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Reproductive Health Nigeria.Rmd
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Reproductive Health Nigeria.Rmd
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## Global -----
```{r load-packages, message = FALSE}
library(tidyverse)
library(changepoint)
```
```{r load-dataset}
df <- read.csv("iot_nigeria_dataset.csv")
df$week <- as.Date(df$week,"%m/%d/%Y")
```
```{r function to detect changepoints}
cptfn <- function(data, pen) {
ans <- cpt.mean(data, test.stat="Normal", method = "PELT", penalty = "Manual", pen.value = pen)
length(cpts(ans)) + 1
}
```
```{r function to plot graphs}
gtplot_cp_nigeria <- function(y, mean_y) {
plot <- ggplot(data = df) +
annotate("rect", xmin = as.Date("2020-03-31"), xmax = as.Date("2020-05-04"),
ymin = -Inf, ymax = Inf, fill = "#38aba6", alpha = 0.8) +
annotate("rect", xmin = as.Date("2020-05-04"), xmax = as.Date("2020-06-01"),
ymin = -Inf, ymax = Inf, fill = "#38aba6", alpha = 0.6) +
annotate("rect", xmin = as.Date("2020-06-01"), xmax = as.Date("2020-09-03"),
ymin = -Inf, ymax = Inf, fill = "#38aba6", alpha = 0.4) +
annotate("rect", xmin = as.Date("2020-09-03"), xmax = as.Date("2021-01-31"),
ymin = -Inf, ymax = Inf, fill = "#38aba6", alpha = 0.2) +
annotate(geom = "text", x = as.Date("2020-04-15"), y = 15, label = "Lockdown phase-1", angle = 90, color = "#3e2c38") +
annotate(geom = "text", x = as.Date("2020-05-20"), y = 15, label = "Lockdown phase-2", angle = 90, color = "#3e2c38") +
annotate(geom = "text", x = as.Date("2020-07-15"), y = 15, label = "Lockdown phase-3", angle = 90, color = "#3e2c38") +
annotate(geom = "text", x = as.Date("2020-11-15"), y = 15, label = "Lockdown phase-4", angle = 90, color = "#3e2c38") +
# geom_line(mapping = aes(x = week, y = y), color = '#157067') +
geom_smooth(aes(x = week, y = y), se = FALSE, method = "loess", color = "#7a6374", span = 0.17) +
geom_line(aes(x= week, y=mean_y), color='red') +
ylim(0,100) +
scale_x_date(date_breaks = "2 months", date_labels = "%b %y") +
ylab("Interest over time") +
xlab("") +
theme_minimal() +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 12))
return(plot)
}
```
## Theme-1: Pregnancy ----
```{r Theme-1: elbow-plot-for-changepoint-analysis}
pen.vals <- seq(0, 1000,10)
elbowplotData <- unlist(lapply(pen.vals, function(p)
cptfn(data = df$iot_pregnancy, pen = p)))
plot(pen.vals,elbowplotData,
xlab = "PELT penalty parameter",
ylab = " ",
main = " ")
## Based on the plot, we choose the penalty value as 420
```
```{r Theme-1: changepoints-computation}
penalty.val <- 420
mcpt <- cpt.mean(df$iot_pregnancy, penalty='Manual', pen.value=penalty.val, method='PELT')
# Get the index of change points:
mcpt.pts <- attributes(mcpt)$cpts
mcpt.pts
# Get the position of the change points:
df$week[mcpt.pts]
# Get mean and variance of the different segments
mcpt.var<- attributes(mcpt)$param.est$var
mcpt.var
mcpt.mean<- attributes(mcpt)$param.est$mean
mcpt.mean
df$mean_preg <- rep(mcpt.mean, c(mcpt.pts[1], diff(mcpt.pts)))
# number of cut points
length(mcpt.pts)
```
## Theme-2: Pregnancy Test----
```{r Theme-2: elbow-plot-for-changepoint-analysis}
pen.vals <- seq(0, 1000,10)
elbowplotData <- unlist(lapply(pen.vals, function(p)
cptfn(data = df$iot_pregnancy_test, pen = p)))
plot(pen.vals,elbowplotData,
xlab = "PELT penalty parameter",
ylab = " ",
main = " ")
## Based on the plot above, we choose the penalty value as 600
```
```{r Theme-2: changepoints-computation}
penalty.val <- 600
mcpt <- cpt.mean(df$iot_pregnancy_test, penalty='Manual', pen.value=penalty.val, method='PELT')
# Get the index of change points:
mcpt.pts <- attributes(mcpt)$cpts
mcpt.pts
# Get the position of the change points:
df$week[mcpt.pts]
# Get mean and variance of the different segments
mcpt.var<- attributes(mcpt)$param.est$var
mcpt.var
mcpt.mean<- attributes(mcpt)$param.est$mean
mcpt.mean
df$mean_preg_test <- rep(mcpt.mean, c(mcpt.pts[1], diff(mcpt.pts)))
# number of cut points
length(mcpt.pts)
```
## Theme-3: Abortion
```{r Theme-3: elbow-plot-for-changepoint-analysis}
pen.vals <- seq(0, 1000,10)
elbowplotData <- unlist(lapply(pen.vals, function(p)
cptfn(data = df$iot_abortion, pen = p)))
plot(pen.vals,elbowplotData,
xlab = "PELT penalty parameter",
ylab = " ",
main = " ")
## Based on the plot above, we choose the penalty value as 550
```
```{r Theme-3: changepoints-computation}
penalty.val <- 550
mcpt <- cpt.mean(df$iot_abortion, penalty='Manual', pen.value=penalty.val, method='PELT')
# Get the index of change points:
mcpt.pts <- attributes(mcpt)$cpts
mcpt.pts
# Get the position of the change points:
df$week[mcpt.pts]
# Get mean and variance of the different segments
mcpt.var<- attributes(mcpt)$param.est$var
mcpt.var
mcpt.mean<- attributes(mcpt)$param.est$mean
mcpt.mean
df$mean_abortion <- rep(mcpt.mean, c(mcpt.pts[1], diff(mcpt.pts)))
# number of cut points
length(mcpt.pts)
```
## Theme-4: Misoprostol
```{r Theme-4: elbow-plot-for-changepoint-analysis}
pen.vals <- seq(0, 1000,10)
elbowplotData <- unlist(lapply(pen.vals, function(p)
cptfn(data = df$iot_misoprostol, pen = p)))
plot(pen.vals,elbowplotData,
xlab = "PELT penalty parameter",
ylab = " ",
main = " ")
## Based on the plot above, we choose the penalty value as 950
```
```{r Theme-3: changepoints-computation}
penalty.val <- 950
mcpt <- cpt.mean(df$iot_misoprostol, penalty='Manual', pen.value=penalty.val, method='PELT')
# Get the index of change points:
mcpt.pts <- attributes(mcpt)$cpts
mcpt.pts
# Get the position of the change points:
df$week[mcpt.pts]
# Get mean and variance of the different segments
mcpt.var<- attributes(mcpt)$param.est$var
mcpt.var
mcpt.mean<- attributes(mcpt)$param.est$mean
mcpt.mean
df$mean_misoprostol <- rep(mcpt.mean, c(mcpt.pts[1], diff(mcpt.pts)))
# number of cut points
length(mcpt.pts)
```
## Plot Graphs ----
```{r plot graphs}
(plot1 <- gtplot_cp_nigeria(y = df$iot_pregnancy, mean_y = df$mean_preg))
(plot2 <- gtplot_cp_nigeria(y = df$iot_pregnancy_test, mean_y = df$mean_preg_test))
(plot3 <- gtplot_cp_nigeria(y = df$iot_abortion, mean_y = df$mean_abortion))
(plot4 <- gtplot_cp_nigeria(y = df$iot_misoprostol, mean_y = df$mean_misoprostol))
```