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Last updated: 2020-12-04

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Knit directory: IITA_2020GS/

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File	Version	Author	Date	Message
Rmd	063cb62	wolfemd	2020-12-04	Making the tables not suck.
html	8ae6386	wolfemd	2020-12-04	Build site.
Rmd	698c5fb	wolfemd	2020-12-04	Making the tables not suck.
html	4ac9fe3	wolfemd	2020-12-04	Build site.
Rmd	79b6430	wolfemd	2020-12-04	Update the analysis of rate-of-gain. Include regressions and output
html	7bae38d	wolfemd	2020-12-03	Build site.
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:

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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")

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
b9bb6f8	wolfemd	2020-12-03
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")

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
b9bb6f8	wolfemd	2020-12-03
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")

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
b9bb6f8	wolfemd	2020-12-03

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")

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
b9bb6f8	wolfemd	2020-12-03

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")

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
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")

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
b9bb6f8	wolfemd	2020-12-03

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)

GETGV vs. “Accession Year”

Start by merging the “accession year” variable with the GETGVs.

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

Key output is a file output/GETGV_IITA_ModelADE_twostage_IITA_2020Dec03_withAccessionYear.csv for use in downstream analyses.

What is yellow?

rm(list = ls())
library(tidyverse)
library(magrittr)
iita_getgvs <- read.csv(here::here("output", "GETGV_IITA_ModelADE_twostage_IITA_2020Dec03_withAccessionYear.csv"), 
    stringsAsFactors = F)

traits <- c("logDYLD", "logFYLD", "MCMDS", "DM", "BCHROMO", "BRLVLS", "HI", "logTOPYLD")

Plot B-value and decide on a threshold for removing “yellow” clones from the analysis.

# 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')

iita_getgvs %>% 
  ggplot(.,aes(x=BCHROMO)) + geom_histogram() +
  theme_bw() + #facet_wrap(~GeneticGroup, nrow=1) + 
  theme(legend.position = 'none') + 
  geom_vline(xintercept = 1, color='darkred') +  # geom_hline(yintercept = 5) + 
  labs(title="Histogram of GETGV for chromometer B-value",
       subtitle = "Cut-offs for `white` roots == 1")

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
4ac9fe3	wolfemd	2020-12-04

Subset years

Remove clones between 2005 and 2012.

Declare the “eras” as PreGS<2012 and GS>=2013.

iita_getgvs %<>% filter(Year_Accession > 2012 | Year_Accession < 2005)

iita_getgvs %<>% mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", "PreGS"))

Analysis by raw GETGVs

Number of clones for each “era” that

iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% count(Nclone = GeneticGroup)

  Nclone    n
1     GS 7800
2  PreGS  449

Number of white root clones (BCHROMO<=1).

iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% filter(BCHROMO <= 
    1) %>% count(NwhiteRoot = GeneticGroup)

  NwhiteRoot    n
1         GS 5568
2      PreGS  346

Group by Era (Genetic Group) and fit a simple linear regression for each trait, i.e. lm(GETGV ~ Year_Accession).

Fit model to “all clones” and then to “white root clones only”.

model_rawgetgvs <- iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% 
    mutate(Dataset = "AllGermplasm") %>% bind_rows(iita_getgvs %>% select(GeneticGroup, 
    GID, Year_Accession, all_of(traits)) %>% filter(BCHROMO <= 1) %>% mutate(Dataset = "WhiteRootClones")) %>% 
    pivot_longer(cols = all_of(traits), names_to = "Trait", values_to = "GETGV") %>% 
    nest(data = c(GID, Year_Accession, GETGV)) %>% mutate(model = map(data, ~lm(formula = "GETGV~Year_Accession", 
    data = .)))

Extract the model effects, etc.

model_rawgetgvs %<>% mutate(out = map(model, ~broom::glance(.))) %>% unnest(out)
model_rawgetgvs %<>% mutate(out = map(model, ~broom::tidy(.)))
model_rawgetgvs %<>% mutate(out = map(out, ~select(., term, estimate) %>% spread(term, 
    estimate))) %>% unnest(out) %>% rename(InterceptEst = `(Intercept)`, YearAccessionEst = Year_Accession) %>% 
    select(Dataset, GeneticGroup, Trait, r.squared, nobs, InterceptEst, YearAccessionEst)

Basic summary of linear models

model_rawgetgvs %>% rmarkdown::paged_table()

Compare slope estimates between “eras”

model_rawgetgvs %>% select(Dataset, GeneticGroup, Trait, YearAccessionEst) %>% spread(GeneticGroup, 
    YearAccessionEst) %>% rmarkdown::paged_table()

Add some summary of the raw data that went into the GETGV analyzed above.

# summarize the raw plot data
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected_2020Dec03.rds")) %>% 
    dplyr::select(-MaxNOHAV) %>% unnest(TrialData) %>% dplyr::select(programName, 
    locationName, studyYear, TrialType, studyName, CompleteBlocks, IncompleteBlocks, 
    yearInLoc, trialInLocYr, repInTrial, blockInRep, observationUnitDbId, germplasmName, 
    FullSampleName, GID, all_of(traits), PropNOHAV) %>% mutate(IncompleteBlocks = ifelse(IncompleteBlocks == 
    TRUE, "Yes", "No"), CompleteBlocks = ifelse(CompleteBlocks == TRUE, "Yes", "No")) %>% 
    pivot_longer(cols = all_of(traits), names_to = "Trait", values_to = "Value") %>% 
    filter(!is.na(Value), !is.na(GID)) %>% nest(MultiTrialTraitData = c(-Trait))

trainingdata_summary <- dbdata %>% mutate(NplotsTotal = map_dbl(MultiTrialTraitData, 
    nrow), nplot = map(MultiTrialTraitData, ~count(., TrialType) %>% mutate(TrialType = paste0("Nplots_", 
    TrialType)) %>% spread(TrialType, n) %>% select(any_of(paste0("Nplots_", c("CrossingBlock", 
    "GeneticGain", "CET", "ExpCET", "PYT", "AYT", "UYT", "NCRP")))))) %>% unnest(nplot) %>% 
    select(-MultiTrialTraitData) %>% # and add a summary of the BLUPs that result which were then later used for
# prediction
left_join(readRDS(file = here::here("output", "iita_blupsForModelTraining_twostage_asreml_2020Dec03.rds")) %>% 
    filter(Trait %in% traits) %>% mutate(NclonesWithBLUPs = map_dbl(blups, nrow)) %>% 
    select(Trait, NclonesWithBLUPs, Vg, Ve, H2))

Print a summary of the raw plots and resulting BLUPs that went into the GETGV .

trainingdata_summary %>% rmarkdown::paged_table()

Write model summaries to disk: output/model_rawgetgvs_vs_year.csv.

Write training data summary to disk: output/training_data_summary.csv

write.csv(trainingdata_summary, file = here::here("output", "training_data_summary.csv"), 
    row.names = F)
write.csv(model_rawgetgvs, file = here::here("output", "model_rawgetgvs_vs_year.csv"), 
    row.names = F)

Plot all germplasm vs. year

iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% pivot_longer(cols = all_of(traits), 
    names_to = "Trait", values_to = "GETGV") %>% mutate(Trait = factor(Trait, traits)) %>% 
    ggplot(., aes(x = Year_Accession, y = GETGV, color = GeneticGroup)) + geom_point(size = 1.25) + 
    geom_smooth(method = lm, se = TRUE, size = 1.5) + 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 = "Regression of raw GETGV vs. Year_Accession by 'era' [GS vs. PreGS]", 
        subtitle = "All Germplasm")

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
4ac9fe3	wolfemd	2020-12-04

iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% filter(BCHROMO <= 
    1) %>% pivot_longer(cols = all_of(traits), names_to = "Trait", values_to = "GETGV") %>% 
    mutate(Trait = factor(Trait, traits)) %>% ggplot(., aes(x = Year_Accession, y = GETGV, 
    color = GeneticGroup)) + geom_point(size = 1.25) + geom_smooth(method = lm, se = TRUE, 
    size = 1.5) + 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 = "Regression of raw GETGV vs. Year_Accession by 'era' [GS vs. PreGS]", 
    subtitle = "White Root Germplasm (BCHROMO<=1)")

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
4ac9fe3	wolfemd	2020-12-04
7bae38d	wolfemd	2020-12-03
b9bb6f8	wolfemd	2020-12-03

Analysis by mean GETGV-by-Year

I recommend using the analysis and maybe also the plots above.

For completeness, below is an analysis and plots using the meanGETGV-by-year.

Compute mean and std. error by Dataset (“all germplasm” vs. “white root clones only”) and GeneticGroup (“GS” vs. “PreGS”).

mean_getgvs <- iita_getgvs %>% select(GeneticGroup, GID, Year_Accession, all_of(traits)) %>% 
    mutate(Dataset = "AllGermplasm") %>% bind_rows(iita_getgvs %>% select(GeneticGroup, 
    GID, Year_Accession, all_of(traits)) %>% filter(BCHROMO <= 1) %>% mutate(Dataset = "WhiteRootClones")) %>% 
    select(Dataset, GeneticGroup, GID, Year_Accession, all_of(traits)) %>% pivot_longer(cols = all_of(traits), 
    names_to = "Trait", values_to = "GETGV") %>% group_by(Dataset, 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)

Group by Era (Genetic Group) and fit a simple linear regression for each trait, i.e. lm(GETGV ~ Year_Accession).

model_meangetgvs <- mean_getgvs %>% nest(data = c(-Dataset, -Trait, -GeneticGroup)) %>% 
    mutate(model = map(data, ~lm(formula = "meanGETGV~Year_Accession", data = .)))

Extract the model effects, etc.

model_meangetgvs %<>% mutate(out = map(model, ~broom::glance(.))) %>% unnest(out) %>% 
    mutate(out = map(model, ~broom::tidy(.))) %>% mutate(out = map(out, ~select(., 
    term, estimate) %>% spread(term, estimate))) %>% unnest(out) %>% rename(InterceptEst = `(Intercept)`, 
    YearAccessionEst = Year_Accession) %>% select(Dataset, GeneticGroup, Trait, r.squared, 
    nobs, InterceptEst, YearAccessionEst)

Basic summary of linear models

model_meangetgvs %>% rmarkdown::paged_table()

Compare slope estimates between “eras”

model_meangetgvs %>% select(Dataset, GeneticGroup, Trait, YearAccessionEst) %>% spread(GeneticGroup, 
    YearAccessionEst) %>% rmarkdown::paged_table()

Save these estimates also to disk at: output/model_meangetgvs_vs_year.csv

write.csv(model_meangetgvs, file = here::here("output", "model_meangetgvs_vs_year.csv"), 
    row.names = F)

Plot all germplasm vs. year

mean_getgvs %>% filter(Dataset == "AllGermplasm") %>% 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 = "meanGETGV vs. Year_Accession - All Germplasm", subtitle = "Mean across all clones within Year_Accession")

Past versions of unnamed-chunk-31-1.png

Version	Author	Date
4ac9fe3	wolfemd	2020-12-04

Plot “white” germplasm vs. year

mean_getgvs %>% filter(Dataset == "WhiteRootClones") %>% 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 = "meanGETGV vs. Year_Accession - White rooted germplasm", subtitle = "Mean across all clones within Year_Accession")

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
4ac9fe3	wolfemd	2020-12-04

Session information

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       Matrix_1.2-18    
[21] rmarkdown_2.5     splines_4.0.2     labeling_0.4.2    munsell_0.5.0    
[25] broom_0.7.2       compiler_4.0.2    httpuv_1.5.4      modelr_0.1.8     
[29] xfun_0.19         pkgconfig_2.0.3   mgcv_1.8-33       htmltools_0.5.0  
[33] tidyselect_1.1.0  fansi_0.4.1       viridisLite_0.3.0 crayon_1.3.4     
[37] dbplyr_2.0.0      withr_2.3.0       later_1.1.0.1     grid_4.0.2       
[41] nlme_3.1-150      jsonlite_1.7.1    gtable_0.3.0      lifecycle_0.2.0  
[45] DBI_1.1.0         git2r_0.27.1      formatR_1.7       scales_1.1.1     
[49] cli_2.2.0         stringi_1.5.3     farver_2.0.3      fs_1.5.0         
[53] promises_1.1.1    xml2_1.3.2        ellipsis_0.3.1    generics_0.1.0   
[57] vctrs_0.3.5       tools_4.0.2       glue_1.4.2        hms_0.5.3        
[61] yaml_2.2.1        colorspace_2.0-0  rvest_0.3.6       knitr_1.30       
[65] haven_2.3.1