https://doi.org/10.5281/zenodo.15690037
.Rhistory
position=position_dodge(0.05), alpha = 0.1) +
geom_errorbar(aes(ymin = min,
ymax = max+0.05, color = as.factor(region)), width=.2,
position=position_dodge(0.05), alpha = 0) +
geom_point(aes(y = media, color = as.factor(region)), size = 0.7) +
facet_wrap(~(region), scales = 'free', ncol = 3) +
geom_text(x = 400, y = (N$MAX+0.05), hjust = 0, vjust = 1,
aes(label = paste0("N = ", N,"\n",
"Zsd = ", ZSD,"\n",
"Chl-a = ", chla,"\n",
"TSS = ", TSS,"\n",
"aCDOM(440) = ", cdom,"\n")), data = df, size = 7) +
scale_y_continuous(name = expression(R[rs]~(sr^-1)), limits = c(NA,NA)) +
scale_x_continuous(name = "Wavelength (nm)", limits = c(400,900),
breaks = seq(from = 400, to = 900, by = 100)) +
scale_fill_manual(values = colorBlindBlack8) +
scale_color_manual(values = colorBlindBlack8) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text=element_text(family = "Tahoma"),
axis.title = element_text( size = 35),
axis.text.x = element_text(colour="black", size = size_axis),
axis.text.y = element_text(colour="black", size = size_axis),
axis.line = element_line(size=2, colour = "black"),
strip.text = element_text(size = 40)) +
#legend.title = element_text(size=20), #change legend title font size
#legend.text = element_text(size=20),
#legend.key.size = unit(2, 'cm')
#change legend text font size) +
theme(plot.margin = unit(c(4,4,4,4), "lines")) +
guides(color = guide_legend(override.aes = list(size=15))) +
theme(legend.position="none")
ggsave(plot = plot_final, filename = 'Outputs/Figures/Figure_04.jpeg',
width = 20, height = 17, dpi = 200, units = 'in')
require(dplyr)
require(ggplot2)
require(tidyr)
require(reshape2)
require(lemon)
require(plotly)
require(data.table)
require(geobr)
require(terra)
require(openxlsx)
estacoes = read.xlsx('Data/stations.xlsx', detectDates = T)
rrs = read.xlsx('Data/rrs.xlsx', detectDates = T)
# Merge data
merged = merge(estacoes, rrs, by = 'BR_ID')
# change to numeric if necessary
for(i in 5:ncol(merged)) {
merged[,i] = as.numeric( merged[,i])
}
# select data to plot
RRS = paste('Rrs_', 400:900, sep = '')
median.na = function(x) { return(median(x, na.rm = T))}
sd.na = function(x) { return(sd(x, na.rm = T))}
max.na = function(x) { return(max(x, na.rm = T))}
min.na = function(x) { return(min(x, na.rm = T))}
res.mean = merged %>% group_by(state) %>% select(contains(RRS)) %>% summarise_each(fun = median.na)
res.sd = merged %>% group_by(state) %>% select(contains(RRS)) %>% summarise_each(fun = sd.na)
max.sd = merged %>% group_by(state) %>% select(contains(RRS)) %>% summarise_each(fun = max.na)
min.sd = merged %>% group_by(state) %>% select(contains(RRS)) %>% summarise_each(fun = min.na)
N = merged %>% group_by(state) %>% summarise(fun = n())
CHLA = merged %>% select('state', 'chla_ugL') %>% group_by(state) %>% summarise_each(fun = median.na)
ZSD = merged %>% select('state', 'secchi_m') %>% group_by(state) %>% summarise_each(fun = median.na)
ZSDzd = merged %>% select('state', 'secchi_m') %>% group_by(state) %>% summarise_each(fun = sd.na)
TSS = merged %>% select('state', 'tss_mgL') %>% group_by(state) %>% summarise_each(fun = median.na)
CDOM = merged %>% select('state', 'acdom440') %>% group_by(state) %>% summarise_each(fun = median.na)
rrs_melt.mean = res.mean %>% melt('state')
rrs_melt.sd = res.sd %>% melt("state")
rrs_melt.max = max.sd %>% melt("state")
rrs_melt.min = min.sd %>% melt("state")
rrs_melt = data.frame(media = rrs_melt.mean$value,
region = rrs_melt.mean$state,
variable = rrs_melt.mean$variable,
sd = rrs_melt.sd$value,
max = rrs_melt.max$value,
min = rrs_melt.min$value)
rrs_melt = data.frame(media = rrs_melt.mean$value,
region = rrs_melt.mean$state,
variable = rrs_melt.mean$variable,
sd = rrs_melt.sd$value,
max = (rrs_melt.mean$value+rrs_melt.sd$value),
min = (rrs_melt.mean$value-rrs_melt.sd$value))
maximos = rrs_melt %>% group_by(region) %>% summarise(fun = max.na(max))
N$MAX = maximos$fun
rrs_melt$min[rrs_melt$min < 0] = 0
df = data.frame(region = N$state,
N = N$fun,
chla = round(CHLA$chla_ugL, 2),
ZSD = round(ZSD$secchi_m, 2),
TSS = round(TSS$tss_mgL, 2),
cdom = round(CDOM$acdom440, 2),
ZSD.sd = round(ZSDzd$secchi_m, 2))
df$ZSD_plus_sd = paste(round(df$ZSD,2), '±', round(df$ZSD.sd,2))
rrs_melt$variable = gsub(rrs_melt$variable, pattern = "Rrs_", replacement = '') %>% as.numeric()
size_axis = 20
plot_final = rrs_melt %>% ggplot(aes(x = variable)) +
geom_errorbar(aes(ymin = min,
ymax = max, color = as.factor(region)), width=.2,
position=position_dodge(0.05), alpha = 0.1) +
geom_errorbar(aes(ymin = min,
ymax = max+0.05, color = as.factor(region)), width=.2,
position=position_dodge(0.05), alpha = 0) +
geom_point(aes(y = media, color = as.factor(region)), size = 0.7) +
facet_wrap(~(region), scales = 'free', ncol = 4) +
geom_text(x = 400, y = (N$MAX+0.05), hjust = 0, vjust = 1,
aes(label = paste0("N = ", N,"\n",
"Zsd = ", ZSD,"\n",
"Chl-a = ", chla,"\n",
"TSS = ", TSS,"\n",
"aCDOM(440) = ", cdom,"\n")), data = df, size = 8) +
scale_y_continuous(name = expression(R[rs]~(sr^-1))) +
scale_x_continuous(name = "Wavelength (nm)", limits = c(400,900),
breaks = seq(from = 400, to = 900, by = 100)) +
scale_fill_viridis_d(name = "region")+
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text=element_text(family = "Tahoma"),
axis.title = element_text( size = 35),
axis.text.x = element_text(colour="black", size = size_axis),
axis.text.y = element_text(colour="black", size = size_axis),
axis.line = element_line(size=2, colour = "black"),
strip.text = element_text(size = 40)) +
#legend.title = element_text(size=20), #change legend title font size
#legend.text = element_text(size=20),
#legend.key.size = unit(2, 'cm')
#change legend text font size) +
theme(plot.margin = unit(c(4,4,4,4), "lines")) +
guides(color = guide_legend(override.aes = list(size=15))) +
theme(legend.position="none")
ggsave(plot = plot_final, filename = 'Outputs/Figures/Figure_04.jpeg',
width = 30, height = 25, dpi = 200, units = 'in')
require(dplyr)
require(ggplot2)
require(tidyr)
require(reshape2)
require(lemon)
require(plotly)
require(data.table)
require(geobr)
require(terra)
require(openxlsx)
estacoes = read.xlsx('Data/stations.xlsx', detectDates = T)
rrs = read.xlsx('Data/rrs.xlsx', detectDates = T)
rrs$Classe = fread('Outputs/SAM_OWT_rrs.csv')$Class
merged = merge(estacoes, rrs, by = 'BR_ID', no.dups = T)
for(i in 5:ncol(merged)) {
merged[,i] = as.numeric( merged[,i])
}
RRS = paste('Rrs_', 400:900, sep = '')
median.na = function(x) { return(median(x, na.rm = T))}
sd.na = function(x) { return(sd(x, na.rm = T))}
max.na = function(x) { return(max(x, na.rm = T))}
min.na = function(x) { return(min(x, na.rm = T))}
merged$estado = merged$Classe
merged = filter(merged, estado > 0)
res.mean = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = median.na)
res.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = sd.na)
max.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = max.na)
min.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = min.na)
N = merged %>% group_by(estado) %>% summarise(fun = n())
N$MAX = apply(max.sd[,-1], 1, max)
CHLA = merged %>% select('estado', 'chla_ugL') %>% group_by(estado) %>% summarise_each(fun = median.na)
ZSD = merged %>% select('estado', 'secchi_m') %>% group_by(estado) %>% summarise_each(fun = median.na)
ZSDzd = merged %>% select('estado', 'secchi_m') %>% group_by(estado) %>% summarise_each(fun = sd.na)
TSS = merged %>% select('estado', 'tss_mgL') %>% group_by(estado) %>% summarise_each(fun = median.na)
CDOM = merged %>% select('estado', 'acdom440') %>% group_by(estado) %>% summarise_each(fun = median.na)
rrs_melt.mean = res.mean %>% melt('estado')
rrs_melt.sd = res.sd %>% melt("estado")
rrs_melt.max = max.sd %>% melt("estado")
rrs_melt.min = min.sd %>% melt("estado")
rrs_melt = data.frame(media = rrs_melt.mean$value,
region = rrs_melt.mean$estado,
variable = rrs_melt.mean$variable,
sd = rrs_melt.sd$value,
max = rrs_melt.max$value,
min = rrs_melt.min$value)
df = data.frame(region = N$estado,
N = N$fun,
chla = round(CHLA$chla_ugL, 2),
ZSD = round(ZSD$secchi_m, 2),
TSS = round(TSS$tss_mgL, 2),
cdom = round(CDOM$acdom440, 2),
ZSD.sd = round(ZSDzd$secchi_m, 2))
df$ZSD_plus_sd = paste(round(df$ZSD,2), '±', round(df$ZSD.sd,2))
rrs_melt$variable = gsub(rrs_melt$variable, pattern = "Rrs_", replacement = '') %>% as.numeric()
rrs_melt = filter(rrs_melt, region > 0)
rrs_melt$region = factor(x = rrs_melt$region, levels = c(1,2,3,4,5,6,7))
df = filter(df, region > 0)
df$region = factor(x = df$region, levels = c(1,2,3,4,5,6,7))
rrs_melt$region = gsub(rrs_melt$region, pattern = 1, replacement = 'OWT-01')
rrs_melt$region = gsub(rrs_melt$region, pattern = 2, replacement = 'OWT-02')
rrs_melt$region = gsub(rrs_melt$region, pattern = 3, replacement = 'OWT-03')
rrs_melt$region = gsub(rrs_melt$region, pattern = 4, replacement = 'OWT-04')
rrs_melt$region = gsub(rrs_melt$region, pattern = 5, replacement = 'OWT-05')
rrs_melt$region = gsub(rrs_melt$region, pattern = 6, replacement = 'OWT-06')
rrs_melt$region = gsub(rrs_melt$region, pattern = 7, replacement = 'OWT-07')
rrs_melt$region = factor(x = rrs_melt$region, levels = c('OWT-01',
'OWT-02',
'OWT-03',
'OWT-04',
'OWT-05',
'OWT-06',
'OWT-07'))
df$region = gsub(df$region, pattern = 1, replacement = 'OWT-01')
df$region = gsub(df$region, pattern = 2, replacement = 'OWT-02')
df$region = gsub(df$region, pattern = 3, replacement = 'OWT-03')
df$region = gsub(df$region, pattern = 4, replacement = 'OWT-04')
df$region = gsub(df$region, pattern = 5, replacement = 'OWT-05')
df$region = gsub(df$region, pattern = 6, replacement = 'OWT-06')
df$region = gsub(df$region, pattern = 7, replacement = 'OWT-07')
df$region = factor(x = df$region, levels = c('OWT-01',
'OWT-02',
'OWT-03',
'OWT-04',
'OWT-05',
'OWT-06',
'OWT-07'))
colorBlindBlack8 <- c("#0000FF", "#0072B2", "#56B4E9", "#009E73",
"#F0E442", "#117733", "#D55E00")
size_axis = 20
plot_final = rrs_melt %>% ggplot(aes(x = variable)) +
geom_errorbar(aes(ymin = min,
ymax = max, color = as.factor(region)), width=.2,
position=position_dodge(0.05), alpha = 0.1) +
geom_errorbar(aes(ymin = min,
ymax = max+0.05, color = as.factor(region)), width=.2,
position=position_dodge(0.05), alpha = 0) +
geom_point(aes(y = media, color = as.factor(region)), size = 0.7) +
facet_wrap(~(region), scales = 'free', ncol = 3) +
geom_text(x = 400, y = (N$MAX+0.05), hjust = 0, vjust = 1,
aes(label = paste0("N = ", N,"\n",
"Zsd = ", ZSD,"\n",
"Chl-a = ", chla,"\n",
"TSS = ", TSS,"\n",
"aCDOM(440) = ", cdom,"\n")), data = df, size = 7) +
scale_y_continuous(name = expression(R[rs]~(sr^-1)), limits = c(NA,NA)) +
scale_x_continuous(name = "Wavelength (nm)", limits = c(400,900),
breaks = seq(from = 400, to = 900, by = 100)) +
scale_fill_manual(values = colorBlindBlack8) +
scale_color_manual(values = colorBlindBlack8) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text=element_text(family = "Tahoma"),
axis.title = element_text( size = 35),
axis.text.x = element_text(colour="black", size = size_axis),
axis.text.y = element_text(colour="black", size = size_axis),
axis.line = element_line(size=2, colour = "black"),
strip.text = element_text(size = 40)) +
#legend.title = element_text(size=20), #change legend title font size
#legend.text = element_text(size=20),
#legend.key.size = unit(2, 'cm')
#change legend text font size) +
theme(plot.margin = unit(c(4,4,4,4), "lines")) +
guides(color = guide_legend(override.aes = list(size=15))) +
theme(legend.position="none")
ggsave(plot = plot_final, filename = 'Outputs/Figures/Figure_05.jpeg',
width = 20, height = 17, dpi = 200, units = 'in')
require(dplyr)
require(ggplot2)
require(tidyr)
require(reshape2)
require(lemon)
require(plotly)
require(data.table)
require(geobr)
require(terra)
require(openxlsx)
estacoes = read.xlsx('Data/stations.xlsx', detectDates = T)
rrs = read.xlsx('Data/rrs.xlsx', detectDates = T)
rrs$Classe = fread('Outputs/SAM_OWT_rrs.csv')$Class
estacoes$OWT_Pahlevan_class = rrs$Classe
merged = merge(estacoes, rrs, by = 'BR_ID', no.dups = T)
for(i in 5:ncol(merged)) {
merged[,i] = as.numeric( merged[,i])
}
RRS = paste('Rrs_', 400:900, sep = '')
median.na = function(x) { return(median(x, na.rm = T))}
sd.na = function(x) { return(sd(x, na.rm = T))}
max.na = function(x) { return(max(x, na.rm = T))}
min.na = function(x) { return(min(x, na.rm = T))}
merged$estado = merged$Classe
merged = filter(merged, estado > 0)
res.mean = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = median.na)
res.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = sd.na)
max.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = max.na)
min.sd = merged %>% group_by(estado) %>% select(contains(RRS)) %>% summarise_each(fun = min.na)
N = merged %>% group_by(estado) %>% summarise(fun = n())
N$MAX = apply(max.sd[,-1], 1, max)
CHLA = merged %>% select('estado', 'chla_ugL') %>% group_by(estado) %>% summarise_each(fun = median.na)
ZSD = merged %>% select('estado', 'secchi_m') %>% group_by(estado) %>% summarise_each(fun = median.na)
ZSDzd = merged %>% select('estado', 'secchi_m') %>% group_by(estado) %>% summarise_each(fun = sd.na)
TSS = merged %>% select('estado', 'tss_mgL') %>% group_by(estado) %>% summarise_each(fun = median.na)
CDOM = merged %>% select('estado', 'acdom440') %>% group_by(estado) %>% summarise_each(fun = median.na)
rrs_melt.mean = res.mean %>% melt('estado')
rrs_melt.sd = res.sd %>% melt("estado")
rrs_melt.max = max.sd %>% melt("estado")
rrs_melt.min = min.sd %>% melt("estado")
rrs_melt = data.frame(media = rrs_melt.mean$value,
region = rrs_melt.mean$estado,
variable = rrs_melt.mean$variable,
sd = rrs_melt.sd$value,
max = rrs_melt.max$value,
min = rrs_melt.min$value)
df = data.frame(region = N$estado,
N = N$fun,
chla = round(CHLA$chla_ugL, 2),
ZSD = round(ZSD$secchi_m, 2),
TSS = round(TSS$tss_mgL, 2),
cdom = round(CDOM$acdom440, 2),
ZSD.sd = round(ZSDzd$secchi_m, 2))
df$ZSD_plus_sd = paste(round(df$ZSD,2), '±', round(df$ZSD.sd,2))
rrs_melt$variable = gsub(rrs_melt$variable, pattern = "Rrs_", replacement = '') %>% as.numeric()
rrs_melt = filter(rrs_melt, region > 0)
rrs_melt$region = factor(x = rrs_melt$region, levels = c(1,2,3,4,5,6,7))
df = filter(df, region > 0)
df$region = factor(x = df$region, levels = c(1,2,3,4,5,6,7))
rrs_melt$region = gsub(rrs_melt$region, pattern = 1, replacement = 'OWT-01')
rrs_melt$region = gsub(rrs_melt$region, pattern = 2, replacement = 'OWT-02')
rrs_melt$region = gsub(rrs_melt$region, pattern = 3, replacement = 'OWT-03')
rrs_melt$region = gsub(rrs_melt$region, pattern = 4, replacement = 'OWT-04')
rrs_melt$region = gsub(rrs_melt$region, pattern = 5, replacement = 'OWT-05')
rrs_melt$region = gsub(rrs_melt$region, pattern = 6, replacement = 'OWT-06')
rrs_melt$region = gsub(rrs_melt$region, pattern = 7, replacement = 'OWT-07')
rrs_melt$region = factor(x = rrs_melt$region, levels = c('OWT-01',
'OWT-02',
'OWT-03',
'OWT-04',
'OWT-05',
'OWT-06',
'OWT-07'))
df$region = gsub(df$region, pattern = 1, replacement = 'OWT-01')
df$region = gsub(df$region, pattern = 2, replacement = 'OWT-02')
df$region = gsub(df$region, pattern = 3, replacement = 'OWT-03')
df$region = gsub(df$region, pattern = 4, replacement = 'OWT-04')
df$region = gsub(df$region, pattern = 5, replacement = 'OWT-05')
df$region = gsub(df$region, pattern = 6, replacement = 'OWT-06')
df$region = gsub(df$region, pattern = 7, replacement = 'OWT-07')
df$region = factor(x = df$region, levels = c('OWT-01',
'OWT-02',
'OWT-03',
'OWT-04',
'OWT-05',
'OWT-06',
'OWT-07'))
colorBlindBlack8 <- c("#0000FF", "#0072B2", "#56B4E9", "#009E73",
"#F0E442", "#117733", "#D55E00")
size_axis = 20
merged = merge(estacoes, rrs, by = 'BR_ID', no.dups = T)
df = data.frame(owt = merged$Classe, state = merged$state) %>%
mutate(owt_estado = paste(owt, state)) %>%
group_by(owt_estado) %>%
summarise(fun = length(owt_estado)) %>% separate(owt_estado, into = c('OWT', "Estado"))
df = filter(df, as.numeric(OWT) > 0)
df$owt_estado = paste(df$Estado, df$OWT)
size_axis = 30
plot_final = df %>%
ggplot(aes(y = fun, x = OWT, color = 'black', fill = (OWT))) +
geom_bar(stat = "identity", color = 'black', linewidth = 2) +#, position = "dodge") + # 'position = "dodge"' para barras separadas, se necessário
facet_wrap(~Estado, scales = 'free_y', ncol = 3) +
labs(y = 'Frequency', x = 'OWT') +
scale_fill_manual(values = c("#0000FF", "#0072B2", "#56B4E9", "#009E73",
"#F0E442", "#117733", "#D55E00")) +
#scale_x_discrete(breaks = 1:7, labels = 1:7, expand = c(0, 0)) + # Expande o eixo X para garantir que apareçam todos os rótulos
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text = element_text(family = "Tahoma"),
axis.title = element_text(size = 35),
axis.text.x = element_text(colour = "black", size = size_axis),
axis.text.y = element_text(colour = "black", size = size_axis),
axis.line = element_line(size = 2, colour = "black"),
strip.text = element_text(size = 40)) +
theme(plot.margin = unit(c(4, 4, 4, 4), "lines")) +
guides(color = guide_legend(override.aes = list(size = 15))) +
theme(legend.position = "none")
ggsave(plot = plot_final, filename = 'Outputs/Figures/Figure_06.jpeg',
width = 25, height = 30, dpi = 200, units = 'in')
require(dplyr)
require(ggplot2)
require(tidyr)
require(reshape2)
require(lemon)
require(plotly)
require(data.table)
require(geobr)
require(terra)
require(openxlsx)
require(scales)
require(viridis)
estacoes = read.xlsx('Data/stations.xlsx', detectDates = T)
a = estacoes %>%
select(c('state', "chla_ugL",'pheophytin_ugL', 'phycocianin_ugL',
'tss_mgL','tso_mgL', 'tsi_mgL', 'secchi_m','dtc_mgL', 'doc_mgL','dic_mgL', 'turbidity_NTU', 'acdom440'))
a$chla_ugL = (as.numeric(a$chla_ugL))
a$pheophytin_ugL = (as.numeric(a$pheophytin_ugL))
a$phycocianin_ugL = (as.numeric(a$phycocianin_ugL))
a$tss_mgL = (as.numeric(a$tss_mgL))
a$tsi_mgL = (as.numeric(a$tsi_mgL))
a$tso_mgL = (as.numeric(a$tso_mgL))
a$acdom440 = (as.numeric(a$acdom440))
a$secchi_m = (as.numeric(a$secchi_m))
a$dtc_mgL = (as.numeric(a$dtc_mgL))
a$dic_mgL = (as.numeric(a$dic_mgL))
a$doc_mgL = (as.numeric(a$doc_mgL))
a$turbidity_NTU = (as.numeric(a$turbidity_NTU))
library(tidyverse)
library(GGally)
names(a) = c("estado_sigla", "Chl-a", 'Phaeophytin', 'Phycocyanin', "TSS", "TSO", "TSI", "Zsd", "DTC", "DOC", 'DIC', "Turb" ,"aCDOM(440)")
melted = melt(a) %>% na.omit()
melted = filter(melted, value < 10000)
plt = ggplot(melted, aes(x = estado_sigla, y = value, fill = estado_sigla)) +
geom_violin(trim=FALSE) +
geom_boxplot(width=0.1, color="grey", alpha=0.2) +
scale_y_log10(labels = label_number()) +
labs(x = "State", y = 'Value') +
facet_wrap(~variable, scale = 'free_y', ncol = 2) +
scale_fill_viridis(discrete = TRUE) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text=element_text(family = "Tahoma", size = 30),
axis.title = element_text( size = 30),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 30, angle = 0),
axis.line = element_line(size=2, colour = "black"),
strip.text = element_text(size=30)) +
#legend.title = element_text(size=20), #change legend title font size
#legend.key.size = unit(2, 'cm')
#change legend text font size) +
theme(plot.margin = unit(c(6,6,6,6), "lines")) +
guides(color = guide_legend(override.aes = list(size=5))) +
theme(legend.position="none")
ggsave(plot = plt, filename = 'Outputs/Figures/Figure_07.jpeg',
width = 30, height = 35, dpi = 200, units = 'in')
require(dplyr)
require(ggplot2)
require(tidyr)
require(reshape2)
require(lemon)
require(plotly)
require(data.table)
require(geobr)
require(terra)
require(openxlsx)
require(ggpubr)
require(ggpubr)
library("gpairs")
library(tidyverse)
library(GGally)
estacoes = read.xlsx('Data/stations.xlsx', detectDates = T)
a = filter(estacoes, as.numeric(chla_ugL) < 1000) %>%
select(c('state', "chla_ugL", 'phycocianin_ugL','tss_mgL','tso_mgL', 'tsi_mgL', 'secchi_m',
'dtc_mgL', 'doc_mgL','dic_mgL', 'turbidity_NTU', 'acdom440'))
a$chla_ugL = (as.numeric(a$chla_ugL))
a$phycocianin_ugL = (as.numeric(a$phycocianin_ugL))
a$tss_mgL = (as.numeric(a$tss_mgL))
a$tsi_mgL = (as.numeric(a$tsi_mgL))
a$tso_mgL = (as.numeric(a$tso_mgL))
a$acdom440 = (as.numeric(a$acdom440))
a$secchi_m = (as.numeric(a$secchi_m))
a$dtc_mgL = (as.numeric(a$dtc_mgL))
a$doc_mgL = (as.numeric(a$doc_mgL))
a$dic_mgL = (as.numeric(a$dic_mgL))
a$turbidity_NTU = (as.numeric(a$turbidity_NTU))
names(a)
names(a) = c("estado_sigla", "Chl-a", 'PC',
"TSS", "TSO", "TSI", "Zsd",'DTC', "DOC", 'DIC', "Turbidity" ,"aCDOM(440)")
a$`Chl-a` = log(a$`Chl-a`)
a$TSS = log(a$TSS)
a$Zsd = log(a$Zsd)
a$`aCDOM(440)` = log(a$`aCDOM(440)`)
plt = a %>% mutate(estado_sigla = as.factor(estado_sigla)) %>%
ggpairs(columns = c("Chl-a",
"TSS", "Zsd","aCDOM(440)"),
aes(color = estado_sigla),
upper = list(continuous = wrap('cor', size = 13)),
lower = list(combo = wrap("facethist", bins = 30)),
diag = list(continuous = wrap("barDiag", alpha = 0.8))) +
#scale_x_log10(labels = label_number()) +
#scale_y_log10(labels = label_number()) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
panel.spacing = unit(2, "lines"),
text=element_text(family = "Tahoma", size = 25),
axis.title = element_text( size = 40),
axis.text.x = element_text(colour="black", size = 40),
axis.text.y = element_text(colour="black", size = 40),
axis.line = element_line(size=2, colour = "black"),
strip.text = element_text(size=40))
ggsave(plot = plt, filename = 'Outputs/Figures/Figure_08_log.jpeg',
width = 30, height = 25, dpi = 200, units = 'in')
plotly::ggplotly(plt)
# rafa
cor(log(a[,-1]), na.rm = T)
