Simple exploration of data with tidyverse and tidyomics

explore_tidyverse_omics.R

library(palmerpenguins) # penguins!
library(ggplot2) # "grammar of graphics" plots
suppressPackageStartupMessages(
  library(dplyr) # data pliers
)

penguins |> 
  slice(1:3) |>
  select(species, island)

penguins |> 
  summarize(ave_mass = mean(body_mass_g, na.rm=TRUE))

penguins |> 
  na.omit() |>
  summarize(ave_mass = mean(body_mass_g))

penguins <- penguins |> 
  na.omit()

penguins |> 
  summarize(ave_mass = mean(body_mass_g))

penguins |> 
  group_by(species, island) |> 
  summarize(ave_mass = mean(body_mass_g))

penguins |> 
  ggplot(aes(species, body_mass_g)) +
  geom_boxplot()

scaled <- penguins |> 
  select(contains(c("mm","mass"))) |> 
  mutate(across(everything(), scale))

scaled |> 
  prcomp()

pca <- scaled |> 
  prcomp()

names(pca)

penguins |> 
  bind_cols( pca$x ) |> 
  ggplot(aes(PC1, PC2, color=species)) + 
  geom_point()

suppressPackageStartupMessages({
  library(macrophage) # some data
  library(SummarizedExperiment) # experiment object
  library(tidySummarizedExperiment) # allow dplyr
})

data(gse)

gse |> 
  distinct(condition)

gse |> 
  filter(condition %in% c("naive","IFNg")) |> 
  filter(SYMBOL == "DPM1") |> 
  ggplot(aes(condition_name, counts)) + 
  geom_boxplot()