Module 2 Lab Quiz
Denise Huynh
2024-09-04
Part 1: HTML Bio
Introduction about Me!
Hello! My name is Denise Huynh. I grew up in central Vietnam; I studied aboard to the US,Pennsylvalnia 5 years ago; and now Cincinnati, OH.
Academic Background
- Currently pursuing Bachelor’s Degree: Business Analytics, Information Systems (University of Cincinnati, Cincinnati, OH)
Professional Background
I currently work as a Student Worker at Aerospace Engineering, and Mechanical Engineering Department of University of Cincinnati. My daily work includes:
- Created reports, spreadsheets, performed data entry tasks with high degree of accuracy and attention to detail.
- Provided front-line support by handling incoming calls, emails, and correspondence in a timely manner.
- Prepared comprehensive reimbursement reports detailing student expenses for processing and disbursement of funds.
- Maintained and updated monthly financial records for departmental professors, tracking and reporting on individual expense accounts for business administration.
Experience with R
- I am a newbie to R and I am really excited about this new experience!
Experience with other Analytic software
In additional to R, I have experience with tools such as:
- Python
- Tableau
- C#, C++
Part 2: Importing Data
- Fill in the blanks below to import the blood_transfusion.csv file (provided via Canvas) and answer the following questions.
df <- readr::read_csv(‘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.
- What are the dimensions of this data (number of rows and columns)?
dim(df)
\[[1] 748 5\]
- What are the data types of each column?
df <- readr::read_csv(‘blood_transfusion.csv’)
Rows: 748 Columns: 5 66MB/s, eta: 0s ── Column specification ────────────── Delimiter: “,” chr (1): Class dbl (4): Recency, Frequency, Monetary, Time
- Are there any missing values?
sum(is.na(df))
\[[1] 0\]
- Check out the first 10 rows? What are the Class values for the first 10 observations?
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 don…
6 4 4 1000 4 not don…
7 2 7 1750 14 donated
8 1 12 3000 35 not don…
9 2 9 2250 22 donated
10 5 46 11500 98 donated
- Check out the last 10 rows? What are the Class values for the last 10 observations?
tail(df, 10)
A tibble: 10 × 5
Recency Frequency Monetary Time Class
<dbl> <dbl> <dbl> <dbl> <chr>
1 23 1 250 23 not don…
2 23 4 1000 52 not don…
3 23 1 250 23 not don…
4 23 7 1750 88 not don…
5 16 3 750 86 not don…
6 23 2 500 38 not don…
7 21 2 500 52 not don…
8 23 3 750 62 not don…
9 39 1 250 39 not don…
10 72 1 250 72 not don…
- Index for the 100th row and just the Monetary column. What is the value?
df[100, ‘Monetary’]
A tibble: 1 × 1
Monetary
<dbl>
1 1750
- 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’]])
[1] 267
Question 2
Fill in the blanks below to import the PDI__Police_Data_Initiative__Crime_Incidents.csv data (provided via Canvas) and answer the questions that follow. Data is taken from the City of Cincinnati Open Data Portal website 4, which you may need to read to place context in your answers.
df <- readr::read_csv(’PDI__Police_Data_Initiative__Crime_Incidents.csv’)
• What are the dimensions of this data (number of rows and columns)?
dim(df)
[1] 15155 40
• What do you think these columns represent?
• Are there any missing values in this data? If so, how many missing values are in each column? Which column has the most missing values?
sum(is.na(df))
[1] 95592
colSums(is.na(df))
INSTANCEID
0
INCIDENT_NO
0
DATE_REPORTED
0
DATE_FROM
2
DATE_TO
9
CLSD
545
UCR
10
DST
0
BEAT
28
OFFENSE
10
LOCATION
2
THEFT_CODE
10167
FLOOR
14127
SIDE
14120
OPENING
14508
HATE_BIAS
0
DAYOFWEEK
423
RPT_AREA
239
CPD_NEIGHBORHOOD
249
WEAPONS
5
DATE_OF_CLEARANCE
2613
HOUR_FROM
2
HOUR_TO
9
ADDRESS_X
148
LONGITUDE_X
1714
LATITUDE_X
1714
VICTIM_AGE
0
VICTIM_RACE
2192
VICTIM_ETHNICITY
2192
VICTIM_GENDER
2192
SUSPECT_AGE
0
SUSPECT_RACE
7082
SUSPECT_ETHNICITY
7082
SUSPECT_GENDER
7082
TOTALNUMBERVICTIMS
33
TOTALSUSPECTS
7082
UCR_GROUP
10
ZIP
1 COMMUNITY_COUNCIL_NEIGHBORHOOD 0 SNA_NEIGHBORHOOD 0
• Using the DATE_REPORTED column, what is the range of dates included in this data?
range(df[[‘DATE_REPORTED’]])
[1] “01/01/2022 01:08:00 AM”
[2] “06/26/2022 12:50:00 AM”
• Using table(), what is the most common age range for known SUSPECT_AGEs?
table(df[[‘SUSPECT_AGE’]])
18-25
1778