# How can a seven-day bandwidth local linear regression be plotted over time using ggplot2?

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Here is an example code to plot a seven-day bandwidth local linear regression using ggplot2:

# Load necessary libraries library(ggplot2) library(dplyr) library(zoo) # Create some dummy data set.seed(123) data <- data.frame(date = seq(as.Date("2021-01-01"), as.Date("2021-01-31"), by = "day"), value = rnorm(31)) # Define the bandwidth for local linear regression bw <- 7 # Calculate the local linear regression using the zooreg function from the zoo package data_llr <- data %>% select(date, value) %>% mutate(date = as.Date(date)) %>% mutate(value_llr = zoo::rollapplyr(value, bw, function(x) lm(value ~ as.numeric(date), data = data.frame(date = x))\$fitted)) # Plot the data and the local linear regression using ggplot2 ggplot(data_llr, aes(x = date)) + geom_line(aes(y = value), color = "blue") + geom_line(aes(y = value_llr), color = "red") + ggtitle("Seven-day bandwidth local linear regression") + xlab("Date") + ylab("Value") 

This code assumes that the data is in a data.frame with columns date and value. The bw variable defines the bandwidth for the local linear regression. The zoo::rollapplyr function is used to apply the local linear regression over a rolling window of width bw. The resulting data frame data_llr contains the original data and the calculated local linear regression values. The ggplot2 functions are used to plot the original data in blue and the local linear regression in red. The resulting plot shows how the local linear regression changes over time, highlighting trends and patterns in the data.

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