---
title: "CO2 Keeling Curve and Hisitorical Ice Core Data"
author: "David Elser"
date: "05-03-2021"
output: pdf_document
---

#### Please note that I am not a Computer Scientist and my programming skills are very limited. 

### Data preprocessing fetch the Data file and check if file already exists

The data is available from the  Web site of the Scripps Institute

"https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html"
"https://scrippsco2.ucsd.edu/assets/data/atmospheric/merged_ice_core_mlo_spo/merged_ice_core_yearly.csv"

Soure script available at : https://github.com/DemirhanOzelTrojan/Keeling_Predict/blob/master/main.R

`
install.packages('forecast', dependencies = TRUE)
install.packages("imputeTS")
install.packages("tidyverse")
`

### Load librarys

```{r}
library(ggplot2)
library(forecast)
library(imputeTS)
library(lubridate)
```

### Fetch CO2 measurements from Mauna Loa observatory, Hawaii, USA

```{r}
data_url = "https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv"
data_file = "CO2.csv"

if (!file.exists(data_file)) {
    download.file(data_url, data_file, method="auto")
}
```

### Fetch Ice Core CO2 Data

```{r}


data_url = "https://scrippsco2.ucsd.edu/assets/data/atmospheric/merged_ice_core_mlo_spo/merged_ice_core_yearly.csv"
data_file1 = "Ice-CO2.csv"

if (!file.exists(data_file1)) {
    download.file(data_url, data_file1, method="auto")
}
```

### Skip the frist 36 lines

```{r}
data_ice = read.csv(data_file1, header=FALSE, skip=36)
```

### Skip the frist 5 lines of the file

```{r}
data = read.csv(data_file, header=TRUE, skip=5)
```

### Let's have a look at what we got and check NA

```{r}
head(data_ice)
tail(data_ice)

na_records = apply(data_ice, 1, function (x) any(is.na(x)))
data[na_records,]



```
### Conversion of decimal-date into Years, rename V2 into ppm


```{r}


data_ice$date<- format(date_decimal(data_ice$V1), "%Y")
head(data_ice)
tail(data_ice)
names(data_ice)[names(data_ice) == "V2"] <- "ppm"
data_ice$Year=as.numeric(data_ice$date)
head(data_ice)
tail(data_ice)


```



### Let's have a look at what we got from Mauna Loa observatory and check NA

```{r}
head(data)
tail(data)

na_records = apply(data, 1, function (x) any(is.na(x)))
data[na_records,]
```

### impute data of column X.ppm..4(CO2, seasonally filled,adjusted filled)

```{r}
data$X.ppm..4[data$X.ppm..4 == -99.99] <- NA
data$X.ppm..4 <- na.interpolation(data$X.ppm..4)
head(data)
tail(data)
```


### data preparation
```{r}
data.ts <- ts(data$X.ppm..4, start = c(1958, 1), end = c(2021, 1), freq = 12)

```

### split training and testing data, use 90% of available data in the co2.csv file
```{r}
train.test.split <- 0.9
split.index <- round(length(data.ts) * train.test.split)
```

### train time series
```{r}
data.train.ts <- window(data.ts, start = c(1958, 1), end = c(1958, split.index))
```

### test time series
```{r}
data.test.ts <- window(data.ts, start = c(1958, split.index + 1), end = c(1958, length(data.ts)))
```

### create a fit using the  time series
```{r}
fit <- ets(data.ts, model = 'AAA')
```

### forecast using the fit
```{r}
predictions <- forecast(fit, 12 * 20, level = 0.95)
```


## Plot Ice Core Data

```{r}

theme_set(theme_minimal())

ggplot(data = data_ice, aes(x = Year, y = ppm))+
  geom_line(color = "#00AFBB", size = 1)


```

## Plot predictions till 2025

```{r}

plot(predictions,
     main = '',
     xlab = 'Time',
     ylab = 'CO2 (ppm)',
     xlim = c(1958, 2025),
     shaded = TRUE,
     shadecols=c('light blue'))
legend('topleft',
       legend=c('Historical Data',
                'Predicted Data',
                '95% CI'),
       fill=c(NA, NA, 'light blue'),
       col=c('black', 'blue', NA),
       lty=c(1, 1, NA),
       lwd=c(1, 2, NA),
       border=c(NA, NA, 'black'),
       inset=0.01)


```