> # Initialize and fit the model
> model <- prophet(prophet_data)
Error in fit.prophet(m, df, ...) : Dataframe has less than 2 non-NA rows.
> # Remove rows with NA values
> prophet_data <- na.omit(prophet_data)
> 
> # Check if there are enough rows
> if (nrow(prophet_data) < 2) {
+     print("Error: Not enough data points to fit the model")
+ } else {
+     # Fit the Prophet model
+     model <- prophet(prophet_data)
+ }
[1] "Error: Not enough data points to fit the model"
> 
> prophet_data
# A tibble: 0 × 2
# ℹ 2 variables: ds <date>, y <int>
> view(prophet_data)
Error in view(prophet_data) : could not find function "view"
> summary(prophet_data)
       ds            y      
 Min.   :NA    Min.   : NA  
 1st Qu.:NA    1st Qu.: NA  
 Median :NA    Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.:NA    3rd Qu.: NA  
 Max.   :NA    Max.   : NA 
for this
# Install Prophet
install.packages("prophet")

### Load Required Libraries

# Load the required libraries:
library(prophet)

### Prepare the Data

# Aggregate the data by date to get the total confirmed cases for each date.
# Preprocess the data:
time_series_data <- data %>%
  group_by(date) %>%
  summarise(total_confirmed = sum(confirmed)) %>%
  ungroup()

# Inspect the data
head(time_series_data)

### Prepare the Data for Prophet

# Rename the columns to ds (date) and y (value) as required by Prophet:
prophet_data <- time_series_data %>%
  rename(ds = date, y = total_confirmed)

# Inspect the renamed data
head(prophet_data)

### Fit the Prophet Model

# Initialize and fit the model
model <- prophet(prophet_data)

### Make Future Predictions

# Specify the number of future days for which you want to make predictions (e.g., next 30 days):
future <- make_future_dataframe(model, periods = 30)

# Make predictions
forecast <- predict(model, future)

### Visualize the Forecast

# Plot the forecast and its components
plot(model, forecast) +
  labs(title = "COVID-19 Confirmed Cases Forecast", x = "Date", y = "Total Confirmed Cases")

# Plot the forecast components
prophet_plot_components(model, forecast)

### Inspect the Forecast Data

# View the forecast dataframe
head(forecast)

### Save the Forecasted Data to a CSV File

# Save the forecasted data to a CSV file
write.csv(forecast, "forecasted_covid19_cases.csv", row.names = FALSE)
# Install Prophet
install.packages("prophet")

### Load Required Libraries

# Load the required libraries:
library(prophet)

### Prepare the Data

# Aggregate the data by date to get the total confirmed cases for each date.
# Preprocess the data:
time_series_data <- data %>%
  group_by(date) %>%
  summarise(total_confirmed = sum(confirmed)) %>%
  ungroup()

# Inspect the data
head(time_series_data)

### Prepare the Data for Prophet

# Rename the columns to ds (date) and y (value) as required by Prophet:
prophet_data <- time_series_data %>%
  rename(ds = date, y = total_confirmed)

# Inspect the renamed data
head(prophet_data)

### Fit the Prophet Model

# Initialize and fit the model
model <- prophet(prophet_data)

### Make Future Predictions

# Specify the number of future days for which you want to make predictions (e.g., next 30 days):
future <- make_future_dataframe(model, periods = 30)

# Make predictions
forecast <- predict(model, future)

### Visualize the Forecast

# Plot the forecast and its components
plot(model, forecast) +
  labs(title = "COVID-19 Confirmed Cases Forecast", x = "Date", y = "Total Confirmed Cases")

# Plot the forecast components
prophet_plot_components(model, forecast)

### Inspect the Forecast Data

# View the forecast dataframe
head(forecast)

### Save the Forecasted Data to a CSV File

# Save the forecasted data to a CSV file
write.csv(forecast, "forecasted_covid19_cases.csv", row.names = FALSE)