Time analysis

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Last updated: 2024-01-01

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Knit directory: mage_2020_marker-gene-benchmarking/

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---

library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(forcats)
library(patchwork)
library(stringr)
library(ggrepel)

source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))

Aim

Measure the time different methods take to run.

Simulated data

sim_pars <- retrive_simulation_parameters()

num_cells_time_data <- sim_pars %>% 
  filter(sim_label == "num_cells") %>% 
  rowwise() %>% 
  mutate(time = readRDS(full_filename)$time) %>%
  ungroup()

num_clusters_time_data <- sim_pars %>% 
  filter(sim_label == "num_clusters") %>% 
  rowwise() %>% 
  mutate(time = readRDS(full_filename)$time) %>%
  ungroup()

All methods, number of cells

label_pars <- c(
  "scanpy_wilcoxontiecorrect_rankby_abs", 
  "scanpy_t_rankby_abs", 
  "edger_ql",
  "scran_t_any", 
  "nsforest", 
  "scran_wilcox_some", 
  "cepo", 
  "rankcorr_2", 
  "seurat_negbinom", 
  "limma_trend", 
  "glmgampoi", 
  "seurat_t", 
  "seurat_MAST", 
  "scanpy_wilcoxon_rankby_abs",
  "presto"
)

time_by_num_cells_plot <- num_cells_time_data %>% 
  # Remove methods we implemented.
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
  group_by(pars, method, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(method = method_lookup[method]) %>% 
  ggplot(aes(x = batchCells, y = time, colour = method)) +
  geom_point() + 
  geom_smooth(se = FALSE, formula = y ~ x, method = "loess", span = 1) +
  geom_text_repel(
    aes(label = if_else(
      pars %in% label_pars & batchCells == 100000, 
      pars_lookup[pars], 
      ""),
    ),
    hjust = -0.1, colour = "black", size = 3, xlim = c(101000, NA)
  ) + 
  external_package_colour + 
  coord_cartesian(xlim = c(1000, 120000)) + 
  scale_x_continuous(
    breaks = c(1000, 25000, 50000, 75000, 100000),
  ) + 
  scale_y_log10(
    breaks = c(1, 10, 60, 600, 3600, 10800, 21600),
    labels = c("1s", "10s", "1m", "10min", "1hr", "3hr", "6hr")
  )  + 
  labs(
    title = "Runtime vs. total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Package"
  ) + 
  guides(color = "none") + 
  theme_bw()

time_by_num_cells_plot

Past versions of plot-num-cells-time-1.png

Version	Author	Date
5cc008f	Jeffrey Pullin	2022-02-09
61ee246	Jeffrey Pullin	2021-04-13

saveRDS(time_by_num_cells_plot, here::here("figures", "raw", "time-num-cells.rds"))

All methods, number of clusters

label_pars <- c(
  "scanpy_wilcoxontiecorrect_rankby_abs", 
  "scanpy_t_rankby_abs", 
  "edger_ql",
  "scran_t_any", 
  "nsforest", 
  "scran_wilcox_some", 
  "cepo", 
  "rankcorr_2", 
  "seurat_negbinom", 
  "limma_trend", 
  "glmgampoi", 
  "seurat_t", 
  "seurat_MAST", 
  "scanpy_wilcoxon_rankby_abs",
  "presto"
)

time_by_num_clusters_plot <- num_clusters_time_data %>% 
  # Remove methods we implemented.
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
  group_by(pars, method, n_clus) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(method = method_lookup[method]) %>% 
  ggplot(aes(x = n_clus, y = time, colour = method)) +
  geom_point() + 
  geom_smooth(se = FALSE, formula = y ~ x, method = "loess", span = 1) +
  external_package_colour + 
  geom_text_repel(
    aes(label = if_else(
      pars %in% label_pars & n_clus == 20,
      pars_lookup[pars], 
      ""),
    ),
    hjust = -0.1, colour = "black", size = 3,  xlim = c(20.5, NA)
  ) + 
  coord_cartesian(xlim = c(5, 24)) + 
  scale_x_continuous(
    breaks = c(5, 10, 15, 20),
  ) + 
  scale_y_log10(
    breaks = c(1, 10, 60, 600, 3600),
    labels = c("1s", "10s", "1m", "10min", "1hr")
  )  + 
  labs(
    title = "Runtime vs. number of clusters",
    x = "Number of clusters", 
    y = "Time (s)", 
    colour = "Package"
  ) + 
  guides(color = "none") + 
  theme_bw()

time_by_num_clusters_plot

Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

saveRDS(time_by_num_clusters_plot, here::here("figures", "raw", "time-num-clusters.rds"))

Specific time values

num_cells_time_data %>% 
 # Remove methods we implemented.
 filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
 group_by(pars, method, batchCells) %>% 
 summarise(time = mean(time), .groups = "drop") %>% 
 mutate(
   min = time / 60, 
   hour = min / 60
 ) %>% 
 filter(pars %in% c("edger_ql", "presto", 
                    "seurat_wilcox", "scanpy_wilcoxontiecorrect_rankby_abs", 
                    "scran_wilcox_any")) %>% 
 print(n = 40)

# A tibble: 20 × 6
   pars                                method batchCells    time     min    hour
   <chr>                               <chr>       <int>   <dbl>   <dbl>   <dbl>
 1 edger_ql                            edger        1000 4.74e+1 7.90e-1 1.32e-2
 2 edger_ql                            edger       25000 2.56e+3 4.27e+1 7.12e-1
 3 edger_ql                            edger       50000 4.20e+3 7.00e+1 1.17e+0
 4 edger_ql                            edger       75000 7.01e+3 1.17e+2 1.95e+0
 5 edger_ql                            edger      100000 9.51e+3 1.59e+2 2.64e+0
 6 presto                              presto       1000 6.73e-1 1.12e-2 1.87e-4
 7 presto                              presto      25000 1.69e+0 2.82e-2 4.71e-4
 8 presto                              presto      50000 2.82e+0 4.69e-2 7.82e-4
 9 presto                              presto      75000 4.95e+0 8.26e-2 1.38e-3
10 presto                              presto     100000 6.91e+0 1.15e-1 1.92e-3
11 scanpy_wilcoxontiecorrect_rankby_a… scanpy       1000 9.73e-1 1.62e-2 2.70e-4
12 scanpy_wilcoxontiecorrect_rankby_a… scanpy      25000 2.03e+1 3.39e-1 5.65e-3
13 scanpy_wilcoxontiecorrect_rankby_a… scanpy      50000 4.40e+1 7.33e-1 1.22e-2
14 scanpy_wilcoxontiecorrect_rankby_a… scanpy      75000 7.24e+1 1.21e+0 2.01e-2
15 scanpy_wilcoxontiecorrect_rankby_a… scanpy     100000 1.07e+2 1.79e+0 2.98e-2
16 seurat_wilcox                       seurat       1000 8.89e+0 1.48e-1 2.47e-3
17 seurat_wilcox                       seurat      25000 1.57e+2 2.62e+0 4.37e-2
18 seurat_wilcox                       seurat      50000 6.82e+2 1.14e+1 1.89e-1
19 seurat_wilcox                       seurat      75000 1.04e+3 1.74e+1 2.90e-1
20 seurat_wilcox                       seurat     100000 1.32e+3 2.20e+1 3.67e-1

Seurat methods, number of cells

num_cells_time_data %>% 
  filter(method == "seurat") %>% 
  group_by(pars, test.use, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(test.use = test.use_lookup[test.use]) %>% 
  ggplot(aes(x = batchCells, y = time, colour = test.use)) +
  geom_jitter(width = 200) + 
  scale_x_continuous(
    breaks = c(1000, 25000, 50000, 75000, 100000)
  ) + 
  scale_y_log10(
    breaks = c(1, 10, 60, 600, 3600, 10800),
    labels = c("1s", "10s", "1m", "10min", "1hr", "3hr")
  )  + 
  labs(
    title = "Runtime vs total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Parameters"
  ) + 
  theme_bw()

scran methods, number of cells

num_cells_time_data %>% 
  filter(method == "scran") %>% 
  group_by(pars, test.type, pval.type, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(scran_pars = paste0(test.type, "_", pval.type)) %>% 
  ggplot(aes(x = batchCells, y = time, colour = scran_pars)) +
  geom_point() + 
  scale_x_continuous(
    breaks = c(1000, 25000, 50000, 75000, 100000)
  ) +  
  labs(
    title = "Runtime vs total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Parameters"
  ) + 
  theme_bw()

Scanpy methods, number of cells

num_cells_time_data %>% 
  filter(method == "scanpy") %>% 
  group_by(pars, test_use, rankby_abs, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(rankby_abs = if_else(rankby_abs == "True", "abs", "raw")) %>% 
  mutate(scran_pars = paste0(test_use, "_", rankby_abs)) %>% 
  ggplot(aes(x = batchCells, y = time, colour = scran_pars)) +
  geom_point() + 
  scale_x_continuous(
    breaks = c(1000, 25000, 50000, 75000, 100000)
  ) +
  labs(
    title = "Runtime vs total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Parameters"
  ) + 
  theme_bw()

Real data

real_data_data <- retrieve_real_data_parameters() %>% 
  rowwise() %>% 
  mutate(time = readRDS(full_filename)$time) %>%
  ungroup()

pbmc3k

real_data_data %>% 
  filter(data_id == "pbmc3k") %>% 
  filter(!(pars %in% c("lm_two_sample", "random"))) %>% 
  mutate(
    pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = plot_method)) + 
  geom_point(alpha = 0.8, size = 3) + 
  external_package_colour + 
  scale_y_log10(
    breaks = c(500, 1000, 10000, 60000, 600000),
    labels = c("0.5s", "1s", "10s", "1m", "10min")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "pbmc3k data",
    colour = "Package"
  ) + 
  theme_bw()

Lawlor

real_data_data %>% 
  filter(data_id == "lawlor") %>% 
  filter(!(pars %in% c("lm_two_sample", "random"))) %>% 
  mutate(
    pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>%  
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = plot_method)) + 
  geom_point(alpha = 0.8, size = 3) + 
  external_package_colour + 
  scale_y_log10(
    breaks = c(500, 1000, 10000, 60000, 600000),
    labels = c("0.5s", "1s", "10s", "1m", "10min")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Lawlor data",
    colour = "Package"
  ) + 
  theme_bw()

Zeisel

real_data_data %>% 
  filter(data_id == "zeisel") %>% 
  filter(!(pars %in% c("lm_two_sample", "random"))) %>% 
  mutate(
    pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>%   
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = plot_method)) + 
  geom_point(alpha = 0.8, size = 3) + 
  external_package_colour + 
  scale_y_log10(
    breaks = c(500, 1000, 10000, 60000, 600000),
    labels = c("0.5s", "1s", "10s", "1m", "10min")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Zeisel data",
    colour = "Package"
  ) + 
  theme_bw()

Endothelial

real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  filter(!(pars %in% c("lm_two_sample", "difference_log_fc", "random"))) %>% 
  mutate(
    pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>%    
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = plot_method)) + 
  geom_point(size = 3) + 
  external_package_colour + 
  scale_y_log10(
    breaks = c(1000, 10000, 60000, 600000, 3600000, 18000000),
    labels = c("1s", "10s", "1m", "10min", "1hr", "5hr")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Endothelial data", 
    colour = "Package"
  ) + 
  theme_bw()

Zhao

zhao_time <- real_data_data %>% 
  filter(data_id == "zhao") %>% 
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
   mutate(
    pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>%     
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = plot_method)) + 
  geom_point(size = 3) + 
  external_package_colour + 
  scale_y_log10(
    breaks = c(1000, 10000, 60000, 600000, 3600000, 18000000, 43200000),
    labels = c("1s", "10s", "1m", "10min", "1hr", "5hr", "12hr")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Zhao data", 
    colour = "Package"
  ) + 
  theme_bw()

zhao_time

saveRDS(zhao_time, here::here("figures", "raw", "zhao-time.rds"))

Overall

overall_time <- real_data_data %>% 
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
  mutate(ms = time * 1000) %>% 
  mutate(log_time = log(ms, base = 10)) %>% 
  mutate(
    plot_method = method_lookup[method], 
    plot_pars = pars_lookup[pars],
    plot_data_id = dataset_lookup[data_id]
  ) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), time)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = log_time), colour = "black") + 
  scale_fill_distiller(
    breaks = log(c(1000, 10000, 60000, 600000, 3600000, 21600000), base = 10),
    labels = c("1s", "10s", "1m", "10min", "1hr", "6hr"),
    palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    title = "Time across datasets",
    x = "Dataset", 
    y = "Method", 
    fill = "Time",
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )
overall_time

saveRDS(overall_time, here::here("figures", "raw", "overall-time.rds"))

Specific values

real_data_data %>% 
 # Remove methods we implemented.
 filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
 filter(data_id %in% c("citeseq", "zhao")) %>% 
 mutate(
   min = time / 60, 
   hour = min / 60
 ) %>% 
 select(data_id, time, pars, min, hour) %>% 
 filter(pars %in% c("edger_ql", "presto", "seurat_t",
                    "seurat_wilcox", "scanpy_t_rankby_abs", 
                    "scran_t_any", "scanpy_t")) %>% 
 print(n = 40)

# A tibble: 10 × 5
   data_id      time pars                     min     hour
   <chr>       <dbl> <chr>                  <dbl>    <dbl>
 1 citeseq  1114.    edger_ql             18.6    0.310   
 2 citeseq     2.00  presto                0.0333 0.000555
 3 citeseq     0.999 scanpy_t_rankby_abs   0.0167 0.000278
 4 citeseq   333.    seurat_t              5.56   0.0926  
 5 citeseq   401.    seurat_wilcox         6.69   0.112   
 6 zhao    25429.    edger_ql            424.     7.06    
 7 zhao        6.18  presto                0.103  0.00172 
 8 zhao       13.2   scanpy_t_rankby_abs   0.220  0.00367 
 9 zhao     6441.    seurat_t            107.     1.79    
10 zhao    12862.    seurat_wilcox       214.     3.57    

Session information

devtools::session_info()

─ Session info  ──────────────────────────────────────────────────────────────
 hash: person walking: dark skin tone, mage: light skin tone, backhand index pointing right: medium skin tone

 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       Red Hat Enterprise Linux 9.2 (Plow)
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_AU.UTF-8
 ctype    en_AU.UTF-8
 tz       Australia/Melbourne
 date     2024-01-01
 pandoc   2.18 @ /apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/RStudio-Server/2022.07.2+576-Java-11-R-4.1.2/bin/pandoc/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version date (UTC) lib source
 assertthat     0.2.1   2019-03-21 [2] CRAN (R 4.1.2)
 bslib          0.3.1   2021-10-06 [1] CRAN (R 4.1.0)
 cachem         1.0.6   2021-08-19 [1] CRAN (R 4.1.0)
 callr          3.7.0   2021-04-20 [2] CRAN (R 4.1.2)
 cli            3.6.1   2023-03-23 [1] CRAN (R 4.1.0)
 colorspace     2.1-0   2023-01-23 [1] CRAN (R 4.1.0)
 crayon         1.5.1   2022-03-26 [1] CRAN (R 4.1.0)
 DBI            1.1.2   2021-12-20 [1] CRAN (R 4.1.0)
 desc           1.4.0   2021-09-28 [2] CRAN (R 4.1.2)
 devtools       2.4.2   2021-06-07 [2] CRAN (R 4.1.2)
 digest         0.6.29  2021-12-01 [1] CRAN (R 4.1.0)
 dplyr        * 1.0.9   2022-04-28 [1] CRAN (R 4.1.0)
 ellipsis       0.3.2   2021-04-29 [2] CRAN (R 4.1.2)
 evaluate       0.14    2019-05-28 [2] CRAN (R 4.1.2)
 fansi          1.0.4   2023-01-22 [1] CRAN (R 4.1.0)
 farver         2.1.1   2022-07-06 [1] CRAN (R 4.1.0)
 fastmap        1.1.0   2021-01-25 [2] CRAN (R 4.1.2)
 forcats      * 0.5.1   2021-01-27 [2] CRAN (R 4.1.2)
 fs             1.5.2   2021-12-08 [1] CRAN (R 4.1.0)
 generics       0.1.3   2022-07-05 [1] CRAN (R 4.1.0)
 ggplot2      * 3.3.6   2022-05-03 [1] CRAN (R 4.1.0)
 ggrepel      * 0.9.1   2021-01-15 [2] CRAN (R 4.1.2)
 git2r          0.28.0  2021-01-10 [2] CRAN (R 4.1.2)
 glue           1.6.0   2021-12-17 [1] CRAN (R 4.1.0)
 gtable         0.3.0   2019-03-25 [2] CRAN (R 4.1.2)
 here           1.0.1   2020-12-13 [1] CRAN (R 4.1.0)
 highr          0.9     2021-04-16 [2] CRAN (R 4.1.2)
 htmltools      0.5.2   2021-08-25 [1] CRAN (R 4.1.0)
 httpuv         1.6.5   2022-01-05 [1] CRAN (R 4.1.0)
 jquerylib      0.1.4   2021-04-26 [2] CRAN (R 4.1.2)
 jsonlite       1.8.0   2022-02-22 [1] CRAN (R 4.1.0)
 knitr          1.36    2021-09-29 [1] CRAN (R 4.1.0)
 labeling       0.4.2   2020-10-20 [2] CRAN (R 4.1.2)
 later          1.3.0   2021-08-18 [1] CRAN (R 4.1.0)
 lattice        0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
 lifecycle      1.0.1   2021-09-24 [1] CRAN (R 4.1.0)
 magrittr       2.0.3   2022-03-30 [1] CRAN (R 4.1.0)
 Matrix         1.3-4   2021-06-01 [2] CRAN (R 4.1.2)
 memoise        2.0.1   2021-11-26 [1] CRAN (R 4.1.0)
 mgcv           1.8-38  2021-10-06 [2] CRAN (R 4.1.2)
 munsell        0.5.0   2018-06-12 [2] CRAN (R 4.1.2)
 nlme           3.1-153 2021-09-07 [2] CRAN (R 4.1.2)
 patchwork    * 1.1.1   2020-12-17 [2] CRAN (R 4.1.2)
 pillar         1.7.0   2022-02-01 [1] CRAN (R 4.1.0)
 pkgbuild       1.2.0   2020-12-15 [2] CRAN (R 4.1.2)
 pkgconfig      2.0.3   2019-09-22 [2] CRAN (R 4.1.2)
 pkgload        1.2.3   2021-10-13 [2] CRAN (R 4.1.2)
 prettyunits    1.1.1   2020-01-24 [2] CRAN (R 4.1.2)
 processx       3.5.2   2021-04-30 [2] CRAN (R 4.1.2)
 promises       1.2.0.1 2021-02-11 [2] CRAN (R 4.1.2)
 ps             1.7.1   2022-06-18 [1] CRAN (R 4.1.0)
 purrr        * 0.3.4   2020-04-17 [2] CRAN (R 4.1.2)
 R6             2.5.1   2021-08-19 [1] CRAN (R 4.1.0)
 RColorBrewer   1.1-3   2022-04-03 [1] CRAN (R 4.1.0)
 Rcpp           1.0.8.3 2022-03-17 [1] CRAN (R 4.1.0)
 remotes        2.4.2   2021-11-30 [1] CRAN (R 4.1.0)
 rlang          1.0.3   2022-06-27 [1] CRAN (R 4.1.0)
 rmarkdown      2.14    2022-04-25 [1] CRAN (R 4.1.0)
 rprojroot      2.0.3   2022-04-02 [1] CRAN (R 4.1.0)
 rstudioapi     0.14    2022-08-22 [1] CRAN (R 4.1.0)
 sass           0.4.1   2022-03-23 [1] CRAN (R 4.1.0)
 scales         1.2.1   2022-08-20 [1] CRAN (R 4.1.0)
 sessioninfo    1.2.0   2021-10-31 [2] CRAN (R 4.1.2)
 stringi        1.7.6   2021-11-29 [1] CRAN (R 4.1.0)
 stringr      * 1.4.0   2019-02-10 [2] CRAN (R 4.1.2)
 testthat       3.1.0   2021-10-04 [2] CRAN (R 4.1.2)
 tibble       * 3.1.7   2022-05-03 [1] CRAN (R 4.1.0)
 tidyr        * 1.2.0   2022-02-01 [1] CRAN (R 4.1.0)
 tidyselect     1.1.2   2022-02-21 [1] CRAN (R 4.1.0)
 usethis        2.1.3   2021-10-27 [2] CRAN (R 4.1.2)
 utf8           1.2.3   2023-01-31 [1] CRAN (R 4.1.0)
 vctrs          0.4.1   2022-04-13 [1] CRAN (R 4.1.0)
 whisker        0.4     2019-08-28 [2] CRAN (R 4.1.2)
 withr          2.5.0   2022-03-03 [1] CRAN (R 4.1.0)
 workflowr      1.7.0   2021-12-21 [1] CRAN (R 4.1.0)
 xfun           0.31    2022-05-10 [1] CRAN (R 4.1.0)
 yaml           2.3.5   2022-02-21 [1] CRAN (R 4.1.0)

 [1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
 [2] /apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/R/4.1.2/lib64/R/library

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