Week3-1

A New Week!

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We aren’t computer scientists and that’s okay!

We make lots of mistakes. Mistakes are funny. You can laugh with us.

Let’s go, Simba, Pumbaa, and Timon!

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1 More data type: factor

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Factors are categorical variables that can be either numeric or character.

They can be ordered and are very useful for statistical analysis and for plotting.

You use factor() to create a factor object

#create factor
a <- factor(1)
a
class(a)

b <- factor("x")
b
class(b)

d <- c("low","middle","high","high")
d

factor_d <- factor(d)
factor_d

levels(factor_d)

# change the order of levels
order_d <- factor(d, levels = c("low","middle","high"))
order_d

levels(order_d)

2 Plots with ggplot2

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In data analysis, it’s super important to visualize your data early and often.

ggplot2 is a plotting package that makes it simple to create complex plots from data frame.

A ggplot has 3 ingredients: base, aesthetics, and layers

2.1 The base plot

library(tidyverse)

ggplot(airquality)

We load the package of ggplot2, but the function to make the plot is only ggplot(). No 2.

2.2 The X, Y aesthetics

The aesthetics assign the columns from the data that you want to use in the chart. This is where you set the X-Y variables that determine the dimensions of the plot.

ggplot(airquality, aes(x = Month, y = Ozone))

2.3 The layers or geometries

ggplot(airquality, aes(x = factor(Month), y = Ozone)) + 
  geom_boxplot()

airquality %>% 
  filter(Month == 8) %>%
ggplot(aes(x = Day, y = Ozone)) + 
  geom_point()

ggplot2 cheat sheet:

https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-visualization.pdf

ggplot2 document:

https://cran.r-project.org/web/packages/ggplot2/index.html

2.4 Additional plot properties

# Change labels
ggplot(airquality, aes(x = factor(Month), y = Ozone)) + 
  geom_boxplot() +
  labs(title = "Boxplot of Ozone in NY, May - Sep 1973",
       x = "Month",
       y = "Ozone(ppb)")

# add color
ggplot(airquality, aes(x = Day, y = Ozone, color = factor(Month))) + 
  geom_line()+
  geom_point()

# create subplots (rows)
ggplot(airquality, aes(x = Day, y = Ozone, color = factor(Month))) + 
  geom_line() +
  facet_grid(factor(Month) ~ .) # facet into rows based on month

# create subplots (columns)
ggplot(airquality, aes(x = Day, y = Ozone, color = factor(Month))) + 
  geom_line() +
  facet_grid( . ~ factor(Month)) # facet into columns based on month

# Add lines
airquality %>%
  group_by(Month) %>% 
  summarise(ozone_avg = mean(Ozone, na.rm = T)) %>% 
  ggplot(aes(x = factor(Month), y = ozone_avg)) +
  geom_col(fill = "steel blue") + 
  geom_hline(yintercept = 50, color = 'orange', size =2)+ # add horizontal line
  scale_y_continuous(breaks = seq(0, 75, by = 10)) +
  labs(title = "Monthly ozone plot", x = "Month", y="Ozone(ppb)")
  
#Themes
  airquality %>%
  group_by(Month) %>% 
  summarise(ozone_avg = mean(Ozone, na.rm = T)) %>% 
  ggplot(aes(x = factor(Month), y = ozone_avg)) +
  geom_col(fill = "steel blue") + 
  geom_hline(yintercept = 50, color = 'orange', size =2)+ # add horizontal line
  scale_y_continuous(breaks = seq(0, 75, by = 10)) +
  labs(title = "Monthly ozone plot", x = "Month", y="Ozone(ppb)")+
  theme_classic()

2.5 Save plots

You’ve made some plots you can be proud of, so let’s learn to save them so we can cherish them forever. There’s a function called ggsave to do just that. How do we ggsave our plots?

#  It defaults to saving the last plot that you displayed. 
ggsave("./output/plot_name.png")


# You can also save the named plot
my_boxplot <- ggplot(airquality, aes(x = factor(Month), y = Ozone)) + 
  geom_boxplot() +
  labs(title = "Boxplot of Ozone in NY, May - Sep 1973",
       x = "Month",
       y = "Ozone(ppb)")

ggsave("./output/my_boxplot.png", plot = my_boxplot)

Exercise

Try to make a plot showing the daily ozone in August 2016 at sites 27-137-7001 and 27-137-7554 using the “ozone_samples_demo.csv” file

3 Recap

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• factor

• ggplot()