South African Weightlifting — Capstone Analysis

1 Ask — Define the Business Task

South Africa has a rich (and often under-recognised) history in Olympic weightlifting. The South African Weightlifting Federation (SAWF) wants to recognise historic bests and showcase performance standards for the next generation.

Primary task Identify and rank the best male and female weightlifters by Sinclair (from their best recorded total).

Secondary questions

• Which weight classes (or eras) produced the strongest results on a relative basis?
• Have performance trends changed over time?
• How does the snatch-to-clean-and-jerk ratio relate to Sinclair efficiency and ranking?

Stakeholders SAWF, national coaches/selectors, sport scientists/analysts, athletes, and the public.

---

2 Prepare — Source, Describe, and Clean the Data

Sources

• International meets: manually collected from the IWF website (by athlete).
• Recent national meets: provided by SAWF general secretary (last ~3–5 years).
• Historic records: archived magazines and coach notes.

Fields athlete, year, bodyweight, snatch, clean_and_jerk, total, sinclair.

Credibility & limitations

• Where possible, results are official or verified.
• Small sample (older national years are sparse; some years missing).
• Sinclair depends on coefficient versions; this analysis treats provided Sinclair values as standardised best performances.

# ---- Libraries ----
library(googlesheets4)
library(dplyr)
library(tidyr)
library(readr)
library(janitor)
library(ggplot2)

# If the sheet is publicly readable this avoids auth prompts during knit:
suppressMessages(gs4_deauth())

# ---- Global styling (consistent theme & colours) ----
theme_set(theme_minimal(base_size = 13))
wal_blue_dark <- "#003566"   # Men
wal_red_dark  <- "#A4161A"   # Women
male_fill_low <- "#90E0EF"; male_fill_high <- "#0077B6"
fem_fill_low  <- "#F8C8DC";  fem_fill_high  <- "#C9184A"

# ---- Data import ----
sheet_url <- "https://docs.google.com/spreadsheets/d/1zhDv-Kvhj86CT6qwcwV1uRskGmXYio7xsqrdtH_sS-g/edit?usp=sharing"
mens_results   <- read_sheet(sheet_url, sheet = "Men")   |> clean_names()
womens_results <- read_sheet(sheet_url, sheet = "Women") |> clean_names()

# ---- Basic cleaning & types ----
num_cols <- c("year","bodyweight","snatch","clean_and_jerk","total","sinclair")
mens_results[num_cols]   <- lapply(mens_results[num_cols],   \(x) suppressWarnings(as.numeric(x)))
womens_results[num_cols] <- lapply(womens_results[num_cols], \(x) suppressWarnings(as.numeric(x)))

# Keep rows usable for rankings/ratios
mens_results   <- mens_results   |> drop_na(sinclair, snatch, clean_and_jerk)
womens_results <- womens_results |> drop_na(sinclair, snatch, clean_and_jerk)

# Quick peek
dplyr::glimpse(mens_results)

## Rows: 37
## Columns: 7
## $ athlete        <chr> "Darryn Anthony", "Bennie Oldewage", "Greg Shushu", "Go…
## $ year           <dbl> 2008, 1977, 2008, 2019, 2014, 2025, 2025, 1988, 1985, 2…
## $ bodyweight     <dbl> 76.70, 87.00, 66.26, 125.00, 55.35, 87.65, 92.97, 95.00…
## $ snatch         <dbl> 143.0, 150.0, 125.0, 176.0, 103.0, 146.0, 142.0, 145.0,…
## $ clean_and_jerk <dbl> 172.0, 185.0, 160.0, 201.0, 135.0, 173.0, 180.0, 180.0,…
## $ total          <dbl> 315.0, 335.0, 285.0, 377.0, 238.0, 319.0, 322.0, 325.0,…
## $ sinclair       <dbl> 412.28, 409.52, 408.86, 400.35, 389.33, 388.51, 381.25,…

dplyr::glimpse(womens_results)

## Rows: 19
## Columns: 7
## $ athlete        <chr> "Mona Pretorius", "Anneke Spies Burger", "Johanni Talja…
## $ year           <dbl> 2018, 2022, 2018, 2010, 2025, 2018, 2008, 2025, 2018, 2…
## $ bodyweight     <dbl> 62.77, 58.89, 58.85, 47.99, 68.65, 63.94, 73.03, 63.00,…
## $ snatch         <dbl> 91, 85, 84, 68, 91, 83, 83, 81, 78, 69, 75, 83, 74, 71,…
## $ clean_and_jerk <dbl> 115, 105, 105, 94, 112, 110, 120, 97, 99, 93, 90, 98, 9…
## $ total          <dbl> 206, 190, 189, 162, 203, 193, 203, 178, 177, 162, 165, …
## $ sinclair       <dbl> 271.02, 260.30, 259.04, 257.49, 253.52, 251.09, 245.34,…

---

3 Process — Transformations for Analysis

I standardised weight-class bins, created the snatch:clean-and-jerk ratio, and built compact Top-10 tables.

# Men classes
mens_results <- mens_results |>
  mutate(weight_class = case_when(
    bodyweight < 65 ~ "<65 kg",
    bodyweight < 75 ~ "65–75 kg",
    bodyweight < 82.5 ~ "75–82.5 kg",
    bodyweight < 90 ~ "82.5–90 kg",
    bodyweight < 100 ~ "90–100 kg",
    TRUE ~ "100+ kg"
  )) |>
  mutate(weight_class = factor(
    weight_class,
    levels = c("<65 kg","65–75 kg","75–82.5 kg","82.5–90 kg","90–100 kg","100+ kg")
  ))

# Women classes
womens_results <- womens_results |>
  mutate(weight_class = case_when(
    bodyweight < 55 ~ "<55 kg",
    bodyweight < 59 ~ "55–59 kg",
    bodyweight < 64 ~ "59–64 kg",
    bodyweight < 71 ~ "64–71 kg",
    bodyweight < 81 ~ "71–81 kg",
    TRUE ~ "81+ kg"
  )) |>
  mutate(weight_class = factor(
    weight_class,
    levels = c("<55 kg","55–59 kg","59–64 kg","64–71 kg","71–81 kg","81+ kg")
  ))

# Ratios
mens_results   <- mens_results   |> mutate(snatch_cj_ratio = snatch / clean_and_jerk)
womens_results <- womens_results |> mutate(snatch_cj_ratio = snatch / clean_and_jerk)

# Top-10 by Sinclair
top10_men   <- mens_results   |> arrange(desc(sinclair)) |> slice_head(n = 10)
top10_women <- womens_results |> arrange(desc(sinclair)) |> slice_head(n = 10)

---

4 Analyse — Findings with Visual Evidence

4.1 A. Who are the top lifters?

ggplot(top10_men, aes(x = reorder(athlete, sinclair), y = sinclair, fill = sinclair)) +
  geom_col(show.legend = FALSE) +
  # Add Sinclair score inside each bar
  geom_text(aes(label = round(sinclair, 1)),
            hjust = 1.2, colour = "white", size = 4, fontface = "bold") +
  scale_fill_gradient(low = male_fill_low, high = male_fill_high) +
  coord_flip() +
  labs(
    title = "Top 10 Weightlifters (All-Time) — Men",
    subtitle = "Sinclair scores displayed inside bars",
    x = "Athlete",
    y = "Sinclair Score"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

ggplot(top10_women, aes(x = reorder(athlete, sinclair), y = sinclair, fill = sinclair)) +
  geom_col(show.legend = FALSE) +
  # Add Sinclair score labels inside bars
  geom_text(aes(label = round(sinclair, 1)),
            hjust = 1.2, colour = "white", size = 4, fontface = "bold") +
  scale_fill_gradient(low = fem_fill_low, high = fem_fill_high) +
  coord_flip() +
  labs(
    title = "Top 10 Weightlifters (All-Time) — Women",
    subtitle = "Sinclair scores displayed inside bars",
    x = "Athlete",
    y = "Sinclair Score"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5))

(Optional quick scatter view for men)

mean_sinclair_men <- mean(top10_men$sinclair, na.rm = TRUE)

top10_men_plot <- top10_men |>
  dplyr::mutate(above_mean = ifelse(sinclair >= mean_sinclair_men, "Above mean", "Below mean"))

ggplot(top10_men_plot, aes(x = reorder(athlete, sinclair), y = sinclair)) +
  geom_segment(aes(xend = athlete, y = mean_sinclair_men, yend = sinclair),
               colour = male_fill_low, linewidth = 1.1, alpha = 0.6) +
  geom_point(aes(colour = above_mean), size = 4) +
  geom_hline(yintercept = mean_sinclair_men, linetype = "dashed", colour = "grey40") +
  coord_flip() +
  scale_colour_manual(values = c("Above mean" = wal_blue_dark, "Below mean" = male_fill_low),
                      guide = "none") +
  labs(
    title = "Top 10 Weightlifters — Men (Deviation from Mean Sinclair)",
    subtitle = paste0("Dashed line = mean Sinclair (", round(mean_sinclair_men, 1), ")"),
    x = "Athlete",
    y = "Sinclair Score"
  ) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

(Optional quick scatter view for women)

mean_sinclair_women <- mean(top10_women$sinclair, na.rm = TRUE)

top10_women_plot <- top10_women |>
  dplyr::mutate(above_mean = ifelse(sinclair >= mean_sinclair_women, "Above mean", "Below mean"))

ggplot(top10_women_plot, aes(x = reorder(athlete, sinclair), y = sinclair)) +
  geom_segment(aes(xend = athlete, y = mean_sinclair_women, yend = sinclair),
               colour = fem_fill_low, linewidth = 1.1, alpha = 0.6) +
  geom_point(aes(colour = above_mean), size = 4) +
  geom_hline(yintercept = mean_sinclair_women, linetype = "dashed", colour = "grey40") +
  coord_flip() +
  scale_colour_manual(values = c("Above mean" = wal_red_dark, "Below mean" = fem_fill_low),
                      guide = "none") +
  labs(title = "Top 10 Weightlifters — Women (Deviation from Mean Sinclair)",
    subtitle = paste0("Dashed line = mean Sinclair (", round(mean_sinclair_women, 1), ")"),
    x = "Athlete",
    y = "Sinclair Score"
  ) +
  theme(plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

4.2 B. Bodyweight vs Sinclair (sanity check)

ggplot(mens_results, aes(x = bodyweight, y = sinclair)) +
  geom_point(alpha = 0.8, colour = male_fill_high) +
  labs(title = "Bodyweight vs Sinclair — Men",
       x = "Bodyweight (kg)", y = "Sinclair Score")

ggplot(womens_results, aes(x = bodyweight, y = sinclair)) +
  geom_point(alpha = 0.8, colour = fem_fill_high) +
  labs(title = "Bodyweight vs Sinclair — Women",
       x = "Bodyweight (kg)", y = "Sinclair Score")

4.3 C. Which weight classes are strongest on average?

avg_by_class_men <- mens_results |>
  group_by(weight_class) |>
  summarise(
    avg_sinclair = mean(sinclair, na.rm = TRUE),
    max_sinclair = max(sinclair, na.rm = TRUE),
    avg_total    = mean(total, na.rm = TRUE),
    n = n(),
    .groups = "drop"
  ) |>
  arrange(desc(avg_sinclair))

ggplot(avg_by_class_men, aes(x = reorder(weight_class, avg_sinclair), y = avg_sinclair, fill = avg_sinclair)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = paste0("n=", n)), hjust = -0.1, size = 3) +
  coord_flip(ylim = c(0, max(avg_by_class_men$avg_sinclair, na.rm = TRUE) * 1.10)) +
  scale_fill_gradient(low = male_fill_low, high = male_fill_high) +
  labs(title = "Average Sinclair by Weight Class — Men",
       x = "Weight Class", y = "Average Sinclair")

avg_by_class_women <- womens_results |>
  group_by(weight_class) |>
  summarise(
    avg_sinclair = mean(sinclair, na.rm = TRUE),
    max_sinclair = max(sinclair, na.rm = TRUE),
    avg_total    = mean(total, na.rm = TRUE),
    n = n(),
    .groups = "drop"
  ) |>
  arrange(desc(avg_sinclair))

ggplot(avg_by_class_women, aes(x = reorder(weight_class, avg_sinclair), y = avg_sinclair, fill = avg_sinclair)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = paste0("n=", n)), hjust = -0.1, size = 3) +
  coord_flip(ylim = c(0, max(avg_by_class_women$avg_sinclair, na.rm = TRUE) * 1.10)) +
  scale_fill_gradient(low = fem_fill_low, high = fem_fill_high) +
  labs(title = "Average Sinclair by Weight Class — Women",
       x = "Weight Class", y = "Average Sinclair")

4.4 D. Performance trends over time (scatter only)

men_time <- mens_results |>
  mutate(year = as.integer(year)) |>
  drop_na(year, sinclair, weight_class)

ggplot(men_time, aes(x = year, y = sinclair, colour = weight_class)) +
  geom_point(aes(alpha = sinclair), size = 3, position = position_jitter(width = 0.2, height = 0)) +
  scale_alpha_continuous(range = c(0.3, 1), guide = "none") +
  labs(title = "Sinclair Over Time by Weight Class — Men",
       x = "Year", y = "Sinclair Score", colour = "Weight Class") +
  theme(legend.position = "bottom")

women_time <- womens_results |>
  mutate(year = as.integer(year)) |>
  drop_na(year, sinclair, weight_class)

ggplot(women_time, aes(x = year, y = sinclair, colour = weight_class)) +
  geom_point(aes(alpha = sinclair), size = 3, position = position_jitter(width = 0.2, height = 0)) +
  scale_alpha_continuous(range = c(0.3, 1), guide = "none") +
  labs(title = "Sinclair Over Time by Weight Class — Women",
       x = "Year", y = "Sinclair Score", colour = "Weight Class") +
  theme(legend.position = "bottom")

(Optional companion: yearly means with sample size to contextualise any “trend” claims.)

trend_by_year_men <- men_time |>
  group_by(year) |>
  summarise(avg_sinclair = mean(sinclair, na.rm = TRUE), n = n(), .groups = "drop")

ggplot(trend_by_year_men, aes(x = year, y = avg_sinclair)) +
  geom_line(size = 1) +
  geom_point(aes(size = n), colour = wal_blue_dark) +
  scale_size(range = c(2, 6), guide = "none") +
  labs(title = "Average Sinclair by Year — Men (Point Size = Sample Size)",
       x = "Year", y = "Average Sinclair")

trend_by_year_women <- women_time |>
  group_by(year) |>
  summarise(avg_sinclair = mean(sinclair, na.rm = TRUE), n = n(), .groups = "drop")

ggplot(trend_by_year_women, aes(x = year, y = avg_sinclair)) +
  geom_line(size = 1) +
  geom_point(aes(size = n), colour = wal_red_dark) +
  scale_size(range = c(2, 6), guide = "none") +
  labs(title = "Average Sinclair by Year — Women (Point Size = Sample Size)",
       x = "Year", y = "Average Sinclair")

4.5 E. Snatch : Clean & Jerk ratio vs Sinclair

# Whole-dataset relationship (context)
cor_m_all <- cor(mens_results$snatch_cj_ratio, mens_results$sinclair, use = "complete.obs")
cor_w_all <- cor(womens_results$snatch_cj_ratio, womens_results$sinclair, use = "complete.obs")
paste("Correlation (Men, all):", round(cor_m_all, 3))

## [1] "Correlation (Men, all): 0.148"

paste("Correlation (Women, all):", round(cor_w_all, 3))

## [1] "Correlation (Women, all): -0.228"

ggplot(mens_results, aes(x = snatch_cj_ratio, y = sinclair)) +
  geom_point(aes(colour = weight_class), size = 3, alpha = 0.85) +
  labs(title = "Snatch:C&J Ratio vs Sinclair — Men",
       x = "Snatch ÷ Clean & Jerk Ratio", y = "Sinclair Score", colour = "Weight Class")

ggplot(womens_results, aes(x = snatch_cj_ratio, y = sinclair)) +
  geom_point(aes(colour = weight_class), size = 3, alpha = 0.85) +
  labs(title = "Snatch:C&J Ratio vs Sinclair — Women",
       x = "Snatch ÷ Clean & Jerk Ratio", y = "Sinclair Score", colour = "Weight Class")

4.6 F. Focus on Top-10 only: correlation & “golden ratio” summary

The snatch-to-clean-and-jerk ratio reflects how balanced a lifter is between explosive power (snatch) and total strength (clean & jerk).
In elite-level lifters, this ratio typically falls within a narrow range — too low suggests a strength bias, while too high suggests technical limitations or poor heavy-lift efficiency.

In this analysis, the term “golden ratio” refers not to the mathematical constant (≈1.618), but to an empirical balance zone — the average snatch-to-clean-and-jerk ratio achieved by South Africa’s top performers.
By examining this range (mean ± SD and median values), we can identify a performance sweet spot that maximises Sinclair efficiency without over-emphasising either lift.

# Top-10 correlations
top10_men   <- top10_men   |> mutate(snatch_cj_ratio = snatch / clean_and_jerk)
top10_women <- top10_women |> mutate(snatch_cj_ratio = snatch / clean_and_jerk)

cor_m_top10 <- cor(top10_men$snatch_cj_ratio, top10_men$sinclair, use = "complete.obs")
cor_w_top10 <- cor(top10_women$snatch_cj_ratio, top10_women$sinclair, use = "complete.obs")
paste("Correlation (Men, Top-10):", round(cor_m_top10, 3))

## [1] "Correlation (Men, Top-10): 0.437"

paste("Correlation (Women, Top-10):", round(cor_w_top10, 3))

## [1] "Correlation (Women, Top-10): 0.115"

# Mean/Median/SD for the ratio among elites (empirical “golden zone”)
men_ratio_summary <- top10_men |>
  summarise(
    mean_ratio   = mean(snatch / clean_and_jerk, na.rm = TRUE),
    median_ratio = median(snatch / clean_and_jerk, na.rm = TRUE),
    sd_ratio     = sd(snatch / clean_and_jerk, na.rm = TRUE)
  ) |> mutate(gender = "Men")

women_ratio_summary <- top10_women |>
  summarise(
    mean_ratio   = mean(snatch / clean_and_jerk, na.rm = TRUE),
    median_ratio = median(snatch / clean_and_jerk, na.rm = TRUE),
    sd_ratio     = sd(snatch / clean_and_jerk, na.rm = TRUE)
  ) |> mutate(gender = "Women")

ratio_summary <- bind_rows(men_ratio_summary, women_ratio_summary) |>
  mutate(across(where(is.numeric), \(x) round(x, 3))) |>
  select(gender, mean_ratio, median_ratio, sd_ratio)

ratio_summary

ggplot(ratio_summary, aes(x = gender)) +
  geom_errorbar(aes(ymin = mean_ratio - sd_ratio, ymax = mean_ratio + sd_ratio, colour = gender),
                width = 0.1, size = 1.1) +
  geom_point(aes(y = mean_ratio, colour = gender), size = 5, shape = 19) +
  geom_point(aes(y = median_ratio, colour = gender), size = 4, shape = 17, alpha = 0.5) +
  scale_colour_manual(values = c("Men" = wal_blue_dark, "Women" = wal_red_dark)) +
  scale_y_continuous(breaks = seq(0.72, 0.86, by = 0.02), limits = c(0.72, 0.86)) +
  labs(title = "Snatch:C&J Ratio Summary (Top-10)",
       subtitle = "Dark circles = Mean ± SD   |   Transparent triangles = Median",
       x = "Gender", y = "Snatch ÷ Clean & Jerk Ratio", colour = "Gender")

4.7 G. (Nice-to-have) Distribution of ratios — clustering

ggplot(mens_results, aes(x = snatch_cj_ratio)) +
  geom_histogram(bins = 12, fill = male_fill_high, alpha = 0.85, colour = "white") +
  labs(title = "Distribution of Snatch:C&J Ratio — Men", x = "Snatch ÷ C&J Ratio", y = "Count")

ggplot(womens_results, aes(x = snatch_cj_ratio)) +
  geom_histogram(bins = 12, fill = fem_fill_high, alpha = 0.85, colour = "white") +
  labs(title = "Distribution of Snatch:C&J Ratio — Women", x = "Snatch ÷ C&J Ratio", y = "Count")

---

5 Share — Insights and Business Recommendations

Headline insights

• All-time rankings: Top-10 lists (by Sinclair) clearly highlight standout lifters in both men and women.
• Weight-class strength: Specific classes show higher average Sinclair; labels include n to avoid over-interpreting small groups.
• Trends over time: Scatter views show where peak years cluster by class; optional yearly means (with point size = sample size) provide context.
• Snatch:C&J balance: Across all athletes, ratio–Sinclair correlation is weak. Among Top-10 men, the relationship strengthens (moderate, r ≈ reported in output).
• Golden ratio (empirical): Top lifters cluster around ~0.78–0.82 snatch:C&J ratio. Mean and median align closely, with small SD — a practical balance zone.

What this means for SAWF

• Publish Top-10 historical lists as part of a recognition series.
• Use average Sinclair by class to set development benchmarks for juniors and talent ID.
• Educate athletes/coaches on the balance zone (ratio ≈ 0.80) as a useful performance heuristic — not a hard rule, especially for women where the relationship is weaker.

---

6 Act — Next Steps

For coaches & selectors

• Incorporate Sinclair-based benchmarks by class into national squad criteria.
• Track athlete snatch:C&J ratio during cycles; if chronically low (<0.76), prioritise snatch technique/mobility; if very high (>0.84), emphasise heavy C&J strength tolerance.

For SAWF

• Build a living dashboard (Google Sheets + RMarkdown/Quarto Publish) that updates rankings as new results arrive.
• Standardise data capture from meets (consistent headers, one row per performance).

For analysts

• Expand dataset (older nationals, youth/juniors) to stabilise year-over-year trends.
• Sensitivity check: replicate Top-10 findings with top-20% threshold to confirm robustness.
• Optional: model a non-linear peak for the ratio using quadratic/LOESS with cross-validation.

---

7 Appendix — Reproducibility

sessionInfo()

## R version 4.5.2 (2025-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_South Africa.utf8  LC_CTYPE=English_South Africa.utf8   
## [3] LC_MONETARY=English_South Africa.utf8 LC_NUMERIC=C                         
## [5] LC_TIME=English_South Africa.utf8    
## 
## time zone: Africa/Johannesburg
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_4.0.0       janitor_2.2.1       readr_2.1.5        
## [4] tidyr_1.3.1         dplyr_1.1.4         googlesheets4_1.1.2
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.10        generics_0.1.4     stringi_1.8.7      hms_1.1.4         
##  [5] digest_0.6.37      magrittr_2.0.4     evaluate_1.0.5     grid_4.5.2        
##  [9] timechange_0.3.0   RColorBrewer_1.1-3 fastmap_1.2.0      cellranger_1.1.0  
## [13] jsonlite_2.0.0     googledrive_2.1.2  httr_1.4.7         purrr_1.1.0       
## [17] scales_1.4.0       jquerylib_0.1.4    cli_3.6.5          rlang_1.1.6       
## [21] withr_3.0.2        cachem_1.1.0       yaml_2.3.10        tools_4.5.2       
## [25] tzdb_0.5.0         gargle_1.6.0       curl_7.0.0         vctrs_0.6.5       
## [29] R6_2.6.1           lifecycle_1.0.4    lubridate_1.9.4    snakecase_0.11.1  
## [33] stringr_1.5.2      fs_1.6.6           pkgconfig_2.0.3    pillar_1.11.1     
## [37] bslib_0.9.0        gtable_0.3.6       glue_1.8.0         xfun_0.54         
## [41] tibble_3.3.0       tidyselect_1.2.1   rstudioapi_0.17.1  knitr_1.50        
## [45] farver_2.1.2       htmltools_0.5.8.1  labeling_0.4.3     rmarkdown_2.30    
## [49] compiler_4.5.2     S7_0.2.0