Introduction to Me!

Hello! I’m a fourth year operation management student at Lindner College of Business. I’m born and raised in Cincinnati, Ohio. I love being outside, playing in a pool, walking in the park and kayaking in the little miami river.

Acdemic Background

  • B.A. in Operations Management (University of Cincinnati,Cincinnati,OH)

Professional Background

Currently, I am warehouse associate and bakery sorter at Manufacturing Inc.. I’ve been there 3 years, rising through the ranks of warehousing industry at Graeters’ Ice Cream. My day-to-day responsibilities include:

  • ordering and managing inventory for commissary items which supplies all 56 stores
  • forecasting and producing all bakery count items for holidays
  • ensure shipping and receiving run in a smoothly and timely manner

Experience with R

I have nearly zero experience with R. I’m taking a data mining class that is exposing me to all characteristics and uses of R. Excel is my only exposure to analytic programs.

library(readr)

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.
print(paste("Data types:", str(df)))
## spc_tbl_ [748 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Recency  : num [1:748] 2 0 1 2 1 4 2 1 2 5 ...
##  $ Frequency: num [1:748] 50 13 16 20 24 4 7 12 9 46 ...
##  $ Monetary : num [1:748] 12500 3250 4000 5000 6000 1000 1750 3000 2250 11500 ...
##  $ Time     : num [1:748] 98 28 35 45 77 4 14 35 22 98 ...
##  $ Class    : chr [1:748] "donated" "donated" "donated" "donated" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Recency = col_double(),
##   ..   Frequency = col_double(),
##   ..   Monetary = col_double(),
##   ..   Time = col_double(),
##   ..   Class = col_character()
##   .. )
##  - attr(*, "problems")=<externalptr> 
## [1] "Data types: "
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']],na.rm=TRUE)
## [1] 1378.676
above_avg <- df[['Monetary']] > mean(df[['Monetary']], na.rm = TRUE)
(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
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
any(is.na(df))
## [1] TRUE
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
range(df[['DATE_REPORTED']], na.rm=TRUE)
## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"
table(df[['SUSPECT_AGE']], useNA="no")
## 
##    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[['DAYOFWEEK']])/ sum(table(df[['DAYOFWEEK']])))
## 
##  THURSDAY    FRIDAY WEDNESDAY   TUESDAY    MONDAY    SUNDAY  SATURDAY 
## 0.1363019 0.1369807 0.1405105 0.1432935 0.1438365 0.1448547 0.1542221