`R`** `for`

`M`**`ake`**

[GNU Make](https://www.gnu.org/software/make/) is a tool that uses a file called a Makefile to specify **dependencies** in a pipeline.

`drake` implements that idea in a way that's more native to how we work in R.

]

Example of a Makefile:

---

## Better Workflows

Does your analysis directory look like this?

`01_import.R`

`02_clean.R`

`03_deep_clean.R`

`04_join.R`

`05_analyze.R`

`06_analyze_more.R`

`07_report.Rmd`
]

#### What's bad about this?

**It doesn't scale well**

Which you know if you've tried to add another intermediate step or reorganize your subdirectories.

]

---

#### Your pipeline depends on

- You keeping file names up-to-date and sourcing things in the right order

- You knowing when the input data changes

- You knowing which objects and functions are used by which other objects and functions

#### If something breaks

- Can you be sure about where it broke?

- Do you know which intermediate data stores are up to date?

- Do you need to re-run the entire pipeline again?

]

---

## Nice features of `drake`

1) Tidy **dataframe** shows how pieces in your pipeline fit together

]

2) **Dependency graph** of all inputs and outputs

]

3) Great for iteration and **reproducibility**, especially if used with git

]

4) Automated parallel and distributed computing

]

5) It's all in R, so no writing config files! 🎉

]

---

## `drake` : `knitr`

(Analogy stolen from Will's [interview on the R podcast](https://www.youtube.com/watch?v=eJQ29CLyDCs&feature=youtu.be&t=1533).)

1) `knitr` can **cache** chunks if they've already been run, and nothing in them has changed.

2) A chunk successfully knitting **depends** on the previous chunk knitting and on any chunk that you specify a [`depedson`](https://twitter.com/drob/status/738786604731490304?lang=en) for.

3) Report lives in a single file, making that part reproducible and **compact**.

???
With knitr, you expect to be able to rerun someone's report from a single file.

---

## `drake` : `knitr`

But, `knitr` is a reporting tool, not a pipelining tool.

- You can't summon values from the `cache` in an interactive session

- If you're outsourcing preprocessing steps to R scripts outside the `Rmd`, we haven't solved any of the usual dependency issues from before

- `Rmd`s quickly get big and unweildy for serious pipelines

---

## Is this also kinda like memoising?

Yes!

But better.

Memoising **caches the return value of a function for a given a set of arguments**

- If the function is called again with the *same* set of arguments, the value is pulled from the cache instead of recomputed
  - Saves time & resources 👍

--

- In R implemented nicely in the [`memoise` package](https://github.com/r-lib/memoise)

---

## On Memoising

The downside:

**Memoising only applies to one function.**

What if a function upstream of the memoised function changes? We could get the wrong answer.

---

## On Memoising

```r
add <- function(a, b) {
 a + b
}

add_and_square <- function(a, b) {
 add(a, b) ^ 2
}
```

```r
add(2, 3)
## [1] 5
add_and_square(2, 3)
## [1] 25
```

---

## On Memoising

If we've memoised `add_and_square`,

```r
add_and_square <- memoise::memoise(add_and_square)
```

we return `add_and_square(2, 3)` from the cache. (Yay, fast!)

```r
add_and_square(2, 3)
## [1] 25
```

**But** if we now redefine `add` so that it *subtracts* `b` from `a`...

```r
add <- function(a, b) {
* a - b
}
```

What will happen when we call `add_and_square(2, 3)`?

---

## On Memoising

We should get

```r
add(2, 3) ^ 2
## [1] 1
```

But instead we return the old answer, which is now wrong:

```r
add_and_square(2, 3)
## [1] 25
```

Luckily, `drake` knows the *all* the dependency relationships between functions as they relate to your targets.

So, `drake` would know that the definition of `add` has changed, meaning that `add_and_square(2, 3)` needs to be recomputed.

---

## A Few Pieces of `drake` Vocab

> **Targets** are the objects that drake generates;

> **Commands** are the pieces of R code that produce them.

> **Plans** wrap up the relationship between targets and commands into a workflow representation: a dataframe.

???

one column for targets, and one column for their corresponding commands.

---

## More on Plans

Plans are like that top-level script that runs your entire pipeline.

```r
source("01_import.R")
source("02_clean.R")
...
source("06_analyze_more.R")

final <- do_more_things(object_in_env)

write_out_my_results(final)
```

*But*, a plan **knows about the dependencies** in your code.

---

## How to `drake`

1) Store functions and any packages you need to load in a file

`funs.R`

2) Store a `drake` **plan** in another file

```r
plan <- 
 drake_plan(
 cleaned_data = clean_my(raw_data),
 results = analyze_my(cleaned_data),
 report = report_out_my(results)
 )
```

3) **Run** the plan

```r
make(plan)
```

---

## What `drake` does

```r
plan <- 
 drake_plan(
 cleaned_data = clean_my(raw_data),
 results = analyze_my(cleaned_data),
 report = report_out_my(results)
 )
```

`drake_plan` stores your plan as targets and commands in a dataframe.

```r
plan
## # A tibble: 3 x 2
## target command 
## <chr> <chr> 
## 1 cleaned_data clean_my(raw_data) 
## 2 results analyze_my(cleaned_data)
## 3 report report_out_my(results)
```

---

## What `drake` does

```r
make(plan)
```

**First run** of `make(plan)`:

`drake` runs the plan from scratch

**Thereafter**:

`drake` will only rebuild targets that are out of date, and everything downstream of them

---

## What makes a target become out of date?

1) A trigger is activated (more on these later)

2) Something used to generate that target *or one of its upstream targets* has changed

```r
plan <-
 drake_plan(
 cleaned_data = clean_my(raw_data),
* results = analyze_my(cleaned_data),
 report = report_out_my(results)
 )
```

`drake` knows that `results` depends on the object `cleaned_data` and the function `analyze_my()`

because those are both part of the command used to generate `results`.

**So, if `cleaned_data` changes or `analyze_my` changes, `results` is out of date.**

---

## Where is all this info stored?

#### **targets**

In a hidden `.drake` directory, or cache, in your project's root. [More on storage.](https://ropensci.github.io/drake/articles/storage.html)

<img src="./img/drake_cache.jpg" height="180px">
 <figcaption style="margin-left: 20%;">Spot the cache among the hidden dirs.</figcaption>

`loadd()` loads targets from the cache into your R session.

`clean()` cleans the cache. (You can recover a cache if you clean it by accident.)

---

## Where is all this info stored?

#### **dependencies**

`drake` **hashes** a target's dependencies to know when one of those dependencies changes

<img src="./img/drake_cache_hashes_small.jpg" height="150px">
 <figcaption style="margin-left: 20%;">Inside the data subdir of the .drake cache</figcaption>

and creates a `config` list that stores a dependency graph (`igraph` object) of the plan along with a bunch of other things.

You can access all of this with `drake_config()`.

???
You can check that the cache is there with `ls -a`.

You have [control](https://ropensci.github.io/drake/articles/storage.html#hash-algorithms) over the hashing algorithm used, location of the cache, etc.

---

## It's all about Functions

`drake` is all built around *functions* rather than scripts.

- A plan works by using functions to create targets

- This allows `drake` to infer **dependencies** between
  - objects and functions
  - functions and other functions

- Running `drake_plan` creates a dataframe relating each target to the command used to generate it

---

## All about Functions

```r
bad_plan <- 
 drake_plan(
 first_target = source("import.R"),
 second_target = source("clean.R")
 )
```

Sourcing files breaks the dependency structure that makes `drake` useful.

```r
source("all_my_funs.R")

good_plan <- 
 drake_plan(
 first_target = do_stuff(my_data),
 second_target = do_more_stuff(first_target)
 )
```

Now `drake` knows`first_target` needs to be built before work on `second_target` can begin.

---

## `drake` things we won't get into

- [Generate ~ big plans ~](https://ropensci.github.io/drake/articles/best-practices.html#generating-workflow-plan-data-frames)

for analyses that require lots of different permutations of a certain analysis.

(`drake` version 7.0.0 has a [new syntax](https://ropenscilabs.github.io/drake-manual/plans.html#create-large-plans-the-easy-way) that makes it easier to create them.)

- Support for [debugging and testing ](https://ropenscilabs.github.io/drake-manual/debug.html) plans

- Compatibility with [high performance computing](https://ropenscilabs.github.io/drake-manual/hpc.html) backends

---

## Moar Resources

- [`drake` user manual](https://ropenscilabs.github.io/drake-manual/index.html)

- [debugging drake](https://ropensci.github.io/drake/articles/debug.html)

- [Kirill Müller's cheat sheet](https://github.com/krlmlr/drake-sib-zurich/blob/master/cheat-sheet.pdf)

- [Sina Rüeger](https://sinarueeger.github.io/2018/10/09/workflow/) and [Christine Stawitz](https://github.com/cstawitz/RLadies_Sea_drake)'s `drake` presentations

- [Drake's Spotify station](https://open.spotify.com/artist/3TVXtAsR1Inumwj472S9r4)

---

## Our Plan

Remember the [crazy blue light](https://twitter.com/NYCFireWire/status/1078478369036165121) from late December?

😱 😱 😱

---

## Our Plan

The Twitter account that let us know that this wasn't in fact aliens is [NYCFireWire](https://twitter.com/NYCFireWire).

Normally they just tweet out fires and their locations in a more or less predictable pattern:

`<borough> ** <some numbers> ** <address> <description of fire>`

We can use their tweets to get some info on where and when fires happen in NYC.

???

I'll illustrate a way you might want to use `drake` with something that's close to home for us.

What if we were constructing an analysis of these tweets and wanted to make sure our pipeline worked end-to-end, but didn't want to unnecessarily re-run outdated parts of it unless we needed to?

---

## The Pipeline

1. Pull in tweets, either the first big batch or any new ones that show up

2. Extract addresses from the tweets (🎶 regex time 🎶)

3. Send addresses to the Google Maps API to grab their latitudes and longitudes

4. Profit

All functions are defined in [`didnt_start_it.R`](https://github.com/aedobbyn/nyc-fires/blob/master/R/didnt_start_it.R), which we'll source in now.

```r
source(here::here("R", "didnt_start_it.R")) 
```

**Caveats**

This analysis relies on the [rtweet](https://github.com/mkearney/rtweet) and [ggmap](https://github.com/dkahle/ggmap) packages.

To be able to run it in full you'll need a [Twitter API access token](https://rtweet.info/articles/auth.html) and [Google Maps Geocoding API key](https://developers.google.com/maps/documentation/geocoding/intro#Geocoding).

---

## Grabbing Tweets

[`get_tweets`](https://github.com/aedobbyn/nyc-fires/blob/master/R/didnt_start_it.R)

*Main idea*:

* **Builds up a file** of the most recent set of tweets from a given account

*Details*:
- If neither file nor `tbl` is supplied as arguments, grabs an initial *seed* batch of tweets
- If either is supplied, checks for new tweets and grabs them if any
- Spits out the latest to the same file

```r
get_tweets(n_tweets_seed = 3)
## # A tibble: 3 x 5
## text user_id status_id created_at screen_name
## <chr> <chr> <chr> <dttm> <chr> 
## 1 Queens **99-75-5985*… 560024… 1090764990… 2019-01-30 19:13:33 NYCFireWire
## 2 Big fire in Bergen C… 560024… 1090762878… 2019-01-30 19:05:10 NYCFireWire
## 3 Blocking the hydrant… 560024… 1090739760… 2019-01-30 17:33:18 NYCFireWire
```

???

- `get_seed_tweets` grabs a batch of tweets *or* reads in seed tweets from a file if the file exists
- `get_more_tweets` checks if there are new tweets and, if so, pulls in the right number of them
- `get_tweets` runs `get_seed_tweets` if given a null `tbl` argument, otherwise runs `get_more_tweets`

---

## Grabbing Seed Tweets

A closer look at just the text of the tweets:

```r
get_tweets(n_tweets_seed = 5) %>% 
  select(text) %>% 
  kable()
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> text </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Queens **99-75-5985** 220-19 145th Avenue Near 221st Street, Battalion 54 reports all hands going to work for a basement fire in A Private Dwelling </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Big fire in Bergen County, NJ. Old timber buildings, they just said the fire is making it's way to the Propane Filling station. </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Blocking the hydrant is dangerous! https://t.co/IFNDVK41u3 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> From NYC OEM: Snow Squall Warning in effect for NYC on 1/30 until 4 PM. Heavy and blowing snow with wind gusts up to 50 mph causing whiteout conditions, zero visibility and life-threatening travel conditions.... https://t.co/HlqrDX2UcX </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Queens *99-75-9298* 60-15 Calloway St. Fire top floor 7 story 200x70 multiple dwelling. </td>
 </tr>
</tbody>
</table>

---

## Reupping Tweets

To show how `get_tweets` can start with a `tbl` of tweets and look for new ones,

we'll grab 10 `seed_tweets` that are all **older** than an old tweet ID.

```r
old_tweet_id <- "1084948074588487680" # From Jan 14

seed_tweets <- 
 get_tweets(
 n_tweets_seed = 10,
 max_id = old_tweet_id
 )

nrow(seed_tweets)
## [1] 10
```

---

## Reupping Tweets

Using `seed_tweets` as an input to the same `get_tweets` function,

we check for new tweets, and, if there are any, pull them in.

```r
full_tweets <- 
 get_tweets(seed_tweets, 
 n_tweets_reup = 5)
## Searching for new tweets.
## 5 new tweet(s) pulled.
```

```r
nrow(seed_tweets)
## [1] 10
nrow(full_tweets)
## [1] 15
```

---

## Getting Addresses

With `pull_addresses` we parse the text of the tweet to pull out borough and street and string them together into an address.

```r
get_tweets(max_id = old_tweet_id) %>% 
* pull_addresses() %>%
  select(text, street, borough, address) %>% 
  kable() 
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> text </th>
 <th style="text-align:left;"> street </th>
 <th style="text-align:left;"> borough </th>
 <th style="text-align:left;"> address </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Bronx *66-75-3049* 1449 Commonwealth ave. Attic fire private dwelling. </td>
 <td style="text-align:left;"> 1449 Commonwealth ave </td>
 <td style="text-align:left;"> The Bronx </td>
 <td style="text-align:left;"> 1449 Commonwealth ave, The Bronx </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Manhattan *66-75-0755* 330 E 39 St. Fire in the duct work 3rd floor. 10-77(HiRise Residential). E-16/TL-7 1st due </td>
 <td style="text-align:left;"> 330 E 39 St </td>
 <td style="text-align:left;"> Manhattan </td>
 <td style="text-align:left;"> 330 E 39 St, Manhattan </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Manhattan 10-77* 66-75-2017* 70 Little West St x 2nd Pl. BC01 has a fire on the 7th floor in the laundry area. </td>
 <td style="text-align:left;"> 70 Little West St x 2nd Pl </td>
 <td style="text-align:left;"> Manhattan </td>
 <td style="text-align:left;"> 70 Little West St x 2nd Pl, Manhattan </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Bronx *66-75-2251* 2922 3rd Avenue at Westchester Avenue, Battalion 14 transmitting a 10-75 for a fire on the 4th floor of a 6 story commercial building. Squad 41 First Due </td>
 <td style="text-align:left;"> 2922 3rd Avenue at Westchester Avenue </td>
 <td style="text-align:left;"> The Bronx </td>
 <td style="text-align:left;"> 2922 3rd Avenue at Westchester Avenue, The Bronx </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Brooklyn **77-75-0270** 330 Bushwick Avenue Near McKibbin Street, Fire on the 4th Floor </td>
 <td style="text-align:left;"> 330 Bushwick Avenue Near McKibbin Street </td>
 <td style="text-align:left;"> Brooklyn </td>
 <td style="text-align:left;"> 330 Bushwick Avenue Near McKibbin Street, Brooklyn </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Brooklyn *77-75-0855* 899 Hancock St. Fire top floor 3 story </td>
 <td style="text-align:left;"> 899 Hancock St </td>
 <td style="text-align:left;"> Brooklyn </td>
 <td style="text-align:left;"> 899 Hancock St, Brooklyn </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Brooklyn **77-75-0855** 899 Hancock Street Near Howard Avenue, All hands going to work for fire I’m the top floor </td>
 <td style="text-align:left;"> 899 Hancock Street Near Howard Avenue </td>
 <td style="text-align:left;"> Brooklyn </td>
 <td style="text-align:left;"> 899 Hancock Street Near Howard Avenue, Brooklyn </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Bronx *66-75-3937* 3840 Orloff Av. Fire 4th floor. </td>
 <td style="text-align:left;"> 3840 Orloff Av </td>
 <td style="text-align:left;"> The Bronx </td>
 <td style="text-align:left;"> 3840 Orloff Av, The Bronx </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Staten Island *MVA/PIN* Box 1744-
490 Harold St off Forest Hill Rd. Hurst tool in operation. </td>
 <td style="text-align:left;"> Box 1744-
490 Harold St off Forest Hill Rd </td>
 <td style="text-align:left;"> Staten Island </td>
 <td style="text-align:left;"> Box 1744-
490 Harold St off Forest Hill Rd, Staten Island </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Queens 99-75-6810 111-15 227 St BC-54 using all hands for a fire in a pvt dwelling </td>
 <td style="text-align:left;"> NA </td>
 <td style="text-align:left;"> Queens </td>
 <td style="text-align:left;"> Queens </td>
 </tr>
</tbody>
</table>

---

## Getting Lat and Long

Last step of the main pipeline!

**Reverse geocoding** = getting latitude and longitude from an address.

The [`ggmap`](https://www.rdocumentation.org/packages/ggmap/versions/2.6.1/topics/geocode) package exposes this feature of the [Google Maps](https://cloud.google.com/maps-platform/) API.

```r
get_tweets(n_tweets_seed = 5,
 max_id = old_tweet_id) %>% 
 pull_addresses() %>% 
* get_lat_long()
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1449%20Commonwealth%20ave%2C%20The%20Bronx&key=xxx
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=330%20E%2039%20St%2C%20Manhattan&key=xxx
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=70%20Little%20West%20St%20x%202nd%20Pl%2C%20Manhattan&key=xxx
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=2922%203rd%20Avenue%20at%20Westchester%20Avenue%2C%20The%20Bronx&key=xxx
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=330%20Bushwick%20Avenue%20Near%20McKibbin%20Street%2C%20Brooklyn&key=xxx
## # A tibble: 5 x 5
## address lat long created_at text 
## <chr> <dbl> <dbl> <dttm> <chr> 
## 1 1449 Commonwealth… 40.8 -73.9 2019-01-14 15:54:18 Bronx *66-75-3049* 14…
## 2 330 E 39 St, Manh… 40.7 -74.0 2019-01-14 13:41:11 Manhattan *66-75-0755…
## 3 70 Little West St… 40.7 -74.0 2019-01-14 12:40:19 Manhattan 10-77* 66-7…
## 4 2922 3rd Avenue a… 40.8 -73.9 2019-01-14 07:06:21 Bronx *66-75-2251* 29…
## 5 330 Bushwick Aven… 40.7 -73.9 2019-01-13 21:27:15 Brooklyn **77-75-0270…
```

---

## Downstream Analysis

Later in the pipeline we'll:

`count_fires`, summing up the total number of fires per `lat`-`long` combo

```r
count_fires <- function(tbl) {
 tbl %>%
 drop_na() %>%
 count(lat, long)
}
```

and plot them on a map (thanks again, `ggmap`)

---

## Downstream Analysis

```r
get_map("new york city") %>% 
  ggmap()
## Source : https://maps.googleapis.com/maps/api/staticmap?center=new%20york%20city&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=new%20york%20city&key=xxx
```

![](index_files/figure-html/unnamed-chunk-30-1.png)

---

Using 3000 tweets:

```r
plot_fire_sums(fire_sums, output_path = NULL)
```

![](index_files/figure-html/unnamed-chunk-32-1.png)

---

## Quick Benchmark

So where does `drake` really come in handy here?

The trips to and from Twitter and Google take a while.

What's the estimate of running the pipeline on a single tweet?

```r
(our_bench <-
 
 bench::mark({
 
 get_tweets(n_tweets_seed = 1) %>% # Hi Twitter
 pull_addresses() %>%
 get_lat_long() # Hi Google
 
 }) %>% 
 
 as_tibble() %>% 
 pull(median))
## [1] 292ms
```

---

## Quick Benchmark

Roughly how many **minutes** would the pipeline take for 3k tweets?

(No batch speedups since we're going `rowwise` on each tweet.)

```r
(n_mins <- 
 (as.numeric(our_bench) # Returns this in seconds
 * 3000) # 3k tweets
 / 60 # 60 seconds in a minute
 ) 
## [1] 14.61218
```

---
## Quick Benchmark

All our downstream analyses depend on this pipeline.

If we tweak some code but `drake` determines we don't need to rerun the pipeline, we will save **15 minutes** of our lives.

![](https://media.giphy.com/media/5VMNcCxVBibZK/giphy.gif)

And we rest assured we have the most up-to-date data.

---

## Our `drake` Plan

We'll set up the `drake` plan and run it for real.

An exclusive sneak peek:

```r
plan <-
 drake_plan(
 seed_fires = get_tweets(), 
 fires = target(
 command = get_tweets(tbl = seed_fires),
 # Always look for new tweets
 trigger = trigger(condition = TRUE)
 ),
 # Extract addresses from tweets
 addresses = pull_addresses(fires), 
 # Send to Google for lat-longs
 lat_long = get_lat_long(addresses), 
 # Sum up n fires per lat-long combo
 fire_sums = count_fires(lat_long),

time_graph = graph_fire_times(lat_long),
    plot = plot_fire_sums(fire_sums)
  )
```

---

## Questions so far?

```r
devtools::session_info()
## ─ Session info ──────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.5.2 (2018-12-20)
##  os       macOS Mojave 10.14          
##  system   x86_64, darwin15.6.0        
##  ui       RStudio                     
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2019-02-12                  
## 
## ─ Packages ──────────────────────────────────────────────────────────────
##  package     * version    date       lib
##  askpass       1.1        2019-01-13 [1]
##  assertthat    0.2.0      2017-04-11 [1]
##  backports     1.1.3      2018-12-14 [1]
##  bench         1.0.1      2018-06-06 [1]
##  bindr         0.1.1      2018-03-13 [1]
##  bindrcpp    * 0.2.2      2018-03-29 [1]
##  bitops        1.0-6      2013-08-17 [1]
##  broom         0.5.0      2018-07-17 [1]
##  callr         3.1.1      2018-12-21 [1]
##  cellranger    1.1.0      2016-07-27 [1]
##  cli           1.0.1      2018-09-25 [1]
##  codetools     0.2-15     2016-10-05 [1]
##  colorspace    1.4-0      2019-01-13 [1]
##  crayon        1.3.4      2018-12-20 [1]
##  curl          3.3        2019-01-10 [1]
##  desc          1.2.0      2018-05-01 [1]
##  devtools      2.0.1      2018-10-26 [1]
##  digest        0.6.18     2018-10-10 [1]
##  dplyr       * 0.7.8      2018-11-10 [1]
##  drake       * 6.2.1      2018-12-10 [1]
##  emo           0.0.0.9000 2018-07-05 [1]
##  emojifont   * 0.5.2      2019-01-17 [1]
##  evaluate      0.12       2018-10-09 [1]
##  forcats     * 0.3.0      2018-02-19 [1]
##  formatR       1.5        2017-04-25 [1]
##  fs          * 1.2.6      2018-08-23 [1]
##  ggmap       * 2.7.904    2019-01-04 [1]
##  ggplot2     * 3.1.0      2018-10-25 [1]
##  glue        * 1.3.0      2018-07-17 [1]
##  gtable        0.2.0      2016-02-26 [1]
##  haven         1.1.2      2018-06-27 [1]
##  here        * 0.1        2017-05-28 [1]
##  hms           0.4.2      2018-03-10 [1]
##  htmltools     0.3.6      2017-04-28 [1]
##  httpuv        1.4.5.1    2018-12-18 [1]
##  httr          1.4.0      2018-12-11 [1]
##  igraph        1.2.2      2018-07-27 [1]
##  jpeg          0.1-8      2014-01-23 [1]
##  jsonlite      1.6        2018-12-07 [1]
##  kableExtra  * 1.0.1      2019-01-22 [1]
##  knitr       * 1.21       2018-12-10 [1]
##  labeling      0.3        2014-08-23 [1]
##  later         0.7.5      2018-09-18 [1]
##  lattice       0.20-38    2018-11-04 [1]
##  lazyeval      0.2.1      2017-10-29 [1]
##  lubridate     1.7.4      2018-04-11 [1]
##  magrittr      1.5        2014-11-22 [1]
##  maps        * 3.3.0      2018-04-03 [1]
##  memoise       1.1.0      2017-04-21 [1]
##  mime          0.6        2018-10-05 [1]
##  modelr        0.1.2      2018-05-11 [1]
##  munsell       0.5.0      2018-06-12 [1]
##  nlme          3.1-137    2018-04-07 [1]
##  openssl       1.2.1      2019-01-17 [1]
##  packrat       0.4.9-10   2018-08-10 [1]
##  pillar        1.3.1.9000 2019-02-03 [1]
##  pkgbuild      1.0.2      2018-10-16 [1]
##  pkgconfig     2.0.2      2018-08-16 [1]
##  pkgload       1.0.2      2018-10-29 [1]
##  plyr          1.8.4      2016-06-08 [1]
##  png           0.1-7      2013-12-03 [1]
##  prettyunits   1.0.2      2015-07-13 [1]
##  processx      3.2.1      2018-12-05 [1]
##  profmem       0.5.0      2018-01-30 [1]
##  promises      1.0.1      2018-04-13 [1]
##  proto         1.0.0      2016-10-29 [1]
##  ps            1.3.0      2018-12-21 [1]
##  purrr       * 0.3.0      2019-01-27 [1]
##  R6            2.3.0      2018-10-04 [1]
##  Rcpp          1.0.0      2018-11-07 [1]
##  readr       * 1.3.1      2018-12-21 [1]
##  readxl        1.1.0.9000 2018-07-05 [1]
##  remotes       2.0.2      2018-10-30 [1]
##  RgoogleMaps   1.4.3      2018-11-07 [1]
##  rjson         0.2.20     2018-06-08 [1]
##  rlang         0.3.1      2019-01-08 [1]
##  rmarkdown     1.11       2018-12-08 [1]
##  rprojroot     1.3-2      2018-01-03 [1]
##  rstudioapi    0.9.0      2019-01-09 [1]
##  rtweet      * 0.6.8      2018-09-28 [1]
##  rvest         0.3.2      2016-06-17 [1]
##  scales        1.0.0      2018-08-09 [1]
##  servr         0.11       2018-10-23 [1]
##  sessioninfo   1.1.1      2018-11-05 [1]
##  showtext      0.6        2019-01-10 [1]
##  showtextdb    2.0        2017-09-11 [1]
##  storr         1.2.1      2018-10-18 [1]
##  stringi       1.2.4      2018-07-20 [1]
##  stringr     * 1.3.1      2018-05-10 [1]
##  sysfonts      0.8        2018-10-11 [1]
##  testthat    * 2.0.1      2018-10-13 [1]
##  tibble      * 2.0.1      2019-01-12 [1]
##  tidyr       * 0.8.2      2018-10-28 [1]
##  tidyselect  * 0.2.5      2018-10-11 [1]
##  tidyverse   * 1.2.1      2017-11-14 [1]
##  usethis       1.4.0      2018-08-14 [1]
##  viridisLite   0.3.0      2018-02-01 [1]
##  webshot       0.5.1      2018-09-28 [1]
##  withr         2.1.2      2018-03-15 [1]
##  xaringan      0.8.6      2019-02-04 [1]
##  xfun          0.4        2018-10-23 [1]
##  xml2          1.2.0      2018-01-24 [1]
##  yaml          2.2.0      2018-07-25 [1]
##  source                                  
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
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##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.2)                          
##  Github (r-lib/crayon@74bee76)           
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.1)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  Github (hadley/emo@02a5206)             
##  Github (GuangchuangYu/emojifont@f7ba3ec)
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  Github (dkahle/ggmap@4dfe516)           
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.1)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.2)                          
##  Github (rstudio/packrat@7d320b1)        
##  Github (r-lib/pillar@3a54b8d)           
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.1)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  Github (tidyverse/readxl@be74f8f)       
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.2)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
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##  CRAN (R 3.5.2)                          
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##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  Github (yihui/xaringan@0d713d5)         
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
##  CRAN (R 3.5.0)                          
## 
## [1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library
```

---

## Live coding time!

The `rtweet` package also supports POSTing tweets, so we can test out whether our trigger successfully pulls in new tweet with our own

## 🔥 **[`burner account!`](https://twitter.com/didntstartit)** 🔥

On to [part 2](https://github.com/aedobbyn/nyc-fires/blob/master/live_code.md) after this message from our sponsor.