```{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://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv 

```{r, echo = TRUE, message=FALSE, error = FALSE}
bike <- read_csv(file = "http://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv")
```


1\. How many rows in the data set? You can use `count()`. Pipe the dataset into this function.

```{r}

```


2\. Inspect the dataset by clicking on the table button in your environment.


3\. Summarize the data to get the `max` of the `length` column using the `summarize` function.

```
# General format 
DATA %>% 
    summarize(SUMMARY_COLUMN_NAME = OPERATOR(SOURCE_COLUMN))
```

```{r}

```


4\. Modify your code from #3 to add the `min` of `length` using the `summarize` function.

```
# General format 
DATA %>% 
    summarize(SUMMARY_COLUMN_NAME = OPERATOR(SOURCE_COLUMN),
              SUMMARY_COLUMN_NAME = OPERATOR(SOURCE_COLUMN)
    )
```

```{r}

```


5\. How many bike lanes are there in each type of lane? Use `count()` on the column named `type`.

```{r}

```


6\. Modify your code from question 5 to break down each lane type by number of lanes. Use `count()` on the columns named `type` and `numLanes`.

```{r}

```


7\. How many bike lanes are there in each type of lane? Use `group_by()`, `summarize()`, and `n()` on the column named `type`.

```
# General format 
DATA %>% 
    group_by(GROUPING_COLUMN_NAME) %>% 
    summarize(SUMMARY_COLUMN_NAME = n())
```

```{r}

```


8\. Modify your code from #7 to also group by `numLanes`.

```{r}

```

