Load compiled database (change path to location on your computer)

database<-read.csv("~/Dropbox/data_files/database/compiled_database.csv")

Here we will make some simple plots with ggplot. Next time, we will look at why ggplot is so useful: making complex and beautiful plots for publications.

library(ggplot2)

p<-ggplot(database, aes(x=f_14c,  y=f_c_org, col=f_property))+
        theme_bw(base_size = 6)+
         geom_point()
plot(p)

## Warning: Removed 5231 rows containing missing values (geom_point).

p<-ggplot(database, aes(x=f_property, col=f_property, y=f_c_tot))+
        geom_boxplot()
plot(p)

## Warning: Removed 5007 rows containing non-finite values (stat_boxplot).

p<-ggplot(database, aes(x=f_13c))+
        geom_histogram()
plot(p)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 4873 rows containing non-finite values (stat_bin).

p<-ggplot(database, aes(x=bd_tot, y=-layer_bot))+
        geom_point()
plot(p)

## Warning: Removed 1354 rows containing missing values (geom_point).