HTML Bio

Who Am I?

My name is Mehak Tiku, and I am 23 years old. I am originally from New Delhi, India. I am a big foodie who loves exploring new places and trying new food. I am also an adventure seeker and have a passion for dancing.

This Is Me!

Mehak Tiku

Academic Background

Bachelors: Computer Science and Business Systems (Completed in India)
Masters: Currently pursuing a Master of Science in Information Systems at the University of Cincinnati

Professional Background

Internship: Front End Development internship during my bachelors
Current Status: Moved directly into my Master’s program after undergraduate studies

Technical Experience

Experience with R

I am new to R, but I have acquired basic knowledge through my coursework.

Experience with Other Software

Excel
Tableau
Power BI
SQL

Part 2: Blood Transfusion Data

# Import blood transfusion data
df_blood <- 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.

# Check for missing values
sum(is.na(df_blood))

## [1] 0

# Dimensions of the data
dim(df_blood)

## [1] 748   5

# First 10 rows
head(df_blood, 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_blood, 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

# 100th row, Monetary column
df_blood[100, "Monetary"]

## # A tibble: 1 × 1
##   Monetary
##      <dbl>
## 1     1750

# Mean of Monetary column
mean(df_blood[["Monetary"]])

## [1] 1378.676

# Subset Monetary > mean
above_avg <- df_blood[["Monetary"]] > mean(df_blood[["Monetary"]])
nrow(df_blood[above_avg, ])

## [1] 267

# Import Police Data
df_police <- readr::read_csv("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
dim(df_police)

## [1] 15155    40

# Missing values (total and per column)
sum(is.na(df_police))

## [1] 95592

colSums(is.na(df_police))

##                     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 of dates reported
range(df_police[["DATE_REPORTED"]])

## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"

# Most common suspect age
table(df_police[["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

# Incidents per zip code (sorted)
sort(table(df_police[["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     1

# Proportion of incidents by day of the week
table(df_police[["DAYOFWEEK"]]) / sum(table(df_police[["DAYOFWEEK"]]))

## 
##    FRIDAY    MONDAY  SATURDAY    SUNDAY  THURSDAY   TUESDAY WEDNESDAY 
## 0.1369807 0.1438365 0.1542221 0.1448547 0.1363019 0.1432935 0.1405105