In [1]:
workDir = '/home/nick/notebook/SIPSim/dev/theory/compositional/'
In [2]:
import os
%load_ext rpy2.ipython
In [3]:
%%R
library(dplyr)
library(tidyr)
library(ggplot2)
library(compositions)
library(coenocliner)
library(vegan)
library(gridExtra)
options(scipen=999)
In [4]:
if not os.path.isdir(workDir):
os.makedirs(workDir)
!cd $workDir
In [5]:
%%R
set.seed(2)
M = 1000 # number of species
ming = 1.67323 # gradient minimum...
maxg = 1.76 # ...and maximum
#meang = mean(c(ming, maxg))
meang = 1.71
locs = seq(ming, maxg, length = 24) # gradient locations
opt = rnorm(M, mean=meang, sd=0.005) # runif(M, min = ming, max = maxg) # species optima
tol = rep(0.005, M) # species tolerances
h = ceiling(rlnorm(M, meanlog = 11)) # max abundances
pars = cbind(opt = opt, tol = tol, h = h) # put in a matrix
In [6]:
%%R
W = c(0.0005, 0.001, 0.005, 0.01)
L = c(0.001, 0.005, 0.01, 0.05)
In [7]:
%%R
# making communities
make_comm = function(x, MM, meang, h, locs){
W = x[1] %>% as.numeric
L = x[2] %>% as.numeric
tol = rep(W, MM)
opt = rnorm(MM, mean=meang, sd=L)
pars = cbind(opt = opt, tol = tol, h = h)
coenocline(locs, responseModel = "gaussian", params = pars, countModel = "poisson")
}
params = expand.grid(W, L)
df = list()
for (i in 1:nrow(params)){
name = paste(params[i,], collapse='__')
tmp = make_comm(params[i,], MM=M, meang=meang, h=h, locs=locs) %>% t
colnames(tmp) = locs
df[[name]] = tmp %>% as.data.frame
}
df = do.call(rbind, df)
df[1:5,1:5]
In [8]:
%%R
# formatting table
df$taxon = gsub('.+\\.', '', rownames(df))
df$params = gsub('\\.[0-9]+$', '', rownames(df))
df = df %>%
gather(BD, count, 1:(ncol(.)-2)) %>%
separate(params, c('W', 'L'), sep='__') %>%
group_by(W, L) %>%
mutate(fraction = BD %>% as.factor %>% as.numeric) %>%
ungroup() %>%
mutate(BD = BD %>% as.character %>% as.numeric,
W = W %>% as.character %>% as.numeric,
L = L %>% as.character %>% as.numeric,
count = count %>% as.character %>% as.numeric) %>%
group_by(fraction, W, L) %>%
mutate(rel_abund = count / sum(count),
rel_abund = ifelse(is.na(rel_abund), 0, rel_abund)) %>%
ungroup() %>%
mutate(W_lab = gsub('^', 'sigma=', W),
L_lab = gsub('^', 'optima=', L),
W_lab = W_lab %>% reorder(W),
L_lab = L_lab %>% reorder(L))
df %>% head(n=3)
In [9]:
%%R -w 800 -h 600
x.lab = expression(paste('Buoyant density (g ml' ^ '-1', ')'))
p.abs = ggplot(df, aes(BD, count, group=taxon)) +
geom_line(alpha=0.5) +
facet_grid(W_lab ~ L_lab) +
labs(x=x.lab, y='Absolute abundance') +
theme_bw() +
theme(
text = element_text(size=16)
)
p.abs
In [10]:
%%R -w 800 -h 600
p.rel = ggplot(df, aes(BD, rel_abund * 100, group=taxon)) +
geom_line(alpha=0.5) +
facet_grid(W_lab ~ L_lab) +
labs(x=x.lab, y='Relative abundance (%)') +
theme_bw() +
theme(
text = element_text(size=16)
)
p.rel
In [288]:
%%R -w 500 -h 800
p.abs.f = p.abs +
theme(
text=element_text(size=12),
axis.text.x = element_text(angle=45, hjust=1)
)
p.rel.f = p.rel +
theme(
text=element_text(size=12),
axis.text.x = element_text(angle=45, hjust=1)
)
p.comb = cowplot::ggdraw() +
geom_rect(aes(xmin=0, ymin=0, xmax=1, ymax=1), fill='white') +
cowplot::draw_plot(p.abs.f, 0, 0.5, 1, 0.5) +
cowplot::draw_plot(p.rel.f, 0.04, 0, 0.96, 0.5) +
cowplot::draw_plot_label(c('A)', 'B)'), c(0, 0), c(1, 0.5))
p.comb
In [290]:
%%R -i workDir
# saving plots
F = file.path(workDir, 'gaus_sim_comb.pdf')
ggsave(F, p.comb, height=10.5, width=7)
cat('File written:', F, '\n')
In [13]:
%%R
# giving value to missing abundances
min.pos.val = df %>%
filter(rel_abund > 0) %>%
group_by() %>%
mutate(min_abund = min(rel_abund)) %>%
ungroup() %>%
filter(rel_abund == min_abund)
min.pos.val = min.pos.val[1,'rel_abund'] %>% as.numeric
imp.val = min.pos.val / 10
# convert numbers
#df[df$rel_abund == 0, 'rel_abund'] = imp.val
# another closure operation
df = df %>%
mutate(rel_abund = ifelse(rel_abund == 0, imp.val, rel_abund)) %>%
group_by(fraction, W, L) %>%
mutate(rel_abund = rel_abund / sum(rel_abund))
# status
cat('Below detection level abundances converted to: ', imp.val, '\n')
In [14]:
%%R
shannon_index_long = function(df, abundance_col, ...){
# calculating shannon diversity index from a 'long' formated table
## community_col = name of column defining communities
## abundance_col = name of column defining taxon abundances
df = df %>% as.data.frame
cmd = paste0(abundance_col, '/sum(', abundance_col, ')')
df.s = df %>%
group_by_(...) %>%
mutate_(REL_abundance = cmd) %>%
mutate(pi__ln_pi = REL_abundance * log(REL_abundance),
shannon = -sum(pi__ln_pi, na.rm=TRUE)) %>%
ungroup() %>%
dplyr::select(-REL_abundance, -pi__ln_pi) %>%
distinct_(...)
return(df.s)
}
In [15]:
%%R -w 700 -h 600
# calculating shannon
df.shan = shannon_index_long(df, 'count', 'fraction', 'BD', 'W', 'L')
df.shan %>% head(n=3)
In [17]:
%%R
# linear interpolation
#approxfun
BDs = seq(1.675, 1.76, (1.76-1.67)/19)
interp = function(df, BDs){
F = approxfun(df$BD, df$shannon)
x = data.frame('BD' = BDs,
'shannon_interp' = F(BDs))
return(x)
}
df.shan.int = df.shan %>%
group_by(W, L) %>%
nest() %>%
mutate(shannon_interp_df = lapply(data, interp, BDs=BDs)) %>%
unnest(shannon_interp = shannon_interp_df %>% purrr::map(function(x) x)) %>%
ungroup() %>%
mutate(shannon_interp = ifelse(is.na(shannon_interp), 0, shannon_interp)) %>%
mutate(W_lab = gsub('^', 'sigma=', W),
L_lab = gsub('^', 'optima=', L),
W_lab = W_lab %>% reorder(W),
L_lab = L_lab %>% reorder(L))
df.shan.int %>% head(n=3)
In [212]:
%%R -w 700 -h 600
# plotting
p.shan.int = ggplot(df.shan.int, aes(BD, shannon_interp)) +
geom_point() +
labs(x='Buoyant density',
y='Shannon index') +
facet_grid(W_lab ~ L_lab) +
theme_bw() +
theme(
text = element_text(size=16),
legend.position = 'none'
)
p.shan.int
In [19]:
%%R -w 650 -h 600
# pairwise correlations for each dataset
#df.shan.bin = df.shan %>%
# group_by(BD_bin = ntile(BD, 24))
calc.pearson = function(x){
xx = x$shannon_interp.x %>% as.matrix %>% as.vector
xy = x$shannon_interp.y %>% as.matrix %>% as.vector
cor(xx, xy, method='pearson')
}
df.shan.corr = inner_join(df.shan.int, df.shan.int, c('BD' = 'BD')) %>%
group_by(W.x, L.x, W.y, L.y) %>%
nest() %>%
mutate(model = purrr::map(data, calc.pearson)) %>%
unnest(pearson = model %>% purrr::map(function(x) x)) %>%
ungroup() %>%
select(-data, -model) %>%
mutate(pearson_txt = round(pearson, 2)) %>%
unite(WL.x, W.x, L.x, sep=':', remove=FALSE) %>%
unite(WL.y, W.y, L.y, sep=':', remove=FALSE)
df.shan.corr %>% head(n=3)
In [23]:
%%R -w 750 -h 600
z = c(4.5, 8.5, 12.5)
lab = 'sigma:optima'
ggplot(df.shan.corr, aes(WL.x, WL.y, fill=pearson)) +
geom_tile() +
geom_text(aes(label=pearson_txt), color='white', size=4) +
geom_vline(xintercept=z, color='white', size=1.2) +
geom_hline(yintercept=z, color='white', size=1.2) +
scale_x_discrete(expand=c(0,0)) +
scale_y_discrete(expand=c(0,0)) +
scale_fill_gradient('Pearson\nCoef.', low='black', high='red') +
labs(x=lab, y=lab) +
theme(
text = element_text(size=16),
axis.text.x = element_text(angle=45, hjust=1)
)
In [151]:
%%R -h 350
df.shan.corr.d = df.shan.corr %>%
mutate(pearson_dist = 1 - pearson) %>%
dplyr::select(WL.x, WL.y, pearson_dist) %>%
spread(WL.y, pearson_dist) %>%
as.data.frame
rownames(df.shan.corr.d) = df.shan.corr.d$WL.x
df.shan.corr.d$WL.x = NULL
df.shan.corr.hc = hclust(df.shan.corr.d %>% as.matrix %>% as.dist)
df.shan.corr.hc %>% plot
In [152]:
%%R
df.shan.corr.dnd = df.shan.corr.hc %>% as.dendrogram
df.shan.corr.dnd.gg = dendextend::as.ggdend(df.shan.corr.dnd)
In [249]:
%%R
# plotting in ggplot
p.dnd = ggplot(df.shan.corr.dnd.gg$segments, aes(y, x)) +
geom_segment(aes(xend=yend, yend=xend)) +
#geom_text(data=df.shan.corr.dnd.gg$labels, aes(y-0.01, x, label=label, hjust=0)) +
scale_x_reverse() +
#scale_x_reverse(limits=c(1, -0.15)) +
labs(x='1 - Pearson Coef.') +
theme_bw() +
theme(
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = 'none',
legend.background = element_blank(),
legend.key = element_blank(),
strip.background = element_blank(),
strip.text = element_blank(),
rect = element_rect(fill = "transparent",colour = NA)
)
p.dnd
In [250]:
%%R -w 300
# adding colored bars for parameters
df.shan.corr.dnd.gg.p = df.shan.corr.dnd.gg$labels %>%
mutate(label = label %>% as.character) %>%
separate(label, c('sigma', 'optima'), sep=':', remove=FALSE) %>%
gather(sig.opt, value, sigma, optima) %>%
mutate(y = ifelse(sig.opt=='sigma', 1, 2),
value = value %>% reorder(value %>% as.numeric),
sig.opt = factor(sig.opt, levels=c('sigma', 'optima')))
p.bar = ggplot(df.shan.corr.dnd.gg.p, aes(sig.opt, x, fill=value)) +
geom_tile() +
scale_x_discrete(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
scale_fill_discrete('Parameter\nvalue') +
labs(x='Parameter') +
theme_bw() +
theme(
text = element_text(size=16),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.background = element_blank(),
legend.key = element_blank(),
strip.background = element_blank(),
strip.text = element_blank()
)
p.bar
In [251]:
%%R -w 300
# adding colored bars for parameters
df.shan.corr.dnd.gg.p = df.shan.corr.dnd.gg$labels %>%
mutate(label = label %>% as.character) %>%
separate(label, c('sigma', 'optima'), sep=':', remove=FALSE) %>%
gather(sig.opt, value, sigma, optima) %>%
mutate(y = ifelse(sig.opt=='sigma', 1, 2),
value = log(value %>% as.character %>% as.numeric),
sig.opt = factor(sig.opt, levels=c('sigma', 'optima')))
p.bar = ggplot(df.shan.corr.dnd.gg.p, aes(sig.opt, x, fill=value)) +
geom_tile() +
scale_x_discrete(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
scale_fill_continuous('Parameter\nvalue\n(log10)') +
labs(x='Parameter') +
theme_bw() +
theme(
text = element_text(size=16),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.background = element_blank(),
legend.key = element_blank(),
strip.background = element_blank(),
strip.text = element_blank(),
rect = element_rect(fill = "transparent",colour = NA)
)
p.bar
In [252]:
%%R
p.shan.dnd = cowplot::ggdraw() +
geom_rect(aes(xmin=0, ymin=0, xmax=1, ymax=1), fill='white') +
cowplot::draw_plot(p.dnd, 0, 0, 0.53, 1) +
cowplot::draw_plot(p.bar, 0.5, 0.01, 0.5, 0.98)
p.shan.dnd
In [253]:
%%R -w 750 -h 600
# # pairwise correlations for each dataset
# df.shan.bin = df.shan %>%
# group_by(BD_bin = ntile(BD, 24)) %>%
# mutate(n=n()) %>%
# ungroup()
# #unite(dataset, W, L, sep=':', remove=FALSE)
# calc.pearson = function(x){
# cor(x[,'shannon.x'], x['shannon.y'], method='pearson')[1,1]
# }
# as.num = function(x) x %>% as.character %>% as.numeric
# df.shan.corr = inner_join(df.shan.bin, df.shan.bin, c('BD_bin' = 'BD_bin')) %>%
# group_by(W.x, L.x, W.y, L.y) %>%
# nest() %>%
# mutate(model = purrr::map(data, calc.pearson)) %>%
# unnest(pearson = model %>% purrr::map(function(x) x)) %>%
# ungroup() %>%
# select(-data, -model) %>%
# mutate(pearson_txt = round(pearson, 2)) %>%
# unite(WL.x, W.x, L.x, sep=':', remove=FALSE) %>%
# unite(WL.y, W.y, L.y, sep=':', remove=FALSE)
# # plotting
# #sig = rep(c(0.0005, 0.001, 0.005, 0.01), 4) %>% sort
# #opt = rep(c(0.001, 0.005, 0.01, 0.05), 4) %>% sort
# #lab = 'sigma:optima'
# ggplot(df.shan.corr, aes(WL.x, WL.y, fill=pearson)) +
# geom_tile() +
# geom_text(aes(label=pearson_txt), color='white', size=4) +
# scale_x_discrete(expand=c(0,0)) +
# scale_y_discrete(expand=c(0,0)) +
# scale_fill_gradient('Pearson\nCoef.', low='black', high='red') +
# # facet_grid(W.y ~ L.x) +
# labs(x='sigma', y='optima') +
# #labs(x=lab, y=lab) +
# theme(
# text = element_text(size=16),
# axis.text.x = element_text(angle=45, hjust=1)
# )
In [254]:
%%R -w 750 -h 600
# # pairwise correlations for each dataset
# df.shan.bin = df.shan %>%
# group_by(BD_bin = ntile(BD, 24)) %>%
# unite(dataset, W, L, sep=':', remove=FALSE)
# calc.pearson = function(x){
# cor(x[,'shannon.x'], x['shannon.y'], method='pearson')[1,1]
# }
# as.num = function(x) x %>% as.character %>% as.numeric
# df.shan.corr = inner_join(df.shan.bin, df.shan.bin, c('BD_bin' = 'BD_bin')) %>%
# group_by(dataset.x, dataset.y) %>%
# nest() %>%
# mutate(model = purrr::map(data, calc.pearson)) %>%
# unnest(pearson = model %>% purrr::map(function(x) x)) %>%
# ungroup() %>%
# select(-data, -model) %>%
# mutate(pearson_txt = round(pearson, 2))
# # dataset.y.opt = gsub('.+:', '', dataset.y) %>% as.numeric,
# #dataset.y = dataset.y %>% reorder(dataset.y.opt),
# # dataset.x.sig = gsub(':.+', '', dataset.x) %>% as.numeric,
# # dataset.x = dataset.x %>% reorder(dataset.x.sig))
# # plotting
# z = c(4.5, 8.5, 12.5)
# #sig = rep(c(0.0005, 0.001, 0.005, 0.01), 4) %>% sort
# #opt = rep(c(0.001, 0.005, 0.01, 0.05), 4) %>% sort
# lab = 'sigma:optima'
# ggplot(df.shan.corr, aes(dataset.x, dataset.y, fill=pearson)) +
# geom_tile() +
# geom_text(aes(label=pearson_txt), color='white', size=4.5) +
# geom_vline(xintercept=z, color='white', size=1.2) +
# geom_hline(yintercept=z, color='white', size=1.2) +
# scale_x_discrete(expand=c(0,0)) +
# scale_y_discrete(expand=c(0,0)) +
# scale_fill_gradient('Pearson\nCoef.', low='black', high='red') +
# #facet_grid(dataset.y.opt ~ dataset.x.sig) +
# #labs(x='sigma', y='optima') +
# labs(x=lab, y=lab) +
# theme(
# text = element_text(size=16),
# axis.text.x = element_text(angle=45, hjust=1)
# )
In [258]:
%%R -w 700 -h 800
p.shan.comb = cowplot::ggdraw() +
geom_rect(aes(xmin=0, ymin=0, xmax=1, ymax=1), fill='white') +
cowplot::draw_plot(p.shan.int, 0.02, 0.40, 0.96, 0.59) +
cowplot::draw_plot(p.dnd, 0.15, 0.01, 0.37, 0.4) +
cowplot::draw_plot(p.bar, 0.5, 0.015, 0.35, 0.39) +
cowplot::draw_plot_label(c('A)', 'B)'), c(0, 0), c(1, 0.4))
p.shan.comb
In [259]:
%%R -i workDir
# saving plot
F = file.path(workDir, 'compData_shan.pdf')
ggsave(F, p.shan.comb, height=10, width=9)
cat('File written:', F, '\n')
In [160]:
%%R
BD.diffs = function(df){
BDs = df$BD %>% unique
df.BD = expand.grid(BDs, BDs)
df.BD$diff = df.BD %>% apply(1, diff) %>% abs %>% as.vector
df.BD = df.BD %>% spread(Var1, diff)
rownames(df.BD) = df.BD$Var2
df.BD$Var2 = NULL
dist.BD = df.BD %>% as.matrix
dist.BD[upper.tri(dist.BD, diag=TRUE)] = 0
dist.BD %>% as.dist
}
vegdist.by = function(df, ...){
df.w = df %>%
select(taxon, rel_abund, fraction, BD) %>%
spread(taxon, rel_abund) %>%
as.data.frame()
rownames(df.w) = df.w$BD %>% as.vector
df.w$BD = NULL
df.w$fraction = NULL
vegan::vegdist(df.w, ...)
}
dist.match = function(X,D){
# making sure matrices match
X.m = X %>% as.matrix
D.m = D %>% as.matrix
d = setdiff(rownames(X.m), rownames(D.m))
if(length(d) > 0){
print(rownames(X.m))
print(rownames(D.m))
print(d)
stop('Distance matrices don\'t match')
}
D.m = D.m[rownames(X.m), colnames(X.m)]
D = D.m %>% as.dist
return(D)
}
m.corr = function(X, D, ...){
res = list()
for (i in 1:length(X)){
X.d = X[[i]]
X.d[is.na(X.d)] = 0
D.d = D[[i]]
D.d = dist.match(X.d, D.d)
tmp = vegan::mantel.correlog(X.d, D.d, ...)
tmp = tmp['mantel.res'][['mantel.res']] %>% as.data.frame
colnames(tmp) = c('class.index', 'n.dist', 'Mantel.corr', 'Pr', 'Pr.corr')
res[[i]] = tmp
}
return(res)
}
# running
df.d = df %>%
ungroup() %>%
group_by(W, L) %>%
nest() %>%
mutate(dist.bray = lapply(data, vegdist.by),
dist.BD = lapply(data, BD.diffs),
mantel.corr = m.corr(dist.bray, dist.BD, n.class=24)) %>%
select(W, L, mantel.corr) %>%
unnest(mantel.corr %>% purrr::map(function(x) x))
df.d %>% head
In [161]:
%%R -w 800 -h 575
df.d.s = df.d %>%
filter(! is.na(Mantel.corr)) %>%
ungroup() %>%
mutate(significant = ifelse(Pr.corr <= 0.05, TRUE, FALSE))
ggplot(df.d.s, aes(class.index, Mantel.corr, color=significant)) +
geom_point() +
scale_color_discrete('P_corr < 0.05') +
labs(x='Class index', y='Mantel correlation') +
facet_grid(W ~ L) +
theme_bw() +
theme(
text = element_text(size=16)
)
In [162]:
%%R -w 750 -h 600
# pairwise correlations for each dataset
df.d.bin = df.d %>%
group_by(BD_bin = ntile(class.index, 12)) %>%
unite(dataset, W, L, sep=':', remove=FALSE)
df.d.bin %>% head
In [163]:
%%R -w 750 -h 600
# pairwise correlations for each dataset
df.d.bin = df.d %>%
filter(! is.na(Mantel.corr)) %>%
group_by(BD_bin = ntile(class.index, 12)) %>%
unite(dataset, W, L, sep=':', remove=FALSE) %>%
as.data.frame
calc.pearson = function(x){
ret = cor(x[,'Mantel.corr.x'], x[,'Mantel.corr.y'], method='pearson')[1,1]
return(ret)
}
as.num = function(x) x %>% as.character %>% as.numeric
df.d.corr = inner_join(df.d.bin, df.d.bin, c('BD_bin' = 'BD_bin')) %>%
group_by(dataset.x, dataset.y) %>%
nest() %>%
mutate(model = purrr::map(data, calc.pearson)) %>%
unnest(pearson = model %>% purrr::map(function(x) x)) %>%
ungroup() %>%
select(-data, -model) %>%
mutate(pearson_txt = round(pearson, 2))
# plotting
lab = 'Width:Location'
ggplot(df.d.corr, aes(dataset.x, dataset.y, fill=pearson)) +
geom_tile() +
geom_text(aes(label=pearson_txt), color='white', size=5) +
scale_fill_gradient(low='black', high='red') +
labs(x=lab, y=lab, title='Bray-Curtis correlogram') +
theme(
text = element_text(size=16),
axis.text.x = element_text(angle=45, hjust=1)
)
In [176]:
%%R -h 350
df.d.corr.d = df.d.corr %>%
mutate(pearson_dist = 1 - pearson) %>%
dplyr::select(dataset.x, dataset.y, pearson_dist) %>%
spread(dataset.y, pearson_dist) %>%
as.data.frame
rownames(df.d.corr.d) = df.d.corr.d$dataset.x
df.d.corr.d$dataset.x = NULL
df.d.corr.hc = hclust(df.d.corr.d %>% as.matrix %>% as.dist, method='complete')
df.d.corr.hc %>% plot
In [177]:
%%R
df.d.corr.dnd = df.d.corr.hc %>% as.dendrogram
df.d.corr.dnd.gg = dendextend::as.ggdend(df.d.corr.dnd)
In [178]:
%%R
# plotting in ggplot
p.dnd = ggplot(df.d.corr.dnd.gg$segments, aes(y, x)) +
geom_segment(aes(xend=yend, yend=xend)) +
#geom_text(data=df.shan.corr.dnd.gg$labels, aes(y-0.01, x, label=label, hjust=0)) +
scale_x_reverse() +
#scale_x_reverse(limits=c(1, -0.15)) +
labs(x='1 - Pearson Coef.') +
theme_bw() +
theme(
axis.title.x = element_text(size=14),
axis.text.x = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = 'none',
legend.background = element_blank(),
legend.key = element_blank(),
strip.background = element_blank(),
strip.text = element_blank()
)
p.dnd
In [179]:
%%R -w 250 -h 400
# adding colored bars for parameters
df.d.corr.dnd.gg.p = df.d.corr.dnd.gg$labels %>%
mutate(label = label %>% as.character) %>%
separate(label, c('sigma', 'optima'), sep=':', remove=FALSE) %>%
gather(sig.opt, value, sigma, optima) %>%
mutate(y = ifelse(sig.opt=='sigma', 1, 2),
value = log(value %>% as.character %>% as.numeric),
sig.opt = factor(sig.opt, levels=c('sigma', 'optima')))
p.bar = ggplot(df.d.corr.dnd.gg.p, aes(sig.opt, x, fill=value)) +
geom_tile() +
scale_x_discrete(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
scale_fill_continuous('Parameter\nvalue\n(log10)') +
labs(x='Parameter') +
theme_bw() +
theme(
text = element_text(size=16),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.background = element_blank(),
legend.key = element_blank(),
strip.background = element_blank(),
strip.text = element_blank()
)
p.bar
In [180]:
%%R
p.d.dnd = cowplot::ggdraw() +
geom_rect(aes(xmin=0, ymin=0, xmax=1, ymax=1), fill='white') +
cowplot::draw_plot(p.dnd, 0, 0, 0.50, 1) +
cowplot::draw_plot(p.bar, 0.5, 0.01, 0.5, 0.98)
p.d.dnd
In [260]:
%%R
# running
df.d = df %>%
ungroup() %>%
group_by(W, L) %>%
nest() %>%
mutate(dist.bray = lapply(data, vegdist.by, method='jaccard'),
dist.BD = lapply(data, BD.diffs),
mantel.corr = m.corr(dist.bray, dist.BD, n.class=24)) %>%
select(W, L, mantel.corr) %>%
unnest(mantel.corr %>% purrr::map(function(x) x))
df.d %>% head
In [277]:
%%R -w 800 -h 575
df.d.s = df.d %>%
filter(! is.na(Mantel.corr)) %>%
ungroup() %>%
mutate(significant = ifelse(Pr.corr <= 0.05, TRUE, FALSE),
W_lab = gsub('^', 'sigma=', W),
L_lab = gsub('^', 'optima=', L))
p.cor = ggplot(df.d.s, aes(class.index, Mantel.corr, color=significant)) +
geom_point() +
scale_color_discrete('Adjusted\nP < 0.05') +
labs(x='Class index', y='Mantel correlation') +
facet_grid(W_lab ~ L_lab) +
theme_bw() +
theme(
text = element_text(size=16)
)
p.cor