library(tidyverse)

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library(tidyr)
library(ggplot2)
library(pastecs)

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##     extract

library(readxl)
Quant_Data_Set <- read_excel("C:/Users/carmo/Downloads/Quant Data Set.xlsx")
View(Quant_Data_Set)

I start by removing the any values listed as “N/A”

Clean_Data <- drop_na(Quant_Data_Set)

After removing the “N/A” values, I run a pairs function for the entire dataset in order to easily visualize postivie or negative correlations amongst all variables in the dataset.

pairs(Clean_Data)

I do notice many postive correlations in this data set. However, most of them are specific to funding. For instance, there is a positive correlation to between the Overall Funding a community receives and the a bucket of funding within that overall bucket. As my topic is specific to understanding the relationship between funding and the rate of homelessness, I chose to view the correlation of the two. As both of the varaibles are not normal, I ran a log to both and used that log value before running cor and pairs.

Clean_Pairs_Data <- Clean_Data %>% select(`Total Amount Awarded`, `Overall Homeless`)

Clean_Log <- log(Clean_Pairs_Data)

cor(Clean_Log)

##                      Total Amount Awarded Overall Homeless
## Total Amount Awarded            1.0000000        0.6914389
## Overall Homeless                0.6914389        1.0000000

pairs(Clean_Log)

cor(Clean_Log, method = 'spearman')

##                      Total Amount Awarded Overall Homeless
## Total Amount Awarded            1.0000000        0.6878916
## Overall Homeless                0.6878916        1.0000000

cor(Clean_Log, method = 'kendall')

##                      Total Amount Awarded Overall Homeless
## Total Amount Awarded            1.0000000        0.5004718
## Overall Homeless                0.5004718        1.0000000

In looking at the three correlations, it determines that is a positive correlation between the Total Amount of Funding a community receives and the Overall Homelessness in that community. Basically, it is saying, that for unit of funding awarded, there is .069 units of homelessness.

HW 5

Marcos Carmona

2025-10-29

I start by removing the any values listed as “N/A”

After removing the “N/A” values, I run a pairs function for the entire dataset in order to easily visualize postivie or negative correlations amongst all variables in the dataset.

In looking at the three correlations, it determines that is a positive correlation between the Total Amount of Funding a community receives and the Overall Homelessness in that community. Basically, it is saying, that for unit of funding awarded, there is .069 units of homelessness.