Module 2 Lab
Kaitlyn Straight
2025-09-04
Introduction to Me!
Hi, I’m Kaitlyn Straight. I’m a recent graduate from UC San Diego, and am excited to work hands-on with Business Analytics throughout this MSBA program! I am interested in the cosmetics industry, particularly suncare and dermatology, and hope to hone my skills in operations and supply chain management, as well as machine learning and portfolio management. I am from Northern California, and enjoy working out, reading, and ceremonial-grade matcha.
Academic Background
- B.S. in Business Psychology (UC San Diego, La Jolla, CA)
- M.S. in Business Analytics (University of Cincinnati, Cincinnati, OH)
Professional Background
I recently concluded a Marketing & Analytics Internship with the Center for Community Energy, during which I managed social media content schedules, outreach to other nonprofits and organizations within the Clean Energy sector, and headed recruitment efforts. I performed analytics and created a marketing strategy to increase public engagement, supporting CCE’s mission to educate and pioneer sustainable energy solutions.
I also work part-time as a Shift Lead at Arteazen Cafe, where I manage inventory, onboarding, and aid in marketing as well as day-to-day function of the shop.
Experience with R
I have been programming with R for just over 1 year, having my first exposure to the language during my studies at UC San Diego in “Business Analytics” and “R for Social Sciences” courses. Prior to—and alongside—R, I have experience with Microsoft Excel, JavaScript, Python, SQL, and Tableau for visualization.
Part 2
Blood Transfusion Questions
- What are the dimensions of this data (number of rows and columns)?
- What are the data types of each column?
# import data and the message print out will also tell you
# what the data types are
df <- readr::read_csv("C:\\Users\\kaitl\\Documents\\U Cincinnati\\Fall 2025\\BANA7025 Data Wrangling\\Module2\\blood_transfusion.csv")## Rows: 748 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Class
## dbl (4): Recency, Frequency, Monetary, Time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.- Are there any missing values?
sum(is.na(df))## [1] 0- What are the dimensions of this data? (Duplicate question)
dim(df)## [1] 748 5- Check out the first 10 rows
head(df, 10)## # A tibble: 10 × 5
## Recency Frequency Monetary Time Class
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 2 50 12500 98 donated
## 2 0 13 3250 28 donated
## 3 1 16 4000 35 donated
## 4 2 20 5000 45 donated
## 5 1 24 6000 77 not donated
## 6 4 4 1000 4 not donated
## 7 2 7 1750 14 donated
## 8 1 12 3000 35 not donated
## 9 2 9 2250 22 donated
## 10 5 46 11500 98 donated- Check out the last 10 rows
tail(df, 10)## # A tibble: 10 × 5
## Recency Frequency Monetary Time Class
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 23 1 250 23 not donated
## 2 23 4 1000 52 not donated
## 3 23 1 250 23 not donated
## 4 23 7 1750 88 not donated
## 5 16 3 750 86 not donated
## 6 23 2 500 38 not donated
## 7 21 2 500 52 not donated
## 8 23 3 750 62 not donated
## 9 39 1 250 39 not donated
## 10 72 1 250 72 not donated- Index for the 100th row and just the
Monetarycolumn. What is the value?
df[100, 'Monetary']## # A tibble: 1 × 1
## Monetary
## <dbl>
## 1 1750- Index for just the
Monetarycolumn. What is the mean of this vector?
mean(df[['Monetary']])## [1] 1378.676- Subset this data frame for all observations where
Monetaryis greater than the mean value. How many rows are in the resulting data frame?
above_avg <- df[['Monetary']] > mean(df[['Monetary']])
df[above_avg, 'Monetary']## # A tibble: 267 × 1
## Monetary
## <dbl>
## 1 12500
## 2 3250
## 3 4000
## 4 5000
## 5 6000
## 6 1750
## 7 3000
## 8 2250
## 9 11500
## 10 5750
## # ℹ 257 more rowssum(above_avg)## [1] 267Police Data Questions
- What are the dimensions of this data (number of rows and columns)?
df <- readr::read_csv("C:\\Users\\kaitl\\Documents\\U Cincinnati\\Fall 2025\\BANA7025 Data Wrangling\\Module2\\PDI__Police_Data_Initiative__Crime_Incidents.csv")## Rows: 15155 Columns: 40
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (34): INSTANCEID, INCIDENT_NO, DATE_REPORTED, DATE_FROM, DATE_TO, CLSD, ...
## dbl (6): UCR, LONGITUDE_X, LATITUDE_X, TOTALNUMBERVICTIMS, TOTALSUSPECTS, ZIP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.- What are the dimensions of this data? (Duplicate question)
dim(df)## [1] 15155 40- Are there any missing data?
sum(is.na(df))## [1] 95592Definitely.
- If so, how many missing values are in each column?
colSums(is.na(df))## INSTANCEID INCIDENT_NO
## 0 0
## DATE_REPORTED DATE_FROM
## 0 2
## DATE_TO CLSD
## 9 545
## UCR DST
## 10 0
## BEAT OFFENSE
## 28 10
## LOCATION THEFT_CODE
## 2 10167
## FLOOR SIDE
## 14127 14120
## OPENING HATE_BIAS
## 14508 0
## DAYOFWEEK RPT_AREA
## 423 239
## CPD_NEIGHBORHOOD WEAPONS
## 249 5
## DATE_OF_CLEARANCE HOUR_FROM
## 2613 2
## HOUR_TO ADDRESS_X
## 9 148
## LONGITUDE_X LATITUDE_X
## 1714 1714
## VICTIM_AGE VICTIM_RACE
## 0 2192
## VICTIM_ETHNICITY VICTIM_GENDER
## 2192 2192
## SUSPECT_AGE SUSPECT_RACE
## 0 7082
## SUSPECT_ETHNICITY SUSPECT_GENDER
## 7082 7082
## TOTALNUMBERVICTIMS TOTALSUSPECTS
## 33 7082
## UCR_GROUP ZIP
## 10 1
## COMMUNITY_COUNCIL_NEIGHBORHOOD SNA_NEIGHBORHOOD
## 0 0- Using the
DATE_REPORTEDcolumn, what is therangeof dates included in this data?
range(df[['DATE_REPORTED']])## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"- Using
table(), what is the most common age range for knownSUSPECT_AGEs?
table(df[['SUSPECT_AGE']])##
## 18-25 26-30 31-40 41-50 51-60 61-70 OVER 70 UNDER 18
## 1778 1126 1525 659 298 121 16 629
## UNKNOWN
## 9003- Use
table()to get the number of incidents per zip code. Sort this table # for those zip codes with the most activity to the least activity.
sort(table(df['ZIP']), decreasing = TRUE)## ZIP
## 45202 45205 45211 45238 45229 45219 45225 45214 45237 45223 45206 45220 45232
## 2049 1110 1094 956 913 863 811 774 699 653 616 477 477
## 45224 45209 45208 45204 45216 45227 45207 45203 45230 45213 45239 45226 45217
## 429 380 359 348 302 286 245 226 214 190 169 112 100
## 45221 45233 45212 45215 45231 45228 42502 45236 45244 45248 4523 5239
## 90 77 61 47 7 5 3 3 3 3 2 1- Using the
DAYOFWEEKcolumn, which day do most incidents occur on? What is the proportion of incidents that fall on this day?
table(df[['DAYOFWEEK']]) / sum(table(df[['DAYOFWEEK']]))##
## FRIDAY MONDAY SATURDAY SUNDAY THURSDAY TUESDAY WEDNESDAY
## 0.1369807 0.1438365 0.1542221 0.1448547 0.1363019 0.1432935 0.1405105