02a-moma-cleaning.Rmd

---
title: "Lab 02a: MOMA cleaning"
subtitle: "CS631"
author: "Alison Hill"
output:
  html_document:
    theme: flatly
---

```{r setup, include = FALSE, cache = FALSE}
knitr::opts_chunk$set(error = TRUE, comment = NA, warning = FALSE, errors = FALSE, message = FALSE, tidy = FALSE, cache = FALSE)
```

```{r load-packages, include = FALSE}
library(tidyverse)
```




# Load the MOMA data

```{r}
library(here)
library(readr)
library(janitor)
library(dplyr)
moma <- read_csv(here::here("data", "artworks.csv"),
                 col_types = cols(
                   BeginDate = col_number(),
                   EndDate = col_number(),
                   `Length (cm)` = col_number(),
                   `Circumference (cm)` = col_number(),
                   `Duration (sec.)` = col_number(),
                   `Diameter (cm)` = col_number()
                 )) %>% 
  clean_names()
problems(moma)
```

Basic cleaning with `stringr` of `gender` variable, which refers to the gender of the artist (a `()` is used a placeholder for "various artists")

```{r}
library(stringr)
moma <- moma %>% 
  mutate(gender = str_replace_all(gender, fixed("(female)", 
                                                    ignore_case = TRUE), "F"),
         gender = str_replace_all(gender, fixed("(male)", 
                                                    ignore_case = TRUE), "M"),
         num_artists = str_count(gender, "[:alpha:]"),
         num_artists = na_if(num_artists, 0),
         n_female_artists = str_count(gender, "F"),
         n_male_artists = str_count(gender, "M"),
         artist_gender = case_when(
           num_artists == 1 & n_female_artists == 1 ~ "Female",
           num_artists == 1 & n_male_artists == 1 ~ "Male"
         ))
```


Let's also do some detecting of strings in the `credit_line` variable.

```{r}
moma <- moma %>% 
  mutate(purchase = str_detect(credit_line, fixed("purchase", ignore_case = TRUE)),
         gift = str_detect(credit_line, fixed("gift", ignore_case = TRUE)),
         exchange = str_detect(credit_line, fixed("exchange", ignore_case = TRUE)))
```

According to [MOMA](https://www.moma.org/momaorg/shared/pdfs/docs/explore/CollectionsMgmtPolicyMoMA_Oct10.pdf):
Acquisitions to the Collection may be made by purchase, gift, fractional interest gift, bequest, or exchange. 

Let's clean up some dates:

- We'll clean up year acquired with `lubridate` to pull out the `year`.
- We'll rename two date variables that are the artist birth/death year, but aren't labelled clearly.
- We'll do a very rough estimate of the date each piece was created, using `stringr::str_extract()`

```{r}
library(lubridate)
moma <- moma %>% 
  mutate(year_acquired = year(date_acquired)) %>% 
  rename(artist_birth_year = begin_date, artist_death_year = end_date) %>% 
  mutate(year_created = str_extract(date, "\\d{4}"),
         artist_birth_year = na_if(artist_birth_year, 0),
         artist_death_year = na_if(artist_death_year, 0))
```



What different kinds of art classifications are available?

```{r}
moma %>% 
  distinct(classification) %>% 
  print(n = Inf)
```

We want to focus on standard rectangular paintings:

- Filter based on classification
- Drop all pieces of art that have either missing (`NA`) height or width measurements, or who have `0` for either height or width.

```{r}
library(tidyr)
moma <- moma %>% 
  filter(classification == "Painting") %>% 
  drop_na(height_cm, width_cm) %>% 
  filter(height_cm > 0 & width_cm > 0)
```

We'll select those columns we want to keep:

```{r}
moma <- moma %>% 
  select(title, contains("artist"), contains("year"), contains("_cm"),
         purchase, gift, exchange, classification, department)
```


Now let's export this data frame for the lab.
```{r}
write_csv(moma, here::here("data", "artworks-cleaned.csv"))
```