---
title: "Data Summarization Lab"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Data used
Circulator Lanes Dataset: the data is from https://data.baltimorecity.gov/Transportation/Charm-City-Circulator-Ridership/wwvu-583r
Available on: https://sisbid.github.io/Data-Wrangling/data/Charm_City_Circulator_Ridership.csv
```{r}
library(tidyverse)
circ <- read_csv("https://sisbid.github.io/Data-Wrangling/data/Charm_City_Circulator_Ridership.csv")
```
1. How many days are in the data set? You can assume each observation/row is a different day (hint: get the number of rows).
```{r q1}
```
2. What is the total (sum) number of boardings on the green bus (`greenBoardings` column)?
```{r q2}
```
3. How many days are missing daily ridership (`daily` column)? Use `is.na()` and `sum()`.
```{r q3}
```
4. Group the data by day of the week (`day`). Next, find the mean daily ridership (`daily` column) and the sample size. (hint: use `group_by` and `summarize` functions)
```{r q4}
```
## **Extra practice:**
5. What is the median of `orangeBoardings`(use `median()`).
```{r q6}
```
6. Take the median of `orangeBoardings`(use `median()`), but this time stratify by day of the week.
```{r q7}
```