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11_domain_stratifications.R
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#Folate
tanzpred <- read.csv("folatemerged_output.csv")
data1 <- merge(tanzpred, tanz4geoloc, by="y4_hhid")
folate1 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(Prediction == 1),
flpredicted4 = (count_1 / total) *100
) %>%
select(domain, flpredicted4)
folate2 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(foladq == 1),
flestimated4 = (count_1 / total) *100
) %>%
select(domain, flestimated4)
folatedf <- merge(folate1, folate2, by="domain")
tanzpred <- read.csv("zincmerged_output.csv")
data1 <- merge(tanzpred, tanz4geoloc, by="y4_hhid")
zinc1 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(Prediction == 1),
znpredicted4 = (count_1 / total) *100
) %>%
select(domain, znpredicted4)
zinc2 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(znadq == 1),
znestimated4 = (count_1 / total) *100
) %>%
select(domain, znestimated4)
zincdf <- merge(zinc1, zinc2, by="domain")
tanz2 <- read.csv("tza.csv") %>%
mutate(znadq = ifelse(zn_mg < 10.2, 1, 0),
foladq = ifelse(folate_mcg < 250, 1, 0)) %>%
select(y5_hhid, znadq, foladq)
#Folate
tanzpred <- read.csv("folatewave5pred.csv")
tanzpred <- merge(tanz2, tanzpred, by="y5_hhid")
tanzpred <- na.omit(tanzpred)
data1 <- merge(tanzpred, tanz5geoloc, by="y5_hhid")
folate1 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(Prediction == 1),
flpredicted5 = (count_1 / total) *100
) %>%
select(domain, flpredicted5)
folate2 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(foladq == 1),
flestimated5 = (count_1 / total) *100
) %>%
select(domain, flestimated5)
folatedf2 <- merge(folate1, folate2, by="domain")
tanzpred <- read.csv("zincwave5pred.csv")
tanzpred <- merge(tanz2, tanzpred, by="y5_hhid")
tanzpred <- na.omit(tanzpred)
data1 <- merge(tanzpred, tanz5geoloc, by="y5_hhid")
zinc1 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(Prediction == 1),
znpredicted5 = (count_1 / total) *100
) %>%
select(domain, znpredicted5)
zinc2 <- data1 %>%
group_by(domain) %>%
summarize(
total = n(),
count_1 = sum(znadq == 1),
znestimated5 = (count_1 / total) *100
) %>%
select(domain, znestimated5)
zincdf2 <- merge(zinc1, zinc2, by="domain")
bin1 <- merge(zincdf, zincdf2, by="domain")
bin2 <- merge(folatedf, folatedf2, by="domain")
merged <- merge(bin2, bin1, by="domain")
merged$domain <- factor(merged$domain,
levels = c(1, 2, 3, 4),
labels = c("Dar es Salaam", "Other urban mainland", "Other rural mainland", "Zanzibar"))
merged_df <- merged
# merged_df <- readRDS("fulldata.Rds")
long_df <- merged_df %>%
select(domain, flestimated4, flestimated5) %>%
pivot_longer(cols = -domain,
names_to = "Variable",
values_to = "Value")
# Create the line plot with customized settings
ggplot(long_df, aes(x = domain, y = Value, color = Variable, group = Variable)) +
geom_line(size = 1) +
geom_point(size = 3) +
scale_color_manual(values = c("flestimated4" = "blue", "flestimated5" = "red")) +
labs(title = "Line Plot of Zn Predictions and Estimates",
x = "Location (Domain)",
y = "Value",
color = "Variables") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
long_df <- merged_df %>%
select(domain, flpredicted4, flpredicted5) %>%
pivot_longer(cols = -domain,
names_to = "Variable",
values_to = "Value")
# Create the line plot with customized settings
ggplot(long_df, aes(x = domain, y = Value, color = Variable, group = Variable)) +
geom_line(size = 1) +
geom_point(size = 3) +
scale_color_manual(values = c("flpredicted4" = "blue", "flpredicted5" = "red")) +
labs(title = "Line Plot of Zn Predictions and Estimates",
x = "Location (Domain)",
y = "Value",
color = "Variables") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
long_df <- merged_df %>%
select(domain, znestimated4, znestimated5) %>%
pivot_longer(cols = -domain,
names_to = "Variable",
values_to = "Value")
# Create the line plot with customized settings
ggplot(long_df, aes(x = domain, y = Value, color = Variable, group = Variable)) +
geom_line(size = 1) +
geom_point(size = 3) +
scale_color_manual(values = c("znestimated4" = "blue", "znestimated5" = "red")) +
labs(title = "Line Plot of Zn Predictions and Estimates",
x = "Location (Domain)",
y = "Value",
color = "Variables") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
long_df <- merged_df %>%
select(domain, znpredicted4, znpredicted5) %>%
pivot_longer(cols = -domain,
names_to = "Variable",
values_to = "Value")
# Create the line plot with customized settings
ggplot(long_df, aes(x = domain, y = Value, color = Variable, group = Variable)) +
geom_line(size = 1) +
geom_point(size = 3) +
scale_color_manual(values = c("znpredicted4" = "blue", "znpredicted5" = "red")) +
labs(title = "Line Plot of Zn Predictions and Estimates",
x = "Location (Domain)",
y = "Value",
color = "Variables") +
theme_minimal() +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
dumbbell_df4 <- merged_df %>%
select(domain, znpredicted4, znpredicted5) %>%
pivot_longer(cols = c(znpredicted4, znpredicted5),
names_to = "Type",
values_to = "Value") %>%
mutate(Type = factor(Type, levels = c("znpredicted4", "znpredicted5"),
labels = c("Predicted4", "Predicted5"))) %>%
arrange(domain)
# Create the Wave 4 dumbbell plot
plot_wave4 <- ggplot(dumbbell_df4, aes(y = factor(domain, levels = unique(dumbbell_df4$domain)))) +
geom_segment(data = merged_df,
aes(x = znpredicted4, xend = znpredicted5, yend = domain, color = "Difference"),
size = 1) +
geom_point(data = dumbbell_df4 %>% filter(Type == "Predicted4"),
aes(x = Value, color = Type), size = 3) +
geom_point(data = dumbbell_df4 %>% filter(Type == "Predicted5"),
aes(x = Value, color = Type), size = 3) +
labs(title = "Dumbbell Plot of Predicted Values Wave 4 vs 5",
x = "Value",
y = "Domain",
color = "Legend") +
scale_color_manual(values = c("Predicted4" = "blue",
"Predicted5" = "red",
"Difference" = "grey")) +
theme_minimal() +
theme(panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(angle = 0),
legend.position = "right")
dumbbell_df5 <- merged_df %>%
select(domain, znestimated4, znestimated5) %>%
pivot_longer(cols = c(znestimated4, znestimated5),
names_to = "Type",
values_to = "Value") %>%
mutate(Type = factor(Type, levels = c("znestimated4", "znestimated5"),
labels = c("Estimated4", "Estimated5"))) %>%
arrange(domain)
# Create the Wave 5 dumbbell plot
plot_wave5 <- ggplot(dumbbell_df5, aes(y = factor(domain, levels = unique(dumbbell_df5$domain)))) +
geom_segment(data = merged_df,
aes(x = znestimated4, xend = znestimated5, yend = domain, color = "Difference"),
size = 1) +
geom_point(data = dumbbell_df5 %>% filter(Type == "Estimated4"),
aes(x = Value, color = Type), size = 3) +
geom_point(data = dumbbell_df5 %>% filter(Type == "Estimated5"),
aes(x = Value, color = Type), size = 3) +
labs(title = "Dumbbell Plot of Estimated Values Wave 4 vs 5",
x = "Value",
y = "Domain",
color = "Legend") +
scale_color_manual(values = c("Estimated4" = "blue",
"Estimated5" = "red",
"Difference" = "grey")) +
theme_minimal() +
theme(panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(angle = 0),
legend.position = "right")
grid.arrange(plot_wave4, plot_wave5, ncol = 2)
####
# Prepare data for Wave 4 plot
dumbbell_df4 <- merged_df %>%
select(domain, znpredicted4, znpredicted5) %>%
pivot_longer(cols = c(znpredicted4, znpredicted5),
names_to = "Type",
values_to = "Value") %>%
mutate(Type = factor(Type, levels = c("znpredicted4", "znpredicted5"),
labels = c("Predicted(Wave4)", "Predicted(Wave5)")))
# Create the Wave 4 dumbbell plot
plot_wave4 <- ggplot(dumbbell_df4, aes(y = domain)) +
geom_segment(data = merged_df,
aes(x = znpredicted4, xend = znpredicted5, yend = domain, color = "Difference"),
size = 1) +
geom_point(data = dumbbell_df4 %>% filter(Type == "Predicted(Wave4)"),
aes(x = Value, color = Type), size = 3) +
geom_point(data = dumbbell_df4 %>% filter(Type == "Predicted(Wave5)"),
aes(x = Value, color = Type), size = 3) +
labs(x = "Proportion(%)",
y = "Strata",
color = "Scenario") +
scale_color_manual(values = c("Predicted(Wave4)" = "blue",
"Predicted(Wave5)" = "red")) +
theme_minimal() +
theme(panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(angle = 0),
legend.position = "right")
# Prepare data for Wave 5 plot
dumbbell_df5 <- merged_df %>%
select(domain, znestimated4, znestimated5) %>%
pivot_longer(cols = c(znestimated4, znestimated5),
names_to = "Type",
values_to = "Value") %>%
mutate(Type = factor(Type, levels = c("znestimated4", "znestimated5"),
labels = c("Estimated(Wave4)", "Estimated(Wave5)")))
# Create the Wave 5 dumbbell plot
plot_wave5 <- ggplot(dumbbell_df5, aes(y = domain)) +
geom_segment(data = merged_df,
aes(x = znestimated4, xend = znestimated5, yend = domain, color = "Difference"),
size = 1) +
geom_point(data = dumbbell_df5 %>% filter(Type == "Estimated(Wave4)"),
aes(x = Value, color = Type), size = 3) +
geom_point(data = dumbbell_df5 %>% filter(Type == "Estimated(Wave5)"),
aes(x = Value, color = Type), size = 3) +
labs(x = "Proportion(%)",
y = "Strata",
color = "Scenario") +
scale_color_manual(values = c("Estimated(Wave4)" = "blue",
"Estimated(Wave5)" = "red")) +
theme_minimal() +
theme(panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.y = element_text(angle = 0),
legend.position = "right")
# Arrange plots side by side
grid.arrange(plot_wave4, plot_wave5, ncol = 2)