week12

# Group by date and average points
nba_tsibble <- nba_data %>%
  group_by(Date) %>%
  summarise(avg_points = mean(PTS, na.rm = TRUE)) %>%
  ungroup() %>%
  as_tsibble(index = Date)

# Plot average points over time
nba_tsibble %>%
  ggplot(aes(x = Date, y = avg_points)) +
  geom_line(color = "darkblue") +
  labs(title = "Average Points Over Time", y = "Average Points", x = "Date")

# Fit linear model


trend_model <- nba_tsibble %>%
  model(lm_trend = TSLM(avg_points ~ trend()))

# Plot with fitted trend
trend_model %>%
  forecast(h = 0) %>%
  autoplot(nba_tsibble) +
  labs(title = "Linear Trend in Average Points")

# Apply smoothing using moving averages
nba_tsibble %>%
  mutate(smoothed = slider::slide_dbl(avg_points, mean, .before = 15, .after = 15, .complete = TRUE)) %>%
  ggplot(aes(x = Date)) +
  geom_line(aes(y = avg_points), alpha = 0.4) +
  geom_line(aes(y = smoothed), color = "red", size = 1.2) +
  labs(title = "Smoothed Average Points", y = "Points")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Removed 30 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(dplyr)
library(tsibble)
library(lubridate)

# Convert date column to proper Date format
nba_data$Date <- as.Date(nba_data$Date, format = "%m/%d/%Y")

# Step 1: Create a sequence of all dates in the range
full_dates <- tibble(Date = seq(min(nba_data$Date), max(nba_data$Date), by = "day"))

# Step 2: Group by date to calculate average points
nba_avg <- nba_data %>%
  group_by(Date) %>%
  summarise(avg_points = mean(PTS, na.rm = TRUE)) %>%
  ungroup()

# Step 3: Left join full date sequence to insert NAs for missing days
nba_filled <- full_dates %>%
  left_join(nba_avg, by = "Date")

# Step 4: Convert to tsibble (now no implicit gaps!)
nba_tsibble <- nba_filled %>%
  as_tsibble(index = Date)




library(fable)
library(feasts)

# Plot time series
autoplot(nba_tsibble, avg_points) +
  ggtitle("Average Points Over Time")

# Model the trend
model_trend <- nba_tsibble %>%
  model(lm_trend = TSLM(avg_points ~ trend()))

report(model_trend)

## Series: avg_points 
## Model: TSLM 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.985  -6.985  -1.549   5.286  54.993 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.345e+01  6.157e-01  38.094  < 2e-16 ***
## trend()     3.311e-04  6.987e-05   4.739 2.37e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.934 on 1373 degrees of freedom
## Multiple R-squared: 0.01609, Adjusted R-squared: 0.01538
## F-statistic: 22.46 on 1 and 1373 DF, p-value: 2.3706e-06

# Forecast
model_trend %>%
  forecast() %>%
  autoplot(nba_tsibble)

# Seasonality checks
nba_tsibble %>% ACF(avg_points) %>% autoplot()

nba_tsibble %>% PACF(avg_points) %>% autoplot()

R Markdown

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

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