Great, let's break this down step by step like a top-level R engineer. ## 1. **Your Error** The error message: ``` Error in plot.default(...) : formal argument "type" matched by multiple actual arguments ``` **Explanation:** This happens because `plot()` **expects a vector or time series** as its first argument, but you're giving it an entire **data frame** (`BikeDemand`). The `type="l"` also conflicts, because internally, `plot.data.frame()` calls `plot.default()` multiple times, which causes this argument clash. ## 2. **What To Do Instead** ### To plot **all 6 stations** time series on one plot: - Extract the columns you want to plot (not the time column, just the 6 demand columns). - Use `matplot()` to plot multiple series at once. ### Example: Suppose your columns are: Time, Station1, Station2, ..., Station6. ```r # Assuming first column is time, stations are columns 2 to 7 matplot(BikeDemand[[1]], BikeDemand[,2:7], 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=names(BikeDemand)[2:7], col=1:6, lty=1) ``` --- ## 3. **Moving Averages / Forecasting** ### **Predict the next 3 hours** (Q1) You need a forecasting model. Moving average is a smoothing technique, **not a forecasting model**. For forecasting, use `forecast` package, e.g. with `auto.arima` or `ets`. #### Example with ARIMA (for one station): ```r library(forecast) # Example for Station1: ts_station1 <- ts(BikeDemand$Station1, frequency=24) # hourly data fit <- auto.arima(ts_station1) forecasted <- forecast(fit, h=3) plot(forecasted, main="Forecast for Station 1 (next 3 hours)") ``` Repeat for each station (`Station2`, etc). #### **Discuss**: - Look at `forecasted$mean` for the predicted values for the next 3 hours. - Discuss trends, seasonality, confidence intervals. --- ### **Aggregate to Daily Data and Predict Next 3 Days** (Q2) - Aggregate using `aggregate` or `dplyr::summarise`. - Then forecast as above. ```r library(dplyr) BikeDemand$Day <- as.Date(BikeDemand[[1]]) # Aggregate each station daily_demand <- BikeDemand %>% group_by(Day) %>% summarise(across(Station1:Station6, sum, na.rm=TRUE)) # Now forecast for daily data (example for Station1) ts_station1_daily <- ts(daily_demand$Station1, frequency=1) # daily data fit_daily <- auto.arima(ts_station1_daily) forecasted_daily <- forecast(fit_daily, h=3) plot(forecasted_daily, main="Forecast for Station 1 (next 3 days)") ``` --- ## 4. **Summary of Key Fixes** - Use `matplot()` for plotting all 6 time series. - Use `auto.arima()` or similar for forecasting, **not just moving averages**. - Aggregate to daily level with `group_by()` + `summarise()` for daily forecasting. --- ## **Tips** - Always inspect your data after load (`str(BikeDemand)`) to verify column types. - For forecasting, check for missing values and deal with them appropriately. --- ## **Minimal Working Example** ```r library(readxl) library(forecast) library(dplyr) # Load data BikeDemand <- read_excel("C:/Users/16092/Desktop/CitiBike_Oct2016_BikeDemand.xlsx", range="A1:G505") # Plot all stations matplot(BikeDemand[[1]], BikeDemand[,2:7], type="l", lty=1, col=1:6, ylim=c(0, 35), main="Bike Usage per Station", xlab="Hour", ylab="Demand") legend("topright", legend=names(BikeDemand)[2:7], col=1:6, lty=1) # Predict next 3 hours for Station 1 ts1 <- ts(BikeDemand[[2]], frequency=24) fit1 <- auto.arima(ts1) forecast1 <- forecast(fit1, h=3) plot(forecast1, main="Station 1: Next 3 hours forecast") # Aggregate to daily BikeDemand$Day <- as.Date(BikeDemand[[1]]) daily <- BikeDemand %>% group_by(Day) %>% summarise(across(2:7, sum)) # Predict next 3 days for Station 1 ts1d <- ts(daily[[2]], frequency=1) fit1d <- auto.arima(ts1d) forecast1d <- forecast(fit1d, h=3) plot(forecast1d, main="Station 1: Next 3 days forecast") ``` --- If you want to discuss the predicted results, **interpret the plots** and **forecast table** for trends (e.g. rising/falling demand, seasonality, uncertainty). --- **If you share your actual column names or a sample of your data, I can tailor the code even better!**