1 Introduction

This is some text.

1.1 Read data

library(tidyverse)
ydat <- read_csv("data/brauer2007_tidy.csv")

that was easy, let’s do some analysis!

1.2 Data analysis

library(knitr)
ydat %>% 
  group_by(nutrient, rate) %>% 
  filter(nutrient!="Glucose") %>% 
  summarize(meanexp=mean(expression)) %>% 
  mutate(meanexp=signif(meanexp, 2)) %>% 
  kable()
nutrient rate meanexp
Ammonia 0.05 0.01400
Ammonia 0.10 0.01500
Ammonia 0.15 0.00410
Ammonia 0.20 0.00250
Ammonia 0.25 -0.01400
Ammonia 0.30 -0.02500
Leucine 0.05 0.02600
Leucine 0.10 0.03000
Leucine 0.15 0.01000
Leucine 0.20 0.00210
Leucine 0.25 -0.01000
Leucine 0.30 -0.02400
Phosphate 0.05 0.02400
Phosphate 0.10 0.00710
Phosphate 0.15 -0.00089
Phosphate 0.20 -0.00400
Phosphate 0.25 -0.01600
Phosphate 0.30 -0.02300
Sulfate 0.05 0.02000
Sulfate 0.10 0.00720
Sulfate 0.15 -0.01200
Sulfate 0.20 -0.01100
Sulfate 0.25 -0.00920
Sulfate 0.30 -0.01800
Uracil 0.05 0.02300
Uracil 0.10 0.02300
Uracil 0.15 0.01100
Uracil 0.20 0.00240
Uracil 0.25 -0.03000
Uracil 0.30 -0.03700

1.3 Let’s plot!

gm <- read_csv("data/gapminder.csv")
ggplot(gm, aes(gdpPercap, lifeExp)) + geom_point() + scale_x_log10() + 
  ggtitle("Life Expectancy vs GDP per capita") + 
  theme_bw() + 
  geom_smooth(se=FALSE, lwd=2)

2 Conclusions