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<title>Case Study 1 - MSDS 6306</title>
<h1>Beers Case Study</h1>
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<h1 class="title toc-ignore">Beers Case Study</h1>
<h4 class="author">Will Sherman</h4>
<h4 class="author">Kati Scheurger</h4>
<h4 class="author">Randy Kim</h4>
<h4 class="date">7/15/2021</h4>
</div>
<p>Hello to everyone—the board and directors of Budweiser for this particular study. The following analysis includes my team’s exploratory data analysis (EDA) and our key findings related to your questions on the Beers and Breweries data sets. The primary focus of this EDA was the relationship between alcohol by volume (ABV), international bitterness units (IBU), and classification of ales as either IPA or some other type of Ale.</p>
</p>Link to R Shiny App:</p>
</p><a href="https://generalshermanw.shinyapps.io/Beers_Case_Study/">Beers Case Study</a></p>
<pre class="r"><code># The necessary libraries for this EDA
require(ggplot2)
require(dplyr)
require(maps)
require(e1071)
require(caret)
require(class)
require(stringr)
require(tidyr)
require(highcharter)
require(usmap)
require(plotly)
require(kable)
require(kableExtra)</code></pre>
<pre class="r"><code>beers <- read.csv("~/Grad School - Classes/MSDS - 6306 - Doing Data Science/MSDS_6306_DDS_Git/Unit 8 and 9 Case Study 1/Beers.csv")
breweries <- read.csv("~/Grad School - Classes/MSDS - 6306 - Doing Data Science/MSDS_6306_DDS_Git/Unit 8 and 9 Case Study 1/Breweries.csv")
beers_df <- beers[order(beers$Brewery_id),]</code></pre>
<div id="question-1-how-many-breweries-are-present-in-each-state" class="section level2">
<h2>Question 1: How many breweries are present in each state?</h2>
<pre class="r"><code>#initial plot
breweries %>% ggplot(aes(x=State)) +
geom_histogram(stat="count") +
labs(title="Count of breweries per state") +
theme(axis.text.x=element_text(angle=45,hjust=0.8))</code></pre>
<p><img src="casestudy1_files/figure-html/Question-1-1.png" width="672" /></p>
<pre class="r"><code>#setting up df to append region to breweries
st_reg <- data.frame(State=state.abb, Region=state.region)
st_reg <- rbind(st_reg, data.frame(State="DC", Region="Northeast"))
st_reg$Region <- as.character(st_reg$Region)
breweries_df <- breweries[order(breweries$State),]
breweries_df$State <- stringr::str_trim(breweries_df$State) #trim whitespace
#append region to breweries
breweries_df <- left_join(breweries_df, st_reg)</code></pre>
<pre><code>## Joining, by = "State"</code></pre>
<pre class="r"><code>#plot breweries by state & region
breweries_df %>% ggplot(aes(x=State)) +
geom_histogram(stat="count") +
labs(title="Count of breweries per state\nsorted by region") +
theme(axis.text.x=element_text(angle=45,hjust=0.8)) +
facet_wrap(~Region, scale="free")</code></pre>
<p><img src="casestudy1_files/figure-html/Question-1-2.png" width="672" /></p>
<pre class="r"><code>#creating df for table analysis
b_tabledata <- breweries_df %>%
group_by(State) %>%
summarise(Breweries=n()) %>%
left_join(st_reg)</code></pre>
<pre><code>## Joining, by = "State"</code></pre>
<pre class="r"><code>#re-order data
b_tabledata <- b_tabledata[order(b_tabledata$Breweries, decreasing=T),]
t1 <- b_tabledata[which(b_tabledata$Region=="North Central"),c(1,2)]
t2 <- b_tabledata[which(b_tabledata$Region=="Northeast"),c(1,2)]
t3 <- b_tabledata[which(b_tabledata$Region=="South"),c(1,2)]
t4 <- b_tabledata[which(b_tabledata$Region=="West"),c(1,2)]
kable(list(t1,t2,t3,t4),
booktabs=T,
align=c("c","c"),
col.names=c("State","&nbsp;&nbsp;&nbsp;Breweries"),
escape=F) %>%
kable_styling(full_width=F,
position="left",
bootstrap_options = "bordered") %>%
add_header_above(c("North Central"=1,"Northeast"=1,"South"=1,"West"=1),align="l") %>%
add_header_above(c("Brewery count by state\nsorted by region"=4),bold=T)</code></pre>
<table class="kable_wrapper table table-bordered" style="width: auto !important; ">
<thead>
<tr>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; font-weight: bold; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Brewery count by state<br>sorted by region
</div>
</th>
</tr>
<tr>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: left; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
North Central
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: left; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Northeast
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: left; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
South
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: left; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
West
</div>
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<table>
<thead>
<tr>
<th style="text-align:center;">
State
</th>
<th style="text-align:center;">
Breweries
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center;">
MI
</td>
<td style="text-align:center;">
32
</td>
</tr>
<tr>
<td style="text-align:center;">
IN
</td>
<td style="text-align:center;">
22
</td>
</tr>
<tr>
<td style="text-align:center;">
WI
</td>
<td style="text-align:center;">
20
</td>
</tr>
<tr>
<td style="text-align:center;">
IL
</td>
<td style="text-align:center;">
18
</td>
</tr>
<tr>
<td style="text-align:center;">
OH
</td>
<td style="text-align:center;">
15
</td>
</tr>
<tr>
<td style="text-align:center;">
MN
</td>
<td style="text-align:center;">
12
</td>
</tr>
<tr>
<td style="text-align:center;">
MO
</td>
<td style="text-align:center;">
9
</td>
</tr>
<tr>
<td style="text-align:center;">
IA
</td>
<td style="text-align:center;">
5
</td>
</tr>
<tr>
<td style="text-align:center;">
NE
</td>
<td style="text-align:center;">
5
</td>
</tr>
<tr>
<td style="text-align:center;">
KS
</td>
<td style="text-align:center;">
3
</td>
</tr>
<tr>
<td style="text-align:center;">
ND
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:center;">
SD
</td>
<td style="text-align:center;">
1
</td>
</tr>
</tbody>
</table>
</td>
<td>
<table>
<thead>
<tr>
<th style="text-align:center;">
State
</th>
<th style="text-align:center;">
Breweries
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center;">
PA
</td>
<td style="text-align:center;">
25
</td>
</tr>
<tr>
<td style="text-align:center;">
MA
</td>
<td style="text-align:center;">
23
</td>
</tr>
<tr>
<td style="text-align:center;">
NY
</td>
<td style="text-align:center;">
16
</td>
</tr>
<tr>
<td style="text-align:center;">
VT
</td>
<td style="text-align:center;">
10
</td>
</tr>
<tr>
<td style="text-align:center;">
ME
</td>
<td style="text-align:center;">
9
</td>
</tr>
<tr>
<td style="text-align:center;">
CT
</td>
<td style="text-align:center;">
8
</td>
</tr>
<tr>
<td style="text-align:center;">
RI
</td>
<td style="text-align:center;">
5
</td>
</tr>
<tr>
<td style="text-align:center;">
NH
</td>
<td style="text-align:center;">
3
</td>
</tr>
<tr>
<td style="text-align:center;">
NJ
</td>
<td style="text-align:center;">
3
</td>
</tr>
<tr>
<td style="text-align:center;">
DC
</td>
<td style="text-align:center;">
1
</td>
</tr>
</tbody>
</table>
</td>
<td>
<table>
<thead>
<tr>
<th style="text-align:center;">
State
</th>
<th style="text-align:center;">
Breweries
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center;">
TX
</td>
<td style="text-align:center;">
28
</td>
</tr>
<tr>
<td style="text-align:center;">
NC
</td>
<td style="text-align:center;">
19
</td>
</tr>
<tr>
<td style="text-align:center;">
VA
</td>
<td style="text-align:center;">
16
</td>
</tr>
<tr>
<td style="text-align:center;">
FL
</td>
<td style="text-align:center;">
15
</td>
</tr>
<tr>
<td style="text-align:center;">
GA
</td>
<td style="text-align:center;">
7
</td>
</tr>
<tr>
<td style="text-align:center;">
MD
</td>
<td style="text-align:center;">
7
</td>
</tr>
<tr>
<td style="text-align:center;">
OK
</td>
<td style="text-align:center;">
6
</td>
</tr>
<tr>
<td style="text-align:center;">
LA
</td>
<td style="text-align:center;">
5
</td>
</tr>
<tr>
<td style="text-align:center;">
KY
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
SC
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
AL
</td>
<td style="text-align:center;">
3
</td>
</tr>
<tr>
<td style="text-align:center;">
TN
</td>
<td style="text-align:center;">
3
</td>
</tr>
<tr>
<td style="text-align:center;">
AR
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:center;">
DE
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:center;">
MS
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:center;">
WV
</td>
<td style="text-align:center;">
1
</td>
</tr>
</tbody>
</table>
</td>
<td>
<table>
<thead>
<tr>
<th style="text-align:center;">
State
</th>
<th style="text-align:center;">
Breweries
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center;">
CO
</td>
<td style="text-align:center;">
47
</td>
</tr>
<tr>
<td style="text-align:center;">
CA
</td>
<td style="text-align:center;">
39
</td>
</tr>
<tr>
<td style="text-align:center;">
OR
</td>
<td style="text-align:center;">
29
</td>
</tr>
<tr>
<td style="text-align:center;">
WA
</td>
<td style="text-align:center;">
23
</td>
</tr>
<tr>
<td style="text-align:center;">
AZ
</td>
<td style="text-align:center;">
11
</td>
</tr>
<tr>
<td style="text-align:center;">
MT
</td>
<td style="text-align:center;">
9
</td>
</tr>
<tr>
<td style="text-align:center;">
AK
</td>
<td style="text-align:center;">
7
</td>
</tr>
<tr>
<td style="text-align:center;">
ID
</td>
<td style="text-align:center;">
5
</td>
</tr>
<tr>
<td style="text-align:center;">
HI
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
NM
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
UT
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
WY
</td>
<td style="text-align:center;">
4
</td>
</tr>
<tr>
<td style="text-align:center;">
NV
</td>
<td style="text-align:center;">
2
</td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
<p>The above graph & table show the number of breweries per state (both are broken down by region for more compact reading).</p>
<pre class="r"><code># Merge the data
combined <- merge(beers_df, breweries_df, by.x="Brewery_id", by.y="Brew_ID")
#head of the merged dataframe
head(combined,6)</code></pre>
<pre><code>## Brewery_id Name.x Beer_ID ABV IBU
## 1 1 Get Together 2692 0.045 50
## 2 1 Maggie's Leap 2691 0.049 26
## 3 1 Wall's End 2690 0.048 19
## 4 1 Pumpion 2689 0.060 38
## 5 1 Stronghold 2688 0.060 25
## 6 1 Parapet ESB 2687 0.056 47
## Style Ounces
## 1 American IPA 16
## 2 Milk / Sweet Stout 16
## 3 English Brown Ale 16
## 4 Pumpkin Ale 16
## 5 American Porter 16
## 6 Extra Special / Strong Bitter (ESB) 16
## Name.y City State
## 1 NorthGate Brewing Minneapolis MN
## 2 NorthGate Brewing Minneapolis MN
## 3 NorthGate Brewing Minneapolis MN
## 4 NorthGate Brewing Minneapolis MN
## 5 NorthGate Brewing Minneapolis MN
## 6 NorthGate Brewing Minneapolis MN
## Region
## 1 North Central
## 2 North Central
## 3 North Central
## 4 North Central
## 5 North Central
## 6 North Central</code></pre>
<pre class="r"><code>#tail of the merged dataframe
tail(combined,6)</code></pre>
<pre><code>## Brewery_id Name.x Beer_ID
## 2405 556 Pilsner Ukiah 98
## 2406 557 Heinnieweisse Weissebier 52
## 2407 557 Snapperhead IPA 51
## 2408 557 Moo Thunder Stout 50
## 2409 557 Porkslap Pale Ale 49
## 2410 558 Urban Wilderness Pale Ale 30
## ABV IBU Style Ounces
## 2405 0.055 NA German Pilsener 12
## 2406 0.049 NA Hefeweizen 12
## 2407 0.068 NA American IPA 12
## 2408 0.049 NA Milk / Sweet Stout 12
## 2409 0.043 NA American Pale Ale (APA) 12
## 2410 0.049 NA English Pale Ale 12
## Name.y City
## 2405 Ukiah Brewing Company Ukiah
## 2406 Butternuts Beer and Ale Garrattsville
## 2407 Butternuts Beer and Ale Garrattsville
## 2408 Butternuts Beer and Ale Garrattsville
## 2409 Butternuts Beer and Ale Garrattsville
## 2410 Sleeping Lady Brewing Company Anchorage
## State Region
## 2405 CA West
## 2406 NY Northeast
## 2407 NY Northeast
## 2408 NY Northeast
## 2409 NY Northeast
## 2410 AK West</code></pre>
<p>Here are the initial views (beginning and end) of the combined dataframe.</p>
<pre class="r"><code>#adding in a df of missing IBU values per state
na_byState <- data.frame()
for(i in st_reg$State){
na_byState[i,1]=length(which(grepl(i,combined$State)))
na_byState[i,2]=length(which(grepl(i,combined$State) & is.na(combined$IBU)))
}
names(na_byState) <- c("Beers_count","IBU_NAs_count")
na_byState %>% mutate(Percent_NA = round(IBU_NAs_count/Beers_count*100,digits=0))</code></pre>
<pre><code>## Beers_count IBU_NAs_count Percent_NA
## AL 10 1 10
## AK 25 8 32
## AZ 47 23 49
## AR 5 4 80
## CA 183 48 26
## CO 265 119 45
## CT 27 21 78
## DE 2 1 50
## FL 58 21 36
## GA 16 9 56
## HI 27 9 33
## ID 30 13 43
## IL 91 52 57
## IN 139 48 35
## IA 30 5 17
## KS 23 4 17
## KY 21 7 33
## LA 19 9 47
## ME 27 20 74
## MD 21 11 52
## MA 82 31 38
## MI 162 124 77
## MN 55 9 16
## MS 11 0 0
## MO 42 13 31
## MT 40 17 42
## NE 25 16 64
## NV 11 3 27
## NH 8 6 75
## NJ 8 0 0
## NM 14 8 57
## NY 74 28 38
## NC 59 29 49
## ND 3 0 0
## OH 49 17 35
## OK 19 8 42
## OR 125 38 30
## PA 100 53 53
## RI 27 7 26
## SC 14 9 64
## SD 7 7 100
## TN 6 1 17
## TX 130 41 32
## UT 26 15 58
## VT 27 10 37
## VA 40 5 12
## WA 68 25 37
## WV 2 0 0
## WI 87 45 52
## WY 15 3 20
## DC 8 4 50</code></pre>
<pre class="r"><code>sum(is.na(combined)) #number of total NA's (equal to NA's in IBU & ABV)</code></pre>
<pre><code>## [1] 1067</code></pre>
<pre class="r"><code>sum(is.na(combined$IBU)) #number of NA's in IBU</code></pre>
<pre><code>## [1] 1005</code></pre>
<pre class="r"><code>sum(is.na(combined$ABV)) #number of NA's in ABV</code></pre>
<pre><code>## [1] 62</code></pre>
<pre class="r"><code>combined_df <- combined #create working df
###set seed
set.seed(7)