library(dplyr) library(ggplot2) library(readr) library(zoo) library(countrycode) library(stringr) library(ggmap) library(rvest) library(gsubfn)

Read in the data

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.

df$Month_acquired=as.yearmon(df$DateAcquired)

stock_month = summarise( group_by(df,Month_acquired),n=n()) stock_month$stock=cumsum(stock_month$n)

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.

what kind of geom do you think is appropriate? why?

- geom_line, since a line graph is good at visualising the movement in stocks

color the geom you have chosen red

add a title and custom axis labels

p = ggplot(stock_month, aes(x=as.numeric(Month_acquired), y=stock)) + geom_line(aes(colour='red')) p + labs(x='Time', y='Number of paintings in stock',title='Number of paintings in stock over time') + theme_minimal() +theme(legend.position="none")

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

stock_month2 = summarise( group_by(df, CuratorApproved ,Month_acquired),n=n()) stock_month2 = mutate(group_by(stock_month2,CuratorApproved), stock=cumsum(n))

p = ggplot(stock_month2, aes(x=as.numeric(Month_acquired), y=stock, group=CuratorApproved)) + geom_line(aes(color=CuratorApproved)) p + labs(x='Time', y='Number of paintings in stock by curator approved or not',title='Number of paintings in stock over time') + theme_minimal()

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

stock_month3 = summarise( group_by(df, Department ,Month_acquired),n=n()) stock_month3 = mutate(group_by(stock_month3,Department), stock=cumsum(n))

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

p = ggplot(stock_month3, aes(x=as.numeric(Month_acquired), y=stock, group=Department)) + geom_line(aes(color=Department)) p + labs(x='Time', y='Number of paintings in stock by department',title='Number of paintings in stock over time') + theme_minimal()

Prins and illustrated books have had the largest increase in their collection.

Question 6: Write a piece of code that counts the number of paintings by each artist in the dataset.

List the 10 painters with the highest number of paintings in MOMA's collection.

artist = df %>% filter(!is.na(Artist),Artist!='Unknown photographer',Artist!='') %>% group_by(Artist) %>% summarise(n=n()) %>% arrange(desc(n))

head(artist,n=10)

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.

Step 1: First we scrape adjectival names of countries from wikipedia

url <- "https://en.wikipedia.org/wiki/List_of_adjectival_and_demonymic_forms_for_countries_and_nations" scrape <- url %>% html() %>% html_nodes(xpath='//*[@id="mw-content-text"]/table[1]') %>% html_table(fill=TRUE) scrape <- scrape[[1]]

cn <- select(scrape, Country name, Adjectivals)

Corrections to the scraped data are added

cn$Country name[94] = "Great Britain" cn$Country name[115] = "Ireland" cn$Country name[119] = "Ivory Coast" cn$Country name[195] = "USSR" cn$Country name[251] = "United Kingdom" cn$Country name[252] = "United States"

cn$Country name <- (str_split_fixed(cn$Country name, ",", n=3)[,1])

Creating references

cn$Country name[1] = "United States" cn$Adjectivals[1] = "U"

cn$Country name[2] = "South Africa" cn$Adjectivals[2] = "South"

cn$Country name[4] = "New Zealand" cn$Adjectivals[4] = "New"

cn$Country name[11] = "Yugoslavia" cn$Adjectivals[4] = "Yugoslav"

cn$Adjectivals[85:87] ="nonamefrench" cn$Adjectivals[94] = "British" cn$Adjectivals[129] = "Korean" cn$Adjectivals[168] = "Dutch" cn$Adjectivals[169] = "newcaldonian" cn$Adjectivals[209] = "Scottish"

cn$Adjectivals[222:224] ="nonamesouthies" cn$Adjectivals[252] = "American"

cn$Adjectivals <- (str_split_fixed(cn$Adjectivals, ",", n=3)[,1]) cn$Adjectivals <- (str_split_fixed(cn$Adjectivals, " or ", n=3)[,1])

Step 2:

We extract adjectivals from the artist bio. regex could be better - but I'm not completely familiar with it yet

Furthermore a charmatch() would be usefull, it is though not supported in my version of RStudio.

df$Adjectivals <- str_extract(df$ArtistBio, "[A-Z][a-z]*")

-------------This could be used for having three options of matches, I just do not know how to combine them with left join------

cn$part1 <- (str_split_fixed(cn$Adjectivals, ",", n=3)[,1])

cn$part2 <- (str_split_fixed(cn$Adjectivals, ",", n=3)[,2])

cn$part3 <- (str_split_fixed(cn$Adjectivals, ",", n=3)[,3])

Step 3: Preparing for maps

Combining the two, we get something that can be read in maps.

dfmaps <- left_join(df, cn, by = "Adjectivals")

mapsum= dfmaps %>% group_by(Country name) %>% summarise(n=n())

world = map_data("world")

mapsum$region = mapsum$Country name

world2=inner_join(world,mapsum)

Step 4: Plot

p = ggplot(world2, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = world2$n)) + scale_fill_gradient(trans = "log") + # Can be used to see the difference, and check that each vountry is actually represented expand_limits() + theme_minimal() plot(p)

I think a better solution would be to have all countries plotted, as one country having 1 contribution isn't a lot different from 0.

I do not know how to do this in maps.

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.

Make two varibles from the dimesions -> muliply to area -> sort by area -> use head and tail functions to find largest and smallest 5

df$test=substring(df$Dimensions,first=str_locate(pattern = '"',df$Dimensions)[,1]+1) df$test2=str_trim(str_replace_all(df$test,pattern = '[( cm ")]', " ")) head(df$test2)

df$dim1=as.numeric(substring(df$test2,first=1,last=str_locate(df$test2,pattern="x")[,1]-1)) head(df$dim1) # First dimension

df$dim2=as.numeric(substring(df$test2,first=str_locate(df$test2,pattern="x")[,1]+1)) head(df$dim2) # Second dimenstion

df$area = df$dim1 * df$dim2 # Calculates area

head(df$area)

Dataframe with the five largest

Largest = head(arrange(df,desc(area)),n=5)

Dataframe with the five smallest

Smallest = head(arrange(df,area),n=5)

sebastianbarfort / sds

Group 25: Assignment 1 #27