# 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: 	https://hutchdatascience.org/SeattleStatSummer_R/data/Bike_Lanes.csv

```{r}

library(readr)
library(tidyverse)

bike <- read_csv(file = "https://hutchdatascience.org/SeattleStatSummer_R/data/Bike_Lanes.csv")

```



1. Use `count` to determine if there are `NA` values for the `route` variable of the `bike` data.

```{r}


```


2.  Filter rows of bike, so that only rows remain that do NOT have missing values for the `route` variable,  using `drop_na`. Assign this to the object `have_route.`

```{r}


```

3.

**New Data set**
Now imagine we work in a clinic and we are trying to understand more about blood types of patients. 

Let's say we the data like so - run the chunk to create the data:

```{r}
BloodType <- tibble(
  type = c(
    "A.-", "AB.+", "O.-", "O.+", "AB.-",
    "B.+", "B.-", "o.-", "O.+", "A.-",
    "A.+", "O.-", "B.-", "o.+", "AB.-"
  ),
  infection = c(
    "Yes", "No", "Yes", "No", "No",
    "No", "Yes", "No", "Yes", "No",
    "No", "Yes", "Yes", "Yes", "NotSure"
  )
)

BloodType
```

There are some issues with this data that we need to figure out!


4. Recode the `type` variable of the `BloodType` data to be consistent. Use `recode`. Hint: the inconsistency has to do with lower case `o` and capital `O`. Don't forget to use quotes for the values!

```{r}

```

5. Check to see that `type` only has these possible values:
 "A.-","A.+", "AB.-", "AB.+", "B-","B+", "O.-", "O.+"

```{r}


```


6. Filtering for patients with type O, how many had the infection?

```{r}


```


