Budgets
Jens Daniel Müller
26 September, 2021
Last updated: 2021-09-26
Checks: 7 0
Knit directory: emlr_obs_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b93e153 | jens-daniel-mueller | 2021-09-26 | filter MLR_basins |
| html | 23cc3a7 | jens-daniel-mueller | 2021-09-26 | Build site. |
| Rmd | ed88932 | jens-daniel-mueller | 2021-09-26 | filter MLR basins |
| html | 0c6be7e | jens-daniel-mueller | 2021-09-24 | Build site. |
| Rmd | 6a79fd0 | jens-daniel-mueller | 2021-09-24 | consider ensemble selection across all analysis |
| html | a324ace | jens-daniel-mueller | 2021-09-24 | Build site. |
| Rmd | c43a6a9 | jens-daniel-mueller | 2021-09-24 | revised ensemble selection |
| html | f54d5db | jens-daniel-mueller | 2021-09-24 | Build site. |
| Rmd | 76c942b | jens-daniel-mueller | 2021-09-24 | 1990 as start year 1 for classic runs |
| html | 31c33cb | jens-daniel-mueller | 2021-09-23 | Build site. |
| Rmd | 9b1df4d | jens-daniel-mueller | 2021-09-23 | 1994 vs 2014 added |
| html | ec4f702 | jens-daniel-mueller | 2021-09-22 | Build site. |
| html | 6525680 | jens-daniel-mueller | 2021-09-21 | Build site. |
| html | 6bf38c9 | jens-daniel-mueller | 2021-09-21 | Build site. |
| html | 63911d0 | jens-daniel-mueller | 2021-09-21 | Build site. |
| html | 499c9d5 | jens-daniel-mueller | 2021-09-21 | Build site. |
| html | 271a3ac | jens-daniel-mueller | 2021-09-21 | Build site. |
| html | 35ad8b5 | jens-daniel-mueller | 2021-09-20 | Build site. |
| Rmd | 919f74b | jens-daniel-mueller | 2021-09-20 | rebuildt with canyon b cleaning analysis |
1 Global
1.1 Read files
# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_10")
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_20")
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_30")
# Version_IDs <- c(Version_IDs_1, Version_IDs_2)
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
# rm(Version_IDs_1, Version_IDs_2)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_global <-
read_csv(paste(path_version_data,
"dcant_budget_global.csv",
sep = ""))
dcant_budget_global_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_global_mod_truth.csv",
sep = ""
))
dcant_budget_global_bias <-
read_csv(paste(path_version_data,
"dcant_budget_global_bias.csv",
sep = ""))
dcant_budget_global <- bind_rows(dcant_budget_global,
dcant_budget_global_mod_truth)
dcant_budget_global <- dcant_budget_global %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_global_bias <- dcant_budget_global_bias %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins = params_local$MLR_basins,
tref1 = params_local$tref1,
tref2 = params_local$tref2,
gap_filling = params_local$gap_filling,
rarefication = params_local$rarefication,
rarefication_threshold = params_local$rarefication_threshold,
MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
vif_max = params_local$vif_max
)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(
median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2)
)
if (exists("dcant_budget_global_all")) {
dcant_budget_global_all <-
bind_rows(dcant_budget_global_all, dcant_budget_global)
}
if (!exists("dcant_budget_global_all")) {
dcant_budget_global_all <- dcant_budget_global
}
if (exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <-
bind_rows(dcant_budget_global_bias_all,
dcant_budget_global_bias)
}
if (!exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <- dcant_budget_global_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1001"
[1] "v_1002"
[1] "v_1003"
[1] "v_1004"
[1] "v_1005"
[1] "v_1006"
[1] "v_1007"
[1] "v_2001"
[1] "v_2002"
[1] "v_2003"
[1] "v_2004"
[1] "v_2005"
[1] "v_2006"
[1] "v_2007"
[1] "v_3001"
[1] "v_3002"
[1] "v_3003"
[1] "v_3004"
[1] "v_3005"
[1] "v_3006"
[1] "v_3007"rm(
dcant_budget_global,
dcant_budget_global_bias,
dcant_budget_global_mod_truth,
params_local,
tref
)
dcant_budget_global_all <- full_join(dcant_budget_global_all,
params_local_all)
dcant_budget_global_bias_all <-
full_join(dcant_budget_global_bias_all,
params_local_all)dcant_budget_global_all <- dcant_budget_global_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_global_all_depth <- dcant_budget_global_all
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)MLR_basins_in <- c("1", "2", "AIP", "SO", "5")
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(MLR_basins %in% MLR_basins_in)
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(MLR_basins %in% MLR_basins_in)1.2 Thresholds
global_bias_rel_max <- 12
regional_bias_rel_max <- 201.3 Individual cases
1.3.1 Absoulte values
dcant_budget_global_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_point() +
scale_color_brewer(palette = "Set1", name = "basin\nseparation") +
facet_grid(. ~ data_source)
1.3.2 Biases
dcant_budget_global_bias_all %>%
ggplot(aes(period, dcant_bias, col = MLR_basins)) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Set1", name = "basin\nseparation") +
geom_point()
dcant_budget_global_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = global_bias_rel_max * 2,
x = "1994 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Set1", name = "basin\nseparation") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins))
1.4 Ensemble
ensemble_MLR_basins_global <- dcant_budget_global_bias_all %>%
filter(dcant_bias_rel < global_bias_rel_max) %>%
count(MLR_basins) %>%
filter(n == 3) %>%
pull(MLR_basins)
ensemble_MLR_basins_global[1] "2" "5" "AIP" "SO" dcant_budget_global_ensemble <- dcant_budget_global_all %>%
filter(data_source %in% c("mod", "obs"),
MLR_basins %in% ensemble_MLR_basins_global) %>%
group_by(data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()1.4.1 Mean
dcant_budget_global_ensemble %>%
ggplot(aes(period, dcant_mean)) +
geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd),
shape = 21) +
facet_grid(. ~ data_source)
1.4.2 Mean bias
dcant_budget_global_ensemble_bias <- full_join(
dcant_budget_global_ensemble %>%
filter(data_source == "mod") %>%
select(period, dcant_mean, dcant_sd),
dcant_budget_global_all %>%
filter(data_source == "mod_truth",
MLR_basins == "2") %>%
select(period, dcant)
)
dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel)) +
geom_hline(yintercept = 0) +
geom_point()
1.4.3 SD vs bias
dcant_budget_global_ensemble_bias %>%
ggplot(aes(dcant_mean_bias, dcant_sd, col = period)) +
geom_point() +
xlim(0, NA) +
ylim(0, NA)
1.5 Vertical patterns
1.5.1 Absoulte values
dcant_budget_global_all_depth %>%
filter(MLR_basins %in% ensemble_MLR_basins_global) %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill=period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
[[2]]
[[3]]
1.5.2 Biases
dcant_budget_global_bias_all_depth %>%
filter(MLR_basins %in% ensemble_MLR_basins_global) %>%
ggplot(aes(dcant_bias, MLR_basins, fill=period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
dcant_budget_global_bias_all_depth %>%
filter(MLR_basins %in% ensemble_MLR_basins_global) %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
rm(dcant_budget_global_all,
dcant_budget_global_all_depth,
dcant_budget_global_bias_all,
dcant_budget_global_bias_all_depth,
dcant_budget_global_ensemble,
dcant_budget_global_ensemble_bias,
params_local_all)2 Regional
2.1 Read files
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_AIP <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP.csv",
sep = ""))
dcant_budget_basin_AIP_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_AIP_mod_truth.csv",
sep = ""
))
dcant_budget_basin_AIP_bias <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_bias.csv",
sep = ""))
dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
dcant_budget_basin_AIP_mod_truth)
dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins = params_local$MLR_basins,
tref1 = params_local$tref1,
tref2 = params_local$tref2,
gap_filling = params_local$gap_filling,
rarefication = params_local$rarefication,
rarefication_threshold = params_local$rarefication_threshold,
MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
vif_max = params_local$vif_max
)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(
median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2)
)
if (exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <-
bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
}
if (!exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
}
if (exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <-
bind_rows(dcant_budget_basin_AIP_bias_all,
dcant_budget_basin_AIP_bias)
}
if (!exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1001"
[1] "v_1002"
[1] "v_1003"
[1] "v_1004"
[1] "v_1005"
[1] "v_1006"
[1] "v_1007"
[1] "v_2001"
[1] "v_2002"
[1] "v_2003"
[1] "v_2004"
[1] "v_2005"
[1] "v_2006"
[1] "v_2007"
[1] "v_3001"
[1] "v_3002"
[1] "v_3003"
[1] "v_3004"
[1] "v_3005"
[1] "v_3006"
[1] "v_3007"rm(
dcant_budget_basin_AIP,
dcant_budget_basin_AIP_bias,
dcant_budget_basin_AIP_mod_truth,
params_local,
tref
)
dcant_budget_basin_AIP_all <- full_join(dcant_budget_basin_AIP_all,
params_local_all)
dcant_budget_basin_AIP_bias_all <-
full_join(dcant_budget_basin_AIP_bias_all,
params_local_all)dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(MLR_basins %in% MLR_basins_in)
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(MLR_basins %in% MLR_basins_in)2.2 Individual cases
2.2.1 Absoulte values
dcant_budget_basin_AIP_all %>%
ggplot(aes(period, dcant, col=MLR_basins)) +
geom_point() +
facet_grid(basin_AIP ~ data_source)
2.2.2 Biases
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin_AIP ~ .)
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(period, dcant_bias_rel, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin_AIP ~ .)
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = "1994 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Set1", name = "basin\nseparation") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins)) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| a324ace | jens-daniel-mueller | 2021-09-24 |
2.3 Ensemble
dcant_budget_basin_AIP_bias_all %>%
filter(dcant_bias_rel < regional_bias_rel_max) %>%
distinct(period, MLR_basins, basin_AIP)[38;5;246m# A tibble: 44 × 3[39m
basin_AIP MLR_basins period
[3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
[38;5;250m 1[39m Indian 1 2004 - 2014
[38;5;250m 2[39m Pacific 1 2004 - 2014
[38;5;250m 3[39m Atlantic 1 2004 - 2014
[38;5;250m 4[39m Indian 2 2004 - 2014
[38;5;250m 5[39m Pacific 2 2004 - 2014
[38;5;250m 6[39m Atlantic 2 2004 - 2014
[38;5;250m 7[39m Indian AIP 2004 - 2014
[38;5;250m 8[39m Pacific AIP 2004 - 2014
[38;5;250m 9[39m Atlantic AIP 2004 - 2014
[38;5;250m10[39m Indian 5 2004 - 2014
[38;5;246m# … with 34 more rows[39mensemble_MLR_basins_regional <- dcant_budget_basin_AIP_bias_all %>%
filter(dcant_bias_rel < regional_bias_rel_max) %>%
count(MLR_basins) %>%
filter(n == 9) %>%
pull(MLR_basins)
ensemble_MLR_basins_regional <-
intersect(ensemble_MLR_basins_global, ensemble_MLR_basins_regional)
ensemble_MLR_basins_regional[1] "2" "5" "AIP" "SO" dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>%
filter(data_source %in% c("mod", "obs"),
MLR_basins %in% ensemble_MLR_basins_global,
MLR_basins %in% ensemble_MLR_basins_regional) %>%
group_by(basin_AIP, data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()2.3.1 Mean
dcant_budget_basin_AIP_ensemble %>%
ggplot(aes(period, dcant_mean, col=basin_AIP)) +
geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd),
shape = 21) +
facet_grid(. ~ data_source)
2.3.2 Mean bias
dcant_budget_basin_AIP_ensemble_bias <- full_join(
dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "mod") %>%
select(basin_AIP, period, dcant_mean, dcant_sd),
dcant_budget_basin_AIP_all %>%
filter(data_source == "mod_truth",
MLR_basins == "2") %>%
select(basin_AIP, period, dcant)
)
dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
2.3.3 SD vs bias
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(dcant_mean_bias, dcant_sd, col=basin_AIP)) +
geom_point() +
facet_grid(. ~ period) +
xlim(0, NA) +
ylim(0, NA)
2.4 Vertical patterns
2.4.1 Absoulte values
unique(
dcant_budget_basin_AIP_all_depth$MLR_basins)[1] "1" "2" "AIP" "5" "SO" "SO_5" "SO_AIP"dcant_budget_basin_AIP_all_depth %>%
filter(
MLR_basins %in% ensemble_MLR_basins_global,
MLR_basins %in% ensemble_MLR_basins_regional
) %>%
group_by(data_source) %>%
group_split() %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
2.4.2 Biases
dcant_budget_basin_AIP_bias_all_depth %>%
filter(
MLR_basins %in% ensemble_MLR_basins_global,
MLR_basins %in% ensemble_MLR_basins_regional
) %>%
ggplot(aes(dcant_bias, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
dcant_budget_basin_AIP_bias_all_depth %>%
filter(
MLR_basins %in% ensemble_MLR_basins_global,
MLR_basins %in% ensemble_MLR_basins_regional
) %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
3 Write ensemble members
ensemble_MLR_basins_regional %>%
write_rds("data/ensemble_members.rds")
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] ggforce_0.3.3 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[5] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.3
[13] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 jsonlite_1.7.1
[4] modelr_0.1.8 bslib_0.2.5.1 assertthat_0.2.1
[7] highr_0.8 blob_1.2.1 cellranger_1.1.0
[10] yaml_2.2.1 pillar_1.6.2 backports_1.1.10
[13] lattice_0.20-41 glue_1.4.2 RcppEigen_0.3.3.7.0
[16] digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1
[19] polyclip_1.10-0 checkmate_2.0.0 rvest_0.3.6
[22] colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.4
[25] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.9
[28] haven_2.3.1 scales_1.1.1 tweenr_1.0.2
[31] whisker_0.4 later_1.2.0 git2r_0.27.1
[34] farver_2.0.3 generics_0.1.0 ellipsis_0.3.2
[37] withr_2.3.0 cli_3.0.1 magrittr_1.5
[40] crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[43] fs_1.5.0 fansi_0.4.1 MASS_7.3-53
[46] xml2_1.3.2 RcppArmadillo_0.10.1.2.0 tools_4.0.3
[49] data.table_1.14.0 hms_0.5.3 lifecycle_1.0.0
[52] munsell_0.5.0 reprex_0.3.0 compiler_4.0.3
[55] jquerylib_0.1.4 rlang_0.4.11 grid_4.0.3
[58] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.10
[61] gtable_0.3.0 DBI_1.1.0 R6_2.5.0
[64] lubridate_1.7.9 knitr_1.33 utf8_1.1.4
[67] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[70] Rcpp_1.0.5 vctrs_0.3.8 dbplyr_1.4.4
[73] tidyselect_1.1.0 xfun_0.25