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<title>Lab 02a: MOMA cleaning</title>
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<h1 class="title toc-ignore">Lab 02a: MOMA cleaning</h1>
<h3 class="subtitle"><em>CS631</em></h3>
<h4 class="author"><em>Alison Hill</em></h4>
</div>
<div id="load-the-moma-data" class="section level1">
<h1>Load the MOMA data</h1>
<pre class="r"><code>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)</code></pre>
<pre><code># A tibble: 8 x 5
row col expected actual file
<int> <chr> <chr> <chr> <chr>
1 103824 DateAcquired "date like " 1998-04 '/Users/hillali/Documents/Proj…
2 128673 DateAcquired "date like " 1977-08 '/Users/hillali/Documents/Proj…
3 131406 DateAcquired "date like " 1998-04 '/Users/hillali/Documents/Proj…
4 131785 DateAcquired "date like " 1975-01 '/Users/hillali/Documents/Proj…
5 132165 DateAcquired "date like " 1998-04 '/Users/hillali/Documents/Proj…
6 132166 DateAcquired "date like " 1998-04 '/Users/hillali/Documents/Proj…
7 132167 DateAcquired "date like " 1995 '/Users/hillali/Documents/Proj…
8 132169 DateAcquired "date like " 1998-04 '/Users/hillali/Documents/Proj…</code></pre>
<p>Basic cleaning with <code>stringr</code> of <code>gender</code> variable, which refers to the gender of the artist (a <code>()</code> is used a placeholder for “various artists”)</p>
<pre class="r"><code>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"
))</code></pre>
<p>Let’s also do some detecting of strings in the <code>credit_line</code> variable.</p>
<pre class="r"><code>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)))</code></pre>
<p>According to <a href="https://www.moma.org/momaorg/shared/pdfs/docs/explore/CollectionsMgmtPolicyMoMA_Oct10.pdf">MOMA</a>: Acquisitions to the Collection may be made by purchase, gift, fractional interest gift, bequest, or exchange.</p>
<p>Let’s clean up some dates:</p>
<ul>
<li>We’ll clean up year acquired with <code>lubridate</code> to pull out the <code>year</code>.</li>
<li>We’ll rename two date variables that are the artist birth/death year, but aren’t labelled clearly.</li>
<li>We’ll do a very rough estimate of the date each piece was created, using <code>stringr::str_extract()</code></li>
</ul>
<pre class="r"><code>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))</code></pre>
<p>What different kinds of art classifications are available?</p>
<pre class="r"><code>moma %>%
distinct(classification) %>%
print(n = Inf)</code></pre>
<pre><code># A tibble: 31 x 1
classification
<chr>
1 Architecture
2 Mies van der Rohe Archive
3 Design
4 Illustrated Book
5 Print
6 Drawing
7 Film
8 Multiple
9 Periodical
10 Photograph
11 Painting
12 (not assigned)
13 Architectural Model
14 Product Design
15 Video
16 Media
17 Performance
18 Sculpture
19 Photography Research/Reference
20 Software
21 Installation
22 Work on Paper
23 Audio
24 Textile
25 Ephemera
26 Collage
27 Film (object)
28 Frank Lloyd Wright Archive
29 Poster
30 Graphic Design
31 Furniture and Interiors </code></pre>
<p>We want to focus on standard rectangular paintings:</p>
<ul>
<li>Filter based on classification</li>
<li>Drop all pieces of art that have either missing (<code>NA</code>) height or width measurements, or who have <code>0</code> for either height or width.</li>
</ul>
<pre class="r"><code>library(tidyr)
moma <- moma %>%
filter(classification == "Painting") %>%
drop_na(height_cm, width_cm) %>%
filter(height_cm > 0 & width_cm > 0)</code></pre>
<p>We’ll select those columns we want to keep:</p>
<pre class="r"><code>moma <- moma %>%
select(title, contains("artist"), contains("year"), contains("_cm"),
purchase, gift, exchange, classification, department)</code></pre>
<p>Now let’s export this data frame for the lab.</p>
<pre class="r"><code>write_csv(moma, here::here("data", "artworks-cleaned.csv"))</code></pre>
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<p>
<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">
<img alt="Creative Commons License" style="border-width:0" src="by-nc.png" height="400" width="65"/></a>
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