---
title: "Missing Data Lab Key"
output: html_document
---

# 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://sisbid.github.io/Data-Wrangling/data/Bike_Lanes.csv 


If you haven't installed  `naniar` yet, you will need to use: `install.packages("naniar")` first. 

```{r}
library(tidyverse)
library(naniar)

bike <-read_csv("https://sisbid.github.io/Data-Wrangling/data/Bike_Lanes.csv")

```


1.  Use the `is.na()`  and `any()` functions to check if the bike `dateInstalled` variable has any `NA` values. 
Hint: You first need to `pull` out the vector version of this variable to use the `is.na()` function.

```{r}
bike %>%
  pull(dateInstalled) %>%
  is.na() %>%
  any()
```


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}
have_rout <- bike %>% drop_na(route)
```


3.  Use `naniar` to make a visual of the amount of data missing for each variable of `bike` (use `gg_miss_var()`). Check out more about this package here: https://www.njtierney.com/post/2018/06/12/naniar-on-cran/

```{r}
gg_miss_var(bike)
```


4. What percentage of the `subType` variable is complete of `bike` ? Hint: use another `naniar` function.
 
```{r}
pull(bike, subType) %>% pct_complete() # this
miss_var_summary(bike) # or this
```
