This is my goal: Plot the average of z according to bins formed by x and y in R










0














So I came across this answer here, How to make 3D histogram in R ,



and my question is, if I have three variable and I want to use the x and y to create bins, like using cut and table in the other answer, how can I then graph the z as the average of all the variable Z data that falls into those bins?



This what I have



library(plot3D)

x <- data$OPEXMKUP_PT_1d
y <- data$prod_opex


z <- data$ab90_ROIC_wogw3

x_c <- cut(x, 20)
y_c <- cut(y, 20)
cutup <- table(x_c, y_c)
mat <- data.frame(cutup)


hist3D(z = cutup, border="black", bty ="g",
main = "Data", xlab = "Markup",
ylab ="Omega", zlab = "Star")


But it show the z as the frequency, and when I try,



hist3D(x, y, z, phi = 0, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5,
xlim=c(0,3),
ylim=c(-10,20),
zlim=c(0,1))


It thinks for a long time and throws an error,



Error: protect(): protection stack overflow
Graphics error: Plot rendering error


It will do the 3dscatter fine but the data doesn't make sense since the Z variable is a ratio that falls mostly between 0 and 1, so you get a bunch of tall lines and and a bunch of short lines. I would like them averaged by bin to show a visual of how the average ratio changes as x and y change. Please let me know if there is a way to do this. Thanks!










share|improve this question























  • It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
    – mickey
    Nov 13 '18 at 0:19















0














So I came across this answer here, How to make 3D histogram in R ,



and my question is, if I have three variable and I want to use the x and y to create bins, like using cut and table in the other answer, how can I then graph the z as the average of all the variable Z data that falls into those bins?



This what I have



library(plot3D)

x <- data$OPEXMKUP_PT_1d
y <- data$prod_opex


z <- data$ab90_ROIC_wogw3

x_c <- cut(x, 20)
y_c <- cut(y, 20)
cutup <- table(x_c, y_c)
mat <- data.frame(cutup)


hist3D(z = cutup, border="black", bty ="g",
main = "Data", xlab = "Markup",
ylab ="Omega", zlab = "Star")


But it show the z as the frequency, and when I try,



hist3D(x, y, z, phi = 0, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5,
xlim=c(0,3),
ylim=c(-10,20),
zlim=c(0,1))


It thinks for a long time and throws an error,



Error: protect(): protection stack overflow
Graphics error: Plot rendering error


It will do the 3dscatter fine but the data doesn't make sense since the Z variable is a ratio that falls mostly between 0 and 1, so you get a bunch of tall lines and and a bunch of short lines. I would like them averaged by bin to show a visual of how the average ratio changes as x and y change. Please let me know if there is a way to do this. Thanks!










share|improve this question























  • It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
    – mickey
    Nov 13 '18 at 0:19













0












0








0







So I came across this answer here, How to make 3D histogram in R ,



and my question is, if I have three variable and I want to use the x and y to create bins, like using cut and table in the other answer, how can I then graph the z as the average of all the variable Z data that falls into those bins?



This what I have



library(plot3D)

x <- data$OPEXMKUP_PT_1d
y <- data$prod_opex


z <- data$ab90_ROIC_wogw3

x_c <- cut(x, 20)
y_c <- cut(y, 20)
cutup <- table(x_c, y_c)
mat <- data.frame(cutup)


hist3D(z = cutup, border="black", bty ="g",
main = "Data", xlab = "Markup",
ylab ="Omega", zlab = "Star")


But it show the z as the frequency, and when I try,



hist3D(x, y, z, phi = 0, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5,
xlim=c(0,3),
ylim=c(-10,20),
zlim=c(0,1))


It thinks for a long time and throws an error,



Error: protect(): protection stack overflow
Graphics error: Plot rendering error


It will do the 3dscatter fine but the data doesn't make sense since the Z variable is a ratio that falls mostly between 0 and 1, so you get a bunch of tall lines and and a bunch of short lines. I would like them averaged by bin to show a visual of how the average ratio changes as x and y change. Please let me know if there is a way to do this. Thanks!










share|improve this question















So I came across this answer here, How to make 3D histogram in R ,



and my question is, if I have three variable and I want to use the x and y to create bins, like using cut and table in the other answer, how can I then graph the z as the average of all the variable Z data that falls into those bins?



This what I have



library(plot3D)

x <- data$OPEXMKUP_PT_1d
y <- data$prod_opex


z <- data$ab90_ROIC_wogw3

x_c <- cut(x, 20)
y_c <- cut(y, 20)
cutup <- table(x_c, y_c)
mat <- data.frame(cutup)


hist3D(z = cutup, border="black", bty ="g",
main = "Data", xlab = "Markup",
ylab ="Omega", zlab = "Star")


But it show the z as the frequency, and when I try,



hist3D(x, y, z, phi = 0, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5,
xlim=c(0,3),
ylim=c(-10,20),
zlim=c(0,1))


It thinks for a long time and throws an error,



Error: protect(): protection stack overflow
Graphics error: Plot rendering error


It will do the 3dscatter fine but the data doesn't make sense since the Z variable is a ratio that falls mostly between 0 and 1, so you get a bunch of tall lines and and a bunch of short lines. I would like them averaged by bin to show a visual of how the average ratio changes as x and y change. Please let me know if there is a way to do this. Thanks!







r 3d histogram






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 12 '18 at 23:54

























asked Nov 12 '18 at 23:32









Nick Hosler

34




34











  • It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
    – mickey
    Nov 13 '18 at 0:19
















  • It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
    – mickey
    Nov 13 '18 at 0:19















It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
– mickey
Nov 13 '18 at 0:19




It will be helpful if you gave a sample of the data, or show how to simulate data similar to what you're working with.
– mickey
Nov 13 '18 at 0:19












1 Answer
1






active

oldest

votes


















0














Not sure exactly what your data looks like, so I made some up. You should be able to adjust to your needs. It's a bit hacky/brute force-ish, but could work just fine if your data isn't too large to slow down the loop.



library(plot3D)

# Fake it til you make it
n = 5000
x = runif(n)
y = runif(n)
z = x + 2*y + sin(x*2*pi)

# Divide into bins
x_c = cut(x, 20)
y_c = cut(y, 20)
x_l = levels(x_c)
y_l = levels(y_c)

# Compute the mean of z within each x,y bin
z_p = matrix(0, 20, 20)
for (i in 1:length(x_l))
for (j in 1:length(y_l))
z_p[i,j] = mean(z[x_c %in% x_l[i] & y_c %in% y_l[j]])



# Get the middle of each bin
x_p = sapply(strsplit(gsub('\(|]', '', x_l), ','), function(x) mean(as.numeric(x)))
y_p = sapply(strsplit(gsub('\(|]', '', y_l), ','), function(x) mean(as.numeric(x)))

# Plot
hist3D(x_p, y_p, z_p, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5)


Basically, we're just manually computing the average bin height z by looping over the bins. There may be a better way to do the computation.



enter image description here






share|improve this answer




















  • Worked great! Thank you so much!
    – Nick Hosler
    Nov 13 '18 at 5:21










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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









0














Not sure exactly what your data looks like, so I made some up. You should be able to adjust to your needs. It's a bit hacky/brute force-ish, but could work just fine if your data isn't too large to slow down the loop.



library(plot3D)

# Fake it til you make it
n = 5000
x = runif(n)
y = runif(n)
z = x + 2*y + sin(x*2*pi)

# Divide into bins
x_c = cut(x, 20)
y_c = cut(y, 20)
x_l = levels(x_c)
y_l = levels(y_c)

# Compute the mean of z within each x,y bin
z_p = matrix(0, 20, 20)
for (i in 1:length(x_l))
for (j in 1:length(y_l))
z_p[i,j] = mean(z[x_c %in% x_l[i] & y_c %in% y_l[j]])



# Get the middle of each bin
x_p = sapply(strsplit(gsub('\(|]', '', x_l), ','), function(x) mean(as.numeric(x)))
y_p = sapply(strsplit(gsub('\(|]', '', y_l), ','), function(x) mean(as.numeric(x)))

# Plot
hist3D(x_p, y_p, z_p, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5)


Basically, we're just manually computing the average bin height z by looping over the bins. There may be a better way to do the computation.



enter image description here






share|improve this answer




















  • Worked great! Thank you so much!
    – Nick Hosler
    Nov 13 '18 at 5:21















0














Not sure exactly what your data looks like, so I made some up. You should be able to adjust to your needs. It's a bit hacky/brute force-ish, but could work just fine if your data isn't too large to slow down the loop.



library(plot3D)

# Fake it til you make it
n = 5000
x = runif(n)
y = runif(n)
z = x + 2*y + sin(x*2*pi)

# Divide into bins
x_c = cut(x, 20)
y_c = cut(y, 20)
x_l = levels(x_c)
y_l = levels(y_c)

# Compute the mean of z within each x,y bin
z_p = matrix(0, 20, 20)
for (i in 1:length(x_l))
for (j in 1:length(y_l))
z_p[i,j] = mean(z[x_c %in% x_l[i] & y_c %in% y_l[j]])



# Get the middle of each bin
x_p = sapply(strsplit(gsub('\(|]', '', x_l), ','), function(x) mean(as.numeric(x)))
y_p = sapply(strsplit(gsub('\(|]', '', y_l), ','), function(x) mean(as.numeric(x)))

# Plot
hist3D(x_p, y_p, z_p, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5)


Basically, we're just manually computing the average bin height z by looping over the bins. There may be a better way to do the computation.



enter image description here






share|improve this answer




















  • Worked great! Thank you so much!
    – Nick Hosler
    Nov 13 '18 at 5:21













0












0








0






Not sure exactly what your data looks like, so I made some up. You should be able to adjust to your needs. It's a bit hacky/brute force-ish, but could work just fine if your data isn't too large to slow down the loop.



library(plot3D)

# Fake it til you make it
n = 5000
x = runif(n)
y = runif(n)
z = x + 2*y + sin(x*2*pi)

# Divide into bins
x_c = cut(x, 20)
y_c = cut(y, 20)
x_l = levels(x_c)
y_l = levels(y_c)

# Compute the mean of z within each x,y bin
z_p = matrix(0, 20, 20)
for (i in 1:length(x_l))
for (j in 1:length(y_l))
z_p[i,j] = mean(z[x_c %in% x_l[i] & y_c %in% y_l[j]])



# Get the middle of each bin
x_p = sapply(strsplit(gsub('\(|]', '', x_l), ','), function(x) mean(as.numeric(x)))
y_p = sapply(strsplit(gsub('\(|]', '', y_l), ','), function(x) mean(as.numeric(x)))

# Plot
hist3D(x_p, y_p, z_p, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5)


Basically, we're just manually computing the average bin height z by looping over the bins. There may be a better way to do the computation.



enter image description here






share|improve this answer












Not sure exactly what your data looks like, so I made some up. You should be able to adjust to your needs. It's a bit hacky/brute force-ish, but could work just fine if your data isn't too large to slow down the loop.



library(plot3D)

# Fake it til you make it
n = 5000
x = runif(n)
y = runif(n)
z = x + 2*y + sin(x*2*pi)

# Divide into bins
x_c = cut(x, 20)
y_c = cut(y, 20)
x_l = levels(x_c)
y_l = levels(y_c)

# Compute the mean of z within each x,y bin
z_p = matrix(0, 20, 20)
for (i in 1:length(x_l))
for (j in 1:length(y_l))
z_p[i,j] = mean(z[x_c %in% x_l[i] & y_c %in% y_l[j]])



# Get the middle of each bin
x_p = sapply(strsplit(gsub('\(|]', '', x_l), ','), function(x) mean(as.numeric(x)))
y_p = sapply(strsplit(gsub('\(|]', '', y_l), ','), function(x) mean(as.numeric(x)))

# Plot
hist3D(x_p, y_p, z_p, bty = "g", type = "h", main = 'NEWer',
ticktype = "detailed", pch = 19, cex = 0.5)


Basically, we're just manually computing the average bin height z by looping over the bins. There may be a better way to do the computation.



enter image description here







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 13 '18 at 0:47









mickey

1,2181216




1,2181216











  • Worked great! Thank you so much!
    – Nick Hosler
    Nov 13 '18 at 5:21
















  • Worked great! Thank you so much!
    – Nick Hosler
    Nov 13 '18 at 5:21















Worked great! Thank you so much!
– Nick Hosler
Nov 13 '18 at 5:21




Worked great! Thank you so much!
– Nick Hosler
Nov 13 '18 at 5:21

















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