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Luke Elmore
2024-09-06
Introduce Myself
My name is Luke Elmore. I am a 4th year Information systems major and BANA minor. I studied abroad last fall in spain and had a blast. I grew up in Columbus Ohio and now live in Cincinnati. My only jobs to this date are at country clubs because I love playing golf.
Professional Background
I am currently working at Hyde Park Golf and Country club in the bag room and I am looking for an intership or co -op for the spring or summer.
Experience with R
In spain I took a class that we used R in them so I have a little experience with R but not the most. I have used python, R and Java for programing languages but at a low level.
Picture of me
Blood transfusion
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.#Dimensions
dim(df)## [1] 748 5#Index
df[100,"Monetary"]## # A tibble: 1 × 1
## Monetary
## <dbl>
## 1 1750# 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#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 just the `Monetary` column. What is the mean of this vector?
mean(df[['Monetary']])## [1] 1378.676# Subset this data frame for all observations where `Monetary` is 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 rowsCincy Crime data
df1 <- readr::read_csv("data/Cincy_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.View(df1)
# dimensions of this data
dim(df1)## [1] 15155 40# Are there any missing data
sum(is.na(df1))## [1] 95592# If so, how many missing values are in each column?
Na_col<- colSums(is.na(df1))
Na_col## 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_REPORTED` column, what is the `range` of dates included in this data?
range(df1$DATE_REPORTED)## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"# Using `table()`, what is the most common age range for known `SUSPECT_AGE`s?
table(df1$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.
table(df1['ZIP'])## ZIP
## 4523 5239 42502 45202 45203 45204 45205 45206 45207 45208 45209 45211 45212
## 2 1 3 2049 226 348 1110 616 245 359 380 1094 61
## 45213 45214 45215 45216 45217 45219 45220 45221 45223 45224 45225 45226 45227
## 190 774 47 302 100 863 477 90 653 429 811 112 286
## 45228 45229 45230 45231 45232 45233 45236 45237 45238 45239 45244 45248
## 5 913 214 7 477 77 3 699 956 169 3 3# Using the `DAYOFWEEK` column, which day do most incidents occur on? What
# is the proportion of incidents that fall on this day?
table(df1[['DAYOFWEEK']]) / sum(table(df1[['DAYOFWEEK']]))##
## FRIDAY MONDAY SATURDAY SUNDAY THURSDAY TUESDAY WEDNESDAY
## 0.1369807 0.1438365 0.1542221 0.1448547 0.1363019 0.1432935 0.1405105Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.