Assignment 1, Group 23

Social Data Science at the University of Copenhagen 14/10-2015

Read in data

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

class(df$DateAcquired) range(df$DateAcquired,na.rm=TRUE)

Question 1

Create a new dataframe of the stock of paintings at MOMA for each month in

the year.

library("dplyr") library("lubridate") df.stock <- df %>% filter(Classification=="Painting") %>% arrange(DateAcquired) %>% group_by(DateAcquired) %>% summarise(count=n()) %>% mutate(stock1=cumsum(count))

Q1 Alternative

df.stock_alt <- df %>% filter(Classification=="Painting") %>% mutate(month=month(DateAcquired, label=TRUE))%>% group_by(month) %>% summarise(total=n())%>% filter(!is.na(month))

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("ggplot2") p = ggplot(df.stock, aes(x=as.Date(DateAcquired), y=stock1, na.rm=TRUE)) p = p + geom_point(color="red",na.rm=TRUE) + geom_line(color="blue") p = p + ggtitle("Stock of paintings at MoMA") + ylab("Stock of paintings") + xlab("Date") p

Q2 Alternative

p = ggplot(df.stock_alt, aes(x=month, na.rm=TRUE)) p = p + geom_bar(aes(y=total), stat="identity", fill="red") + labs(x="Month", y="Paintings", title="Stock of paintings per month") p

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

We use a histogram to look at seasonal trends across year, and use the point to look at development over time.

-color the geom you have chosen red

-add a title and custom axis labels

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.stock1 <- df %>% arrange(DateAcquired) %>% filter(CuratorApproved=="N") %>% filter(Classification=="Painting") %>% group_by(DateAcquired) %>% summarise(count1=n())

df.stock2=left_join(df.stock,df.stock1) %>% mutate(count1 = ifelse(!is.na(count1),count1,0)) %>% mutate(stock2=cumsum(count1)) %>% mutate(stock=cumsum(count-count1))

df.stock2 <- df.stock2[-c(447),]

p = ggplot(df.stock2, aes(x=DateAcquired, y=stock1)) p = p + geom_area(fill="red",na.rm=TRUE) p = p + geom_area(aes(x=DateAcquired,y=stock2),fill="blue",colour="blue") p = p + ggtitle("Stock of paintings at\nMuseum of Modern Art") + ylab("Stock of paintings") + xlab("Date") p

Q3 Alternative, not correct

df.stock_alt3 <- df %>% filter(Classification=="Painting") %>% mutate(month=month(DateAcquired, label=TRUE))%>% group_by(month,CuratorApproved) %>% summarise(total=n())

df.stock_alt1 <- df.stock_alt3 %>% filter(CuratorApproved=="Y")%>% filter(!is.na(month))

df.stock_alt2 <- df.stock_alt3 %>% filter(CuratorApproved=="N") %>% filter(!is.na(month)) %>% select(month,total1=total)%>% right_join(df.stock_alt)%>% ungroup()%>% mutate(total1=ifelse(is.na(total1),0,total1))

p = ggplot(df.stock_alt, aes(x=month),na.rm=TRUE) p = p + geom_bar(aes(y=df.stock_alt1$total), stat="identity", fill="red") p = p + geom_bar(aes(y=df.stock_alt2$total1), stat="identity", fill="blue") p = p + labs(x="Month", y="Paintings", title="Stock of paintings per month") p

Question 4

Create a new dataframe of the stock of paintings grouped by what department

the painting belongs to.

df.department <- df %>% filter(Classification=="Painting") %>% filter(!is.na(DateAcquired)) %>% group_by(Department) %>% arrange(DateAcquired) %>% group_by(Department, DateAcquired) %>% arrange(DateAcquired) %>% summarise(count=n()) %>% mutate(stock1=cumsum(count))

Question 5

Plot this data frame using ggplot2. Which department has had the highest

increase in their stock of paintings?

p = ggplot(df.department, aes(x=as.Date(DateAcquired), y=stock1, na.rm=TRUE)) p = p + geom_point(fill="red",na.rm=TRUE) + facet_wrap(~ Department, scales="free") p = p + geom_line(color="blue") p + ggtitle("Stock of paintings at MoMA") + ylab("Stock of paintings") + xlab("Date")

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.

df.artist <- df %>% filter(Classification=="Painting") %>% group_by(Artist) %>% arrange(Artist) %>% summarise(count=n()) %>% arrange(desc(count))

head(df.artist, 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.

library(maps) library(stringr) library(data.table)

df.artistbio <- df %>% filter(Classification=="Painting") %>% mutate(artborn1 = str_extract(ArtistBio,"[:alpha:]{1,15}")) %>% mutate(artborn2 = gsub("^.*?,","",ArtistBio)) %>% mutate(artborn2 = gsub("????????","",artborn2)) %>% mutate(artborn2 = gsub("born","",artborn2)) %>% mutate(artborn2 = gsub("The","",artborn2)) %>% mutate(artborn2 = str_extract(artborn2,"[:alpha:]{1,15}")) %>% mutate(artborn = artborn1) %>% mutate(region = ifelse(!is.na(artborn2), artborn2,artborn))%>% left_join(df.artist) %>% select(Artist,region,count)

df.nationality = read.csv("https://raw.githubusercontent.com/trondur88/SDS_G23/master/nationality.csv", stringsAsFactors = FALSE, header=TRUE) %>% select(V1, region=V2) %>% mutate(region = str_trim(region))

df.plot_art <- df.artistbio %>% left_join(df.nationality) %>% mutate(region = ifelse(is.na(V1),region,V1)) %>% unique() %>% arrange(region) %>% mutate(region=str_trim(region)) %>% group_by(region) %>% summarise(stock=sum(count)) %>% mutate(region = gsub("The United States","USA",region)) %>% mutate(region = gsub("Russia","USSR",region))

country <- map_data("world") df.plot_art <- left_join(df.plot_art,country)

p = ggplot(df.plot_art, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = stock)) expand_limits() theme_minimal() p

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.

df.dimension <- df %>% filter(Classification=="Painting") %>% mutate(size = str_extract(Dimensions, "([0.0-9.9]+ x [0.0-9.9]+ cm)")) %>% mutate(size1 = as.numeric(gsub("x [0.0-9.9]+ cm","", size))) %>% mutate(size2 = as.numeric(gsub("cm","",gsub("[0.0-9.9]+ x","", size)))) %>% mutate(area = size1*size2) %>% arrange(desc(area)) %>% select(Artist, area) %>% filter(!is.na(area)) head(df.dimension,5) tail(df.dimension,5)

sebastianbarfort / sds

Group 23 Assignment 1 #21