Cross-plot themes

Load example data:

# Load data
library(microbiome)
data(atlas1006)

# Rename the data
pseq <- subset_samples(atlas1006, DNA_extraction_method == "r")
library(hrbrthemes)
library(gcookbook)
library(tidyverse)
# Themes from https://github.com/hrbrmstr/hrbrthemse

p <- ggplot(meta(pseq), aes(age, diversity)) +
  geom_point() +
  labs(x="Age (y)", y="Diversity (Shannon)",
       title="HITChip Atlas",
       subtitle="Age-diversity relation",
       caption="Caption shown here 'g'") 


print(p + theme_ipsum() + ggtitle("HITChip - theme_ipsum"))
print(p + theme_ipsum_rc() + ggtitle("HITChip - theme_ipsum_rc"))

Color fill example

# Theme from https://github.com/hrbrmstr/hrbrthemse
p <- ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + 
  geom_area() +
  scale_fill_ipsum() +
  scale_x_continuous(expand=c(0,0)) +
  scale_y_comma() +
  labs(title="Age distribution of population in the U.S., 1900-2002",
       subtitle="Example data from the R Graphics Cookbook",
       caption="Source: R Graphics Cookbook") +
  theme_ipsum_rc(grid="XY") +
  theme(axis.text.x=element_text(hjust=c(0, 0.5, 0.5, 0.5, 1))) +
  theme(legend.position="bottom")
print(p)

Barplot example

update_geom_font_defaults(font_rc_light)
df <- count(mpg, class) %>% 
  mutate(n = n*2000) %>% 
  arrange(n) %>% 
  mutate(class=factor(class, levels=class)) 

p <- ggplot(df, aes(class, n)) +
  geom_col() +
  geom_text(aes(label=scales::comma(n)), hjust=0, nudge_y=2000) +
  scale_y_comma(limits=c(0,150000)) +
  coord_flip() +
  labs(x="Fuel effiiency (mpg)", y="Weight (tons)",
       title="Seminal ggplot2 column chart example with commas",
       subtitle="A plot that is only useful for demonstration purposes, esp since you'd never\nreally want direct labels and axis labels",
       caption="Caption goes here") + 
  theme_ipsum_rc(grid="X")

print(p)

Viridis scale

# Load data
library(microbiome)
data(peerj32)

# Rename the data
pseq <- peerj32$phyloseq

# Calculate compositional version of the data
# (relative abundances)
pseq.rel <- microbiome::transform(pseq, "compositional")

# With compositional (relative) abundances
det <- c(0, 0.1, 0.5, 2, 5, 20)/100
prevalences <- seq(.05, 1, .05)
p <- plot_core(pseq.rel, prevalences = prevalences, detections = det, plot.type = "lineplot") + xlab("Relative Abundance (%)")

# Same with the viridis color palette
# color-blind friendly and uniform
# options: viridis, magma, plasma, inferno
# https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
# Also discrete=TRUE versions available
library(viridis)
print(p + scale_fill_viridis())