Historical Trends in Gold Prices

Introduction

In this analysis, I explore the historical trends of gold prices. By visualizing the actual prices alongside a Six-Year Moving Average using data sourced from: datahub.io

The aim is to gain insights into the long-term behavior of gold prices and identify patterns or deviations in recent years. The moving average helps smooth out short-term fluctuations and provides a clearer view of overarching trends.

# Load necessary packages
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)
library(slider)
library(ggplot2)

# Specify the URL
url <- "https://raw.githubusercontent.com/hbedros/gold_price_index/main/annual_csv.csv"

# Fetch the data
gold_price <- read_csv(url)

## Rows: 70 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Date
## dbl (1): Price
## 
## 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.

# View the first few rows of the data
head(gold_price)

## # A tibble: 6 × 2
##   Date    Price
##   <chr>   <dbl>
## 1 1950-12  34.7
## 2 1951-12  34.7
## 3 1952-12  34.8
## 4 1953-12  34.8
## 5 1954-12  35.0
## 6 1955-12  35.0

# Arrange the data based on year in descending order.
gold_price <- gold_price %>%
  mutate(Date = as.Date(paste0(Date, "-01"), format = "%Y-%m-%d")) %>%
  arrange(desc(Date))

# Process the data
gold_price_processed <- gold_price %>%
  arrange(Date) %>%
  mutate(
    YTD_avg = cummean(Price),
    SixYear_MA = slide_dbl(.x = Price, .f = ~mean(.), .before = 5, .complete = TRUE)
  )

# Print the processed data
print(gold_price_processed)

## # A tibble: 70 × 4
##    Date       Price YTD_avg SixYear_MA
##    <date>     <dbl>   <dbl>      <dbl>
##  1 1950-12-01  34.7    34.7       NA  
##  2 1951-12-01  34.7    34.7       NA  
##  3 1952-12-01  34.8    34.7       NA  
##  4 1953-12-01  34.8    34.8       NA  
##  5 1954-12-01  35.0    34.8       NA  
##  6 1955-12-01  35.0    34.8       34.8
##  7 1956-12-01  34.9    34.8       34.9
##  8 1957-12-01  35.0    34.9       34.9
##  9 1958-12-01  35.1    34.9       35.0
## 10 1959-12-01  35.0    34.9       35.0
## # ℹ 60 more rows

The slide_dbl function from the slider package is used to compute a rolling average.
* .before = 5 means the calculation will consider the current year and the 5 years before it, giving us a total of 6 years for each average calculation.
* .complete = TRUE ensures we only get an average for periods where we have data for all 6 years. If there’s data for fewer than 6 years, the result for that period will be NA (meaning “Not Available”).

ggplot(data = gold_price_processed, aes(x = Date)) +
  geom_line(aes(y = Price, color = "Price")) +
  geom_line(aes(y = YTD_avg, color = "YTD Average")) +
  geom_line(aes(y = SixYear_MA, color = "Six-Year MA")) +
  labs(title = "Gold Price Over Time",
       y = "Price in USD",
       x = "Date",
       color = "Metric") +
  theme_minimal()

## Warning: Removed 5 rows containing missing values (`geom_line()`).

#### Explanation:
- Price: Shows raw fluctuations over time.
- YTD Average: Indicates intra-year performance.
- Six-Year MA: Highlights longer-term trends and smoothens short-term noise.

Historical Trends in Gold Prices

Haig Bedros

2023-09-19

Introduction

Conclusion: