CS_15.Rmd

---
title: Final Challenge
week: 12
type: Case Study
---

## Write a short script to perform the following steps

1. Import the data on atmospheric $CO_2$ from the link below into R

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

2. Remove the header of the file to retain only the data
3. Filter out missing data (`-99.99`)
4. Update the column names using the `colnames` vector below

```{r setup, message=F}
colnames=c("year","month","excel","decimal_year","co2","co2_seasonal","co2_fit","co2_seasonalfit","co2_filled","co2_seasonalfilled")
```

5. Calculate the mean $CO_2$ for each year from the `co2` column
6. plot a simple ggplot line graph of mean annual $CO_2$ concentration each year as shown below




```{r, purl=F, echo=F, message=F}
library(tidyverse)

httr::set_config(httr::config(ssl_verifypeer = 0L)) 
tf=tempfile()
httr::GET(data_url, httr::write_disk(tf, overwrite=TRUE))

read_csv(tf,
  skip=57,
  col_names=colnames,
  na="-99.99") %>% 
  group_by(year) %>% 
  summarize(mean=mean(co2,na.rm=T)) %>% 
  ggplot(aes(x=year,y=mean))+
  geom_line()

if(F){
  library(tidyverse)
  read_csv(data_url,
  skip=57,
  col_names=colnames,
  na="-99.99") %>% 
  group_by(year) %>% 
  summarize(mean=mean(co2,na.rm=T)) %>% 
  ggplot(aes(x=year,y=mean))+
  geom_line()

}

```