• 1 Workflow for Lab 02
  • 2 Start by Loading Libraries
  • 3 Know Your Data
    • 3.1 How Many Paintings? 2253 painting, 23 variables
    • 3.2 What is the first painting that was acquired?
    • 3.3 What is the oldest painting?
    • 3.4 Odilon Redon Landscape at Daybreak 1872
    • 3.5 How many artists? 989
    • 3.6 Which artist has the most paintings? Pablo Picasso had 55 paintings in the collection
    • 3.7 How many paintings, by gender? Female’s paintings 252, male’s paintings 1991
    • 3.8 How many artists, by gender? 837 males, 143 females
    • 3.9 In which years were the most paintings in the collection acquired? 1985
    • 3.10 In which years were the most paintings in the collection created? 1977
    • 3.11 What about the first painting by a solo female artist?
    • 3.12 by the year acquired: Landscape, 47 Natalia Goncharova
    • 3.13 by the year created: Self-Portrait with Two Flowers in H… Paula Modersohn…
  • 4 Basic Plotting!
    • 4.1 Year painted vs. year acquired
    • 4.2 Faceting by gender
  • 5 Exploring Painting Dimensions
    • 5.1 Challenge #4
    • 5.2 Experimenting with geom_annotate()
  • 6 Challenge #5, on your own!

1 Workflow for Lab 02

The lab instructions can be found here; we will work through its contents together via Webex. You will use this RMarkdown file as your workspace and final document. Don’t forget to update the “author” metadata field at the top of the file!

2 Start by Loading Libraries

3 Know Your Data

moma<-read_csv(here::here("data", "artworks-cleaned.csv"))
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   title = col_character(),
##   artist = col_character(),
##   artist_bio = col_character(),
##   artist_gender = col_character(),
##   circumference_cm = col_logical(),
##   diameter_cm = col_logical(),
##   length_cm = col_logical(),
##   seat_height_cm = col_logical(),
##   purchase = col_logical(),
##   gift = col_logical(),
##   exchange = col_logical(),
##   classification = col_character(),
##   department = col_character()
## )
## See spec(...) for full column specifications.

3.1 How Many Paintings? 2253 painting, 23 variables

glimpse(moma)
## Observations: 2,253
## Variables: 23
## $ title             <chr> "Rope and People, I", "Fire in the Evening", "Portr…
## $ artist            <chr> "Joan Miró", "Paul Klee", "Paul Klee", "Pablo Picas…
## $ artist_bio        <chr> "(Spanish, 1893–1983)", "(German, born Switzerland.…
## $ artist_birth_year <dbl> 1893, 1879, 1879, 1881, 1880, 1879, 1943, 1880, 183…
## $ artist_death_year <dbl> 1983, 1940, 1940, 1973, 1946, 1953, 1977, 1950, 190…
## $ num_artists       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ n_female_artists  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ n_male_artists    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ artist_gender     <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Ma…
## $ year_acquired     <dbl> 1936, 1970, 1966, 1955, 1939, 1968, 1997, 1931, 193…
## $ year_created      <dbl> 1935, 1929, 1927, 1919, 1925, 1919, 1970, 1929, 188…
## $ circumference_cm  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ depth_cm          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ diameter_cm       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ height_cm         <dbl> 104.8, 33.8, 60.3, 215.9, 50.8, 129.2, 200.0, 54.6,…
## $ length_cm         <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ width_cm          <dbl> 74.6, 33.3, 36.8, 78.7, 54.0, 89.9, 200.0, 38.1, 96…
## $ seat_height_cm    <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ purchase          <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ gift              <lgl> TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, …
## $ exchange          <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FAL…
## $ classification    <chr> "Painting", "Painting", "Painting", "Painting", "Pa…
## $ department        <chr> "Painting & Sculpture", "Painting & Sculpture", "Pa…

3.2 What is the first painting that was acquired?

moma %>%
  select(artist,title,year_acquired) %>%
  arrange(year_acquired)
## # A tibble: 2,253 x 3
##    artist           title                                          year_acquired
##    <chr>            <chr>                                                  <dbl>
##  1 Edward Hopper    House by the Railroad                                   1930
##  2 Bernard Karfiol  Seated Nude                                             1930
##  3 Pierre Roy       Daylight Savings Time                                   1931
##  4 Preston Dickins… Plums on a Plate                                        1931
##  5 Otto Dix         Dr. Mayer-Hermann                                       1932
##  6 Paul Cézanne     The Bather                                              1934
##  7 Paul Cézanne     Pines and Rocks (Fontainebleau?)                        1934
##  8 Paul Cézanne     Still Life with Ginger Jar, Sugar Bowl, and O…          1934
##  9 Paul Cézanne     Still Life with Apples                                  1934
## 10 Arthur B. Davies Italian Landscape                                       1934
## # … with 2,243 more rows

##Edward Hopper House by the Railroad 1930

3.3 What is the oldest painting?

moma %>%
  select(artist,title,year_created) %>%
  arrange(year_created)
## # A tibble: 2,253 x 3
##    artist       title                                    year_created
##    <chr>        <chr>                                           <dbl>
##  1 Odilon Redon Landscape at Daybreak                            1872
##  2 Odilon Redon Apache (Man on Horseback)                        1875
##  3 Odilon Redon Apache (Man on Horseback II)                     1875
##  4 Odilon Redon Fishing Boat                                     1875
##  5 Odilon Redon Rocky Peak                                       1875
##  6 Odilon Redon The Rocky Slope                                  1875
##  7 Odilon Redon Landscape with Rocks, near Royan                 1875
##  8 Paul Cézanne Still Life with Fruit Dish                       1879
##  9 Paul Cézanne L'Estaque                                        1879
## 10 Claude Monet On the Cliff at Pourville, Clear Weather         1882
## # … with 2,243 more rows

3.4 Odilon Redon Landscape at Daybreak 1872

3.5 How many artists? 989

moma %>%
  distinct(artist)
## # A tibble: 989 x 1
##    artist           
##    <chr>            
##  1 Joan Miró        
##  2 Paul Klee        
##  3 Pablo Picasso    
##  4 Arthur Dove      
##  5 Francis Picabia  
##  6 Blinky Palermo   
##  7 Pierre Roy       
##  8 Paul Cézanne     
##  9 Enrico Prampolini
## 10 Jankel Adler     
## # … with 979 more rows

3.6 Which artist has the most paintings? Pablo Picasso had 55 paintings in the collection

moma %>%
  count(artist, sort = TRUE)
## # A tibble: 989 x 2
##    artist               n
##    <chr>            <int>
##  1 Pablo Picasso       55
##  2 Henri Matisse       32
##  3 On Kawara           32
##  4 Jacob Lawrence      30
##  5 Batiste Madalena    25
##  6 Jean Dubuffet       25
##  7 Odilon Redon        25
##  8 Ben Vautier         24
##  9 Frank Stella        23
## 10 Philip Guston       23
## # … with 979 more rows
moma %>%
  count(artist, sort=TRUE) %>%
  count()
## # A tibble: 1 x 1
##       n
##   <int>
## 1   989

3.7 How many paintings, by gender? Female’s paintings 252, male’s paintings 1991

moma %>%
  count(artist_gender)
## # A tibble: 3 x 2
##   artist_gender     n
##   <chr>         <int>
## 1 Female          252
## 2 Male           1991
## 3 <NA>             10

3.8 How many artists, by gender? 837 males, 143 females

moma %>%
  count(artist_gender, artist, sort=TRUE) %>%
  filter(artist_gender=="Female")
## # A tibble: 143 x 3
##    artist_gender artist                    n
##    <chr>         <chr>                 <int>
##  1 Female        Sherrie Levine           12
##  2 Female        Agnes Martin              9
##  3 Female        Elizabeth Murray          8
##  4 Female        Susan Rothenberg          8
##  5 Female        Joan Mitchell             6
##  6 Female        Loren MacIver             6
##  7 Female        R. H. Quaytman            6
##  8 Female        Helen Frankenthaler       5
##  9 Female        Georgia O'Keeffe          4
## 10 Female        Lynette Yiadom-Boakye     4
## # … with 133 more rows
moma %>%
  count(artist_gender, artist, sort=TRUE) %>%
  filter(artist_gender=="Male")
## # A tibble: 837 x 3
##    artist_gender artist               n
##    <chr>         <chr>            <int>
##  1 Male          Pablo Picasso       55
##  2 Male          Henri Matisse       32
##  3 Male          On Kawara           32
##  4 Male          Jacob Lawrence      30
##  5 Male          Batiste Madalena    25
##  6 Male          Jean Dubuffet       25
##  7 Male          Odilon Redon        25
##  8 Male          Ben Vautier         24
##  9 Male          Frank Stella        23
## 10 Male          Philip Guston       23
## # … with 827 more rows

3.9 In which years were the most paintings in the collection acquired? 1985

moma %>%
  count(year_acquired, sort=TRUE)
## # A tibble: 88 x 2
##    year_acquired     n
##            <dbl> <int>
##  1          1985    86
##  2          1942    71
##  3          1979    71
##  4          1991    67
##  5          2005    67
##  6          1967    65
##  7          2008    55
##  8          1961    45
##  9          1969    45
## 10          1956    42
## # … with 78 more rows

3.10 In which years were the most paintings in the collection created? 1977

moma %>%
  count(year_created, sort=TRUE)
## # A tibble: 139 x 2
##    year_created     n
##           <dbl> <int>
##  1         1977    57
##  2         1940    56
##  3         1964    56
##  4         1961    50
##  5         1962    49
##  6         1963    44
##  7         1959    42
##  8         1968    40
##  9         1960    39
## 10         1914    37
## # … with 129 more rows

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

3.12 by the year acquired: Landscape, 47 Natalia Goncharova

3.13 by the year created: Self-Portrait with Two Flowers in H… Paula Modersohn…

moma %>%
  filter(num_artists == 1 & n_female_artists == 1) %>%
  select(title, artist, year_acquired, year_created) %>%
  arrange(year_acquired)
## # A tibble: 252 x 4
##    title                        artist                year_acquired year_created
##    <chr>                        <chr>                         <dbl>        <dbl>
##  1 Landscape, 47                Natalia Goncharova             1937         1912
##  2 Shack                        Loren MacIver                  1938         1934
##  3 Hopscotch                    Loren MacIver                  1940         1940
##  4 Shadows with Painting        Irene Rice Pereira             1941         1940
##  5 Figure                       Varvara Stepanova              1941         1921
##  6 Still Life in Red            Amelia Peláez Del Ca…          1942         1938
##  7 White Lines                  Irene Rice Pereira             1942         1942
##  8 Musical Squash               Maud Morgan                    1942         1942
##  9 Desolation                   Raquel Forner                  1942         1942
## 10 Self-Portrait with Cropped … Frida Kahlo                    1943         1940
## # … with 242 more rows
moma %>%
  filter(num_artists == 1 & n_female_artists == 1) %>%
  select(title, artist, year_acquired, year_created) %>%
  arrange(year_created)
## # A tibble: 252 x 4
##    title                              artist          year_acquired year_created
##    <chr>                              <chr>                   <dbl>        <dbl>
##  1 Self-Portrait with Two Flowers in… Paula Modersoh…          2017         1907
##  2 Girl with Bare Shoulders           Gwen John                1958         1909
##  3 Girl Reading at a Window           Gwen John                1971         1911
##  4 Landscape, 47                      Natalia Goncha…          1937         1912
##  5 Cubist Nude                        Alexandra Exter          1991         1912
##  6 Rayonism, Blue-Green Forest        Natalia Goncha…          1985         1913
##  7 The Factory and the Bridge         Olga Rozanova            1985         1913
##  8 Subject from a Dyer's Shop         Lyubov Popova            1985         1914
##  9 Portuguese Market                  Sonia Delaunay…          1955         1915
## 10 Girl with a Blue Scarf             Gwen John                1963         1915
## # … with 242 more rows

4 Basic Plotting!

4.1 Year painted vs. year acquired

ggplot(moma, aes(year_created, year_acquired))+
  geom_point(alpha=0.1, na.rm=TRUE)+
  geom_abline(intercept=c(0,0), color="red")+
  labs(x="Year Painted", y="Year Acquired")+
  ggtitle("MoMA Keeps Its Collection Current")

4.2 Faceting by gender

ggplot(moma, aes(year_created, year_acquired))+
  geom_point(alpha=0.1, na.rm=TRUE)+
  geom_abline(intercept=c(0,0), color="red")+
  labs(x="Year Painted", y="Year Acquired")+
  ggtitle("MoMA Keeps Its Collection Current")+
  facet_wrap(~artist_gender)

5 Exploring Painting Dimensions

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"
         ))

5.1 Challenge #4

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")

## Different colors

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")

5.2 Experimenting with geom_annotate()

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)

6 Challenge #5, on your own!

For this exrcise I’ve decided to look for a correlation between an age, when the artist made his/her masterpiece and the remainer of their life. To my surprise, despite some significant correlation of younger age = longer life, there were some really tragic stories of very young artitsts dying prematurely…

moma_age <- moma %>% 
  mutate(artist_age = year_created - artist_birth_year,
  artist_life = artist_death_year-year_created,
  life_expectancy = case_when(
           artist_life < 1 ~ "short life",
           artist_life < 10 ~ "decent life",
           artist_life < 26 ~ "good life",
           artist_life >25 ~ "long life"))

library(ggthemes)
ggplot(moma_age, aes(x = artist_age, y = artist_life, color=life_expectancy)) +
  geom_point(alpha = .5, show.legend = FALSE) +
  ggtitle("MoMA Paintings, Artist Age vs Life Left") +
  scale_colour_manual(name = "",
                      values = c("gray50", "#00b0f6", "#ee5863", "#6999cd")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_text()) +
  labs(x = "Artist's Age", y = "Life Left") +
  annotate(x = 50, y = 65, geom = "text", 
           label = "Early Start", color = "#ee5863", 
           size = 5, family = "Lato", hjust = 1, fontface = 2) +
    annotate(x = 75, y = 25, geom = "text", 
             label = "Late Bloomer", color = "#6999cd", 
             size = 5, family = "Lato", hjust = 0, fontface = 2)
## Warning: Removed 633 rows containing missing values (geom_point).