Last updated: 2020-12-03

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Knit directory: IITA_2020GS/

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Rmd 3cd0f44 wolfemd 2020-12-03 Refresh BLUPs and GBLUPs with trials harvested so far. Include
html b9bb6f8 wolfemd 2020-12-03 Build site.
Rmd 9718666 wolfemd 2020-12-03 Refresh BLUPs and GBLUPs with trials harvested so far. Include
html c97b21b wolfemd 2020-11-27 Build site.
Rmd 1f8cd99 wolfemd 2020-11-27 Added plots of genetic gain for 4 traits. Initial analysis of GEBV vs.
html d72a9ed wolfemd 2020-09-21 Build site.
html 9194239 wolfemd 2020-09-21 Build site.
Rmd 97778e7 wolfemd 2020-09-21 Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs:

Cross-validation accuracy

Conducted 5-fold x 5-reps of cross-validation (here). Three traits only, MCMDS, logFYLD, DM.

library(tidyverse)
library(magrittr)
cvresults <- readRDS(here::here("output", "cvresults_ModelA_chunk1.rds")) %>% bind_rows(readRDS(here::here("output", 
    "cvresults_ModelA_chunk2.rds"))) %>% bind_rows(readRDS(here::here("output", "cvresults_ModelA_chunk3.rds"))) %>% 
    mutate(Model = "A") %>% bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk1.rds")) %>% 
    bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk2.rds"))) %>% 
    bind_rows(readRDS(here::here("output", "cvresults_ModelADE_chunk3.rds"))) %>% 
    mutate(Model = "ADE"))
cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGEBV, Model) %>% ggplot(., 
    aes(x = Model, y = accGEBV, fill = VersionOfBLUPs)) + geom_boxplot() + theme_bw() + 
    facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + labs(title = "GEBV: Compare 3-stage and 2-stage prediction pipelines")

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c97b21b wolfemd 2020-11-27
9194239 wolfemd 2020-09-21
cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGETGV, Model) %>% ggplot(., 
    aes(x = Model, y = accGETGV, fill = VersionOfBLUPs)) + geom_boxplot() + theme_bw() + 
    facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + labs(title = "GETGV: Compare 3-stage and 2-stage prediction pipelines")

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c97b21b wolfemd 2020-11-27
cvresults %>% select(Trait, Model, repeats, id, VersionOfBLUPs, accGEBV) %>% spread(VersionOfBLUPs, 
    accGEBV) %>% mutate(diffAcc = blups2stage - blups3stage) %>% ggplot(., aes(x = Model, 
    y = diffAcc, fill = Trait)) + geom_hline(yintercept = 0, color = "darkred") + 
    geom_boxplot() + theme_bw() + facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + 
    labs(y = "Accuracy Difference (2-stage minus 3-stage)", title = "GEBV")

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cvresults %>% select(Trait, Model, repeats, id, VersionOfBLUPs, accGETGV) %>% spread(VersionOfBLUPs, 
    accGETGV) %>% mutate(diffAcc = blups2stage - blups3stage) %>% ggplot(., aes(x = Model, 
    y = diffAcc, fill = Trait)) + geom_hline(yintercept = 0, color = "darkred") + 
    geom_boxplot() + theme_bw() + facet_wrap(~Trait, scales = "free") + scale_fill_viridis_d() + 
    labs(y = "Accuracy Difference (2-stage minus 3-stage)", title = "GETGV")

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cvresults %>% filter(VersionOfBLUPs == "blups2stage") %>% select(Trait, repeats, 
    id, VersionOfBLUPs, accGETGV, Model) %>% ggplot(., aes(x = Trait, y = accGETGV, 
    fill = Model)) + geom_boxplot(color = "gray60", notch = T) + theme_bw() + facet_wrap(~Trait, 
    scales = "free") + scale_fill_viridis_d() + labs(title = "Compare accuracy: models A vs. ADE")

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b9bb6f8 wolfemd 2020-12-03

Genetic Gain

September 2020

library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"), 
    stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")
iita_gebvs %>% select(GID, GeneticGroup, any_of(traits)) %>% pivot_longer(cols = any_of(traits), 
    names_to = "Trait", values_to = "GEBV") %>% group_by(Trait, GeneticGroup) %>% 
    summarize(meanGEBV = mean(GEBV), stdErr = sd(GEBV)/sqrt(n()), upperSE = meanGEBV + 
        stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = GeneticGroup, 
    y = meanGEBV, fill = Trait)) + geom_bar(stat = "identity", color = "gray60", 
    size = 1.25) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), color = "gray60", 
    size = 1.25) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + theme_bw() + 
    geom_hline(yintercept = 0, size = 1.15, color = "black") + theme(axis.text.x = element_text(face = "bold", 
    angle = 0, size = 12), axis.title.y = element_text(face = "bold", size = 14), 
    legend.position = "none", strip.background.x = element_blank(), strip.text = element_text(face = "bold", 
        size = 14)) + scale_fill_viridis_d() + labs(x = NULL, y = "Mean GEBVs")

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Rate of gain

List of trials from 2020 to Prasad and Ismail… should I download fresh data?

dbdata <- readRDS(here::here("output", "IITA_CleanedTrialData.rds"))
trialsHarvested2019to2020 <- dbdata %>% filter(studyYear >= 2019) %>% group_by(studyYear, 
    locationName, studyName, plantingDate, harvestDate) %>% summarize(Nhav = sum(!is.na(NOHAV)))
trialsHarvested2019to2020 %>% write.csv(., file = here::here("output", "trials_uploaded_by_Nharvested_15Sep2020.csv"), 
    row.names = F)

September 2020 GEBV

library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"), 
    stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")

ggcycletime <- readxl::read_xls(here::here("data", "PedigreeGeneticGainCycleTime_aafolabi_01122020.xls"))
table(ggcycletime$Accession %in% iita_gebvs$GID)

FALSE 
  807 

Need germplasmName field from raw trial data to match GEBV and cycle time

library(tidyverse)
library(magrittr)
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected.rds"))
iita_gebvs %<>% left_join(dbdata %>% select(-MaxNOHAV) %>% unnest(TrialData) %>% 
    distinct(germplasmName, GID)) %>% group_by(GID) %>% slice(1) %>% ungroup()
table(ggcycletime$Accession %in% iita_gebvs$germplasmName)

FALSE  TRUE 
  193   614 
table(ggcycletime$Year_Accession)

               1973                1974                1975                1976 
                  5                   5                   1                   2 
               1977                1978                1981                1982 
                  3                   2                   2                   4 
               1983                1984                1985                1987 
                  1                   4                   2                   2 
               1988                1989                1990                1991 
                  6                   3                   2                  20 
               1992                1993                1994                1995 
                 25                  20                  17                  37 
               1996                1997                1998                1999 
                 55                  43                  29                  33 
               2000                2001                2002                2003 
                 35                  74                  25                  34 
               2005                2006                2007                2008 
                 24                  21                  59                  39 
               2009                2010                2011                2012 
                 37                  22                   1                   4 
               2013                2014                2015                2016 
                 19                  19                  26                  10 
     BENIN BEN86052 CARICASS II LIBERIA    CARICASS LIBERIA                CIAT 
                  1                   1                   1                   1 
      COTE D'IVOIRE          EastAfrica         GHANA ANKRA          KENYA GUZO 
                  1                  15                   1                   1 
  KIROBA EastAfrica               NRCRI     RWANDA CREOLINA               SLARI 
                  1                   6                   1                   1 
      TOGO TOMA 326       TOGO TOMA 393    ZAMBIA Bangweulu 
                  1                   1                   1 
iita_gebvs %<>% left_join(., ggcycletime %>% rename(germplasmName = Accession) %>% 
    mutate(Year_Accession = as.numeric(Year_Accession)))
iita_gebvs %<>% mutate(Year_Accession = case_when(grepl("2013_|TMS13", germplasmName) ~ 
    2013, grepl("TMS14", germplasmName) ~ 2014, grepl("TMS15", germplasmName) ~ 2015, 
    grepl("TMS18", germplasmName) ~ 2018, !grepl("2013_|TMS13|TMS14|TMS15|TMS18", 
        germplasmName) ~ Year_Accession))
iita_gebvs %>% ggplot(., aes(x = TCHART, y = BCHROMO)) + geom_hex() + theme_bw() + 
    facet_wrap(~GeneticGroup, nrow = 1) + theme(legend.position = "none")

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# iita_gebvs %>% select(germplasmName,GeneticGroup,Year_Accession,any_of(traits))
# %>% pivot_longer(cols=any_of(traits),names_to = 'Trait',values_to = 'GEBV') %>%
# ggplot(.,aes(x=Year_Accession,y=GEBV,color=GeneticGroup)) + geom_point() +
# facet_wrap(~Trait,scales='free_y', ncol=1) + theme_bw() + #
# geom_hline(yintercept = 0, size=1.15, color='black') + theme(axis.text.x =
# element_text(face = 'bold',angle = 0, size=12), axis.title.y =
# element_text(face = 'bold',size=14), #legend.position = 'none',
# strip.background.x = element_blank(), strip.text =
# element_text(face='bold',size=14)) + scale_color_viridis_d()
# labs(x=NULL,y='Mean GEBVs')
iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(traits)) %>% 
    mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", "PreGS")) %>% pivot_longer(cols = any_of(traits), 
    names_to = "Trait", values_to = "GEBV") %>% group_by(Trait, GeneticGroup, Year_Accession) %>% 
    summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()), 
        upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession, 
    y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) + 
    geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), 
    color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + 
    theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14), 
    axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(), 
    strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()

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iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(c(traits, 
    "TCHART", "BCHROMO"))) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, 
    "GS", "PreGS")) %>% pivot_longer(cols = any_of(c(traits, "TCHART", "BCHROMO")), 
    names_to = "Trait", values_to = "GEBV") %>% mutate(Trait = factor(Trait, c(traits, 
    "TCHART", "BCHROMO"))) %>% group_by(Trait, GeneticGroup, Year_Accession) %>% 
    summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()), 
        upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession, 
    y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) + 
    geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), 
    color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + 
    theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14), 
    axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(), 
    strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()

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iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(c(traits, 
    "TCHART", "BCHROMO"))) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, 
    "GS", "PreGS")) %>% filter(BCHROMO < 5, TCHART < 0.5) %>% pivot_longer(cols = any_of(c(traits, 
    "TCHART", "BCHROMO")), names_to = "Trait", values_to = "GEBV") %>% mutate(Trait = factor(Trait, 
    c(traits, "TCHART", "BCHROMO"))) %>% group_by(Trait, GeneticGroup, Year_Accession) %>% 
    summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()), 
        upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession, 
    y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) + 
    geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), 
    color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + 
    theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14), 
    axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(), 
    strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()

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December 2020 GETGV

library(tidyverse); library(magrittr);
iita_getgvs<-read.csv(here::here("output","GETGV_IITA_ModelADE_twostage_IITA_2020Dec03.csv"), stringsAsFactors = F)
#traits<-c("DM","logFYLD","logTOPYLD","MCMDS")
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI",
          "logDYLD", # <-- logDYLD now included. 
          "logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")

ggcycletime<-readxl::read_xls(here::here("data","PedigreeGeneticGainCycleTime_aafolabi_01122020.xls"))
# table(ggcycletime$Accession %in% iita_getgvs$GID)
# FALSE 
#   807 
# Need germplasmName field from raw trial data to match GEBV and cycle time
dbdata<-readRDS(here::here("output","IITA_ExptDesignsDetected_2020Dec03.rds"))
iita_getgvs %<>% 
  left_join(dbdata %>% 
  select(-MaxNOHAV) %>% unnest(TrialData) %>% 
  distinct(germplasmName,GID)) %>% 
  group_by(GID) %>% 
  slice(1) %>% 
  ungroup()
rm(dbdata)
# table(ggcycletime$Accession %in% iita_getgvs$germplasmName)
# FALSE  TRUE 
#   193   614 

# table(ggcycletime$Year_Accession)
iita_getgvs %<>% 
  left_join(.,ggcycletime %>% 
              rename(germplasmName=Accession) %>% 
              mutate(Year_Accession=as.numeric(Year_Accession)))
iita_getgvs %<>% 
  mutate(Year_Accession=case_when(grepl("2013_|TMS13",germplasmName)~2013,
                                  grepl("TMS14",germplasmName)~2014,
                                  grepl("TMS15",germplasmName)~2015,
                                  grepl("TMS18",germplasmName)~2018,
                                  !grepl("2013_|TMS13|TMS14|TMS15|TMS18",germplasmName)~Year_Accession)) 

write.csv(iita_getgvs, file = here::here("output","GETGV_IITA_ModelADE_twostage_IITA_2020Dec03_withAccessionYear.csv"), row.names = F)

What is yellow?

iita_getgvs %>% ggplot(., aes(x = TCHART, y = BCHROMO)) + geom_hex() + theme_bw() + 
    facet_wrap(~GeneticGroup, nrow = 1) + theme(legend.position = "none") + geom_vline(xintercept = 0.5) + 
    geom_hline(yintercept = 5) + labs(title = "Arbitrary suggested cut-offs for `white` rooted GETGVs", 
    subtitle = "horiz. and vert. lines")

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Mean GETGV-by-Year

mean_getgvs <- iita_getgvs %>% select(germplasmName, GeneticGroup, Year_Accession, 
    any_of(traits)) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", 
    "PreGS")) %>% pivot_longer(cols = any_of(traits), names_to = "Trait", values_to = "GETGV") %>% 
    group_by(Trait, GeneticGroup, Year_Accession) %>% summarize(meanGETGV = mean(GETGV), 
    Nclones = n(), stdErr = sd(GETGV)/sqrt(n()), upperSE = meanGETGV + stdErr, lowerSE = meanGETGV - 
        stdErr) %>% ungroup()

write.csv(mean_getgvs, file = here::here("output", "meanGETGVbyYear_IITA_2020Dec03.csv"), 
    row.names = F)
# traits<-c('logDYLD','logFYLD','MCMDS','DM','TCHART','BCHROMO')
traits <- c("logDYLD", "logFYLD", "MCMDS", "DM", "TCHART", "BCHROMO", "PLTHT", "BRNHT1", 
    "BRLVLS", "HI", "logTOPYLD", "logRTNO", "LCHROMO", "ACHROMO")

Plot all germplasm vs. year

mean_getgvs %>% mutate(Trait = factor(Trait, traits)) %>% ggplot(., aes(x = Year_Accession, 
    y = meanGETGV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) + 
    geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), 
    color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 2) + 
    theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14), 
    axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(), 
    strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()

Version Author Date
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Plot “white” germplasm vs. year

mean_getgvs_whiteroots <- iita_getgvs %>% select(germplasmName, GeneticGroup, Year_Accession, 
    any_of(traits)) %>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", 
    "PreGS")) %>% filter(BCHROMO <= 5, TCHART <= 0.5) %>% pivot_longer(cols = any_of(traits), 
    names_to = "Trait", values_to = "GETGV") %>% group_by(Trait, GeneticGroup, Year_Accession) %>% 
    summarize(meanGETGV = mean(GETGV), Nclones = n(), stdErr = sd(GETGV)/sqrt(n()), 
        upperSE = meanGETGV + stdErr, lowerSE = meanGETGV - stdErr) %>% ungroup()
mean_getgvs_whiteroots %>% mutate(Trait = factor(Trait, traits)) %>% ggplot(., aes(x = Year_Accession, 
    y = meanGETGV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) + 
    geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), 
    color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 2) + 
    theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14), 
    axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(), 
    strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d() + 
    labs(title = "Mean GETGV vs. Accession Year of White-rooted Clones Only")

Version Author Date
b9bb6f8 wolfemd 2020-12-03

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] magrittr_2.0.1  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
 [9] ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        lattice_0.20-41   lubridate_1.7.9.2 here_1.0.0       
 [5] ps_1.4.0          assertthat_0.2.1  rprojroot_2.0.2   digest_0.6.27    
 [9] R6_2.5.0          cellranger_1.1.0  backports_1.2.0   reprex_0.3.0     
[13] evaluate_0.14     httr_1.4.2        pillar_1.4.7      rlang_0.4.9      
[17] readxl_1.3.1      rstudioapi_0.13   whisker_0.4       hexbin_1.28.1    
[21] Matrix_1.2-18     rmarkdown_2.5     splines_4.0.2     labeling_0.4.2   
[25] munsell_0.5.0     broom_0.7.2       compiler_4.0.2    httpuv_1.5.4     
[29] modelr_0.1.8      xfun_0.19         pkgconfig_2.0.3   mgcv_1.8-33      
[33] htmltools_0.5.0   tidyselect_1.1.0  fansi_0.4.1       viridisLite_0.3.0
[37] crayon_1.3.4      dbplyr_2.0.0      withr_2.3.0       later_1.1.0.1    
[41] grid_4.0.2        nlme_3.1-150      jsonlite_1.7.1    gtable_0.3.0     
[45] lifecycle_0.2.0   DBI_1.1.0         git2r_0.27.1      formatR_1.7      
[49] scales_1.1.1      cli_2.2.0         stringi_1.5.3     farver_2.0.3     
[53] fs_1.5.0          promises_1.1.1    xml2_1.3.2        ellipsis_0.3.1   
[57] generics_0.1.0    vctrs_0.3.5       tools_4.0.2       glue_1.4.2       
[61] hms_0.5.3         yaml_2.2.1        colorspace_2.0-0  rvest_0.3.6      
[65] knitr_1.30        haven_2.3.1