ggtrail

Little disclaimer: This is an unsupported package and you will use it at your own risk.

Features

• geom_roundseg: Make round segments that start and end where they should.
• geom_colorpath: Lines with alternating colors.
• scale_aes_craftfermenter: Extended brewer scales for binned scales (for color and fill aesthetic).
• long guide ticks for color scales (doesn’t really work yet)

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("tjebo/ggtrail")

Examples

geom_roungseg

Make round segments that start and end where they should. Big shoutout to Allan Cameron who created this amazing geom on Stack Overflow. There were only minor modifications needed for this package.

df_hor <- data.frame(
  x = 0, xend = 1, y = 1:3, yend = 1:3,
  linewidth = c(2, 30, 6)
)

## Works with different thickness
ggplot(df_hor, aes(x, y)) +
  geom_vline(xintercept = c(0, 1), lty = 2, linewidth = .2) +
  geom_roundseg(aes(xend = xend, yend = yend, linewidth = linewidth),
    alpha = 0.5
  ) +
  scale_linewidth_identity()

## For example, this can be used to make nice looking rotas
rota <- structure(list(day = structure(c(3L, 6L, 7L, 5L, 2L, 1L, 5L, 
5L, 7L, 1L, 2L, 3L, 4L, 1L), levels = c("Sunday", "Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", 
"Monday"), class = "factor"), 
    on = c(17.5, 7.5, 17.5, 6.5, NA, 11.5, 17.5, 21.3, 13.5, 
    15.5, NA, 19, 7.5, 7.5), off = c(18.5, 18.75, 22.5, 7.5, 
    NA, 14.75, 18.5, 22.75, 14.75, 19.75, NA, 19.75, 15.75, 
    10.75)), class = "data.frame")

ggplot(rota) +
  geom_roundseg(aes(y = day, yend = day, x = on, xend = off),
    color = "darkred", linewidth = 4, na.rm = T
  ) +
  scale_x_continuous(limits = c(min(rota$on), 24), expand = c(0, 0), breaks = seq(0, 24, 4)) +
  labs(y = NULL, x = NULL) +
  cowplot::theme_minimal_vgrid()

geom_colorpath

library(patchwork)

dat <- data.frame(x = seq(2,10, 2), y = seq(4,20, 4))

p1 <- ggplot(dat, aes(x = x, y = y)) +
  geom_colorpath(linewidth = 2)+
  ggtitle("Default colors")

p2 <- ggplot(dat, aes(x, y)) +
  geom_colorpath(linewidth = 2,
        cols = c("lightblue", "darkblue"))+
  ggtitle("Two colors")

p3 <- ggplot(dat, aes(x, y)) +
  geom_colorpath(linewidth = 2, 
    cols = c("darkred", "darkblue", "lightblue"))+
  ggtitle("Three colors")

p4 <- ggplot(dat, aes(x, y)) +
  geom_colorpath(linewidth = 2, 
                 cols = c("darkred", "darkblue", "lightblue", "white"))+
  ggtitle("Four colors")

wrap_plots(mget(ls(pattern = "p[1-9]")))

air_df <- data.frame(x = 1: length(AirPassengers), y = c(AirPassengers))

a1 <- ggplot(air_df, aes(x, y)) +
  geom_colorpath(cols = c("darkred", "darkblue", "lightblue"))+
  ggtitle("Works also with more complex curves")

a2 <- ggplot(air_df, aes(x, y)) +
  geom_colorpath(cols = c("darkred", "darkblue", "lightblue"), n_seg = 150)+
  ggtitle("... more color segments")

a1 / a2

An extended brewer scale for binned scales

ggplot(mtcars, aes(mpg, disp, fill = hp)) +
  geom_point(shape = 21) +
  labs(title = "Craft brewer - an extended brewer scale") +
  scale_fill_craftfermenter(
    breaks = seq(0,520,40),
    limits = c(0,520),
    palette = "Spectral",
    guide =  guide_colorsteps(even.steps = FALSE, # workaround for issues #4019/#4100
                             barheight = 15) 
  )
#> Warning: 13 colours used, but Spectral has only 11 - New palette generated
#> based on all colors of Spectral

Long ticks for color bars

(Currently not working)

ggplot(iris, aes(Sepal.Length, y = Sepal.Width, fill = Petal.Length))+
  geom_point(shape = 21) +
  scale_fill_fermenter(breaks = c(1:3,5,7), palette = "Reds") +
  guides(fill = guide_longticks(
    ticks = TRUE,
    even.steps = FALSE,
    frame.colour = "black",
    ticks.colour = "black")) +
  theme(legend.position = "bottom")

About reproducible scripts, and using ggplot2

This readme is not meant to dive into detail how to use ggplot2, an amazing package which allows for very creative data visualization in a fairly user-friendly manner. A good place to start learning about ggplot2 is http://www.cookbook-r.com/Graphs/

First, the data needs to be prepared. I often find that especially rather inexperienced users hesitate to manipulate / shape their data before doing any visualization or analysis.

However, there is really no need to fret. It is an often very important and necessary step. Actually, it can make your life much easier. And if you follow simple precautions, you can not cause much damage to your actual raw data.

(Those simple precautions are: Do not use functions in your script or report which write a file on your disk. This may easily overwrite your raw data! E.g. write.csv and friends should be used only with utmost care.)

Any wrong assignments where you may need to rerun your scripts can be annoying, but are in itself not detrimental (provided your scripts are sound).

On this note, I recommend to always start in a fresh session and to make sure that your script does not rely on objects that have not been correctly created. This is another advantage of using rmarkdown: Knitting your script to a report makes you aware of inconsistencies in your script.