Line color and width by slope in ggplot2

Question

This is relates very closely to this question , as well as this , answers to which I don't understand, at least in this context. I would like to make the difference between increasing and decreasing scores (eg repeated psychological measurements from T1 to T2) prominent by placing a heatmap-style gradient on lines based on their slopes. In other words, I'd like to use eg Viridis 's inferno-scale, so that the lines which decrease most sharply tend towards darkness and those which increase most sharply tend towards light.

Many thanks for any ideas!

data <- data.frame(id = 1:500, 
                               Intrinsic_01_T1 = sample(1:5, 500, replace = TRUE), 
                               Intrinsic_02_T1 = sample(1:5, 500, replace = TRUE), 
                               Intrinsic_03_T1 = sample(1:5, 500, replace = TRUE), 
                               Intrinsic_01_T2 = sample(1:5, 500, replace = TRUE, prob = c(0.1, 0.1, 0.2, 0.3, 0.3)), 
                               Intrinsic_02_T2 = sample(1:5, 500, replace = TRUE), 
                               Intrinsic_03_T2 = sample(1:5, 500, replace = TRUE, prob = c(0.3, 0.3, 0.2, 0.1, 0.1)))

pd <- position_dodge(0.4)

data %>% 
  tidyr::gather(variable, value, -id) %>% 
  tidyr::separate(variable, c("item", "time"), sep = "_T") %>% 
  dplyr::mutate(value = jitter(value, amount = 0.1)) %>% # Y-axis jitter to make points more readable
ggplot(aes(x = time, y = value, group = id)) +
  geom_point(size = 1, alpha = .2, position = pd) +
  geom_line(alpha = .2, position = pd) +
  ggtitle('Multiple indicator LCS model') + 
  ylab('Intrinsic motivation scores') +
  xlab('Time points') + 
  facet_wrap("item")

Answer 1

The trick is to calculate your slope for each line before plotting. To do this you can group by the time and item and then calculate the slope for each line.

data %>% 
  tidyr::gather(variable, value, -id) %>% 
  tidyr::separate(variable, c("item", "time"), sep = "_T") %>% 
  dplyr::mutate(value = jitter(value, amount = 0.1)) %>%  # Y-axis jitter to make points more readable
  group_by(id,item) %>% 
  mutate(slope = (value[time==2] - value[time==1])/(2-1)) %>% 
  ggplot(aes(x = time, y = value, group = id)) +
  geom_point(size = 1, alpha = .2, position = pd) +
  geom_line(alpha = .2, position = pd, aes(color = slope)) +
  scale_color_viridis_c(option = "inferno")+
   ggtitle('Multiple indicator LCS model') + 
  ylab('Intrinsic motivation scores') +
  xlab('Time points') + 
  facet_wrap("item")

Resulting in: