[If this thing is true], "Do this", "Otherwise do this"

Here’s a handy ifelse statement to help you identify lightsabers.

Or say we wanted to label all the porgs over 60 cm as tall, and everyone else as short. Whenever we want to add a column where the value depends on the value found in another column. We can use ifelse().

Ok, say we have a list of prices for scrap and we want to flag the ones that cost less than 500 credits. How?

mutate() + ifelse() is powerful!

# Add an affordable column
scrap <- scrap %>% 
         mutate(affordable = ifelse(price_per_unit < 500, "Cheap", "Expensive"))

Plot the data, Plot the data, Plot the data

Who remembers how to plot???

The ggplot() sandwich

A ggplot has 3 ingredients.

1. The base plot

library(ggplot2)

ggplot(scrap)

2. The the X, Y aesthetics

The aesthetics assign the components from the data that you want to use in the chart. These also determine the dimensions of the plot.

ggplot(scrap, aes(x = origin, y = credits)) 

3. The layers or geometries

ggplot(scrap, aes(x = origin, y = credits)) + geom_point()

Colors

Now let’s use color to show the destination of the scrap

ggplot(scrap, aes(x = origin, y = credits, color = destination)) +
  geom_point()

Columns charts

Yikes! That point chart has too much detail. Let’s make a column chart and add up the sales to make it easier to understand. Note that we used fill = instead of color = below. Go ahead and try using color instead and see what happens.

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col()

We can change the position of the bars to make it easier to compare sales by destination for each origin. For that we’ll use the – drum roll please – position argument. Remember, you can use help(geom_col) to learn about the different options for that type of plot.

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col(position = "dodge")

Facet wrap

Does the chart feel crowded to you? Let’s use facet wrap to put each destination in a separate chart.

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col(position = "dodge") +
  facet_wrap("destination")

Themes

Want to shake up the appearance of your plots? ggplot2 uses theme functions to change the general appearance of a plot. Try some different themes out. Here’s theme_dark().

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col(position = "dodge") +
  facet_wrap("destination") +
  theme_dark()

Labels

You can set the axis and title labels using the labs function.

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col(position = "dodge") +
  facet_wrap("destination") +
  scale_fill_viridis_d() +
  labs(title    = "Scrap sales by origin and destination", 
       subtitle = "Planet Jakku",
       x        = "Origin", 
       y        = "Total sales",
       caption  = "Data gracefully provided by BB8")

Drop 1.0e+10 scientific notation

Is your boss scared of scientific notation? To hide it we can use options(scipen = 999). Note that this is a general setting in R. Once you use options(scipen = 999) in your current session you won’t have to use it again. Like loading a package, you only need to run the line once when you start RStudio.

options(scipen = 999)

ggplot(scrap, aes(x = origin, y = credits, fill = destination)) +
  geom_col(position = "dodge") +
  facet_wrap("destination") +
  scale_fill_viridis_d() +
  labs(title = "Scrap sales by origin and destination", 
       x     = "Origin", 
       y     = "Total sales") +
  theme_bw()

Save plots

You’ve hopefully made some plots you’re 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? HELP! Let’s type help(ggsave).

# Get help
help(ggsave)
?ggsave

# Copy and paste the r code of your favorite plot here
ggplot(data, aes()) +
   .... +
   ....


# Save your plot to a png file of your choosing
ggsave("your_results_folder/plot_name.png")

# Name the ggplot you want to save
my_plot <- ggplot(...) + geom_point(...)

# Save it!
ggsave(filename = "Save my plot here.png", plot = my_plot)