Story 6 Data 608
CN
2024-04-16
\[Libraries & Data\]
library(readr)## Warning: package 'readr' was built under R version 4.3.2library(plotly)## Warning: package 'plotly' was built under R version 4.3.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(ggplot2)
library(tidyverse)## Warning: package 'tidyr' was built under R version 4.3.2## Warning: package 'purrr' was built under R version 4.3.2## Warning: package 'dplyr' was built under R version 4.3.2## Warning: package 'stringr' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ stringr 1.5.1
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata= read.csv("C:/Users/Chafiaa/Downloads/Food security.csv")
head(data)## X State Year POVALL_2021 PCTPOVALL_2021 POV017_2021 PCTPOV017_2021
## 1 0 AL 2021 800848 16.3 250327 22.7
## 2 1 AK 2021 77736 10.8 23760 13.5
## 3 2 AZ 2021 919680 12.9 281696 17.8
## 4 3 AR 2021 471195 16.0 150353 21.8
## 5 4 CA 2021 4742405 12.3 1362903 15.8
## 6 5 CO 2021 554126 9.7 144163 11.8
## POV517_2021 PCTPOV517_2021 MEDHHINC_2021 POV04_2021 PCTPOV04_2021
## 1 176596 21.6 53990 71220 25.1
## 2 16316 12.7 78437 6633 14.1
## 3 197841 16.7 68967 76649 19.7
## 4 102718 20.1 52577 45096 25.5
## 5 993484 15.4 84831 339169 15.8
## 6 99663 10.9 82228 39308 12.8
## OverallFoodInsecurityRate SeniorFoodInsecurityRate.StateofSeniorHunger.
## 1 0.148 0.089
## 2 0.107 0.084
## 3 0.103 0.074
## 4 0.155 0.076
## 5 0.105 0.074
## 6 0.092 0.074
## X.ofFoodInsecureSeniors.StateofSeniorHunger.
## 1 103567
## 2 12149
## 3 129412
## 4 55600
## 5 620899
## 6 102243
## OlderAdultFoodInsecurityRate.StateofSeniorHunger.
## 1 0.144
## 2 0.080
## 3 0.085
## 4 0.198
## 5 0.104
## 6 0.082
## X.ofFoodInsecureOlderAdults.StateofSeniorHunger. ChildFoodInsecurityRate
## 1 100289 0.183
## 2 6184 0.128
## 3 79721 0.140
## 4 75302 0.191
## 5 485709 0.135
## 6 55579 0.105
## X.ofFoodInsecureChildren
## 1 204830
## 2 22960
## 3 226080
## 4 134690
## 5 1182720
## 6 129900\[Food\ insecurity \ by\ state\]
state_bp <- ggplot(data, aes(x=reorder(State, OverallFoodInsecurityRate), y=OverallFoodInsecurityRate, fill=State)) +
geom_col() + theme_minimal() + coord_flip()
state_bp <- state_bp + theme(legend.position="none")
state_bp <- state_bp + theme(text = element_text(size=8), axis.title=element_text(size=12))
state_bp <- state_bp + labs(title = "Food Insecurity by State", x= "State", y= "Food Insecurity rate")
state_bp <- state_bp + theme(plot.title = element_text(size=8))
state_bp
## From the plot "Food Insecurity by State" we can see the highest rate of poverty is Mississippi, Arizona, Alabama, Texas\[Poverty\ Rate\ by\ State\]
map <- plot_geo(data, locationmode = 'USA-states') %>%
add_trace(
z = ~POVALL_2021,
locations = ~State,
color = ~POVALL_2021,
colorscale = 'purple',
colorbar = list(title = 'Poverty_Rate (%)')
) %>%
layout(
title = 'US Poverty Rate in 2021',
geo = list(scope = 'usa')
)
map## Warning: Ignoring 102 observations##From the plot "US Poverty Rate in 2021" the states with the highest poverty rate is CA and Texas, Arizona, ...etc\[From \ the \ two\ plots\ above\ I\ can\ say\ that\ the \ states \ with\ highest\ poverty\ rate \ has\ food \ insecurity \] \[Correlation\ between\ poverty \ rate\ and\ food\ insecurity\]
ggplot(data, aes(x = POVALL_2021, y = OverallFoodInsecurityRate)) +
geom_point(color = "blue") +
geom_smooth(method = "lm", formula = y ~ x, se = FALSE, color = "red") +
geom_abline(intercept = 0, slope = 0, linetype = "dashed", color = "black") +
labs(x = "Poverty Rate", y = "Food Insecurity ", title = "Correlation between Poverty Rate and Food Insecurity Prevalence") ## Warning: Removed 102 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 102 rows containing missing values (`geom_point()`).
from the correlation plot we can confirm the conclusion on my first 2 plots, poverty lead to food insecurity.
\[Food\ insecurite\ on \ US \ by \ Age\]
plot_ly(data, x = ~State, y = ~SeniorFoodInsecurityRate.StateofSeniorHunger., color = ~SeniorFoodInsecurityRate.StateofSeniorHunger.
, type = 'scatter', mode = 'lines') %>%
layout(title = "Food Insecurity-Seniors",
xaxis = list(title = "State", tickangle = 35),
yaxis = list(title = "Food Insecurity Rate"),
legend = list(title = "Senior age"),
margin = list(l = 45, r = 45, b = 45, t = 45))## Warning: line.color doesn't (yet) support data arrays
## Warning: line.color doesn't (yet) support data arraysplot_ly(data, x = ~State, y = ~ChildFoodInsecurityRate
, color = ~ChildFoodInsecurityRate
, type = 'scatter', mode = 'lines') %>%
layout(title = "Food Insecurity-Children",
xaxis = list(title = "State", tickangle = 35),
yaxis = list(title = "Food Insecurity Rate"),
legend = list(title = "Children age"),
margin = list(l = 45, r = 45, b = 45, t = 45)) ## Warning: line.color doesn't (yet) support data arrays
## Warning: line.color doesn't (yet) support data arraysplot_ly(data, x = ~State, y = ~OlderAdultFoodInsecurityRate.StateofSeniorHunger.
, color = ~OlderAdultFoodInsecurityRate.StateofSeniorHunger.
, type = 'scatter', mode = 'lines') %>%
layout(title = "Food Insecurity-Adults",
xaxis = list(title = "State", tickangle = 35),
yaxis = list(title = "Food Insecurity Rate"),
legend = list(title = "Adults age"),
margin = list(l = 45, r = 45, b = 45, t = 45)) ## Warning: line.color doesn't (yet) support data arrays
## Warning: line.color doesn't (yet) support data arrays##From the 03 plot of food insecurity by age we see that children suffering from food insecurity then adults but senior citizen have less rate of food insecurity .
##Conclusion: Food insecurity is related to poverty, unfortunately in the USA children suffer more from food insecurity and adults come in the second place because a lot of adults have expensive housing bills, medical bills and unemoployment problems which lead to poverty and less money to spend to feed them self and their children but senior citizen we see them in better place because they get some discounts on medical bills (Medicaid), social security and most of them their children grow up and left the household.