Introduction
Sudip Dahal
2025-01-26
Who Am I?
Photo of myself when I was 21 years old.
Hello! My name is Sudip Dahal. I am currently a graduate student at UC Linder and I am super excited to learn R and data analysis.
Academic Background
- Master in Information Systems
- University of Cincinnati
Professional Background
- I work at Medpace as a Application Administrator.
Experience with R
I am new to R but excited to learn it for data analysis and visualization.
Experience with Other Analytic Software
- Excel
- Python
- SQL
Blood Transfusion Data Analysis
Import the Data
# Import the data
df <- 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.# Dimensions of the data
dim(df)## [1] 748 5# Data structure
str(df)## spc_tbl_ [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>Data Cleaning and Exploration
# Check for missing values
sum(is.na(df))## [1] 0# 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# Value in the 100th row and 'Monetary' column
df[100, 'Monetary']## # A tibble: 1 × 1
## Monetary
## <dbl>
## 1 1750# Mean of the 'Monetary' column
mean_monetary <- mean(df[['Monetary']], na.rm = TRUE)
mean_monetary## [1] 1378.676# Subset where 'Monetary' > mean
above_avg <- df[df[['Monetary']] > mean_monetary, ]
nrow(above_avg)## [1] 267Police Data Initiative Analysis
Import the Data
# Import the data
df <- 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 of the data
dim(df)## [1] 15155 40Missing Values and Summary
# Check for missing values
colSums(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 0# Range of 'DATE_REPORTED'
range(df[['DATE_REPORTED']], na.rm = TRUE)## [1] "01/01/2022 01:08:00 AM" "06/26/2022 12:50:00 AM"# Most common 'SUSPECT_AGE'
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
## 9003Data Exploration
# Number of incidents per ZIP code
zip_table <- table(df[['ZIP']])
zip_table_sorted <- sort(zip_table, decreasing = TRUE)
zip_table_sorted##
## 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# Most common day of incidents
day_table <- table(df[['DAYOFWEEK']])
most_common_day <- names(which.max(day_table))
most_common_day## [1] "SATURDAY"# Proportion of incidents on the most common day
proportion <- max(day_table) / sum(day_table)
proportion## [1] 0.1542221