geom_line : How to connect only a few points

Question

I have this dataframe and this plot :

df <- data.frame(Groupe = rep(c("A","B"),4),
                 Period = gl(4,2,8,c("t0","t1","t2","t3","t4")),
                 rate = c(0.83,0.96,0.75,0.93,0.67,0.82,0.65,0.73))

ggplot(data = df, mapping = aes(y = rate, x = Period ,group = Groupe, colour=Groupe, shape=Groupe)) +
   geom_line(size=1.2) +
   geom_point(size=5) 

How could i organize my data so that the points between t1 and t2 are not connected with a line ? I'd like t0 and t1 to be connected (blue or red according to the group), t2 and t3 connected in the same way, but no lines between t1 and t2. I tried several things by looking at similar questions, but it always mess up my grouping colors :/

Answer 1

Creating a new grouping variable manually is mostly not the best way. So, a slightly different approach which requires less hardcoding:

# create new grouping variable
df$grp <- c(1,2)[df$Period %in% c("t2","t3","t4") + 1L]

# create the plot and use the interaction between 'Group' and 'grp' as group
ggplot(df, aes(x = Period, y = rate,
               group = interaction(Groupe,grp),
               colour = Groupe,
               shape = Groupe)) +
  geom_line(size=1.2) +
  geom_point(size=5)

this gives the same plot as in the other answer:

Answer 2

The best way to handle a problem like this in ggplot is often to create an additional column in your data frame that indicates the grouping you want to work with in your data. For example, here I've added an extra column gp to your data frame:

df$gp <- c(1,2,1,2,3,4,3,4)
ggplot(data = df, aes(y = rate, x = Period, group = gp, colour=Groupe, shape=Groupe)) +
  geom_line(size=1.2) +
  geom_point(size=5)

The result is, I believe, what you are looking for:

If you make Period a numerical column rather than a character vector or factor, you can more easily generate a column like gp automatically rather than manually specifying it (perhaps using ifelse or cases to create it) - this would be useful if you wanted to do the same thing many times or with a large data frame.