R - ggplot2 contour plot

Question

I am trying to replicate the code from Andrew Ng's Machine Learning course on Coursera in R (as the course is in Octave).

Basically I have to plot a non linear decision boundary (at p = 0.5) for a polynomial regularized logistic regression.

I can easily replicate the plot with the base library:

contour(u, v, z, levels = 0)
points(x = data$Test1, y = data$Test2)

where:

u <- v <- seq(-1, 1.5, length.out = 100)

and z is a matrix 100x100 with the values of z for every point of the grid. Dimension of data is 118x3.

I cannot do it in ggplot2. Does somebody know how to replicate the same in ggplot2? I tried with:

z = as.vector(t(z))
ggplot(data, aes(x = Test1, y = Test2) + geom_contour(aes(x = u, y = 
v, z = z))

But I get the error: Aesthetics must be either length 1 or the same as the data (118): colour, x, y, shape

Thanks.

EDIT (Adding plot created from code of missuse):

Answer 1

What you need is to convert the coordinates into long format. Here is an example using volcano data set:

data(volcano)

in base R:

contour(volcano)

with ggplot2:

library(tidyverse)
as.data.frame(volcano) %>% #convert the matrix to data frame
  rownames_to_column() %>% #get row coordinates
  gather(key, value, -rowname) %>% #convert to long format
  mutate(key = as.numeric(gsub("V", "", key)), #convert the column names to numbers
         rowname = as.numeric(rowname)) %>%
  ggplot() +
  geom_contour(aes(x = rowname, y = key, z = value))

if you would like to label it directly as in base R plot you can use library directlabels :

First map the color/fill to a variable:

as.data.frame(volcano) %>%
  rownames_to_column() %>%
  gather(key, value, -rowname) %>%
  mutate(key = as.numeric(gsub("V", "", key)),
         rowname = as.numeric(rowname)) %>%
  ggplot() +
  geom_contour(aes(x = rowname,
                   y = key,
                   z = value,
                   colour = ..level..)) -> some_plot

and then

library(directlabels)

direct.label(some_plot, list("far.from.others.borders", "calc.boxes", "enlarge.box", 
                     box.color = NA, fill = "transparent", "draw.rects"))

to add markers at specific coordinates you just need to add another layer with appropriate data:

the previous plot

as.data.frame(volcano) %>% 
  rownames_to_column() %>% 
  gather(key, value, -rowname) %>% 
  mutate(key = as.numeric(gsub("V", "", key)), 
         rowname = as.numeric(rowname)) %>%
  ggplot() +
  geom_contour(aes(x = rowname, y = key, z = value)) -> plot_cont

add layer with points for instance:

plot_cont +
  geom_point(data = data.frame(x = c(35, 47, 61),
                               y = c(22, 37, 15)),
             aes(x = x, y = y), color = "red")

you can add any type of layer this way: geom_line , geom_text to name a few.

EDIT2: to change the scale of the axis there are several options, one is to assign appropriate rownames and colnames to the matrix:

I will assign a sequence from 0 - 2 for the x axis and 0 - 5 to the y axis:

rownames(volcano) <- seq(from = 0,
                         to = 2,
                         length.out = nrow(volcano)) #or some vector like u
colnames(volcano) <- seq(from = 0,
                         to = 5,
                         length.out = ncol(volcano)) #or soem vector like v

as.data.frame(volcano) %>% 
  rownames_to_column() %>% 
  gather(key, value, -rowname) %>% 
  mutate(key = as.numeric(key), 
         rowname = as.numeric(rowname)) %>%
  ggplot() +
  geom_contour(aes(x = rowname, y = key, z = value))

Answer 2

ggplot2 works most efficiently with data in long format. Here's an example with fake data:

library(tidyverse)  

u <- v <- seq(-1, 1.5, length.out = 100)

# Generate fake data
z = outer(u, v, function(a, b) sin(2*a^3)*cos(5*b^2))
rownames(z) = u
colnames(z) = v

# Convert data to long format and plot
as.data.frame(z) %>% 
  rownames_to_column(var="row") %>% 
  gather(col, value, -row) %>% 
  mutate(row=as.numeric(row), 
         col=as.numeric(col)) %>% 
ggplot(aes(col, row, z=value)) +
  geom_contour(bins=20) +
  theme_classic()