A look at iris

Let’s have a look at the iris data set. The dataset contains 150 observations. We’ll also have a look at some chicken weights later.

Count

iris %>% 
  group_by(Species) %>% 
  count(name = "Count")

Species	Count
setosa	50
versicolor	50
virginica	50

Scatter plot

iris %>% 
  ggplot(aes(Sepal.Length, Sepal.Width, color = Species)) + 
  geom_point() + 
  labs(title = "The iris data-set") +
  theme_bw(base_size = 18) + 
  theme(legend.position = "bottom")

Distribution

iris %>% 
  ggplot(aes(Species, Sepal.Length)) + 
  geom_violin(aes(fill = Species)) +
  geom_boxplot(width = 0.1) + 
  theme_bw(base_size = 18) +
  guides(fill = FALSE)

Chicken Data

Let’s now have a look at ChickWeight data. The dataset contains 578 observations and 50 chicks.

Chickens increase weight over time

ChickWeight %>% 
  ggplot(aes(Time, weight, color = Diet)) + 
  geom_point() +
  facet_wrap(~Chick) + 
  theme_minimal(base_size = 18)

Diet effect

sumdat <- ChickWeight %>% 
  filter(Time == max(Time)) %>% 
  group_by(Diet) %>% 
  summarise(Median = median(weight))

ChickWeight %>% 
  filter(Time == max(Time)) %>% 
  ggplot(aes(Diet, weight)) + 
  geom_point(size = 3, alpha = 1/3) +
  theme_minimal(base_size = 18) +
  geom_point(data = sumdat, aes(Diet, Median), color = "red", size = 5)