A repo to enable Emily’s methane problem. Bonus features include functions and scripts to analyze a global dataset of methane in flowing waters.
This repo only contains scripts and example data. The data, analyses, figures, etc. are saved on your local dropbox folder. To use the functions and scripts in this package, you will create an R object with the path to your local dropbox directory. You should only need to do this once, and all functions will access and create files within that folder.
#Replace with your own dropbox folder location
path_to_dropbox <- "C:/Users/emsta/Dropbox/MethDB2.0" #Emily's folder
path_to_dropbox <- "C:/Users/lloken/OneDrive - DOI/GlobalRiverMethane" #Luke's USGS computerThere is a .rda file in the dropbox folder that contains several data
tables from the MethDB excel file. Some of column names have been
changed to remove blanks. These include conc_df, flux_df,
sites_df, papers_df, and gis_df. Both the concentrations and flux
tables have been converted to a common unit (uM and mmol m-2 d-1) for
all variables (CH4, CO2, N2O, nutrients)
library(dplyr)
library(ggplot2)
library(grid)
#Load custom ggplot functions
source("R/ggplot2_utils.R")
# load formatted and converted tables into your R environment
load(file.path(path_to_dropbox, "db_processingR",
"MethDB_tables_converted.rda"))
head(conc_df)
#> # A tibble: 6 x 68
#> Publication_Nid Site_Nid SiteName Conc_Name `SampleDate(sta~ `SampleDate(end~
#> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 2222 1000 Kytalyk~ Kytalyk ~ 42583 42583
#> 2 2222 1000 Kytalyk~ Kytalyk ~ 42587 42587
#> 3 2222 1000 Kytalyk~ Kytalyk ~ 42590 42590
#> 4 2222 1001 Kytalyk~ Kytalyk ~ 42583 42583
#> 5 2222 1001 Kytalyk~ Kytalyk ~ 42587 42587
#> 6 2222 1001 Kytalyk~ Kytalyk ~ 42590 42590
#> # ... with 62 more variables: SampleSeason <chr>, AggregatedSpace <chr>,
#> # AggregatedTime <chr>, `Flux?` <chr>, CH4min <dbl>, CH4max <dbl>,
#> # CH4mean <dbl>, CH4StDev <dbl>, CH4median <dbl>, orig_CH4unit <chr>,
#> # SampleCount <dbl>, CO2min <dbl>, CO2max <dbl>, CO2mean <dbl>,
#> # CO2stdev <dbl>, CO2median <dbl>, orig_CO2unit <chr>,
#> # CO2measurementtype <chr>, N2Omin <dbl>, N2Omax <dbl>, N2Omean <dbl>,
#> # N2Ostdev <dbl>, N2Omedian <dbl>, orig_N2Ounit <chr>, WaterTempactual <dbl>,
#> # WaterTempest <dbl>, `ConduS/cm` <chr>, pH <chr>, `DOmg/L` <dbl>,
#> # `DO%sat` <dbl>, NO3actual <dbl>, NO3aggregated <dbl>, orig_NO3unit <chr>,
#> # NH4actual <dbl>, NH4aggregated <dbl>, orig_NH4unit <chr>, TNactual <dbl>,
#> # TNaggregated <dbl>, orig_TNunit <chr>, SRPactual <dbl>,
#> # SRPaggregated <dbl>, orig_SRPunit <chr>, TPactual <dbl>,
#> # TPaggregated <dbl>, orig_TPunit <chr>, DOCactual <dbl>,
#> # DOCaggregated <dbl>, orig_DOCunit <chr>, Q <dbl>, orig_Qunit <chr>,
#> # Comments <chr>, Elevation_m <dbl>, new_CH4unit <chr>, new_CO2unit <chr>,
#> # new_N2Ounit <chr>, new_NO3unit <chr>, new_NH4unit <chr>, new_TNunit <chr>,
#> # new_TPunit <chr>, new_SRPunit <chr>, new_DOCunit <chr>, new_Qunit <chr>
head(flux_df)
#> # A tibble: 6 x 54
#> Publication_Nid Site_Nid SiteName FluxName `SampleDate(start)~
#> <dbl> <dbl> <chr> <chr> <dttm>
#> 1 2222 1000 Kytalyk~ Kytalyk~ 2016-08-01 00:00:00
#> 2 2222 1000 Kytalyk~ Kytalyk~ 2016-08-05 00:00:00
#> 3 2222 1000 Kytalyk~ Kytalyk~ 2016-08-08 00:00:00
#> 4 2222 1001 Kytalyk~ Kytalyk~ 2016-08-01 00:00:00
#> 5 2222 1001 Kytalyk~ Kytalyk~ 2016-08-05 00:00:00
#> 6 2222 1001 Kytalyk~ Kytalyk~ 2016-08-08 00:00:00
#> # ... with 49 more variables: `SampleDate(end)...6` <chr>,
#> # SampleSeason...7 <chr>, AggregatedSpace <chr>, AggregatedTime <chr>,
#> # DiffusiveCH4FluxMin <dbl>, DiffusiveCH4FluxMax <dbl>,
#> # DiffusiveCH4FluxMean <dbl>, DiffusiveCH4FluxStddev <dbl>,
#> # DiffusiveCH4FluxMedian <dbl>, DiffusiveFluxunit <chr>,
#> # SampleCount...16 <chr>, FluxMethod <chr>, BubbleCH4FluxMin <dbl>,
#> # BubbleCH4FluxMax <dbl>, BubbleCH4FluxMean <dbl>, BubbleCH4FluxStddev <dbl>,
#> # BubbleCH4FluxMedian <dbl>, BubbleCH4Fluxunit <chr>, Ebullitionmethod <chr>,
#> # `SampleDate(start)...25` <dttm>, `SampleDate(end)...26` <dttm>,
#> # SampleSeason...27 <chr>, SampleCount...28 <dbl>, TotalCH4FluxMin <dbl>,
#> # TotalCH4FluxMax <dbl>, TotalCH4FluxMean <dbl>, TotalCH4FluxStddev <dbl>,
#> # TotalCH4FluxMedian <dbl>, TotalCH4FluxUnit <chr>, Totalmethod <chr>,
#> # SampleCount...36 <dbl>, CO2FluxMin <dbl>, CO2FluxMax <dbl>,
#> # CO2FluxMean <dbl>, CO2FluxStddev <dbl>, CO2FluxMedian <dbl>,
#> # CO2Fluxunit <chr>, N2OFluxMin <dbl>, N2OFluxMax <dbl>, N2OFluxMean <dbl>,
#> # N2OFluxStddev <dbl>, N2OFluxMedian <dbl>, N2OFluxunit <chr>,
#> # Comments <chr>, new_CH4DiffusiveFluxunit <chr>,
#> # new_CH4BubbleFluxunit <chr>, new_CH4TotalFluxunit <chr>,
#> # new_CO2Fluxunit <chr>, new_N2OFluxunit <chr>
head(sites_df)
#> # A tibble: 6 x 23
#> Publication_Nid Site_Nid SiteName StreamName `Aggregated?` NSitesAggregated
#> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 2222 1000 Kytalyk~ Indigirka~ No NA
#> 2 2222 1001 Kytalyk~ Indigirka~ No NA
#> 3 2222 1002 Kytalyk~ Indigirka~ No NA
#> 4 2222 1003 Kytalyk~ Indigirka~ No NA
#> 5 2223 1004 Annweil~ <NA> No NA
#> 6 2223 1005 Bellhei~ <NA> No NA
#> # ... with 17 more variables: `Basin/Region` <chr>, Latitude <dbl>,
#> # Longitude <dbl>, Country <chr>, Continent <chr>, Elevation_m <dbl>,
#> # Landuse <chr>, Channeltype <chr>, SystemSize <chr>, StrahlerOrder <dbl>,
#> # Depth_m <chr>, Width_m <dbl>, `avgQm3/s` <chr>, Catchmentsizekm2 <dbl>,
#> # `Targeted?` <chr>, Comments <chr>, ...23 <chr>
head(papers_df)
#> # A tibble: 6 x 10
#> Title Authorlastname Journal PubYear Publication_Nid `Additionaldata~
#> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 Patt~ Abbott Biogeo~ 2015 2297 <NA>
#> 2 Carb~ Abril Global~ 2005 2292 <NA>
#> 3 Gree~ Adams Intern~ 2009 7243 <NA>
#> 4 Diff~ Aho Journa~ 2019 12484 Yes
#> 5 Expo~ Alshboul Enviro~ 2016 2223 <NA>
#> 6 Meth~ Anthony Limnol~ 2012 7171 yes
#> # ... with 4 more variables: `Embargo?` <chr>, `2xcheck` <chr>, Comments <chr>,
#> # ...10 <chr>Let’s plot the distribution of mean CH4 concentration.
commontheme <- list(
theme_bw(),
scale_x_log10nice(omag = seq(-20, 20, 2)),
scale_y_continuous(expand = expansion(mult = c(0, .05)))
)
CH4mean_hist_fig <- ggplot(conc_df) +
geom_histogram(aes(x = CH4mean)) +
commontheme
print(CH4mean_hist_fig)
# Save image to dropbox or another folder
# ggsave(file.path(path_to_dropbox, "db_processingR",
# "Figures", "CH4_meanconc_hist.png"),
# CH4mean_hist_fig)Let’s plot the distribution of mean CH4, CO2, and N2O concentration and fluxes.
CO2mean_hist_fig <- ggplot(conc_df) +
geom_histogram(aes(x = CO2mean)) +
commontheme +
theme(axis.title.y = element_blank())
# print(CO2mean_hist_fig)
N2Omean_hist_fig <- ggplot(conc_df) +
geom_histogram(aes(x = N2Omean)) +
commontheme +
theme(axis.title.y = element_blank())
# print(N2Omean_hist_fig)
CH4meanflux_hist_fig <- ggplot(flux_df) +
geom_histogram(aes(x = DiffusiveCH4FluxMean)) +
commontheme
# print(CH4meanflux_hist_fig)
CO2meanflux_hist_fig <- ggplot(flux_df) +
geom_histogram(aes(x = CO2FluxMean)) +
commontheme +
theme(axis.title.y = element_blank())
# print(CO2meanflux_hist_fig)
N2Omeanflux_hist_fig <- ggplot(flux_df) +
geom_histogram(aes(x = N2OFluxMean)) +
commontheme +
theme(axis.title.y = element_blank())
# print(N2Omeanflux_hist_fig)
# To save this figure use package "grid"
# Need to uncomment png() and dev.off()
# png(file.path(path_to_dropbox, "db_processingR",
# "Figures", "Mean_gas_concflux.png"),
# height = 6, width = 8, units = "in", res = 300)
grid.newpage()
plots <- grid.draw(rbind(cbind(ggplotGrob(CH4mean_hist_fig),
ggplotGrob(CO2mean_hist_fig),
ggplotGrob(N2Omean_hist_fig)),
cbind(ggplotGrob(CH4meanflux_hist_fig),
ggplotGrob(CO2meanflux_hist_fig),
ggplotGrob(N2Omeanflux_hist_fig)),
size = "first"))
# dev.off()