Module 2 Lab
Will Kegg
2023-09-03
Introduction to Me
Hello everyone! My name is William Kegg and I am a fulltime MS BANA Student. I am from Batavia, Ohio (I still commute from there) and recently graduated from UC in the Spring with undergraduate degrees in Business Analytics and Insurance & Risk Management. I am currently on my 13th internship rotation at Great American Insurance Group (6th with them specifically) as a Business Analyst for their IT Asset Management Team. Based on my work in my undergraduate program and my work experience, I would say I am between a beginner and intermediate level for R. I would say that I am at the same level for Python and SQL as well. I look forward to working with you all this semester!
Academic Background
- High School: Glen Este High School (2015-2017)
- High School: West Clermont High School (2017-2019)
- B.S. in Business Analytics: University of Cincinnati (2019-2023)
- B.A. in Insurance & Risk Management: University of Cincinnati (2019-2023)
- M.S. in Business Analytics: University of Cincinnati (2023-2024)
Professional Background
- Sales Associate: Skyzone ( September 2018-June 2020)
- Personal Lines Underwriting Intern: American Modern (May 2020-August 2020)
- Claims Representative: American Modern (August 2020-December 2020)
- Analytics & Risk Management Intern: Hylant (May 2021-December 2021)
- IT Asset Management Analytics Intern: Great American Insurance Group (December 2021-Now)
Experience with R and Other Programming Languages
As stated before, I have a basic understanding of R and other programming languages like Python and SQL. Here are a few examples of what I can do with these languages:
- Simulation Modeling
- Data Mining
- Multivariate Regression
Question 1
Import data and the message print out will also tell you
What are the data types?
df <- readr::read_csv("Lab 2 Data Source/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.str(df)## spec_tbl_df [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>There are Numeric and Character variables.
Are there any missing values?
sum(is.na(df))## [1] 0There are 0 missing values.
What are the dimensions of this data?
# Number of Rows
nrow(df)## [1] 748# Number of Columns
ncol(df)## [1] 5# Number of Both Columns and Rows
dim(df)## [1] 748 5# Length
length(df)## [1] 5Check 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 donatedCheck 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 donatedIndex for the 100th row and just the Monetary column.
What is the value?
df[100, 'Monetary']## # A tibble: 1 × 1
## Monetary
## <dbl>
## 1 1750Index for just the Monetary column. What is the mean of
this vector?
mean(df[['Monetary']])## [1] 1378.676Subset 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
## # … with 257 more rowsQuestion 2
df2 <- readr::read_table("Lab 2 Data Source/dayton.txt", col_names = c('Month', 'Day', 'Year', 'Avg_temp'))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Month = col_double(),
## Day = col_double(),
## Year = col_double(),
## Avg_temp = col_double()
## )## Warning: 3 parsing failures.
## row col expected actual file
## 4623 -- 4 columns 5 columns 'Lab 2 Data Source/dayton.txt'
## 5016 -- 4 columns 5 columns 'Lab 2 Data Source/dayton.txt'
## 5213 -- 4 columns 5 columns 'Lab 2 Data Source/dayton.txt'These columns represent specifics with the weather.
Dimensions of imported data
dim(df2)## [1] 9265 4Any missing data
mean(is.na(df2))## [1] 0subset for the 365th observation
df2[365, ]## # A tibble: 1 × 4
## Month Day Year Avg_temp
## <dbl> <dbl> <dbl> <dbl>
## 1 12 31 1995 39.3Subset for all observations that happened during January of 2000
jan_2000 <- df2[df2[['Month']] == 1 & df2[['Year']] == 2000, ]compute the median avg temp of this month
median(jan_2000[['Avg_temp']])## [1] 27.1Which date was the highest average temp recorded
df2[which.max(df2[['Avg_temp']]),]## # A tibble: 1 × 4
## Month Day Year Avg_temp
## <dbl> <dbl> <dbl> <dbl>
## 1 7 7 2012 89.2Which date was the coldest average temp recorded?
df2[which.min(df2[['Avg_temp']]),]## # A tibble: 1 × 4
## Month Day Year Avg_temp
## <dbl> <dbl> <dbl> <dbl>
## 1 12 24 1998 -99-99 does nit make sense and must be error.
How many of these -99s are there?
sum(df2[['Avg_temp']] == -99)## [1] 14Mean of avg temp column
mean(df2[['Avg_temp']])## [1] 54.39876recode -99s to NA
bad_values <- df2[['Avg_temp']] == -99
df2[bad_values, 'Avg_temp'] <- NArecompute mean of avg temp column
mean(df2[['Avg_temp']], na.rm = TRUE)## [1] 54.6309Question 3
df3 <- readr::read_csv("Lab 2 Data Source/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.dimensions of this data
dim(df3)## [1] 15155 40Are there any missing data
sum(is.na(df3))## [1] 95592If so, how many missing values are in each column?
colSums(is.na(df3))## 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 0Using the DATE_REPORTED column, what is the
range of dates included in this data?
range(df3[['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_AGEs?
table(df3[['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
## 9003Use 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(df3['ZIP']), decreasing = TRUE)##
## 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 1Using the DAYOFWEEK column, which day do most incidents
occur on? What is the proportion of incidents that fall on
table(df3[['DAYOFWEEK']]) / sum(table(df3[['DAYOFWEEK']]))##
## FRIDAY MONDAY SATURDAY SUNDAY THURSDAY TUESDAY WEDNESDAY
## 0.1369807 0.1438365 0.1542221 0.1448547 0.1363019 0.1432935 0.1405105Looking at the information this data set provides, what are some insights you’d be interested in assessing? Analyze three different columns that could start to provide you with these insights. Are there missing values in these columns?
# I chose the columns of HATE_BIAS, WEAPONS, AND TOTALNUMBERVICTIMS. These seemed like the most significant for the kind of analysis I wanted to do.
colSums(is.na(df3))## 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 0HATE_BIAS had 0 missing values, WEAPONS had 5 AND TOTALNUMBERVICTIMS had 33.
What are some summary statistics you can compute for these columns?
Most common weapons, quantity of hate filled crimes, and range of the number of victims.
Are there any outliers or aberrant values in these columns? How do you know?
table(df3[['HATE_BIAS']])##
## 11--ANTI-WHITE 12--ANTI-BLACK
## 7 6
## 13--ANTI-AMERICAN INDIAN/ALASKAN NATIVE 21--ANTI-JEWISH
## 6 2
## 24--ANTI-ISLAMIC (MOSLEM) 41--ANTI-MALE HOMOSEXUAL (GAY)
## 1 3
## 43--ANTI-HOMOSEXUAL (GAY AND LESBIAN) 88--DOMESTIC VIOLENCE
## 1 1241
## 89--GANG RELATED N--NO BIAS/NOT APPLICABLE
## 3 13885table(df3[['WEAPONS']])##
## 11 - FIREARM (TYPE NOT STATED)
## 531
## 12 - HANDGUN
## 934
## 12A - AUTOMATIC HANDGUN
## 16
## 13 - RIFLE
## 42
## 14 - SHOTGUN
## 13
## 15 - OTHER FIREARM
## 3
## 16 - IMITATION FIREARM
## 4
## 17 - SIMULATED FIREARM
## 6
## 18 - BB AND PELLET GUNS
## 32
## 20 - KNIFE/CUTTING INSTRUMENT (ICEPICK, AX, ETC.)
## 247
## 30 - BLUNT OBJECT (CLUB, HAMMER, ETC.)
## 333
## 35 - MOTOR VEHICLE (WHEN USED AS WEAPON)
## 122
## 40 - PERSONAL WEAPONS (HANDS, FEET, TEETH, ETC.)
## 2827
## 50 - POISON
## 1
## 60 - EXPLOSIVES
## 1
## 65 - FIRE/INCENDIARY DEVICE
## 7
## 70 - DRUGS/NARCOTICS/SLEEPING PILLS
## 2
## 80 - OTHER WEAPON
## 348
## 85 - ASPHYXIATION (BY DROWNING, STRANGULATION, SUFFOCATION)
## 2
## 99 - NONE
## 8106
## U - UNKNOWN
## 1573table(df3[['TOTALNUMBERVICTIMS']])##
## 1 2 3 4 5 6 7 8 9 10 12 13
## 12146 1919 481 228 40 120 35 42 52 10 36 13