Data 608 Story 6
Mikhail Broomes
2024-04-15
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## mapdf_state <- read.csv("https://raw.githubusercontent.com/MAB592/DATA-608-SP2024/main/foodsecurity(state)%20(2).csv")
df_all <- read.csv("https://raw.githubusercontent.com/MAB592/DATA-608-SP2024/main/foodsecurity(allhouseholds).xlsx.csv")
head(df_all)## Year Category Subcategory Sub.subcategory
## 1 2001 All households
## 2 2001 Household composition With children < 18 years
## 3 2001 Household composition With children < 18 years With children < 6 years
## 4 2001 Household composition With children < 18 years Married-couple families
## 5 2001 Household composition With children < 18 years Female head, no spouse
## 6 2001 Household composition With children < 18 years Male head, no spouse
## Total Food.secure.1.000 Food.secure.percent Food.insecure.1.000
## 1 107824 96303 89.3 11521
## 2 38330 32141 83.9 6189
## 3 16858 13920 82.6 2938
## 4 26182 23389 89.3 2793
## 5 9080 6185 68.1 2895
## 6 2389 2009 84.1 380
## Food.insecure.percent Low.food.security.1.000 Low.food.security.percent
## 1 10.7 8010 7.4
## 2 16.1 4744 12.4
## 3 17.4 2304 13.7
## 4 10.7 2247 8.6
## 5 31.9 2101 23.1
## 6 15.9 298 12.5
## Very.low.food.security.1.000 Very.low.food.security.percent
## 1 3511 3.3
## 2 1445 3.8
## 3 634 3.8
## 4 546 2.1
## 5 794 8.7
## 6 82 3.4Data Cleanining
df_all_household <- df_all[df_all$Sub.subcategory != "", ]df_state <- df_state[!(df_state$State %in% c("U.S. total", "U.S.")), ]
head(df_state)## Year State Food.insecurity.prevalence Food.insecurity.margin.of.error
## 2 2006–2008 AK 11.6 1.66
## 3 2006–2008 AL 13.3 1.66
## 4 2006–2008 AR 15.9 3.19
## 5 2006–2008 AZ 13.2 1.51
## 6 2006–2008 CA 12.0 0.74
## 7 2006–2008 CO 11.6 1.13
## Very.low.food.security.prevalence Very.low.food.security.margin.of.error
## 2 4.4 1.31
## 3 5.4 1.02
## 4 5.6 1.50
## 5 4.9 0.84
## 6 4.3 0.48
## 7 5.0 0.67specific_items <- c("Male head, no spouse", "Female head, no spouse", "Married-couple families")
df_children_household <- df_all_household[df_all_household$Sub.subcategory %in% specific_items, ]df_race <- df_all %>%
filter(`Subcategory` %in% c("White non-Hispanic", "Black non-Hispanic", "Hispanic"))
# View the filtered dataframe
print(df_race)## Year Category Subcategory Sub.subcategory Total
## 1 2001 Race/ethnicity of households White non-Hispanic 80337
## 2 2001 Race/ethnicity of households Black non-Hispanic 13134
## 3 2001 Race/ethnicity of households Hispanic 9864
## 4 2002 Race/ethnicity of households White non-Hispanic 80266
## 5 2002 Race/ethnicity of households Black non-Hispanic 13515
## 6 2002 Race/ethnicity of households Hispanic 10344
## 7 2003 Race/ethnicity of households White non-Hispanic 81080
## 8 2003 Race/ethnicity of households Black non-Hispanic 13156
## 9 2003 Race/ethnicity of households Hispanic 12034
## 10 2004 Race/ethnicity of households White non-Hispanic 81388
## 11 2004 Race/ethnicity of households Black non-Hispanic 13509
## 12 2004 Race/ethnicity of households Hispanic 12014
## 13 2005 Race/ethnicity of households White non-Hispanic 82144
## 14 2005 Race/ethnicity of households Black non-Hispanic 13732
## 15 2005 Race/ethnicity of households Hispanic 12397
## 16 2006 Race/ethnicity of households White non-Hispanic 82268
## 17 2006 Race/ethnicity of households Black non-Hispanic 14054
## 18 2006 Race/ethnicity of households Hispanic 12879
## 19 2007 Race/ethnicity of households White non-Hispanic 82882
## 20 2007 Race/ethnicity of households Black non-Hispanic 14209
## 21 2007 Race/ethnicity of households Hispanic 13378
## 22 2008 Race/ethnicity of households White non-Hispanic 82935
## 23 2008 Race/ethnicity of households Black non-Hispanic 14441
## 24 2008 Race/ethnicity of households Hispanic 13504
## 25 2009 Race/ethnicity of households White non-Hispanic 83259
## 26 2009 Race/ethnicity of households Black non-Hispanic 14519
## 27 2009 Race/ethnicity of households Hispanic 13566
## 28 2010 Race/ethnicity of households White non-Hispanic 83113
## 29 2010 Race/ethnicity of households Black non-Hispanic 14600
## 30 2010 Race/ethnicity of households Hispanic 14109
## 31 2011 Race/ethnicity of households White non-Hispanic 83304
## 32 2011 Race/ethnicity of households Black non-Hispanic 14765
## 33 2011 Race/ethnicity of households Hispanic 14410
## 34 2012 Race/ethnicity of households White non-Hispanic 83217
## 35 2012 Race/ethnicity of households Black non-Hispanic 14976
## 36 2012 Race/ethnicity of households Hispanic 15613
## 37 2013 Race/ethnicity of households White non-Hispanic 83496
## 38 2013 Race/ethnicity of households Black non-Hispanic 15311
## 39 2013 Race/ethnicity of households Hispanic 15648
## 40 2014 Race/ethnicity of households White non-Hispanic 84127
## 41 2014 Race/ethnicity of households Black non-Hispanic 15424
## 42 2014 Race/ethnicity of households Hispanic 16148
## 43 2015 Race/ethnicity of households White non-Hispanic 83931
## 44 2015 Race/ethnicity of households Black non-Hispanic 15734
## 45 2015 Race/ethnicity of households Hispanic 16803
## 46 2016 Race/ethnicity of households White non-Hispanic 84087
## 47 2016 Race/ethnicity of households Black non-Hispanic 15940
## 48 2016 Race/ethnicity of households Hispanic 17054
## 49 2017 Race/ethnicity of households White non-Hispanic 84548
## 50 2017 Race/ethnicity of households Black non-Hispanic 16358
## 51 2017 Race/ethnicity of households Hispanic 17197
## 52 2018 Race/ethnicity of households White non-Hispanic 84975
## 53 2018 Race/ethnicity of households Black non-Hispanic 16613
## 54 2018 Race/ethnicity of households Hispanic 18101
## 55 2019 Race/ethnicity of households White non-Hispanic 85196
## 56 2019 Race/ethnicity of households Black non-Hispanic 16504
## 57 2019 Race/ethnicity of households Hispanic 18035
## 58 2020 Race/ethnicity of households White non-Hispanic 85466
## 59 2020 Race/ethnicity of households Black non-Hispanic 16686
## 60 2020 Race/ethnicity of households Hispanic 18543
## 61 2021 Race/ethnicity of households White non-Hispanic 85866
## 62 2021 Race/ethnicity of households Black non-Hispanic 16951
## 63 2021 Race/ethnicity of households Hispanic 19082
## 64 2022 Race/ethnicity of households White non-Hispanic 85603
## 65 2022 Race/ethnicity of households Black non-Hispanic 17271
## 66 2022 Race/ethnicity of households Hispanic 19507
## Food.secure.1.000 Food.secure.percent Food.insecure.1.000
## 1 74230 92.4 6107
## 2 10331 78.7 2803
## 3 7717 78.2 2147
## 4 73859 92.0 6407
## 5 10546 78.0 2969
## 6 8099 78.3 2245
## 7 74733 92.2 6347
## 8 10251 77.9 2905
## 9 9347 77.7 2687
## 10 74383 91.4 7005
## 11 10303 76.3 3206
## 12 9404 78.3 2610
## 13 75444 91.8 6700
## 14 10658 77.6 3074
## 15 10176 82.1 2221
## 16 75810 92.2 6458
## 17 10991 78.2 3063
## 18 10367 80.5 2512
## 19 76342 92.1 6540
## 20 11057 77.8 3152
## 21 10694 79.9 2684
## 22 74041 89.3 8894
## 23 10732 74.3 3709
## 24 9873 73.1 3631
## 25 74066 89.0 9193
## 26 10908 75.1 3611
## 27 9922 73.1 3644
## 28 74117 89.2 8996
## 29 10941 74.9 3659
## 30 10416 73.8 3693
## 31 73823 88.6 9481
## 32 11066 74.9 3699
## 33 10629 73.8 3781
## 34 73914 88.8 9303
## 35 11287 75.4 3689
## 36 11982 76.7 3631
## 37 74636 89.4 8860
## 38 11322 73.9 3989
## 39 11939 76.3 3709
## 40 75335 89.5 8792
## 41 11393 73.9 4031
## 42 12527 77.6 3621
## 43 75563 90.0 8368
## 44 12357 78.5 3377
## 45 13592 80.9 3211
## 46 76271 90.7 7816
## 47 12346 77.5 3594
## 48 13907 81.5 3147
## 49 77083 91.2 7465
## 50 12799 78.2 3559
## 51 14108 82.0 3089
## 52 78106 91.9 6869
## 53 13087 78.8 3526
## 54 15164 83.8 2937
## 55 78438 92.1 6758
## 56 13356 80.9 3148
## 57 15216 84.4 2819
## 58 79387 92.9 6079
## 59 13058 78.3 3628
## 60 15359 82.8 3184
## 61 79843 93.0 6023
## 62 13597 80.2 3354
## 63 15983 83.8 3099
## 64 77682 90.7 7921
## 65 13406 77.6 3865
## 66 15453 79.2 4054
## Food.insecure.percent Low.food.security.1.000 Low.food.security.percent
## 1 7.6 4072 5.1
## 2 21.3 1986 15.1
## 3 21.8 1613 16.4
## 4 8.0 4294 5.3
## 5 22.0 1999 14.8
## 6 21.7 1654 16.0
## 7 7.8 4169 5.1
## 8 22.1 2010 15.3
## 9 22.3 2034 16.9
## 10 8.6 4632 5.7
## 11 23.7 2108 15.6
## 12 21.7 1903 15.8
## 13 8.2 4305 5.2
## 14 22.4 1894 13.8
## 15 17.9 1559 12.6
## 16 7.8 3937 4.8
## 17 21.8 1944 13.8
## 18 19.5 1780 13.8
## 19 7.9 4008 4.8
## 20 22.2 2064 14.5
## 21 20.1 1798 13.4
## 22 10.7 5154 6.2
## 23 25.7 2251 15.6
## 24 26.9 2439 18.1
## 25 11.0 5394 6.5
## 26 24.9 2259 15.6
## 27 26.9 2388 17.6
## 28 10.8 5512 6.6
## 29 25.1 2332 16.0
## 30 26.2 2507 17.8
## 31 11.4 5689 6.8
## 32 25.1 2155 14.6
## 33 26.2 2583 17.9
## 34 11.2 5452 6.6
## 35 24.6 2136 14.3
## 36 23.3 2469 15.8
## 37 10.6 5033 6.0
## 38 26.1 2442 15.9
## 39 23.7 2660 17.0
## 40 10.5 4966 5.9
## 41 26.1 2434 15.8
## 42 22.4 2502 15.5
## 43 10.0 4759 5.7
## 44 21.5 2127 13.5
## 45 19.1 2132 12.7
## 46 9.3 4591 5.5
## 47 22.5 2048 12.8
## 48 18.5 2152 12.7
## 49 8.8 4389 5.2
## 50 21.8 2164 13.2
## 51 18.0 2137 12.4
## 52 8.1 4109 4.9
## 53 21.2 2021 12.1
## 54 16.2 2018 11.1
## 55 7.9 3966 4.7
## 56 19.1 1891 11.5
## 57 15.6 1937 10.7
## 58 7.1 3547 4.1
## 59 21.7 2295 13.7
## 60 17.2 2263 12.2
## 61 7.0 3754 4.4
## 62 19.8 2019 11.9
## 63 16.2 2059 10.7
## 64 9.3 4522 5.3
## 65 22.4 2283 13.2
## 66 20.8 2682 13.8
## Very.low.food.security.1.000 Very.low.food.security.percent
## 1 2035 2.5
## 2 817 6.2
## 3 534 5.4
## 4 2113 2.6
## 5 970 7.2
## 6 591 5.7
## 7 2178 2.7
## 8 895 6.8
## 9 653 5.4
## 10 2373 2.9
## 11 1098 8.1
## 12 707 5.9
## 13 2395 2.9
## 14 1180 8.6
## 15 662 5.3
## 16 2521 3.1
## 17 1119 8.0
## 18 732 5.7
## 19 2532 3.1
## 20 1088 7.7
## 21 886 6.6
## 22 3740 4.5
## 23 1458 10.1
## 24 1192 8.8
## 25 3799 4.6
## 26 1352 9.3
## 27 1256 9.3
## 28 3484 4.2
## 29 1327 9.1
## 30 1186 8.4
## 31 3792 4.6
## 32 1544 10.5
## 33 1198 8.3
## 34 3851 4.6
## 35 1553 10.4
## 36 1162 7.4
## 37 3827 4.6
## 38 1547 10.1
## 39 1049 6.7
## 40 3826 4.5
## 41 1597 10.4
## 42 1119 6.9
## 43 3609 4.3
## 44 1250 7.9
## 45 1079 6.4
## 46 3225 3.8
## 47 1546 9.7
## 48 995 5.8
## 49 3076 3.6
## 50 1395 8.5
## 51 952 5.5
## 52 2760 3.2
## 53 1505 9.1
## 54 919 5.1
## 55 2792 3.3
## 56 1257 7.6
## 57 882 4.9
## 58 2532 3.0
## 59 1333 8.0
## 60 921 5.0
## 61 2269 2.6
## 62 1335 7.9
## 63 1040 5.5
## 64 3399 4.0
## 65 1582 9.2
## 66 1372 7.0unique(df_state$Year)## [1] "2006–2008" "2007–2009" "2008–2010" "2009–2011" "2010–2012" "2011–2013"
## [7] "2012–2014" "2013–2015" "2014–2016" "2015–2017" "2016–2018" "2017–2019"
## [13] "2018–2020" "2019–2021" "2020–2022"filtered_data <- df_state %>%
filter(Year %in% c("2020–2022"))Analyzing Food Security and Nutrition Data
We start by examining household-level data on food security and nutrition in the US. This data provides insights into the prevalence of food insecurity across different household compositions, including those with children and different racial and ethnic backgrounds.
Food Insecurity Trends Let’s first explore the trends in food insecurity among different household compositions over the years. The graph below illustrates the percentage of food-insecure households from 2008 to 2022.
# Food Insecurity by Household Composition
plot_ly(
data = df_all_household,
x = ~Year,
y = ~Food.insecure.percent,
color = ~Sub.subcategory,
type = 'scatter',
mode = 'lines+markers',
text = ~paste("Year: ", Year, "<br>Food Insecure Households Percent: ", Food.insecure.percent),
hoverinfo = 'text',
width = 800,
height = 400
) %>%
layout(
title = "<b>FOOD INSECURITY BY HOUSEHOLD COMPOSITION IN THE U.S.</b>",
xaxis = list(
title = "Year",
tickmode = "linear",
dtick = 1
),
yaxis = list(title = "Food Insecurity Households Percent"),
legend = list(x = 1, y = 1),
margin = list(b = 60, l = 60, r = 60, t = 60),
annotations = list(
list(
text = "Food Insecurity by household composition, 2008 - 2022",
x = 0, y = 1.1,
xref = "paper", yref = "paper",
showarrow = FALSE,
font = list(size = 14)
)
)
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsFood Insecurity by State
Next, let’s examine the variation in food insecurity prevalence across different states in the US from 2020 to 2022.
# Food Insecurity by State
ggplot(filtered_data, aes(x = reorder(State, -Food.insecurity.prevalence), y = Food.insecurity.prevalence, fill = State)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = rep(c("#FF9999", "#66CCCC", "#99CC99"), length(unique(filtered_data$State)))) +
labs(title = "Food Insecurity Prevalence by State (2020-2022)",
x = "State", y = "Food Insecurity Prevalence") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
coord_flip() +
guides(fill = 'none')
Food Insecurity Among Children
Lastly, we investigate food insecurity among children, focusing on different household compositions and racial/ethnic backgrounds.
# Food Insecurity Among Children by Household Composition
plot_ly(
data = df_children_household,
x = ~Year,
y = ~Food.insecure.percent,
color = ~Sub.subcategory,
type = 'scatter',
mode = 'lines+markers',
text = ~paste("Year: ", Year, "<br>Food Insecure Households Percent: ", Food.insecure.percent),
hoverinfo = 'text',
width = 800,
height = 400
) %>%
layout(
title = "<b>FOOD INSECURITY AMONG CHILDREN BY HOUSEHOLD COMPOSITION IN THE U.S.</b>",
xaxis = list(
title = "Year",
tickmode = "linear",
dtick = 1
),
yaxis = list(title = "Percent of Households with Children"),
legend = list(x = 1.00, y = 1),
margin = list(b = 60, l = 60, r = 60, t = 60),
annotations = list(
list(
text = "Food Insecurity among children by household composition, 2008 - 2022",
x = 0.0, y = 1.10,
xref = "paper", yref = "paper",
showarrow = FALSE,
font = list(size = 14)
)
)
)# Food Insecurity Among Children by Race and Ethnicity
plot_ly(
data = df_race,
x = ~Year,
y = ~Food.insecure.percent,
color = ~Subcategory,
type = 'scatter',
mode = 'lines+markers',
text = ~paste("Year: ", Year, "Food Insecurity Among Children: ", Food.insecure.percent),
hoverinfo = 'text',
width = 800,
height = 400
) %>%
layout(
title = list(
text = "<b>FOOD INSECURITY AMONG CHILDREN BY RACE AND ETHNICITY IN THE U.S.</b>",
x = -.10
),
xaxis = list(title = "Year"),
yaxis = list(title = "Food Insecurity Among Children"),
legend = list(x = 1.00, y = 1),
margin = list(b = 60, l = 60, r = 60, t = 60),
height = 400,
width = 800,
annotations = list(
list(
text = "Food Insecurity among children by race and ethnicity of head of household, 2008 - 2022",
x = 1.01, y = 1.10,
xref = "paper", yref = "paper",
showarrow = FALSE,
font = list(size = 14)
)
)
)## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()##Conclusion
Analyzing the data on food security and nutrition in the US reveals significant disparities across different household compositions, racial and ethnic backgrounds, and states. It’s evident that addressing food insecurity requires targeted interventions addressing the root causes, including poverty, systemic inequalities, and access to resources. As citizens, it’s imperative to advocate for policies and initiatives that aim to eradicate food insecurity and ensure the well-being of all individuals and families in the US.