| title | output | editor_options | ||||||
|---|---|---|---|---|---|---|---|---|
Population Differentiation Analysis |
|
|
library('tidyverse')
library('poppr')
library('ggcompoplot')load(file.path(PROJHOME, "data", "sclerotinia_16_loci.rda"))
setPop(dat11) <- ~Host/Source/Region/Year
dat11cc <- clonecorrect(dat11, ~Host/Source/Region/Year, keep = 1:4)
dat11cc##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 165 original multilocus genotypes
## 318 haploid individuals
## 11 codominant loci
##
## Population information:
##
## 5 strata - MCG, Region, Source, Year, Host
## 128 populations defined -
## GH_unk_NE_2003, GH_unk_NY_2003, G122_wmn_MN_2003, ..., unk_pmc_ND_2010, unk_wlc_ND_2010, unk_flds_France_2012
# Asserting that nothing messed up with the metadata.
stopifnot(identical(indNames(dat11cc), other(dat11cc)$meta$Isolate))
dat11.bruvo <- bruvo.dist(dat11, replen = other(dat11)$REPLEN)
dat11cc.bruvo <- bruvo.dist(dat11cc, replen = other(dat11)$REPLEN)set.seed(2017-08-02)
ptab <- dat11cc %>% setPop(~Region) %>% poppr(sample = 999, total = FALSE)Warning: values for NY, WI, ID could not be plotted.
ptab %>%
arrange(desc(N), desc(MLG)) %>%
write_csv(file.path(PROJHOME, "results/tables/genotype_diversity_table_region.csv"), col_names = TRUE) %>%
mutate(Ia = case_when(
!is.finite(p.Ia) ~ "-",
p.Ia == 0.001 ~ paste0(round(Ia, 2), "*"),
p.Ia <= 0.01 ~ paste0(round(Ia, 2), "-"),
p.Ia <= 0.05 ~ paste0(round(Ia, 2), "~"),
TRUE ~ as.character(round(Ia, 2))
)) %>%
mutate(rbarD = case_when(
!is.finite(p.rD) ~ "-",
p.rD == 0.001 ~ paste0(round(rbarD, 2), "*"),
p.rD <= 0.01 ~ paste0(round(rbarD, 2), "-"),
p.rD <= 0.05 ~ paste0(round(rbarD, 2), "~"),
TRUE ~ as.character(round(rbarD, 2))
)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
mutate(N = paste(N, MLG, sep = " (") %>% paste0(")") %>% formatC()) %>%
mutate(eMLG = paste(eMLG, SE, sep = " (") %>% paste0(")") %>% formatC()) %>%
rename(`*h*` = Hexp) %>%
rename(`$E_5$` = E.5) %>%
rename(`$I_A$` = Ia) %>%
rename(`$\\lambda$` = lambda) %>%
rename(`$\\bar{r}_d$` = rbarD) %>%
select(-File, -MLG, -SE, -p.rD, -p.Ia) %>%
huxtable::as_huxtable(add_colnames = TRUE) %>%
huxtable::set_number_format(huxtable::everywhere, 4:5, 1) %>%
huxtable::set_align(huxtable::everywhere, -1, "right") %>%
huxtable::set_col_width(c(1.1, 0.9, 1.3, 0.5, 0.5, 1.2, 0.7, 0.6, 0.6, 0.8)) %>%
huxtable::print_md(max_width = 80)--------------------------------------------------------------------------
Pop N eMLG H G $\lambda$ $E_5$ *h* $I_A$ $\bar{
r}_d$
---------- ------- ---------- --- ---- --------- ----- ----- ----- -------
WA 58 (56) 9.95 4.0 54.3 0.98 0.98 0.60 0.65* 0.07*
(0.23)
MI 58 (43) 9.3 (0.79) 3.6 29.0 0.97 0.78 0.54 1.25* 0.14*
ND 41 (35) 9.44 3.5 25.9 0.96 0.82 0.54 1* 0.1*
(0.73)
NE 37 (28) 8.93 3.2 17.8 0.94 0.75 0.55 2.22* 0.25*
(0.94)
CO 34 (28) 9.46 3.3 24.1 0.96 0.92 0.56 2.33* 0.27*
(0.67)
France 21 (14) 8.5 (0.85) 2.6 12.6 0.92 0.95 0.48 0.94* 0.11*
CA 18 (15) 9.12 2.7 13.5 0.93 0.94 0.51 0.28~ 0.03~
(0.72)
OR 17 (13) 8.52 2.5 10.7 0.91 0.89 0.47 0.92* 0.1*
(0.85)
Mexico 15 (9) 7.1 (0.85) 2.1 7.3 0.86 0.89 0.28 2.57* 0.37*
MN 9 (7) 7 (0) 1.9 6.2 0.84 0.93 0.47 1.34* 0.19*
Australia 6 (6) 6 (0) 1.8 6.0 0.83 1.00 0.48 0.70 0.12
WI 2 (2) 2 (0) 0.7 2.0 0.50 1.00 0.27 - -
NY 1 (1) 1 (0) 0.0 1.0 0.00 NaN NaN - -
ID 1 (1) 1 (0) 0.0 1.0 0.00 NaN NaN - -
--------------------------------------------------------------------------
mtab <- mlg.table(dat11, ~Region, background = TRUE)
First, we can conduct an AMOVA analysis across all populations, with respect to year.
(res <- poppr.amova(dat11cc, ~Region/Host/Year, dist = dat11cc.bruvo))##
## No missing values detected.
## $call
## ade4::amova(samples = xtab, distances = xdist, structures = xstruct)
##
## $results
## Df Sum Sq Mean Sq
## Between Region 13 10.19459 0.7841991
## Between Host Within Region 61 14.01673 0.2297824
## Between samples Within Host 36 10.13126 0.2814238
## Within samples 207 35.25497 0.1703139
## Total 317 69.59754 0.2195506
##
## $componentsofcovariance
## Sigma %
## Variations Between Region 0.024231812 10.879560
## Variations Between Host Within Region -0.008652608 -3.884834
## Variations Between samples Within Host 0.036834798 16.538027
## Variations Within samples 0.170313877 76.467247
## Total variations 0.222727878 100.000000
##
## $statphi
## Phi
## Phi-samples-total 0.23532753
## Phi-samples-Host 0.17781817
## Phi-Host-Region -0.04359083
## Phi-Region-total 0.10879560
set.seed(999)
(res.p <- randtest(res, nrepet = 999))## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: randtest.amova(xtest = res, nrepet = 999)
##
## Number of tests: 4
##
## Adjustment method for multiple comparisons: none
## Permutation number: 999
## Test Obs Std.Obs Alter Pvalue
## 1 Variations within samples 0.170313877 -16.0843846 less 0.001
## 2 Variations between samples 0.036834798 7.0114136 greater 0.001
## 3 Variations between Host -0.008652608 -0.8984575 greater 0.818
## 4 Variations between Region 0.024231812 7.0499262 greater 0.001
This can tell us how well our regions separate
set.seed(2017-07-05)
region.xval <- xvalDapc(tab(dat11cc), strata(dat11cc)$Region, n.pca = 15:25,
n.rep = 100)## Warning in xvalDapc.matrix(tab(dat11cc), strata(dat11cc)$Region, n.pca =
## 15:25, : 2 groups have only 1 member: these groups cannot be represented in
## both training and validation sets.
region.dapc <- region.xval$DAPCregion.dapc## #################################################
## # Discriminant Analysis of Principal Components #
## #################################################
## class: dapc
## $call: dapc.data.frame(x = as.data.frame(x), grp = ..1, n.pca = ..2,
## n.da = ..3)
##
## $n.pca: 21 first PCs of PCA used
## $n.da: 13 discriminant functions saved
## $var (proportion of conserved variance): 0.881
##
## $eig (eigenvalues): 104.8 17.77 9.831 9.152 6.637 ...
##
## vector length content
## 1 $eig 13 eigenvalues
## 2 $grp 318 prior group assignment
## 3 $prior 14 prior group probabilities
## 4 $assign 318 posterior group assignment
## 5 $pca.cent 69 centring vector of PCA
## 6 $pca.norm 69 scaling vector of PCA
## 7 $pca.eig 58 eigenvalues of PCA
##
## data.frame nrow ncol
## 1 $tab 318 21
## 2 $means 14 21
## 3 $loadings 21 13
## 4 $ind.coord 318 13
## 5 $grp.coord 14 13
## 6 $posterior 318 14
## 7 $pca.loadings 69 21
## 8 $var.contr 69 13
## content
## 1 retained PCs of PCA
## 2 group means
## 3 loadings of variables
## 4 coordinates of individuals (principal components)
## 5 coordinates of groups
## 6 posterior membership probabilities
## 7 PCA loadings of original variables
## 8 contribution of original variables
scatter(region.dapc,
scree.pca = TRUE,
bg = "grey95",
col = "black",
# col = rev(funky(nlevels(strata(dat11cc)$Region))),
pch = seq(nlevels(strata(dat11cc)$Region)),
legend = "true",
posi.leg = "topleft",
posi.pca = "topright"
)
ggcompoplot(region.dapc, setPop(dat11cc, ~Region), pal = rev(funky(nlevels(strata(dat11cc)$Region))), cols = 2)
LDS <- bind_cols(Population = region.dapc$grp, as.data.frame(region.dapc$ind.coord)) %>%
as_tibble()
LDS_pop <- LDS %>%
group_by(Population) %>%
summarize_all(mean) %>%
rename_all(function(x) gsub("LD", "mean", x))
LDS <- full_join(LDS, LDS_pop)## Joining, by = "Population"
LDS_PLOT <- ggplot(LDS, aes(x = LD1, y = LD2, color = Population)) +
geom_point(aes(fill = Population), alpha = 0.5, pch = 21, color = "black", size = 2) +
geom_segment(aes(x = mean1, y = mean2, xend = LD1, yend = LD2), alpha = 0.5) +
stat_ellipse(type = "norm", level = 0.66, alpha = 0.75) +
ggrepel::geom_label_repel(aes(x = mean1, y = mean2, label = Population),
data = LDS_pop, show.legend = FALSE, color = "black") +
theme_bw() +
# theme(aspect.ratio = 1/1.618) +
theme(legend.position = "bottom") +
theme(axis.text = element_blank()) +
theme(axis.title = element_blank()) +
theme(axis.ticks = element_blank()) +
viridis::scale_color_viridis(discrete = TRUE, direction = -1) +
viridis::scale_fill_viridis(discrete = TRUE, direction = -1) +
scale_y_continuous(breaks = 0) +
scale_x_continuous(breaks = 0) +
theme(panel.background = element_rect(fill = "grey95")) +
theme(panel.grid.major = element_line(color = "black"))
LDS_PLOT## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
LDS_PLOT + facet_wrap(~Population)## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
Another way to visualize this is to look at the probability of assignment by source pouplation.
region_summary <- region.dapc$posterior %>%
as.data.frame() %>%
as_tibble() %>%
add_column(Individual = indNames(dat11cc)) %>%
add_column(`Original Population` = as.character(strata(dat11cc)$Region)) %>%
group_by(`Original Population`) %>%
mutate(n = n()) %>%
gather(Population, Posterior, -Individual, -`Original Population`, -n) %>%
group_by(Population, `Original Population`, n) %>%
summarize(`Mean Assignment` = mean(Posterior)) %>%
arrange(`Mean Assignment`) %>%
mutate(Source = case_when(
Population == `Original Population` ~ "Original Population",
TRUE ~ "Other"
)) %>%
ungroup() %>%
arrange(desc(n)) %>%
mutate(`Original Population` = forcats::fct_inorder(`Original Population`)) %>%
mutate(Population = factor(Population, levels(`Original Population`))) %>%
mutate(`Original Population` = paste0(`Original Population`, " (", n, ")")) %>%
mutate(`Original Population` = forcats::fct_inorder(`Original Population`))
REGION_PLOT <- region_summary %>%
filter(n >= 10) %>%
ggplot(aes(x = Population, y = `Mean Assignment`)) +
geom_segment(aes(xend = Population, yend = 0)) +
geom_point(aes(fill = Source), pch = 21, size = 2) +
facet_wrap(~`Original Population`, ncol = 3, strip.position = "top") +
scale_fill_grey() +
# scale_x_discrete(position = "top") +
scale_y_continuous(limits = c(0, 1), minor_breaks = c(seq(0, 1, by = 0.125)), breaks = c(0, 0.5, 1), position = "right") +
theme_bw(base_size = 16, base_family = "Helvetica") +
theme(legend.position = "top") +
theme(aspect.ratio = 1/2) +
theme(axis.text = element_text(color = "black")) +
theme(axis.ticks.y = element_blank()) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
# theme(axis.text.x.top = element_text(angle = 90, vjust = 0.5, hjust = 0)) +
theme(panel.grid.major.x = element_line(linetype = 0, color = "grey50")) +
theme(panel.grid.major = element_line(colour = "grey20")) +
theme(panel.grid.minor = element_line(linetype = 3, colour = "grey50")) +
# theme(panel.spacing.y = unit(0, "line")) +
theme(strip.background = element_rect(color = NA, fill = "grey90")) +
theme(strip.text = element_text(face = "bold", hjust = 0.05)) +
theme(panel.border = element_blank()) +
xlab("Population Assignment") +
ylab("Mean Probability") +
labs(list(fill = "Source:"))
REGION_PLOT
LEG <- cowplot::get_legend(LDS_PLOT +
theme(legend.position = "right") +
theme(legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "lines")) +
theme(legend.text = element_text(size = 12)))## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
cowplot::plot_grid(LEG,
LDS_PLOT +
theme(legend.position = "none") +
theme(plot.margin = unit(c(0, 0, 0, 0), "lines")) +
theme(panel.border = element_rect(color = "grey50")) +
theme(aspect.ratio = 0.935),
REGION_PLOT +
theme(plot.margin = unit(c(0, 1, 0, 0), "lines")),
nrow = 1,
rel_widths = c(0.25, 0.75, 1),
label_size = 25,
label_fontfamily = "Helvetica",
labels = c(NA, "A", "B"),
align = "v",
axis = "t")## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Warning: Removed 1 rows containing missing values (geom_text).
cowplot::ggsave(filename = file.path(PROJHOME, "results/figures/publication/DAPC.pdf"),
width = 13,
height = 6
)## Warning: Removed 1 rows containing missing values (geom_text).
Since we saw MLGs crossing populations, we want to find out what happens when we remove these MLGs, run DAPC on the regions, and then predict where they came from.
minds <- mlg.crosspop(dat11, ~Region, indexreturn = TRUE)
dat11.nocross <- dat11[!mll(dat11) %in% minds]
dat11.nocross##
## This is a genclone object
## -------------------------
## Genotype information:
##
## 89 original multilocus genotypes
## 114 haploid individuals
## 11 codominant loci
##
## Population information:
##
## 5 strata - MCG, Region, Source, Year, Host
## 54 populations defined -
## GH_unk_NE_2003, Beryl_wmn_MI_2003, Bunsi_wmn_MI_2003, ..., unk_pmc_ND_2010, unk_wlc_ND_2010, unk_flds_France_2012
dat11.nc.dapc <- dapc(dat11.nocross, strata(dat11.nocross)$Region, n.pca = 11, n.da = 14)## Warning in lda.default(x, grouping, ...): group NY is empty
scatter(dat11.nc.dapc)
dat11.cross <- clonecorrect(dat11[mll(dat11) %in% minds], NA)
pred <- predict.dapc(dat11.nc.dapc, dat11.cross)
ggcompoplot(pred, setPop(dat11.cross, ~Region), cols = 2, pal = funky)
posterior <- as.data.frame(pred$posterior) %>%
rownames_to_column("Sample") %>%
bind_cols(data_frame(MLG = mll(dat11.cross))) %>%
gather(key = "population", value = "posterior", -Sample, -MLG) %>%
group_by(Sample) %>%
mutate(entropy = vegan::diversity(posterior)) %>%
mutate(label = ifelse(posterior == max(posterior), MLG, NA)) %>%
ungroup() %>%
mutate(bins = cut(entropy, quantile(entropy)))
ggplot(posterior, aes(x = population, y = posterior, color = entropy, group = Sample)) +
geom_line() +
ggrepel::geom_label_repel(aes(label = label)) +
viridis::scale_color_viridis() +
facet_wrap(~bins, ncol = 1)## Warning: Removed 912 rows containing missing values (geom_label_repel).
top5 <- table(mll(dat11)) %>%
sort(decreasing = TRUE) %>%
head(5) %>%
names() %>%
as.integer()
top5## [1] 25 163 65 140 66
center_point <- function(n){
fromto <- c(-1, 1)
if (n < 4){
fromto <- fromto/8
} else if (n < 10){
fromto <- fromto/4
} else {
fromto <- fromto/2
}
seq(from = fromto[1], to = fromto[2], length.out = n)
}
origins <- mlg.crosspop(dat11, ~Region, mlgsub = top5, quiet = TRUE, df = TRUE) %>%
mutate(MLG = gsub("MLG.", "", MLG)) %>%
mutate(population = match(as.character(Population), posterior$population %>% unique %>% sort)) %>%
# group_by(Population) %>%
# mutate(population = population + center_point(n())) %>%
I()
posterior %>%
filter(MLG %in% top5) %>%
ggplot(aes(x = population, y = posterior, color = factor(MLG), group = MLG)) +
geom_line() +
geom_vline(aes(xintercept = population), data = origins, lty = 2) +
scale_color_brewer(palette = "Dark2") +
facet_wrap(~MLG, ncol = 1) +
labs(list(
lty = "Populations discovered in",
color = "MLG"
))
scatter(dat11.nc.dapc)
Session Information
## Session info --------------------------------------------------------------------------------------
## setting value
## version R version 3.4.2 (2017-09-28)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz UTC
## date 2018-04-12
## Packages ------------------------------------------------------------------------------------------
## package * version date source
## ade4 * 1.7-8 2017-08-09 cran (@1.7-8)
## adegenet * 2.1.0 2017-10-12 cran (@2.1.0)
## ape 5.0 2017-10-30 cran (@5.0)
## assertr 2.0.2.2 2017-06-06 cran (@2.0.2.2)
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.2)
## base * 3.4.2 2018-03-01 local
## bindr 0.1 2016-11-13 CRAN (R 3.4.2)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.2)
## boot 1.3-20 2017-07-30 cran (@1.3-20)
## broom 0.4.3 2017-11-20 CRAN (R 3.4.2)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.2)
## cli 1.0.0 2017-11-05 CRAN (R 3.4.2)
## cluster 2.0.6 2017-03-16 CRAN (R 3.4.2)
## coda 0.19-1 2016-12-08 cran (@0.19-1)
## codetools 0.2-15 2016-10-05 CRAN (R 3.4.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.2)
## compiler 3.4.2 2018-03-01 local
## cowplot 0.9.1 2017-11-16 cran (@0.9.1)
## crayon 1.3.4 2017-09-16 CRAN (R 3.4.2)
## datasets * 3.4.2 2018-03-01 local
## deldir 0.1-14 2017-04-22 cran (@0.1-14)
## devtools 1.13.4 2017-11-09 CRAN (R 3.4.2)
## digest 0.6.12 2017-01-27 CRAN (R 3.4.2)
## dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.2)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.4.2)
## expm 0.999-2 2017-03-29 cran (@0.999-2)
## ezknitr 0.6 2016-09-16 cran (@0.6)
## fastmatch 1.1-0 2017-01-28 cran (@1.1-0)
## forcats * 0.2.0 2017-01-23 CRAN (R 3.4.2)
## foreign 0.8-69 2017-06-21 CRAN (R 3.4.2)
## gdata 2.18.0 2017-06-06 cran (@2.18.0)
## ggcompoplot * 0.1.0 2018-04-09 Github (zkamvar/ggcompoplot@bcf007d)
## ggforce 0.1.1 2016-11-28 cran (@0.1.1)
## ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.2)
## ggraph * 1.0.0 2017-02-24 cran (@1.0.0)
## ggrepel 0.7.0 2017-09-29 cran (@0.7.0)
## glue 1.2.0 2017-10-29 CRAN (R 3.4.2)
## gmodels 2.16.2 2015-07-22 cran (@2.16.2)
## graphics * 3.4.2 2018-03-01 local
## grDevices * 3.4.2 2018-03-01 local
## grid 3.4.2 2018-03-01 local
## gridExtra 2.3 2017-09-09 CRAN (R 3.4.2)
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.2)
## gtools 3.5.0 2015-05-29 cran (@3.5.0)
## haven 1.1.0 2017-07-09 CRAN (R 3.4.2)
## highr 0.6 2016-05-09 CRAN (R 3.4.2)
## hms 0.4.0 2017-11-23 CRAN (R 3.4.2)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.2)
## htmlwidgets 0.9 2017-07-10 CRAN (R 3.4.2)
## httpuv 1.3.5 2017-07-04 CRAN (R 3.4.2)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.2)
## huxtable 1.1.0 2017-10-20 cran (@1.1.0)
## igraph * 1.1.2 2017-07-21 CRAN (R 3.4.2)
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.2)
## KernSmooth 2.23-15 2015-06-29 cran (@2.23-15)
## knitr * 1.17 2017-08-10 CRAN (R 3.4.2)
## labeling 0.3 2014-08-23 CRAN (R 3.4.2)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.2)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.2)
## LearnBayes 2.15 2014-05-29 cran (@2.15)
## lubridate 1.7.1 2017-11-03 CRAN (R 3.4.2)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.2)
## MASS 7.3-47 2017-04-21 CRAN (R 3.4.2)
## Matrix 1.2-12 2017-11-16 CRAN (R 3.4.2)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.2)
## methods * 3.4.2 2018-03-01 local
## mgcv 1.8-22 2017-09-19 CRAN (R 3.4.2)
## mime 0.5 2016-07-07 CRAN (R 3.4.2)
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.2)
## modelr 0.1.1 2017-07-24 CRAN (R 3.4.2)
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.2)
## nlme 3.1-131 2017-02-06 CRAN (R 3.4.2)
## parallel 3.4.2 2018-03-01 local
## pegas 0.10 2017-05-03 cran (@0.10)
## permute 0.9-4 2016-09-09 cran (@0.9-4)
## phangorn 2.3.1 2017-11-01 cran (@2.3.1)
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.2)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.2)
## poppr * 2.5.0 2017-09-11 cran (@2.5.0)
## psych 1.7.8 2017-09-09 CRAN (R 3.4.2)
## purrr * 0.2.4 2017-10-18 CRAN (R 3.4.2)
## quadprog 1.5-5 2013-04-17 cran (@1.5-5)
## R.methodsS3 1.7.1 2016-02-16 cran (@1.7.1)
## R.oo 1.21.0 2016-11-01 cran (@1.21.0)
## R.utils 2.6.0 2017-11-05 cran (@2.6.0)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.2)
## RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.4.2)
## Rcpp 0.12.14 2017-11-23 CRAN (R 3.4.2)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.2)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.2)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.4.2)
## rlang 0.1.4 2017-11-05 CRAN (R 3.4.2)
## rstudioapi 0.7 2017-09-07 CRAN (R 3.4.2)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.2)
## scales 0.5.0 2017-08-24 CRAN (R 3.4.2)
## seqinr 3.4-5 2017-08-01 cran (@3.4-5)
## shiny 1.0.5 2017-08-23 CRAN (R 3.4.2)
## sp 1.2-5 2017-06-29 CRAN (R 3.4.2)
## spData 0.2.6.7 2017-11-28 cran (@0.2.6.7)
## spdep 0.7-4 2017-11-22 cran (@0.7-4)
## splines 3.4.2 2018-03-01 local
## stats * 3.4.2 2018-03-01 local
## stringi 1.1.6 2017-11-17 CRAN (R 3.4.2)
## stringr * 1.2.0 2017-02-18 CRAN (R 3.4.2)
## tibble * 1.3.4 2017-08-22 CRAN (R 3.4.2)
## tidyr * 0.7.2 2017-10-16 CRAN (R 3.4.2)
## tidyselect 0.2.3 2017-11-06 CRAN (R 3.4.2)
## tidyverse * 1.2.1 2017-11-14 CRAN (R 3.4.2)
## tools 3.4.2 2018-03-01 local
## tweenr 0.1.5 2016-10-10 cran (@0.1.5)
## udunits2 0.13 2016-11-17 cran (@0.13)
## units 0.4-6 2017-08-27 cran (@0.4-6)
## utils * 3.4.2 2018-03-01 local
## vegan 2.4-4 2017-08-24 cran (@2.4-4)
## viridis * 0.4.0 2017-03-27 CRAN (R 3.4.2)
## viridisLite * 0.2.0 2017-03-24 CRAN (R 3.4.2)
## visNetwork * 2.0.1 2017-07-30 cran (@2.0.1)
## withr 2.1.0 2017-11-01 CRAN (R 3.4.2)
## xml2 1.1.1 2017-01-24 CRAN (R 3.4.2)
## xtable 1.8-2 2016-02-05 CRAN (R 3.4.2)