Story 6
Jonathan Cruz
2024-04-14
hh_fs <- read.csv("https://raw.githubusercontent.com/jamilton08/FoodSecurity/main/household_fs.csv") |> rename(cat = Sub.subcategory) |>
mutate(cat = case_when(cat == "Male head, no spouse" ~ "MH",
cat == "Female head, no spouse" ~ "FH",
cat == "Women living alone" ~ "WA",
cat == "Men living alone" ~ "MA",
cat == "Other household with child" ~ "OHC",
cat == "With children < 6 years" ~ "WC",
cat == "Married-couple families" ~ "MC",
cat == "Elderly living alone" ~ "E",
cat == "More than one adult" ~ "MTA",
TRUE ~ cat)
) |>
filter(cat %in% c("MH" , "FH" , "WA" , "MA" , "OHC" , "WC", "MC", "E", "MTA")) |>
rename(sc = Subcategory) |>
mutate(children = ifelse(sc == "With children < 18 years", "y", "n")) |>
mutate(sex = case_when(cat == "MH" ~ "m",
cat == "MA" ~ "m",
cat == "FH" ~ "f",
cat == "WA" ~ "f")
) |>
mutate(relationship = case_when(cat == "MH" ~ "s",
cat == "FH" ~ "s",
cat == "WA" ~ "s",
cat == "MA" ~ "s",
cat == "MC" ~ "t",
cat == "MTA" ~ "t")
)|>
mutate(age_group = case_when(cat == "MH" ~ "Child",
cat == "FH" ~ "Child",
cat == "WA" ~ "Adult",
cat == "MA" ~ "Adult",
cat == "MTA" ~ "Adult",
cat == "OHC" ~ "Child",
cat == "WC" ~ "Child",
cat == "MC" ~ "Child",
cat == "E" ~ "Elderly")
)Generate Data For Gender and Child Analysis
t <- hh_fs |>
group_by(Year, sex, children) |>
summarise(food_security = mean(Food.secure.percent) / 100) |>
na.omit() |>
mutate(id = str_c(sex, children)) |>
mutate(groups = case_when(id == "fn" ~ "Female, no Children",
id == "fy" ~ "Female, with Children",
id == "mn" ~ "Male, no Children",
id == "my" ~ "Male, with Children")
)## `summarise()` has grouped output by 'Year', 'sex'. You can override using the
## `.groups` argument.Gender and Guardian Analysis
ggplot(shape=t$groups) +
geom_line(data=t,aes(x=Year,y= food_security,color=groups),size=1)+
geom_point(data=t,aes(x=Year,y=food_security,color=groups,shape = groups),size=3) +
theme_bw() +
labs(
title = "Food security status of single individuals",
subtitle = "The average food security of singles over 22 years, categorized by gender and guardianship status."
) +
theme(
plot.title = element_text(color = "#0099f9", size = 20, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 8, face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
scale_y_continuous(labels = scales::percent) +
ylab("Food Security Percent")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Generate Data For Child Analysis
child_a <- hh_fs |>
group_by(Year, children) |>
summarise(food_security = mean(Food.secure.percent) / 100) |>
na.omit() ## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.Child Food Security Analysis
Singles with kids have less food security then singles without kids.
ggplot(shape=child_a$children) +
geom_line(data=child_a,aes(x=Year,y= food_security,color=children),size=1)+
geom_point(data=child_a,aes(x=Year,y=food_security,color=children,shape = children),size=3) +
theme_bw() +
labs(
title = "Average Food Security Status For Children",
subtitle = "The average food security of adults with no children vs adults with children over 22 years"
) +
theme(
plot.title = element_text(color = "#0099f9", size = 20, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 8, face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
scale_y_continuous(labels = scales::percent) +
ylab("Food Security Percent") +
guides(color = guide_legend(title = "Has Children"), shape = guide_legend(title = "Has Children") ) 
## Generate Data For Age Group Analysis
age_group <- hh_fs |>
group_by(Year, age_group) |>
summarise(food_security = mean(Food.secure.percent) / 100) ## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.Age Group Analysis
As you get older you see that the drop off in food security, hence the drop in support makes a difference. This also contradicts the idea that kids have less food security however I believe that families being included gives it a diffirent perspective.
ggplot(shape=age_group$age_group) +
geom_line(data=age_group,aes(x=Year,y= food_security,color=age_group),size=1)+
geom_point(data=age_group,aes(x=Year,y=food_security,color=age_group,shape = age_group),size=3) +
theme_bw() +
labs(
title = "Average Food Security Status Categorized By Age Group",
subtitle = "Food Security Status for Adults, Children and Elders"
) +
theme(
plot.title = element_text(color = "#0099f9", size = 15, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 8, face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
scale_y_continuous(labels = scales::percent) +
ylab("Food Security Percent") +
guides(color = guide_legend(title = "Has Children"), shape = guide_legend(title = "Has Children") )
Generate Data For State Food Security Analysis
states_fs <- read.csv("https://raw.githubusercontent.com/jamilton08/FoodSecurity/main/state_fs.csv") |>
filter(Year == "2020–2022")
states_fs <- states_fs[2:nrow(states_fs),]Food Security By State Analysis
ggplot(states_fs,
aes(x=reorder(State, Food.insecurity.prevalence),
y = Food.insecurity.prevalence,
group = 1, fill = "States")) +
geom_bar(stat="identity", width = 0.8) +
coord_flip() +
theme_ipsum() +
scale_x_discrete(guide = guide_axis(angle = 10)) +
geom_text(aes(label = Food.insecurity.prevalence),
colour = "white",
size = 2.4,
vjust =0.45,
hjust = 1.2,
position = position_dodge(0.1)
) +
labs(title = "Food Insecurity Status By State",
subtitle = "From 2020 to 2022 ",
y= "Food Insecurity Prevalence",
x = "States") +
theme(axis.title = element_text(family = "Helvetica",
size = (20),
colour = "steelblue4",
face = "bold.italic"),
plot.title = element_text(family ="Helvetica", size = (20), colour = "steelblue4", face = "bold.italic"),
plot.subtitle = element_text(family = "Helvetica",
size = (12), colour = "lightblue3",
face = "bold.italic"))
poverty <- read.csv("poverty.csv") |>
rename(below_pov = X2022.Percent.below.poverty.level)
poverty$state <- state.abb[match(poverty$State,state.name)]
poverty <- poverty |> na.omit()
states_fs <- states_fs |> rename(state = State)
pov_an <- inner_join(poverty, states_fs) |>
rename(fip = Food.insecurity.prevalence) |>
select(state, fip, below_pov)## Joining with `by = join_by(state)`Poverty vs Food Security per State Analysis
As the poverty is higher, we see that there’s more insecurity.
cc<-ggplot(data = pov_an) +
geom_point(mapping = aes(x=state, y=below_pov, color = fip, size = fip)) +
labs(
title = "The Effect of Poverty on Food Security",
) +
theme_minimal() +
theme(
plot.title = element_text(color = "#0099f9", size = 20, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 13, face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(size = 10, face = "bold", angle = 60, hjust=1)
) +
ylab("Percent Below Poverty Level") +
xlab("State") +
scale_color_continuous(limits=c(1, 7), breaks=seq(1, 7, by=1)) +
guides(color= guide_legend(title = "Food Insecurity Prevalence"), size=guide_legend(title = "Food Insecurity Prevalence"))
cc +
scale_color_gradient(name = "fip", low = "#ffff00", high = "#8b0000") ## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.