Atmospheric CO2
Jens Daniel Müller
10 July, 2024
Last updated: 2024-07-10
Checks: 7 0
Knit directory:
heatwave_co2_flux_2023/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20240307) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 334ff26. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data
Ignored: output/
Unstaged changes:
Modified: code/Workflowr_project_managment.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/atm_co2.Rmd) and HTML
(docs/atm_co2.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 334ff26 | jens-daniel-mueller | 2024-07-10 | manual commit |
| html | 430e926 | jens-daniel-mueller | 2024-07-10 | manual commit |
| html | caec400 | jens-daniel-mueller | 2024-06-13 | Build site. |
| html | 3148fef | jens-daniel-mueller | 2024-06-13 | Build site. |
| html | a60be97 | jens-daniel-mueller | 2024-06-12 | Build site. |
| html | d46002d | jens-daniel-mueller | 2024-06-12 | manual commit |
| html | 03c415f | jens-daniel-mueller | 2024-06-11 | Build site. |
| html | 5261667 | jens-daniel-mueller | 2024-06-11 | manual commit |
| html | 2b34bf8 | jens-daniel-mueller | 2024-06-11 | manual commit |
| html | 6954c65 | jens-daniel-mueller | 2024-06-06 | Build site. |
| html | 009791f | jens-daniel-mueller | 2024-05-14 | Build site. |
| html | 7f9c687 | jens-daniel-mueller | 2024-04-23 | Build site. |
| html | ce4e2a6 | jens-daniel-mueller | 2024-04-17 | Build site. |
| Rmd | 6b3a080 | jens-daniel-mueller | 2024-04-17 | rebuild entire website with NRT_fco2residual |
| html | 58e3680 | jens-daniel-mueller | 2024-04-11 | Build site. |
| html | dfcf790 | jens-daniel-mueller | 2024-04-11 | Build site. |
| html | 139bc97 | jens-daniel-mueller | 2024-04-11 | manual deletion of files |
| html | 69dc18c | jens-daniel-mueller | 2024-04-04 | Build site. |
| html | c9d994c | jens-daniel-mueller | 2024-04-04 | Build site. |
| html | 40cb158 | jens-daniel-mueller | 2024-04-03 | Build site. |
| html | a83c8fc | jens-daniel-mueller | 2024-04-03 | Build site. |
| html | 6bb7ce2 | jens-daniel-mueller | 2024-03-25 | Build site. |
| html | 4589270 | jens-daniel-mueller | 2024-03-24 | Build site. |
| html | 62ea4dd | jens-daniel-mueller | 2024-03-24 | Build site. |
| html | 1a5167d | jens-daniel-mueller | 2024-03-24 | Build site. |
| html | 934da22 | jens-daniel-mueller | 2024-03-22 | Build site. |
| html | ae4041c | jens-daniel-mueller | 2024-03-22 | Build site. |
| html | dc2068e | jens-daniel-mueller | 2024-03-22 | Build site. |
| html | 47238da | jens-daniel-mueller | 2024-03-21 | Build site. |
| html | 83fcd67 | jens-daniel-mueller | 2024-03-21 | Build site. |
| html | 342018b | jens-daniel-mueller | 2024-03-20 | Build site. |
| html | 8698b51 | jens-daniel-mueller | 2024-03-20 | Build site. |
| html | 03321bd | jens-daniel-mueller | 2024-03-19 | Build site. |
| html | de8f158 | jens-daniel-mueller | 2024-03-19 | Build site. |
| Rmd | 0fb4008 | jens-daniel-mueller | 2024-03-19 | relative path |
| html | b41fa51 | jens-daniel-mueller | 2024-03-19 | Build site. |
| Rmd | e492444 | jens-daniel-mueller | 2024-03-19 | correct knit root folder |
| html | bd3c1fe | jens-daniel-mueller | 2024-03-19 | Build site. |
| html | 5c97a86 | jens-daniel-mueller | 2024-03-19 | Build site. |
| html | 604281a | jens-daniel-mueller | 2024-03-19 | Build site. |
| html | 14a6ce5 | jens-daniel-mueller | 2024-03-19 | Build site. |
| html | d3f3f52 | jens-daniel-mueller | 2024-03-18 | Build site. |
| html | 2b04797 | jens-daniel-mueller | 2024-03-18 | Build site. |
| html | 426fd51 | jens-daniel-mueller | 2024-03-15 | Build site. |
| html | 1f85de2 | jens-daniel-mueller | 2024-03-15 | Build site. |
| html | fc68c94 | jens-daniel-mueller | 2024-03-15 | Build site. |
| html | 85c6275 | jens-daniel-mueller | 2024-03-15 | Build site. |
| html | 2e0907d | jens-daniel-mueller | 2024-03-14 | Build site. |
| html | 7a258b9 | jens-daniel-mueller | 2024-03-14 | Build site. |
| html | baf5167 | jens-daniel-mueller | 2024-03-14 | Build site. |
| html | 5ae6c32 | jens-daniel-mueller | 2024-03-14 | Build site. |
| html | 4eb2ea7 | jens-daniel-mueller | 2024-03-14 | Build site. |
| html | f1a1ba8 | jens-daniel-mueller | 2024-03-13 | Build site. |
| html | a91341f | jens-daniel-mueller | 2024-03-13 | Build site. |
| html | 86b52e9 | jens-daniel-mueller | 2024-03-13 | Build site. |
| html | 1e3279c | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | ff205b8 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | e760672 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | caeb7f1 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | b6c0bec | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | 3f69dc8 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | 0473a50 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | f3b86fa | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | 3f11106 | jens-daniel-mueller | 2024-03-12 | Build site. |
| html | cfe3967 | jens-daniel-mueller | 2024-03-11 | Build site. |
| html | 45a623c | jens-daniel-mueller | 2024-03-11 | Build site. |
| Rmd | f4af74f | jens-daniel-mueller | 2024-03-11 | started pCO2 products analysis |
| html | 41d435b | jens-daniel-mueller | 2024-03-11 | Build site. |
| Rmd | 8ac0750 | jens-daniel-mueller | 2024-03-11 | included setup source functions |
| html | 581fb88 | jens-daniel-mueller | 2024-03-08 | Build site. |
| Rmd | af44790 | jens-daniel-mueller | 2024-03-08 | atm co2 2023 prediction |
| html | a2ce6d8 | jens-daniel-mueller | 2024-03-07 | Build site. |
| Rmd | 36de8c8 | jens-daniel-mueller | 2024-03-07 | atm co2 2023 prediction |
| html | 5ac6300 | jens-daniel-mueller | 2024-03-07 | Build site. |
| Rmd | dda379e | jens-daniel-mueller | 2024-03-07 | atm co2 data added |
| html | 7d24a99 | jens-daniel-mueller | 2024-03-07 | Build site. |
| html | a9267cc | jens-daniel-mueller | 2024-03-07 | Build site. |
| Rmd | 694c584 | jens-daniel-mueller | 2024-03-07 | added atm co2 |
center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)
worldmap <- ne_countries(scale = 'small',
type = 'map_units',
returnclass = 'sf')
worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans)
coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans)
bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)
bbox_graticules <- st_graticule(
x = bbox_trans,
crs = st_crs(bbox_trans),
datum = st_crs(bbox_trans),
lon = c(20, 20.001),
lat = c(-78,65),
ndiscr = 1e3,
margin = 0.001
)
bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans)
lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans, linewidth = 1) +
# coord_sf(crs = target_crs,
# ylim = lat_lim,
# xlim = lon_lim,
# expand = FALSE) +
# theme(
# panel.border = element_blank(),
# axis.text = element_blank(),
# axis.ticks = element_blank()
# )
latitude_graticules <- st_graticule(
x = bbox_graticules,
crs = st_crs(bbox_graticules),
datum = st_crs(bbox_graticules),
lon = c(20, 20.001),
lat = c(-60,-30,0,30,60),
ndiscr = 1e3,
margin = 0.001
)
latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)
latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))
latitude_labels <- st_as_sf(x = latitude_labels,
coords = c("lon", "lat"),
crs = "+proj=longlat")
latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans) +
# geom_sf(data = latitude_graticules_trans,
# col = "grey60",
# linewidth = 0.2) +
# geom_sf_text(data = latitude_labels_trans,
# aes(label = lat_label),
# size = 3,
# col = "grey60")Read data
co2_mm_gl <- read_csv("../data/co2_mm_gl.csv",
skip = 38)
global_co2_merged <- read_table("../data/global_co2_merged.txt",
comment = "!",
col_names = c("decimal", "average"))
atm_co2 <-
bind_rows(
co2_mm_gl %>%
select(decimal, average) %>%
mutate(source = "NOAA - Global marine surface"),
global_co2_merged %>%
mutate(source = "GCB - MLO & SPO")
)
atm_co2 <-
atm_co2 %>%
mutate(date = date_decimal(decimal),
year = year(date),
month = month(date))co2_surface <- read_table(here::here("data/input/atm_co2/co2_GHGreference.767706493_surface.txt"),
col_names = FALSE, skip = 76)
co2_surface <-
co2_surface[c(1, seq(2, 83, 2))]
names(co2_surface) <- c("time",
as.character(seq(-1, 1, 0.05)))
# conversion to degrees latitude
# 180/pi * asin(-1)
co2_surface <-
co2_surface %>%
pivot_longer(-time,
names_to = "lat",
values_to = "xco2")
co2_surface <-
co2_surface %>%
mutate(lat = as.numeric(lat))
co2_surface %>%
filter(time > 2010) %>%
ggplot(aes(time, lat, fill = xco2)) +
geom_tile() +
scale_fill_viridis_c(option = "magma") +
# scale_y_continuous(sec.axis = sec_axis(~ sin(.))) +
coord_cartesian(expand = 0)
surface_mbl <- read_table("data/input/atm_co2/surface.mbl.co2",
col_names = FALSE)
names(surface_mbl) <- c("time",
as.character(seq(-1, 1, 0.05)))
# conversion to degrees latitude
# 180/pi * asin
surface_mbl <-
surface_mbl %>%
pivot_longer(-time,
names_to = "lat",
values_to = "xco2")
surface_mbl <-
surface_mbl %>%
mutate(lat = as.numeric(lat))
surface_mbl %>%
filter(time > 2010) %>%
ggplot(aes(time, lat, fill = xco2)) +
geom_tile() +
scale_fill_viridis_c(option = "magma") +
coord_cartesian(expand = 0)
co2_merge <-
bind_rows(co2_surface %>% mutate(product = "website"),
surface_mbl %>% mutate(product = "preliminary"))
co2_merge %>%
filter(time > 2010) %>%
ggplot(aes(time, lat, fill = xco2)) +
geom_tile() +
scale_fill_viridis_c(option = "magma") +
coord_cartesian(expand = 0) +
facet_wrap(~ product, ncol = 1)
library(colorspace)
co2_merge_delta <-
co2_merge %>%
pivot_wider(names_from = product,
values_from = xco2) %>%
mutate(delta_xco2 = preliminary - website) %>%
filter(!is.na(delta_xco2))
co2_merge_delta %>%
filter(time > 2010) %>%
ggplot(aes(time, lat, fill = delta_xco2)) +
geom_tile() +
scale_fill_divergent() +
scale_x_continuous(breaks = seq(1900,2100,5)) +
coord_cartesian(expand = 0)
library(khroma)
co2_merge_delta %>%
mutate(lat = cut(lat,sin(c(-90,seq(-75,75,30),90)/180*pi))) %>%
group_by(lat, time) %>%
summarise(delta_xco2 = mean(delta_xco2, na.rm = TRUE)) %>%
ungroup() %>%
# filter(time > 2010) %>%
ggplot(aes(time, delta_xco2, col = lat)) +
geom_path() +
scale_color_bright() +
coord_cartesian(expand = 0)atm_co2 %>%
filter(decimal > 2010) %>%
ggplot(aes(decimal, average, col = source)) +
geom_path() +
geom_point() +
labs(y = expression(Atm.~CO[2]~(ppm))) +
geom_smooth(method = "lm", se = FALSE) +
scale_color_okabeito() +
theme_bw() +
scale_x_continuous(breaks = seq(1900,2100,2)) +
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
legend.position = c(0.8,0.2))
atm_co2 <-
atm_co2 %>%
filter(year < 2023 | source != "GCB - MLO & SPO")
atm_co2 <-
atm_co2 %>%
group_by(year, source) %>%
mutate(annual_mean = mean(average)) %>%
ungroup() %>%
mutate(monthly_anomaly = average - annual_mean)
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
ggplot(aes(month, monthly_anomaly, col = source, group = interaction(source, year))) +
geom_path() +
geom_point() +
labs(y = expression(Monthly~anomaly~from~annual~mean~(ppm)),
title = expression(Atmospheric~CO[2]~seasonality),
subtitle = "2013 - 2022") +
scale_color_manual(values = c("#000000", "#E69F00", "#56B4E9")) +
theme_bw() +
scale_x_continuous(breaks = seq(1,12,1), expand = c(0.01,0)) +
theme(legend.title = element_blank(),
legend.position = c(0.3,0.2))
| Version | Author | Date |
|---|---|---|
| a60be97 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| 03c415f | jens-daniel-mueller | 2024-06-11 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| dfcf790 | jens-daniel-mueller | 2024-04-11 |
| d5075c5 | jens-daniel-mueller | 2024-04-11 |
| 581fb88 | jens-daniel-mueller | 2024-03-08 |
| a2ce6d8 | jens-daniel-mueller | 2024-03-07 |
ggsave(
here::here(
paste0(
"output/atm_CO2_seasonality.png"
)
),
width = 6,
height = 4,
dpi = 600,
bg = "white"
)
atm_co2 %>%
filter(decimal > 1980,
decimal < 2023) %>%
ggplot(aes(month, monthly_anomaly, group = interaction(year), col = year)) +
geom_path() +
geom_point() +
labs(y = expression(Monthly~anomaly~atm.~CO[2]~(ppm))) +
scale_color_viridis_c() +
theme_bw() +
scale_x_continuous(breaks = seq(1,12,3)) +
facet_wrap(~ source) +
theme(legend.title = element_blank())
atm_co2_monthly_anomaly <-
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
group_by(source, month) %>%
summarise(monthly_anomaly = mean(monthly_anomaly),
decimal_mean = mean(decimal - year)) %>%
ungroup()
atm_co2_annual_means <-
atm_co2 %>%
group_by(source, year) %>%
summarise(annual_mean = mean(average)) %>%
ungroup()
atm_co2_annual_means %>%
filter(year >= 2013,
year <= 2022) %>%
group_by(source) %>%
summarise(long_term_mean = mean(annual_mean)) %>%
ungroup() %>%
pull(long_term_mean)[1] 405.7722 406.2577annual_mean_2023_predicted <-
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
group_by(source, year) %>%
summarise(annual_mean = mean(average)) %>%
ungroup() %>%
nest(data = -source) %>%
mutate(fit = map(data, ~ lm(annual_mean ~ year, data = .x)),
tidied = map(fit, tidy)) %>%
unnest(tidied) %>%
select(source, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(annual_mean = `(Intercept)` + year * 2023) %>%
filter(source == "GCB - MLO & SPO") %>%
pull(annual_mean)
atm_co2_monthly_anomaly <-
atm_co2_monthly_anomaly %>%
filter(source == "GCB - MLO & SPO") %>%
mutate(
year = 2023,
decimal = year + decimal_mean,
annual_mean = annual_mean_2023_predicted,
average = annual_mean + monthly_anomaly,
source = "GCB - MLO & SPO (linear prediction)",
date = date_decimal(decimal)
) %>%
select(-decimal_mean)
atm_co2_predicted <-
bind_rows(atm_co2 %>% filter(year < 2023 |
source != "GCB - MLO & SPO"),
atm_co2_monthly_anomaly)
atm_co2_annual_means %>%
filter(year > 2021)# A tibble: 3 × 3
source year annual_mean
<chr> <dbl> <dbl>
1 GCB - MLO & SPO 2022 416.
2 NOAA - Global marine surface 2022 417.
3 NOAA - Global marine surface 2023 419.atm_co2_predicted %>%
filter(decimal > 2013) %>%
ggplot() +
geom_path(aes(decimal, average, col = source)) +
geom_point(aes(decimal, average, col = source)) +
geom_path(aes(decimal, annual_mean, col = source, group = interaction(source, year),
linetype = "annual mean")) +
scale_linetype_manual(values = 2) +
labs(y = expression(CO[2]~(ppm)),
title = expression(Atmospheric~CO[2]~timeseries),
subtitle = "2013 - 2022") +
scale_color_manual(values = c("#000000", "#56B4E9", "#E69F00")) +
theme_bw() +
scale_x_continuous(breaks = seq(1900,2100,2), expand = c(0.01,0)) +
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
legend.position = c(0.3,0.7))
| Version | Author | Date |
|---|---|---|
| a60be97 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| 03c415f | jens-daniel-mueller | 2024-06-11 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| dfcf790 | jens-daniel-mueller | 2024-04-11 |
| d5075c5 | jens-daniel-mueller | 2024-04-11 |
| 581fb88 | jens-daniel-mueller | 2024-03-08 |
| a2ce6d8 | jens-daniel-mueller | 2024-03-07 |
ggsave(
here::here(
paste0(
"output/atm_CO2_timeseries.png"
)
),
width = 6,
height = 4,
dpi = 600,
bg = "white"
)
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_1.0.5 khroma_1.9.0 lubridate_1.9.0
[4] timechange_0.1.1 terra_1.7-65 sf_1.0-9
[7] rnaturalearth_0.1.0 geomtextpath_0.1.1 colorspace_2.0-3
[10] marelac_2.1.10 shape_1.4.6 ggforce_0.4.1
[13] metR_0.13.0 scico_1.3.1 patchwork_1.1.2
[16] collapse_1.8.9 forcats_0.5.2 stringr_1.5.0
[19] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3
[22] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[25] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 ellipsis_0.3.2 class_7.3-20
[4] rprojroot_2.0.3 fs_1.5.2 rstudioapi_0.15.0
[7] proxy_0.4-27 farver_2.1.1 bit64_4.0.5
[10] fansi_1.0.3 xml2_1.3.3 splines_4.2.2
[13] codetools_0.2-18 cachem_1.0.6 knitr_1.41
[16] polyclip_1.10-4 jsonlite_1.8.3 gsw_1.1-1
[19] dbplyr_2.2.1 compiler_4.2.2 httr_1.4.4
[22] backports_1.4.1 Matrix_1.5-3 assertthat_0.2.1
[25] fastmap_1.1.0 gargle_1.2.1 cli_3.6.1
[28] later_1.3.0 tweenr_2.0.2 htmltools_0.5.3
[31] tools_4.2.2 rnaturalearthdata_0.1.0 gtable_0.3.1
[34] glue_1.6.2 Rcpp_1.0.11 cellranger_1.1.0
[37] jquerylib_0.1.4 vctrs_0.6.4 nlme_3.1-160
[40] xfun_0.35 ps_1.7.2 rvest_1.0.3
[43] lifecycle_1.0.3 googlesheets4_1.0.1 oce_1.7-10
[46] getPass_0.2-2 MASS_7.3-58.1 scales_1.2.1
[49] vroom_1.6.0 ragg_1.2.4 hms_1.1.2
[52] promises_1.2.0.1 parallel_4.2.2 yaml_2.3.6
[55] memoise_2.0.1 sass_0.4.4 stringi_1.7.8
[58] highr_0.9 e1071_1.7-12 checkmate_2.1.0
[61] rlang_1.1.1 pkgconfig_2.0.3 systemfonts_1.0.4
[64] evaluate_0.18 lattice_0.20-45 SolveSAPHE_2.1.0
[67] labeling_0.4.2 bit_4.0.5 processx_3.8.0
[70] tidyselect_1.2.0 here_1.0.1 seacarb_3.3.1
[73] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[76] DBI_1.1.3 mgcv_1.8-41 pillar_1.9.0
[79] haven_2.5.1 whisker_0.4 withr_2.5.0
[82] units_0.8-0 sp_1.5-1 modelr_0.1.10
[85] crayon_1.5.2 KernSmooth_2.23-20 utf8_1.2.2
[88] tzdb_0.3.0 rmarkdown_2.18 grid_4.2.2
[91] readxl_1.4.1 data.table_1.14.6 callr_3.7.3
[94] git2r_0.30.1 reprex_2.0.2 digest_0.6.30
[97] classInt_0.4-8 httpuv_1.6.6 textshaping_0.3.6
[100] munsell_0.5.0 viridisLite_0.4.1 bslib_0.4.1