Synopsis

I am currently in the MSBANA Program at UC. I received my undergrad in Marketing from UC and have been in the Cincinnati area all my life. I have a dog Finn, a double doodle, and enjoy reading, chess, and basketball in my free time.

Academic Background

  • B.S. in Marketing (University of Cincinnati)
  • Graduate Certificate in Data Analytics (University of Cincinnati)

Professional Background

Having just recently made the change into the Business Analytics, my previous jobs were more in line with my marketing degree. My post collegiate jobs have been at Enterprise in the management program, sales at Renewal by Andersen, and customer service at Retiremed.

Experience with R and Other Software

My experience with R comes entirely from other classes, notably Data Mining, and Data Analysis Methods. I do have experience with Python, SQL, and Tableau as well, all from the program.

Week 2 Exercises

df <- readr::read_csv("Data/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.
sum(is.na(df))
## [1] 0
dim(df)
## [1] 748   5
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
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
df[100, 'Monetary']
## # A tibble: 1 × 1
##   Monetary
##      <dbl>
## 1     1750
mean(df[['Monetary']])
## [1] 1378.676
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 rows
dim(df[above_avg, 'Monetary'])
## [1] 267   1
df <- readr::read_csv("Data/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.
dim(df)
## [1] 15155    40
sum(is.na(df))
## [1] 95592
sort(colSums(is.na(df)), decreasing = TRUE)
##                        OPENING                          FLOOR 
##                          14508                          14127 
##                           SIDE                     THEFT_CODE 
##                          14120                          10167 
##                   SUSPECT_RACE              SUSPECT_ETHNICITY 
##                           7082                           7082 
##                 SUSPECT_GENDER                  TOTALSUSPECTS 
##                           7082                           7082 
##              DATE_OF_CLEARANCE                    VICTIM_RACE 
##                           2613                           2192 
##               VICTIM_ETHNICITY                  VICTIM_GENDER 
##                           2192                           2192 
##                    LONGITUDE_X                     LATITUDE_X 
##                           1714                           1714 
##                           CLSD                      DAYOFWEEK 
##                            545                            423 
##               CPD_NEIGHBORHOOD                       RPT_AREA 
##                            249                            239 
##                      ADDRESS_X             TOTALNUMBERVICTIMS 
##                            148                             33 
##                           BEAT                            UCR 
##                             28                             10 
##                        OFFENSE                      UCR_GROUP 
##                             10                             10 
##                        DATE_TO                        HOUR_TO 
##                              9                              9 
##                        WEAPONS                      DATE_FROM 
##                              5                              2 
##                       LOCATION                      HOUR_FROM 
##                              2                              2 
##                            ZIP                     INSTANCEID 
##                              1                              0 
##                    INCIDENT_NO                  DATE_REPORTED 
##                              0                              0 
##                            DST                      HATE_BIAS 
##                              0                              0 
##                     VICTIM_AGE                    SUSPECT_AGE 
##                              0                              0 
## COMMUNITY_COUNCIL_NEIGHBORHOOD               SNA_NEIGHBORHOOD 
##                              0                              0
range(df[['DATE_REPORTED']])
## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"
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
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
table(df[['DAYOFWEEK']])
## 
##    FRIDAY    MONDAY  SATURDAY    SUNDAY  THURSDAY   TUESDAY WEDNESDAY 
##      2018      2119      2272      2134      2008      2111      2070
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