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I have been working on data visualization of a dataset.

I've found a baseplot version that allow me to visualize the effect of pre- and post treatment.

I would like to know if there is ggplot version of this plot.

> dput(data)
structure(list(ID = c("far001", "far002", "far003", "far004", 
"far005", "far006", "far007", "far008", "far009", "far010"), 
    HBA1Cpre = c(7, 8, 7.5, 9, 8.1, 7.9, 7.3, 7.4, 7.8, 7.1), 
    HBA1Cpost = c(6.5, 6.6, 6.8, 7, 6.3, 6.9, 6.7, 7.1, 7, 6.5
    )), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
))

matplot(t(data.frame(HBA1Cpre,HBA1Cpost)), type="b", xaxt="n", pch=19, col=1, lty=1, ylab="HBAIC(%)")
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1 Answer 1

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Here are two approaches.

1) Use geom_segment to specify the start and end of each point, with a geom_point for the starts and another for the ends.

library(ggplot2)
ggplot(data, aes(x = "HBA1Cpre", xend = "HBA1Cpost",
                 y = HBA1Cpre, yend = HBA1Cpost)) +
  geom_segment() +
  geom_point() +
  geom_point(aes(x = "HBA1Cpost", y = HBA1Cpost)) +
  scale_x_discrete(limits = c("HBA1Cpre", "HBA1Cpost"))

enter image description here

2) Do a little reshaping to put the data in longer form, then map the time status to x. (Here there's an extra step making the time status into a factor so it can be displayed in non-alphabetical order. Could also be accomplished by manipulating scale_x_discrete like above.)

library(dplyr)
data %>%
  pivot_longer(-ID, names_to = "time", values_to = "val") %>%
  mutate(time = factor(time, levels = c("HBA1Cpre", "HBA1Cpost"))) %>%
  ggplot(aes(x = time, y = val, group = ID)) +
  geom_line() +
  geom_point()

enter image description here

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