---
title: "Data Summarization Lab Key"
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```



# Data used

Bike Lanes Dataset: BikeBaltimore is the Department of Transportation's bike program. 
The data is from http://data.baltimorecity.gov/Transportation/Bike-Lanes/xzfj-gyms

You can Download as a CSV in your current working directory.  Note its also available at: 	http://johnmuschelli.com/intro_to_r/data/Bike_Lanes.csv 

```{r}
library(readr)
library(dplyr)
library(tidyverse)
library(jhur)

bike = read_csv(
  "http://johnmuschelli.com/intro_to_r/data/Bike_Lanes.csv")
```


or use 
```{r}
library(jhur)
bike = read_bike()
```

# Part 1

1. How many bike "lanes" are currently in Baltimore?  You can assume each observation/row is a different bike "lane".  (hint: how to get the number of rows of a data set)
```{r q1}

```

2. How many (a) feet and (b) miles of bike "lanes" are currently in Baltimore? (`5280` feet in a mile.

```{r q2}

```

# Part 2

3. How many types of bike lanes are there?   (Hints: `unique`, `table`, or `bike %>% count`). Which `type` has (a) the most number of and (b) longest average bike lane length? (Hint: `group_by` and `summarize`)
   
```{r q3}



```


4. How many different projects do the "bike" lanes fall into? 
		Which `project` category has the longest average bike lane? 

```{r q4}



```

			  
5. What was the average bike lane length per year that they were installed? 
 Set `bike$dateInstalled` to `NA` if it is equal to zero.
```{r q5}



```

# Part 3

6. (a) Numerically [hint: `quantile()`, using `bike$length`] and 
		(b) graphically [hint: `qplot(length, geom = "histogram")` or `qplot( geom = "density")`] describe the distribution of bike "lane" lengths.
```{r q6}



```

7. Then describe as above, after stratifying by  i) `type` then ii) number of lanes (`numLanes`) using `boxplot`: 
```{r q7}



```

