Great, let's break down your scenario and **identify the problem, the cause, and the solution**. Then, **we'll discuss how to approach your two analysis questions**. --- ## **1. Understanding the Error** Your error is: ``` Error in plot.default(...): formal argument "type" matched by multiple actual arguments ``` **What does this mean?** - The `plot()` function in R is "generic", meaning it can handle different kinds of data (vectors, matrices, data frames). - When you call `plot(BikeDemand, ...)`, R interprets `BikeDemand` as a data frame and tries to plot it accordingly. - The data frame method for `plot()` does not accept the `type` argument in the same way as `plot.default()`, leading to a conflict. - The error says that the argument `type` is being given more than once or in an invalid context. **The root cause**: You're trying to plot a data frame and passing arguments (`type`, `col`, etc.) that are meant for plotting *vectors* or *matrices*. `plot.data.frame` (the method for data frames) does not accept the same arguments as `plot.default`. --- ## **2. What Should You Do Instead?** ### **A. Choose what you want to plot** You have a data frame `BikeDemand` with 7 columns: - The **first column** is time (likely a date-time or hour index). - The **next six columns** are bike demands for six stations. #### **If you want to plot time series for all six stations:** You can **matplot** (for multiple lines) or plot individual columns. Here's how: ### **Option 1: Using `matplot()` for multiple time series** ```r # Assuming first column is time, others are demand time <- BikeDemand[[1]] demands <- as.matrix(BikeDemand[, 2:7]) matplot(time, demands, type = "l", lty = 1, col = 1:6, ylim = c(0, 35), main = "Types of moving averages", sub = "Oct 3 2016--Oct 23 2016", xlab = "Hours", ylab = "Bike Usage/Demand") legend("topright", legend = colnames(BikeDemand)[2:7], col = 1:6, lty = 1) ``` ### **Option 2: Plotting a single station** ```r plot(time, BikeDemand[[2]], type = "l", col = 1, ylim = c(0, 35), ...) ``` --- ## **3. Addressing Your Analysis Questions** ### **Q1. Predict the next 3 hours of bike demand for each station** #### **Logic:** - For each of the six station columns, you could use a simple moving average (your `movavg` function) or a more sophisticated time series model (e.g., `auto.arima` from the `forecast` package). - To **predict the next 3 hours**, you need a model that can forecast beyond the existing data. #### **Example with Moving Average (not ideal for forecasting):** ```r # For station 1: ma <- movavg(BikeDemand[[2]], n = 5, type = "s") # Last value is your smoothed estimate, but moving averages do not forecast future values. # For real forecasting, consider ARIMA: library(forecast) fit <- auto.arima(BikeDemand[[2]]) forecast_hours <- forecast(fit, h = 3) plot(forecast_hours) ``` Repeat for each station. --- ### **Q2. Aggregate data to daily and forecast 3 days** #### **Logic:** - Aggregate each station's hourly data into daily totals (or means). - Use a time series model (e.g., ARIMA) to forecast the next 3 days. #### **Example:** ```r # Suppose your time column is POSIXct or Date, otherwise convert it BikeDemand$Date <- as.Date(BikeDemand[[1]]) # Aggregate by Date for each station daily <- aggregate(. ~ Date, data = BikeDemand[, -1], FUN = sum) # For station 1: library(forecast) fit_daily <- auto.arima(daily[[2]]) forecast_days <- forecast(fit_daily, h = 3) plot(forecast_days) ``` --- ## **Summary Table** | Step | What you did | What went wrong | What to do instead | |-------------|--------------------------|------------------------|--------------------------------| | plot(df,...)| Plotted entire data frame | Argument mismatch | Use `matplot()` or plot each column individually | --- ## **Key Takeaways** - Use `matplot()` for plotting multiple series from a data frame. - Use time series models (`auto.arima`, `forecast`) for forecasting, not moving averages. - To aggregate, use `aggregate()` to sum or mean by day. - Always check what kind of object your plotting function expects (vector, matrix, etc.). --- ## **References:** - [`matplot` documentation](https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/matplot.html) - [`forecast` package documentation](https://pkg.robjhyndman.com/forecast/) --- ### **If you want code tailored to your exact data, please paste a snippet of the first few rows!** --- **Let me know if you want sample code for `ggplot2` or further explanation on forecasting models!**

Your error occurs because `plot()` is being called on a data frame with conflicting arguments; instead, use `matplot()` with your time and demand columns to plot multiple series, e.g., `matplot(time, as.matrix(BikeDemand[, 2:7]), type="l", col=1:6)`. For forecasting, apply time series models like `auto.arima()` to each station's data, and for daily aggregation, sum or average hourly data by date before modeling.