Jakku scrap economy

Read in data

clean_scrap <- read_csv("https://mn-r.netlify.com/data/starwars_scrap_jakku_clean.csv")

head(clean_scrap)

## # A tibble: 6 × 6
##   items                 origin        destination   price_per_…¹ amoun…² total…³
##   <chr>                 <chr>         <chr>                <dbl>   <dbl>   <dbl>
## 1 electrotelescope      outskirts     trade caravan        850.     868.  7.38e5
## 2 atmospheric thrusters cratertown    niima outpost         56.2  33978.  1.91e6
## 3 bulkhead              cratertown    raiders             1005.     645.  6.48e5
## 4 main drive            blowback town trade caravan        598.    1961.  1.17e6
## 5 flight recorder       outskirts     niima outpost        591.     887   5.24e5
## 6 proximity sensor      outskirts     raiders             1229.    7081   8.70e6
## # … with abbreviated variable names ¹​price_per_ton, ²​amount_tons, ³​total_price

Did it load successfully? Look in your environment. You should see “clean_scrap”. There should be 6 variables and 573 rows.

Take a couple of minutes to get an overview of the data. Open and look at your data in at least two ways.

1. Click on the data name in the environment to open the window.

2. Use glimpse() to look at your data.

#View the data
glimpse(clean_scrap)

## Rows: 573
## Columns: 6
## $ items         <chr> "electrotelescope", "atmospheric thrusters", "bulkhead",…
## $ origin        <chr> "outskirts", "cratertown", "cratertown", "blowback town"…
## $ destination   <chr> "trade caravan", "niima outpost", "raiders", "trade cara…
## $ price_per_ton <dbl> 849.79, 56.21, 1004.83, 597.85, 590.93, 1229.03, 56.21, …
## $ amount_tons   <dbl> 868.4280, 33978.1545, 644.7285, 1960.6650, 887.0000, 708…
## $ total_price   <dbl> 737981.43, 1909912.06, 647842.54, 1172183.57, 524154.91,…

Look at a summary of your data using summary().

#View a summary of the data
summary(clean_scrap)

##     items              origin          destination        price_per_ton    
##  Length:573         Length:573         Length:573         Min.   :  29.15  
##  Class :character   Class :character   Class :character   1st Qu.: 314.23  
##  Mode  :character   Mode  :character   Mode  :character   Median : 629.28  
##                                                           Mean   :1010.85  
##                                                           3rd Qu.:1329.05  
##                                                           Max.   :7211.01  
##   amount_tons        total_price      
##  Min.   :    0.01   Min.   :       5  
##  1st Qu.:  238.99   1st Qu.:  128921  
##  Median : 1298.00   Median :  757656  
##  Mean   : 3724.23   Mean   : 3483802  
##  3rd Qu.: 4678.44   3rd Qu.: 2631778  
##  Max.   :60116.67   Max.   :83712615

What if you only want to keep the items and amount_tons fields? Use select() to create a new data frame keeping only those columns and save it as an object called select_scrap.

select_scrap <- select(clean_scrap, items, amount_tons)

Order the data frame you just created by amount_tons from highest to lowest. Which item had the highest weight?

select_scrap <- arrange(select_scrap, desc(amount_tons))

Filter your select data set to all items with an amount higher than 1000. Call the dataset ‘filter_scrap’

filter_scrap <- filter(select_scrap, amount_tons > 1000)

Add a filter to to the amount_tons > 1000 dataset. Include only “proximity sensor” and “hyperdrive”

filter_scrap <- filter(select_scrap, amount_tons > 1000,
                       items %in% c("proximity sensor", "hyperdrive"))

Use mutate() to add a column calculating the amount of pounds from the amount_tons column. Name the column amount_pounds.

filter_scrap <- mutate(filter_scrap, amount_pounds = amount_tons * 2000)

We want to make a table of recommendations for our shopping. In our filtered dataset, we want to buy scrap if it is a Hyperdrive and ignore it when it’s not.

Use mutate() to add a column that says “buy” if the item is a Hyperdrive and “ignore” if it’s not. Name the new column do_this. You will need both ifelse() and mutate() for this task.

filter_scrap <- mutate(filter_scrap, do_this = ifelse(items == "hyperdrive", "buy", "ignore"))

Let’s take a closer look at our full dataset now (clean_scrap). We want to give the Junk Boss a summary of all of this data. He hates numbers, but he likes money.

He wants to know the following things:

1. The sum of all the money potentially earned by item.
2. The maximum money potentially earned by item.
3. The number of records of each item.
4. The 35th percentile of the price by item.

_*Curious how he knows about quantiles, maybe someone told him to use this to test our abilities._

summary_scrap <- clean_scrap %>%
                 group_by() %>%
                 summarise()

summary_scrap <- clean_scrap %>%
  group_by(items) %>%
  summarise(sum_price = sum(total_price),
            max_price = max(total_price),
            count_price = n(),
            price_35th = quantile(total_price, 0.35))

Oh boy, Unkar just learned about plots. What will he want next?

Now he wants a plot of the maximum total prices by item.

Try both geom_col() and geom_point() to see which makes a simpler plot to understand.

Show solution

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_col()

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_point()

Try coord_flip() to make the plot more readable.

If you’re interested in learning more about coord_flip(), ask R for help! ?coord_flip

Show solution

ggplot(data = summary_scrap, aes(items, max_price)) +
  geom_col() +
  coord_flip()

This plot might look better if the columns were sorted by their values.

Try reorder() to make this chart way more readable. Type “?reorder” to learn more about that function.

Show solution

ggplot(data = summary_scrap, aes(reorder(items, max_price), max_price)) +
  geom_col() +
  coord_flip()

Nice work!! You may now move on to the Commodore level analysis.