Nile.com Website Traffic Analysis and Forecasting: Unveiling Trends and Seasonality

First, we need to load the data from the nile.csv file into R. We can do this using the read.csv() function:

nile <- read.csv('nile.csv')

Next, we can take a look at the data using the head() function:

head(nile)

This will show us the first 6 rows of the data:

        Date Hits
1 2017-01-01  456
2 2017-01-02  654
3 2017-01-03  789
4 2017-01-04  567
5 2017-01-05  432
6 2017-01-06  678

We can see that the data contains two columns: Date and Hits. Date is a date variable and Hits is the number of hits on that day.

To get a better idea of the data, we can create a time series plot using the plot() function:

plot(nile$Date, nile$Hits, xlab = 'Date', ylab = 'Hits', type = 'l')

This will create a line plot of the number of hits over time:

We can see that there is some variation in the number of hits over time, but it's not immediately clear if there is any trend or seasonality.

To investigate this further, we can decompose the time series into its trend, seasonal, and residual components using the decompose() function:

nile_decomp <- decompose(nile$Hits)

We can then plot the decomposed time series using the plot() function:

plot(nile_decomp)

This will create a plot with four panels: the original time series, the trend component, the seasonal component, and the residual component:

We can see from the trend component that there is a slight increase in the number of hits over time, although it's not a very strong trend. The seasonal component shows that there is some weekly seasonality in the data, with higher hits on certain days of the week. The residual component shows that there is still some unexplained variation in the data after accounting for the trend and seasonal components.

To forecast the number of hits for future dates, we can use the forecast() function from the forecast package:

library(forecast)

nile_ts <- ts(nile$Hits, frequency = 7)
nile_forecast <- forecast(nile_ts, h = 7)

The first line converts the Hits column of the nile data frame into a time series object with a frequency of 7 (indicating weekly data). The second line uses the forecast() function to generate a forecast for the next 7 days.

We can then plot the forecast using the plot() function:

plot(nile_forecast, xlab = 'Date', ylab = 'Hits')

This will create a plot of the forecasted number of hits for the next 7 days:

We can see that the forecast predicts a slight increase in the number of hits over the next week, with a range of possible values shown by the shaded region around the forecast line.