---
title: "Homework 5: MORE Data Wrangling"
author: "PUT YOUR NAME HERE"
date: now
date-format: "YYYY-MM-DDTHH:mm:ssZ"
format:
  html:
    toc: true
    toc-depth: 2
    embed-resources: true
    code-tools: true
    df-print: paged
---

```{r include = FALSE}
# This chunk just sets up some styling (eg: default size of our images)
knitr::opts_chunk$set(
  collapse = TRUE, 
  warning = FALSE,
  message = FALSE,
  fig.height = 3, 
  fig.width = 5,
  fig.align = 'center')
```

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**DIRECTIONS**

-   Save this file as `homework_5.qmd` in your `DS112 > homework` folder.
-   Type your name in line 3 above (where it says "author").
-   Type your responses in this template.
-   Do not modify the structure of this document (e.g. don't change section headers, spacing, etc).
-   There are lots of ways to do things in R. In these exercises, be sure to use the `tidyverse` code and style / structure we've learned in this class.


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**GOALS**

Practice some data wrangling and data viz in guided settings. The content of the exercises is not necessarily in the order that we learned it, so you'll need to practice identifying appropriate tools for a given task.

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# Exercise 1: More names

In this exercise, let's revisit the `babynames` dataset from the previous homework.
This dataset, provided by the U.S. Social Security Administration, provides information on the names of every baby born in the U.S. from 1880-2017:

```{r}
# Load the tidyverse 
library(tidyverse)

# Import the data
library(babynames)
data(babynames)
head(babynames)
```


Along with names, there's information on the `sex` assigned at birth.
This information reflects that collected by the U.S. government at birth.
We'll refer to sex assigned at birth as `sex` throughout.
In this exercise, we'll examine the neutrality or non-neutrality of names assigned to babies by `sex`.

## Part a

Create a dataset that has one row per name observed during the study period. For each observed name, calculate the total number of females and males born with that name. Use `values_fill = 0` to replace NAs with 0s. Store this as `babynames_total` and print out the first 3 rows which should match the following:

| name  |   M |   F |
|:------|----:|----:|
| Aaban | 107 |   0 |
| Aabha |   0 |  35 |
| Aabid |  10 |   0 |

```{r}
# Define babynames_total


# Print out the first 3 rows


```

## Part b

After completing Part a, run the following code. Fill in the blanks (`___`) below to comment on what each wrangling row *does*. (You can check your claims by running the code, but should first try to do this without running any code.)

```{r}
# popular_names <- babynames_total %>%
#   filter(M > 25000, F > 25000) %>%  # ___
#   mutate(ratio = F / (F + M)) %>%   # ___
#   arrange(desc(ratio))              # ___
# 
# head(popular_names)
```

## Part c

Starting from `popular_names`, identify the names that are most popular among *both* male and female babies. Specifically, names for which 45-55% of babies born with that name are female (thus 45-55% are male.)

```{r}

```

## Part d

Pick one "neutral" name from Part c. Construct a line plot of the number of babies with this name by year and sex. You should have 2 lines. Discuss your observations.

```{r}

```

> Discussion:

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# Exercise 2: Laughing

In [Laughing On Line](https://pudding.cool/2019/10/laugh/), The Pudding analyzed the use of different laughter expressions (e.g. "lol", "haha") in Reddit comments. And how these have changed over time.

```{r}
# Import data (shared by The Pudding at https://github.com/the-pudding/data/tree/master/laugh)
laughs <- read.csv("https://mac-stat.github.io/data/reddit-laughs.csv")

# Check it out
head(laughs)
```

## Part a

What are the units of observation in `laughs`?

**ANSWER:**

## Part b

Our goal is to create a line plot similar to that in the section titled "The evolution of lol":

![](https://mac-stat.github.io/images/112/laughs.png)

First, create a new `laugh_plot_data` set that you'll need to make this plot. Show the first 6 rows of the data and confirm that it includes 55 rows (one per laugh type / year combination), and 3 columns: `id`, `year`, and `share`. HINTS:

-   Remove any columns you don't need for this analysis.
-   Only keep track of 5 laugh types: "lol", "haha", "lmao", "ha", "heh"
-   Make sure that `share` is on the scale from 0-100, not 0-1.

```{r}
# Define your dataset


# Show the first 6 rows


# Confirm that the dataset has 55 rows and 3 columns


```

## Part c

Use your data from Part b to create a plot that's *similar* to that from the article. For example, the colors and styling don't have to be the same. Discuss your observations.

```{r}

```

> Discussion:

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# Exercise 3: Bikes

In the next 5 exercises, you'll explore two datasets related to Capital Bikeshare, a bike share company, in Washington, DC. To begin, the `trips` data includes information about 10,000 bike rentals during the last quarter of 2014:

```{r}
# Import the data
# Only keep certain variables of interest
trips <- readRDS(gzcon(url("https://mac-stat.github.io/data/2014-Q4-Trips-History-Data-Small.rds"))) %>% 
  select(client, sstation, sdate, duration) %>% 
  mutate(duration = as.numeric(hms(duration))/60)

# Check it out
head(trips)
```

For each rental, `trips` has information on:

-   `client` = whether the renter is a `Registered` member of the bike share service or a `Casual` renter

-   `sstation` = the name of the station where the rental *started*

-   `sdate` = the date and time that a rental *started*

-   `duration` = the duration of the rental in minutes


## Part a

The `sdate` variable contains a lot of information! Use it to define (and store) 5 new variables in the `trips` data:

-   `s_date = as_date(sdate)`: the date (not including time) that the rental started
-   `s_day_of_week` = day of week that the rental started, labeled `Sun`, `Mon`, etc
-   `s_hour` = hour of day that the rentals started (0-23)
-   `s_minute` = minute within the hour that the rentals started (0-59)
-   `s_time_of_day` = time of day that the rentals started in decimal notation (e.g. 3:30 should be 3.5)

HINTS:

-   Recall that the `lubridate` package (part of the `tidyverse`) has some handy functions, including `hour()`, `minute()`, `wday(label = TRUE)`.
-   The time of day can be calculated by hour + minute/60.

```{r}
# Define the new variables
# Once you're confident, store them under trips


# Confirm that your new dataset has 10000 rows and 9 columns
dim(trips)

# Convince yourself that the first entry has
# s_date = 2014-11-06, s_day_of_week = Thu, s_hour = 16, s_minute = 26, s_time_of_day = 16.43333

```

## Part b

Let's warm up by exploring some basic patterns in the data.

```{r}
# Calculate the shortest, average, and longest rental duration 


# Show the rentals that left from Lincoln Memorial on a Monday and lasted under 10 minutes
# HINT: You should get two data points!


# Calculate the total number of rides taken on each day of the week
# Sort from highest to lowest


# Identify the 2 dates (s_date) on which there were the fewest rentals
# (Think about the significance of these dates)


```

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# Exercise 4: When do people ride?

Let's explore temporal patterns in bike rentals using data viz. You will make a series of plots and summarize them in the last part of the exercise.

## Part a

How did the volume of bike trips vary throughout the study period? Construct a univariate plot of `s_date`.

```{r}

```

## Part b

How did the volume of bike trips vary at different times of the day? Construct a univariate plot of `s_time_of_day`.

```{r}

```

## Part c

How do ridership patterns vary by both time of day and day of week? *Facet* your plot from Part b by `s_day_of_week`.

```{r}

```

## Part d

Summarize, in words, the temporal patterns you observed in Parts a, b, and c.

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# Exercise 5: Registered vs casual riders

Let's explore how ridership patterns might differ among registered and casual riders. You will make a series of plots and calculations, and summarize them in the last part of the problem.

## Part a

How do trip durations compare among registered and casual riders? Calculate the shortest, average, and longest rental duration among registered and casual riders.

```{r}

```

## Part b

On what days of the week do registered and casual riders tend to ride more? Construct a plot of `s_day_of_week`, faceted by `client`.

```{r}

```

## Part c

At what times of day *and* days of the week do registered and casual riders tend to ride more? Construct density plots of `s_time_of_day` faceted by `s_day_of_week` and colored by `client` status.

```{r}

```

## Part d

Summarize some key takeaways from Parts a, b, and c. What do they tell us about registered vs casual riders? What is your explanation for why these observations make sense?

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# Exercise 6: Where do people ride?

Beyond information about *when* the bike trips started, the `trips` data includes information about `sstation`, the stations *where* each bike trip started. Let's explore.

## Part a

Make a table with the *ten* stations with the *highest* number of departures (`head(10)`). Define this as `popular_stations` and print / show the data table. HINT: Your first row should have 2 entries: `Columbus Circle / Union Station`, `241`

```{r}
# Define popular_stations

# Print out popular_stations (just type popular_stations)

```

## Part b

Get a dataset of only the `trips` that departed from the ten most `popular_stations`. Store this as `popular_trips`. HINT: Use a join operation.

```{r}
# Define popular_trips

# Confirm that popular_trips has 1525 rows and 9 columns

```

## Part c

Get a dataset of the `trips` that did *not* depart from the most `popular_stations`. Store this as `unpopular_trips`.

```{r}
# Define unpopular_trips

# Confirm that unpopular_trips has 8475 rows and 9 columns

```

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# Exercise 7: Spatial trends

Let's bring in *spatial* information about the bike stations! The `stations` dataset includes the latitude / longitude coordinates and other details for the bike rental stations:

```{r}
# Import data on the bike stations
# Only keep certain variables of interest
stations <- read_csv("https://mac-stat.github.io/data/DC-Stations.csv") %>% 
  select(name, lat, long)

# Check it out
head(stations)
```

## Part a

To the `popular_stations` data, tack on new information about the latitude and longitude of the 10 most popular stations.

```{r}
# Define popular_stations with the additional lat / long variables
# NOTE: Don't store the data until you know it's what you want!


# Confirm that popular_stations has 10 rows and 4 columns

```

## Part b

Construct a leaflet map of the `stations` data:

-   include *circles* representing each station in the Capital Bikeshare system
-   add *markers* (the upside-down teardrop / flag symbols) that indicate the locations of the 10 most popular stations. NOTE: Since these 10 locations are in the `popular_stations` not `stations` data, you'll need to add a `data = popular_stations` argument to this layer.

```{r}
library(leaflet)

```

## Part c

Write a 1-sentence summary of what you learned from this map.



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# Finalize your homework

- Render your qmd one more time and check out the rendered html.
    - Confirm that the html appears as you expect it and that it's correctly formatted.
    - Confirm that you haven't accidentally printed out long datasets.
    - Review your answers and make sure you addressed each question. For example, several questions ask for *both* some code / plot and a *discussion* or *summary* in words.

-   Submit your **html** file to the Homework 5 assignment on Moodle.

-   You're done with Homework 5. Congrats!!
