chapter 4 version 7 agg graph code
James Lin
8/6/2021
1 Set up
1.1 1) Load packages
1.2 2) Import data
#import data
soilhealth <- read_excel("7-30-21 part 11 nrcs soil health final data.xlsx")
soilhealth$bdepth=as.numeric(soilhealth$bdepth)1.3 3) Data Wrangling
#change column names
soilhealth1 <- soilhealth %>%
clean_names()
#str(soilhealth)
#View(soilhealth)
#names(soilhealth1)
soilhealth1 <- as.data.frame(soilhealth1)
# keeps aggregate data
agg <- soilhealth1 %>%
dplyr::select(location, treatment, bdepth, horizon, blk, replication, x20wsa2000, x20wsa250, x20wsa53, x20wsa20, x20mwd, x5wsa2000, x5wsa250, x5wsa53, x5wsa20, x5mwd, nagg)
soils <- agg %>%
filter(location!="Ottawa", treatment!="IR")
agg$location_f =factor(agg$location, levels=c('Tribune', 'Hays', 'Manhattan'))
#str(agg)
#View(agg)
agg1 <- agg%>%
filter(horizon=="1", location!="Ottawa", treatment!="IR")
agg2 <- agg%>%
filter(horizon=="2", location!="Ottawa", treatment!="IR")
agg3 <- agg%>%
filter(horizon=="3", location!="Ottawa", treatment!="IR")
agg4 <- agg%>%
filter(horizon=="4", location!="Ottawa", treatment!="IR")
aggmwd <- agg %>%
filter(location!="Ottawa", treatment!="IR") %>%
na.omit()
agg1$location_f =factor(agg1$location, levels=c('Tribune', 'Hays', 'Manhattan'))1.4 4) Theme James Bar
theme_James <- function(base_size=14, base_family="TT Times New Roman") {
(theme_foundation(base_size=base_size, base_family=base_family)+
theme_bw()+
theme(panel.background = element_rect(colour = NA),
plot.background = element_rect(colour = NA),
axis.title = element_text(color="black",size=rel(1.2)),
axis.text = element_text(color="black", size = 12),
legend.key = element_rect(colour = NA),
legend.spacing = unit(0, "cm"),
legend.text = element_text(size=12),
legend.title = element_blank(),
panel.grid = element_blank(),
plot.title = element_text(color="Black",size = rel(1.5),face = "bold",hjust = 0.5),
strip.text = element_text(color="Black",size = rel(1),face="bold")
))
}1.5 5) Theme James Correlation
theme_James2 <- function(base_size=14, base_family="TT Times New Roman") {
(theme_foundation(base_size=base_size, base_family=base_family)+
theme_bw()+
theme(panel.background = element_rect(colour = NA),
plot.background = element_rect(colour = NA),
axis.title = element_text(color="black",size=rel(1.2)),
axis.text = element_text(color="black", size = 12),
legend.key = element_rect(colour = NA),
legend.spacing = unit(0, "cm"),
legend.text = element_text(size=12),
panel.grid = element_blank(),
plot.title = element_text(color="Black",size = rel(1.5),face = "bold",hjust = 0.5),
strip.text = element_text(color="Black",size = rel(1),face="bold")
))
}2 Correlation Matrix
names(agg)## [1] "location" "treatment" "bdepth" "horizon" "blk"
## [6] "replication" "x20wsa2000" "x20wsa250" "x20wsa53" "x20wsa20"
## [11] "x20mwd" "x5wsa2000" "x5wsa250" "x5wsa53" "x5wsa20"
## [16] "x5mwd" "nagg" "location_f"aggmatrix <- agg %>%
filter(location!="Ottawa", treatment!="IR") %>%
dplyr::select(-location, -treatment, -bdepth, -horizon, -blk, -replication) %>%
dplyr::select(where(is.numeric)) %>% drop_na()
aggmatrix <- na.omit(aggmatrix)
aggmatrix1 <- aggmatrix[, c(5,1,2,3,4,10,6,7,8,9,11)]
#aggmatrix1
aggmat <- cor(aggmatrix1, method = "spearman")
#aggmat
#library("writexl")
#aggmatdf <- as.data.frame(aggmat)
#write_xlsx(aggmatdf,"aggmatdf.xlsx")
library("PerformanceAnalytics")
chart.Correlation(aggmatrix1, histogram = TRUE, pch = 19, method="spearman")
# use exact=FALSE
chart.Correlation(aggmatrix1, histogram = TRUE, pch = 19, method="spearman", exact=FALSE)
colnames(aggmat) <- c( "20 min MWD","20 min 2 mm", "20 min 0.250 mm", "20 min 0.053 mm", "20 min 0.020 mm", "5 min MWD", "5 min 2 mm", "5 min 0.25 mm", "5 min 0.053 mm", "5 min 0.020 mm", "NRCS")
rownames(aggmat) <- c( "20 min MWD","20 min 2 mm", "20 min 0.250 mm", "20 min 0.053 mm", "20 min 0.020 mm", "5 min MWD", "5 min 2 mm", "5 min 0.25 mm", "5 min 0.053 mm", "5 min 0.020 mm", "NRCS")
corrplot(aggmat, method = "square", tl.col = "black", type = "lower", tl.srt = 45, tl.cex = 0.7)
#corrplot.mixed(aggmat, lower.col = "black", number.cex = .7)
#corrplot(aggmat, type = "lower")
corrplot(aggmat, method = "pie",type = "lower", tl.srt = 20, tl.col="black")
#head(p.matr[,])
# cl.* is for color legend, and tl.* if for text legend.
res1<- cor.mtest(aggmat, conf.level=0.95)
#Significance level
fcor <- corrplot(aggmat, method = "pie",type = "upper", tl.srt = 20, tl.col="black", p.mat=res1$p, sig.level = 0.05, title = "Spearman Correlation Matrix p > 0.05")
corrplot(aggmat, method = "number",type = "upper", tl.srt = 20, tl.col="black", p.mat=res1$p, sig.level = 0.05, title = "Spearman Correlation Matrix p > 0.05")
3 Methods Comparison
3.1 Data Manipulation
#remove Ottawa and irrigation dataset since 5MWD isnt available
# data frame for (20 minutes vs 5 minutes) & (5 minutes vs NRCS)- only has 4 horizons
soils <- agg %>%
filter(location!="Ottawa", treatment!="IR") %>%
na.omit()
soils$location_a =factor(soils$location, levels=c('Tribune', 'Hays', 'Manhattan'))
soils$horizon_a =factor(soils$horizon, levels=c('1', '2', '3', '4'))
# data frame for 20 minutes vs NRCS- has all horizons
soils_b <- agg %>%
dplyr::select(-x5mwd, -x5wsa2000, -x5wsa250, -x5wsa53, -x5wsa20) %>%
dplyr::filter(location!="Ottawa", treatment!="IR") %>%
na.omit()
soils_b$location_b =factor(soils_b$location, levels=c('Tribune', 'Hays', 'Manhattan'))
soils_b$horizon_b =factor(soils_b$horizon, levels=c('1', '2', '3', '4', '5', '6', '7'))3.2 20mwd and 5mwd Correlation graphs
use soils data frame and location_a and horizon_a
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(soils, aes(x=x20mwd, y= x5mwd))+
geom_point(size=.5)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD") +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
theme_James() +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.1, label.y=3.4, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=3)
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD") +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
#group by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
# just to find p value for tribune
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, show.legend = F)+
stat_regline_equation(label.x=.5, show.legend = F)
3.2.1 Correlation plots for group depths
#20 minute Mean Weight Diameter vs 5 minute Method
#group by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=1.3, show.legend = F)
#ggsave("20min5mincor.png", height=6, width=9)
#Has correlation by treatment by location and has eclipses
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
geom_mark_ellipse(expand=0, aes(fill=treatment), show.legend = F)
## 8-2 mm 20 and 5 Correlation graphs use soils data frame and location_a and horizon_a
#20 minute vs 5 minute Method 8-2 mm
ggplot(soils, aes(x=x20wsa2000, y= x5wsa2000))+
geom_point(size=.5)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 8-2 mm") +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
theme_James() +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.1, label.y=50, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=45)
#cor(soils$x20wsa2000, soils$x5wsa2000, method="spearman", use="complete.obs")
#One graph by location
soils %>%
ggplot(aes(x=x20wsa2000, y= x5wsa2000, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 8-2 mm ") +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=9, show.legend = F)
#group by location
soils %>%
ggplot(aes(x=x20wsa2000, y= x5wsa2000))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 8-2 mm by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
# just to find p value for tribune
soils %>%
ggplot(aes(x=x20wsa2000, y= x5wsa2000))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 8-2 mm by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, show.legend = F)+
stat_regline_equation(label.x=.5, show.legend = F) ## 2-0.25 mm 20 and 5 Correlation graphs use soils data frame and location_a and horizon_a
#20 minute vs 5 minute Method 8-2 mm
ggplot(soils, aes(x=x20wsa250, y= x5wsa250))+
geom_point(size=.5)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 2-0.25 mm") +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
theme_James() +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.1, label.y=70, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=65)
#cor(soils$x20wsa2000, soils$x5wsa2000, method="spearman", use="complete.obs")
#One graph by location
soils %>%
ggplot(aes(x=x20wsa250, y= x5wsa250, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 2-0.25 mm ") +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=14, show.legend = F)
#group by location
soils %>%
ggplot(aes(x=x20wsa250, y= x5wsa250))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 2-0.25 mm by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
# just to find p value for tribune
soils %>%
ggplot(aes(x=x20wsa250, y= x5wsa250))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (%)",
y="5 Minute Method (%)",
title="Correlation between 20 vs 5 minute 2-0.25 mm by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, show.legend = F)+
stat_regline_equation(label.x=.5, show.legend = F)
3.2.2 end of 8-2 mm
3.3 20mwd and NRCS Correlation graphs
use soils_b data frame and location_b and horizon_b
#20 minute Mean Weight Diameter vs NRCS Hand Method
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg))+
geom_point(size=.5)+
labs(x="20 minute method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS") +
theme_classic()+
geom_smooth(method="lm", se=FALSE) +
theme_James() +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.1, label.y=85, p.accuracy = 0.001) +
stat_regline_equation(label.y=75)
#One graph by location
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg, color=location_b, shape=location_b))+
geom_point(size=1)+
labs(x="20 minute method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS") +
theme_classic()+
geom_smooth(method="lm", se=FALSE) +
theme_James() +
stat_cor(aes(color=location_b, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
#group by location
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg))+
facet_wrap(~location_b)+
labs(x="20 Minute Method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5) +
theme_James() +
stat_cor(aes(color=location_b, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
3.3.1 Correlation plots for group depths
#20 minute Mean Weight Diameter vs NRCS
#group by location
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg, color=treatment))+
facet_wrap(~location_b)+
labs(x="20 Minute Method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,100)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg, color=treatment))+
facet_wrap(~location_b)+
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,100)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_b))+
scale_shape_manual(values=c(19, 17, 15, 3, 7, 8, 10)) +
theme_James2() +
stat_regline_equation(label.x=1.3, show.legend = F)
ggsave("20minNRCScor.png", height=6, width=9)
#Has correlation by treatment by location and has eclipses
soils_b %>%
ggplot(aes(x=x20mwd, y= nagg, color=treatment))+
facet_wrap(~location_b)+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="NRCS Hand Method (%)",
title="Correlation between 20 Minute MWD vs NRCS by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,100)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_b), na.rm = T)+
scale_shape_manual(values=c(19, 17, 15, 3, 7, 8, 10)) +
theme_James2() +
geom_mark_ellipse(expand=0, aes(fill=treatment), show.legend = F)
3.4 NRCS and 5mwd Correlation graphs
use soils data frame and location_a and horizon_a
#NRCS vs 5 minute Method
soils %>%
ggplot(aes(x=nagg, y= x5mwd))+
geom_point(size=.5)+
labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD") +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
theme_James() +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=1, label.y=3.4, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=3)
#One graph by location
soils %>%
ggplot(aes(x=nagg, y= x5mwd, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD") +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=15, show.legend = F)
#group by location
soils %>%
ggplot(aes(x=nagg, y= x5mwd))+
facet_wrap(~location_a)+
labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD by Location") +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
theme_James() +
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)
3.4.1 Correlation plots for group depths
#NRCS vs 5 minute Method
#group by location
soils %>%
ggplot(aes(x=nagg, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
soils %>%
ggplot(aes(x=nagg, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=30, show.legend = F) 
ggsave("NRCS5mincor.png", height=6, width=9)
#Has correlation by treatment by location and has eclipses
soils %>%
ggplot(aes(x=nagg, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="NRCS Hand Method (%)",
y="5 Minute Method (mm)",
title="Correlation between NRCS vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
geom_mark_ellipse(expand=0, aes(fill=treatment), show.legend = F)
3.5 MWD Bland-Altman plots
library(BlandAltmanLeh)
nrcs <-as.numeric(soils$nagg, na.rm=FALSE)
n20mwd <- as.numeric(soils$x20mwd, na.rm=FALSE)
n5mwd <- as.numeric(soils$x5mwd, na.rm=FALSE)
#Bland-Altman Plots for 20 minute Mean Weight Diameter vs NRCS Hand Method
soils %>%
ggplot(aes(x=((x20mwd+nagg)/2), y= (x20mwd-nagg)))+
geom_point(size=.5)+
labs(x="20 minute method",
y="NRCS hand method",
title="Bland-Altman Plots for 20 minute Mean Weight Diameter vs NRCS Hand Method") +
theme_classic()
bland.altman.plot(n20mwd, nrcs, xlab="Means", ylab="Differences", na.rm=FALSE)
## NULL#Bland-Altman Plots for 20 minute Mean Weight Diameter vs 5 minute Method
soils %>%
ggplot(aes(x=((x20mwd+x5mwd)/2), y= (x5mwd-x20mwd)))+
geom_point(size=.5)+
labs(x="Means",
y="Differences",
title="Bland-Altman Plots for 20 minute Mean Weight Diameter vs 5 minute Method") +
theme_classic()
bland.altman.plot(n5mwd, n20mwd, xlab="Means", ylab="Differences")
## NULLmodmwd <- lm(x5mwd ~ x20mwd, data=soils)
summary(modmwd)##
## Call:
## lm(formula = x5mwd ~ x20mwd, data = soils)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80351 -0.29802 -0.05178 0.24953 1.69249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.47932 0.08718 5.498 1.8e-07 ***
## x20mwd 0.77334 0.07969 9.704 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5395 on 138 degrees of freedom
## Multiple R-squared: 0.4056, Adjusted R-squared: 0.4013
## F-statistic: 94.17 on 1 and 138 DF, p-value: < 2.2e-163.5.1 Correlation plots for group depths
#20 minute Mean Weight Diameter vs 5 minute Method
#group by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2()
#Has correlation by treatment by location and has eclipses
soils %>%
ggplot(aes(x=x20mwd, y= x5mwd, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+ labs(x="20 Minute Method (mm)",
y="5 Minute Method (mm)",
title="Correlation between 20 vs 5 minute MWD by Location", shape="Horizon", color="Treatment") +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
geom_mark_ellipse(expand=0, aes(fill=treatment), show.legend = F)
4 Regression
5 Data wrangling
reg <- soilhealth1 %>%
dplyr::filter(location!="Ottawa", treatment!="IR", bdepth<=15) %>%
dplyr::select(location, treatment, bdepth, horizon, blk, replication, x20wsa2000, x20wsa250, x20wsa53, x20wsa20, x20mwd, x5wsa2000, x5wsa250, x5wsa53, x5wsa20, x5mwd, nagg, amf,fungi, mb)
reg$location_a =factor(reg$location, levels=c('Tribune', 'Hays', 'Manhattan'))
reg$horizon_a =factor(reg$horizon, levels=c('1', '2', '3'))5.1 20mwd and AMF Correlation graphs
ggplot(reg, aes(y=x20mwd, x= amf))+
geom_point(size=.5)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between AMF and MWD") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=3, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=2.5, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(y=x20mwd, x= amf, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between AMF and MWD") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=5, show.legend = F) +
theme_James() 
ggsave("mwd_amf1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(y=x20mwd, x= amf))+
facet_wrap(~location_a)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between AMF and MWD") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.1.1 MWD vs AMF Correlation plots for group depths
#20 minute Mean Weight Diameter vs 5 minute Method
#group by location
reg %>%
ggplot(aes(x=amf, y= x20mwd, color=treatment))+
facet_wrap(~location_a)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between AMF and MWD by Location", shape="Horizon", color="Treatment") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20mwd, x= amf, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between AMF and MWD", shape="Horizon", color="Treatment") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=8, show.legend = F)
ggsave("mwd_amfcor.png", height=5, width=9)5.2 20mwd and MB Correlation graphs
ggplot(reg, aes(y=x20mwd, x= mb))+
geom_point(size=.5)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between Microbial Biomass and MWD") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=3, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=2.5, label.x=.3) +
theme_James() 
#One graph by location
ggplot(reg, aes(y=x20mwd, x= mb, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between Microbial Biomass and MWD") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=50, show.legend = F) +
theme_James() 
ggsave("mwd_mb1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(y=x20mwd, x= mb))+
facet_wrap(~location_a)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between Microbial Biomass and MWD") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.2.1 MWD vs MB Correlation plots for group depths
reg %>%
ggplot(aes(x=mb, y= x20mwd, color=treatment))+
facet_wrap(~location_a)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between Microbial Biomass and MWD", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20mwd, x= mb, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Mean Weight Diameter (mm)",
title="Correlation between Microbial Biomass and MWD", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,5)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=50, show.legend = F)
ggsave("mwd_mbcor.png", height=5, width=9)5.3 20mwd and Fungi Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(x=x20mwd, y= fungi))+
geom_point(size=.5)+
labs(x="Mean Weight Diameter (mm)",
title="Correlation between MWD and Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=3, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=4, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(x=x20mwd, y= fungi, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="Mean Weight Diameter (mm)",
title="Correlation between MWD and Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F) +
theme_James() 
#group by location
reg %>%
ggplot(aes(x=x20mwd, y= fungi))+
facet_wrap(~location_a)+
labs(x="Mean Weight Diameter (mm)",
title="Correlation between MWD vs Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.4 8-2 mm and AMF Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(y=x20wsa2000, x= amf))+
geom_point(size=.5)+
labs(y="Percent 8-2 mm Aggregate fraction(%)",
title="Correlation between 8-2 mm fraction and AMF") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=35, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=30, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(y=x20wsa2000, x= amf, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between AMF and 8-2 mm fraction ") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=10, show.legend = F) +
theme_James() 
ggsave("x8_2mm_amf1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(y=x20wsa2000, x= amf))+
facet_wrap(~location_a)+
labs(y="Percent 8-2 mm Aggregate fraction(%)",
title="Correlation between AMF and 8-2 mm fraction ") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.4.1 8-2 mm vs AMF Correlation plots for group depths
reg %>%
ggplot(aes(x=amf, y= x20wsa2000, color=treatment))+
facet_wrap(~location_a)+
labs(y="Percent 8-2 mm Aggregate fraction(%)",
title="Correlation between AMF and 8-2 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20wsa2000, x= amf, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between AMF and 8-2 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~Arbuscular ~Mycorrhizal ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=9, show.legend = F)
ggsave("x8_2mm_amfcor.png", height=5, width=9)5.5 8-2 mm and MB Correlation graphs
ggplot(reg, aes(y=x20wsa2000, x= mb))+
geom_point(size=.5)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 8-2 mm fraction") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=35, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=40, label.x=.3) +
theme_James() 
#One graph by location
ggplot(reg, aes(y=x20wsa2000, x= mb, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 8-2 mm fraction") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=50, show.legend = F) +
theme_James() 
ggsave("x8_2mm_mb1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(y=x20wsa2000, x= mb))+
facet_wrap(~location_a)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 8-2 mm fraction") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.5.1 8-2 mm vs MB Correlation plots for group depths
reg %>%
ggplot(aes(x=mb, y= x20wsa2000, color=treatment))+
facet_wrap(~location_a)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 8-2 mm fraction", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,50)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20wsa2000, x= mb, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 8-2 mm fraction", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,50)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=50, show.legend = F)
ggsave("8_2mm_mbcor.png", height=5, width=9)5.6 8-2 mm and fungi Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(x=x20wsa2000, y= fungi))+
geom_point(size=.5)+
labs(x="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between 8-2 mm fraction and Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=5, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=4, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(x=x20wsa2000, y= fungi, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(x="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between 8-2 mm fraction and Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=10, show.legend = F) +
theme_James() 
#group by location
reg %>%
ggplot(aes(x=x20wsa2000, y= fungi))+
facet_wrap(~location_a)+
labs(x="Percent 8-2 mm Aggregate fraction (%)",
title="Correlation between 8-2 mm fraction and Fungi") +
ylab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=3, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.7 2-0.25 and AMF Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(x=x20wsa250, y= amf))+
geom_point(size=.5)+
labs(x="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and AMF") +
ylab(expression(~AMF ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=15, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=14, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(y=x20wsa250, x= amf, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between AMF and 2-0.25 mm fraction ") +
xlab(expression(~AMF ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=10, show.legend = F) +
theme_James() 
ggsave("x2_025mm_amf1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(x=x20wsa250, y= amf))+
facet_wrap(~location_a)+
labs(x="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and AMF") +
ylab(expression(~AMF ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.7.1 2-0.25 mm vs AMF Correlation plots for group depths
reg %>%
ggplot(aes(x=amf, y= x20wsa250, color=treatment))+
facet_wrap(~location_a)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between AMF and 2-0.25 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~AMF ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20wsa250, x= amf, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between AMF and 2-0.25 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~AMF ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=8, show.legend = F)
ggsave("x2_025mm_amfcor.png", height=5, width=9)5.8 2-0.25 and MB Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(y=x20wsa250, x= mb))+
geom_point(size=.5)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and Microbial Biomass") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=100, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=85, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(y=x20wsa250, x= mb, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 2-0.25 mm fraction ") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..r.label..,..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=50, show.legend = F) +
theme_James() 
ggsave("x2_025mm_mb1.png", height=5, width=9)
#group by location
reg %>%
ggplot(aes(y=x20wsa250, x= mb))+
facet_wrap(~location_a)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and Microbial Biomass") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 
5.8.1 2-0.25 mm vs MB Correlation plots for group depths
reg %>%
ggplot(aes(x=mb, y= x20wsa250, color=treatment))+
facet_wrap(~location_a)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 2-0.25 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_point(size=2)+
theme_James2()+
stat_cor(aes(label = paste(..r.label..,..rr.label.., ..p.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)
#Has correlation by treatment by location
reg %>%
ggplot(aes(y=x20wsa250, x= mb, color=treatment))+
facet_wrap(~location_a)+
stat_cor(aes(label = paste(..r.label.., sep = "~`,`~")), method="spearman", na.rm=F, p.accuracy = 0.001, show.legend = F)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between Microbial Biomass and 2-0.25 mm fraction ", shape="Horizon", color="Treatment") +
xlab(expression(~Microbial ~Biomass ~(nmol ~PLFA ~g^{-1} ~soil))) +
scale_y_continuous(limits=c(0,60)) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=2, aes(shape=horizon_a))+
theme_James2() +
stat_regline_equation(label.x=60, show.legend = F)
ggsave("x2_025mm_mbcor.png", height=5, width=9)5.9 2-0.25 mm and Fungi Correlation graphs
#20 minute Mean Weight Diameter vs 5 minute Method
ggplot(reg, aes(y=x20wsa250, x= fungi))+
geom_point(size=.5)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and Fungi") +
xlab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_bw(base_size=12, base_family='TT Times New Roman')+
geom_smooth(method="lm", se=FALSE)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
stat_cor(aes(label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.3, label.y=5, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.y=4, label.x=.3) +
theme_James() 
#cor(soils$x20mwd, soils$x5mwd, method="spearman", use="complete.obs")
#One graph by location
ggplot(reg, aes(y=x20wsa250, x= fungi, color=location_a, shape=location_a))+
geom_point(size=1)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and Fungi") +
xlab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.x=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=13, show.legend = F) +
theme_James() 
#group by location
reg %>%
ggplot(aes(y=x20wsa250, x= fungi))+
facet_wrap(~location_a)+
labs(y="Percent 2-0.25 mm Aggregate fraction (%)",
title="Correlation between 2-0.25 mm fraction and Fungi") +
xlab(expression(~Saprophytic ~Fungi ~(nmol ~PLFA ~g^{-1} ~soil))) +
theme_classic()+
geom_smooth(method="lm", se=FALSE, color= "black") +
geom_point(size=.5)+
stat_cor(aes(color=location_a, label = paste(..rr.label.., sep = "~`,`~")), method="spearman", na.rm=F, label.y=.01, p.accuracy = 0.001, show.legend = F) +
stat_regline_equation(label.x=.5, show.legend = F)+
theme_James() 