Use dendrograms and hierarchical clustering to analyze similarity among passengers in the Titanic dataset based on selected attributes such as Age, Fare, Passenger Class, and family size.

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library(sf) |> suppressPackageStartupMessages() par(mfrow = c(2,4)) par(mar = c(1,1,1.2,1)) # 1 p <- st_point(0:1) plot(p, pch = 16) title("point") box(col = 'grey') # 2 mp <- st_multipoint(rbind(c(1,1), c(2, 2), c(4, 1), c(2, 3), c(1,4))) plot(mp, pch = 16) title("multipoint") box(col = 'grey') # 3 ls <- st_linestring(rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3))) plot(ls, lwd = 2) title("linestring") box(col = 'grey') # 4 mls <- st_multilinestring(list( rbind(c(1,1), c(5,5), c(5, 6), c(4, 6), c(3, 4), c(2, 3)), rbind(c(3,0), c(4,1), c(2,1)))) plot(mls, lwd = 2) title("multilinestring") box(col = 'grey') # 5 polygon po <- st_polygon(list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2)))) plot(po, border = 'black', col = '#ff8888', lwd = 2) title("polygon") box(col = 'grey') # 6 multipolygon mpo <- st_multipolygon(list( list(rbind(c(2,1), c(3,1), c(5,2), c(6,3), c(5,3), c(4,4), c(3,4), c(1,3), c(2,1)), rbind(c(2,2), c(3,3), c(4,3), c(4,2), c(2,2))), list(rbind(c(3,7), c(4,7), c(5,8), c(3,9), c(2,8), c(3,7))))) plot(mpo, border = 'black', col = '#ff8888', lwd = 2) title("multipolygon") box(col = 'grey') # 7 geometrycollection gc <- st_geometrycollection(list(po, ls + c(0,5), st_point(c(2,5)), st_point(c(5,4)))) plot(gc, border = 'black', col = '#ff6666', pch = 16, lwd = 2) title("geometrycollection") box(col = 'grey')

In this project, I analyze a dataset of pets to investigate which characteristics are associated with higher adoption likelihood. By identifying these patterns, this analysis can provide insights that may help improve adoption strategies in shelters.