About Me
Harshitha Baratam
2026-01-22
Part 1: HTML Bio
Introduction to Me
My name is Harshitha Baratam, and I am currently pursuing a Master’s degree in Information Systems at the University of Cincinnati.
Prior to graduate school, I worked at Tech Mahindra, where I contributed to an ETL migration project for Scotiabank, collaborating with cross-functional teams to deliver reliable and impactful data solutions.
Academic Background
- Bachelor’s degree in Computer Science and Engineering
- Currently pursuing a Master’s in Information Systems
- Relevant coursework includes:
- Data Wrangling
- Statistics
- Analytics Programming
- Data Wrangling
Professional Background
- Software Engineer at Tech Mahindra
- Experience in software development and data-related projects
- Strong interest in analytics, data engineering, and data-driven decision making
Experience with R
I have beginner-to-intermediate experience with R, including:
- Writing and executing R scripts
- Creating reproducible reports using R Markdown
- Importing, cleaning, and exploring datasets
- Working with tidyverse packages
Experience with Other Analytic Software
- Microsoft Excel (pivot tables, formulas)
- SQL
- Python
- Tableau / Power BI
Fun Fact
In my free time, I enjoy painting and crafting, which helps me relax and express creativity outside of academics.
Part 2: Importing Data
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.sum(is.na(df))## [1] 0dim(df)## [1] 748 5head(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 donatedtail(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 donateddf[100, "Monetary"]## # A tibble: 1 × 1
## Monetary
## <dbl>
## 1 1750mean(df[["Monetary"]])## [1] 1378.676above_avg <- df[["Monetary"]] > mean(df[["Monetary"]])
nrow(df[above_avg, ])## [1] 267Blood Transfusion Data Summary
- The dataset contains 748 rows and 5 columns.
- There are no missing values in the dataset.
- The average monetary value is 1378.676.
- There are 267 observations with a monetary value greater than the average.
# Import Police Crime Data
df <- 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.dim(df)## [1] 15155 40sum(is.na(df))## [1] 95592colSums(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 0range(df[["DATE_REPORTED"]])## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"table(df[["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
## 9003sort(table(df[["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 1day_tbl <- table(df[["DAYOFWEEK"]])
day_tbl / sum(day_tbl)##
## FRIDAY MONDAY SATURDAY SUNDAY THURSDAY TUESDAY WEDNESDAY
## 0.1369807 0.1438365 0.1542221 0.1448547 0.1363019 0.1432935 0.1405105Police Crime Data Summary
- The dataset contains 15,155 rows and 40 columns.
- Several columns contain missing values, particularly demographic-related fields.
- The data ranges from January 1, 2022 to June 26, 2022.
- The most common suspect age category is UNKNOWN.
- ZIP code 45202 has the highest number of reported incidents.
- Crime incidents occur most frequently on Saturday.
Part 3: Computing Environment
library(sessioninfo)
session_info(pkgs = "attached")## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.5.2 (2025-10-31)
## os macOS Tahoe 26.2
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2026-01-23
## pandoc 3.6.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
## quarto 1.8.25 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## sessioninfo * 1.2.3 2025-02-05 [1] CRAN (R 4.5.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library
## * ── Packages attached to the search path.
##
## ──────────────────────────────────────────────────────────────────────────────