N_RHealth<-read.csv("Nthapo RangeMod.csv")
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
N_RHealth %>%
group_by(data.time_date.site,data.rangel_form.rl_nearest_perennial_sp) %>%
summarise(count=n()) %>%
mutate(freq=count/sum(count)) %>%
ggplot(aes(x=reorder(data.rangel_form.rl_nearest_perennial_sp, -freq),y=freq)) + geom_bar(stat="identity", fill= "seagreen") +theme_bw(base_size=9) +
theme(axis.text.x = element_text(angle= 90, hjust=1)) + xlab("Species Name") + ylab("Relative Frequency")+facet_wrap(~data.time_date.site, ncol=2)
## `summarise()` has grouped output by 'data.time_date.site'. You can override
## using the `.groups` argument.
library(dplyr)
RYield<-N_RHealth%>%
group_by(data.time_date.site,data.time_date.plot,data.rangel_form.rl_dpm_height)%>%
summarise(MeanDPM= mean(data.rangel_form.rl_dpm_height,na.rm = TRUE))
## `summarise()` has grouped output by 'data.time_date.site',
## 'data.time_date.plot'. You can override using the `.groups` argument.
library(ggplot2)
RYield%>%
mutate(Yield=(31.7176*0.3218^(1/MeanDPM)*MeanDPM^0.2834)^2)%>%
group_by(data.time_date.site,Yield)%>%
ggplot()+geom_boxplot(aes(x=data.time_date.site,y=Yield,fill=data.time_date.site)) +coord_flip() +
ggtitle("Yield per grazing unit")+theme(legend.position = "none")+xlab("Grazing Unit")+ylab("Yield(Kg/ha)")
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
N_RHealth%>%
group_by(data.time_date.site)%>%
mutate(count=n()) %>%
filter(count>0 & !is.na(data.time_date.site)) %>%
ggplot(aes(y =data.rangel_form.rl_dist_perennial, x = data.time_date.site)) +
geom_boxplot(fill="grey50", width=0.4, alpha=0.7) +
geom_hline(aes(yintercept=8),lwd=0.5, lty=3, col="black")+ ggtitle('Distance to tuft per grazing unit ') + theme_gray(base_size=10)+ ylab('Distance to the nearest tuft (cm)')+ xlab('grazing unit')+coord_flip()
N_RHealth%>%
group_by(data.time_date.site)%>%
mutate(count=n()) %>%
filter(count>0 & !is.na(data.time_date.site)) %>%
ggplot(aes(y =data.rangel_form.rl_leaflitter_depth_cm, x = data.time_date.site)) +
geom_boxplot(fill="grey50", width=0.4, alpha=0.7) +
geom_hline(aes(yintercept=8),lwd=0.5, lty=3, col="black")+ ggtitle('Litter thickness per grazing unit ') + theme_gray(base_size=10)+ ylab('Litter thickness (cm)')+ xlab('grazing unit')+coord_flip()
N_RHealth %>%
group_by (data.time_date.site) %>%
summarise(uniqueSpecies = length(unique(data.rangel_form.rl_nearest_perennial_sp)),
meanElevation=mean(data.gps.altitude)) %>%
ggplot(aes(x=reorder(data.time_date.site, -uniqueSpecies), y=uniqueSpecies,fill=data.time_date.site))+ geom_boxplot()+ theme_bw()+theme(legend.position = "none") +ggtitle("Unique Plant Species by grazing Unit")+ ylab("Number of Unique Herbaceous Species") + xlab("Grazing unit")+theme(axis.text.x = element_text(angle=45))
N_Browse<-read.csv("Nthapo Shrubs.csv")
sort(unique(N_Browse$data.browse_sp1.tree_shrub_sp1))
## [1] " " "Aloe maculata"
## [3] "Chrysocoma ciliata" "Cliffortia nitidula"
## [5] "Diospyros austro-africana" "Euryops annae"
## [7] "Felicia filifolia" "Gymnopentzia bifurcata"
## [9] "Helichrysum splendidum" "Helichrysum trilineatum"
## [11] "Leucosidea sericea" "Passerina montana"
## [13] "Pentzia cooperi" "Relhania dieterlenii"
## [15] "Rhumnus prinoides" "Searsia divericata"
## [17] "Stachys rugosa"
N_Browse%>%
mutate(AvShrubDensity=as.integer((data.browse_sp1.tree_shrub_sp_n1)/1*100))%>%
group_by(data.time_date.site)%>%
ggplot()+geom_boxplot(aes(x=data.time_date.site,y=AvShrubDensity,fill=data.time_date.site)) +ggtitle("Woody plant density per site")+theme(legend.position = "none")+xlab("Sample Site")+ylab("Woody Plant Density/(No./ha)")+theme(axis.text.x = element_text(angle=45))
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
N_Browse%>%
group_by(data.time_date.site,data.browse_sp1.tree_shrub_sp1)%>%
mutate(ShrubVol=(pi*(sum(data.browse_sp1.tree_shrub_sp_height1,data.browse_sp1.shrub_average_width1)/2)^2*data.browse_sp1.shrub_average_height1)/4)%>%
ggplot()+geom_boxplot(aes(x=data.browse_sp1.tree_shrub_sp1,y=ShrubVol,fill=data.browse_sp1.tree_shrub_sp1)) +
ggtitle("Shrub volume per species in all grazing units")+ coord_flip()+theme(legend.position = "none")+xlab("Woody plant species")+ylab("Shrub Volume(m^3))") + facet_wrap(~data.time_date.site,nrow = 2)+theme(axis.text.x = element_text(angle=60))
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
SP_List<-sort(unique(N_RHealth$data.rangel_form.rl_nearest_perennial_sp),decreasing = FALSE)# herbaceous Species Checklist
Herb_Sp<-as.data.frame(SP_List)
Herb_Sp
## SP_List
## 1 Agrostis lachnantha
## 2 Aristida bipartita
## 3 Aristida congesta
## 4 Aristida diffusa
## 5 Bidens pilosa
## 6 Catalepis gracilis
## 7 Cheilanthes hirta
## 8 Cotyledon orbiculata var. oblonga
## 9 Cymbopogon excavatus
## 10 Cynodon dactylon
## 11 Cyperus marginatus
## 12 Elionurus muticus
## 13 Eragrostis capensis
## 14 Eragrostis curvula
## 15 Eragrostis plana
## 16 Eragrostis racemosa
## 17 Fingerhuthia sesleriiformis
## 18 Gazania krebsiana
## 19 Harpochloa falx
## 20 Helichrysum caespititium
## 21 Helichrysum ecklonis
## 22 Helichrysum krookii
## 23 Helichrysum odoratissimum
## 24 Heteropogon contortus
## 25 Hyperrhenia hirta
## 26 Jamesbrettania breviflora
## 27 Jamesbrittenia breviflora
## 28 Jamesbrittenia prestisepala
## 29 Malva parviflora
## 30 Melenis repens
## 31 Melolobium microphyllum
## 32 Merxmuellera disticha
## 33 Microchloa caffra
## 34 Pelargonium alchemelloides
## 35 Pennisetum sphacelatum
## 36 Pennisetum thunbergii
## 37 Psammotropha mucronata
## 38 Salsola kali
## 39 Salvia stenophylla
## 40 Senecio asperulus
## 41 Senecio dissimulans
## 42 Sonchus nanus
## 43 Tagetes minuta
## 44 Themeda triandra
## 45 Trachypogon spicatus
## 46 Trifolium africanum
## 47 Xanthium spinosa
SpEco<-Herb_Sp %>%
mutate(
EcoStatus = case_when(
SP_List == "Fingerhuthia sesleriiformis" ~ "Decreaser",
SP_List == "Aristida diffusa" ~ "Increaser3",
SP_List == "Eragrostis plana" ~ "Increaser2",
SP_List == "Eragrostis racemosa" ~ "Increaser2",
SP_List == "Agrostis lachnantha" ~ "Increaser2",
SP_List == "Eragrostis capensis" ~ "Increaser2",
SP_List == "Themeda triandra" ~ "Decreaser",
SP_List == "Festuca caprina" ~ "Decreaser",
SP_List == "Eragrostis curvula" ~ "Increaser2",
SP_List == "Eragrostis caesia" ~ "Increaser2",
SP_List == "Trachypogon spicatus" ~ "Increaser1",
SP_List == "Merxmuellera disticha" ~ "Increaser3",
SP_List == "Cynodon dactylon" ~ "Increaser2",
SP_List == "Melica decumbens" ~ "Increaser3",
SP_List == "Aristida bipartita" ~ "Increaser2",
SP_List == "Aristida congesta" ~ "Increaser2",
SP_List == "Catalepis gracilis" ~ "Increaser1",
SP_List == "Cymbopogon excavatus" ~ "Increaser1",
SP_List == "Cyperus marginatus" ~ "Increaser1",
SP_List == "Elionurus muticus" ~ "Increaser3",
SP_List == "Harpochloa falx" ~ "Increaser1",
SP_List == "Heteropogon contortus" ~ "Increaser2",
SP_List == "Hyperrhenia hirta" ~ "Increaser1",
SP_List == "Melenis repens" ~ "Increaser2",
SP_List == "Microchloa caffra" ~ "Increaser2",
SP_List == "Pennisetum sphacelatum" ~ "Increaser1",
SP_List == "Pennisetum thunbergii" ~ "Increaser1",
TRUE ~ "other"
)
)
SpEcoName<-rename(SpEco,data.rangel_form.rl_nearest_perennial_sp = SP_List)
SpEcoName
## data.rangel_form.rl_nearest_perennial_sp EcoStatus
## 1 Agrostis lachnantha Increaser2
## 2 Aristida bipartita Increaser2
## 3 Aristida congesta Increaser2
## 4 Aristida diffusa Increaser3
## 5 Bidens pilosa other
## 6 Catalepis gracilis Increaser1
## 7 Cheilanthes hirta other
## 8 Cotyledon orbiculata var. oblonga other
## 9 Cymbopogon excavatus Increaser1
## 10 Cynodon dactylon Increaser2
## 11 Cyperus marginatus Increaser1
## 12 Elionurus muticus Increaser3
## 13 Eragrostis capensis Increaser2
## 14 Eragrostis curvula Increaser2
## 15 Eragrostis plana Increaser2
## 16 Eragrostis racemosa Increaser2
## 17 Fingerhuthia sesleriiformis Decreaser
## 18 Gazania krebsiana other
## 19 Harpochloa falx Increaser1
## 20 Helichrysum caespititium other
## 21 Helichrysum ecklonis other
## 22 Helichrysum krookii other
## 23 Helichrysum odoratissimum other
## 24 Heteropogon contortus Increaser2
## 25 Hyperrhenia hirta Increaser1
## 26 Jamesbrettania breviflora other
## 27 Jamesbrittenia breviflora other
## 28 Jamesbrittenia prestisepala other
## 29 Malva parviflora other
## 30 Melenis repens Increaser2
## 31 Melolobium microphyllum other
## 32 Merxmuellera disticha Increaser3
## 33 Microchloa caffra Increaser2
## 34 Pelargonium alchemelloides other
## 35 Pennisetum sphacelatum Increaser1
## 36 Pennisetum thunbergii Increaser1
## 37 Psammotropha mucronata other
## 38 Salsola kali other
## 39 Salvia stenophylla other
## 40 Senecio asperulus other
## 41 Senecio dissimulans other
## 42 Sonchus nanus other
## 43 Tagetes minuta other
## 44 Themeda triandra Decreaser
## 45 Trachypogon spicatus Increaser1
## 46 Trifolium africanum other
## 47 Xanthium spinosa other
R_HealthJoin<-N_RHealth %>%
mutate(
EcoStatus = case_when(
data.rangel_form.rl_nearest_perennial_sp == "Fingerhuthia sesleriiformis" ~ "Decreaser",
data.rangel_form.rl_nearest_perennial_sp == "Aristida diffusa" ~ "Increaser3",
data.rangel_form.rl_nearest_perennial_sp == "Eragrostis plana" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Eragrostis racemosa" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Agrostis lachnantha" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Eragrostis capensis" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Themeda triandra" ~ "Decreaser",
data.rangel_form.rl_nearest_perennial_sp == "Festuca caprina" ~ "Decreaser",
data.rangel_form.rl_nearest_perennial_sp == "Eragrostis curvula" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Eragrostis caesia" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Trachypogon spicatus" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Merxmuellera disticha" ~ "Increaser3",
data.rangel_form.rl_nearest_perennial_sp == "Cynodon dactylon" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Melica decumbens" ~ "Increaser3",
data.rangel_form.rl_nearest_perennial_sp == "Aristida bipartita" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Aristida congesta" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Catalepis gracilis" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Cymbopogon excavatus" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Cyperus marginatus" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Elionurus muticus" ~ "Increaser3",
data.rangel_form.rl_nearest_perennial_sp == "Harpochloa falx" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Heteropogon contortus" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Hyperrhenia hirta" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Melenis repens" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Microchloa caffra" ~ "Increaser2",
data.rangel_form.rl_nearest_perennial_sp == "Pennisetum sphacelatum" ~ "Increaser1",
data.rangel_form.rl_nearest_perennial_sp == "Pennisetum thunbergii" ~ "Increaser1",
TRUE ~ "other"
)
)
R_HealthJoin %>%
group_by(data.time_date.site,EcoStatus) %>%
summarise(count=n()) %>%
mutate(freq=count/sum(count)*100) %>%
ggplot(aes(x=reorder(EcoStatus, -freq),y=freq)) + geom_bar(stat="identity", fill= "tomato") +theme_bw(base_size=9) +
theme(axis.text.x = element_text(angle= 90, hjust=1)) + xlab("Ecological Status") + ylab("Fractional Cover(%)")+facet_wrap(~data.time_date.site, ncol=5)+ theme_dark()+theme(axis.text.x = element_text(angle=90))
## `summarise()` has grouped output by 'data.time_date.site'. You can override
## using the `.groups` argument.
N_Browse%>%
mutate(AvShrubDensity=as.integer((data.browse_sp1.tree_shrub_sp_n1)/1*100))%>%
group_by(data.time_date.site)%>%
ggplot()+geom_violin(aes(x=data.time_date.site,y=AvShrubDensity,
stat = "ydensity",
position = "dodge",draw_quantiles = TRUE,
trim = TRUE,scale = "area",
inherit.aes = TRUE,fill=data.time_date.site)) +ggtitle("Woody plant density per site")+theme(legend.position = "none")+xlab("Sample Site")+ylab("Woody Plant Density/(No./ha)")+theme(axis.text.x = element_text(angle=45))
## Warning in geom_violin(aes(x = data.time_date.site, y = AvShrubDensity, : Ignoring unknown aesthetics: stat, position, draw_quantiles, trim, scale, and
## inherit.aes
## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).
