Read the data

library("readr")
df = read_csv("https://raw.githubusercontent.com/MuseumofModernArt/collection/master/Artworks.csv")

Question 1: Create a new dataframe of the stock of paintings at MOMA for each month in the year

library("dplyr")
library("lubridate")
library("zoo")

df.q1 = df %>% 
  mutate(
    year = year(DateAcquired),
    month = month(DateAcquired),
    date = ymd(paste(year, month, "01",sep = "-"))
  ) %>% 
  group_by(date) %>% 
  summarise(
    supply = n()
  ) %>% 
  arrange(date) %>% 
  mutate(
    stock = cumsum(supply)
  )
head(df.q1)
## Source: local data frame [6 x 3]
## 
##         date supply stock
##       (time)  (int) (int)
## 1 1929-11-01      9     9
## 2 1930-01-01      3    12
## 3 1930-04-01      2    14
## 4 1930-06-01      1    15
## 5 1930-10-01      2    17
## 6 1931-01-01      2    19

Question 2: Use ggplot2 and your new data frame to plot the the stock of paintings on the y-axis and the date on the x-axis

library("zoo")
library("ggplot2")
library("ggthemes")
library("scales")
library("viridis")

p = ggplot(df.q1, aes(x = date, y = stock))
p + geom_line(colour = "red", size = 1) +
  theme_tufte() +
  theme(axis.title = element_text(), 
        axis.title.y = element_text(angle = 90)) + 
  labs("stock of paintings", title = "Question 2", x = NULL) + 
  scale_y_continuous(labels=comma)

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Question 3: Create the same plot but this time the color should reflect the stock of paintings for curator approved and non-curator approved paintings, respectively

df.q3 = df %>% 
  mutate(
    year = year(DateAcquired),
    month = month(DateAcquired),
    date = ymd(paste(year, month, "01",sep = "-"))
  ) %>% 
  group_by(date, CuratorApproved) %>% 
  summarise(
    supply = n()
  ) %>% 
  ungroup() %>% 
  group_by(CuratorApproved) %>% 
  arrange(CuratorApproved, date) %>% 
  mutate(
    stock = cumsum(supply)
  )
head(df.q3)
## Source: local data frame [6 x 4]
## Groups: CuratorApproved [1]
## 
##         date CuratorApproved supply stock
##       (time)           (chr)  (int) (int)
## 1 1931-01-01               N      1     1
## 2 1932-12-01               N      1     2
## 3 1933-01-01               N      1     3
## 4 1933-04-01               N     90    93
## 5 1934-04-01               N     14   107
## 6 1934-05-01               N     65   172
p = ggplot(df.q3, aes(x = date, y = stock, colour = CuratorApproved))
p + geom_line(size = 1) +
  theme_tufte()  +
  theme(axis.title = element_text(), 
        axis.title.y = element_text(angle = 90)) + 
  labs("stock of paintings", title = "Question 3", x = NULL) + 
  scale_y_continuous(labels=comma) +
  scale_color_viridis(discrete = TRUE)

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Question 4: Create a new data frame of the stock of paintings grouped by what department the painting belongs to

df.q4 = df %>% 
  mutate(
    year = year(DateAcquired),
    month = month(DateAcquired),
    date = ymd(paste(year, month, "01",sep = "-"))
  ) %>% 
  group_by(date, Department) %>% 
  summarise(
    supply = n()
  ) %>% 
  ungroup() %>% 
  group_by(Department) %>% 
  arrange(Department, date) %>% 
  mutate(
    stock = cumsum(supply)
  )
head(df.q4)
## Source: local data frame [6 x 4]
## Groups: Department [1]
## 
##         date            Department supply stock
##       (time)                 (chr)  (int) (int)
## 1 1932-01-01 Architecture & Design      2     2
## 2 1934-01-01 Architecture & Design      2     4
## 3 1934-04-01 Architecture & Design     43    47
## 4 1934-09-01 Architecture & Design      4    51
## 5 1935-11-01 Architecture & Design     22    73
## 6 1935-12-01 Architecture & Design      1    74

Question 5: Plot this data frame using ggplot2. Which department has had the highest increase in their stock of paintings?

p = ggplot(df.q4, aes(x = date, y = stock, colour = Department))
p + geom_line(size = 1) +
  theme_tufte() +
  theme(axis.title = element_text(), 
        axis.title.y = element_text(angle = 90)) + 
  labs("stock of paintings", title = "Question 5", x = NULL) + 
  scale_y_continuous(labels=comma) +
  scale_color_viridis(discrete = TRUE)

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p = ggplot(df.q4, aes(x = date, y = stock, colour = Department))
p + geom_line(size = 1) +
  theme_tufte() +
  theme(axis.title = element_text(), 
        axis.title.y = element_text(angle = 90)) + 
  labs("stock of paintings", title = "Question 5", x = NULL) + 
  scale_y_log10() +
  scale_color_viridis(discrete = TRUE)

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# Alternative:
df.q4.alt = df %>% 
  group_by(Department) %>% 
  summarise(
    stock = n()
  ) 
head(df.q4.alt)
## Source: local data frame [6 x 2]
## 
##                              Department stock
##                                   (chr) (int)
## 1                 Architecture & Design 15828
## 2 Architecture & Design - Image Archive    18
## 3                              Drawings 10738
## 4                                  Film  2587
## 5                     Fluxus Collection  2547
## 6             Media and Performance Art  2350
p = ggplot(df.q4.alt, aes(x = Department, y = stock, fill = Department))
p + geom_bar(stat="identity") +
  theme_tufte() + 
  scale_y_continuous(labels=comma) +
  theme(axis.text.x = element_blank(),
        legend.position = "none") + 
  facet_wrap(~ Department, scales = "free_x") +
  scale_fill_viridis(discrete = TRUE)

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Question 6: Write a piece of code that counts the number of paintings by each artist in the data set. List the 10 painters with the highest number of paintings in MOMA’s collection.

df.artist = df %>% 
  filter(Artist != "") %>% 
  group_by(Artist) %>% 
  summarise(count = n()) %>% 
  arrange(-count)

head(df.artist, 10)
## Source: local data frame [10 x 2]
## 
##                      Artist count
##                       (chr) (int)
## 1              Eugène Atget  5050
## 2          Louise Bourgeois  3224
## 3  Ludwig Mies van der Rohe  2497
## 4      Unknown photographer  1573
## 5             Jean Dubuffet  1426
## 6           Lee Friedlander  1317
## 7             Pablo Picasso  1309
## 8              Marc Chagall  1146
## 9             Henri Matisse  1064
## 10           Pierre Bonnard   940

Question 7: The variable ArtistBio lists the birth place of each painter. Use this information to create a world map where each country is colored according to the stock of paintings in MOMA’s collection.

library("stringr")
df$Nationality = str_extract(df$ArtistBio, "[A-Z][a-z]+")

df.nationality = df %>% 
  group_by(Nationality) %>% 
  summarise(count = n())

head(df.nationality)
## Source: local data frame [6 x 2]
## 
##   Nationality count
##         (chr) (int)
## 1      Active     3
## 2      Afghan     1
## 3    Albanian    23
## 4    Algerian     5
## 5    American 54031
## 6       Anglo     4

Information on country and country adjective available here

Scrape and create data frame

library("rvest")
link = "https://www.englishclub.com/vocabulary/world-countries-nationality.htm"
css.selector = "td:nth-child(2) , td:nth-child(1)"

country = link %>% 
  read_html() %>% 
  html_nodes(css = "td:nth-child(1)") %>% 
  html_text()

adjective = link %>% 
  read_html() %>% 
  html_nodes(css = "td:nth-child(2)") %>% 
  html_text()

df.info = data.frame(country = country, adjective = adjective)
df.info$adjective = tolower(df.info$adjective)

df.info$adjective = ifelse(df.info$country == "United States of America (USA)",
                           "american", df.info$adjective)

head(df.info)
##       country   adjective
## 1 Afghanistan      afghan
## 2     Albania    albanian
## 3     Algeria    algerian
## 4     Andorra    andorran
## 5      Angola     angolan
## 6   Argentina argentinian
df.nationality$Nationality = tolower(df.nationality$Nationality)

df.map = inner_join(df.nationality, df.info, by = c("Nationality" = "adjective"))

library("ggmap")
world.map = map_data("world")

library("countrycode")
world.map$iso2c = countrycode(world.map$region, 
                           origin = "country.name", 
                           destination = "iso2c")

df.map$iso2c = countrycode(df.map$country, 
                                 origin = "country.name", 
                                 destination = "iso2c")

df.map = inner_join(world.map, df.map, by = "iso2c")

p = ggplot(df.map, aes(x = long, y = lat, group = group, fill = count)) 
p + geom_polygon() +
  theme_tufte() +
  labs(title = "Painters by country") +
  scale_fill_viridis()

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p = ggplot(df.map, aes(x = long, y = lat, group = group, fill = count)) 
p + geom_polygon() +
  theme_tufte() +
  labs(title = "Painters by country") +
  scale_fill_viridis(trans = "log", breaks = c(20, 8000), 
                     labels = c("low", "high"),
                     name = "count\n(log transformed)")

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Question 8: The Dimensions variable lists the dimensions of each painting. Use your data manipulation skills to calculate the area of each painting (in cm’s). Create a data frame of the five largest and five smallest paintings in MOMA’s collection.

dim = str_extract(df$Dimensions, "\\([^()]+\\)")
dim = gsub("\\(|\\)", "", dim)
dim = gsub("[a-z]", "", dim)
dim = str_trim(dim)
dim = str_split(dim, "  ")
df$width = unlist(lapply(dim, function(x) x[1]))
df$length = unlist(lapply(dim, function(x) x[2]))
df$dim.length = unlist(lapply(dim, length))
df$height = NA
df$height[df$dim.length == 3] = unlist(lapply(dim[df$dim.length == 3], 
                                              function(x) x[3]))
df = df %>% 
  mutate(
    width = as.numeric(width),
    length = as.numeric(length),
    height = as.numeric(height),
    area = ifelse(dim.length == 3, width*length*height, width*length)
    ) 

summary(df$area)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
## 0.000e+00 3.530e+02 7.300e+02 9.604e+04 2.134e+03 1.419e+09     35288