R Markdown - A tall white fountain played

options (scipen = 100) #Once more, still couldn't figure out how to get rid of exponents in data. Opted for this instead
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union
library(readxl)
library(tidyverse)
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## ✔ ggplot2   4.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.3.0
## ✔ purrr     1.1.0     ✔ tidyr     1.3.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
CAFB_SetUp <- read.csv("Capital_Area_Food_Bank_Hunger_Estimates.csv")
CAFB_Report <- CAFB_SetUp[c("TRACT", "F15_FI_RATE", "F15_FI_POP", "F15_LB_NEED", "F15_DISTRIB", "F15_LB_UNME")]
summary(CAFB_Report)
##      TRACT         F15_FI_RATE       F15_FI_POP      F15_LB_NEED    
##  Min.   :   100   Min.   :0.0000   Min.   :   0.0   Min.   :     0  
##  1st Qu.:201802   1st Qu.:0.0470   1st Qu.: 174.7   1st Qu.: 36689  
##  Median :492400   Median :0.0900   Median : 351.2   Median : 73760  
##  Mean   :512323   Mean   :0.1068   Mean   : 427.7   Mean   : 89816  
##  3rd Qu.:800607   3rd Qu.:0.1415   3rd Qu.: 609.1   3rd Qu.:127911  
##  Max.   :920200   Max.   :0.4710   Max.   :2178.6   Max.   :457514  
##   F15_DISTRIB      F15_LB_UNME    
##  Min.   :     0   Min.   :     0  
##  1st Qu.:  8478   1st Qu.: 23523  
##  Median : 21884   Median : 46737  
##  Mean   : 32752   Mean   : 57063  
##  3rd Qu.: 44673   3rd Qu.: 79975  
##  Max.   :243138   Max.   :290836
#For this, I believe it is important to see how much food is commonly needed. With this information, food banks can potentially have food portioned more appropriately to fit the need for each tract.
hist(CAFB_Report$F15_LB_NEED,
     main = "Food needed by census tract",
     xlab = "Estimated poundage")

plot(CAFB_Report$F15_FI_POP,CAFB_Report$F15_LB_UNME, 
     xlab = "Estimated population experiencing food insecurity based on census tract", ylab = "Unmet food need in LBS", col = c("red", "blue"))

cor(CAFB_Report$F15_FI_RATE, CAFB_Report$F15_LB_UNME)
## [1] 0.6624265
  1. Data only comes with sparse dictionary. The 6 chosen columns for my dataset were picked as they were provided a description for these columns.
  2. Yes, all data collected is scrubbed of personal information.
  3. Yes, this was provided to the District of Columbia’s government as a courtesy from the Capital Area Food Bank.
  4. Yes, this data set is composed of over a thousand observations of food disparity within several tracts in D.C.
  5. Yes, this data is relevant to public/nonprofit administration to address the hunger issues in the region.

https://catalog.data.gov/dataset/capital-area-food-bank-hunger-estimates