sebastianbarfort
commented
9 years ago Hi, I'm getting the following errors for Question 1 and 2. Please fix and resubmit.
Loading .csv file
library("readr") moma = read_csv("https://raw.githubusercontent.com/MuseumofModernArt/collection/master/Artworks.csv") Warning message: 6 problems parsing 'https://raw.githubusercontent.com/MuseumofModernArt/collection/master/Artworks.csv'. See problems(...) for more details.
Accordingly to the assignment, we're asked to only look at paintings
which is why, we condition on the piece of art belongs to the "Painting" classification
if this is misunderstood, simply dont run the following line
moma = moma %>%
- filter(Classification=="Painting")
############################################### Part 1 ###
Using lubridate to translate dates into months/years by some standard
library("lubridate") moma.df = moma[-which(is.na(moma$DateAcquired)),] moma.df$mnyr = ymd(moma.df$DateAcquired) # Translating AquiredDate into PROSIX1t format moma.df$mon = month(moma.df$mnyr, label =TRUE, abbr = TRUE) # Reading mnyr and translating only month into new variable, "mon", abbr is abbreviation and TRUE -> Jan instead of January Error in month(moma.df$mnyr, label = TRUE, abbr = TRUE) : unused arguments (label = TRUE, abbr = TRUE) library("ggplot2")
p = ggplot(data = moma.df, aes(x = mon)) p = p + geom_histogram(fill = "red", alpha = 0.9) + labs(x = "Month", y = "Number of Painting in holding", title = "MoMA Paintings by Month") p = p + theme(panel.grid.major = element_blank(),
- panel.grid.minor = element_blank(),
- panel.background = element_blank(),
- axis.line = element_line(colour = "blue")) plot(p) Error in eval(expr, envir, enclos) : object 'mon' not found
Loading .csv file
library("readr") moma = read_csv("https://raw.githubusercontent.com/MuseumofModernArt/collection/master/Artworks.csv")
Accordingly to the assignment, we're asked to only look at paintings
which is why, we condition on the piece of art belongs to the "Painting" classification
if this is misunderstood, simply dont run the following line
moma = moma %>% filter(Classification=="Painting")
Part 1
Using lubridate to translate dates into months/years by some standard
library("lubridate") moma.df = moma[-which(is.na(moma$DateAcquired)),] moma.df$mnyr = ymd(moma.df$DateAcquired) # Translating AquiredDate into PROSIX1t format moma.df$mon = month(moma.df$mnyr, label =TRUE, abbr = TRUE) # Reading mnyr and translating only month into new variable, "mon", abbr is abbreviation and TRUE -> Jan instead of January
Part 2
library("ggplot2")
p = ggplot(data = moma.df, aes(x = mon)) p = p + geom_histogram(fill = "red", alpha = 0.9) + labs(x = "Month", y = "Number of Painting in holding", title = "MoMA Paintings by Month") p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "blue")) plot(p)
Nothing new here - themes are only to colour axis and make background blank
Part 3
p = ggplot(data = moma.df, aes(x = mon, fill = CuratorApproved)) p = p + geom_histogram(alpha = 0.9) + labs(x = "Month", y = "Number of Painting in holding", title = "MoMA Paintings by Month") p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) plot(p)
Part 4
library("dplyr") moma.dep = moma %>% filter(!is.na(Department)) %>% group_by(Department)%>% summarise(antalpr.dep= n())
Part 5
p = ggplot(data = moma.dep, aes(x = Department, y=antalpr.dep)) p = p + geom_histogram(stat = "identity", alpha = 0.9) + labs(x = "Department", y = "Number of Painting in holding", title = "MoMA Paintings by Department") p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text(angle = 90)) plot(p)
or
p = ggplot(data = moma.dep, aes(x = Department, y=antalpr.dep)) p = p + geom_histogram(stat = "identity", alpha = 0.9) + labs(x = "Department", y = "Number of Painting in holding", title = "MoMA Paintings by Department") p = p + coord_flip() p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) plot(p)
Another, more colourful representation using facet-wrap
p = ggplot(data = moma.df, aes(x = DateAcquired, fill = Department)) p = p + geom_bar() + labs(x = "Yearr", y = "# Paintings", title = "MoMA Paintings over time by Department") p = p + facet_wrap(~Department, scales = "free_y") plot(p)
Part 6
The following summarises the number of contributions pr artist in a new dataframe
and sorts it descendingly
moma.hp = moma %>% filter(!is.na(Artist)) %>% group_by(Artist) %>% summarise(worksprartist= n()) %>% arrange(desc(worksprartist))
It is however also possible to show a table from the raw dataframe
Showing the 10 higehst contributors by a table
sort(table(moma$Artist), decreasing = TRUE)[1:10]
It's seen that Pablo Picasso with 55 paintings, is the single highest
contributor to the paintings collection
Part 7
Firstly, using gsub and word for extracting nationalities from the ArtistBio-variable
library("stringr") moma$Nationality = gsub("(", "", moma$ArtistBio) # Replace ( with "blank" moma$Nationality = word(moma$Nationality, +1) #Pick the 1st word moma$Nationality = gsub(",", "", moma$Nationality) # Replace , with "blank"
Read external files from Adam's GitHub repository
Data on Countries and Nationalities, tab-separated from (https://www.englishclub.com/vocabulary/world-countries-nationality.htm) and more.
Nat1 <- read.csv("https://raw.githubusercontent.com/adamingwersen/Data_for_assignment1_SDS/master/Nationalities2.txt", sep="\t", header = FALSE)
Data on population-size by iso3c codes from (http://data.worldbank.org/indicator/SP.POP.TOTL)
Pop1 <- read.csv("https://raw.githubusercontent.com/adamingwersen/Data_for_assignment1_SDS/master/Countrypop.txt", sep = "\t", header = TRUE)
Next, we clean up the Nat1 Dataframe by renaming variables and adding an iso3c countrycode
library("plyr") Nat1 = rename(Nat1, c("V1"="Country", "V2"="Nationality"))
Creating a joined dataframe by nationalities, so that Country.names based on the artists birthplace are linked to each artwork
Plyr to dplyr issue - disable plyr
library("dplyr") combi.df = inner_join(moma, Nat1, by = "Nationality")
In order to create a map of the world, the ggmap-package is used to create a new dataframe
library("ggmap") map.df = map_data("world")
Adding countrycode (iso2c) to each of our dataframes:
library("countrycode") map.df$iso2c = countrycode(map.df$region, origin = "country.name", destination = "iso2c") combi.df$iso2c = countrycode(combi.df$Country, origin = "country.name", destination = "iso2c") Pop1$iso2c = countrycode(Pop1$iso3c, origin = "iso3c", destination = "iso2c")
Summarising over the total number of paintings attributed to each country in order to reduce final dataframe significantly
library("dplyr") momamap.df = combi.df %>% filter(!is.na(iso2c)) %>% select(iso2c) %>% group_by(iso2c) %>% summarise(number = n())
Joining dataframes:
library("dplyr")
Adding long/lat to the summarised dataframe created above
momamap.df = inner_join(map.df, momamap.df, by = "iso2c")
Matching with 2014 population in each country
momamap.df2 = inner_join(momamap.df, Pop1, by = "iso2c")
Finally, create new variable using mutate to obtain a pr. capita number of paintings for each country
momamutate.df = mutate(momamap.df2, paint.by.pop = number/Population)
Due to the fact that American artists can be attributed the majority of paintings held in the MoMA, we can use log - however the meaning of this plot is somewhat nonexistent.
library("ggplot2") library("ggmap") p = ggplot(momamutate.df, aes(x = long, y = lat, group = group, fill = log(number))) p = p + geom_polygon() p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) p = p + labs(title = "Number of MoMA Paintings around the world") p = p + scale_fill_gradient(low = "#00b3b3", high = "#cccc00", guide = "colorbar") plot(p)
Instead we could use the pr. capita paintings by country to obtain a more meaningful map-plot
p = ggplot(momamutate.df, aes(x = long, y = lat, group = group, fill = paint.by.pop)) p = p + geom_polygon() p = p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) # Removing exessive lines, background and text
p = p + labs(title = "Number of MoMA Paintings around the world pr. capita (2014)") p = p + scale_fill_gradient(low = "#ffa500", high = "#228b22", guide = "colorbar") # Using different colours for the two plots plot(p)
Part 8
library("stringr")
Extract the 1st word within "Dimensions" and call this "Dimensions1"
moma$Dimensions1 = str_extract_all(moma$Dimensions, "([^()]+)"[[1]])
Remove parentheses
moma$Dimensions1 = gsub("(", "", moma$Dimensions1) moma$Dimensions1 = gsub(")", "", moma$Dimensions1)
Remove "cm"
moma$Dimensions1 = gsub("\cm", "", moma$Dimensions1)
str_split could have been used - this was easier to do, however
First chr
moma$H = word(moma$Dimensions1, +1)
Second chr
moma$L = word(moma$Dimensions1, -2)
We need to transform chr to num in order to perform math-expressions as below
moma$H = as.numeric(moma$H) moma$L = as.numeric(moma$L)
A = H * L, if i remember correctly
moma$PicArea = (moma$L * moma$H)
library("dplyr")
Multiple approaches
top5 = moma %>% arrange_(~ desc(PicArea)) %>% slice(1:5)
bot5 = moma %>% arrange_(~ (PicArea)) %>% slice(1:5)
mix10 <- rbind(top5, bot5)