Analysis of cant estimates
Jens Daniel Müller
18 June, 2021
Last updated: 2021-06-18
Checks: 7 0
Knit directory: emlr_obs_v_XXX/
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1 Version ID
The results displayed on this site correspond to the Version_ID: v_XXX
2 Data sources
Required are:
Cant from Sabine 2004 (S04)
Cant from Gruber 2019 (G19)
annual mean atmospheric pCO2
Mean eMLR-Cant per grid cell (lat, lon, depth)
cant_3d_ss <- read_csv(paste(path_version_data,
"cant_3d_tref.csv",
sep = ""))
cant_3d_emlr <-
read_csv(paste(path_version_data,
"cant_3d.csv",
sep = ""))
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))
tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
cant_3d_mod_truth <-
read_csv(paste(path_version_data,
"cant_3d_mod_truth.csv", sep = ""))
cant_3d_mod_truth_cc <-
read_csv(paste(path_version_data,
"cant_3d_mod_truth_cc.csv", sep = ""))3 Prepare delta Cant
3.1 Steady state
# join with basinmask
cant_3d_ss <- inner_join(cant_3d_ss, basinmask)
# calculate delta cant as the difference between total cant at tref
# this includes the G19 anomaly
cant_3d_ss <- cant_3d_ss %>%
pivot_wider(names_from = era,
values_from = "cant_pos") %>%
mutate(delta_cant_pos = !!sym(tref$era[2]) - !!sym(tref$era[1])) %>%
select(-c(!!sym(tref$era[2]), !!sym(tref$era[1]))) %>%
drop_na()3.2 Model truth
cant_3d_mod_truth <- cant_3d_mod_truth %>%
select(lon, lat, depth, basin_AIP, delta_cant_pos = cant_pos) %>%
mutate(data_source = "mod_truth")
cant_3d_mod_truth_cc <- cant_3d_mod_truth_cc %>%
select(lon, lat, depth, basin_AIP, delta_cant_pos = cant_pos) %>%
mutate(data_source = "mod_truth_cc")3.3 eMLR(C*)
cant_3d_emlr <- cant_3d_emlr %>%
select(data_source, lon, lat, depth, basin_AIP, delta_cant_pos = cant_pos)4 Join data
4.1 Model
cant_mod_3d <- bind_rows(
cant_3d_emlr %>%
filter(data_source == "mod") %>%
select(-data_source) %>%
mutate(estimate = "emlr"),
cant_3d_mod_truth %>%
filter(data_source == "mod_truth") %>%
select(-data_source) %>%
mutate(estimate = "mod_truth"),
cant_3d_mod_truth_cc %>%
filter(data_source == "mod_truth_cc") %>%
select(-data_source) %>%
mutate(estimate = "mod_truth_cc"),
cant_3d_ss %>%
filter(data_source == "mod") %>%
select(-data_source) %>%
mutate(estimate = "ss")
)4.2 Observations
cant_3d_obs <- bind_rows(
cant_3d_emlr %>% filter(data_source == "obs") %>% select(-data_source) %>% mutate(estimate = "emlr"),
cant_3d_ss %>% filter(data_source == "obs") %>% select(-data_source) %>% mutate(estimate = "ss")
)5 Model
5.1 Bias correlation
cant_mod_3d_bias <- cant_mod_3d %>%
pivot_wider(names_from = estimate,
values_from = delta_cant_pos) %>%
drop_na()
cant_mod_3d_bias <- cant_mod_3d_bias %>%
mutate(ss_mod_truth = ss - mod_truth,
emlr_mod_truth = emlr - mod_truth,
emlr_ss = emlr - ss,
nss = mod_truth_cc - mod_truth) %>%
select(-c(emlr:ss))
axis_limit <- cant_mod_3d_bias %>%
pivot_longer(ss_mod_truth:emlr_ss,
names_to = "estimate",
values_to = "bias") %>%
pull(bias) %>%
abs() %>%
max()
cant_mod_3d_bias %>%
ggplot(aes(emlr_mod_truth, emlr_ss)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap( ~ basin_AIP)
cant_mod_3d_bias %>%
ggplot(aes(emlr_mod_truth, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
cant_mod_3d_bias %>%
ggplot(aes(emlr_ss, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
cant_mod_3d_bias %>%
ggplot(aes(nss, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
5.1.1 Vertical patterns
cant_mod_3d_bias_profile <- cant_mod_3d_bias %>%
pivot_longer(ss_mod_truth:nss,
names_to = "estimate",
values_to = "bias") %>%
group_by(depth, estimate, basin_AIP) %>%
summarise(bias = mean(bias)) %>%
ungroup()
cant_mod_3d_bias_profile %>%
ggplot(aes(bias, depth, col=estimate)) +
geom_path() +
scale_y_reverse() +
facet_grid(~ basin_AIP)
cant_mod_3d_bias_profile <- cant_mod_3d_bias %>%
pivot_longer(ss_mod_truth:nss,
names_to = "estimate",
values_to = "bias") %>%
group_by(depth, estimate) %>%
summarise(bias = mean(bias)) %>%
ungroup()
cant_mod_3d_bias_profile %>%
ggplot(aes(bias, depth, col=estimate)) +
geom_path() +
scale_y_reverse()
rm(cant_mod_3d_bias_profile)5.2 Zonal mean sections
5.2.1 delta Cant
# calculate zonal mean section
cant_mod_section <- cant_mod_3d %>%
group_by(estimate) %>%
nest() %>%
mutate(section = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
select(-data) %>%
unnest(section)
cant_mod_section %>%
group_by(basin_AIP, estimate) %>%
group_split() %>%
# head(1) %>%
map( ~ p_section_zonal(
df = .x,
var = "delta_cant_pos_mean",
plot_slabs = "n",
subtitle_text = paste("Basin:", .x$basin_AIP, "| Estimate:", .x$estimate)
))[[1]]
[[2]]
[[3]]
[[4]]
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5.2.2 Bias
cant_mod_section_bias <- cant_mod_section %>%
select(-delta_cant_pos_sd) %>%
pivot_wider(names_from = estimate,
values_from = delta_cant_pos_mean) %>%
drop_na()
cant_mod_section_bias <- cant_mod_section_bias %>%
mutate(ss_mod_truth = ss - mod_truth,
emlr_mod_truth = emlr - mod_truth,
emlr_ss = emlr - ss,
nss = mod_truth_cc - mod_truth) %>%
select(-c(emlr:ss))
cant_mod_section_bias_long <- cant_mod_section_bias %>%
pivot_longer(ss_mod_truth:nss,
values_to = "delta_cant_pos_bias",
names_to = "estimate")
cant_mod_section_bias_long %>%
group_by(basin_AIP, estimate) %>%
group_split() %>%
# head(1) %>%
map( ~ p_section_zonal(
df = .x,
var = "delta_cant_pos_bias",
col = "divergent",
breaks = params_global$breaks_cant_offset,
plot_slabs = "n",
subtitle_text = paste("Basin:", .x$basin_AIP, "| Estimate:", .x$estimate)
))[[1]]
| Version | Author | Date |
|---|---|---|
| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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axis_limit <- cant_mod_section_bias %>%
pivot_longer(ss_mod_truth:nss,
names_to = "estimate",
values_to = "bias") %>%
pull(bias) %>%
abs() %>%
max()
cant_mod_section_bias %>%
ggplot(aes(emlr_mod_truth, emlr_ss)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap( ~ basin_AIP)
| Version | Author | Date |
|---|---|---|
| bf487c8 | jens-daniel-mueller | 2021-06-16 |
cant_mod_section_bias %>%
ggplot(aes(emlr_mod_truth, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
| Version | Author | Date |
|---|---|---|
| bf487c8 | jens-daniel-mueller | 2021-06-16 |
cant_mod_section_bias %>%
ggplot(aes(emlr_ss, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
| Version | Author | Date |
|---|---|---|
| bf487c8 | jens-daniel-mueller | 2021-06-16 |
5.3 Inventories
5.3.1 delta Cant
# calculate zonal mean section
cant_mod_inv <- cant_mod_3d %>%
rename(cant_pos = delta_cant_pos) %>%
mutate(cant = 0,
eras = "blank") %>%
group_by(estimate) %>%
nest() %>%
mutate(inv = map(.x = data, ~m_cant_inv(.x))) %>%
select(-data) %>%
unnest(inv) %>%
select(-c(cant_inv, eras))
cant_mod_inv %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
group_by(estimate) %>%
group_split() %>%
# head(1) %>%
map( ~ p_map_cant_inv(
df = .x,
var = "cant_pos_inv",
subtitle_text = paste("Estimate:", .x$estimate)
))[[1]]
| Version | Author | Date |
|---|---|---|
| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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| bf487c8 | jens-daniel-mueller | 2021-06-16 |
5.3.2 Bias
cant_mod_inv_bias <- cant_mod_inv %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
select(-inv_depth) %>%
pivot_wider(names_from = estimate,
values_from = cant_pos_inv) %>%
drop_na()
cant_mod_inv_bias <- cant_mod_inv_bias %>%
mutate(ss_mod_truth = ss - mod_truth,
emlr_mod_truth = emlr - mod_truth,
emlr_ss = emlr - ss,
nss = mod_truth_cc - mod_truth) %>%
select(-c(emlr:ss))
cant_mod_inv_bias_long <- cant_mod_inv_bias %>%
pivot_longer(ss_mod_truth:nss,
values_to = "cant_pos_inv_bias",
names_to = "estimate")
cant_mod_inv_bias_long %>%
group_by(estimate) %>%
group_split() %>%
# head(1) %>%
map( ~ p_map_cant_inv_offset(
df = .x,
var = "cant_pos_inv_bias",
subtitle_text = paste("Estimate:", .x$estimate)
))[[1]]
| Version | Author | Date |
|---|---|---|
| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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|---|---|---|
| 71c4934 | jens-daniel-mueller | 2021-06-15 |
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axis_limit <- cant_mod_inv_bias %>%
pivot_longer(ss_mod_truth:emlr_ss,
names_to = "estimate",
values_to = "bias") %>%
pull(bias) %>%
abs() %>%
max()
cant_mod_inv_bias %>%
ggplot(aes(emlr_mod_truth, emlr_ss)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap( ~ basin_AIP)
cant_mod_inv_bias %>%
ggplot(aes(emlr_mod_truth, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
cant_mod_inv_bias %>%
ggplot(aes(emlr_ss, ss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)) +
facet_wrap(~ basin_AIP)
| Version | Author | Date |
|---|---|---|
| bf487c8 | jens-daniel-mueller | 2021-06-16 |
5.4 Budgets
cant_mod_inv_budget <- cant_mod_inv %>%
mutate(surface_area = earth_surf(lat, lon),
# cant_inv_grid = cant_inv*surface_area,
cant_pos_inv_grid = cant_pos_inv*surface_area) %>%
group_by(basin_AIP, estimate, inv_depth) %>%
summarise(#cant_total = sum(cant_inv_grid)*12*1e-15,
#cant_total = round(cant_total,1),
cant_pos_total = sum(cant_pos_inv_grid)*12*1e-15,
cant_pos_total = round(cant_pos_total,1)) %>%
ungroup()
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
cant_mod_inv_budget %>%
group_by(estimate, inv_depth) %>%
summarise(#cant_total = sum(cant_total),
#cant_total = round(cant_total,1),
cant_pos_total = sum(cant_pos_total),
cant_pos_total = round(cant_pos_total,1),
#cant_total_rate = cant_total / duration,
cant_pos_total_rate = cant_pos_total / duration) %>%
ungroup()# A tibble: 20 x 4
estimate inv_depth cant_pos_total cant_pos_total_rate
<chr> <dbl> <dbl> <dbl>
1 emlr 100 2.8 0.35
2 emlr 500 9.5 1.19
3 emlr 1000 13.3 1.66
4 emlr 3000 17.7 2.21
5 emlr 10000 18.5 2.31
6 mod_truth 100 2.9 0.362
7 mod_truth 500 10.8 1.35
8 mod_truth 1000 14.8 1.85
9 mod_truth 3000 16.8 2.1
10 mod_truth 10000 17.1 2.14
11 mod_truth_cc 100 2.8 0.35
12 mod_truth_cc 500 10.7 1.34
13 mod_truth_cc 1000 14.6 1.82
14 mod_truth_cc 3000 16.6 2.08
15 mod_truth_cc 10000 16.6 2.08
16 ss 100 2.7 0.338
17 ss 500 9.5 1.19
18 ss 1000 12.5 1.56
19 ss 3000 13.9 1.74
20 ss 10000 14.1 1.76 5.4.1 Standard depth
Results integrated over the upper 3000 m
cant_mod_inv_budget %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
ggplot(aes(estimate, cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
geom_col()
cant_mod_inv_budget %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
gt(rowname_col = "basin_AIP",
groupname_col = c("estimate", "inv_depth"),
row_group.sep = " | Depth: ") %>%
summary_rows(
groups = TRUE,
fns = list(total = "sum")
)| cant_pos_total | |
|---|---|
| emlr | Depth: 3000 | |
| Atlantic | 5.7 |
| Indian | 4.2 |
| Pacific | 7.8 |
| total | 17.70 |
| mod_truth | Depth: 3000 | |
| Atlantic | 4.2 |
| Indian | 4.5 |
| Pacific | 8.1 |
| total | 16.80 |
| mod_truth_cc | Depth: 3000 | |
| Atlantic | 4.2 |
| Indian | 4.5 |
| Pacific | 7.9 |
| total | 16.60 |
| ss | Depth: 3000 | |
| Atlantic | 3.5 |
| Indian | 3.7 |
| Pacific | 6.7 |
| total | 13.90 |
5.4.2 Other depths
Results integrated over the upper 100, 500, 1000, 3000, 10^{4} m
cant_mod_inv_budget %>%
filter(inv_depth != params_global$inventory_depth_standard) %>%
ggplot(aes(estimate, cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
geom_col() +
facet_grid(inv_depth ~ ., scales = "free_y")
cant_mod_inv_budget %>%
filter(inv_depth != params_global$inventory_depth_standard) %>%
gt(rowname_col = "basin_AIP",
groupname_col = c("estimate", "inv_depth"),
row_group.sep = " | Depth: ") %>%
summary_rows(
groups = TRUE,
fns = list(total = "sum")
)| cant_pos_total | |
|---|---|
| emlr | Depth: 100 | |
| Atlantic | 0.8 |
| Indian | 0.5 |
| Pacific | 1.5 |
| total | 2.80 |
| emlr | Depth: 500 | |
| Atlantic | 2.9 |
| Indian | 1.8 |
| Pacific | 4.8 |
| total | 9.50 |
| emlr | Depth: 1000 | |
| Atlantic | 4.1 |
| Indian | 3.0 |
| Pacific | 6.2 |
| total | 13.30 |
| emlr | Depth: 10000 | |
| Atlantic | 5.9 |
| Indian | 4.3 |
| Pacific | 8.3 |
| total | 18.50 |
| mod_truth | Depth: 100 | |
| Atlantic | 0.7 |
| Indian | 0.6 |
| Pacific | 1.6 |
| total | 2.90 |
| mod_truth | Depth: 500 | |
| Atlantic | 2.7 |
| Indian | 2.5 |
| Pacific | 5.6 |
| total | 10.80 |
| mod_truth | Depth: 1000 | |
| Atlantic | 3.7 |
| Indian | 3.8 |
| Pacific | 7.3 |
| total | 14.80 |
| mod_truth | Depth: 10000 | |
| Atlantic | 4.2 |
| Indian | 4.6 |
| Pacific | 8.3 |
| total | 17.10 |
| mod_truth_cc | Depth: 100 | |
| Atlantic | 0.7 |
| Indian | 0.6 |
| Pacific | 1.5 |
| total | 2.80 |
| mod_truth_cc | Depth: 500 | |
| Atlantic | 2.7 |
| Indian | 2.5 |
| Pacific | 5.5 |
| total | 10.70 |
| mod_truth_cc | Depth: 1000 | |
| Atlantic | 3.6 |
| Indian | 3.8 |
| Pacific | 7.2 |
| total | 14.60 |
| mod_truth_cc | Depth: 10000 | |
| Atlantic | 4.2 |
| Indian | 4.5 |
| Pacific | 7.9 |
| total | 16.60 |
| ss | Depth: 100 | |
| Atlantic | 0.7 |
| Indian | 0.6 |
| Pacific | 1.4 |
| total | 2.70 |
| ss | Depth: 500 | |
| Atlantic | 2.4 |
| Indian | 2.2 |
| Pacific | 4.9 |
| total | 9.50 |
| ss | Depth: 1000 | |
| Atlantic | 3.1 |
| Indian | 3.2 |
| Pacific | 6.2 |
| total | 12.50 |
| ss | Depth: 10000 | |
| Atlantic | 3.6 |
| Indian | 3.7 |
| Pacific | 6.8 |
| total | 14.10 |
rm(cant_mod_inv_budget)6 Observations
6.1 Zonal mean sections
# calculate zonal mean section
cant_obs_section <- cant_3d_obs %>%
group_by(estimate) %>%
nest() %>%
mutate(section = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
select(-data) %>%
unnest(section)
cant_obs_section %>%
group_by(basin_AIP, estimate) %>%
group_split() %>%
# head(1) %>%
map( ~ p_section_zonal(
df = .x,
var = "delta_cant_pos_mean",
plot_slabs = "n",
subtitle_text = paste("Basin:", .x$basin_AIP, "| Estimate:", .x$estimate)
))[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
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] gt_0.2.2 marelac_2.1.10 shape_1.4.5 metR_0.9.0
[5] scico_1.2.0 patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 gsw_1.0-5
[4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 utf8_1.1.4
[10] R6_2.5.0 DBI_1.1.0 colorspace_1.4-1
[13] withr_2.3.0 tidyselect_1.1.0 compiler_4.0.3
[16] git2r_0.27.1 cli_2.1.0 rvest_0.3.6
[19] xml2_1.3.2 isoband_0.2.2 sass_0.2.0
[22] labeling_0.4.2 scales_1.1.1 checkmate_2.0.0
[25] digest_0.6.27 rmarkdown_2.5 oce_1.2-0
[28] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[31] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.11
[34] generics_0.0.2 farver_2.0.3 jsonlite_1.7.1
[37] magrittr_1.5 Matrix_1.2-18 Rcpp_1.0.5
[40] munsell_0.5.0 fansi_0.4.1 lifecycle_1.0.0
[43] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[46] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[49] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[52] haven_2.3.1 hms_0.5.3 seacarb_3.2.14
[55] knitr_1.30 pillar_1.4.7 reprex_0.3.0
[58] glue_1.4.2 evaluate_0.14 RcppArmadillo_0.10.1.2.0
[61] data.table_1.13.2 modelr_0.1.8 vctrs_0.3.5
[64] httpuv_1.5.4 testthat_2.3.2 cellranger_1.1.0
[67] gtable_0.3.0 assertthat_0.2.1 xfun_0.18
[70] broom_0.7.5 RcppEigen_0.3.3.7.0 later_1.1.0.1
[73] viridisLite_0.3.0 ellipsis_0.3.1 here_0.1