A scrappy ransom

Mr. Baddy Plutt is demanding 10,000 items of scrap for Finn. Sounds expensive, but lucky for us he didn’t clarify the exact items. Let’s find the scrap that weighs the least per shipment and try to make this transaction as light as possible.

Take a look at our NEW scrap data and see if we have the weight of all the items.

# What unit types are in the data?
unique(scrap$units)

## [1] "Cubic Yards" "Items"       "Tons"

Or return results as a data frame

distinct(scrap, units)

## # A tibble: 3 x 1
##   units      
##   <chr>      
## 1 Cubic Yards
## 2 Items      
## 3 Tons

Hmmm…. So how much does a cubic yard of Hull Panel weigh?

A lot? 32? Maybe…

I think we’re going to need some more data.

Get the weights

It took a while, but with a few droid bribes BB8 was able to track down a Weight conversion table from his old droid buddies. Our current data shows the total cubic yards for some scrap shipments, but not how much the shipment weighs.

# The data's URL
convert_url <- "https://rtrain.netlify.com/data/conversion_table.csv"

# Read the conversion data
convert <- read_csv(convert_url)

head(convert, 3)

## # A tibble: 3 x 3
##   item               units       pounds_per_unit
##   <chr>              <chr>                 <dbl>
## 1 Bulkhead           Cubic Yards             321
## 2 Hull Panel         Cubic Yards             641
## 3 Repulsorlift array Cubic Yards             467

Join 2 tables

Adding porg names

Remember our porg friends? How rude of us not to share their names. Wups!

Here they are:

Hey now! That’s not very helpful. Who’s who? Let’s join their names to the rest their data.

The result

Back to scrap land

Let’s apply our new left_join() skills to the scrap data.

scrap <- left_join(scrap, convert, 
                   by = c("item" = "item", "units" = "units"))

scrap <- left_join(scrap, convert)

head(scrap, 4)

## # A tibble: 4 x 8
##   receipt_date item  origin destination amount units price_per_pound
##   <chr>        <chr> <chr>  <chr>        <dbl> <chr>           <dbl>
## 1 4/1/2013     Bulk~ Crate~ Raiders       4017 Cubi~           1005.
## 2 4/2/2013     Star~ Outsk~ Trade cara~   1249 Cubi~           1129.
## 3 4/3/2013     Star~ Outsk~ Niima Outp~   4434 Cubi~           1129.
## 4 4/4/2013     Hull~ Crate~ Raiders        286 Cubi~            843.
## # ... with 1 more variable: pounds_per_unit <dbl>

Total pounds per shipment

Let’s mutate!

Now that we have pounds per unit we can use mutate to calculate the total pounds for each shipment.

Fill in the blank

scrap <- scrap %>% 
         mutate(total_pounds = amount *  _____________ )

scrap <- scrap %>% 
         mutate(total_pounds = amount * pounds_per_unit)

Total price per shipment

# Calculate the total price for each shipment
scrap <- scrap %>% 
         mutate(credits = ________ * ________ )
         

# Calculate the total price for each shipment
scrap <- scrap %>% 
         mutate(credits = total_pounds  * ________ )

# Calculate the total price for each shipment
scrap <- scrap %>% 
         mutate(credits = total_pounds * price_per_pound)
         

Price per item

Great! Let’s add one last column. We can divide the shipment’s credits by the amount of items to get the price_per_unit.

# Calculate the price per unit
scrap <- scrap %>% 
         mutate(price_per_unit = credits / amount)

sum()

Use summarize() and sum() to find the total credits of all the scrap.

summarize(scrap, total_credits = sum(credits))

mean()

Use summarize() and mean() to calculate the mean price_per_pound in the scrap report.

summarize(scrap, mean_price = mean(price_per_pound, na.rm = T))

Note the na.rm = TRUE in the mean() function. This tells R to ignore empty cells or missing values that show up in R as NA. If you leave na.rm out, the mean function will return ‘NA’ if it finds a missing value in the data.

median()

Use summarize to calculate the median price_per_pound in the scrap report.

summarize(scrap, median_price = median(price_per_pound, na.rm = T))

max()

Use summarize to calculate the maximum price per pound any scrapper got for their scrap.

summarize(scrap, max_price = max(price_per_pound, na.rm = T))

min()

Use summarize to calculate the minimum price per pound any scrapper got for their scrap.

summarize(scrap, min_price = min(price_per_pound, na.rm = T))

nth()

Use summarize() and nth(Origin, 12) to find the name of the Origin City that had the 12th highest scrapper haul.

Hint: Use arrange() first.

arrange(scrap, desc(credits)) %>% summarize(price_12 = nth(origin, 12))

quantile()

Quantiles are great for finding the upper or lower range of a column. Use the quantile() function to find the the 5th and 95th quantile of the prices.

summarize(scrap, 
          price_5th_pctile  = quantile(price_per_pound, 0.05, na.rm = T),
          price_95th_pctile = quantile(price_per_pound, 0.95))

Hint: Add na.rm = T to quantile().

summary <- scrap %>% 
             summarize(max_credits      =  __________ ,
                       mean_credits     =  __________ ,
                       min_pounds       =  __________  )

n()

n() stands for count. It has the smallest function name I know of, but is super useful.

Use summarize and n() to count the number of reported scrap records going to Niima outpost.

Hint: Use filter() first.

niima_scrap <- filter(scrap, destination == "Niima Outpost") 

niima_scrap <- summarize(niima_scrap, scrap_records = n())

Ok. That was fun. Now let’s do a summary for Cratertown. And then for Blowback Town. And then for Tuanul. And then for…

Bargain hunters

Who’s selling goods for cheap?

Use group_by with the column origin, and then usesummarize to find the mean(price_per_unit) at each origin city.

mean_prices <- group_by(scrap, origin) %>% 
                 summarize(mean_price = mean(price_per_unit, na.rm = T)) %>%
                 ungroup()

mean_prices <- group_by(scrap, origin) %>% 
                 summarize(mean_price       = mean(price_per_unit, na.rm = T),
                           mean_price_round = round(mean_price, digits = 2)) %>%  
                 arrange(mean_price_round) %>%
                 ungroup()

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

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

Say you want to label all the porgs over 60 cm as tall, and everyone else as short. In other words, we want to add a column where the value depends on the value found in the height column. We use ifelse() for this.

Or maybe you have a list of prices for scrap, and you want to flag only the ones that cost less than 500 credits.

mutate() + ifelse() is powerful!

library(dplyr)
library(readr)

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

Plot the data, Plot the data, Plot the data

The ggplot() sandwich

A ggplot has 3 ingredients.

1. The base plot

library(ggplot2)

ggplot(scrap)

Note when we load the package it’s library (ggplot2), but the function to make a plot is ggplot(scrap). We admit, it is a bit silly.

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()

ggplot(scrap, aes(x = __column1__, y = __column2__)) + 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 had 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 =. 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 origin 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") +
  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") +
  theme_bw() +
  labs(title = "Scrap sales by origin and destination", 
       x     = "Origin", 
       y     = "Total sales")

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")

# The ggplot you want to save
my_plot <- ggplot(...)
  
# The name of the file the chart will be saved to.
where_to_save_it <- "___.png"

# Save it!
ggsave(filename = where_to_save_it, plot = my_plot)