# Week 3 Data Visualization Lab
# Install the package ggplot
#install.packages("ggplot2")
library(ggplot2)
#View(cars)
#A quick base R plot - this is not gglot
plot(cars)
# Our first ggplot, we need data + aes + geoms
ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point()
p <- ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point()
# Add a line with geom with geom_line()
p + geom_line()
# Add a trend line close to the data
p + geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
p + geom_smooth(method="lm")
## `geom_smooth()` using formula = 'y ~ x'
#---------------------------------------------------
# Read in our drug expression data
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
# Q. How many genes are in this dataset?
nrow(genes)
## [1] 5196
# Q. How many 'up' regulated genes?
table( genes$State )
##
## down unchanging up
## 72 4997 127
# Q. What fraction of total genes is up-regulated?
round((table(genes$State) / nrow(genes)) * 100, 2)
##
## down unchanging up
## 1.39 96.17 2.44
# Let's make a first plot attempt
g <- ggplot(data=genes) + aes(x=Condition1, y=Condition2, col=State) + geom_point()
g
# Add some color
g + scale_color_manual(values=c("pink", "lightyellow", "lightblue")) +
labs(title="Gene expression changes", x= "Control (no drug)", y= "Drug Treatment") +
theme_bw()
