To provide a solution, we’ll need to analyze your question and the provided R code. However, there seems to be some missing information, such as:
- The specific model used for prediction (e.g., linear regression, decision tree, etc.)
- The library or package used for data manipulation and visualization (e.g., dplyr, tidyr, ggplot2, etc.)
- The exact code for creating the plots
Assuming you’re using R Studio and have loaded the necessary libraries (e.g., dplyr, tidyr, ggplot2), here’s a general approach to address your concerns:
Analysis of the Provided Code
The provided R code is mostly a data frame df with some features:
set.seed(123)
n <- 100
x <- rnorm(n)
y <- rnorm(n) + 1.5 * x + rnorm(n)
df <- data.frame(
date = format(c(2022-01-01, 2022-01-02, ..., 2022-12-31), "%Y-%m-%d"),
value = c(x, y)
)
df$group <- ifelse(df$value > 1.5 * df$x, "Group A", "Group B")
# Group by date and calculate the mean
df %>%
group_by(date) %>%
summarise(mean_value = mean(value)) %>%
arrange(mean_value) %>%
print()
Predictions vs Actual Values with Test Set
To create a plot of predictions vs actual values using the test set, we’ll need to define the test set and the prediction model. Let’s assume you have a function predict_model() that takes in your data and returns predicted values.
# Assume predict_model() is defined elsewhere
set.seed(123)
n <- 100
x <- rnorm(n)
y <- rnorm(n) + 1.5 * x + rnorm(n)
test_set <- df %>%
filter(date >= "2022-06-01" & date <= "2022-07-31") # Adjust this interval
predictions <- predict_model(test_set)
# Create the plot
ggplot(data.frame(actual = test_set$value, predicted = predictions),
aes(x = actual, y = predicted)) +
geom_point() +
geom_abline(intercept=0, slope=1, color="red") +
labs(title="Predictions vs Actual Values with Test Set", x = "Actual Value", y = "Predicted Value")
Using Another Data Set but Same Features
To address your second question, we’ll assume you have another data set df2 with the same features (i.e., date, value, and possibly a new column).
# Assume df2 is defined elsewhere
set.seed(123)
n <- 100
x <- rnorm(n)
y <- rnorm(n) + 1.5 * x + rnorm(n)
df2$group <- ifelse(df2$value > 1.5 * df2$x, "Group A", "Group B")
predictions_2 <- predict_model(df2)
# Create the plot
ggplot(data.frame(actual = df2$value, predicted = predictions_2),
aes(x = actual, y = predicted)) +
geom_point() +
geom_abline(intercept=0, slope=1, color="red") +
labs(title="Predictions vs Actual Values with Another Data Set", x = "Actual Value", y = "Predicted Value")
Please note that this is a simplified example and you may need to adjust the code according to your specific requirements. Additionally, without knowing the specifics of predict_model(), it’s difficult to provide an accurate implementation.
I hope this helps! If you have further questions or would like more detailed explanations, feel free to ask.
Last modified on 2025-03-21