BSTA504 Midterm Project

1. Define the Question

Coffee is American’s most popular beverage. According to Food Beverage Insider, Americans consume more coffee than tap water (https://www.foodbeverageinsider.com/beverages/coffee-consumption-hits-record-high-us). As a graduate student on a budget, I wanted to answer the question, how can I best spend my coffee dollars? The last bag of coffee I bought was a 12 oz bag of Portland Coffee Roasters, Goose Hollow coffee beans for $11.99 at Safeway. While this exact blend was not listed on Coffee Review’s website, other blends of Portland Coffee Roasters (previously called Portland Roasters, https://www.portlandcoffeeroasters.com/consumer/learn/about-us/mission-history) are listed. For this analysis, I will using the Mocha Java blend, which was rated at 89, as a substitute for Goose Hollow.

In order to answer this question, I used the data from Coffee Review’s website (https://www.coffeereview.com/). This website was started in 1997 by two people who were determined to provide a wine style rating to coffee. The data used for this analysis was sourced from (https://www.kaggle.com/datasets/hanifalirsyad/coffee-scrap-coffeereview/versions/2?resource=download&select=coffee_fix.csv) and the file used was coffee_fix.csv. The dataset in question includes multiple currencies and unit of measure. In order to streamline my analysis, I filtered the dataset to included only coffees whose prices were listed in USD.

2. Import Data

Prior to importing the data, I looked at the excel sheet to check that there were not any missing lines that needed to be addressed during the import. There were not so I was able to improve the data using a basic read.csv() command.

# coffee budget
budget_per_bag <- 11.99 #cost of 12oz of Portland roasters from Safeway

coffee_data <- read.csv(here("data", "coffee_fix.csv"), na = "NA")
glimpse(coffee_data)

## Rows: 2,282
## Columns: 20
## $ slug        <chr> "https://www.coffeereview.com/review/sweety-espresso-blend…
## $ all_text    <chr> " \n\n\n95\n\n\nA.R.C.\n“Sweety” Espresso Blend\n\n\n\n\n\…
## $ rating      <int> 95, 94, 92, 92, 94, 93, 93, 93, 93, 94, 95, 93, 92, 90, 90…
## $ roaster     <chr> "A.R.C.", "A.R.C.", "Revel Coffee", "Roast House", "Big Cr…
## $ name        <chr> "“Sweety” Espresso Blend", "Flora Blend Espresso", "Ethiop…
## $ location    <chr> "Hong Kong, China", "Hong Kong, China", "Billings, Montana…
## $ origin      <chr> "Panama; Ethiopia", "Africa; Asia Pacific", "Guji Zone, so…
## $ roast       <chr> "Medium-Light", "Medium-Light", "Medium-Light", "Medium-Li…
## $ est_price   <chr> "HKD $250/227 grams", "HKD $158/227 grams", "$16.00/12 oun…
## $ review_date <chr> "November 2017", "November 2017", "November 2017", "Novemb…
## $ agtron      <chr> "50/73", "54/77", "54/70", "53/79", "48/70", "66/92", "54/…
## $ aroma       <int> 9, 9, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 7, 8, 9, 8…
## $ acid        <int> NA, NA, 8, 8, 9, 8, 8, 8, 9, 9, 9, 8, NA, 8, NA, NA, NA, 8…
## $ body        <int> 9, 9, 8, 9, 9, 9, 9, 9, 8, 9, 9, 9, 8, 7, 8, 8, 8, 9, 8, 9…
## $ flavor      <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 9, 8, 8, 9, 9, 8…
## $ aftertaste  <int> 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 8, 8, 8, 7, 8, 7, 8, 8, 7…
## $ with_milk   <int> 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, NA, 8, 7,…
## $ desc_1      <chr> "Evaluated as espresso. Sweet-toned, deeply rich, chocolat…
## $ desc_2      <chr> "An espresso blend comprised of coffees from Panama and Et…
## $ desc_3      <chr> "A radiant espresso blend that shines equally in the strai…

3. Overview of ratings

In order to visualize the range of ratings within this dataset, I split up the ratings into smaller ranges. Using glimpse() below, you can see that the dataset was successful split into the different rating ranges.

coffee_data <- coffee_data %>% 
  mutate(
    rating_ranges = case_when(
      rating < 80 ~ "<80",
      rating >= 80 & rating < 85 ~ "80-84",
      rating >= 85 & rating < 90 ~ "85-89",
      rating >= 90 & rating < 95 ~ "90-94",
      rating >= 95 ~"95 and up"
    )
  )
glimpse(coffee_data)

## Rows: 2,282
## Columns: 21
## $ slug          <chr> "https://www.coffeereview.com/review/sweety-espresso-ble…
## $ all_text      <chr> " \n\n\n95\n\n\nA.R.C.\n“Sweety” Espresso Blend\n\n\n\n\…
## $ rating        <int> 95, 94, 92, 92, 94, 93, 93, 93, 93, 94, 95, 93, 92, 90, …
## $ roaster       <chr> "A.R.C.", "A.R.C.", "Revel Coffee", "Roast House", "Big …
## $ name          <chr> "“Sweety” Espresso Blend", "Flora Blend Espresso", "Ethi…
## $ location      <chr> "Hong Kong, China", "Hong Kong, China", "Billings, Monta…
## $ origin        <chr> "Panama; Ethiopia", "Africa; Asia Pacific", "Guji Zone, …
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium-Light", "Medium-…
## $ est_price     <chr> "HKD $250/227 grams", "HKD $158/227 grams", "$16.00/12 o…
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "50/73", "54/77", "54/70", "53/79", "48/70", "66/92", "5…
## $ aroma         <int> 9, 9, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 7, 8, 9,…
## $ acid          <int> NA, NA, 8, 8, 9, 8, 8, 8, 9, 9, 9, 8, NA, 8, NA, NA, NA,…
## $ body          <int> 9, 9, 8, 9, 9, 9, 9, 9, 8, 9, 9, 9, 8, 7, 8, 8, 8, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 9, 8, 8, 9, 9,…
## $ aftertaste    <int> 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 8, 8, 8, 7, 8, 7, 8, 8,…
## $ with_milk     <int> 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, NA, 8, …
## $ desc_1        <chr> "Evaluated as espresso. Sweet-toned, deeply rich, chocol…
## $ desc_2        <chr> "An espresso blend comprised of coffees from Panama and …
## $ desc_3        <chr> "A radiant espresso blend that shines equally in the str…
## $ rating_ranges <chr> "95 and up", "90-94", "90-94", "90-94", "90-94", "90-94"…

ggplot(coffee_data, 
       aes(x = rating_ranges))+
  geom_bar()+
  labs(x = "Rating Ranges", 
       y = "Count",
       title = "Bar Graph of Coffee Ratings")

In the bar graph below, we can see that the large majority of ratings are above Java Mocha’s rating of 89. Using summarize() below, we can see that the range of ratings are from 63 to 98, but the median and mean are both 93 so I am confident that based on this analysis, I’ll be able to find a higher rated coffee for the same price. I also looked at the ratings grouped by roast and for whatever reason, the dark roasts are rated the worst based on median and mean. I am surprised to see that. I had assumed that dark roasts were considered better by coffee snobs experts, but based on the values in the table below, it appears that dark roasts are rated differently than light and medium roasts.

# Range and mean of all coffee ratings
coffee_data %>% 
  summarize(minimum_rating = min(rating, na.rm = TRUE),
            maximum_rating = max(rating, na.rm = TRUE),
            mean_rating = round(mean(rating, na.rm = TRUE), digits = 1),
            median_rating = median(rating, na.rm = TRUE))

##   minimum_rating maximum_rating mean_rating median_rating
## 1             63             98          93            93

coffee_data %>% 
  group_by(roast) %>% 
  summarize(minimum_rating = min(rating, na.rm = TRUE),
            maximum_rating = max(rating, na.rm = TRUE),
            mean_rating = round(mean(rating, na.rm = TRUE), digits = 0),
            median_rating = median(rating, na.rm = TRUE))

## # A tibble: 7 × 5
##   roast        minimum_rating maximum_rating mean_rating median_rating
##   <chr>                 <int>          <int>       <dbl>         <dbl>
## 1 Dark                     84             94          88            87
## 2 Light                    63             97          93            94
## 3 Medium                   85             97          92            92
## 4 Medium-Dark              83             96          91            92
## 5 Medium-Light             86             98          93            93
## 6 Very Dark                89             89          89            89
## 7 <NA>                     72             96          92            93

4. Convert all the data within the dataset to cost per 12oz

There are some coffees that are sold in other measurements such as capsules, packets, and pouches. Since those aren’t standardized measures, I’ve removed these values from the dataset. This evaluation will only consider coffee measured in grams or ounces

# from my code file

knitr::include_graphics(here("pics", "est_price.png"))

Taking the data, we can see that within the “est_price” column, we can fix inconsistencies between the currency and the mass measured for the beans. I am assuming that every column that does not call out a specific currency is using USD so I am splitting the columns up so that we can see the specific currencies evaluated. To simplify my analysis, I decided to filter out all non-US currencies and those coffees which were measured in values other than ounces and grams. I did remove any coffee that had “Cold Brew” in the name. This is because the first time I ran this analysis, I found that the highest rated coffee within my budget was a Cold Brew. Since the volume of a cold brew includes water and coffee, it would be an accurate analysis as I’m only looking to evaluate coffee beans.

glimpse(coffee_data)

## Rows: 2,282
## Columns: 21
## $ slug          <chr> "https://www.coffeereview.com/review/sweety-espresso-ble…
## $ all_text      <chr> " \n\n\n95\n\n\nA.R.C.\n“Sweety” Espresso Blend\n\n\n\n\…
## $ rating        <int> 95, 94, 92, 92, 94, 93, 93, 93, 93, 94, 95, 93, 92, 90, …
## $ roaster       <chr> "A.R.C.", "A.R.C.", "Revel Coffee", "Roast House", "Big …
## $ name          <chr> "“Sweety” Espresso Blend", "Flora Blend Espresso", "Ethi…
## $ location      <chr> "Hong Kong, China", "Hong Kong, China", "Billings, Monta…
## $ origin        <chr> "Panama; Ethiopia", "Africa; Asia Pacific", "Guji Zone, …
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium-Light", "Medium-…
## $ est_price     <chr> "HKD $250/227 grams", "HKD $158/227 grams", "$16.00/12 o…
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "50/73", "54/77", "54/70", "53/79", "48/70", "66/92", "5…
## $ aroma         <int> 9, 9, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 7, 8, 9,…
## $ acid          <int> NA, NA, 8, 8, 9, 8, 8, 8, 9, 9, 9, 8, NA, 8, NA, NA, NA,…
## $ body          <int> 9, 9, 8, 9, 9, 9, 9, 9, 8, 9, 9, 9, 8, 7, 8, 8, 8, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 9, 8, 8, 9, 9,…
## $ aftertaste    <int> 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 8, 8, 8, 7, 8, 7, 8, 8,…
## $ with_milk     <int> 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, NA, 8, …
## $ desc_1        <chr> "Evaluated as espresso. Sweet-toned, deeply rich, chocol…
## $ desc_2        <chr> "An espresso blend comprised of coffees from Panama and …
## $ desc_3        <chr> "A radiant espresso blend that shines equally in the str…
## $ rating_ranges <chr> "95 and up", "90-94", "90-94", "90-94", "90-94", "90-94"…

#cleaning data so that it only considers grams and ounces
coffee_data <- coffee_data %>% 
  filter(
    str_detect(est_price, 'grams|gram|ounces|oz|ounce')
  ) %>% 
  filter(
        !str_detect(name, 'cold brew|Cold Brew')
  )

glimpse(coffee_data)

## Rows: 2,222
## Columns: 21
## $ slug          <chr> "https://www.coffeereview.com/review/sweety-espresso-ble…
## $ all_text      <chr> " \n\n\n95\n\n\nA.R.C.\n“Sweety” Espresso Blend\n\n\n\n\…
## $ rating        <int> 95, 94, 92, 92, 94, 93, 93, 93, 93, 94, 95, 93, 92, 90, …
## $ roaster       <chr> "A.R.C.", "A.R.C.", "Revel Coffee", "Roast House", "Big …
## $ name          <chr> "“Sweety” Espresso Blend", "Flora Blend Espresso", "Ethi…
## $ location      <chr> "Hong Kong, China", "Hong Kong, China", "Billings, Monta…
## $ origin        <chr> "Panama; Ethiopia", "Africa; Asia Pacific", "Guji Zone, …
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium-Light", "Medium-…
## $ est_price     <chr> "HKD $250/227 grams", "HKD $158/227 grams", "$16.00/12 o…
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "50/73", "54/77", "54/70", "53/79", "48/70", "66/92", "5…
## $ aroma         <int> 9, 9, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 7, 8, 9,…
## $ acid          <int> NA, NA, 8, 8, 9, 8, 8, 8, 9, 9, 9, 8, NA, 8, NA, NA, NA,…
## $ body          <int> 9, 9, 8, 9, 9, 9, 9, 9, 8, 9, 9, 9, 8, 7, 8, 8, 8, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 9, 8, 8, 9, 9,…
## $ aftertaste    <int> 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 8, 8, 8, 7, 8, 7, 8, 8,…
## $ with_milk     <int> 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, NA, 8, …
## $ desc_1        <chr> "Evaluated as espresso. Sweet-toned, deeply rich, chocol…
## $ desc_2        <chr> "An espresso blend comprised of coffees from Panama and …
## $ desc_3        <chr> "A radiant espresso blend that shines equally in the str…
## $ rating_ranges <chr> "95 and up", "90-94", "90-94", "90-94", "90-94", "90-94"…

Looking at the data above, I’m seeing additional difficulties with the data. There are some tubes, bottles, and cans that are measured in grams or ounces so my previous filter did not remove them. Further down in my analysis, I do filter for values of only “ounces” or “grams” so this will be addressed later in the code.

Below, I took the est_price and split it at the “$” into currency and prices. I add “US” to those that had “$”, but did not have a currency listed at the beginning of their strings

coffee_currency <- coffee_data %>% 
  separate( #splitting currency into its own column
    col = est_price,
    into = c("currency","prices"),
    sep = "\\$",
    remove = FALSE)

## Warning: Expected 2 pieces. Additional pieces discarded in 7 rows [218, 783, 823, 827,
## 830, 852, 1703].

## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 25 rows [254, 388, 531,
## 562, 770, 1170, 1171, 1274, 1275, 1372, 1373, 1374, 1375, 1398, 1436, 1467,
## 1602, 1643, 1644, 1646, ...].

coffee_currency$currency[coffee_currency$currency == ""] <- "US"
glimpse(coffee_currency)

## Rows: 2,222
## Columns: 23
## $ slug          <chr> "https://www.coffeereview.com/review/sweety-espresso-ble…
## $ all_text      <chr> " \n\n\n95\n\n\nA.R.C.\n“Sweety” Espresso Blend\n\n\n\n\…
## $ rating        <int> 95, 94, 92, 92, 94, 93, 93, 93, 93, 94, 95, 93, 92, 90, …
## $ roaster       <chr> "A.R.C.", "A.R.C.", "Revel Coffee", "Roast House", "Big …
## $ name          <chr> "“Sweety” Espresso Blend", "Flora Blend Espresso", "Ethi…
## $ location      <chr> "Hong Kong, China", "Hong Kong, China", "Billings, Monta…
## $ origin        <chr> "Panama; Ethiopia", "Africa; Asia Pacific", "Guji Zone, …
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium-Light", "Medium-…
## $ est_price     <chr> "HKD $250/227 grams", "HKD $158/227 grams", "$16.00/12 o…
## $ currency      <chr> "HKD ", "HKD ", "US", "US", "US", "US", "US", "US", "US"…
## $ prices        <chr> "250/227 grams", "158/227 grams", "16.00/12 ounces", "19…
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "50/73", "54/77", "54/70", "53/79", "48/70", "66/92", "5…
## $ aroma         <int> 9, 9, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 7, 8, 9,…
## $ acid          <int> NA, NA, 8, 8, 9, 8, 8, 8, 9, 9, 9, 8, NA, 8, NA, NA, NA,…
## $ body          <int> 9, 9, 8, 9, 9, 9, 9, 9, 8, 9, 9, 9, 8, 7, 8, 8, 8, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 9, 8, 8, 9, 9,…
## $ aftertaste    <int> 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 8, 8, 8, 7, 8, 7, 8, 8,…
## $ with_milk     <int> 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, NA, 8, …
## $ desc_1        <chr> "Evaluated as espresso. Sweet-toned, deeply rich, chocol…
## $ desc_2        <chr> "An espresso blend comprised of coffees from Panama and …
## $ desc_3        <chr> "A radiant espresso blend that shines equally in the str…
## $ rating_ranges <chr> "95 and up", "90-94", "90-94", "90-94", "90-94", "90-94"…

coffee_currency %>%
  tabyl(currency)

## Warning: Using one column matrices in `filter()` was deprecated in dplyr 1.1.0.
## ℹ Please use one dimensional logical vectors instead.
## ℹ The deprecated feature was likely used in the dplyr package.
##   Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.

##                     currency    n     percent
##             #23.00/12 ounces    1 0.000450045
##              18.00/12 ounces    1 0.000450045
##          500 pesos/200 grams    1 0.000450045
##          9.9 Euros/225 grams    1 0.000450045
##                         AED     3 0.001350135
##                         AUD     2 0.000900090
##                         CAD    25 0.011251125
##                          HK     5 0.002250225
##                         HKD    17 0.007650765
##                         IDR     2 0.000900090
##                         KRW     2 0.000900090
##                           NT    9 0.004050405
##                          NT   512 0.230423042
##             NT 400/227 grams    1 0.000450045
##                         NTD     3 0.001350135
##             RM 120/350 grams    2 0.000900090
##             RM 130/350 grams    1 0.000450045
##                         THB     1 0.000450045
##                           US 1613 0.725922592
##                          US     1 0.000450045
##                         USD     2 0.000900090
##         £100/150-gram packet    1 0.000450045
##     £25.00/8 18-gram pouches    1 0.000450045
##            ¥ 1,580/100 grams    1 0.000450045
##            ¥ 2,420/200 grams    2 0.000900090
##             ¥ 2,640/50 grams    1 0.000450045
##             ¥1,050/100 grams    1 0.000450045
##              ¥1280/100 grams    4 0.001800180
##              ¥1680/100 grams    1 0.000450045
##              ¥2980/100 grams    1 0.000450045
##                ¥78/16 ounces    2 0.000900090
##                ¥88/16 ounces    1 0.000450045
##  €29.95/1 kilo (35.3 ounces)    1 0.000450045

Looking at the tabyl above, I noticed that there are additional currency signs found within the coffee_dix dataset. I am going to filter those out to limit my analysis to just US. USD and US is the same currency.

coffee_currency_filter <- coffee_currency %>%
  filter(
      grepl('US', currency) #|
  )



coffee_currency_filter %>% 
  group_by(currency) %>% 
  summarise(n = n())

## # A tibble: 3 × 2
##   currency     n
##   <chr>    <int>
## 1 "US"      1613
## 2 "US "        1
## 3 "USD "       2

In the table above, it appears that there are two “US” columns. They likely have whitespace so they are not exactly the same value. At this point, all that remain in the sample set are in USD. Now I am going to split the price columns into numbers and units.

coffee_price <- coffee_currency_filter %>% #splitting the prices into price in dollars and units
  separate( #splitting price into its own column
    col = prices,
    into = c("price_dollars","unit_size"),
    sep = "/",
    remove = FALSE)
glimpse(coffee_price)

## Rows: 1,616
## Columns: 25
## $ slug          <chr> "https://www.coffeereview.com/review/ethiopia-shakiso-mo…
## $ all_text      <chr> " \n\n\n92\n\n\nRevel Coffee\nEthiopia Shakiso Mormora\n…
## $ rating        <int> 92, 92, 94, 93, 93, 93, 93, 95, 90, 87, 93, 97, 92, 93, …
## $ roaster       <chr> "Revel Coffee", "Roast House", "Big Creek Coffee Roaster…
## $ name          <chr> "Ethiopia Shakiso Mormora", "Ethiopia Suke Quto", "Ethio…
## $ location      <chr> "Billings, Montana", "Spokane, Washington", "Hamilton, M…
## $ origin        <chr> "Guji Zone, southern Ethiopia", "Guji Zone, Oromia Regio…
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium", "Light", "Medi…
## $ est_price     <chr> "$16.00/12 ounces", "$19.00/16 ounces", "$16.50/12 ounce…
## $ currency      <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", "U…
## $ prices        <chr> "16.00/12 ounces", "19.00/16 ounces", "16.50/12 ounces",…
## $ price_dollars <chr> "16.00", "19.00", "16.50", "17.49", "17.99", "17.50", "1…
## $ unit_size     <chr> "12 ounces", "16 ounces", "12 ounces", "12 ounces", "16 …
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "54/70", "53/79", "48/70", "66/92", "54/75", "58/80", "5…
## $ aroma         <int> 9, 8, 9, 9, 9, 9, 9, 9, 8, 7, 9, 10, 9, 9, 8, 8, 8, 9, 8…
## $ acid          <int> 8, 8, 9, 8, 8, 8, 9, 9, 8, 7, 8, 9, 8, 8, 8, 7, 9, 7, 8,…
## $ body          <int> 8, 9, 9, 9, 9, 9, 8, 9, 7, 8, 9, 9, 8, 8, 9, 8, 9, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 10, 9, 9, 9, 8, 9, 9, 9…
## $ aftertaste    <int> 8, 8, 8, 8, 8, 8, 8, 9, 8, 7, 8, 9, 8, 9, 8, 8, 8, 8, 8,…
## $ with_milk     <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ desc_1        <chr> "Crisply sweet, cocoa-toned. Lemon blossom, roasted caca…
## $ desc_2        <chr> "This coffee tied for the third-highest rating in a tast…
## $ desc_3        <chr> "A gently spice-toned, floral- driven wet-processed Ethi…
## $ rating_ranges <chr> "90-94", "90-94", "90-94", "90-94", "90-94", "90-94", "9…

unit_size refers to the amount of coffee sold at the price listed. For some coffees that is 16oz, 120z, 200 grams, etc. Now I’m going to split the unit size between the numbers and the units at the whitespace in between.

coffee_price_split <- coffee_price %>% 
  separate(
    col = unit_size,
    into = c("amount", "unit"),
    sep = " ",
    remove = FALSE
  ) 

## Warning: Expected 2 pieces. Additional pieces discarded in 33 rows [160, 169, 185, 203,
## 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 332, 499, 500, 513, 586, 595,
## ...].

glimpse(coffee_price_split)

## Rows: 1,616
## Columns: 27
## $ slug          <chr> "https://www.coffeereview.com/review/ethiopia-shakiso-mo…
## $ all_text      <chr> " \n\n\n92\n\n\nRevel Coffee\nEthiopia Shakiso Mormora\n…
## $ rating        <int> 92, 92, 94, 93, 93, 93, 93, 95, 90, 87, 93, 97, 92, 93, …
## $ roaster       <chr> "Revel Coffee", "Roast House", "Big Creek Coffee Roaster…
## $ name          <chr> "Ethiopia Shakiso Mormora", "Ethiopia Suke Quto", "Ethio…
## $ location      <chr> "Billings, Montana", "Spokane, Washington", "Hamilton, M…
## $ origin        <chr> "Guji Zone, southern Ethiopia", "Guji Zone, Oromia Regio…
## $ roast         <chr> "Medium-Light", "Medium-Light", "Medium", "Light", "Medi…
## $ est_price     <chr> "$16.00/12 ounces", "$19.00/16 ounces", "$16.50/12 ounce…
## $ currency      <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", "U…
## $ prices        <chr> "16.00/12 ounces", "19.00/16 ounces", "16.50/12 ounces",…
## $ price_dollars <chr> "16.00", "19.00", "16.50", "17.49", "17.99", "17.50", "1…
## $ unit_size     <chr> "12 ounces", "16 ounces", "12 ounces", "12 ounces", "16 …
## $ amount        <chr> "12", "16", "12", "12", "16", "12", "12", "12", "12", "1…
## $ unit          <chr> "ounces", "ounces", "ounces", "ounces", "ounces", "ounce…
## $ review_date   <chr> "November 2017", "November 2017", "November 2017", "Nove…
## $ agtron        <chr> "54/70", "53/79", "48/70", "66/92", "54/75", "58/80", "5…
## $ aroma         <int> 9, 8, 9, 9, 9, 9, 9, 9, 8, 7, 9, 10, 9, 9, 8, 8, 8, 9, 8…
## $ acid          <int> 8, 8, 9, 8, 8, 8, 9, 9, 8, 7, 8, 9, 8, 8, 8, 7, 9, 7, 8,…
## $ body          <int> 8, 9, 9, 9, 9, 9, 8, 9, 7, 8, 9, 9, 8, 8, 9, 8, 9, 9, 8,…
## $ flavor        <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 10, 9, 9, 9, 8, 9, 9, 9…
## $ aftertaste    <int> 8, 8, 8, 8, 8, 8, 8, 9, 8, 7, 8, 9, 8, 9, 8, 8, 8, 8, 8,…
## $ with_milk     <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ desc_1        <chr> "Crisply sweet, cocoa-toned. Lemon blossom, roasted caca…
## $ desc_2        <chr> "This coffee tied for the third-highest rating in a tast…
## $ desc_3        <chr> "A gently spice-toned, floral- driven wet-processed Ethi…
## $ rating_ranges <chr> "90-94", "90-94", "90-94", "90-94", "90-94", "90-94", "9…

coffee_price_split %>% 
  tabyl(unit)

##       unit    n      percent
##   1.6-gram    1 0.0006188119
##         12    2 0.0012376238
##        3.3    2 0.0012376238
##     5-gram    7 0.0043316832
##    6-ounce    1 0.0006188119
##          7    1 0.0006188119
##          8    1 0.0006188119
##    8-ounce    1 0.0006188119
##  9.5-ounce    2 0.0012376238
##     bottle    1 0.0006188119
##        can    2 0.0012376238
##      grams   70 0.0433168317
##     grams;    1 0.0006188119
##     ounces 1519 0.9399752475
##    ounces*    1 0.0006188119
##    ounces;    3 0.0018564356
##        tin    1 0.0006188119

At this point, I am going to split my table into two. One table is going to be for ounces and one is going to be for grams. The table containing the grams will be converted to ounces and below tables will have standardized cost to the cost of 12 ounces of coffee. These tables will be reconnected further down.

#removing everything that isn't ounces
coffee_price_ounces <- coffee_price_split %>%
  filter(
    unit == "ounces" | unit == "ounces*" | unit == "ounces;"
  )

coffee_price_ounces %>% 
  tabyl(unit)

##     unit    n      percent
##   ounces 1519 0.9973736047
##  ounces*    1 0.0006565988
##  ounces;    3 0.0019697965

#filtering to make a table of grams
coffee_price_grams <- coffee_price_split %>%
  filter(
    unit == "gram" | unit == "grams"
  )

coffee_price_grams %>% 
  tabyl(unit)

##   unit  n percent
##  grams 70       1

#calculate the total prices per 12 oz, because that's typically how coffee is sold in the grocery store
coffee_price_per_ounces <-coffee_price_ounces %>% 
  mutate(
    price_per_12oz = (12*as.numeric(price_dollars)/as.numeric(amount)) 
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `price_per_12oz = (12 *
##   as.numeric(price_dollars)/as.numeric(amount))`.
## Caused by warning:
## ! NAs introduced by coercion

coffee_price_per_ounces %>%
  summarize(price_dollars,amount,unit, price_per_12oz) %>% gt()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.

price_dollars	amount	unit	price_per_12oz
16.00	12	ounces	16.000000
19.00	16	ounces	14.250000
16.50	12	ounces	16.500000
17.49	12	ounces	17.490000
17.99	16	ounces	13.492500
17.50	12	ounces	17.500000
18.00	12	ounces	18.000000
24.50	12	ounces	24.500000
19.50	12	ounces	19.500000
15.00	12	ounces	15.000000
17.95	12	ounces	17.950000
74.95	8	ounces	112.425000
18.00	12	ounces	18.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
11.90	12	ounces	11.900000
19.95	12	ounces	19.950000
15.00	12	ounces	15.000000
14.99	12	ounces	14.990000
14.99	12	ounces	14.990000
19.00	12	ounces	19.000000
16.00	12	ounces	16.000000
16.95	12	ounces	16.950000
16.95	12	ounces	16.950000
25.00	12	ounces	25.000000
14.99	12	ounces	14.990000
17.00	12	ounces	17.000000
22.00	8	ounces	33.000000
22.00	12	ounces	22.000000
15.00	12	ounces	15.000000
20.00	12	ounces	20.000000
28.00	8	ounces	42.000000
55.00	12	ounces	55.000000
25.00	12	ounces	25.000000
37.00	4	ounces	111.000000
19.25	12	ounces	19.250000
49.99	16	ounces	37.492500
44.95	8	ounces	67.425000
19.95	12	ounces	19.950000
16.95	12	ounces	16.950000
29.48	8	ounces	44.220000
19.95	8	ounces	29.925000
12.95	12	ounces	12.950000
16.95	12	ounces	16.950000
18.95	12	ounces	18.950000
15.75	12	ounces	15.750000
60.00	8	ounces	90.000000
40.00	8	ounces	60.000000
27.50	8	ounces	41.250000
30.00	8	ounces	45.000000
17.00	12	ounces	17.000000
17.95	12	ounces	17.950000
20.00	12	ounces	20.000000
25.00	12	ounces	25.000000
24.00	12	ounces	24.000000
13.99	12	ounces	13.990000
17.25	12	ounces	17.250000
17.25	12	ounces	17.250000
18.75	12	ounces	18.750000
206.00	8	ounces	309.000000
206.00	8	ounces	309.000000
29.48	8	ounces	44.220000
16.99	12	ounces	16.990000
17.00	12	ounces	17.000000
51.00	8	ounces	76.500000
54.00	8	ounces	81.000000
19.00	12	ounces	19.000000
20.50	12	ounces	20.500000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
24.00	12	ounces	24.000000
15.50	12	ounces	15.500000
17.50	12	ounces	17.500000
16.49	12	ounces	16.490000
23.00	12	ounces	23.000000
13.75	12	ounces	13.750000
21.00	16	ounces	15.750000
14.00	12	ounces	14.000000
17.00	12	ounces	17.000000
22.00	12	ounces	22.000000
32.00	8	ounces	48.000000
15.75	12	ounces	15.750000
15.75	12	ounces	15.750000
16.95	12	ounces	16.950000
9.94	4	ounces	29.820000
9.94	4	ounces	29.820000
9.94	4	ounces	29.820000
25.00	12	ounces	25.000000
12.00	12	ounces	12.000000
12.00	12	ounces	12.000000
15.00	12	ounces	15.000000
14.00	12	ounces	14.000000
11.35	12	ounces	11.350000
13.99	12	ounces	13.990000
14.95	12	ounces	14.950000
14.50	12	ounces	14.500000
17.60	12	ounces	17.600000
44.95	8	ounces	67.425000
17.95	12	ounces	17.950000
16.49	12	ounces	16.490000
13.95	8	ounces	20.925000
17.00	12	ounces	17.000000
20.00	12	ounces	20.000000
30.50	4	ounces	91.500000
17.00	12	ounces	17.000000
21.00	12	ounces	21.000000
51.00	8	ounces	76.500000
37.50	8	ounces	56.250000
18.75	12	ounces	18.750000
15.75	12	ounces	15.750000
20.00	12	ounces	20.000000
15.00	12	ounces	15.000000
24.00	12	ounces	24.000000
22.50	12	ounces	22.500000
15.50	12	ounces	15.500000
16.95	12	ounces	16.950000
12.00	12	ounces	12.000000
16.49	12	ounces	16.490000
75.00	8	ounces	112.500000
75.00	8	ounces	112.500000
145.00	8	ounces	217.500000
19.50	12	ounces	19.500000
12.60	12	ounces	12.600000
17.95	12	ounces	17.950000
45.95	8	ounces	68.925000
18.00	12	ounces	18.000000
28.00	8	ounces	42.000000
17.00	12	ounces	17.000000
18.00	12	ounces	18.000000
19.95	12	ounces	19.950000
24.95	12	ounces	24.950000
17.60	12	ounces	17.600000
16.00	12	ounces	16.000000
35.00	8	ounces	52.500000
13.99	12	ounces	13.990000
16.50	12	ounces	16.500000
15.50	12	ounces	15.500000
16.00	10	ounces	19.200000
15.00	10	ounces	18.000000
18.00	12	ounces	18.000000
15.50	12	ounces	15.500000
16.99	16	ounces	12.742500
19.98	16	ounces	14.985000
12.50	12	ounces	12.500000
15.00	12	ounces	15.000000
15.50	12	ounces	15.500000
15.99	16	ounces	11.992500
18.00	12	ounces	18.000000
20.50	16	ounces	15.375000
19.50	12	ounces	19.500000
19.20	12	ounces	19.200000
15.00	12	ounces	15.000000
18.99	12	ounces	18.990000
18.50	12	ounces	18.500000
18.49	12	ounces	18.490000
13.99	12	ounces	13.990000
18.00	12	ounces	18.000000
17.75	12	ounces	17.750000
18.00	12	ounces	18.000000
35.00	12	ounces	35.000000
16.95	12	ounces	16.950000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
19.00	12	ounces	19.000000
13.95	12	ounces	13.950000
13.95	12	ounces	13.950000
21.00	12	ounces	21.000000
20.50	12	ounces	20.500000
22.50	12	ounces	22.500000
18.95	12	ounces	18.950000
18.00	12	ounces	18.000000
11.95	12	ounces	11.950000
19.50	12	ounces	19.500000
24.00	12	ounces	24.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
13.34	4	ounces	40.020000
13.34	4	ounces	40.020000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
14.99	12	ounces	14.990000
8.99	8	ounces	13.485000
9.99	7	ounces	17.125714
17.00	12	ounces	17.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
15.00	12	ounces	15.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
17.50	12	ounces	17.500000
13.75	12	ounces	13.750000
17.50	12	ounces	17.500000
20.50	12	ounces	20.500000
21.00	12	ounces	21.000000
18.75	12	ounces	18.750000
18.75	12	ounces	18.750000
15.75	12	ounces	15.750000
14.75	12	ounces	14.750000
19.95	12	ounces	19.950000
13.34	4	ounces	40.020000
19.50	12	ounces	19.500000
19.50	12	ounces	19.500000
19.50	12	ounces	19.500000
75.00	8	ounces	112.500000
16.75	12	ounces	16.750000
17.00	12	ounces	17.000000
13.50	12	ounces	13.500000
16.50	12	ounces	16.500000
11.95	12	ounces	11.950000
20.00	12	ounces	20.000000
21.00	12	ounces	21.000000
16.00	12	ounces	16.000000
16.95	12	ounces	16.950000
12.00	12	ounces	12.000000
15.00	12	ounces	15.000000
16.95	12	ounces	16.950000
13.50	10	ounces	16.200000
19.95	12	ounces	19.950000
12.25	12	ounces	12.250000
18.00	12	ounces	18.000000
17.50	12	ounces	17.500000
16.25	10	ounces	19.500000
15.00	12	ounces	15.000000
75.00	8	ounces	112.500000
45.00	8	ounces	67.500000
14.45	12	ounces	14.450000
17.95	16	ounces	13.462500
22.00	12	ounces	22.000000
15.45	12	ounces	15.450000
18.50	12	ounces	18.500000
17.50	12	ounces	17.500000
19.95	12	ounces	19.950000
16.25	10	ounces	19.500000
17.00	12	ounces	17.000000
14.99	12	ounces	14.990000
13.99	12	ounces	13.990000
70.00	8	ounces	105.000000
45.00	8	ounces	67.500000
21.00	12	ounces	21.000000
18.00	12	ounces	18.000000
18.95	12	ounces	18.950000
19.99	12	ounces	19.990000
25.00	8	ounces	37.500000
19.50	12	ounces	19.500000
18.00	8	ounces	27.000000
24.00	6	ounces	48.000000
16.99	12	ounces	16.990000
45.00	8	ounces	67.500000
54.00	12	ounces	54.000000
39.99	12	ounces	39.990000
39.99	12	ounces	39.990000
16.95	12	ounces	16.950000
19.00	12	ounces	19.000000
9.00	8	ounces	13.500000
15.00	12	ounces	15.000000
16.95	12	ounces	16.950000
13.95	12	ounces	13.950000
26.00	12	ounces	26.000000
19.50	12	ounces	19.500000
21.50	12	ounces	21.500000
44.95	8	ounces	67.425000
10.00	8	ounces	15.000000
17.50	12	ounces	17.500000
15.99	12	ounces	15.990000
16.00	8	ounces	24.000000
13.99	12	ounces	13.990000
17.99	12	ounces	17.990000
17.50	12	ounces	17.500000
17.99	12	ounces	17.990000
20.00	12	ounces	20.000000
16.00	12	ounces	16.000000
55.00	12	ounces	55.000000
19.50	12	ounces	19.500000
17.50	12	ounces	17.500000
25.00	12	ounces	25.000000
23.00	12	ounces	23.000000
21.00	12	ounces	21.000000
44.95	8	ounces	67.425000
15.00	8	ounces	22.500000
18.50	12	ounces	18.500000
15.90	12	ounces	15.900000
17.00	12	ounces	17.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
22.00	12	ounces	22.000000
16.90	12	ounces	16.900000
17.95	12	ounces	17.950000
60.00	8	ounces	90.000000
15.00	8	ounces	22.500000
12.95	6	ounces	25.900000
24.00	12	ounces	24.000000
18.00	12	ounces	18.000000
22.00	12	ounces	22.000000
14.50	12	ounces	14.500000
20.99	12	ounces	20.990000
13.95	12	ounces	13.950000
19.00	12	ounces	19.000000
13.95	12	ounces	13.950000
13.99	12	ounces	13.990000
19.95	8	ounces	29.925000
19.95	8	ounces	29.925000
18.00	12	ounces	18.000000
13.34	4	ounces	40.020000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.99	16	ounces	14.992500
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
12.95	6	ounces	25.900000
23.50	12	ounces	23.500000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
28.85	8	ounces	43.275000
16.00	12	ounces	16.000000
30.00	8	ounces	45.000000
30.00	8	ounces	45.000000
15.00	12	ounces	15.000000
16.50	12	ounces	16.500000
17.99	12	ounces	17.990000
19.49	12	ounces	19.490000
19.00	12	ounces	19.000000
12.00	8	ounces	18.000000
18.00	12	ounces	18.000000
16.95	12	ounces	16.950000
17.00	12	ounces	17.000000
17.00	12	ounces	17.000000
48.95	8	ounces	73.425000
36.95	8	ounces	55.425000
19.00	12	ounces	19.000000
15.95	12	ounces	15.950000
17.00	12	ounces	17.000000
18.00	16	ounces	13.500000
24.00	8	ounces	36.000000
18.00	12	ounces	18.000000
28.99	16	ounces	21.742500
15.00	12	ounces	15.000000
27.00	16	ounces	20.250000
16.95	6	ounces	33.900000
13.00	12	ounces	13.000000
18.50	12	ounces	18.500000
19.00	12	ounces	19.000000
16.20	12	ounces	16.200000
16.20	12	ounces	16.200000
13.99	12	ounces	13.990000
15.95	12	ounces	15.950000
37.50	8	ounces	56.250000
23.00	12	ounces	23.000000
15.95	12	ounces	15.950000
18.00	12	ounces	18.000000
19.00	8	ounces	28.500000
20.99	12	ounces	20.990000
19.95	12	ounces	19.950000
15.90	12	ounces	15.900000
19.95	12	ounces	19.950000
19.95	4	ounces	59.850000
42.00	4	ounces	126.000000
31.00	10	ounces	37.200000
29.00	10	ounces	34.800000
63.95	8	ounces	95.925000
49.00	8	ounces	73.500000
40.00	8	ounces	60.000000
24.00	8	ounces	36.000000
32.00	8	ounces	48.000000
75.00	12	ounces	75.000000
16.00	12	ounces	16.000000
28.00	8	ounces	42.000000
14.00	12	ounces	14.000000
12.95	12	ounces	12.950000
13.56	12	ounces	13.560000
13.99	12	ounces	13.990000
14.95	12	ounces	14.950000
19.50	12	ounces	19.500000
24.95	12	ounces	24.950000
20.00	12	ounces	20.000000
40.00	8	ounces	60.000000
49.50	4	ounces	148.500000
60.00	8	ounces	90.000000
19.00	12	ounces	19.000000
15.90	12	ounces	15.900000
21.20	12	ounces	21.200000
30.00	12	ounces	30.000000
15.00	12	ounces	15.000000
15.00	12	ounces	15.000000
15.00	12	ounces	15.000000
15.00	10	ounces	18.000000
21.00	12	ounces	21.000000
15.90	12	ounces	15.900000
16.00	12	ounces	16.000000
17.25	12	ounces	17.250000
16.95	12	ounces	16.950000
18.95	12	ounces	18.950000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
17.00	12	ounces	17.000000
19.00	12	ounces	19.000000
60.00	10	ounces	72.000000
15.95	12	ounces	15.950000
35.00	8	ounces	52.500000
49.95	4	ounces	149.850000
18.95	8	ounces	28.425000
30.00	8	ounces	45.000000
206.00	8	ounces	309.000000
16.40	12	ounces	16.400000
13.34	4	ounces	40.020000
15.50	12	ounces	15.500000
15.00	12	ounces	15.000000
21.20	12	ounces	21.200000
15.45	12	ounces	15.450000
22.00	12	ounces	22.000000
19.65	8	ounces	29.475000
16.20	12	ounces	16.200000
34.99	16	ounces	26.242500
25.00	12	ounces	25.000000
45.00	16	ounces	33.750000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
23.00	12	ounces	23.000000
19.00	12	ounces	19.000000
25.75	4	ounces	77.250000
60.00	8	ounces	90.000000
40.00	4	ounces	120.000000
50.00	8	ounces	75.000000
19.00	12	ounces	19.000000
17.10	12	ounces	17.100000
17.10	12	ounces	17.100000
12.00	12	ounces	12.000000
12.00	12	ounces	12.000000
18.70	12	ounces	18.700000
18.70	12	ounces	18.700000
39.95	4	ounces	119.850000
13.50	8	ounces	20.250000
15.00	12	ounces	15.000000
20.00	12	ounces	20.000000
15.00	10	ounces	18.000000
14.50	12	ounces	14.500000
39.95	8	ounces*	59.925000
19.95	8	ounces	29.925000
16.25	10	ounces	19.500000
60.00	8	ounces	90.000000
54.95	6	ounces	109.900000
15.00	10	ounces	18.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
40.00	8	ounces	60.000000
23.95	12	ounces	23.950000
22.00	12	ounces	22.000000
39.95	8	ounces	59.925000
39.95	8	ounces	59.925000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
21.95	12	ounces	21.950000
17.95	12	ounces	17.950000
20.95	12	ounces	20.950000
24.50	12	ounces	24.500000
17.20	12	ounces	17.200000
19.20	12	ounces	19.200000
15.75	12	ounces	15.750000
16.90	12	ounces	16.900000
125.00	4	ounces;	375.000000
60.00	8	ounces	90.000000
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30.00	6	ounces	60.000000
24.95	12	ounces	24.950000
24.00	12	ounces	24.000000
14.95	6	ounces	29.900000
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18.95	12	ounces	18.950000
20.00	12	ounces	20.000000
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16.00	12	ounces	16.000000
15.95	12	ounces	15.950000
25.00	12	ounces	25.000000
23.50	12	ounces	23.500000
17.00	12	ounces	17.000000
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15.95	12	ounces	15.950000
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10.99	10.5	ounces	12.560000
14.95	12	ounces	14.950000
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5.00	12	ounces	5.000000
16.50	12	ounces	16.500000
16.50	12	ounces	16.500000
13.00	12	ounces	13.000000
5.00	12	ounces	5.000000
16.00	10	ounces	19.200000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.25	10.5	ounces	14.000000
11.75	10.5	ounces	13.428571
25.00	4	ounces	75.000000
22.00	12	ounces	22.000000
19.00	12	ounces	19.000000
30.00	4	ounces	90.000000
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20.00	12	ounces	20.000000
17.00	12	ounces	17.000000
30.00	8	ounces	45.000000
50.00	4	ounces	150.000000
25.00	12	ounces	25.000000
20.00	8	ounces	30.000000
26.00	12	ounces	26.000000
24.00	8	ounces	36.000000
26.00	12	ounces	26.000000
13.95	4	ounces	41.850000
16.95	12	ounces	16.950000
29.00	12	ounces	29.000000
24.00	12	ounces	24.000000
25.00	8	ounces	37.500000
19.00	12	ounces	19.000000
25.00	8	ounces	37.500000
19.00	12	ounces	19.000000
25.00	12	ounces	25.000000
11.75	10.5	ounces	13.428571
11.75	10.5	ounces	13.428571
12.25	10.5	ounces	14.000000
12.25	10.5	ounces	14.000000
35.00	16	ounces	26.250000
34.95	8	ounces	52.425000
15.99	12	ounces	15.990000
15.99	12	ounces	15.990000
24.95	8	ounces	37.425000
12.99	12	ounces	12.990000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
18.00	8	ounces	27.000000
19.00	12	ounces	19.000000
38.50	12	ounces	38.500000
25.00	12	ounces	25.000000
23.00	12	ounces	23.000000
20.00	12	ounces	20.000000
11.75	10.5	ounces	13.428571
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
16.00	12	ounces	16.000000
16.00	12	ounces	16.000000
22.00	12	ounces	22.000000
45.00	16	ounces	33.750000
19.00	12	ounces	19.000000
17.50	12	ounces	17.500000
17.99	12	ounces	17.990000
25.00	12	ounces	25.000000
17.99	12	ounces	17.990000
15.99	12	ounces	15.990000
19.00	12	ounces	19.000000
36.00	8	ounces	54.000000
49.95	8	ounces	74.925000
24.99	12	ounces	24.990000
18.99	12	ounces	18.990000
18.00	8	ounces	27.000000
25.00	8	ounces	37.500000
21.00	12	ounces	21.000000
17.00	12	ounces	17.000000
22.00	12	ounces	22.000000
14.50	12	ounces	14.500000
17.75	12	ounces	17.750000
22.00	12	ounces	22.000000
69.00	6	ounces	138.000000
40.00	4	ounces	120.000000
40.95	4	ounces	122.850000
40.00	4	ounces	120.000000
25.00	4	ounces	75.000000
18.00	6	ounces	36.000000
18.00	6	ounces	36.000000
42.00	4	ounces	126.000000
26.00	8	ounces	39.000000
20.00	4	ounces	60.000000
21.00	12	ounces	21.000000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
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26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
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24.00	12	ounces	24.000000
21.00	12	ounces	21.000000
30.00	4	ounces	90.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
65.95	8	ounces	98.925000
30.00	12	ounces	30.000000
24.50	12	ounces	24.500000
19.95	7	ounces	34.200000
14.50	12	ounces	14.500000
17.95	16	ounces	13.462500
13.25	12	ounces	13.250000
15.00	12	ounces	15.000000
12.99	12	ounces	12.990000
18.20	24	ounces	9.100000
9.98	12	ounces	9.980000
17.99	12	ounces	17.990000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
17.00	4	ounces	51.000000
19.50	12	ounces	19.500000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
24.00	12	ounces	24.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.95	12	ounces	19.950000
18.00	12	ounces	18.000000
16.95	12	ounces	16.950000
23.50	12	ounces	23.500000
22.00	12	ounces	22.000000
24.00	12	ounces	24.000000
140.00	4	ounces	420.000000
24.00	12	ounces	24.000000
25.00	4	ounces	75.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
19.50	12	ounces	19.500000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
30.00	4	ounces	90.000000
30.00	4	ounces	90.000000
30.00	4	ounces	90.000000
35.00	12	ounces	35.000000
16.00	4	ounces	48.000000
22.00	12	ounces	22.000000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
22.00	16	ounces	16.500000
18.50	12	ounces	18.500000
23.00	12	ounces	23.000000
35.00	8	ounces	52.500000
16.00	5	ounces	38.400000
28.00	12	ounces	28.000000
18.50	16	ounces	13.875000
18.95	12	ounces	18.950000
30.00	12	ounces	30.000000
19.00	6	ounces	38.000000
26.00	14	ounces	22.285714
20.00	12	ounces	20.000000
18.00	12	ounces	18.000000
25.00	6	ounces	50.000000
30.00	12	ounces	30.000000
19.50	12	ounces	19.500000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.99	12	ounces	19.990000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
17.75	12	ounces	17.750000
18.75	12	ounces	18.750000
18.00	12	ounces	18.000000
25.00	8	ounces	37.500000
30.00	12	ounces	30.000000
25.00	12	ounces	25.000000
18.45	12	ounces	18.450000
62.95	8	ounces	94.425000
22.50	10	ounces	27.000000
65.00	8	ounces	97.500000
30.00	4	ounces	90.000000
18.99	12	ounces	18.990000
18.99	12	ounces	18.990000
18.75	12	ounces	18.750000
23.00	12	ounces	23.000000
19.50	12	ounces	19.500000
24.50	5.5	ounces	53.454545
26.50	6	ounces	53.000000
26.50	6	ounces	53.000000
14.00	12	ounces	14.000000
19.50	6	ounces	39.000000
13.99	12	ounces	13.990000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
13.99	12	ounces	13.990000
23.00	12	ounces	23.000000
20.00	12	ounces	20.000000
21.00	12	ounces	21.000000
22.00	12	ounces	22.000000
15.95	12	ounces	15.950000
19.95	12	ounces	19.950000
39.45	12	ounces	39.450000
45.00	12	ounces	45.000000
21.00	12	ounces	21.000000
26.00	12	ounces	26.000000
20.00	12	ounces	20.000000
25.00	12	ounces	25.000000
17.95	12	ounces	17.950000
17.95	12	ounces	17.950000
30.00	4	ounces	90.000000
19.00	12	ounces	19.000000
19.00	6	ounces	38.000000
17.50	12	ounces	17.500000
17.50	12	ounces	17.500000
17.50	12	ounces	17.500000
18.65	12	ounces	18.650000
65.00	8	ounces	97.500000
22.00	12	ounces	22.000000
15.00	12	ounces	15.000000
16.99	12	ounces	16.990000
21.00	12	ounces	21.000000
21.50	16	ounces	16.125000
24.00	12	ounces	24.000000
18.50	16	ounces	13.875000
8.00	4	ounces	24.000000
19.00	12	ounces	19.000000
50.00	4	ounces	150.000000
18.00	8	ounces	27.000000
50.00	4	ounces	150.000000
25.00	8	ounces	37.500000
18.99	16	ounces	14.242500
17.00	12	ounces	17.000000
17.00	12	ounces	17.000000
49.95	8	ounces	74.925000
17.50	12	ounces	17.500000
22.00	12	ounces	22.000000
24.00	8	ounces	36.000000
22.00	12	ounces	22.000000
28.50	12	ounces	28.500000
18.50	12	ounces	18.500000
19.95	12	ounces	19.950000
24.00	12	ounces	24.000000
17.00	12	ounces	17.000000
12.00	12	ounces	12.000000
13.00	12	ounces	13.000000
22.00	12	ounces	22.000000
19.00	12	ounces	19.000000
25.00	14	ounces	21.428571
26.00	12	ounces	26.000000
16.70	12	ounces	16.700000
24.00	12	ounces	24.000000
39.50	12	ounces	39.500000
80.00	8	ounces	120.000000
17.00	12	ounces	17.000000
17.99	12	ounces	17.990000
17.99	12	ounces	17.990000
16.99	12	ounces	16.990000
23.00	8	ounces	34.500000
18.95	12	ounces	18.950000
26.00	10	ounces	31.200000
20.00	12	ounces	20.000000
19.00	12	ounces	19.000000
25.00	12	ounces	25.000000
29.00	4	ounces	87.000000
49.00	10	ounces	58.800000
25.50	12	ounces	25.500000
22.00	12	ounces	22.000000
22.00	12	ounces	22.000000
32.25	8	ounces	48.375000
16.00	12	ounces	16.000000
16.95	16	ounces	12.712500
22.00	12	ounces	22.000000
15.00	8	ounces	22.500000
26.00	12	ounces	26.000000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
21.95	12	ounces	21.950000
16.95	12	ounces	16.950000
47.95	8	ounces	71.925000
27.00	14	ounces	23.142857
19.50	10	ounces	23.400000
27.00	14	ounces	23.142857
30.00	4	ounces	90.000000
25.00	12	ounces	25.000000
25.00	12	ounces	25.000000
8.40	4	ounces	25.200000
18.05	12	ounces	18.050000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
22.00	12	ounces	22.000000
18.00	12	ounces	18.000000
17.50	12	ounces	17.500000
30.00	12	ounces	30.000000
13.00	12	ounces	13.000000
16.50	12	ounces	16.500000
5.00	12	ounces	5.000000
5	12	ounces	5.000000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
17.00	12	ounces	17.000000
10.00	16	ounces	7.500000
10.00	4	ounces	30.000000
45.99	12	ounces	45.990000
18.95	12	ounces	18.950000
23.00	12	ounces	23.000000
22.00	6	ounces	44.000000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
25.99	10	ounces	31.188000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
45.00	12	ounces	45.000000
23.00	10	ounces	27.600000
26.00	12	ounces	26.000000
16.00	12	ounces	16.000000
16.00	12	ounces	16.000000
10.00	12	ounces	10.000000
10.00	12	ounces	10.000000
16.00	12	ounces	16.000000
20.00	12	ounces	20.000000

When converting grams to ounces, I used the following formula: \[ Price \ per \ 12 \ ounces \ of \ coffee = \frac{price}{amount \ in \ grams} \times \frac{1 \ gram}{0.035274 \ ounces} \times 12 \ ounces \] Data was transformed below to make the characters numeric.

#converting grams to ounces then calculating the total prices per 12 oz
coffee_price_per_ounces_from_grams <-coffee_price_grams %>% 
  mutate(
    price_per_12oz = ((12*as.numeric(gsub(",","", price_dollars)))/(as.numeric(amount)*0.035274)) #gsub is used because some of the prices included commas. 
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `price_per_12oz = (...)`.
## Caused by warning:
## ! NAs introduced by coercion

coffee_price_per_ounces_from_grams %>%
  summarize(price_dollars,amount,unit, price_per_12oz) %>% gt()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.

price_dollars	amount	unit	price_per_12oz
14.00	250	grams	19.050859
35.00	100	grams	119.067869
15.00	250	grams	20.411635
15.00	250	grams	20.411635
35.00	100	grams	119.067869
35.00	100	grams	119.067869
35.00	100	grams	119.067869
6.00	250	grams	8.164654
35.00	100	grams	119.067869
20.00	250	grams	27.215513
20.00	250	grams	27.215513
15.16	500	grams	10.314679
22.15	500	grams	15.070590
16.98	45	grams	128.366502
6.00	250	grams	8.164654
6.00	250	grams	8.164654
37.00	120	grams	104.893122
15.00	250	grams	20.411635
30.00	250	grams	40.823269
15.00	250	grams	20.411635
15.00	250	grams	20.411635
29.00	150	grams	65.770823
100	200	grams	170.096955
28.00	500	grams	19.050859
28.00	500	grams	19.050859
7.12	120	grams	20.184839
19.00	200	grams	32.318422
25.00	150	grams	56.698985
NT 700	227	grams	NA
15.00	250	grams	20.411635
15.00	250	grams	20.411635
100.00	21	grams	1619.971003
100	18	grams	1889.966170
60.00	100	grams	204.116346
15.00	500	grams	10.205817
8.00	100	grams	27.215513
35,000 LAK	250	grams	NA
12.00	500	grams	8.164654
12.00	500	grams	8.164654
17.88	227	grams	26.795890
16.88	227	grams	25.297239
19.99	340	grams	20.001401
17.00	150	grams	38.555310
60.00	150	grams	136.077564
10.00	250	grams	13.607756
10.00	250	grams	13.607756
25.00	150	grams	56.698985
14.00	225	grams	21.167621
28.80	500	grams	19.595169
16.00	230	grams	23.665663
60.00	150	grams	136.077564
30.00	300	grams	34.019391
21.00	113	grams	63.221877
21.00	113	grams	63.221877
45.00	175	grams	87.478434
28.00	225	grams	42.335242
16.50	250	grams	22.452798
35.00	300	grams	39.689290
16.00	250	grams	21.772410
15.00	300	grams	17.009696
33.00	200	grams	56.131995
60.00	150	grams	136.077564
14.00	230	grams	20.707455
14.00	250	grams	19.050859
13.00	250	grams	17.690083
13.00	250	grams	17.690083
13.00	250	grams	17.690083
12.50	100	grams	42.524239
25.00	250	grams	34.019391
16.00	150	grams	36.287350

Now that the prices have been standardized, I am reconnecting the data sets.

#joining the two datasets with rbind(ounces and grams datasets)
coffee_data_standardized <- 
  rbind(coffee_price_per_ounces, coffee_price_per_ounces_from_grams)

glimpse(coffee_data_standardized)

## Rows: 1,593
## Columns: 28
## $ slug           <chr> "https://www.coffeereview.com/review/ethiopia-shakiso-m…
## $ all_text       <chr> " \n\n\n92\n\n\nRevel Coffee\nEthiopia Shakiso Mormora\…
## $ rating         <int> 92, 92, 94, 93, 93, 93, 93, 95, 90, 87, 93, 97, 92, 93,…
## $ roaster        <chr> "Revel Coffee", "Roast House", "Big Creek Coffee Roaste…
## $ name           <chr> "Ethiopia Shakiso Mormora", "Ethiopia Suke Quto", "Ethi…
## $ location       <chr> "Billings, Montana", "Spokane, Washington", "Hamilton, …
## $ origin         <chr> "Guji Zone, southern Ethiopia", "Guji Zone, Oromia Regi…
## $ roast          <chr> "Medium-Light", "Medium-Light", "Medium", "Light", "Med…
## $ est_price      <chr> "$16.00/12 ounces", "$19.00/16 ounces", "$16.50/12 ounc…
## $ currency       <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", "…
## $ prices         <chr> "16.00/12 ounces", "19.00/16 ounces", "16.50/12 ounces"…
## $ price_dollars  <chr> "16.00", "19.00", "16.50", "17.49", "17.99", "17.50", "…
## $ unit_size      <chr> "12 ounces", "16 ounces", "12 ounces", "12 ounces", "16…
## $ amount         <chr> "12", "16", "12", "12", "16", "12", "12", "12", "12", "…
## $ unit           <chr> "ounces", "ounces", "ounces", "ounces", "ounces", "ounc…
## $ review_date    <chr> "November 2017", "November 2017", "November 2017", "Nov…
## $ agtron         <chr> "54/70", "53/79", "48/70", "66/92", "54/75", "58/80", "…
## $ aroma          <int> 9, 8, 9, 9, 9, 9, 9, 9, 8, 7, 9, 10, 9, 9, 8, 8, 8, 9, …
## $ acid           <int> 8, 8, 9, 8, 8, 8, 9, 9, 8, 7, 8, 9, 8, 8, 8, 7, 9, 7, 8…
## $ body           <int> 8, 9, 9, 9, 9, 9, 8, 9, 7, 8, 9, 9, 8, 8, 9, 8, 9, 9, 8…
## $ flavor         <int> 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 9, 10, 9, 9, 9, 8, 9, 9, …
## $ aftertaste     <int> 8, 8, 8, 8, 8, 8, 8, 9, 8, 7, 8, 9, 8, 9, 8, 8, 8, 8, 8…
## $ with_milk      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ desc_1         <chr> "Crisply sweet, cocoa-toned. Lemon blossom, roasted cac…
## $ desc_2         <chr> "This coffee tied for the third-highest rating in a tas…
## $ desc_3         <chr> "A gently spice-toned, floral- driven wet-processed Eth…
## $ rating_ranges  <chr> "90-94", "90-94", "90-94", "90-94", "90-94", "90-94", "…
## $ price_per_12oz <dbl> 16.0000, 14.2500, 16.5000, 17.4900, 13.4925, 17.5000, 1…

You can see in the summary below that the datasets now contain both grams and ounces in the units with a columns labeled ‘price_per_12oz’.

coffee_data_standardized %>%
  summarize(price_dollars,amount,unit, price_per_12oz) %>% gt()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.

price_dollars	amount	unit	price_per_12oz
16.00	12	ounces	16.000000
19.00	16	ounces	14.250000
16.50	12	ounces	16.500000
17.49	12	ounces	17.490000
17.99	16	ounces	13.492500
17.50	12	ounces	17.500000
18.00	12	ounces	18.000000
24.50	12	ounces	24.500000
19.50	12	ounces	19.500000
15.00	12	ounces	15.000000
17.95	12	ounces	17.950000
74.95	8	ounces	112.425000
18.00	12	ounces	18.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
11.90	12	ounces	11.900000
19.95	12	ounces	19.950000
15.00	12	ounces	15.000000
14.99	12	ounces	14.990000
14.99	12	ounces	14.990000
19.00	12	ounces	19.000000
16.00	12	ounces	16.000000
16.95	12	ounces	16.950000
16.95	12	ounces	16.950000
25.00	12	ounces	25.000000
14.99	12	ounces	14.990000
17.00	12	ounces	17.000000
22.00	8	ounces	33.000000
22.00	12	ounces	22.000000
15.00	12	ounces	15.000000
20.00	12	ounces	20.000000
28.00	8	ounces	42.000000
55.00	12	ounces	55.000000
25.00	12	ounces	25.000000
37.00	4	ounces	111.000000
19.25	12	ounces	19.250000
49.99	16	ounces	37.492500
44.95	8	ounces	67.425000
19.95	12	ounces	19.950000
16.95	12	ounces	16.950000
29.48	8	ounces	44.220000
19.95	8	ounces	29.925000
12.95	12	ounces	12.950000
16.95	12	ounces	16.950000
18.95	12	ounces	18.950000
15.75	12	ounces	15.750000
60.00	8	ounces	90.000000
40.00	8	ounces	60.000000
27.50	8	ounces	41.250000
30.00	8	ounces	45.000000
17.00	12	ounces	17.000000
17.95	12	ounces	17.950000
20.00	12	ounces	20.000000
25.00	12	ounces	25.000000
24.00	12	ounces	24.000000
13.99	12	ounces	13.990000
17.25	12	ounces	17.250000
17.25	12	ounces	17.250000
18.75	12	ounces	18.750000
206.00	8	ounces	309.000000
206.00	8	ounces	309.000000
29.48	8	ounces	44.220000
16.99	12	ounces	16.990000
17.00	12	ounces	17.000000
51.00	8	ounces	76.500000
54.00	8	ounces	81.000000
19.00	12	ounces	19.000000
20.50	12	ounces	20.500000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
24.00	12	ounces	24.000000
15.50	12	ounces	15.500000
17.50	12	ounces	17.500000
16.49	12	ounces	16.490000
23.00	12	ounces	23.000000
13.75	12	ounces	13.750000
21.00	16	ounces	15.750000
14.00	12	ounces	14.000000
17.00	12	ounces	17.000000
22.00	12	ounces	22.000000
32.00	8	ounces	48.000000
15.75	12	ounces	15.750000
15.75	12	ounces	15.750000
16.95	12	ounces	16.950000
9.94	4	ounces	29.820000
9.94	4	ounces	29.820000
9.94	4	ounces	29.820000
25.00	12	ounces	25.000000
12.00	12	ounces	12.000000
12.00	12	ounces	12.000000
15.00	12	ounces	15.000000
14.00	12	ounces	14.000000
11.35	12	ounces	11.350000
13.99	12	ounces	13.990000
14.95	12	ounces	14.950000
14.50	12	ounces	14.500000
17.60	12	ounces	17.600000
44.95	8	ounces	67.425000
17.95	12	ounces	17.950000
16.49	12	ounces	16.490000
13.95	8	ounces	20.925000
17.00	12	ounces	17.000000
20.00	12	ounces	20.000000
30.50	4	ounces	91.500000
17.00	12	ounces	17.000000
21.00	12	ounces	21.000000
51.00	8	ounces	76.500000
37.50	8	ounces	56.250000
18.75	12	ounces	18.750000
15.75	12	ounces	15.750000
20.00	12	ounces	20.000000
15.00	12	ounces	15.000000
24.00	12	ounces	24.000000
22.50	12	ounces	22.500000
15.50	12	ounces	15.500000
16.95	12	ounces	16.950000
12.00	12	ounces	12.000000
16.49	12	ounces	16.490000
75.00	8	ounces	112.500000
75.00	8	ounces	112.500000
145.00	8	ounces	217.500000
19.50	12	ounces	19.500000
12.60	12	ounces	12.600000
17.95	12	ounces	17.950000
45.95	8	ounces	68.925000
18.00	12	ounces	18.000000
28.00	8	ounces	42.000000
17.00	12	ounces	17.000000
18.00	12	ounces	18.000000
19.95	12	ounces	19.950000
24.95	12	ounces	24.950000
17.60	12	ounces	17.600000
16.00	12	ounces	16.000000
35.00	8	ounces	52.500000
13.99	12	ounces	13.990000
16.50	12	ounces	16.500000
15.50	12	ounces	15.500000
16.00	10	ounces	19.200000
15.00	10	ounces	18.000000
18.00	12	ounces	18.000000
15.50	12	ounces	15.500000
16.99	16	ounces	12.742500
19.98	16	ounces	14.985000
12.50	12	ounces	12.500000
15.00	12	ounces	15.000000
15.50	12	ounces	15.500000
15.99	16	ounces	11.992500
18.00	12	ounces	18.000000
20.50	16	ounces	15.375000
19.50	12	ounces	19.500000
19.20	12	ounces	19.200000
15.00	12	ounces	15.000000
18.99	12	ounces	18.990000
18.50	12	ounces	18.500000
18.49	12	ounces	18.490000
13.99	12	ounces	13.990000
18.00	12	ounces	18.000000
17.75	12	ounces	17.750000
18.00	12	ounces	18.000000
35.00	12	ounces	35.000000
16.95	12	ounces	16.950000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
19.00	12	ounces	19.000000
13.95	12	ounces	13.950000
13.95	12	ounces	13.950000
21.00	12	ounces	21.000000
20.50	12	ounces	20.500000
22.50	12	ounces	22.500000
18.95	12	ounces	18.950000
18.00	12	ounces	18.000000
11.95	12	ounces	11.950000
19.50	12	ounces	19.500000
24.00	12	ounces	24.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
13.34	4	ounces	40.020000
13.34	4	ounces	40.020000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
14.99	12	ounces	14.990000
8.99	8	ounces	13.485000
9.99	7	ounces	17.125714
17.00	12	ounces	17.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
15.00	12	ounces	15.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
17.50	12	ounces	17.500000
13.75	12	ounces	13.750000
17.50	12	ounces	17.500000
20.50	12	ounces	20.500000
21.00	12	ounces	21.000000
18.75	12	ounces	18.750000
18.75	12	ounces	18.750000
15.75	12	ounces	15.750000
14.75	12	ounces	14.750000
19.95	12	ounces	19.950000
13.34	4	ounces	40.020000
19.50	12	ounces	19.500000
19.50	12	ounces	19.500000
19.50	12	ounces	19.500000
75.00	8	ounces	112.500000
16.75	12	ounces	16.750000
17.00	12	ounces	17.000000
13.50	12	ounces	13.500000
16.50	12	ounces	16.500000
11.95	12	ounces	11.950000
20.00	12	ounces	20.000000
21.00	12	ounces	21.000000
16.00	12	ounces	16.000000
16.95	12	ounces	16.950000
12.00	12	ounces	12.000000
15.00	12	ounces	15.000000
16.95	12	ounces	16.950000
13.50	10	ounces	16.200000
19.95	12	ounces	19.950000
12.25	12	ounces	12.250000
18.00	12	ounces	18.000000
17.50	12	ounces	17.500000
16.25	10	ounces	19.500000
15.00	12	ounces	15.000000
75.00	8	ounces	112.500000
45.00	8	ounces	67.500000
14.45	12	ounces	14.450000
17.95	16	ounces	13.462500
22.00	12	ounces	22.000000
15.45	12	ounces	15.450000
18.50	12	ounces	18.500000
17.50	12	ounces	17.500000
19.95	12	ounces	19.950000
16.25	10	ounces	19.500000
17.00	12	ounces	17.000000
14.99	12	ounces	14.990000
13.99	12	ounces	13.990000
70.00	8	ounces	105.000000
45.00	8	ounces	67.500000
21.00	12	ounces	21.000000
18.00	12	ounces	18.000000
18.95	12	ounces	18.950000
19.99	12	ounces	19.990000
25.00	8	ounces	37.500000
19.50	12	ounces	19.500000
18.00	8	ounces	27.000000
24.00	6	ounces	48.000000
16.99	12	ounces	16.990000
45.00	8	ounces	67.500000
54.00	12	ounces	54.000000
39.99	12	ounces	39.990000
39.99	12	ounces	39.990000
16.95	12	ounces	16.950000
19.00	12	ounces	19.000000
9.00	8	ounces	13.500000
15.00	12	ounces	15.000000
16.95	12	ounces	16.950000
13.95	12	ounces	13.950000
26.00	12	ounces	26.000000
19.50	12	ounces	19.500000
21.50	12	ounces	21.500000
44.95	8	ounces	67.425000
10.00	8	ounces	15.000000
17.50	12	ounces	17.500000
15.99	12	ounces	15.990000
16.00	8	ounces	24.000000
13.99	12	ounces	13.990000
17.99	12	ounces	17.990000
17.50	12	ounces	17.500000
17.99	12	ounces	17.990000
20.00	12	ounces	20.000000
16.00	12	ounces	16.000000
55.00	12	ounces	55.000000
19.50	12	ounces	19.500000
17.50	12	ounces	17.500000
25.00	12	ounces	25.000000
23.00	12	ounces	23.000000
21.00	12	ounces	21.000000
44.95	8	ounces	67.425000
15.00	8	ounces	22.500000
18.50	12	ounces	18.500000
15.90	12	ounces	15.900000
17.00	12	ounces	17.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
22.00	12	ounces	22.000000
16.90	12	ounces	16.900000
17.95	12	ounces	17.950000
60.00	8	ounces	90.000000
15.00	8	ounces	22.500000
12.95	6	ounces	25.900000
24.00	12	ounces	24.000000
18.00	12	ounces	18.000000
22.00	12	ounces	22.000000
14.50	12	ounces	14.500000
20.99	12	ounces	20.990000
13.95	12	ounces	13.950000
19.00	12	ounces	19.000000
13.95	12	ounces	13.950000
13.99	12	ounces	13.990000
19.95	8	ounces	29.925000
19.95	8	ounces	29.925000
18.00	12	ounces	18.000000
13.34	4	ounces	40.020000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.99	16	ounces	14.992500
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
12.95	6	ounces	25.900000
23.50	12	ounces	23.500000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
28.85	8	ounces	43.275000
16.00	12	ounces	16.000000
30.00	8	ounces	45.000000
30.00	8	ounces	45.000000
15.00	12	ounces	15.000000
16.50	12	ounces	16.500000
17.99	12	ounces	17.990000
19.49	12	ounces	19.490000
19.00	12	ounces	19.000000
12.00	8	ounces	18.000000
18.00	12	ounces	18.000000
16.95	12	ounces	16.950000
17.00	12	ounces	17.000000
17.00	12	ounces	17.000000
48.95	8	ounces	73.425000
36.95	8	ounces	55.425000
19.00	12	ounces	19.000000
15.95	12	ounces	15.950000
17.00	12	ounces	17.000000
18.00	16	ounces	13.500000
24.00	8	ounces	36.000000
18.00	12	ounces	18.000000
28.99	16	ounces	21.742500
15.00	12	ounces	15.000000
27.00	16	ounces	20.250000
16.95	6	ounces	33.900000
13.00	12	ounces	13.000000
18.50	12	ounces	18.500000
19.00	12	ounces	19.000000
16.20	12	ounces	16.200000
16.20	12	ounces	16.200000
13.99	12	ounces	13.990000
15.95	12	ounces	15.950000
37.50	8	ounces	56.250000
23.00	12	ounces	23.000000
15.95	12	ounces	15.950000
18.00	12	ounces	18.000000
19.00	8	ounces	28.500000
20.99	12	ounces	20.990000
19.95	12	ounces	19.950000
15.90	12	ounces	15.900000
19.95	12	ounces	19.950000
19.95	4	ounces	59.850000
42.00	4	ounces	126.000000
31.00	10	ounces	37.200000
29.00	10	ounces	34.800000
63.95	8	ounces	95.925000
49.00	8	ounces	73.500000
40.00	8	ounces	60.000000
24.00	8	ounces	36.000000
32.00	8	ounces	48.000000
75.00	12	ounces	75.000000
16.00	12	ounces	16.000000
28.00	8	ounces	42.000000
14.00	12	ounces	14.000000
12.95	12	ounces	12.950000
13.56	12	ounces	13.560000
13.99	12	ounces	13.990000
14.95	12	ounces	14.950000
19.50	12	ounces	19.500000
24.95	12	ounces	24.950000
20.00	12	ounces	20.000000
40.00	8	ounces	60.000000
49.50	4	ounces	148.500000
60.00	8	ounces	90.000000
19.00	12	ounces	19.000000
15.90	12	ounces	15.900000
21.20	12	ounces	21.200000
30.00	12	ounces	30.000000
15.00	12	ounces	15.000000
15.00	12	ounces	15.000000
15.00	12	ounces	15.000000
15.00	10	ounces	18.000000
21.00	12	ounces	21.000000
15.90	12	ounces	15.900000
16.00	12	ounces	16.000000
17.25	12	ounces	17.250000
16.95	12	ounces	16.950000
18.95	12	ounces	18.950000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
17.00	12	ounces	17.000000
19.00	12	ounces	19.000000
60.00	10	ounces	72.000000
15.95	12	ounces	15.950000
35.00	8	ounces	52.500000
49.95	4	ounces	149.850000
18.95	8	ounces	28.425000
30.00	8	ounces	45.000000
206.00	8	ounces	309.000000
16.40	12	ounces	16.400000
13.34	4	ounces	40.020000
15.50	12	ounces	15.500000
15.00	12	ounces	15.000000
21.20	12	ounces	21.200000
15.45	12	ounces	15.450000
22.00	12	ounces	22.000000
19.65	8	ounces	29.475000
16.20	12	ounces	16.200000
34.99	16	ounces	26.242500
25.00	12	ounces	25.000000
45.00	16	ounces	33.750000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
23.00	12	ounces	23.000000
19.00	12	ounces	19.000000
25.75	4	ounces	77.250000
60.00	8	ounces	90.000000
40.00	4	ounces	120.000000
50.00	8	ounces	75.000000
19.00	12	ounces	19.000000
17.10	12	ounces	17.100000
17.10	12	ounces	17.100000
12.00	12	ounces	12.000000
12.00	12	ounces	12.000000
18.70	12	ounces	18.700000
18.70	12	ounces	18.700000
39.95	4	ounces	119.850000
13.50	8	ounces	20.250000
15.00	12	ounces	15.000000
20.00	12	ounces	20.000000
15.00	10	ounces	18.000000
14.50	12	ounces	14.500000
39.95	8	ounces*	59.925000
19.95	8	ounces	29.925000
16.25	10	ounces	19.500000
60.00	8	ounces	90.000000
54.95	6	ounces	109.900000
15.00	10	ounces	18.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
40.00	8	ounces	60.000000
23.95	12	ounces	23.950000
22.00	12	ounces	22.000000
39.95	8	ounces	59.925000
39.95	8	ounces	59.925000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
21.95	12	ounces	21.950000
17.95	12	ounces	17.950000
20.95	12	ounces	20.950000
24.50	12	ounces	24.500000
17.20	12	ounces	17.200000
19.20	12	ounces	19.200000
15.75	12	ounces	15.750000
16.90	12	ounces	16.900000
125.00	4	ounces;	375.000000
60.00	8	ounces	90.000000
20.00	12	ounces	20.000000
19.95	12	ounces	19.950000
21.00	12	ounces	21.000000
19.95	12	ounces	19.950000
21.00	12	ounces	21.000000
18.00	12	ounces	18.000000
16.95	12	ounces	16.950000
16.95	12	ounces	16.950000
69.95	6	ounces	139.900000
20.99	12	ounces	20.990000
19.95	4	ounces	59.850000
15.95	12	ounces	15.950000
16.80	12	ounces	16.800000
16.20	12	ounces	16.200000
17.99	12	ounces	17.990000
17.99	12	ounces	17.990000
17.99	12	ounces	17.990000
18.75	12	ounces	18.750000
18.99	12	ounces	18.990000
15.00	12	ounces	15.000000
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
15.25	8	ounces	22.875000
18.00	16	ounces	13.500000
19.95	12	ounces	19.950000
18.00	12	ounces	18.000000
19.95	8	ounces	29.925000
14.25	12	ounces	14.250000
16.00	14	ounces	13.714286
19.95	4	ounces	59.850000
19.75	12	ounces	19.750000
15.00	8	ounces	22.500000
13.99	12	ounces	13.990000
10.95	12	ounces	10.950000
14.99	12	ounces	14.990000
13.99	12	ounces	13.990000
14.99	10	ounces	17.988000
13.99	12	ounces	13.990000
11.29	12	ounces	11.290000
6.99	16	ounces	5.242500
4.99	12	ounces	4.990000
9.29	22	ounces	5.067273
8.99	32	ounces	3.371250
10.49	12	ounces	10.490000
62.95	8	ounces	94.425000
19.00	12	ounces	19.000000
22.99	12	ounces	22.990000
17.90	12	ounces	17.900000
12.00	12	ounces	12.000000
17.50	12	ounces	17.500000
18.00	12	ounces	18.000000
22.50	12	ounces	22.500000
19.50	12	ounces	19.500000
19.75	12	ounces	19.750000
19.00	12	ounces	19.000000
12.00	12	ounces	12.000000
18.40	12	ounces	18.400000
30.00	8	ounces	45.000000
17.75	12	ounces	17.750000
16.20	12	ounces	16.200000
14.99	12	ounces	14.990000
17.99	12	ounces	17.990000
16.00	12	ounces;	16.000000
17.90	12	ounces	17.900000
17.90	12	ounces	17.900000
24.00	8	ounces	36.000000
18.00	16	ounces	13.500000
20.00	12	ounces	20.000000
14.00	12	ounces	14.000000
12.95	16	ounces	9.712500
34.95	7	ounces	59.914286
34.95	7	ounces	59.914286
20.99	12	ounces	20.990000
19.95	12	ounces	19.950000
15.00	12	ounces	15.000000
17.00	12	ounces	17.000000
17.00	12	ounces	17.000000
33.00	12	ounces	33.000000
15.00	8	ounces	22.500000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
14.00	12	ounces	14.000000
18.00	12	ounces	18.000000
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16.00	12	ounces	16.000000
16.00	12	ounces	16.000000
22.00	12	ounces	22.000000
25.00	12	ounces	25.000000
95.00	4	ounces	285.000000
60.00	8	ounces	90.000000
120.00	6	ounces	240.000000
40.00	4	ounces	120.000000
18.50	12	ounces	18.500000
89.95	8	ounces	134.925000
49.95	8	ounces	74.925000
15.99	12	ounces	15.990000
23.50	12	ounces	23.500000
15.99	12	ounces	15.990000
20.00	12	ounces	20.000000
12.99	12	ounces	12.990000
28.00	12	ounces	28.000000
21.00	12	ounces	21.000000
49.95	8	ounces	74.925000
150.00	4	ounces	450.000000
145.00	4	ounces	435.000000
50.00	6	ounces	100.000000
19.00	4	ounces	57.000000
22.00	12	ounces	22.000000
21.99	12	ounces	21.990000
20.99	12	ounces	20.990000
75.00	12	ounces	75.000000
20.00	6	ounces	40.000000
25.00	12	ounces	25.000000
62.95	8	ounces	94.425000
17.00	12	ounces	17.000000
18.50	16	ounces	13.875000
20.00	12	ounces	20.000000
15.00	12	ounces	15.000000
13.00	12	ounces	13.000000
13.00	12	ounces	13.000000
13.00	12	ounces	13.000000
10.00	12	ounces	10.000000
13.00	12	ounces	13.000000
16.50	12	ounces	16.500000
5.00	12	ounces	5.000000
16.50	12	ounces	16.500000
16.50	12	ounces	16.500000
13.00	12	ounces	13.000000
5.00	12	ounces	5.000000
16.00	10	ounces	19.200000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.00	8	ounces	18.000000
12.25	10.5	ounces	14.000000
11.75	10.5	ounces	13.428571
25.00	4	ounces	75.000000
22.00	12	ounces	22.000000
19.00	12	ounces	19.000000
30.00	4	ounces	90.000000
20.50	12	ounces	20.500000
20.00	12	ounces	20.000000
17.00	12	ounces	17.000000
30.00	8	ounces	45.000000
50.00	4	ounces	150.000000
25.00	12	ounces	25.000000
20.00	8	ounces	30.000000
26.00	12	ounces	26.000000
24.00	8	ounces	36.000000
26.00	12	ounces	26.000000
13.95	4	ounces	41.850000
16.95	12	ounces	16.950000
29.00	12	ounces	29.000000
24.00	12	ounces	24.000000
25.00	8	ounces	37.500000
19.00	12	ounces	19.000000
25.00	8	ounces	37.500000
19.00	12	ounces	19.000000
25.00	12	ounces	25.000000
11.75	10.5	ounces	13.428571
11.75	10.5	ounces	13.428571
12.25	10.5	ounces	14.000000
12.25	10.5	ounces	14.000000
35.00	16	ounces	26.250000
34.95	8	ounces	52.425000
15.99	12	ounces	15.990000
15.99	12	ounces	15.990000
24.95	8	ounces	37.425000
12.99	12	ounces	12.990000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
18.00	8	ounces	27.000000
19.00	12	ounces	19.000000
38.50	12	ounces	38.500000
25.00	12	ounces	25.000000
23.00	12	ounces	23.000000
20.00	12	ounces	20.000000
11.75	10.5	ounces	13.428571
18.00	12	ounces	18.000000
16.00	12	ounces	16.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
16.00	12	ounces	16.000000
16.00	12	ounces	16.000000
22.00	12	ounces	22.000000
45.00	16	ounces	33.750000
19.00	12	ounces	19.000000
17.50	12	ounces	17.500000
17.99	12	ounces	17.990000
25.00	12	ounces	25.000000
17.99	12	ounces	17.990000
15.99	12	ounces	15.990000
19.00	12	ounces	19.000000
36.00	8	ounces	54.000000
49.95	8	ounces	74.925000
24.99	12	ounces	24.990000
18.99	12	ounces	18.990000
18.00	8	ounces	27.000000
25.00	8	ounces	37.500000
21.00	12	ounces	21.000000
17.00	12	ounces	17.000000
22.00	12	ounces	22.000000
14.50	12	ounces	14.500000
17.75	12	ounces	17.750000
22.00	12	ounces	22.000000
69.00	6	ounces	138.000000
40.00	4	ounces	120.000000
40.95	4	ounces	122.850000
40.00	4	ounces	120.000000
25.00	4	ounces	75.000000
18.00	6	ounces	36.000000
18.00	6	ounces	36.000000
42.00	4	ounces	126.000000
26.00	8	ounces	39.000000
20.00	4	ounces	60.000000
21.00	12	ounces	21.000000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
48.00	12	ounces	48.000000
20.00	12	ounces	20.000000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
18.00	8	ounces	27.000000
24.00	12	ounces	24.000000
21.00	12	ounces	21.000000
30.00	4	ounces	90.000000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
65.95	8	ounces	98.925000
30.00	12	ounces	30.000000
24.50	12	ounces	24.500000
19.95	7	ounces	34.200000
14.50	12	ounces	14.500000
17.95	16	ounces	13.462500
13.25	12	ounces	13.250000
15.00	12	ounces	15.000000
12.99	12	ounces	12.990000
18.20	24	ounces	9.100000
9.98	12	ounces	9.980000
17.99	12	ounces	17.990000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
17.00	4	ounces	51.000000
19.50	12	ounces	19.500000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
24.00	12	ounces	24.000000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.95	12	ounces	19.950000
18.00	12	ounces	18.000000
16.95	12	ounces	16.950000
23.50	12	ounces	23.500000
22.00	12	ounces	22.000000
24.00	12	ounces	24.000000
140.00	4	ounces	420.000000
24.00	12	ounces	24.000000
25.00	4	ounces	75.000000
19.00	12	ounces	19.000000
20.00	12	ounces	20.000000
19.50	12	ounces	19.500000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
17.00	12	ounces	17.000000
30.00	4	ounces	90.000000
30.00	4	ounces	90.000000
30.00	4	ounces	90.000000
35.00	12	ounces	35.000000
16.00	4	ounces	48.000000
22.00	12	ounces	22.000000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
22.00	16	ounces	16.500000
18.50	12	ounces	18.500000
23.00	12	ounces	23.000000
35.00	8	ounces	52.500000
16.00	5	ounces	38.400000
28.00	12	ounces	28.000000
18.50	16	ounces	13.875000
18.95	12	ounces	18.950000
30.00	12	ounces	30.000000
19.00	6	ounces	38.000000
26.00	14	ounces	22.285714
20.00	12	ounces	20.000000
18.00	12	ounces	18.000000
25.00	6	ounces	50.000000
30.00	12	ounces	30.000000
19.50	12	ounces	19.500000
18.00	12	ounces	18.000000
18.00	12	ounces	18.000000
19.99	12	ounces	19.990000
18.00	12	ounces	18.000000
23.00	12	ounces	23.000000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
17.75	12	ounces	17.750000
18.75	12	ounces	18.750000
18.00	12	ounces	18.000000
25.00	8	ounces	37.500000
30.00	12	ounces	30.000000
25.00	12	ounces	25.000000
18.45	12	ounces	18.450000
62.95	8	ounces	94.425000
22.50	10	ounces	27.000000
65.00	8	ounces	97.500000
30.00	4	ounces	90.000000
18.99	12	ounces	18.990000
18.99	12	ounces	18.990000
18.75	12	ounces	18.750000
23.00	12	ounces	23.000000
19.50	12	ounces	19.500000
24.50	5.5	ounces	53.454545
26.50	6	ounces	53.000000
26.50	6	ounces	53.000000
14.00	12	ounces	14.000000
19.50	6	ounces	39.000000
13.99	12	ounces	13.990000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
14.00	12	ounces	14.000000
13.99	12	ounces	13.990000
23.00	12	ounces	23.000000
20.00	12	ounces	20.000000
21.00	12	ounces	21.000000
22.00	12	ounces	22.000000
15.95	12	ounces	15.950000
19.95	12	ounces	19.950000
39.45	12	ounces	39.450000
45.00	12	ounces	45.000000
21.00	12	ounces	21.000000
26.00	12	ounces	26.000000
20.00	12	ounces	20.000000
25.00	12	ounces	25.000000
17.95	12	ounces	17.950000
17.95	12	ounces	17.950000
30.00	4	ounces	90.000000
19.00	12	ounces	19.000000
19.00	6	ounces	38.000000
17.50	12	ounces	17.500000
17.50	12	ounces	17.500000
17.50	12	ounces	17.500000
18.65	12	ounces	18.650000
65.00	8	ounces	97.500000
22.00	12	ounces	22.000000
15.00	12	ounces	15.000000
16.99	12	ounces	16.990000
21.00	12	ounces	21.000000
21.50	16	ounces	16.125000
24.00	12	ounces	24.000000
18.50	16	ounces	13.875000
8.00	4	ounces	24.000000
19.00	12	ounces	19.000000
50.00	4	ounces	150.000000
18.00	8	ounces	27.000000
50.00	4	ounces	150.000000
25.00	8	ounces	37.500000
18.99	16	ounces	14.242500
17.00	12	ounces	17.000000
17.00	12	ounces	17.000000
49.95	8	ounces	74.925000
17.50	12	ounces	17.500000
22.00	12	ounces	22.000000
24.00	8	ounces	36.000000
22.00	12	ounces	22.000000
28.50	12	ounces	28.500000
18.50	12	ounces	18.500000
19.95	12	ounces	19.950000
24.00	12	ounces	24.000000
17.00	12	ounces	17.000000
12.00	12	ounces	12.000000
13.00	12	ounces	13.000000
22.00	12	ounces	22.000000
19.00	12	ounces	19.000000
25.00	14	ounces	21.428571
26.00	12	ounces	26.000000
16.70	12	ounces	16.700000
24.00	12	ounces	24.000000
39.50	12	ounces	39.500000
80.00	8	ounces	120.000000
17.00	12	ounces	17.000000
17.99	12	ounces	17.990000
17.99	12	ounces	17.990000
16.99	12	ounces	16.990000
23.00	8	ounces	34.500000
18.95	12	ounces	18.950000
26.00	10	ounces	31.200000
20.00	12	ounces	20.000000
19.00	12	ounces	19.000000
25.00	12	ounces	25.000000
29.00	4	ounces	87.000000
49.00	10	ounces	58.800000
25.50	12	ounces	25.500000
22.00	12	ounces	22.000000
22.00	12	ounces	22.000000
32.25	8	ounces	48.375000
16.00	12	ounces	16.000000
16.95	16	ounces	12.712500
22.00	12	ounces	22.000000
15.00	8	ounces	22.500000
26.00	12	ounces	26.000000
26.00	12	ounces	26.000000
19.00	12	ounces	19.000000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
21.95	12	ounces	21.950000
16.95	12	ounces	16.950000
47.95	8	ounces	71.925000
27.00	14	ounces	23.142857
19.50	10	ounces	23.400000
27.00	14	ounces	23.142857
30.00	4	ounces	90.000000
25.00	12	ounces	25.000000
25.00	12	ounces	25.000000
8.40	4	ounces	25.200000
18.05	12	ounces	18.050000
23.50	12	ounces	23.500000
18.00	12	ounces	18.000000
20.00	12	ounces	20.000000
22.00	12	ounces	22.000000
18.00	12	ounces	18.000000
17.50	12	ounces	17.500000
30.00	12	ounces	30.000000
13.00	12	ounces	13.000000
16.50	12	ounces	16.500000
5.00	12	ounces	5.000000
5	12	ounces	5.000000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
25.00	8	ounces	37.500000
17.00	12	ounces	17.000000
10.00	16	ounces	7.500000
10.00	4	ounces	30.000000
45.99	12	ounces	45.990000
18.95	12	ounces	18.950000
23.00	12	ounces	23.000000
22.00	6	ounces	44.000000
21.00	12	ounces	21.000000
19.00	12	ounces	19.000000
25.99	10	ounces	31.188000
20.00	12	ounces	20.000000
20.00	12	ounces	20.000000
45.00	12	ounces	45.000000
23.00	10	ounces	27.600000
26.00	12	ounces	26.000000
16.00	12	ounces	16.000000
16.00	12	ounces	16.000000
10.00	12	ounces	10.000000
10.00	12	ounces	10.000000
16.00	12	ounces	16.000000
20.00	12	ounces	20.000000
14.00	250	grams	19.050859
35.00	100	grams	119.067869
15.00	250	grams	20.411635
15.00	250	grams	20.411635
35.00	100	grams	119.067869
35.00	100	grams	119.067869
35.00	100	grams	119.067869
6.00	250	grams	8.164654
35.00	100	grams	119.067869
20.00	250	grams	27.215513
20.00	250	grams	27.215513
15.16	500	grams	10.314679
22.15	500	grams	15.070590
16.98	45	grams	128.366502
6.00	250	grams	8.164654
6.00	250	grams	8.164654
37.00	120	grams	104.893122
15.00	250	grams	20.411635
30.00	250	grams	40.823269
15.00	250	grams	20.411635
15.00	250	grams	20.411635
29.00	150	grams	65.770823
100	200	grams	170.096955
28.00	500	grams	19.050859
28.00	500	grams	19.050859
7.12	120	grams	20.184839
19.00	200	grams	32.318422
25.00	150	grams	56.698985
NT 700	227	grams	NA
15.00	250	grams	20.411635
15.00	250	grams	20.411635
100.00	21	grams	1619.971003
100	18	grams	1889.966170
60.00	100	grams	204.116346
15.00	500	grams	10.205817
8.00	100	grams	27.215513
35,000 LAK	250	grams	NA
12.00	500	grams	8.164654
12.00	500	grams	8.164654
17.88	227	grams	26.795890
16.88	227	grams	25.297239
19.99	340	grams	20.001401
17.00	150	grams	38.555310
60.00	150	grams	136.077564
10.00	250	grams	13.607756
10.00	250	grams	13.607756
25.00	150	grams	56.698985
14.00	225	grams	21.167621
28.80	500	grams	19.595169
16.00	230	grams	23.665663
60.00	150	grams	136.077564
30.00	300	grams	34.019391
21.00	113	grams	63.221877
21.00	113	grams	63.221877
45.00	175	grams	87.478434
28.00	225	grams	42.335242
16.50	250	grams	22.452798
35.00	300	grams	39.689290
16.00	250	grams	21.772410
15.00	300	grams	17.009696
33.00	200	grams	56.131995
60.00	150	grams	136.077564
14.00	230	grams	20.707455
14.00	250	grams	19.050859
13.00	250	grams	17.690083
13.00	250	grams	17.690083
13.00	250	grams	17.690083
12.50	100	grams	42.524239
25.00	250	grams	34.019391
16.00	150	grams	36.287350

#5. Visualizing and Summarizing the Data

#calling my coffee budget from the beginning, split the table so that only coffees within my budget are in it and those with 
coffees_within_budget <- coffee_data_standardized %>% 
  arrange(
    rating
  ) %>% 
  filter(
    price_per_12oz <= budget_per_bag &
      rating > 89
  )

#for box plot below
coffees_within_budget_unrated <- coffee_data_standardized %>% 
  arrange(
    rating
  ) %>% 
  filter(
    price_per_12oz <= budget_per_bag
  )


coffees_within_budget %>% 
  tabyl(rating)

##  rating n   percent
##      90 5 0.2173913
##      91 6 0.2608696
##      92 3 0.1304348
##      93 9 0.3913043

Despite my budget being on the lower end of the coffees listed, I can still get a coffee at a better rating the Java Mocha I am using as my standard at 89. This dataset includes 23 coffees with a rating greater than 89.

#density scatterplot of price vs rating
ratingplot <- ggplot(coffee_data_standardized)+
               aes(x = price_per_12oz,
                   y = rating)+
                 geom_point(alpha = 0.2, colour = "blue")+
                 labs(title = "Price per 12oz vs Rating",
                      x = "Price per 12oz of Coffee ($)",
                      y = "Rating")
ratingplot

## Warning: Removed 3 rows containing missing values (`geom_point()`).

I created a scatterplot to have a visual representation of the coffee ratings and costs. There does appear to be an increase in quality with an increase in price, however given the very expensive outliers, it is difficult to determine how much. Within my budget, I wanted to see how many coffees were above the 89 benchmark and of those which coffees were highest rated. They are listed below with their ratings and prices per 12 ounces.

coffees_within_budget_unrated <- coffees_within_budget_unrated %>% 
  mutate(
    rating_ranges = case_when(
      rating < 89 ~ "lower rated than Java Mocha",
      rating == 89 ~ "same rating as Java Mocha",
      rating > 89 ~ "better rating than Java Mocha"
    )
  )

ggplot(coffees_within_budget_unrated, 
       aes(x = rating_ranges))+
  geom_bar()+
  labs(x = "Ratings compared to Java Mocha", 
       y = "Count",
       title = "Bar Graph of Coffee Ratings compared to Java Mocha")

To see how the coffees within my budget were rated in comparison to Java Mocha, I created a bar graph to visualize the distribution. I was surprised to find how many coffees there were with a better rating than Java Mocha.

#Pull the coffees from this group with the highest ratings
final <- subset(coffees_within_budget, rating == max(rating))

final %>%
  summarize(roaster, name, rating, location, origin, roast, price_per_12oz, review_date) %>% gt()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.

roaster	name	rating	location	origin	roast	price_per_12oz	review_date
Paradise Roasters	Organic Dulce de Guatemala	93	Ramsey, Minnesota	Huehuetenango growing region, Guatemala	Medium-Light	11.95000	August 2018
Mystic Monk Coffee	Kenya AA Top Lot	93	Clark, Wyoming	South-central Kenya	Medium	11.95000	October 2018
Columbia Street Roastery	40-Hour Ethiopia Girma Eshetu	93	Champaign, Illinois	Bita Genet District, Keffa Zone, southwestern Ethiopia	Medium-Light	9.00000	July 2020
Euphora Coffee	The World Espresso Blend	93	Taipei, Taiwan	Guatemala; Nicaragua; Ethiopia; Costa Rica; Indonesia; Brazil	Medium-Light	7.50000	May 2021
Geisha Coffee Roaster	Elida Natural ASD (Panama)	93	Orlando, Florida	Boquete growing region, western Panama	Medium-Light	11.05500	May 2021
Euphora	World 2.0 Espresso Blend	93	Taipei, Taiwan	Guatemala; Nicaragua; Ethiopia; Costa Rica; Indonesia; Brazil	Medium-Light	7.50000	October 2022
El Gran Cafe	Proyecto Aurora	93	Antigua, Guatemala	Antigua growing region, Guatemala	Medium-Light	10.00000	November 2022
El Gran Cafe	Finca El Potrero	93	Antigua, Guatemala	Antigua growing region, Guatemala	Medium-Light	10.00000	November 2022
Kunshan Kokei co., LTD	H Coffee Espresso Blend 01	93	Shanghai, China	Ethiopia; Colombia; Brazil	Medium	10.31468	September 2018

For my own curiosity, I increased my coffee budget and re-ran my analysis to see if I would be able find to find a significantly better coffee by spend 4 more dollars which would make my coffee budget \(\$ 15.99\).

coffees_within_higher_budget <- coffee_price_per_ounces %>% 
  arrange(
    rating
  ) %>% 
  filter(
    price_per_12oz <= (budget_per_bag + 4) &
      rating > 89
  )

coffees_within_higher_budget %>% 
  tabyl(rating)

##  rating  n percent
##      90 42   0.168
##      91 45   0.180
##      92 77   0.308
##      93 60   0.240
##      94 23   0.092
##      95  1   0.004
##      96  2   0.008

final2 <- subset(coffees_within_higher_budget, rating == max(rating))

final2 %>%
  summarize(roaster, name, rating, location, origin, roast, price_per_12oz, review_date) %>% gt()

## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
##   always returns an ungrouped data frame and adjust accordingly.

roaster	name	rating	location	origin	roast	price_per_12oz	review_date
Barrington Coffee Roasting	Twenty Five	96	Lee, Massachusetts	Ethiopia; Papua New Guinea; Guatemala	Medium-Light	14.45	October 2018
Thanksgiving Coffee Company	Mocha Java	96	Fort Bragg, California	Sumatra; Ethiopia	Medium	14.50	May 2020

By adding just 4 dollars to my coffee budget, I am able to choose from 250 coffees that are rated above 90 and 2 of which that are rated at 96.

#6. Final Summary At the beginning of this project, I set out to determine if I could find a better rated coffee for the same price as I spend on a 12oz bag of Portland Coffee Roaster’s Goose Hollow beans. I was not sure if that would be possible as good coffee can be expensive. Based on this analysis, I can say that with the prices listed in the dataset, I can find a lot of better rated coffees for the same or lower price. I was surpised to see how many options there were because I tend to find whatever local coffee is the cheapest at the grocery store. One caveat to this conclusion is that the prices listed in this dataset may not have been updated since the review occurred for the coffee. Some of the reviews are as old as 2010 and due to global inflation, coffee prices have drastically changed in this time. Without an updated cost list, it is difficult to determine how well my analysis translates to the cost of coffee at your local grocery store this. That beginning said, with an updated cost list, this tool can be quickly updated to accommodate the new prices. At this point, the only thing left is to actually buy the coffees and determine if the additional few dollars are worth it. Cheers!