week11 materials

data/womens_track.csv

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+m100,m200,m400,m800,m1500,m3000,marathon,country
+11.61,22.94,54.5,2.15,4.43,9.79,178.52,argentin
+11.2,22.35,51.08,1.98,4.13,9.08,152.37,australi
+11.43,23.09,50.62,1.99,4.22,9.34,159.37,austria
+11.41,23.04,52,2,4.14,8.88,157.85,belgium
+11.46,23.05,53.3,2.16,4.58,9.81,169.98,bermuda
+11.31,23.17,52.8,2.1,4.49,9.77,168.75,brazil
+12.14,24.47,55,2.18,4.45,9.51,191.02,burma
+11,22.25,50.06,2,4.06,8.81,149.45,canada
+12,24.52,54.9,2.05,4.23,9.37,171.38,chile
+11.95,24.41,54.97,2.08,4.33,9.31,168.48,china
+11.6,24,53.26,2.11,4.35,9.46,165.42,columbia
+12.9,27.1,60.4,2.3,4.84,11.1,233.22,cookis
+11.96,24.6,58.25,2.21,4.68,10.43,171.8,costa
+11.09,21.97,47.99,1.89,4.14,8.92,158.85,czech
+11.42,23.52,53.6,2.03,4.18,8.71,151.75,denmark
+11.79,24.05,56.05,2.24,4.74,9.89,203.88,domrep
+11.13,22.39,50.14,2.03,4.1,8.92,154.23,finland
+11.15,22.59,51.73,2,4.14,8.98,155.27,france
+10.81,21.71,48.16,1.93,3.96,8.75,157.68,gdr
+11.01,22.39,49.75,1.95,4.03,8.59,148.53,frg
+11,22.13,50.46,1.98,4.03,8.62,149.72,gbni
+11.79,24.08,54.93,2.07,4.35,9.87,182.2,greece
+11.84,24.54,56.09,2.28,4.86,10.54,215.08,guatemal
+11.45,23.06,51.5,2.01,4.14,8.98,156.37,hungary
+11.95,24.28,53.6,2.1,4.32,9.98,188.03,india
+11.85,24.24,55.34,2.22,4.61,10.02,201.28,indonesi
+11.43,23.51,53.24,2.05,4.11,8.89,149.38,ireland
+11.45,23.57,54.9,2.1,4.25,9.37,160.48,israel
+11.29,23,52.01,1.96,3.98,8.63,151.82,italy
+11.73,24,53.73,2.09,4.35,9.2,150.5,japan
+11.73,23.88,52.7,2,4.15,9.2,181.05,kenya
+11.96,24.49,55.7,2.15,4.42,9.62,164.65,korea
+12.25,25.78,51.2,1.97,4.25,9.35,179.17,dprkorea
+12.03,24.96,56.1,2.07,4.38,9.64,174.68,luxembou
+12.23,24.21,55.09,2.19,4.69,10.46,182.17,malaysia
+11.76,25.08,58.1,2.27,4.79,10.9,261.13,mauritiu
+11.89,23.62,53.76,2.04,4.25,9.59,158.53,mexico
+11.25,22.81,52.38,1.99,4.06,9.01,152.48,netherla
+11.55,23.13,51.6,2.02,4.18,8.76,145.48,nz
+11.58,23.31,53.12,2.03,4.01,8.53,145.48,norway
+12.25,25.07,56.96,2.24,4.84,10.69,233,png
+11.76,23.54,54.6,2.19,4.6,10.16,200.37,philippi
+11.13,22.21,49.29,1.95,3.99,8.97,160.82,poland
+11.81,24.22,54.3,2.09,4.16,8.84,151.2,portugal
+11.44,23.46,51.2,1.92,3.96,8.53,165.45,rumania
+12.3,25,55.08,2.12,4.52,9.94,182.77,singapor
+11.8,23.98,53.59,2.05,4.14,9.02,162.6,spain
+11.16,22.82,51.79,2.02,4.12,8.84,154.48,sweden
+11.45,23.31,53.11,2.02,4.07,8.77,153.42,switzerl
+11.22,22.62,52.5,2.1,4.38,9.63,177.87,taipei
+11.75,24.46,55.8,2.2,4.72,10.28,168.45,thailand
+11.98,24.44,56.45,2.15,4.37,9.38,201.08,turkey
+10.79,21.83,50.62,1.96,3.95,8.5,142.72,usa
+11.06,22.19,49.19,1.89,3.87,8.45,151.22,ussr
+12.74,25.85,58.73,2.33,5.81,13.04,306,wsamoa

docs/data/landmine.R

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+# Generating the landmine data
+# mystery data generated using locator function
+plot(-1:1, -1:1, type = "n", xlab = "Re", ylab = "Im")
+text(x=0, y=0, labels="you found me", cex=5)
+d <- locator(512)
+plot(d$x, d$y)
+save(d, file = "data/mystery.rda")
+d2 <- d
+d2$y <- d2$y
+d3$x <- d2$x*cos(pi/6) - d2$y*sin(5*pi/6)
+d3 <- d2
+d3$x <- d2$x*cos(pi/6) - d2$y*sin(pi/6)
+d3$y <- d2$x*sin(pi/6) + d2$y*cos(pi/6)
+d3 <- as_tibble(d3)
+df <- tibble(x=runif(7500, -2, 2), y=runif(7500, -2, 2))
+df <- bind_rows(df, d3)
+df <- df[sample(1:nrow(df)),]
+write_csv(df, file="data/landmine1.csv")
+
+df4 <- read_csv("data/landmine1.csv")
+df4 <- df4 |>
+filter(!((x-1)^2+(y-1)^2)<0.01) |>
+filter(!((x+1)^2+(y+1)^2)<0.01) |>
+filter(!((x+1)^2+(y-1)^2)<0.01) |>
+filter(!((x-1)^2+(y+1)^2)<0.01)
+ggplot(df4, aes(x, y)) + geom_point(alpha=0.1)
+ggplot(df4, aes(x, y)) + geom_point()
+ggplot(df4, aes(x, y)) + geom_density_2d()
+ggplot(df4, aes(x, y)) + geom_density_2d_filled()
+ggplot(lineup(null_permute("x"), df4), aes(x, y)) + geom_point(alpha=0.5) + facet_wrap(~.sample, ncol=5)
+ggplot(lineup(null_permute("x"), df4), aes(x, y)) + geom_point(alpha=0.1) + facet_wrap(~.sample, ncol=5)
+
+
+