Update module3/exo3/CO2_Trend.Rmd

module3/exo3/exercice_en.Rmd

 ---
-title: "Your title"
-author: "Your name"
-date: "Today's date"
+title: "Subject 1: CO2 concentration in the atmosphere since 1958"
+author: "NJ"
+date: "11 July 2025"
 output: html_document
+Link: https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html
 ---
 
 
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
+```{r}
+data_url <- 
+  "https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv"
+data <- read.csv(data_url, skip=1, na.strings=c("-"))
 ```
 
-## Some explanations
+```{r}
+D <- is.data.frame(data)
+D
+```
+
+```{r}
+df <- read.csv(
+  data_url,
+  skip = 64,
+  header = FALSE,
+  col.names = c("Year", "Month", "ExcelDate", "DecimalDate", "CO2", 
+                "CO2_Adjusted", "Fit", "Fit_Adjusted", 
+                "Filled", "Filled_Adjusted", "Station")
+)
+
+head(df)
+```
+
+```{r}
+library(ggplot2)
+
+ggplot(df, aes(x = DecimalDate, y = CO2)) +
+  geom_line(color = "steelblue") +
+  labs(title = "Mauna Loa Atmospheric CO2",
+       x = "Year",
+       y = "CO2 (ppm)") +
+  theme_minimal()
+```
+
+```{r}
+##1. Make a plot that shows the superposition of 
+#a periodic oscillation and a slower systematic evolution.
+
+# Remove missing values (like -99.99)
+df_clean <- df[df$CO2 > 0, ]
+
+# Plot
+ggplot(df_clean, aes(x = DecimalDate, y = CO2)) +
+  geom_line(color = "darkblue", size = 0.6, alpha = 0.8) +
+  geom_smooth(span = 0.3, se = FALSE, color = "red", size = 1.2) +
+  scale_x_continuous(
+    limits = c(1958, 2025),  # x-axis range
+    breaks = seq(1960, 2025, by = 5)  # tick marks every 5 years
+  ) +
+  labs(
+    title = "Superposition of Seasonal Oscillation and Long-Term CO2 Trend",
+    subtitle = "Mauna Loa Observatory (MLO)",
+    x = "Year",
+    y = "CO2 concentration (ppm)"
+  ) +
+  theme_minimal()
+```
+
+```{r}
+#2. Separate these two phenomena. 
+#2.1. Characterize the periodic oscillation. 
+
+# Create a time variable in years and months
+df_clean$time <- df_clean$DecimalDate
+```
+
+```{r}
+# Fit a quadratic model (simplified but effective)
+trend_model <- lm(CO2 ~ poly(time, 2), data = df_clean)
+
+# Add predicted trend to dataframe
+df_clean$trend <- predict(trend_model)
+
+df_clean$seasonal <- df_clean$CO2 - df_clean$trend
+
+```
+
+```{r}
+# create the date like..."YYYY-MM-15" # 15 = mid month
+df_clean$Date <- as.Date(paste(df_clean$Year, df_clean$Month, "15", sep = "-"))
+
+df_clean$month <- as.numeric(format(df_clean$Date, "%m"))
+```
+
+```{r}
+p1 <- ggplot(df_clean, aes(x = DecimalDate)) +
+  geom_line(aes(y = CO2), color = "gray60", size = 0.5) +
+  geom_line(aes(y = trend), color = "blue", size = 1.2) +
+  scale_x_continuous(
+    limits = c(1958, 2025),  # x-axis range
+    breaks = seq(1960, 2025, by = 5)  # tick marks every 5 years
+  ) + 
+  labs(
+    title = "Long-Term Trend in CO2 Concentration",
+    x = "Year", y = "CO2 (ppm)"
+  ) +
+  theme_minimal()
 
-This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
+p1
+```
+
+```{r}
+#2.2. Find a simple model for the slow contribution, estimate its parameters, 
+#and attempt an extrapolation until 2025 (for validating the model using future observations).
+
+# Fit sine curve with 1-year periodne
+season_model <- nls(seasonal ~ A*sin(2*pi*DecimalDate) + B*cos(2*pi*DecimalDate),
+                    data = df_clean, start = list(A = 1, B = 1))
 
-When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
+summary(season_model)
 
-```{r cars}
-summary(cars)
+df_clean$time <- df_clean$DecimalDate
 ```
 
-It is also straightforward to include figures. For example:
 
-```{r pressure, echo=FALSE}
-plot(pressure)
+```{r}
+# Create new data from last year to 2030
+future <- data.frame(time = seq(from = max(df_clean$time), to = 2030, by = 1/12))
+
+# Predict trend
+future$trend <- predict(trend_model, newdata = future)
+
+# Predict seasonal using fitted sine model
+A <- coef(season_model)[1]
+B <- coef(season_model)[2]
+future$seasonal <- A * sin(2 * pi * future$time) + B * cos(2 * pi * future$time)
+
+# Combine to get total CO2 estimate
+future$CO2_predicted <- future$trend + future$seasonal
+
+CO2_predicted <- future$trend + future$seasonal
+
+```
+
+```{r}
+# Trend of the CO2 (ppm) predicted from 2025 to 2030 year
+ggplot() +
+  geom_line(data = future, aes(x = time, y = trend), color = "blue") +
+  geom_line(data = future, aes(x = time, y = CO2_predicted), color = "red") +
+  labs(title = "CO2 Projection with Trend + Seasonal Model",
+       y = "CO2 (ppm)", x = "Year") +
+  theme_minimal()
 ```
 
-Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
+```{r}
+# the inceasing of the CO2 (ppm) predicted within 2030 year
+ggplot() +
+  geom_line(data = df_clean, aes(x = DecimalDate, y = CO2), color = "black") +
+  geom_line(data = future, aes(x = time, y = CO2_predicted), color = "darkgreen") +
+  geom_vline(xintercept = 2023, linetype = "dashed", color = "red") +
+  annotate("text", x = 2023.5, y = max(df_clean$CO2), 
+           label = "Prediction zone", color = "red", hjust = 0) +
+  scale_x_continuous(
+    limits = c(1958, 2030),  # x-axis range
+    breaks = seq(1960, 2030, by = 5)  # tick marks every 5 years
+  ) + 
+  labs(
+    title = "CO2 Prediction Until 2030",
+    x = "Year", y = "CO2 (ppm)"
+  ) +
+  theme_minimal()
+
+```
+
+
+
+
+
+
+
+
+
+
+
 
-Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
 
-Now it's your turn! You can delete all this information and replace it by your computational document.
+Note: write your solution in the file module3/exo3/exercice_en.Rmd. 
+Generate a PDF file using the KnitR tool and store it as module3/exo3/YourFileName.pdf (don't forget to commit it).