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< section id="introduction" class="level1">
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Introduction
Ever felt those data points were a bit too jittery? Smoothing out trends and revealing underlying patterns is a breeze with rolling averages in R. Ready to roll? Let’s dive in!
< section id="rolling-with-the-zoo" class="level1">Rolling with the ‘zoo’
Meet the ‘zoo’ package, your trusty companion for time series data wrangling. It’s got a handy function called ‘rollmean’ that handles those rolling averages with ease.
< section id="installing-and-loading" class="level1">Installing and Loading
# install.packages("zoo") # Grab it if you haven't already
library(zoo) # Bring it into your workspace
Example
< section id="creating-a-simple-time-series" class="level2">Creating a Simple Time Series
set.seed(123) # Set seed for reproducibility (optional # Let's imagine some daily sales data sales <- trunc(runif(112, min = 100, max = 500)) # Generate some random sales days <- as.Date(1:112, origin = "2022-12-31") # Add some dates! data_zoo <- zoo(sales, days) # Convert to a zoo object
Calculating Rolling Averages
# Say we want a 7-day rolling average: rolling_avg7 <- rollmean(data_zoo, k = 7) rolling_avg7_left <- rollmean(data_zoo, k = 7, align = "left") rolling_avg7_right <- rollmean(data_zoo, k = 7, align = "right") # How about a 28-day one? rolling_avg28 <- rollmean(data_zoo, k = 28) rolling_avg28_left <- rollmean(data_zoo, k = 28, align = "left") rolling_avg28_right <- rollmean(data_zoo, k = 28, align = "right")
Visualizing the Smoothness
plot(data_zoo, type = "l", col = "black", lwd = 1, ylab = "Sales")
lines(rolling_avg7, col = "red", lwd = 2, lty = 2)
lines(rolling_avg7_left, col = "green", lwd = 2, lty = 2)
lines(rolling_avg7_right, col = "orange", lwd = 2, lty = 2)
legend(
"bottomleft",
legend = c(
"Original Data", "7-day Avg", "7-day Avg (left-aligned)",
"7-day Avg (right-aligned)"
),
col = c("black", "red", "green", "orange"),
lwd = 1, lty = 1:2,
cex = 0.628
)
plot(data_zoo, type = "l", col = "black", lwd = 1, ylab = "Sales")
lines(rolling_avg28, col = "green", lwd = 2, lty = 2)
lines(rolling_avg28_left, col = "steelblue", lwd = 2, lty = 2)
lines(rolling_avg28_right, col = "brown", lwd = 2, lty = 2)
legend(
"bottomleft",
legend = c(
"Original Data", "28-day Avg", "28-day Avg (left-aligned)",
"28-day Avg (right-aligned)"
),
col = c("black", "green", "steelblue", "brown"),
lwd = 1, lty = 1:2,
cex = 0.628
)
Experimenting and Interpreting
Play with different ‘k’ values to see how they affect the smoothness. Remember, larger ‘k’ means more smoothing, but potential loss of detail.
< section id="your-turn-to-roll" class="level1">Your Turn to Roll!
Grab your data and start exploring rolling averages! It’s a powerful tool to uncover hidden patterns and trends. Share your discoveries and join the rolling conversation!
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