plotly-master/tests/testthat/test-cookbook-scatterplots.R

In franciscorichter/dmea: Diversification and Macro-Evolution Analysis

# Make some noisily increasing data
dat <- data.frame(
  cond = rep(c("A", "B"), each = 10),
  xvar = c(1.475957, -3.423712, 1.966129, 5.575364, 2.954719, 2.768286, 3.507499, 6.945000, 12.135050, 10.231673, 13.040393, 12.231689, 13.506993, 13.590874, 15.455178, 28.431185, 17.758937, 24.730797, 22.954238, 21.122766),
  yvar = c(-1.315387, 3.323239, 4.452183, 4.597885, 5.697203, 5.991221, 5.764561, 10.163165, 14.805634, 11.447913, 12.163597, 10.930851, 13.491366, 11.800783, 19.246991, 13.870457, 11.031923, 22.700302, 24.877547, 22.520114)
)
# cond         xvar         yvar
#    A -4.252354091  3.473157275
#    A  1.702317971  0.005939612
#   ...
#    B 17.793359218 19.718587761
#    B 19.319909163 19.647899863
g <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1)      # Use hollow circles
save_outputs(g, "scatterplots-hollow")

g <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +
  geom_smooth(method=lm)   # Add linear regression line
save_outputs(g, "scatterplots-smooth-lm")

g <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +
  geom_smooth(method=lm, se=FALSE)    # Don't add shaded confidence region
save_outputs(g, "scatterplots-smooth-lm-se-false")


g <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()            # Add a loess smoothed fit curve with confidence region
save_outputs(g, "scatterplots-loess")

# Set color by cond
g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1)
save_outputs(g, "scatterplots-color")

# # Same, but with different colors and add regression lines
g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) +
  scale_colour_hue(l=50) + # Use a slightly darker palette than normal
  geom_smooth(method=lm, se=FALSE)
save_outputs(g, "scatterplots-scale-color-hue")

# Extend the regression lines beyond the domain of the data
g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) +
  scale_colour_hue(l=50) +
  geom_smooth(method=lm, se=FALSE, fullrange=T)
save_outputs(g, "scatterplots-full-range")

# Set shape by cond
g <- ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point()
save_outputs(g, "scatterplots-shape")

# Same, but with different shapes
g <- ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point() +
  scale_shape_manual(values=c(1,2))  # Use a hollow circle and triangle
save_outputs(g, "scatterplots-shape-manual")

# Round xvar and yvar to the nearest 5
dat$xrnd <- round(dat$xvar/5)*5
dat$yrnd <- round(dat$yvar/5)*5

# Make each dot partially transparent, with 1/4 opacity
# For heavy overplotting, try using smaller values
g <- ggplot(dat, aes(x=xrnd, y=yrnd)) +
  geom_point(shape=19,      # Use solid circles
             alpha=1/4)     # 1/4 opacity
save_outputs(g, "scatterplots-overlap")

# Jitter the points
# Jitter range is 1 on the x-axis, .5 on the y-axis
g <- ggplot(dat, aes(x=xrnd, y=yrnd)) +
  geom_point(shape=1,      # Use hollow circles
             position=position_jitter(width=1,height=.5))
save_outputs(g, "scatterplots-jitter")

# Jitter the points using geom_jitter
# Jitter range is 1 on the x-axis, .5 on the y-axis
g <- ggplot(dat, aes(x = xrnd, y = yrnd)) +
  geom_jitter(shape = 1,      # Use hollow circles
             width = 1, height = 0.5)
save_outputs(g, "scatterplots-geom_jitter")