Is there a way to draw a vertical line using geom_abline?

Question

(Context: I'm using the soc.ca package and hoping to illustrate the differences in angles between the overall MCA results and each of the class-specific results.)

I have a data frame of pairs of slopes, and I'm hoping to illustrate how they differ from axes where x = 0 and y = 0. This works OK in still images. However, I'd ideally like to use gganimate to illustrate each pair of slopes rotating from those axes.

In order to do so, I think I need to use geom_abline. This works fine to illustrate the difference between the different slopes, but not for the difference between those slopes and x = 0 and y = 0.

My data is currently of the following shape:

# set seed
set.seed(24601)

# load packages
library(tidyverse)
library(gganimate)

# generate vector of slopes
slopes <- 
  runif(12,
      -2,
      2)

# generate vector of cases i'm cycling through
cases <- 
  c(rep("a", 2),
    rep("b", 2),
    rep("c", 2),
    rep("d", 2),
    rep("e", 2),
    rep("f", 2)
  )

# generate vector of primary or secondary axes
axes <- 
  (rep(c("primary",
         "secondary"),
       6))

# generate vector of zeroes
zeroes <- 
  rep(0, 12)

# combine into a data frame
df <- 
  data.frame(slopes,
             cases,
             axes,
             zeroes)

The still image can be generated as follows

ggplot(df) +
  geom_abline(aes(slope = slopes,
                  linetype = axes,
                  intercept = 0)) +
  geom_hline(yintercept = 0) +
  geom_vline(xintercept = 0) +
  facet_wrap(~ cases)

while the animation is currently as follows:

ggplot(df) +
  geom_abline(aes(intercept = zeroes,
                  slope = slopes,
                  linetype = axes)) +
  transition_states(cases) +
  scale_x_continuous(limits = c(-1, 1)) +
  scale_y_continuous(limits = c(-1, 1))

What I ideally want is for the axes to "snap" back to x = 0 and y = 0 between each phase. However, in order to do so I need the slope of geom_abline to be infinity for the relevant cases.

Any suggestions for how to do this very welcome!

Update, with solution

mhovd has shown me geom_spoke, which addresses the problem that I had. They were having a couple of issues with the animation, which I think I've managed to resolve: below is my tweaking of their code such that the axes rotate in the ways that I was hoping they would.

library(tidyverse)
library(gganimate)

# Seed
set.seed(24601)

# Given the following slopes
# nb I diverge from the helpful solution here
# by having the x and y slopes as separate variables

x_slopes <- 
  runif(6,
        -2,
        2)

y_slopes <- 
  runif(6,
        -2,
        2)

# What are the angles?
df = data.frame(
  x = 0, # Draw from zero
  y = 0, # Draw from zero
  angle_1 = atan(x_slopes), # generate primary axis slopes
  angle_2 = atan(y_slopes) + pi/2, # generate secondary axis slopes
  radius = 5, # Make sufficiently long lines
  cases =c("a", "b", "c", "d", "e", "f")
) %>% 
  mutate(  angle_3 = angle_1 + pi, # opposite angle for primary axis
           angle_4 = angle_2 + pi, # opposite angle for secondary axis
  )
  
df

# Add horizontal and vertical lines
# again this differs from the solution as i have 
# separate variables for the (originally) horizontal and vertical lines
original_axis_data = data.frame(x = 0, 
                        y = 0, 
                        angle_1 = atan(0), 
                        angle_2 = atan(0) + pi/2,
                        angle_3 = atan(0) + pi,
                        angle_4 = atan(0) + pi*1.5,
                        radius = 5, 
                        cases = "reset")

# combine these objects
df = rbind(df, original_axis_data)

# generate list (from solution)
animation_list = list(
  df %>% filter(cases == "a") %>% mutate(event = 1),
  df %>% filter(cases == "reset") %>% mutate(event = 2),
  df %>% filter(cases == "b") %>% mutate(event = 3),
  df %>% filter(cases == "reset") %>% mutate(event = 4),
  df %>% filter(cases == "c") %>% mutate(event = 5),
  df %>% filter(cases == "reset") %>% mutate(event = 6),
  df %>% filter(cases == "d") %>% mutate(event = 7),
  df %>% filter(cases == "reset") %>% mutate(event = 8),
  df %>% filter(cases == "e") %>% mutate(event = 9),
  df %>% filter(cases == "reset") %>% mutate(event = 10),
  df %>% filter(cases == "f") %>% mutate(event = 11),
  df %>% filter(cases == "reset") %>% mutate(event = 12)
)

# convert to data frame (again from solution)
animation_data = bind_rows(animation_list) %>% 
  mutate(cases = factor(cases))  # To make ggplot respect the order

# animate!
animation_data %>% 
  ggplot(aes(x = x, y = y, radius = radius)) + # i have simplified these aes
  geom_spoke(aes(angle = angle_1)) + # primary axis, original
  geom_spoke(aes(angle = angle_2),
             linetype = "dashed") + # secondary axis, original, linetype explicit
  geom_spoke(aes(angle = angle_3)) + # primary axis plus pi
  geom_spoke(aes(angle = angle_4), 
             linetype = "dashed") + # secondary axis plus pi
  coord_cartesian(ylim=c(-1, 1), xlim = c(-1, 1)) + # Zoom in without removing data like scale_*_continous does
  theme(legend.position = NULL, aspect.ratio = 1) +
  transition_states(event) +
  labs(title = "{closest_state}")

Answer 1

Instead of working with slopes, maybe you can achieve what you want with angles using geom_spoke. The angle inclination of a line is theta = atan(slope). The opposite angle would be theta + pi.

For the horizontal and vertical lines, would you pass a slope of 0 and Inf, respectively.

EDIT: I made a poor attempt at showing how you could build the animation, but I was unable to make it transition smoothly. Hopefully you will be able to build on this.

library(tidyverse)
library(gganimate)

# Seed
set.seed(24601)

# Given the following slopes
slopes <- runif(12,-2, 2)

# What are the angles?
df = data.frame(
  x = 0, # Draw from zero
  y = 0, # Draw from zero
  angle1 = atan(slopes), # First segment, actual angle
  angle2 = atan(slopes) + pi, # Second segment, opposite angle
  radius = 5, # Make sufficiently long lines
  cases =c(rep("a", 2), rep("b", 2), rep("c", 2), rep("d", 2), rep("e", 2), rep("f", 2)),
  axes = c("primary", "secondary")
)

# Add horizontal and vertical lines
hline_data = data.frame(x = 0, y  = 0, angle1 = atan(0), angle2 = atan(0) + pi, radius = 5, cases = "reset", axes = "primary")
vline_data = data.frame(x = 0, y  = 0, angle1 = atan(Inf), angle2 = atan(Inf) + pi, radius = 5, cases = "reset", axes = "primary")

df = rbind(df, vline_data, hline_data)

df %>% 
  ggplot(aes(x = x, y = y, radius = radius, col = cases, linetype = axes)) +
  geom_spoke(aes(angle = angle1)) +
  geom_spoke(aes(angle = angle2)) +
  coord_cartesian(ylim=c(-1, 1), xlim = c(-1, 1)) + # Zoom in without removing data like scale_*_continous does
  theme(legend.position = NULL, aspect.ratio = 1)

# Poor attempt at building the animation
animation_list = list(
  df %>% filter(cases == "a") %>% mutate(event = 1),
  df %>% filter(cases == "reset") %>% mutate(event = 2),
  df %>% filter(cases == "b") %>% mutate(event = 3),
  df %>% filter(cases == "reset") %>% mutate(event = 4),
  df %>% filter(cases == "c") %>% mutate(event = 5),
  df %>% filter(cases == "reset") %>% mutate(event = 6),
  df %>% filter(cases == "d") %>% mutate(event = 7),
  df %>% filter(cases == "reset") %>% mutate(event = 8),
  df %>% filter(cases == "e") %>% mutate(event = 9),
  df %>% filter(cases == "reset") %>% mutate(event = 10),
  df %>% filter(cases == "f") %>% mutate(event = 11),
  df %>% filter(cases == "reset") %>% mutate(event = 12)
)

animation_data = bind_rows(animation_list) %>% 
  mutate(cases = factor(cases))  # To make ggplot respect the order

animation_data %>% 
  ggplot(aes(x = x, y = y, radius = radius, linetype = axes, group = event)) +
  geom_spoke(aes(angle = angle1)) +
  geom_spoke(aes(angle = angle2)) +
  coord_cartesian(ylim=c(-1, 1), xlim = c(-1, 1)) + # Zoom in without removing data like scale_*_continous does
  theme(legend.position = NULL, aspect.ratio = 1) +
  transition_states(event) +
  labs(title = "{closest_state}")

^{Created on 2021-07-09 by the reprex package (v2.0.0)}