Week7 Assignment

setup

library(data.table)
library(ggplot2)
library(tidyverse)

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library(TTR)

Exercise 1: Insert the graph you produced in your workshop this week from exercise 6 in Chapter 7 that has the SOI values plotted with the running 5 monthly mean SOI superimposed.

SOIdata <- read_csv("/Users/benjaminrowe/Library/CloudStorage/OneDrive-JamesCookUniversity/UniversityOD/2025/Trimester3/SC1109/Workshop/SOI2007-10.csv")

## Rows: 48 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): Year, Month, SOI
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

SOIdata2 <- SOIdata %>% mutate(MonthContinuum = seq(1,48))
SOIdata3 <- SOIdata2 %>% mutate(RunningMeans = SMA(SOIdata2$SOI,n=5))
ggplot(data=SOIdata3)+
  geom_line(aes(x=MonthContinuum, y=SOI, colour = "SOI"))+
  geom_line(aes(x=MonthContinuum, y=RunningMeans, colour = "Running Mean"))+
  labs(colour = "Legend")

## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).

2. How does the line representing the running means differ from the line that represents the monthly values?

The curve for running mean is “smoother” in the sense that it is not as sharply affected between months.

Exercise 2: Insert the graph you produced in your workshop this week from exercise 7 in Chapter 7.

ONIdata <- read_csv("/Users/benjaminrowe/Library/CloudStorage/OneDrive-JamesCookUniversity/UniversityOD/2025/Trimester3/SC1109/Workshop/sst34_1997to2002.csv")

## Rows: 73 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Median Month, MONTHS
## dbl (2): Anomaly, YEAR
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#run following lines at once
ONIdata$phase=with(ONIdata, cut(Anomaly, breaks=c(-10,-0.5,0.5,10), labels=c("La Nina", "Neutral", "El Nino")))
palette(c("green", "lightgrey", "red"))
barplot(ONIdata$Anomaly, space=1, ylim=c(-2,3), col=as.numeric(ONIdata$phase), las=1, ylab="SOI Anomaly", main="ONI and El Nino Phases Between 1997 and 2002")
abline(h=0)
axis(1, at=c(0.5,24.5,48.5,72.5,96.5,120.5,144.5), labels=c("Jan 1997", "Jan 1998", "Jan 1999", "Jan 2000","Jan 2001", "Jan 2002", "Jan 2003"))
legend("bottomright", legend=c("La Nina", "Neutral", "El Nino"), fill=1:3, bty="n")
box()

2. Describe two key features in this plot. (write in complete sentences)

Changes between La Nina and El Nino are always gradual and occurr over a period of time rather than month to month. After 5 years the graph seems to reurn to where it started, entering El Nino again. This means the pattern could likely repeat over many years. El Nino -> La Nina -> El Nino -> La Nina