Libraries and data were loaded/cleaned. Knitr was added as a package to create the tables.
library(readxl)
library(tidyverse)
library(knitr)
cary<-read_xls("Transect_Data_Set/temp.namerica/CARY.xls")
hbm<-read_xls("Transect_Data_Set/temp.namerica/HEUSTOBM.xls")
ccary<- cary %>%
select(-voucher...5,-voucher...6,-voucher...7,-voucher...8,-voucher...9,
-voucher...10,-voucher...11,-voucher...12,-Liana, -"N(Ind.)") %>%
pivot_longer(cols = starts_with("Stemdbh"),
values_to = "Stemdbh") %>%
filter(!is.na(Stemdbh)) %>%
select(-name)
chbm<- hbm %>%
select(-voucher...5,-voucher...6,-voucher...7,-voucher...8,-voucher...9,
-voucher...10,-voucher...11,-voucher...12,-Liana, -"N(Ind.)") %>%
pivot_longer(cols = starts_with("Stemdbh"),
values_to = "Stemdbh") %>%
filter(!is.na(Stemdbh)) %>%
select(-name)Preparing for the calculations that come before the actual indices such as the D value for each species and line and calculations that come before the H value
calcD<- function(n,N){
(n/N)^2
}
n_count <- ccary %>% #indiviuals per species CARY
group_by(Line, Species) %>%
summarize(n = n()) %>%
ungroup()
N_count <- ccary %>% #total individuals per line
group_by(Line) %>%
summarize(N = n()) %>%
ungroup()
nandN<-left_join(n_count,N_count, by=c("Line"="Line"))
n_count2 <- chbm %>% #indiviuals per species HEUSTO
group_by(Line, Species) %>%
summarize(n = n()) %>%
ungroup()
N_count2 <- chbm %>% #total individuals per line
group_by(Line) %>%
summarize(N = n()) %>%
ungroup()
nandN2<-left_join(n_count2,N_count2, by=c("Line"="Line"))The calculations for D, n/N, etc. for each species and line which was then used in the meanD dataframes to get indices for each transect line. Tables were also created for each site.
withD <- nandN %>%
mutate(D = calcD(n,N)) %>% #D calc
mutate(ndivN= n/N) %>% #n/N
mutate(lnndivN= log(n/N)) %>% #ln(n/N)
mutate(befH= ndivN * lnndivN) # negative H
meanD<- withD %>% #CARY site indices per transect
group_by(Line) %>%
summarize(meanD= round(mean(D),3),
meanbefH= round(mean(befH),3),
totspecies= n_distinct(Species)) %>%
mutate(Index_of_Div= round(1-meanD,3)) %>%
mutate(Recip_Index= round(1/meanD,3)) %>%
mutate(ESimp= round(Recip_Index/totspecies,3)) %>%
mutate(H= round(-1 * meanbefH,3)) %>%
mutate(EShan= round(H/log(totspecies),3)) %>%
arrange(as.numeric(Line)) %>%
mutate(Site="Cary") %>%
select(-meanbefH)
withD2 <- nandN2 %>%
mutate(D = calcD(n,N)) %>%
mutate(ndivN= n/N) %>%
mutate(lnndivN= log(n/N)) %>%
mutate(befH= ndivN * lnndivN)
meanD2<- withD2 %>% #HEUSTO site indices per transect
group_by(Line) %>%
summarize(meanD= round(mean(D),3),
meanbefH= round(mean(befH),3),
totspecies= n_distinct(Species)) %>%
mutate(Index_of_Div= round(1-meanD,3)) %>%
mutate(Recip_Index= round(1/meanD,3)) %>%
mutate(ESimp= round(Recip_Index/totspecies,3)) %>%
mutate(H= round(-1 * meanbefH,3)) %>%
mutate(EShan= round(H/log(totspecies),3)) %>%
arrange(as.numeric(Line)) %>%
mutate(Site="Heusto B/M") %>%
select(-meanbefH)
carytable<-kable(meanD, format="html")
kable(meanD, format="html")| Line | meanD | totspecies | Index_of_Div | Recip_Index | ESimp | H | EShan | Site |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.040 | 6 | 0.960 | 25.000 | 4.167 | 0.266 | 0.148 | Cary |
| 2 | 0.018 | 10 | 0.982 | 55.556 | 5.556 | 0.201 | 0.087 | Cary |
| 3 | 0.028 | 7 | 0.972 | 35.714 | 5.102 | 0.256 | 0.132 | Cary |
| 4 | 0.017 | 9 | 0.983 | 58.824 | 6.536 | 0.224 | 0.102 | Cary |
| 5 | 0.069 | 4 | 0.931 | 14.493 | 3.623 | 0.332 | 0.239 | Cary |
| 6 | 0.097 | 4 | 0.903 | 10.309 | 2.577 | 0.288 | 0.208 | Cary |
| 7 | 0.041 | 6 | 0.959 | 24.390 | 4.065 | 0.261 | 0.146 | Cary |
| 8 | 0.021 | 9 | 0.979 | 47.619 | 5.291 | 0.210 | 0.096 | Cary |
| 9 | 0.031 | 6 | 0.969 | 32.258 | 5.376 | 0.289 | 0.161 | Cary |
| 10 | 0.022 | 8 | 0.978 | 45.455 | 5.682 | 0.236 | 0.113 | Cary |
heustobmtable<- kable(meanD2, format="html")
kable(meanD2, format="html")| Line | meanD | totspecies | Index_of_Div | Recip_Index | ESimp | H | EShan | Site |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.053 | 5 | 0.947 | 18.868 | 3.774 | 0.287 | 0.178 | Heusto B/M |
| 2 | 0.032 | 6 | 0.968 | 31.250 | 5.208 | 0.283 | 0.158 | Heusto B/M |
| 3 | 0.048 | 5 | 0.952 | 20.833 | 4.167 | 0.301 | 0.187 | Heusto B/M |
| 4 | 0.086 | 4 | 0.914 | 11.628 | 2.907 | 0.303 | 0.219 | Heusto B/M |
| 5 | 0.031 | 6 | 0.969 | 32.258 | 5.376 | 0.291 | 0.162 | Heusto B/M |
| 6 | 0.033 | 6 | 0.967 | 30.303 | 5.051 | 0.283 | 0.158 | Heusto B/M |
| 7 | 0.055 | 6 | 0.945 | 18.182 | 3.030 | 0.238 | 0.133 | Heusto B/M |
| 8 | 0.032 | 8 | 0.968 | 31.250 | 3.906 | 0.213 | 0.102 | Heusto B/M |
| 9 | 0.116 | 3 | 0.884 | 8.621 | 2.874 | 0.360 | 0.328 | Heusto B/M |
| 10 | 0.030 | 8 | 0.970 | 33.333 | 4.167 | 0.210 | 0.101 | Heusto B/M |
Joining the two two meanD tables to get the average index values per site. Made a table from the dataframe.
avgsite<- meanD %>% #table with indices for the two sites
rbind.data.frame(meanD2) %>%
group_by(Site) %>%
summarize(D=round(mean(meanD),3),
"1-D"=round(mean(Index_of_Div),3),
"D'"=round(mean(Recip_Index),3),
ESimpson=round(mean(ESimp),3),
H=round(mean(H),3),
EShannon=round(mean(EShan),3))
avgsitetable<- kable(avgsite, format="html")
kable(avgsite, format="html") | Site | D | 1-D | D’ | ESimpson | H | EShannon |
|---|---|---|---|---|---|---|
| Cary | 0.038 | 0.962 | 34.962 | 4.798 | 0.256 | 0.143 |
| Heusto B/M | 0.052 | 0.948 | 23.653 | 4.046 | 0.277 | 0.173 |