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# STEP 1 --------------------------------------- Pre-workshop to-do
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# Install the packages you"ll require for the workshop
# First we creating an object with a list of the packages that we"ll need
list.of.packages <- c("tidyverse", "conflicted", "datapasta", "data.table", "esquisse", "flextable", "ggbeeswarm", "ggalluvial", "gganimate", "ggrepel", "glue", "gridExtra", "igraph", "installr", "janitor", "kableExtra", "knitr", "likert", "nycflights13", "ordinal", "palmerpenguins", "patchwork","plotly", "png", "pxR", "reprex", "rmarkdown", "rms","sjPlot", "summarytools", "table1", "shiny", "swirl", "synthpop", "testthat", "viridis")
# Now we will check to see if any of the packages required are not yet on our system
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
# Any package missing will be added to the ‘new.packages’ object
# which can then be used to install any missing ones
if(length(new.packages)) install.packages(new.packages)
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# STEP 2 --------------------------------------- Load the tidyverse package
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library(tidyverse)
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# STEP 3 --------------------------------------- # Does this work on your system?
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nutrient_names <- c(G = "Glucose", L = "Leucine", P = "Phosphate",
S = "Sulfate", N = "Ammonia", U = "Uracil")
# Create and object that is the weblink to the data
url <- "http://varianceexplained.org/files/Brauer2008_DataSet1.tds"
# Some example R code we"ll see again during the workshop
# Here we"re reading in data from a remote source and cleaning it
cleaned_genes_tbl <- read_delim(url,
delim = "\t") %>%
separate(NAME,
c("name", "BP", "MF", "systematic_name", "number"),
sep = "\\|\\|") %>%
mutate_at(vars(name:systematic_name), list(trimws)) %>%
select(-number, -GID, -YORF, -GWEIGHT) %>%
gather(sample, expression, G0.05:U0.3) %>%
separate(sample, c("nutrient", "rate"), sep = 1, convert = TRUE) %>%
mutate(nutrient = plyr::revalue(nutrient, nutrient_names)) %>%
filter(!is.na(expression), systematic_name != "")
# Plot the clean data
cleaned_genes_tbl %>%
filter(BP == "leucine biosynthesis") %>%
ggplot(mapping = aes(x = rate, y = expression, color = nutrient)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap(~ name)
