#shannon_index_functions Seascape_shannon = function(data1, group, daterange){ #data$seascape_variable <- factor(data$seascape_variable, levels = c("33","1", "2","3","4","5","6","7","8"," 9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32")) maxDate = daterange[2] startDate = daterange[1] data1 = data1 %>% filter(mpa_id %in% group, date.time>=startDate & date.time<=maxDate) # convert seascape to factor data1$seascape_variable <- as.factor(data1$seascape_variable) # calculate frequency of each seascape variable for each MPA data2 <- summarize(data1, freq_33 =sum(seascape_variable=="33", na.rm=T),freq_7 =sum(seascape_variable=="7", na.rm=T), freq_11 =sum(seascape_variable=="11", na.rm=T),freq_12 =sum(seascape_variable=="12", na.rm=T), freq_14 =sum(seascape_variable=="14", na.rm=T),freq_15 =sum(seascape_variable=="15", na.rm=T), freq_17 =sum(seascape_variable=="17", na.rm=T),freq_19 =sum(seascape_variable=="19", na.rm=T), freq_20 =sum(seascape_variable=="20", na.rm=T),freq_21 =sum(seascape_variable=="21", na.rm=T), freq_22 =sum(seascape_variable=="22", na.rm=T),freq_23 =sum(seascape_variable=="23", na.rm=T), freq_27 =sum(seascape_variable=="27", na.rm=T)) data2 <-as.data.frame(data2) data2$freq_33 <- as.numeric(data2$freq_33) data2$freq_7 <- as.numeric(data2$freq_7) data2$freq_11 <- as.numeric(data2$freq_11) data2$freq_12 <- as.numeric(data2$freq_12) data2$freq_14 <- as.numeric(data2$freq_14) data2$freq_15 <- as.numeric(data2$freq_15) data2$freq_17 <- as.numeric(data2$freq_17) data2$freq_19 <- as.numeric(data2$freq_19) data2$freq_20 <- as.numeric(data2$freq_20) data2$freq_21 <- as.numeric(data2$freq_21) data2$freq_22 <- as.numeric(data2$freq_22) data2$freq_23 <- as.numeric(data2$freq_23) data2$freq_27 <- as.numeric(data2$freq_27) data2$sum <- sum(data2$freq_33, data2$freq_7, data2$freq_11, data2$freq_12, data2$freq_14, data2$freq_15, data2$freq_17, data2$freq_19, data2$freq_20, data2$freq_21, data2$freq_22, data2$freq_23, data2$freq_27) shannon_in <- summarize(data2, plnp33 = (freq_33/sum) * log(freq_33/sum), plnp7 = (freq_7/sum) * log(freq_7/sum), plnp11 = (freq_11/sum) * log(freq_11/sum), plnp12 = (freq_12/sum) * log(freq_12/sum), plnp14 = (freq_14/sum) * log(freq_14/sum), plnp15 = (freq_15/sum) * log(freq_15/sum), plnp17 = (freq_17/sum) * log(freq_17/sum), plnp19 = (freq_19/sum) * log(freq_19/sum), plnp20 = (freq_20/sum) * log(freq_20/sum), plnp21 = (freq_21/sum) * log(freq_21/sum), plnp22 = (freq_22/sum) * log(freq_22/sum), plnp23 = (freq_23/sum) * log(freq_23/sum), plnp27 = (freq_27/sum) * log(freq_27/sum)) is.nan.data.frame <- function(x) do.call(cbind, lapply(x, is.nan)) shannon_in[is.nan(shannon_in)] <- 0 shannon <- (-1*sum(shannon_in$plnp33, shannon_in$plnp7, shannon_in$plnp11, shannon_in$plnp12, shannon_in$plnp14, shannon_in$plnp15, shannon_in$plnp17, shannon_in$plnp19, shannon_in$plnp20, shannon_in$plnp21, shannon_in$plnp22, shannon_in$plnp23, shannon_in$plnp27)) #print(shannon) } # # data1 = mpa_sea %>% # filter(mpa_id %in% "Pyramid Point SMCA", # date.time>=date.time[500] & date.time<=date.time[1000]) # # # convert seascape to factor # data1$seascape_variable <- as.factor(data1$seascape_variable) # # # calculate frequency of each seascape variable for each MPA # #mpa_index <- group_by(data1, mpa_id) # data2 <- summarize(data1, freq_33 =sum(seascape_variable=="33", na.rm=T),freq_7 =sum(seascape_variable=="7", na.rm=T), # freq_11 =sum(seascape_variable=="11", na.rm=T),freq_12 =sum(seascape_variable=="12", na.rm=T), # freq_14 =sum(seascape_variable=="14", na.rm=T),freq_15 =sum(seascape_variable=="15", na.rm=T), # freq_17 =sum(seascape_variable=="17", na.rm=T),freq_19 =sum(seascape_variable=="19", na.rm=T), # freq_20 =sum(seascape_variable=="20", na.rm=T),freq_21 =sum(seascape_variable=="21", na.rm=T), # freq_22 =sum(seascape_variable=="22", na.rm=T),freq_23 =sum(seascape_variable=="23", na.rm=T), # freq_27 =sum(seascape_variable=="27", na.rm=T)) # # data2 <-as.data.frame(data2) # # data2$freq_33 <- as.numeric(data2$freq_33) # data2$freq_7 <- as.numeric(data2$freq_7) # data2$freq_11 <- as.numeric(data2$freq_11) # data2$freq_12 <- as.numeric(data2$freq_12) # data2$freq_14 <- as.numeric(data2$freq_14) # data2$freq_15 <- as.numeric(data2$freq_15) # data2$freq_17 <- as.numeric(data2$freq_17) # data2$freq_19 <- as.numeric(data2$freq_19) # data2$freq_20 <- as.numeric(data2$freq_20) # data2$freq_21 <- as.numeric(data2$freq_21) # data2$freq_22 <- as.numeric(data2$freq_22) # data2$freq_23 <- as.numeric(data2$freq_23) # data2$freq_27 <- as.numeric(data2$freq_27) # # data2$sum <- sum(data2$freq_33, data2$freq_7, data2$freq_11, data2$freq_12, data2$freq_14, data2$freq_15, data2$freq_17, data2$freq_19, data2$freq_20, data2$freq_21, data2$freq_22, data2$freq_23, data2$freq_27) # head(data2$sum) # # # # shannon_in <- summarize(data2, plnp33 = (freq_33/sum) * log(freq_33/sum), plnp7 = (freq_7/sum) * log(freq_7/sum), # plnp11 = (freq_11/sum) * log(freq_11/sum), plnp12 = (freq_12/sum) * log(freq_12/sum), # plnp14 = (freq_14/sum) * log(freq_14/sum), plnp15 = (freq_15/sum) * log(freq_15/sum), # plnp17 = (freq_17/sum) * log(freq_17/sum), plnp19 = (freq_19/sum) * log(freq_19/sum), # plnp20 = (freq_20/sum) * log(freq_20/sum), plnp21 = (freq_21/sum) * log(freq_21/sum), # plnp22 = (freq_22/sum) * log(freq_22/sum), plnp23 = (freq_23/sum) * log(freq_23/sum), # plnp27 = (freq_27/sum) * log(freq_27/sum)) # # is.nan.data.frame <- function(x) # do.call(cbind, lapply(x, is.nan)) # shannon_in[is.nan(shannon_in)] <- 0 # # shannon <- (-1*sum(shannon_in$plnp33, shannon_in$plnp7, shannon_in$plnp11, shannon_in$plnp12, # shannon_in$plnp14, shannon_in$plnp15, shannon_in$plnp17, shannon_in$plnp19, # shannon_in$plnp20, shannon_in$plnp21, shannon_in$plnp22, shannon_in$plnp23, # shannon_in$plnp27)) # print(shannon) # # # # #create new dataframe # # mpa_index3 <- data.frame(mpa_index2) # # # # #### read new csv with removed MPAs (with only NAs) # # mpa_shannon = read.csv('data/shannon_index.csv') # # # # # relative abundance of species times natural log of relative abundance # # shannon_in <- group_by(mpa_shannon, mpa_id) # # shannon_in2 <- summarize(mpa_shannon, plnp33 = (freq_33/sum) * log(freq_33/sum), plnp7 = (freq_7/sum) * log(freq_7/sum), # # plnp11 = (freq_11/sum) * log(freq_11/sum), plnp12 = (freq_12/sum) * log(freq_12/sum), # # plnp14 = (freq_14/sum) * log(freq_14/sum), plnp15 = (freq_15/sum) * log(freq_15/sum), # # plnp17 = (freq_17/sum) * log(freq_17/sum), plnp19 = (freq_19/sum) * log(freq_19/sum), # # plnp20 = (freq_20/sum) * log(freq_20/sum), plnp21 = (freq_21/sum) * log(freq_21/sum), # # plnp22 = (freq_22/sum) * log(freq_22/sum), plnp23 = (freq_23/sum) * log(freq_23/sum), # # plnp27 = (freq_27/sum) * log(freq_27/sum)) # # # # shannon_in2 <- cbind(mpa_shannon, shannon_in2) # # head(shannon_in2) # # # # is.nan.data.frame <- function(x) # # do.call(cbind, lapply(x, is.nan)) # # shannon_in2[is.nan(shannon_in2)] <- 0 # # # # shannon_in2$shannon <- 0 # # head(shannon_in2) # # # # for (i in 1:length(shannon_in2$shannon)) { # # shannon_in2$shannon[i] <- (-1*sum(shannon_in2$plnp33[i], shannon_in2$plnp7[i], shannon_in2$plnp11[i], shannon_in2$plnp12[i], # # shannon_in2$plnp14[i], shannon_in2$plnp15[i], shannon_in2$plnp17[i], shannon_in2$plnp19[i], # # shannon_in2$plnp20[i], shannon_in2$plnp21[i], shannon_in2$plnp22[i], shannon_in2$plnp23[i], # # shannon_in2$plnp27[i])) # # } # # head(shannon_in2) # # tail(shannon_in2) # #