eMLR - data preparation
Jens Daniel Müller
12 December, 2020
Last updated: 2020-12-12
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Knit directory: emlr_obs_v_XXX/
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| html | 7d82772 | jens-daniel-mueller | 2020-12-11 | Build site. |
| Rmd | 6069c23 | jens-daniel-mueller | 2020-12-11 | selectable basinmask, try 5 |
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| Rmd | bc61ce3 | Jens Müller | 2020-11-30 | Initial commit |
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1 Required data
Required are:
- GLODAPv2.2020
- cleaned data file
- horizontal grid of sampling coordinates
- Cant from Sabine 2004
- annual mean atmospheric pCO2
GLODAP <-
read_csv(paste(path_version_data,
"GLODAPv2.2020_clean.csv",
sep = ""))
GLODAP_obs_grid <-
read_csv(paste(path_version_data,
"GLODAPv2.2020_clean_obs_grid.csv",
sep = ""))
S04_cant_3d <-
read_csv(paste(path_preprocessing,
"S04_cant_3d.csv",
sep = ""))
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))2 PO4*
2.1 Calculation
The predictor PO4* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an erroneous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.
GLODAP <- GLODAP %>%
mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))3 C*
C* serves as a conservative tracer of anthropogenic CO2 uptake. It is derived from measured DIC by removing the impact of
- organic matter formation and respiration
- calcification and calcium carbonate dissolution
Contributions of those processes are estimated from phosphate and alkalinity concentrations.
3.1 Stoichiometric ratios
The stoichiometric nutrient ratios for the production and mineralization of organic matter were set to:
- C/P: 117
- N/P: 16
3.2 Calculation
C* was calculated as:
print(b_cstar)function (tco2, phosphate, talk)
{
cstar = tco2 - (params_local$rCP * phosphate) - 0.5 * (talk -
(params_local$rNP * phosphate))
return(cstar)
}GLODAP <- GLODAP %>%
mutate(rCP_phosphate = -params_local$rCP * phosphate,
talk_05 = -0.5 * talk,
rNP_phosphate_05 = -0.5 * params_local$rNP * phosphate,
cstar = b_cstar(tco2, phosphate, talk))3.3 Reference year adjustment
The reference year adjustment relies on an apriori estimate of Cant at a given location and depth, which is used as a scaling factor for the concurrent change in atmospheric CO2. The underlying assumption is a transient steady state for the oceanic Cant uptake. Here, Cant from the GLODAP mapped Climatology was used.
Note that eq. 6 in Clement and Gruber (2018) misses pCO2 pre-industrial in the denominator. Here we use the equation published in Gruber et al. (2019).
3.3.1 Combine GLODAP + Cant
S04_cant_3d_obs <- left_join(
GLODAP_obs_grid,
S04_cant_3d %>% select(-c(cant, eras))
)
# calculate number of cant data points per grid cell
S04_cant_3d_obs <- S04_cant_3d_obs %>%
group_by(lon, lat) %>%
mutate(n = n()) %>%
ungroup()
# S04_cant_3d_obs %>%
# filter(n <= 1) %>%
# ggplot(aes(lon,lat)) +
# geom_point(data = GLODAP_obs_grid, aes(lon, lat)) +
# geom_point(col = "red")
rm(S04_cant_3d, GLODAP_obs_grid)
GLODAP_cant_obs <- full_join(GLODAP, S04_cant_3d_obs)
rm(GLODAP, S04_cant_3d_obs)
# fill number of cant data points per grid cell to all observations
GLODAP_cant_obs <- GLODAP_cant_obs %>%
group_by(lon, lat) %>%
fill(n, .direction = "updown") %>%
ungroup()The mapped Cant product was merged with GLODAP observation by:
- using an identical 1x1° horizontal grid
- linear interpolation of Cant from standard to sampling depth
# interpolate cant to observation depth
GLODAP_cant_obs_int <- GLODAP_cant_obs %>%
filter(n > 1) %>%
group_by(lat, lon) %>%
arrange(depth) %>%
mutate(cant_pos_int = approxfun(depth, cant_pos, rule = 2)(depth)) %>%
ungroup()
# set cant for observation depth if only one cant available
GLODAP_cant_obs_set <- GLODAP_cant_obs %>%
filter(n == 1) %>%
group_by(lat, lon) %>%
mutate(cant_pos_int = mean(cant_pos, na.rm = TRUE)) %>%
ungroup()
# bin data sets with interpolated and set cant
GLODAP_cant_obs <- bind_rows(GLODAP_cant_obs_int, GLODAP_cant_obs_set)
rm(GLODAP_cant_obs_int, GLODAP_cant_obs_set)
ggplot() +
geom_path(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
arrange(depth),
aes(cant_pos, depth, col = "mapped")
) +
geom_point(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
arrange(depth),
aes(cant_pos, depth, col = "mapped")
) +
geom_point(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
aes(cant_pos_int, depth, col = "interpolated")
) +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", name = "") +
labs(title = "Cant interpolation to sampling depth - example profile")
# remove cant data at grid cells without observations
GLODAP <- GLODAP_cant_obs %>%
filter(!is.na(cstar)) %>%
mutate(cant_pos = cant_pos_int) %>%
select(-cant_pos_int, n)
rm(GLODAP_cant_obs)3.3.2 Merge GLODAP + atm. pCO2
GLODAP observations were merged with mean annual atmospheric pCO2 levels by year.
GLODAP <- left_join(GLODAP, co2_atm)3.3.3 Calculation
Cant for median year of each era was calculated by applying alpha = 0.28/13 * (median year - 1994) to the estimate of Sabine et al. (2004).
# assign reference year
GLODAP <- GLODAP %>%
group_by(era) %>%
mutate(tref = median(year)) %>%
ungroup()
# calculate reference year
tref <- GLODAP %>%
group_by(era) %>%
summarise(year = median(year)) %>%
ungroup()
# extract atm pCO2 at reference year
co2_atm_tref <- right_join(co2_atm, tref) %>%
select(-year) %>%
rename(pCO2_tref = pCO2)
# merge atm pCO2 at tref with GLODAP
GLODAP <- full_join(GLODAP, co2_atm_tref)
rm(co2_atm, tref)
# scale cant to reference year
GLODAP <- GLODAP %>%
mutate(alpha = (tref - 1994) * (0.28 / 13),
cant_pos = cant_pos * (1 + alpha))
# calculate cstar for reference year
GLODAP <- GLODAP %>%
mutate(
cstar_tref_delta =
((pCO2 - pCO2_tref) / (pCO2_tref - params_local$preind_atm_pCO2)) * cant_pos,
cstar_tref = cstar - cstar_tref_delta)3.4 Control plots
GLODAP %>%
ggplot(aes(cstar_tref_delta)) +
geom_histogram(binwidth = 1) +
labs(title = "Histogramm with binwidth = 1")
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| 7788175 | jens-daniel-mueller | 2020-12-09 |
| 24a632f | jens-daniel-mueller | 2020-12-07 |
| 6a8004b | jens-daniel-mueller | 2020-12-07 |
| 70bf1a5 | jens-daniel-mueller | 2020-12-07 |
| 7555355 | jens-daniel-mueller | 2020-12-07 |
| 143d6fa | jens-daniel-mueller | 2020-12-07 |
| 37e9dac | jens-daniel-mueller | 2020-12-02 |
| 0ff728b | jens-daniel-mueller | 2020-12-01 |
| b02b7a4 | jens-daniel-mueller | 2020-12-01 |
| 196be51 | jens-daniel-mueller | 2020-11-30 |
| bc61ce3 | Jens Müller | 2020-11-30 |
GLODAP %>%
sample_n(1e4) %>%
ggplot(aes(year, cstar_tref_delta, col = cant_pos)) +
geom_point() +
scale_color_viridis_c() +
labs(title = "Time series of random subsample 1e4")
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| 7788175 | jens-daniel-mueller | 2020-12-09 |
| 24a632f | jens-daniel-mueller | 2020-12-07 |
| 6a8004b | jens-daniel-mueller | 2020-12-07 |
| 70bf1a5 | jens-daniel-mueller | 2020-12-07 |
| 7555355 | jens-daniel-mueller | 2020-12-07 |
| 143d6fa | jens-daniel-mueller | 2020-12-07 |
| 37e9dac | jens-daniel-mueller | 2020-12-02 |
| 0ff728b | jens-daniel-mueller | 2020-12-01 |
| b02b7a4 | jens-daniel-mueller | 2020-12-01 |
| 196be51 | jens-daniel-mueller | 2020-11-30 |
| bc61ce3 | Jens Müller | 2020-11-30 |
GLODAP %>%
ggplot(aes(year, cstar_tref_delta)) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
labs(title = "Heatmap with binwidth = 1")
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| 7788175 | jens-daniel-mueller | 2020-12-09 |
| 24a632f | jens-daniel-mueller | 2020-12-07 |
| 6a8004b | jens-daniel-mueller | 2020-12-07 |
| 70bf1a5 | jens-daniel-mueller | 2020-12-07 |
| 7555355 | jens-daniel-mueller | 2020-12-07 |
| 143d6fa | jens-daniel-mueller | 2020-12-07 |
| 090e4d5 | jens-daniel-mueller | 2020-12-02 |
| 37e9dac | jens-daniel-mueller | 2020-12-02 |
| 0ff728b | jens-daniel-mueller | 2020-12-01 |
| b02b7a4 | jens-daniel-mueller | 2020-12-01 |
| 196be51 | jens-daniel-mueller | 2020-11-30 |
| bc61ce3 | Jens Müller | 2020-11-30 |
4 Selected section plots
A selected section is plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% params_global$cruises_meridional)map +
geom_path(data = GLODAP_cruise %>%
arrange(date),
aes(lon, lat)) +
geom_point(data = GLODAP_cruise %>%
arrange(date),
aes(lon, lat, col = date)) +
scale_color_viridis_c(trans = "date") +
labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_fill_viridis_c() +
theme(axis.title.x = element_blank())
for (i_var in c("tco2",
"rCP_phosphate",
"talk_05",
"rNP_phosphate_05",
"cstar",
"cstar_tref")) {
print(lat_section +
stat_summary_2d(aes(z = tco2))+
scale_fill_viridis_c(name = i_var)
)
}
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| 7788175 | jens-daniel-mueller | 2020-12-09 |
| 6a8004b | jens-daniel-mueller | 2020-12-07 |
| 7555355 | jens-daniel-mueller | 2020-12-07 |
| 143d6fa | jens-daniel-mueller | 2020-12-07 |
| 3c8a83c | jens-daniel-mueller | 2020-12-04 |
| 090e4d5 | jens-daniel-mueller | 2020-12-02 |
| 37e9dac | jens-daniel-mueller | 2020-12-02 |
| 0ff728b | jens-daniel-mueller | 2020-12-01 |
| b02b7a4 | jens-daniel-mueller | 2020-12-01 |
| 196be51 | jens-daniel-mueller | 2020-11-30 |
| bc61ce3 | Jens Müller | 2020-11-30 |
rm(lat_section, GLODAP_cruise)
# remove columns not required for further analysis
GLODAP <- GLODAP %>%
select(-c(rCP_phosphate:cstar, cant_pos:cstar_tref_delta))5 Isoneutral slabs
The following boundaries for isoneutral slabs were defined:
- Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
- Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,
Continuous neutral densities (gamma) values from GLODAP are grouped into isoneutral slabs.
GLODAP <- m_cut_gamma(GLODAP, "gamma")GLODAP_cruise <- GLODAP %>%
filter(cruise %in% params_global$cruises_meridional)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
theme(legend.position = "bottom")
lat_section +
geom_point(aes(col = gamma_slab)) +
scale_color_viridis_d()
rm(lat_section, GLODAP_cruise)# this section was only used to calculate gamma locally, and compare it to the value provided in GLODAP data set
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% params_global$cruises_meridional)
library(oce)
library(gsw)
# calculate pressure from depth
GLODAP_cruise <- GLODAP_cruise %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
GLODAP_cruise <- GLODAP_cruise %>%
mutate(THETA = swTheta(salinity = sal,
temperature = temp,
pressure = CTDPRS,
referencePressure = 0,
longitude = lon-180,
latitude = lat))
GLODAP_cruise <- GLODAP_cruise %>%
rename(LATITUDE = lat,
LONGITUDE = lon,
SALNTY = sal,
gamma_provided = gamma)
library(reticulate)
source_python(here::here("code/python_scripts",
"Gamma_GLODAP_python.py"))
GLODAP_cruise <- calculate_gamma(GLODAP_cruise)
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_delta = gamma_provided - GAMMA)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(LATITUDE, CTDPRS)) +
scale_y_reverse() +
theme(legend.position = "bottom")
lat_section +
stat_summary_2d(aes(z = gamma_delta)) +
scale_color_viridis_c()
GLODAP_cruise %>%
ggplot(aes(gamma_delta))+
geom_histogram()
rm(lat_section, GLODAP_cruise, cruises_meridional)6 Observations coverage
GLODAP <- GLODAP %>%
mutate(gamma_slab = factor(gamma_slab),
gamma_slab = factor(gamma_slab, levels = rev(levels(gamma_slab))))
for (i_basin in unique(GLODAP$basin)) {
# i_basin <- unique(GLODAP$basin)[3]
print(
GLODAP %>%
filter(basin == i_basin) %>%
ggplot(aes(lat, gamma_slab)) +
geom_bin2d(binwidth = 5) +
scale_fill_viridis_c(
option = "magma",
direction = -1,
trans = "log10"
) +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(params_global$lat_min,
params_global$lat_max)) +
facet_grid(era ~ .) +
labs(title = paste("MLR region: ", i_basin))
)
}
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 7d82772 | jens-daniel-mueller | 2020-12-11 |
6.1 Histograms
GLODAP_vars <- GLODAP %>%
select(cstar_tref,
sal,
temp,
oxygen,
aou,
silicate,
phosphate,
phosphate_star)
GLODAP_vars_long <- GLODAP_vars %>%
pivot_longer(cstar_tref:phosphate_star, names_to = "variable", values_to = "value")
GLODAP_vars_long %>%
ggplot(aes(value)) +
geom_histogram() +
facet_wrap(~ variable,
ncol = 2,
scales = "free")
rm(GLODAP_vars, GLODAP_vars_long)7 Individual cruise sections
Zonal and meridional section plots are produce for each cruise individually and can be downloaded here.
if (params_local$plot_all_figures == "y") {
cruises <- GLODAP %>%
group_by(cruise) %>%
summarise(date_mean = mean(date, na.rm = TRUE),
n = n()) %>%
ungroup() %>%
arrange(date_mean)
GLODAP <- full_join(GLODAP, cruises)
n <- 0
for (i_cruise in unique(cruises$cruise)) {
# i_cruise <- unique(cruises$cruise)[1]
n <- n + 1
print(n)
GLODAP_cruise <- GLODAP %>%
filter(cruise == i_cruise) %>%
arrange(date)
cruises_cruise <- cruises %>%
filter(cruise == i_cruise)
map_plot <-
map +
geom_point(data = GLODAP_cruise,
aes(lon, lat, col = date)) +
scale_color_viridis_c(trans = "date") +
labs(title = paste("Mean date:", cruises_cruise$date_mean,
"| cruise:", cruises_cruise$cruise,
"| n(samples):", cruises_cruise$n))
lon_section <- GLODAP_cruise %>%
ggplot(aes(lon, depth)) +
scale_y_reverse() +
scale_fill_viridis_c()
lon_tco2 <- lon_section+
stat_summary_2d(aes(z=tco2))
lon_talk <- lon_section+
stat_summary_2d(aes(z=talk))
lon_phosphate <- lon_section+
stat_summary_2d(aes(z=phosphate))
lon_oxygen <- lon_section+
stat_summary_2d(aes(z=oxygen))
lon_aou <- lon_section+
stat_summary_2d(aes(z=aou))
lon_phosphate_star <- lon_section+
stat_summary_2d(aes(z=phosphate_star))
lon_nitrate <- lon_section+
stat_summary_2d(aes(z=nitrate))
lon_cstar <- lon_section+
stat_summary_2d(aes(z=cstar_tref))
lat_section <- GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_fill_viridis_c()
lat_tco2 <- lat_section+
stat_summary_2d(aes(z=tco2))
lat_talk <- lat_section+
stat_summary_2d(aes(z=talk))
lat_phosphate <- lat_section+
stat_summary_2d(aes(z=phosphate))
lat_oxygen <- lat_section+
stat_summary_2d(aes(z=oxygen))
lat_aou <- lat_section+
stat_summary_2d(aes(z=aou))
lat_phosphate_star <- lat_section+
stat_summary_2d(aes(z=phosphate_star))
lat_nitrate <- lat_section+
stat_summary_2d(aes(z=nitrate))
lat_cstar <- lat_section+
stat_summary_2d(aes(z=cstar_tref))
hist_tco2 <- GLODAP_cruise %>%
ggplot(aes(tco2)) +
geom_histogram()
hist_talk <- GLODAP_cruise %>%
ggplot(aes(talk)) +
geom_histogram()
hist_phosphate <- GLODAP_cruise %>%
ggplot(aes(phosphate)) +
geom_histogram()
hist_oxygen <- GLODAP_cruise %>%
ggplot(aes(oxygen)) +
geom_histogram()
hist_aou <- GLODAP_cruise %>%
ggplot(aes(aou)) +
geom_histogram()
hist_phosphate_star <- GLODAP_cruise %>%
ggplot(aes(phosphate_star)) +
geom_histogram()
hist_nitrate <- GLODAP_cruise %>%
ggplot(aes(nitrate)) +
geom_histogram()
hist_cstar <- GLODAP_cruise %>%
ggplot(aes(cstar_tref)) +
geom_histogram()
(map_plot /
((hist_tco2 / hist_talk / hist_phosphate / hist_cstar) |
(hist_oxygen / hist_phosphate_star / hist_nitrate / hist_aou)
)) |
((lat_tco2 / lat_talk / lat_phosphate / lat_oxygen / lat_aou / lat_phosphate_star / lat_nitrate / lat_cstar) |
(lon_tco2 / lon_talk / lon_phosphate / lon_oxygen / lon_aou /lon_phosphate_star / lon_nitrate / lon_cstar))
ggsave(
path = paste(path_version_figures, "Cruise_sections_histograms/", sep = ""),
filename = paste(
"Cruise_date",
cruises_cruise$date_mean,
"count",
cruises_cruise$n,
"cruiseID",
cruises_cruise$cruise,
".png",
sep = "_"
),
width = 20, height = 12)
rm(map_plot,
lon_section, lat_section,
lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
GLODAP_cruise, cruises_cruise)
}
}8 Write files
# rename the cstar variable,
# in scripts downstrea from here, we refer to cstar as the value adjusted to the reference year
GLODAP <- GLODAP %>%
rename(cstar = cstar_tref)
GLODAP %>% write_csv(paste(path_version_data,
"GLODAPv2.2020_MLR_fitting_ready.csv",
sep = ""))
co2_atm_tref %>% write_csv(paste(path_version_data,
"co2_atm_tref.csv",
sep = ""))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] lubridate_1.7.9 marelac_2.1.10 shape_1.4.5 metR_0.9.0
[5] scico_1.2.0 patchwork_1.1.0 collapse_1.4.2 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 viridisLite_0.3.0 jsonlite_1.7.1
[4] here_0.1 modelr_0.1.8 assertthat_0.2.1
[7] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[10] pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 RcppEigen_0.3.3.7.0 digest_0.6.27
[16] RColorBrewer_1.1-2 promises_1.1.1 checkmate_2.0.0
[19] rvest_0.3.6 colorspace_2.0-0 htmltools_0.5.0
[22] httpuv_1.5.4 Matrix_1.2-18 pkgconfig_2.0.3
[25] broom_0.7.2 seacarb_3.2.14 haven_2.3.1
[28] scales_1.1.1 whisker_0.4 later_1.1.0.1
[31] git2r_0.27.1 farver_2.0.3 generics_0.0.2
[34] ellipsis_0.3.1 withr_2.3.0 cli_2.2.0
[37] magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1
[40] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[43] xml2_1.3.2 RcppArmadillo_0.10.1.2.0 oce_1.2-0
[46] tools_4.0.3 data.table_1.13.2 hms_0.5.3
[49] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[52] gsw_1.0-5 compiler_4.0.3 rlang_0.4.9
[55] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[58] rmarkdown_2.5 testthat_3.0.0 gtable_0.3.0
[61] DBI_1.1.0 R6_2.5.0 knitr_1.30
[64] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[67] Rcpp_1.0.5 vctrs_0.3.5 dbplyr_1.4.4
[70] tidyselect_1.1.0 xfun_0.18