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Correlation Analysis

I am using my Texas Department of Family and Protective Services dataset to examine the relationship between fiscal year and the number of victims.

Loading the Data

dfps_data <- read.csv("dfps_data.csv", check.names = FALSE)
head(dfps_data)
##   Fiscal Year Program     County    Region Confirmed Victims # of Victims
## 1        2025     DCI Hutchinson 1-Lubbock     Not Confirmed            1
## 2        2025     DCI    Lubbock 1-Lubbock     Not Confirmed           36
## 3        2025     DCI    Lubbock 1-Lubbock  Confirmed Victim            4
## 4        2025     DCI  Ochiltree 1-Lubbock     Not Confirmed            1
## 5        2025     DCI     Potter 1-Lubbock     Not Confirmed           13
## 6        2025     DCI     Potter 1-Lubbock  Confirmed Victim            1
str(dfps_data)
## 'data.frame':    3121 obs. of  6 variables:
##  $ Fiscal Year      : int  2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 ...
##  $ Program          : chr  "DCI" "DCI" "DCI" "DCI" ...
##  $ County           : chr  "Hutchinson" "Lubbock" "Lubbock" "Ochiltree" ...
##  $ Region           : chr  "1-Lubbock" "1-Lubbock" "1-Lubbock" "1-Lubbock" ...
##  $ Confirmed Victims: chr  "Not Confirmed" "Not Confirmed" "Confirmed Victim" "Not Confirmed" ...
##  $ # of Victims     : chr  "1" "36" "4" "1" ...

Preparing the Data

The number of victims variable must be numeric for correlation analysis.

dfps_data$`# of Victims` <- as.numeric(dfps_data$`# of Victims`)
## Warning: NAs introduced by coercion
correlation_data <- dfps_data %>%
  select(`Fiscal Year`, `# of Victims`)
correlation_data <- na.omit(correlation_data)
head(correlation_data)
##   Fiscal Year # of Victims
## 1        2025            1
## 2        2025           36
## 3        2025            4
## 4        2025            1
## 5        2025           13
## 6        2025            1

Correlation Matrix

cor(correlation_data)
##              Fiscal Year # of Victims
## Fiscal Year    1.0000000    0.0437523
## # of Victims   0.0437523    1.0000000

The correlation coefficient shows the direction and strength of the relationship between fiscal year and victim counts.

Visual Examination Using Pairs

pairs(correlation_data)

The pairs plot provides a visual examination of the relationship between fiscal year and victim counts.

Spearman’s Rho Correlation

The number of victims was strongly right-skewed in Homework 4, so I selected Spearman’s correlation.

cor.test(correlation_data$`Fiscal Year`,
         correlation_data$`# of Victims`,
         method = "spearman",
         exact = FALSE)
## 
##  Spearman's rank correlation rho
## 
## data:  correlation_data$`Fiscal Year` and correlation_data$`# of Victims`
## S = 4856082213, p-value = 0.03912
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.03695855

Conclusion

The correlation coefficient describes the relationship between fiscal year and victim counts. The p-value indicates whether the association is statistically significant. Correlation does not establish causation.