02-moma.Rmd

---
title: "Lab 02: MoMA Museum Tour"
subtitle: "CS631"
author: "Alison Hill"
output:
  html_document:
    theme: flatly
    toc: TRUE
    toc_float: TRUE
    toc_depth: 2
    number_sections: TRUE
    code_folding: hide
---
```{r setup, include = FALSE, cache = FALSE}
knitr::opts_chunk$set(error = TRUE, comment = NA, warning = FALSE, errors = FALSE, message = FALSE, tidy = FALSE, cache = FALSE)
```

# Goals for Lab 02

- Review `dplyr` functions learned in last lab and DataCamp course
- Practice using `dplyr` functions to get to know a new dataset
- Map global plot aesthetics to variables in `ggplot2`
- Create facetted plots with `ggplot2`

# Slides for today

```{r}
knitr::include_url("slides/02-slides.html")
```




# Inspiration + data

We'll use data from the Museum of Modern Art (MoMA)

- Publicly available on [GitHub](https://github.com/MuseumofModernArt/collection)
- As analyzed by [fivethirtyeight.com](https://fivethirtyeight.com/features/a-nerds-guide-to-the-2229-paintings-at-moma/)
- And by [others](https://medium.com/@foe/here-s-a-roundup-of-how-people-have-used-our-data-so-far-80862e4ce220)

# Packages needed

```{r}
library(here) # to set file path if working from local file
library(tidyverse) # readr, ggplot2, dplyr
```


# Read in the data

Note! This is not the original data- I did a lot of cleaning and decision-making in the pre-processing. The below contains only paintings and drawings in the MoMA collection.

Use this code chunk to read in the data available at [http://bit.ly/cs631-moma](http://bit.ly/cs631-moma):

```{r eval = FALSE}
library(readr)
moma <- read_csv("http://bit.ly/cs631-moma")
```

I called my cleaned data `artworks-cleaned.csv`, and stored it in a folder called `data`. You can use this code if you want to read in the local CSV file.

```{r}
library(here)
library(readr)
library(dplyr)
moma <- read_csv(here::here("data", "artworks-cleaned.csv"))
```




# Know your data

<div class="panel panel-success">
  <div class="panel-heading">Challenge #1:</div>
  <div class="panel-body">
Try to answer all of these questions using `dplyr`. Answers are below but try them on your own first!

1. How many paintings (rows) are in `moma`? How many variables (columns) are in `moma`?
1. What is the first painting acquired by MoMA? Which year? Which artist? What title?
1. What is the oldest painting in the collection? Which year? Which artist? What title?
1. How many distinct artists are there?
1. Which artist has the most paintings in the collection? How many paintings are by this artist?
1. How many paintings by male vs female artists?


If you want more:

1. How many artists of each gender are there?
1. In what year were the most paintings acquired? Created?
1. In what year was the first painting by a (solo) female artist acquired? When was that painting created? Which artist? What title?
  </div>
</div>

## How many paintings?

- How many rows/observations are in `moma`?
- How many variables are in `moma`?

<p class="text-info"> __Hint:__ These questions can be answered using the `dplyr` function `glimpse`.</p>


```{r}
library(dplyr)
moma
glimpse(moma)
```

There are `r nrow(moma)` paintings in `moma`.

## What is the first painting acquired?


- What is the first painting acquired by MoMA (since they started tracking)? 
- What year was it acquired?
- Which artist?
- What title?

<p class="text-info"> __Hint:__ These questions can be answered by combining two `dplyr` functions: `select` and `arrange`.</p>


```{r}
moma %>% 
  select(artist, title, year_acquired) %>% 
  arrange(year_acquired)
```

## What is the oldest painting in the MoMA collection?


- What is the oldest painting in the MoMA collection historically (since they started tracking)? 
- What year was it created?
- Which artist?
- What title?

<p class="text-info"> __Hint:__ These questions can be answered by combining two `dplyr` functions: `select` and `arrange`.</p>



```{r}
moma %>% 
  select(artist, title, year_created) %>% 
  arrange(year_created)
```

```{r include = FALSE}
oldest <- moma %>% 
  select(artist, title, year_created) %>% 
  arrange(year_created) %>% 
  slice(1)
```

To do inline comments, I could say that the oldest painting is `r oldest %>% pull(title)`, painted by `r oldest %>% pull(artist)` in `r oldest %>% pull(year_created)`.

## How many artists?

- How many distinct artists are there?

<p class="text-info"> __Hint:__ Try `dplyr::distinct`.</p>
 

```{r}
moma %>% 
  distinct(artist)
```

You could add a `tally()` too to get just the number of rows. You can also then use `pull()` to get that single number out of the tibble:

```{r}
num_artists <- moma %>% 
  distinct(artist) %>% 
  tally() %>% 
  pull()
num_artists
```

Then I can refer to this number in inline comments like: there are `r num_artists` total.

## Which artist has the most paintings?

- Which artist has the most paintings ever owned by `moma`? 
- How many paintings in the MoMA collection by that artist?

<p class="text-info"> __Hint:__ Try `dplyr::count`. Use `?count` to figure out how to sort the output.</p>


```{r}
moma %>% 
  count(artist, sort = TRUE)
```

```{r include = FALSE}
pablo <- moma %>% 
  count(artist, sort = TRUE) %>% 
  slice(1)
```

In the `?count` documentation, it says: "`count` and `tally` are designed so that you can call them repeatedly, each time rolling up a level of detail." Try running `count()` again (leave parentheses empty) on your last code chunk.

```{r}
moma %>% 
  count(artist, sort = TRUE) %>% 
  count()
```

## How many paintings by male vs female artists?


```{r}
moma %>% 
  count(artist_gender)
```


Now together we'll count the number of artists by gender. You'll need to give `count` two variable names in the parentheses: `artist_gender` and `artist`.

```{r}
moma %>% 
  count(artist_gender, artist, sort = TRUE) 
```

This output is not superhelpful as we already know that `r pablo %>% pull(artist)` has `r pablo %>% pull(n)` paintings in the MoMA collection. But how can we find out which female artist has the most paintings? We have a few options. Let's first add a `filter` for females.

```{r}
moma %>% 
  count(artist_gender, artist, sort = TRUE) %>% 
  filter(artist_gender == "Female")
```

Another option is to use another `dplyr` function called `top_n()`. Use `?top_n` to see how it works. How it won't work in this context:

```{r}
moma %>% 
  count(artist_gender, artist, sort = TRUE) %>% 
  top_n(2)
```

How it will work better is following a `group_by(artist_gender)`:

```{r}
moma %>% 
  count(artist_gender, artist, sort = TRUE) %>% 
  group_by(artist_gender) %>% 
  top_n(1)
```


```{r include = FALSE}
sherrie <- moma %>% 
  count(artist_gender, artist, sort = TRUE) %>% 
  filter(artist_gender == "Female") %>% 
  slice(1)
```

Now we can see that `r sherrie %>% pull(artist)` has `r sherrie %>% pull(n)` paintings. This is a pretty far cry from the `r pablo %>% pull(n)` paintings by `r pablo %>% pull(artist)`.

## How many artists of each gender are there?

This is a harder question to answer than you think! This is because the level of observation in our current `moma` dataset is *unique paintings*. We have multiple paintings done by the same artists though, so counting just the number of unique paintings is different than counting the number of unique artists. 

Remember how `count` can be used back-to-back to roll up a level of detail? Try running `count(artist_gender)` again on your last code chunk.

```{r}
moma %>% 
  count(artist_gender, artist) %>% 
  count(artist_gender)
```


This output takes the previous table (made with `count(artist_gender, artist)`), and essentially ignores the `n` column. So we no longer care about how *many* paintings each individual artist created. Instead, we want to `count` the rows in this *new* table where each row is a unique artist. By counting by `artist_gender` in the last line, we are grouping by levels of that variable (so Female/Male/`NA`) and `nn` is the number of unique artists for each gender category recorded.

## When were the most paintings in the collection acquired?


<p class="text-info"> __Hint:__ Try `dplyr::count`. Use `?count` to figure out how to sort the output.</p>

```{r}
moma %>% 
  count(year_acquired, sort = TRUE)
```

## When were the most paintings in the collection created?


<p class="text-info"> __Hint:__ Try `dplyr::count`. Use `?count` to figure out how to sort the output.</p>

```{r}
moma %>% 
  count(year_created, sort = TRUE)
```


## What about the first painting by a solo female artist?



<p class="text-info"> __Hint:__ Try combining three `dplyr` functions: `filter`, `select`, and `arrange`.</p>

When was the first painting by a solo female artist acquired?

```{r}
moma %>% 
  filter(num_artists == 1 & n_female_artists == 1) %>% 
  select(title, artist, year_acquired, year_created) %>% 
  arrange(year_acquired)
```

What is the oldest painting by a solo female artist, and when was it created?

```{r}
moma %>% 
  filter(num_artists == 1 & n_female_artists == 1) %>% 
  select(title, artist, year_acquired, year_created) %>% 
  arrange(year_created)
```

```{r eval = FALSE}
# or, because artist_gender is missing when num_artists > 1
moma %>% 
  filter(artist_gender == "Female") %>% 
  select(title, artist, year_acquired, year_created) %>% 
  arrange(year_acquired)
```

# Basics of `ggplot2`

<div class="panel panel-success">
  <div class="panel-heading">Challenge #2:</div>
  <div class="panel-body">
We'll do this together *(nothing to turn in)*: see [slides](https://apreshill.github.io/data-vis-labs-2018/slides/02-slides.html#16).
  </div>
</div>



# Plot your data



## Plot year painted vs year acquired

 



<div class="panel panel-success">
  <div class="panel-heading">Challenge #3:</div>
  <div class="panel-body">
Let's recreate this plot from [fivethirtyeight](https://fivethirtyeight.com/features/a-nerds-guide-to-the-2229-paintings-at-moma/) (mostly)!

![](https://espnfivethirtyeight.files.wordpress.com/2015/08/roeder-feature-moma-1.png?w=1150&quality=90&strip=info)

Things to consider:

- You'll want to play around with setting an `alpha` value here- keep in mind that `0` is totally transparent and `1` is opaque. 
- Try using `geom_abline()` to add the line in red (use the default intercept value of 0). The actual red line is difficult to recreate- here is what the authors say: "The red regression line shows the “modernizing” of MoMA’s collection — how quickly the museum has moved toward acquiring recent paintings."
- Go back to [Lab 01](https://apreshill.github.io/data-vis-labs-2018/01-eda_hot_dogs.html) to review how to do the following:
    - Change the x- and y-axis labels and the plot title to match the plot above
  </div>
</div>


```{r}
ggplot(moma, aes(year_created, year_acquired)) +
  geom_point(alpha = .1, na.rm = TRUE) +
  geom_abline(intercept = c(0,0), colour = "red") +
  labs(x = "Year Painted", y = "Year Acquired") +
  ggtitle("MoMA Keeps Its Collection Current") 
```



## Facet by artist gender

Can you make the same plot above, but facet by artist gender? 

<p class="text-info"> __Hint:__ For this to make sense, you probably want to do some filtering to select only those paintings where there was one "solo" artist.</p>

```{r}
moma_solo <- moma %>% 
  filter(num_artists == 1)
ggplot(moma_solo, aes(year_created, year_acquired)) +
  geom_point(alpha = .1) +
  geom_abline(intercept = c(0,0), colour = "red") +
  labs(x = "Year Painted", y = "Year Acquired") +
  ggtitle("MoMA Keeps Its Collection Current") +
  facet_wrap(~artist_gender)
```


# Plot painting dimensions

<div class="panel panel-success">
  <div class="panel-heading">Challenge #4:</div>
  <div class="panel-body">
Let's (somewhat) try to recreate this scatterplot from [fivethirtyeight](https://fivethirtyeight.com/features/a-nerds-guide-to-the-2229-paintings-at-moma/). 


![](https://espnfivethirtyeight.files.wordpress.com/2015/08/roeder-feature-moma-3.png?w=1150&quality=90&strip=info)

To recreate, some things to consider:

- Try filtering all paintings with height less than 600 cm and width less than 760 cm. 
- If you want to add color as in the original, you'll need to create a new variable using `mutate`. 


<p class="text-info"> __Hint:__ You'll probably also want to look into `case_when` to create a categorical variable to color by.</p>
  </div>
</div>







```{r}
moma_dim <- moma %>% 
  filter(height_cm < 600, width_cm < 760) %>% 
  mutate(hw_ratio = height_cm / width_cm,
         hw_cat = case_when(
           hw_ratio > 1 ~ "taller than wide",
           hw_ratio < 1 ~ "wider than tall",
           hw_ratio == 1 ~ "perfect square"
         ))
library(ggthemes)
ggplot(moma_dim, aes(x = width_cm, y = height_cm, colour = hw_cat)) +
  geom_point(alpha = .5) +
  ggtitle("MoMA Paintings, Tall and Wide") +
  scale_colour_manual(name = "",
                      values = c("gray50", "#FF9900", "#B14CF0")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text()) +
  labs(x = "Width", y = "Height") 
```


Because Grace is right, we can do better with colors!

```{r}
library(ggthemes)
ggplot(moma_dim, aes(x = width_cm, y = height_cm, colour = hw_cat)) +
  geom_point(alpha = .5) +
  ggtitle("MoMA Paintings, Tall and Wide") +
  scale_colour_manual(name = "",
                      values = c("gray50", "#ee5863", "#6999cd")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text()) +
  labs(x = "Width", y = "Height") 
```

We could also do away with the legend and use `geom_annotate` instead.

```{r}
library(ggthemes)
ggplot(moma_dim, aes(x = width_cm, y = height_cm, colour = hw_cat)) +
  geom_point(alpha = .5, show.legend = FALSE) +
  ggtitle("MoMA Paintings, Tall and Wide") +
  scale_colour_manual(name = "",
                      values = c("gray50", "#ee5863", "#6999cd")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text()) +
  labs(x = "Width", y = "Height") +
  annotate(x = 200, y = 380, geom = "text", 
           label = "Taller than\nWide", color = "#ee5863", 
           size = 5, family = "Lato", hjust = 1, fontface = 2) +
    annotate(x = 375, y = 100, geom = "text", 
             label = "Wider than\nTall", color = "#6999cd", 
             size = 5, family = "Lato", hjust = 0, fontface = 2)
```

# Plot something new & different!

<div class="panel panel-success">
  <div class="panel-heading">Challenge #5:</div>
  <div class="panel-body">
It can be anything- you can change colors, add annotations, switch the geoms, add new variables to examine- the world is your oyster! The only requirements are:

1. You *make* one new plot that is original, and 
2. You *write* 1-2 sentences to present the plot and why it makes sense. What questions do you think your plot can help you to answer?

It does not have to be pretty right now, but it must make sense as a visualization- you must be able to intelligently and succintly tell us about it in real words.
  </div>
</div>