| Title: | Packages, data and scripts for ATSA course and lab book |
|---|---|
| Description: | This package will load the needed packages and data files for the ATSA course material when students install from GitHub. |
| Authors: | Elizabeth Eli Holmes [aut, cre], Mark D. Scheuerell [aut], Eric J. Ward [aut] |
| Maintainer: | Elizabeth E. Holmes <[email protected]> |
| License: | GPL-2 |
| Version: | 1.5 |
| Built: | 2024-11-08 04:25:13 UTC |
| Source: | https://github.com/atsa-es/atsalibrary |
This package will load the needed packages and data files for the ATSA course material when students install from GitHub.
Monthly (WA, OR and CA) and yearly (AK, CA, MI, OR, PA, and WA) data on commercial landings and value of Chinook salmon. Yearly data were downloaded from the NOAA Fisheries, Fisheries Statistics Division and the monthly data were downloaded from PacFIN.
data(chinook)data(chinook)
Objects of class "data.frame". Columns are Year,
Month (if monthly), Species, State, log.metric.tons, metric.tons, and value.usd (USD)
There are two datasets included: chinook.month and chinook.year. The monthly data are available from 1990 and the annual data are available from 1950. The raw data and R script to process the data are in the inst/original_data folder.
From the NOAA Fisheries Statistics Divison: "Collecting these data is a joint state and federal responsibility. State-federal systems gather landings data from state-mandated fishery trip-tickets, landing weighout reports from seafood dealers, federal logbooks of fishery catch and effort, and shipboard and portside interviews and biological sampling of catches. State fishery agencies are usually the main collectors of these data, though they and NOAA Fisheries gather data jointly in some states. Surveys are done differently in different states; NOAA Fisheries takes supplemental surveys to ensure that the data from different states and years are comparable."
In addition from NOAA Fisheries Statistics Division: "Statistics for each state represent a census of the volume and value of finfish and shellfish landed and sold at the dock, not an expanded estimate of landings based on sampling data. The main statistics collected are the pounds and ex-vessel dollar value of landings identified by species, year, month, state, county, port, water, and fishing gear. Most states get their landings data from seafood dealers who submit monthly reports of the weight and value of landings by vessel. Increasingly, though, states are getting landings data from mandatory trip-tickets—filled out by seafood dealers and fishermen at the end of every fishing trip, indicating their landings by species."
Yearly NOAA Commercial Landings Statistics
Monthly PacFIN
NOAA Fisheries Office of Science and Technology, Commercial Landings Query, Available at: www.fisheries.noaa.gov/foss, Accessed 14 April 2023
Pacific Fisheries Information Network (PacFIN) retrieval dated 14 April 2023, Pacific States Marine Fisheries Commission, Portland, Oregon (www.psmfc.org). Available at https://reports.psmfc.org/pacfin
data(chinook) dat <- subset(chinook.month, state="WA")$log.metric.tons datts <- ts(dat, start=c(1990,1), frequency=12) plot(datts) ggplot( chinook.month %>% mutate(t = zoo::as.yearmon(paste(Year, Month), "%Y %b")), aes(x=t, y=log.metric.tons)) + geom_line() + facet_wrap(~State)data(chinook) dat <- subset(chinook.month, state="WA")$log.metric.tons datts <- ts(dat, start=c(1990,1), frequency=12) plot(datts) ggplot( chinook.month %>% mutate(t = zoo::as.yearmon(paste(Year, Month), "%Y %b")), aes(x=t, y=log.metric.tons)) + geom_line() + facet_wrap(~State)
UK Environmental Change Network (ECN) atmospheric nitrogen chemistry data: 1993-2015
data(ECNNO2) ECNNO2 ECNmetadata(ECNNO2) ECNNO2 ECNmeta
ECNNO2 is the data and is an object of class "data.frame".
Columns are Year, Month, TubeID, SiteCode, and Value. ECNmeta is the site locations.
An object of class data.frame with 9108 rows and 7 columns.
An object of class data.frame with 12 rows and 4 columns.
From https://catalogue.ceh.ac.uk/documents/baf51776-c2d0-4e57-9cd3-30cd6336d9cf: "Atmospheric Nitrogen Dioxide data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected by diffusion tubes at all of ECN's terrestrial sites using a standard protocol. They represent continuous fortnightly records from 1993 to 2015." The data in ECNNO2 are the NO2 (micrograms/m3). Note although the dataset information do not state this, I believe that the data are averaged iver the time exposure (minutes), i.e. are micrograms/m3-minute, because the exposure time does not affect the N02 values in the dataset, i.e. longer exposure does not equal higher NO2 values. The link above has all the background information on the dataset. The original data are taken every 7-31 days, with 14 days apart being the target and most common time difference. ECNNO2 is the monthly average of of all the readings for the experimental tubes. The average for the E1 tube will be average of the 1-3 E1 tubes from that month. Same for E2, E3 etc.
These data are part of a long-term monitoring program in the UK. N02 is just one component that is monitored. The sites locations are in ECNmeta.
UK Centre for Ecolgoy & Hydrology
Rennie, S.; Adamson, J.; Anderson, R.; Andrews, C.; Bater, J.; Bayfield, N.; Beaton, K.; Beaumont, D.; Benham, S.; Bowmaker, V.; Britt, C.; Brooker, R.; Brooks, D.; Brunt, J.; Common, G.; Cooper, R.; Corbett, S.; Critchley, N.; Dennis, P.; Dick, J.; Dodd, B.; Dodd, N.; Donovan, N.; Easter, J.; Flexen, M.; Gardiner, A.; Hamilton, D.; Hargreaves, P.; Hatton-Ellis, M.; Howe, M.; Kahl, J.; Lane, M.; Langan, S.; Lloyd, D.; McCarney, B.; McElarney, Y.; McKenna, C.; McMillan, S.; Milne, F.; Milne, L.; Morecroft, M.; Murphy, M.; Nelson, A.; Nicholson, H.; Pallett, D.; Parry, D.; Pearce, I.; Pozsgai, G.; Rose, R.; Schafer, S.; Scott, T.; Sherrin, L.; Shortall, C.; Smith, R.; Smith, P.; Tait, R.; Taylor, C.; Taylor, M.; Thurlow, M.; Turner, A.; Tyson, K.; Watson, H.; Whittaker, M.; Wood, C. (2017). UK Environmental Change Network (ECN) atmospheric nitrogen chemistry data: 1993-2015. NERC Environmental Information Data Centre. https://doi.org/10.5285/baf51776-c2d0-4e57-9cd3-30cd6336d9cf
# Data were prepared from the downloaded data set as follows: ## Not run: library(tidyr) library(reshape2) dat <- read.csv("ECN_AN1.csv", stringsAsFactors = FALSE) dat$Date <- as.Date(dat$SDATE, "%d-%B-%y") dat$Year <- format(dat$Date, "%Y") dat$Mon <- format(dat$Date, "%b") a <- subset(dat, TUBEID %in% c("E1","E2","E3")) b <- spread(a, FIELDNAME, VALUE) b <- b[order(b$Date), ] bad <- c(103, 104, 107:120) # bad quality codes. Burning nearby or contaminant in tube. b$NO2[b$Q1 %in% bad | b$Q2 %in% bad | b$Q3 %in% bad] <- NA val <- tapply(b$NO2, list(b$Year, b$Mon, b$TUBEID, b$SITECODE), mean, na.rm=TRUE) dat.mon <- melt(data=val, value.name="NO2") colnames(dat.mon) <- c("Year", "Month", "TubeID", "SiteCode", "Value") dat.mon$Date <- as.Date(paste(1,dat.mon$Mon, dat.mon$Year), "%d %b %Y") dat.mon <- dat.mon[order(dat.mon$Date),] rownames(dat.mon) <- NULL ECNNO2 <- dat.mon ## End(Not run) data(ECNNO2)# Data were prepared from the downloaded data set as follows: ## Not run: library(tidyr) library(reshape2) dat <- read.csv("ECN_AN1.csv", stringsAsFactors = FALSE) dat$Date <- as.Date(dat$SDATE, "%d-%B-%y") dat$Year <- format(dat$Date, "%Y") dat$Mon <- format(dat$Date, "%b") a <- subset(dat, TUBEID %in% c("E1","E2","E3")) b <- spread(a, FIELDNAME, VALUE) b <- b[order(b$Date), ] bad <- c(103, 104, 107:120) # bad quality codes. Burning nearby or contaminant in tube. b$NO2[b$Q1 %in% bad | b$Q2 %in% bad | b$Q3 %in% bad] <- NA val <- tapply(b$NO2, list(b$Year, b$Mon, b$TUBEID, b$SITECODE), mean, na.rm=TRUE) dat.mon <- melt(data=val, value.name="NO2") colnames(dat.mon) <- c("Year", "Month", "TubeID", "SiteCode", "Value") dat.mon$Date <- as.Date(paste(1,dat.mon$Mon, dat.mon$Year), "%d %b %Y") dat.mon <- dat.mon[order(dat.mon$Date),] rownames(dat.mon) <- NULL ECNNO2 <- dat.mon ## End(Not run) data(ECNNO2)
Annual commercial landings of anchovy, sardine and mackerel from Greek fisheries compiled by the Hellenic Statistical Authority. Also included are covariates for the catch.
data(greeklandings) greeklandings covs covsmean.year covsmean.mondata(greeklandings) greeklandings covs covsmean.year covsmean.mon
Objects of class "data.frame". Columns are Year, Species, log.metric.tons, metric.tons
An object of class data.frame with 264 rows and 4 columns.
An object of class list of length 3.
An object of class data.frame with 55 rows and 6 columns.
An object of class data.frame with 656 rows and 7 columns.
Data are from Table IV in the "Sea Fishery by Motor Vessels" statistical reports published by the Hellenic Statisitcal Authority. The reports are available in Digital Library (ELSTAT), Special Publications, Agriculture-Livestock-Fisheries, Fisheries. In Table IV, the landings data were taken from the total column, units are metric tons. In the table, sardine is denoted ' Pilchard'. The data were assembled manually for the Fish Forecast eBook.
The covariates are Year, air.degC (air temperature), slp.millibars, sst.degC (sea surface temperature), vwnd.m/s (N/S wind speed), wspd3.m3/s3 (overall wind speed cubed). covsmean.mon and covsmean.year are averages over the 3 regions in the covs list. See the the Fish Forecast eBook for details.
Hellenic Statisitcal Authority Digital Library
data(greeklandings) anchovy = ts(subset(greeklandings, Species=="Anchovy")$log.metric.tons, start=1964) plot(anchovy) library(ggplot2) ggplot(greeklandings, aes(x=Year, y=log.metric.tons)) + geom_line() + facet_wrap(~Species)data(greeklandings) anchovy = ts(subset(greeklandings, Species=="Anchovy")$log.metric.tons, start=1964) plot(anchovy) library(ggplot2) ggplot(greeklandings, aes(x=Year, y=log.metric.tons)) + geom_line() + facet_wrap(~Species)
no information about these data
data(hourlyphyto)data(hourlyphyto)
Objects of class "data.frame" with column V1
data(hourlyphyto)data(hourlyphyto)
Spawner-recruit data for sockeye salmon from the Kvichak River.
data(KvichakSockeye)data(KvichakSockeye)
Object of class "data.frame"
brood_year Brood year.
spawners Spawners (count) in thousands
recruits Recruits (count) in thousands
pdo_summer_t2 Pacific Decadal Oscillation (PDO) from Apr-Sep of brood year $t+2$
pdo_winter_ts Pacific Decadal Oscillation (PDO) from Oct (brood year $t+2$) to Mar (brood year $t+3$)
Spawner-recruit data for sockeye salmon (Oncorhynchus nerka) from the Kvichak River in SW Alaska that span the years 1952-1989. The data come from a large public database begun by Ransom Myers many years ago. The database is now maintained as the RAM Legacy Stock Assessment Database.
RAM Legacy Stock Assessment Database
RAM Legacy Stock Assessment Database. 2018. Version 4.44-assessment-only. Released 2018-12-22. Retrieved from DOI:10.5281/zenodo.2542919.
Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) Evaluating the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries 13 (4) 380-398. DOI: 10.1111/j.1467-2979.2011.00435.x
Spawner escapement estimates are originally from: 1997-2017 Appendix Table A12.
data(KvichakSockeye)data(KvichakSockeye)
Monthly Lake Washington (WA, USA) plankton, temperature, total phosphorous, and pH data 1962 to 1994.
data(lakeWA)data(lakeWA)
Object of class "data.frame".
#'
The lakeWA is a 32-year time series (1962-1994) of monthly plankton counts from Lake Washington, Washington, USA. lakeWA is a transformed version of the raw data (available in the MARSS package data(lakeWAplanktonRaw, package="MARSS")). Zeros have been replaced with NAs (missing). The plankton counts are logged (natural log) and standardized to a mean of zero and variance of 1 (so logged and then z-scored). Temperature, TP & pH were also z-scored but not logged (so z-score of the untransformed values for these covariates). The single missing temperature value was replaced with -1 and the single missing TP value was replaced with -0.3. The two missing pH values were interpolated. Monthly anomalies for temperature, TP and pH were computed by removing the monthly means (computed over the 1962-1994 period). The anomalies were then z-scored to remove mean and standardize variance to 1.
Adapted from the Lake Washington database of Dr. W. T. Edmondson, as funded by the Andrew Mellon Foundation; data courtesy of Dr. Daniel Schindler, University of Washington, Seattle, WA.
Hampton, S. E. Scheuerell, M. D. Schindler, D. E. (2006) Coalescence in the Lake Washington story: Interaction strengths in a planktonic food web. Limnology and Oceanography, 51, 2042-2051.
# The lakeWA data frame was created with the following code: ## Not run: data(lakeWAplankton, package = "MARSS") lakeWA <- data.frame(lakeWAplanktonTrans) # add on month and date columns lakeWA$Month.abb <- month.abb[lakeWA$Month] lakeWA$Date <- as.Date(paste0(lakeWA$Year, "-", lakeWA$Month,"-01")) # interpolate 2 missing values in pH lakeWA$pH[is.na(lakeWA$pH)] <- MARSS::MARSS(lakeWA$pH)$states[1, is.na(lakeWA$pH)] # create monthly anomalies lakeWA$Temp.anom <- residuals(lm(Temp ~ Month.abb, data=lakeWA)) lakeWA$TP.anom <- residuals(lm(TP ~ Month.abb, data=lakeWA)) lakeWA$pH.anom <- residuals(lm(pH ~ Month.abb, data=lakeWA)) # resort the columns lakeWA <- lakeWA[,c(22,1:2,21,3:5,23:25,6:20)] # zscore everything for (i in 5:25) lakeWA[[i]] <- MARSS::zscore(lakeWA[[i]]) save( lakeWA, file="data/lakeWA.RData" ) ## End(Not run) library(ggplot2) library(tidyr) df <- lakeWA %>% pivot_longer( cols = Temp:Non.colonial.rotifers, names_to = "variable", values_to = "value" ) ggplot(df, aes(x=Date, y=value)) + geom_line() + facet_wrap(~variable) data(lakeWA)# The lakeWA data frame was created with the following code: ## Not run: data(lakeWAplankton, package = "MARSS") lakeWA <- data.frame(lakeWAplanktonTrans) # add on month and date columns lakeWA$Month.abb <- month.abb[lakeWA$Month] lakeWA$Date <- as.Date(paste0(lakeWA$Year, "-", lakeWA$Month,"-01")) # interpolate 2 missing values in pH lakeWA$pH[is.na(lakeWA$pH)] <- MARSS::MARSS(lakeWA$pH)$states[1, is.na(lakeWA$pH)] # create monthly anomalies lakeWA$Temp.anom <- residuals(lm(Temp ~ Month.abb, data=lakeWA)) lakeWA$TP.anom <- residuals(lm(TP ~ Month.abb, data=lakeWA)) lakeWA$pH.anom <- residuals(lm(pH ~ Month.abb, data=lakeWA)) # resort the columns lakeWA <- lakeWA[,c(22,1:2,21,3:5,23:25,6:20)] # zscore everything for (i in 5:25) lakeWA[[i]] <- MARSS::zscore(lakeWA[[i]]) save( lakeWA, file="data/lakeWA.RData" ) ## End(Not run) library(ggplot2) library(tidyr) df <- lakeWA %>% pivot_longer( cols = Temp:Non.colonial.rotifers, names_to = "variable", values_to = "value" ) ggplot(df, aes(x=Date, y=value)) + geom_line() + facet_wrap(~variable) data(lakeWA)
Monthly Snow Water Equivalent (SWE) at Stations in Washington State
data(MLCO2)data(MLCO2)
Objects of class "data.frame". Columns are year, month, ppm (part per million)
These are monthly average C02 values recorded on Mauna Loa in Hawaii since 1958. Data are provided by NOAA Earth System Rearch Laboratory, Global Monitoring Division.
Data from March 1958 through April 1974 have been obtained by C. David Keeling of the Scripps Institution of Oceanography (SIO) and were obtained from the Scripps website (scrippsco2.ucsd.edu). The ppm column contains the monthly mean CO2 mole fraction determined from daily averages. The mole fraction of CO2, expressed as parts per million (ppm) is the number of molecules of CO2 in every one million molecules of dried air (water vapor removed). If there are missing days, the value is the interpolated value. See www.esrl.noaa.gov/gmd/ccgg/trends/ for additional details.
NOAA ESRL Trends in Atmospheic Carbon Dioxide
Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).
# We downloaded this data and created the `MLCO2` dataframe with the following code: ## Not run: library(RCurl) ## get CO2 data from Mauna Loa observatory ww1 <- "ftp://aftp.cmdl.noaa.gov/products/" ww2 <- "trends/co2/co2_mm_mlo.txt" CO2fulltext <- getURL(paste0(ww1,ww2)) MLCO2 <- read.table(text=CO2fulltext)[,c(1,2,5)] ## assign better column names colnames(MLCO2) <- c("year","month","ppm") save(MLCO2, file="MLCO2.RData") ## End(Not run) data(MLCO2)# We downloaded this data and created the `MLCO2` dataframe with the following code: ## Not run: library(RCurl) ## get CO2 data from Mauna Loa observatory ww1 <- "ftp://aftp.cmdl.noaa.gov/products/" ww2 <- "trends/co2/co2_mm_mlo.txt" CO2fulltext <- getURL(paste0(ww1,ww2)) MLCO2 <- read.table(text=CO2fulltext)[,c(1,2,5)] ## assign better column names colnames(MLCO2) <- c("year","month","ppm") save(MLCO2, file="MLCO2.RData") ## End(Not run) data(MLCO2)
Lake Barco data for the EFI 2021 Forecasting Challenge
data(neon_barc)data(neon_barc)
neon_barc is a tibble with columns are date, siteID, oxygen, temperature, oxygen_sd, temperature_sd, depth_oxygen, depth_temperature, neon_product_ids, year, and cal_day.
The data for the aquatics challenge comes from a NEON site at Lake Barco (Florida). More about the data and challenge is here and the Github repository for getting all the necessary data is eco4cast.
The neon_barc data set was created with the aquatics-targets.csv.gz file produced in the neon4cast-aquatics GitHub repository and saved in the inst folder in the atsalibrary package.
From that file, neon_barc is created with
library(tidyverse)
library(lubridate)
# This taken from code on NEON aquatics challenge
targets <- readr::read_csv("aquatics-targets.csv.gz", guess_max = 10000)
site_data_var <- targets %>%
filter(siteID == "BARC")
# This is key here - I added the forecast horizon on the end of the data for the forecast period
full_time <- tibble(time = seq(min(site_data_var$time), max(site_data_var$time), by = "1 day"))
# Join the full time with the site_data_var so there aren't gaps in the time column
site_data_var <- left_join(full_time, site_data_var) %>%
dplyr::rename(date = time)
site_data_var$year <- year(site_data_var$date)
site_data_var$cal_day <- yday(site_data_var$date)
neon_barc <- site_data_var
https://github.com/eco4cast/neon4cast-aquatics
Ecological Forecasting Initiative https://ecoforecast.org/
Neon4Cast Aquatics GitHub repository https://github.com/eco4cast/neon4cast-aquatics
data(neon_barc)data(neon_barc)
Northern Hemisphere land and ocean temperature anomalies from NOAA
data(NHTemp)data(NHTemp)
Object of class "data.frame". Columns are Year and Value.
#'
From https://www.ncdc.noaa.gov/cag/global/data-info: "Global temperature anomaly data come from the Global Historical Climatology Network-Monthly (GHCN-M) data set and International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which have data from 1880 to the present. These two datasets are blended into a single product to produce the combined global land and ocean temperature anomalies. The available timeseries of global-scale temperature anomalies are calculated with respect to the 20th century average."
# We downloaded this data and created the `NHTemp` dataframe with the following code: ## Not run: library(RCurl) ww1 <- "https://www.ncdc.noaa.gov/cag/time-series/" ww2 <- "global/nhem/land_ocean/p12/12/1880-2014.csv" NHTemp <- read.csv(text=getURL(paste0(ww1,ww2)), skip=4) save(NHTemp, file="NHTemp.RData") ## End(Not run) data(NHTemp)# We downloaded this data and created the `NHTemp` dataframe with the following code: ## Not run: library(RCurl) ww1 <- "https://www.ncdc.noaa.gov/cag/time-series/" ww2 <- "global/nhem/land_ocean/p12/12/1880-2014.csv" NHTemp <- read.csv(text=getURL(paste0(ww1,ww2)), skip=4) save(NHTemp, file="NHTemp.RData") ## End(Not run) data(NHTemp)
Monthly Snow Water Equivalent (SWE) at Stations in Washington State
data(snotel) snotel snotelmetadata(snotel) snotel snotelmeta
snotel: object of class "data.frame". Columns are Station, Station.Id, Year, Month, SWE, and Date
snotelmeta: Station_Name, Station.Id, State.Code, Network.Code, Network.Name, Elevation, Latitude, Longitude"
An object of class data.frame with 27720 rows and 6 columns.
An object of class data.frame with 70 rows and 8 columns.
These data are the snow water equivalent percent of normal.
This represents the snow water equivalent (SWE) compared to the average value for
that site on the same day. The data were downloaded from the USDA
Natural Resources Conservation Service website for the Washington Snow Survey Program. The snotel object is the SWE data and the snotelmeta is the station metadata (lat/lon and elevation).
data(snotel)data(snotel)
Spawner-recruit data for sockeye salmon from the KVICHAK, WOOD, IGUSHIK, NAKNEK, EGEGIK, NUSHAGAK, UGASHIK, TOGIAK Rivers.
data(sockeye)data(sockeye)
Object of class "data.frame"
brood_year Brood year.
region River.
spawners Spawners (count) in thousands
recruits Recruits (count) in thousands
pdo_summer_t2 Pacific Decadal Oscillation (PDO) from Apr-Sep of brood year $t+2$
pdo_winter_ts Pacific Decadal Oscillation (PDO) from Oct (brood year $t+2$) to Mar (brood year $t+3$)
Spawner-recruit data for sockeye salmon (Oncorhynchus nerka) from the Bristol Bay region in SW Alaska that span the years 1952-2005. The data come from a large public database begun by Ransom Myers many years ago. The database is now maintained as the RAM Legacy Stock Assessment Database.
The R script which created the data can be found in the atsalibrary GitHub site in the inst/orginal_data/sockeye folder.
For convenience in the lab book, KvichakSockeye is also created which is just the Kvichak data.
RAM Legacy Stock Assessment Database v4.491
RAM Legacy Stock Assessment Database. 2018. Version 4.44-assessment-only. Released 2018-12-22. Retrieved from DOI:10.5281/zenodo.2542919.
Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) Evaluating the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries 13 (4) 380-398. DOI: 10.1111/j.1467-2979.2011.00435.x
Spawner escapement estimates are originally from: Bristol Bay Area Annual Management Reports from the Alaska Department of Fish and Game and are based on in river spawner counts (tower counts and aerial surveys).
See an example application of these data see: Rogers, L.A. and Schindler, D.E. 2008. Asynchrony in Population Dynamics of Sockeye Salmon in Southwest Alaska. Oikos 117: 1578-1586
data(sockeye) a <- pivot_longer(sockeye, cols=spawners:recruits, names_to="value", values_to="count") ggplot(a,aes(brood_year, count, color=value, fill=value)) + geom_area() + theme(axis.text.x = element_text(angle = 90)) + ylab("Count (thousands)") + xlab("Brood Year") + facet_wrap(~region, scales="free_y")data(sockeye) a <- pivot_longer(sockeye, cols=spawners:recruits, names_to="value", values_to="count") ggplot(a,aes(brood_year, count, color=value, fill=value)) + geom_area() + theme(axis.text.x = element_text(angle = 90)) + ylab("Count (thousands)") + xlab("Brood Year") + facet_wrap(~region, scales="free_y")