### Load Prophet R Package

Source: https://facebook.github.io/prophet/docs/index

Loads the Prophet library in R. Ensure Prophet is installed before running this command.

```R
library(prophet)
```

--------------------------------

### Import Libraries (Python)

Source: https://facebook.github.io/prophet/docs/index

Imports the necessary libraries, pandas for data manipulation and Prophet for forecasting. This is a standard starting point for any Prophet project.

```python
import pandas as pd
from prophet import Prophet
```

--------------------------------

### Plot Forecast with Plotly (Python)

Source: https://facebook.github.io/prophet/docs/index

Generates an interactive forecast plot using Plotly. Requires Plotly, notebook, and ipywidgets to be installed.

```python
from prophet.plot import plot_plotly, plot_components_plotly

plot_plotly(m, forecast)
```

--------------------------------

### Plot Forecast in R

Source: https://facebook.github.io/prophet/docs/index

Creates a plot of the Prophet forecast, showing the historical data, forecast line, and uncertainty intervals.

```R
plot(m, forecast)
```

--------------------------------

### Plot Forecast (Python)

Source: https://facebook.github.io/prophet/docs/index

Visualizes the forecast generated by the Prophet model. The `plot` method displays the predicted values against the historical data.

```python
fig1 = m.plot(forecast)
```

--------------------------------

### Instantiate and Fit Prophet Model (Python)

Source: https://facebook.github.io/prophet/docs/index

Creates an instance of the Prophet model and fits it to the prepared historical data. The `fit` method trains the model to learn patterns from the time series.

```python
m = Prophet()
m.fit(df)
```

--------------------------------

### Plot Prophet Components in R

Source: https://facebook.github.io/prophet/docs/index

Visualizes the individual components of the Prophet forecast, such as trend, weekly seasonality, and yearly seasonality.

```R
prophet_plot_components(m, forecast)
```

--------------------------------

### Plot Forecast Components with Plotly (Python)

Source: https://facebook.github.io/prophet/docs/index

Generates interactive plots of forecast components using Plotly. This provides a detailed breakdown of seasonality and trend.

```python
plot_components_plotly(m, forecast)
```

--------------------------------

### Create Future Dataframe for Forecasting in R

Source: https://facebook.github.io/prophet/docs/index

Generates a dataframe with future dates for making predictions. Includes historical dates by default for in-sample evaluation.

```R
future <- make_future_dataframe(m, periods = 365)
tail(future)
```

--------------------------------

### Plot Forecast Components (Python)

Source: https://facebook.github.io/prophet/docs/index

Visualizes the components of the forecast, such as trend, seasonality, and holidays. The `plot_components` method helps in understanding the different factors contributing to the forecast.

```python
fig2 = m.plot_components(forecast)
```

--------------------------------

### Load and Inspect Data (Python)

Source: https://facebook.github.io/prophet/docs/index

Loads a CSV dataset from a URL into a pandas DataFrame and displays the first few rows. This step is crucial for understanding the structure and content of the time series data.

```python
df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')
df.head()
```

--------------------------------

### Fit Prophet Model in R

Source: https://facebook.github.io/prophet/docs/index

Fits the Prophet time series model to the provided historical dataframe. The dataframe must have 'ds' and 'y' columns.

```R
m <- prophet(df)
```

--------------------------------

### Generate Forecast in R

Source: https://facebook.github.io/prophet/docs/index

Predicts future values using the fitted Prophet model and the future dataframe. The output includes forecast ('yhat') and uncertainty intervals.

```R
forecast <- predict(m, future)
tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])
```

--------------------------------

### Load Data for Prophet in R

Source: https://facebook.github.io/prophet/docs/index

Reads a CSV file from a specified URL into an R dataframe. The dataframe must contain 'ds' (date) and 'y' (numeric value) columns for Prophet.

```R
df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')
```

--------------------------------

### Create Future Dataframe (Python)

Source: https://facebook.github.io/prophet/docs/index

Generates a dataframe containing future dates for which predictions will be made. The `make_future_dataframe` method extends the historical data by a specified number of periods.

```python
future = m.make_future_dataframe(periods=365)
future.tail()
```

--------------------------------

### Generate Forecast (Python)

Source: https://facebook.github.io/prophet/docs/index

Predicts future values based on the fitted Prophet model and the future dataframe. The `predict` method returns a dataframe with forecasts, including `yhat` (predicted value) and uncertainty intervals.

```python
forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.