TCC
Leonardo Fernandes Wink
24/01/2023
1 Brief explanation
Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).
The river flows in the east-west direction as shown in the image below.
The logic behind the sorting in the boxplots is because of 2 main reasons:
- The original monitoring point isn’t easy to understand (8 digits, like 87409900)
- Changing the original nomenclature to PM1, PM2 (…) makes it easier to understand that the last point has water contributions of every other point upstream.
Some features that I want to add:
If the parameter is x, then use x’s classes (with its own classes background color plotted)
Define the timescale, should act just like a filter
# plan_wide_19902020 %>%
# filter(ano_coleta > "1990" &
# ano_coleta <= "2000")2 Anotações de coisas por fazer:
- Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900
Aprender a segmentar o meu dataset por períodos- aprender a criar uma nova coluna com a segmentação dos períodos
- maybe use
~facet.grid - aprender a colocar a legenda dentro do gráfico
- reduzir o tamanho da legenda
corrigir os valores 0 de IQA pra NA- descobrir como conseguir a equação do lm
aprender a pivotar o sumário-> meu sumário do google docs ta batendo direitinho com o do R- descobrir se há outros TCCs com disponibilização de códigos
Namontá com com casa decimal","eptottá com"."- correlação forte entre condutividade e Namon/Ptot/DBO
| 1990-2000 | 2000-2010 | 2010-2020 |
|---|---|---|
| 1990-2000 | 2000-2010 | 2010-2020 |
3 Instalar os pacotes
# install.packages(tidyverse)3.1 acessar os pacotes
pacman::p_load(readr, rmarkdown, readxl, janitor,
pillar, dplyr, tidyverse,
knitr, kableExtra, see,
gridExtra, #modelsummary,
gtsummary, ggplot2,
ggbeeswarm, GGally, ggtext, cowplot,
report)
# pacman::p_load(tibbletime)
# cite_packages()knitr::knit_hooks$set(time_it = local({
now <- NULL
function(before, options) {
if (before) {
# record the current time before each chunk
now <<- Sys.time()
} else {
# calculate the time difference after a chunk
res <- difftime(Sys.time(), now)
# return a character string to show the time
paste("Tempo para esse code chunk ser rodado:", round(res, digits = 2), "s")
}
}
}))
knitr::opts_chunk$set(time_it = TRUE)3.1.1 referenciando os pacotes
version$version.string## [1] "R version 4.2.2 (2022-10-31 ucrt)"# citation(package = "tidyverse")Tempo para esse code chunk ser rodado: 0.01 s
3.2 importando a planilha
Tempo para esse code chunk ser rodado: 0.97 s
Tempo para esse code chunk ser rodado: 0.48 s
4 data wrangling
Como há dados faltantes, no cálculo entre o produto das colunas, o R
acaba interpretando como se fosse zero, mas na verdade é
NA.
plan_wide_19902020 <- plan_wide_19902020 %>%
mutate(iqa = ifelse(iqa == 0, NA, iqa))
parametros_IQA <- plan_wide_19902020 %>%
select(
codigo,
ponto_monitoramento,
pH,
oxigenio_dissolvido,
dbo,
fosforo_total,
escherichia_coli,
nitrogenio_amoniacal,
nitrogenio_kjeldahl,
nitrogenio_total,
turbidez,
temperatura_agua,
solidos_totais,
condutividade,
ano_coleta
)
write.csv(parametros_IQA,
"./parametros_IQA.csv",
row.names = FALSE)Tempo para esse code chunk ser rodado: 0.16 s
5 setting theme
theme_grafs <- function(bg = "white",
coloracao_letra = "black"){
theme(
plot.title =
element_text(
hjust = 0.5,
color = coloracao_letra,
size = 19),
axis.title.x =
# element_text(
# color = coloracao_letra,
# size = 15,
# angle = 0,),
element_blank(),
axis.title.y = element_text(
color = coloracao_letra,
size = 15,
angle = 90),
axis.text.x = element_text(
color = coloracao_letra,
size = 17),
axis.text.y = element_text(
color = coloracao_letra,
size = 17,
angle = 0),
strip.background = element_rect(fill = bg,
linetype = 1,
size = 0.5,
color = "black"),
strip.text = element_text(size = 17),
panel.background = element_rect(fill = bg),
plot.background = element_rect(fill = bg),
plot.margin = margin(l = 5, r = 10,
b = 5, t = 5)
)
}Tempo para esse code chunk ser rodado: 0.01 s
6 setting different timescales
plan_wide_19902020 <- plan_wide_19902020 %>%
mutate(
periodo = if_else(
ano_coleta <= 2000,
"1990-2000",
if_else(
ano_coleta <= 2010,
"2000-2010",
"2010-2020"
)
)
)Tempo para esse code chunk ser rodado: 0.01 s
7 setting sumaries
Tempo para esse code chunk ser rodado: 0 s
8 Funções
8.1 criando função para gerar boxplots com percentil 20 e 80
f <- function(x) {
r <- quantile(x, probs = c(0.05, 0.20, 0.50, 0.80, 0.95))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
return(r)
}Tempo para esse code chunk ser rodado: 0 s
8.2 Oxigênio Dissolvido
Tempo para esse code chunk ser rodado: 0.01 s
8.3 DBO
Tempo para esse code chunk ser rodado: 0.01 s
8.4 Ptot
Tempo para esse code chunk ser rodado: 0.01 s
8.5 E coli
Tempo para esse code chunk ser rodado: 0.01 s
8.6 Nitrogênio Amoniacal
Tempo para esse code chunk ser rodado: 0.01 s
8.7 Turbidez
Tempo para esse code chunk ser rodado: 0 s
8.8 pH
Tempo para esse code chunk ser rodado: 0.01 s
8.9 Sólidos Totais
Tempo para esse code chunk ser rodado: 0.01 s
8.10 Condutividade
Tempo para esse code chunk ser rodado: 0.01 s
9 Parâmetros físico-químicos
9.0.1 Oxigênio Dissolvido
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chunk ser rodado: 3.68 s
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chunk ser rodado: 1.02 s
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para esse code chunk ser rodado: 1.06 s
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ggsave("od.png",
units = c("px"),
width = 4500,
height = 2993,
plot = od,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p1.png",
plot = od_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p2.png",
plot = od_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p3.png",
plot = od_p3,
path = "./graficos",
dpi = 300,
type = "cairo")Tempo para esse code chunk ser rodado: 7.05 s
Tempo
para esse code chunk ser rodado: 1.28 s
Tempo
para esse code chunk ser rodado: 1.53 s
Tempo
para esse code chunk ser rodado: 1.29 s
## # A tibble: 9 × 8
## par PM1 PM2 PM3 PM4 PM5 PM6 PM7
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 max 10.8 10.5 10.3 12.1 19.9 10.2 11.1
## 2 p95 9.24 9.8 9.16 9.56 8.92 6.45 8.38
## 3 p80 7.76 8.3 7.52 8.42 6.2 5.5 5.7
## 4 median 6.4 6.9 5.95 6.3 4.2 2.6 2.9
## 5 mean 5.99 6.78 5.98 7.01 4.22 2.98 3.60
## 6 p20 3.84 5.2 4.3 5.72 0.760 0.2 0.8
## 7 p05 2 4.3 3.14 4.94 0.28 0.1 0.128
## 8 min 0.8 2 2.5 4.2 0.1 0.1 0.1
## 9 n 101 101 68 30 97 32 65## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.4 3.5 4.9 5.01 6.65 10.9
## 2 87398900 1.9 4 5.5 5.33 6.6 12
## 3 87398950 1.7 3.2 5.3 5.06 6.18 8.9
## 4 87398980 1.2 3.8 5.6 5.38 6.6 9.2
## 5 87405500 0.2 1.4 2.55 3.28 4 14.2
## 6 87406900 0 1.1 1.9 2.59 3.15 16
## 7 87409900 0 0.7 2.3 3.12 3.7 10.6## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.38 3.11 4.41 4.57 6.2 12.4
## 2 87398900 3.52 5.25 5.96 6.61 7.3 13.8
## 3 87398950 1.62 3.68 4.92 5.28 6.64 11.9
## 4 87398980 3.37 5.5 6.17 6.48 7.14 13.1
## 5 87405500 0.2 1.3 2.53 2.83 3.66 9.8
## 6 87406900 0.1 0.865 2.4 2.43 3.05 9.1
## 7 87409900 0.1 0.92 2.03 2.43 3.5 8.1Tempo para esse code chunk ser rodado: 0.7 s
9.0.2 Demanda Bioquímica de Oxigênio
(dbo <- ggplot(plan_wide_19902020,
aes(x = codigo,
y = dbo))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=10,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=5,
ymax=10,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3,
ymax=5,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_summary(
fun.data = f,
geom = 'errorbar',
width = 0.3,
position = position_dodge(width = 0.65),
)+
stat_summary(
fun.data = f,
geom = "boxplot",
width = 0.7,
fill = '#F8F8FF',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
)+
facet_wrap(~periodo)+
labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
x="Estação",
y="mg/L",
# caption = "Leonardo Fernandes Wink"
)+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(1,100),
trans = "log10")+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 60 rows containing non-finite values (`stat_summary()`).
## Removed 60 rows containing non-finite values (`stat_summary()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 22 rows containing missing values.## Warning: Removed 30 rows containing missing values.## Warning: Removed 8 rows containing missing values.
Tempo para
esse code chunk ser rodado: 2.07 s
Tempo
para esse code chunk ser rodado: 1.04 s
Tempo
para esse code chunk ser rodado: 0.99 s
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para esse code chunk ser rodado: 0.93 s
Tempo
para esse code chunk ser rodado: 1.12 s
Tempo
para esse code chunk ser rodado: 0.92 s
Tempo
para esse code chunk ser rodado: 0.84 s
(sum_dbo_p1 <- plan_wide_19902020 %>%
select(codigo, dbo, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(dbo,
na.rm = TRUE),
q1 =
quantile(dbo, 0.25,
na.rm = TRUE),
median =
median(dbo,
na.rm = TRUE),
mean =
mean(dbo,
na.rm= TRUE),
q3 =
quantile(dbo, 0.75,
na.rm = TRUE),
max =
max(dbo,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 2 1.86 2 13
## 2 87398900 1 1 1 1.52 2 6
## 3 87398950 1 1 1 1.66 2 6
## 4 87398980 1 1 1 1.13 1 2
## 5 87405500 1 2 3 5.37 5 64
## 6 87406900 1 4 5 9 11 26
## 7 87409900 2 3 4 6.97 9.5 31(sum_dbo_p2 <- plan_wide_19902020 %>%
select(codigo, dbo, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(dbo,
na.rm = TRUE),
q1 =
quantile(dbo, 0.25,
na.rm = TRUE),
median =
median(dbo,
na.rm = TRUE),
mean =
mean(dbo,
na.rm= TRUE),
q3 =
quantile(dbo, 0.75,
na.rm = TRUE),
max =
max(dbo,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1 1.58 2 5
## 2 87398900 1 1 1 1.40 2 5
## 3 87398950 1 1 1 1.66 2 5
## 4 87398980 1 1 1 1.30 1 5
## 5 87405500 1 2 4 4.67 6.5 14
## 6 87406900 1 3 5 6.53 8 28
## 7 87409900 1 3 6 6.31 9 15(sum_dbo_p3 <- plan_wide_19902020 %>%
select(codigo, dbo, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(dbo,
na.rm = TRUE),
q1 =
quantile(dbo, 0.25,
na.rm = TRUE),
median =
median(dbo,
na.rm = TRUE),
mean =
mean(dbo,
na.rm= TRUE),
q3 =
quantile(dbo, 0.75,
na.rm = TRUE),
max =
max(dbo,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1.5 2.15 3 7
## 2 87398900 1 1 1 1.51 2 5
## 3 87398950 1 1 2 2.65 2 18
## 4 87398980 1 1 1 1.32 2 2
## 5 87405500 1 3 4 5.28 6.25 21
## 6 87406900 1 3 5 6.58 10 24
## 7 87409900 1 3 4.5 6.18 8 18Tempo para esse code chunk ser rodado: 0.34 s
ggsave("dbo.png",
units = c("px"),
width = 4500,
height = 2993,
plot = dbo,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p1.png",
plot = dbo_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p2.png",
plot = dbo_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p3.png",
plot = dbo_p3,
path = "./graficos",
dpi = 300,
type = "cairo")Tempo para esse code chunk ser rodado: 5.78 s
9.0.3 Fósforo total
(ptot <- ggplot(plan_wide_19902020,
aes(codigo,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_summary(
fun.data = f,
geom = 'errorbar',
width = 0.3,
position = position_dodge(width = 0.65),
)+
stat_summary(
fun.data = f,
geom = "boxplot",
width = 0.7,
fill = '#F8F8FF',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
)+
facet_wrap(~periodo)+
labs(title = "Fósforo total no período 1990-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " ")
)+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 134 rows containing non-finite values (`stat_summary()`).
## Removed 134 rows containing non-finite values (`stat_summary()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 47 rows containing missing values.## Warning: Removed 31 rows containing missing values.## Warning: Removed 56 rows containing missing values.
Tempo para esse code chunk
ser rodado: 2.04 s
(ptot_p1<-ggplot(plan_wide_19902020%>%
filter(ano_coleta>"1990" &
ano_coleta<="2000"),
aes(codigo,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 1990-2000",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.04 s
(ptot_p2 <- ggplot(plan_wide_19902020%>%
filter(ano_coleta>"2000" &
ano_coleta<="2010"),
aes(codigo,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2000-2010",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.21 s
(ptot_p3 <- ggplot(plan_wide_19902020%>%
filter(ano_coleta>"2010" &
ano_coleta<="2020"),
aes(codigo,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2010-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.1 s
(sum_ptot_p1 <- plan_wide_19902020 %>%
select(codigo, fosforo_total, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.0097 0.0593 0.0881 0.123 0.14 0.863
## 2 87398900 0.0023 0.0468 0.0678 0.0747 0.0883 0.247
## 3 87398950 0.0202 0.0544 0.0737 0.0751 0.0904 0.179
## 4 87398980 0.01 0.0254 0.0547 0.0708 0.114 0.189
## 5 87405500 0.017 0.171 0.281 0.417 0.492 2.32
## 6 87406900 0.156 0.270 0.508 0.785 1.07 2.79
## 7 87409900 0.107 0.258 0.384 0.489 0.712 1.53(sum_ptot_p2 <- plan_wide_19902020 %>%
select(codigo, fosforo_total, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.025 0.094 0.131 0.148 0.16 0.637
## 2 87398900 0.015 0.0764 0.104 0.140 0.164 0.646
## 3 87398950 0.036 0.116 0.171 0.180 0.207 0.485
## 4 87398980 0.0115 0.052 0.076 0.101 0.103 1
## 5 87405500 0.046 0.261 0.406 0.547 0.681 1.98
## 6 87406900 0.056 0.338 0.599 0.752 0.967 3.49
## 7 87409900 0.043 0.325 0.624 0.677 0.989 1.57(sum_ptot_p3 <- plan_wide_19902020 %>%
select(codigo, fosforo_total, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.061 0.118 0.163 0.166 0.186 0.381
## 2 87398900 0.057 0.0935 0.130 0.163 0.168 0.444
## 3 87398950 0.07 0.132 0.156 0.292 0.221 3.11
## 4 87398980 0.019 0.0625 0.106 0.144 0.170 0.59
## 5 87405500 0.013 0.187 0.332 0.361 0.45 0.803
## 6 87406900 0.089 0.254 0.364 0.448 0.560 1.26
## 7 87409900 0.203 0.259 0.369 0.488 0.564 1.7Tempo para esse code chunk ser rodado: 0.61 s
ggsave("ptot.png",
units = c("px"),
width = 4500,
height = 2993,
plot = ptot,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p1.png",
plot = ptot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p2.png",
plot = ptot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p3.png",
plot = ptot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")Tempo para esse code chunk ser rodado: 5.63 s
9.0.4 Escherichia coli
(ecoli <- boxplot_ecoli(
titulo = "*Escherichia coli* no período 1990-2020"
)+
facet_wrap(~periodo)
)
Escherichia-coli-gravataí no período 1990-2020
(ecoli <- ggplot(plan_wide_19902020,
aes(codigo,
escherichia_coli))+
annotate("rect",
xmin=-Inf, xmax=Inf,
ymin=3200, ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf, xmax=Inf,
ymin=800, ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf, xmax=Inf,
ymin=160, ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf, xmax=Inf,
ymin=0, ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_summary(
fun.data = f,
geom = 'errorbar',
width = 0.3,
position = position_dodge(width = 0.65),
)+
stat_summary(
fun.data = f,
geom = "boxplot",
width = 0.7,
fill = '#F8F8FF',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
)+
facet_wrap(~periodo)+
labs(title = "*Escherichia coli* no período 1990-2020",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
# n.breaks = 9,
n.breaks = 6,
limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()+
theme(
axis.text.y = element_text(
angle = 90,
# size=15,
# face=2
),
plot.title =
element_markdown(
hjust = 0.5,
color = "black",
size = 19)
)
)Tempo para esse
code chunk ser rodado: 4.16 s
(ecoli_p1 <- ggplot(plan_wide_19902020 %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000"),
aes(codigo,
escherichia_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 1990-2000",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.07 s
(ecoli_p2 <- ggplot(plan_wide_19902020 %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010"),
aes(codigo,
escherichia_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2000-2010",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.01 s
(ecoli_p3 <- ggplot(plan_wide_19902020 %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020"),
aes(codigo,
escherichia_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2010-2020",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Tempo
para esse code chunk ser rodado: 1.11 s
(sum_ecoli_p1 <- plan_wide_19902020 %>%
select(codigo, escherichia_coli, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(escherichia_coli,
na.rm = TRUE),
q1 =
quantile(escherichia_coli, 0.25,
na.rm = TRUE),
median =
median(escherichia_coli,
na.rm = TRUE),
mean =
mean(escherichia_coli,
na.rm= TRUE),
q3 =
quantile(escherichia_coli, 0.75,
na.rm = TRUE),
max =
max(escherichia_coli,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 32 136 240 854. 720 19200
## 2 87398900 16 68 160 548. 480 7760
## 3 87398950 2.4 12.8 268 4039. 10000 28000
## 4 87398980 4 160 243. 2907. 446 25600
## 5 87405500 1.6 12.8 24 545. 128 18400
## 6 87406900 13.6 61.6 192 718. 414 12800
## 7 87409900 2.4 12.8 64 97.7 128 720(sum_ecoli_p2 <- plan_wide_19902020 %>%
select(codigo, escherichia_coli, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(escherichia_coli,
na.rm = TRUE),
q1 =
quantile(escherichia_coli, 0.25,
na.rm = TRUE),
median =
median(escherichia_coli,
na.rm = TRUE),
mean =
mean(escherichia_coli,
na.rm= TRUE),
q3 =
quantile(escherichia_coli, 0.75,
na.rm = TRUE),
max =
max(escherichia_coli,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 21.6 91 150 1335. 308 27200
## 2 87398900 11 70 133. 444. 414. 2600
## 3 87398950 20 400 720 935. 1120 5500
## 4 87398980 24 110. 195 410. 289. 8800
## 5 87405500 4.7 162 2400 25445. 12950 490000
## 6 87406900 8 172 12800 66370. 62300 650000
## 7 87409900 16 7355. 35500 72440. 68750 460000(sum_ecoli_p3 <- plan_wide_19902020 %>%
select(codigo, escherichia_coli, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(escherichia_coli,
na.rm = TRUE),
q1 =
quantile(escherichia_coli, 0.25,
na.rm = TRUE),
median =
median(escherichia_coli,
na.rm = TRUE),
mean =
mean(escherichia_coli,
na.rm= TRUE),
q3 =
quantile(escherichia_coli, 0.75,
na.rm = TRUE),
max =
max(escherichia_coli,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 90 155. 260 409. 451 2420
## 2 87398900 10 52.8 107 245. 313 1553.
## 3 87398950 108. 250 487 1424. 1553. 10462
## 4 87398980 40.8 140. 242. 529. 738. 2400
## 5 87405500 632 8965 19232. 109992. 70750 1400000
## 6 87406900 1440 23100 34500 230828. 140500 3400000
## 7 87409900 2000 20100 38400 83128. 83680 345000Tempo para esse code chunk ser rodado: 0.33 s
ggsave("ecoli.png",
plot = ecoli,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p1.png",
plot = ecoli_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p2.png",
plot = ecoli_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p3.png",
plot = ecoli_p3,
path = "./graficos",
dpi = 300,
type = "cairo")Tempo para esse code chunk ser rodado: 5.56 s
9.0.5 Nitrogênio amoniacal
(namon <- plan_wide_19902020 %>%
boxplot_namon(
eixo_y = nitrogenio_amoniacal,
titulo = "Nitrogênio Amoaniacal no período 1990-2020"
)+
facet_wrap(~periodo)
)
Tempo para esse code
chunk ser rodado: 1.92 s
periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01", "%Y-%m-%d")
(nitro_line <-
plan_wide_19902020 %>%
filter(ano_coleta > "1990" &
ano_coleta <= "2020") %>%
dplyr::select(codigo, nitrogenio_amoniacal, data_coleta, periodo) %>%
# group_by(codigo) %>%
mutate(
ponto_monitoramento = case_when(
codigo == "87398500" ~ "PM1",
codigo == "87398980" ~ "PM2",
codigo == "87398900" ~ "PM3",
codigo == "87398950" ~ "PM4",
codigo == "87405500" ~ "PM5",
codigo == "87406900" ~ "PM6",
codigo == "87409900" ~ "PM7"
)
) %>%
# pivot_wider(
# names_from = codigo,
# values_from = nitro_amon,
# id_cols = data_coleta
# ) %>%
ggplot(
aes(x = data_coleta,
y = nitrogenio_amoniacal,
# color = codigo
))+
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin = 13.3,
# ymax = Inf,
# alpha= 0.005,
# fill= "#ac5079"),
# show.legend = FALSE)+ #>pior classe
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin= 3.7,
# ymax= 13.3,
# alpha= 0.005,
# fill= "#fcf7ab"),
# show.legend = FALSE)+ #classe 3
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin= 0,
# ymax= 3.7,
# alpha= 0.005,
# fill= "blue"
# # "#8dcdeb"
# ),
# show.legend = FALSE)+ #classe 1
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin=13.3,
ymax=Inf,
alpha= 0.7,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin=3.7,
ymax=13.3,
alpha= 0.7,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin= -Inf,
ymax=3.7,
alpha= 0.7,
fill="#8dcdeb")+ #classe 1
geom_line(
# aes(color = codigo),
na.rm = TRUE)+
geom_point(
# aes(color = codigo),
na.rm = TRUE)+
scale_x_date(
limits = as.Date(c(
"1990-01-01",
"2021-01-01"
# NA #pode usar NA também
)),
expand = c(0.0, 0.0),
date_breaks = "10 years",
minor_breaks = "5 years",
date_labels = "%Y",
)+
# geom_smooth(
# # aes(color = codigo),
# method = "lm",
# # formula = y ~ poly(x, 2),
# # span = 0.2,
# se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
# aes(group = 1),
# alpha =.5,
# na.rm = TRUE,
# size = 0.3,
# # fullrange = TRUE,
# # show.legend = TRUE
# )+
# stat_smooth(
# geom = "smooth",
# # span = 0.2,
# se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
# # aes(group = 1),
# # alpha =.5,
# na.rm = TRUE,
# # size = 0.3,
# fullrange = TRUE,
# show.legend = TRUE
# )+
facet_wrap(
~ponto_monitoramento,
nrow = 4,
)+
theme_bw()
)
Tempo
para esse code chunk ser rodado: 1.34 s
(namon_p1 <- plan_wide_19902020 %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
boxplot_namon(
titulo = "Nitrogênio Amoniacal no período 1990-2000"
)
)
Tempo
para esse code chunk ser rodado: 0.98 s
(namon_p2 <- plan_wide_19902020 %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
boxplot_namon(
titulo = "Nitrogênio Amoniacal no período 2000-2010"
)
)
Tempo
para esse code chunk ser rodado: 1.62 s
(namon_p3 <- plan_wide_19902020 %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
boxplot_namon(
titulo = "Nitrogênio Amoniacal no período 2010-2020"
)
)
Tempo
para esse code chunk ser rodado: 1.33 s
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 3.61 s
(sum_namon_p1 <- plan_wide_19902020 %>%
select(codigo, nitrogenio_amoniacal, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(nitrogenio_amoniacal,
na.rm = TRUE),
q1 =
quantile(nitrogenio_amoniacal, 0.25,
na.rm = TRUE),
median =
median(nitrogenio_amoniacal,
na.rm = TRUE),
mean =
mean(nitrogenio_amoniacal,
na.rm= TRUE),
q3 =
quantile(nitrogenio_amoniacal, 0.75,
na.rm = TRUE),
max =
max(nitrogenio_amoniacal,
na.rm = TRUE),
n =
length(nitrogenio_amoniacal)
)
)## # A tibble: 7 × 8
## codigo min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 0.08 0.165 0.205 0.246 0.28 0.91 101
## 2 87398900 0.087 0.17 0.2 0.248 0.242 1.13 101
## 3 87398950 Inf NA NA NaN NA -Inf 68
## 4 87398980 0.027 0.12 0.15 0.158 0.188 0.3 30
## 5 87405500 0.43 0.875 1.74 4.28 5.88 17.7 97
## 6 87406900 0.51 1.14 2.78 5.35 7.50 26 32
## 7 87409900 Inf NA NA NaN NA -Inf 65(sum_namon_p2 <- plan_wide_19902020 %>%
select(codigo, nitrogenio_amoniacal, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(nitrogenio_amoniacal,
na.rm = TRUE),
q1 =
quantile(nitrogenio_amoniacal, 0.25,
na.rm = TRUE),
median =
median(nitrogenio_amoniacal,
na.rm = TRUE),
mean =
mean(nitrogenio_amoniacal,
na.rm= TRUE),
q3 =
quantile(nitrogenio_amoniacal, 0.75,
na.rm = TRUE),
max =
max(nitrogenio_amoniacal,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.031 0.16 0.21 0.259 0.29 1.22
## 2 87398900 0.03 0.16 0.21 0.284 0.342 2.77
## 3 87398950 0.044 0.214 0.276 0.268 0.349 0.413
## 4 87398980 0.024 0.098 0.14 0.161 0.21 0.551
## 5 87405500 0.038 0.79 2.09 3.49 4.54 18
## 6 87406900 0.03 1.05 2.66 4.27 5.62 19.4
## 7 87409900 0.453 0.78 2.46 3.84 4.77 16.6(sum_namon_p3 <- plan_wide_19902020 %>%
select(codigo, nitrogenio_amoniacal, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(nitrogenio_amoniacal,
na.rm = TRUE),
q1 =
quantile(nitrogenio_amoniacal, 0.25,
na.rm = TRUE),
median =
median(nitrogenio_amoniacal,
na.rm = TRUE),
mean =
mean(nitrogenio_amoniacal,
na.rm= TRUE),
q3 =
quantile(nitrogenio_amoniacal, 0.75,
na.rm = TRUE),
max =
max(nitrogenio_amoniacal,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.03 0.085 0.1 0.150 0.198 0.419
## 2 87398900 0.03 0.1 0.192 0.213 0.248 0.699
## 3 87398950 0.02 0.165 0.263 0.315 0.4 0.951
## 4 87398980 0.02 0.06 0.1 0.179 0.235 0.717
## 5 87405500 0.05 0.808 1.60 2.55 3.78 9.12
## 6 87406900 0.03 1.09 2.16 3.61 5.02 21.2
## 7 87409900 0.04 1.44 2.5 3.40 4.39 18.8Tempo para esse code chunk ser rodado: 0.55 s
ggsave("namon.png",
units = c("px"),
width = 4500,
height = 2993,
plot = namon,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p1.png",
plot = namon_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p2.png",
plot = namon_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p3.png",
plot = namon_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 11.92 s
9.0.6 Turbidez
(turb <- plan_wide_19902020 %>%
boxplot_turb(
titulo = "Turbidez no período 1990-2020"
)+
facet_wrap(~periodo)
)
Tempo para esse code
chunk ser rodado: 1.95 s
(turb_line <- plan_wide_19902020 %>%
filter(ano_coleta > "1990" &
ano_coleta <= "2020") %>%
select(codigo, turbidez, data_coleta, periodo) %>%
group_by(codigo) %>%
ggplot(
aes(x = data_coleta,
y = turbidez,
color = codigo
))+
geom_line(
# aes(color = codigo),
na.rm = TRUE)+
geom_point(
# aes(color = codigo),
na.rm = TRUE)+
scale_x_date(
limits = as.Date(c(
"1990-01-01",
"2021-01-01"
# NA #pode usar NA também
)),
expand = c(0.0, 0.0),
date_breaks = "10 years",
minor_breaks = "5 years",
date_labels = "%Y",
)+
# geom_smooth(
# # aes(color = codigo),
# method = "lm",
# # formula = y ~ poly(x, 2),
# # span = 0.2,
# se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
# aes(group = 1),
# alpha =.5,
# na.rm = TRUE,
# size = 0.3,
# # fullrange = TRUE,
# # show.legend = TRUE
# )+
# stat_smooth(
# geom = "smooth",
# # span = 0.2,
# se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
# # aes(group = 1),
# # alpha =.5,
# na.rm = TRUE,
# # size = 0.3,
# fullrange = TRUE,
# show.legend = TRUE
# )+
facet_wrap(
~codigo,
nrow = 4,
)+
theme_bw()
)
Tempo
para esse code chunk ser rodado: 1.38 s
(turb_p1 <- plan_wide_19902020 %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
boxplot_turb(
titulo = "Turbidez no período 1990-2000"
)
)
Tempo
para esse code chunk ser rodado: 0.89 s
(turb_p2 <- plan_wide_19902020 %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
boxplot_turb(
titulo = "Turbidez no período 2000-2010"
)
)
Tempo
para esse code chunk ser rodado: 1.02 s
(turb_p3 <- plan_wide_19902020 %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
boxplot_turb(
titulo = "Turbidez no período 2010-2020"
)
)
Tempo
para esse code chunk ser rodado: 0.97 s
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 2.62 s
(sum_turb_p1 <- plan_wide_19902020 %>%
select(codigo, turbidez, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 6.2 19 34.5 63.5 67 461
## 2 87398900 9 19 49.5 61.5 73.8 460
## 3 87398950 9.6 16 22 33.3 48.8 144
## 4 87398980 16 32.8 43 66.8 90.5 190
## 5 87405500 8.5 23.5 47 47.5 58 159
## 6 87406900 33 54.8 67 77.7 81.5 199
## 7 87409900 5.8 15 25 32.2 48 76(sum_turb_p2 <- plan_wide_19902020 %>%
select(codigo, turbidez, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9 41.2 55.5 71.1 74.2 428
## 2 87398900 39 57 78 107. 116. 475
## 3 87398950 39 47 64 96.5 90 330
## 4 87398980 24 37 50 64.5 87 176
## 5 87405500 32 46 63.5 70.3 76 341
## 6 87406900 35 49 62 69.9 75.5 284
## 7 87409900 40 45 60 70.4 90 151(sum_turb_p3 <- plan_wide_19902020 %>%
select(codigo, turbidez, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
) ## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 8.52 16.4 29 33.3 43 85
## 2 87398900 14.8 39.2 48.3 66.7 73.4 299
## 3 87398950 16 29.9 41 51.6 65 230
## 4 87398980 11 19.4 33.6 39.5 42.2 110.
## 5 87405500 10.0 29.0 41 42.9 54.5 131
## 6 87406900 9.62 23 39 41.2 52 122
## 7 87409900 9.68 22.0 34.0 40.5 47 182.Tempo para esse code chunk ser rodado: 0.28 s
ggsave("turb.png",
units = c("px"),
width = 4500,
height = 2993,
plot = turb,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p1.png",
plot = turb_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p2.png",
plot = turb_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p3.png",
plot = turb_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 9.06 s
9.0.7 pH
(pH <- boxplot_pH(
titulo = "pH no período 1990-2020"
)+
facet_wrap(~periodo)
)
Tempo para esse code chunk ser
rodado: 1.84 s
(pH_p1 <- plan_wide_19902020 %>%
filter(ano_coleta > "1990" &
ano_coleta <= "2000") %>%
boxplot_pH(
titulo = "pH no período 1990-2000"
)
)
Tempo
para esse code chunk ser rodado: 0.89 s
(pH_p2 <- plan_wide_19902020 %>%
filter(ano_coleta > "2000" &
ano_coleta <= "2010") %>%
boxplot_pH(
titulo = "pH no período 2000-2010"
)
)
Tempo
para esse code chunk ser rodado: 0.87 s
(pH_p3 <- plan_wide_19902020 %>%
filter(ano_coleta > "2010" &
ano_coleta <= "2020") %>%
boxplot_pH(
titulo = "pH no período 2010-2020"
)
)
Tempo
para esse code chunk ser rodado: 0.85 s
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 2.32 s
(sum_pH_p1 <- plan_wide_19902020 %>%
select(codigo, pH, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5 6.18 6.59 6.51 6.82 7.9
## 2 87398900 5.2 6 6.3 6.33 6.63 7.9
## 3 87398950 5.4 6.29 6.4 6.49 6.72 8.1
## 4 87398980 5.3 5.93 6.2 6.16 6.3 7.3
## 5 87405500 5 6.3 6.4 6.47 6.7 9.3
## 6 87406900 5.5 6.18 6.45 6.43 6.8 7.3
## 7 87409900 4.5 6.2 6.4 6.44 6.7 7.4(sum_pH_p2 <- plan_wide_19902020 %>%
select(codigo, pH, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
) ## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.3 6.3 6.6 6.59 6.88 7.9
## 2 87398900 5.5 6.4 6.65 6.63 6.9 7.5
## 3 87398950 6 6.6 6.8 6.89 7.25 7.6
## 4 87398980 5.8 6.3 6.5 6.63 7 7.5
## 5 87405500 5.2 6.4 6.6 6.68 6.9 8.3
## 6 87406900 5.5 6.4 6.7 6.66 6.9 8.6
## 7 87409900 5.8 6.5 6.8 6.77 7 8.4(sum_pH_p3 <- plan_wide_19902020 %>%
select(codigo, pH, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.47 6.28 6.42 6.47 6.60 7.3
## 2 87398900 5.68 6.36 6.5 6.57 6.84 7.4
## 3 87398950 5.71 6.28 6.46 6.46 6.68 7
## 4 87398980 5.42 6.10 6.36 6.39 6.6 7.2
## 5 87405500 5.64 6.34 6.5 6.49 6.7 7.01
## 6 87406900 5.6 6.4 6.48 6.51 6.77 7.3
## 7 87409900 5.59 6.46 6.6 6.57 6.76 7.2Tempo para esse code chunk ser rodado: 0.36 s
ggsave("pH.png",
units = c("px"),
width = 4500,
height = 2993,
plot = pH,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p1.png",
plot = pH_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p2.png",
plot = pH_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p3.png",
plot = pH_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 8.59 s
9.0.8 Sólidos totais
(SolTot <- plan_wide_19902020 %>%
boxplot_solidos_totais(
titulo = "Sólidos totais no período 1990-2020"
)+
facet_wrap(~periodo)
)
Tempo para esse
code chunk ser rodado: 1.92 s
(SolTot_p1 <- plan_wide_19902020 %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
boxplot_solidos_totais(
titulo = "Sólidos totais no período 1990-2000"
)
)
Tempo
para esse code chunk ser rodado: 1.03 s
(SolTot_p2 <- plan_wide_19902020 %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
boxplot_solidos_totais(
titulo = "Sólidos totais no período 2000-2010"
)
)
Tempo
para esse code chunk ser rodado: 0.94 s
(SolTot_p3 <- plan_wide_19902020 %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
boxplot_solidos_totais(
titulo = "Sólidos totais no período 2010-2020"
)
)
Tempo
para esse code chunk ser rodado: 0.91 s
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 2.48 s
(sum_SolTot_p1 <- plan_wide_19902020 %>%
select(codigo, solidos_totais, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 46 84.5 95 122. 120 510
## 2 87398900 18 74.5 97 111. 122. 474
## 3 87398950 10 76.5 91 90.9 106. 155
## 4 87398980 48 63.5 81.5 104. 126. 337
## 5 87405500 70 101 121 133. 151 361
## 6 87406900 89 118 155 165. 210 279
## 7 87409900 20 99.5 122 128. 143 381(sum_SolTot_p2 <- plan_wide_19902020 %>%
select(codigo, solidos_totais, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 28 80 100 111. 123. 412
## 2 87398900 42 82 102. 128. 140. 489
## 3 87398950 46 94.2 108. 126. 127. 318
## 4 87398980 40 61 77 85.3 96 228
## 5 87405500 48 102 133 148. 170. 522
## 6 87406900 50 109 134. 154. 170. 670
## 7 87409900 56 112. 156 167. 190. 599(sum_SolTot_p3 <- plan_wide_19902020 %>%
select(codigo, solidos_totais, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 61 69 90 82.8 96 101
## 2 87398900 41 77 104 120. 127 308
## 3 87398950 45 93 101 109. 117 221
## 4 87398980 55 62.8 80 79.9 95 109
## 5 87405500 83 89.2 108. 124. 162. 195
## 6 87406900 50 106 117 135. 163 246
## 7 87409900 75 103 115 131. 145 251Tempo para esse code chunk ser rodado: 0.33 s
ggsave("SolTot.png",
units = c("px"),
width = 4500,
height = 2993,
plot = SolTot,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p1.png",
plot = SolTot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p2.png",
plot = SolTot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p3.png",
plot = SolTot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 10.19 s
9.1 Condutividade elétrica
(cond_elet <- plan_wide_19902020 %>%
boxplot_cond_elet(
titulo = "Condutividade elétrica no período 1990-2020"
)+
facet_wrap(~periodo)
)
Tempo para
esse code chunk ser rodado: 3.2 s
(cond_elet_p1 <- plan_wide_19902020 %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
boxplot_cond_elet(
titulo = "Condutividade elétrica no período 1990-2000"
)
)
Tempo
para esse code chunk ser rodado: 1.31 s
(cond_elet_p2 <- plan_wide_19902020 %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
boxplot_cond_elet(
titulo = "Condutividade elétrica no período 2000-2010"
)
)
Tempo
para esse code chunk ser rodado: 1 s
(cond_elet_p3 <- plan_wide_19902020 %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
boxplot_cond_elet(
titulo = "Condutividade elétrica no período 2010-2020"
)
)
Tempo
para esse code chunk ser rodado: 0.83 s
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 2.34 s
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
select(codigo, condutividade, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(condutividade,
na.rm = TRUE),
q1 =
quantile(condutividade, 0.25,
na.rm = TRUE),
median =
median(condutividade,
na.rm = TRUE),
mean =
mean(condutividade,
na.rm= TRUE),
q3 =
quantile(condutividade, 0.75,
na.rm = TRUE),
max =
max(condutividade,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9.4 51.1 67 75.1 83.2 340
## 2 87398900 10 41.5 51 55.3 64.2 160
## 3 87398950 9 41.5 51.5 60.1 69.5 160
## 4 87398980 11.3 42.4 52.0 53.0 67.0 83.8
## 5 87405500 25 68.7 88.2 130. 170 560
## 6 87406900 52 88.4 133. 193. 256. 576
## 7 87409900 29 80 110. 134. 168. 460(sum_cond_elet_p2 <- plan_wide_19902020 %>%
select(codigo, condutividade, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(condutividade,
na.rm = TRUE),
q1 =
quantile(condutividade, 0.25,
na.rm = TRUE),
median =
median(condutividade,
na.rm = TRUE),
mean =
mean(condutividade,
na.rm= TRUE),
q3 =
quantile(condutividade, 0.75,
na.rm = TRUE),
max =
max(condutividade,
na.rm = TRUE))
)## # A tibble: 7 × 7
## codigo min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 11.9 67.0 82.6 84.8 102. 164.
## 2 87398900 11 44.4 52.3 57.1 72.6 136.
## 3 87398950 39.8 58.4 76 82.3 98.3 160
## 4 87398980 9.4 42.4 49.7 51.5 62 114.
## 5 87405500 17 77.5 107 142. 171. 679
## 6 87406900 23.1 85.6 124. 164. 199. 619
## 7 87409900 56.1 114. 177 200. 242 454(sum_cond_elet_p3 <- plan_wide_19902020 %>%
select(codigo, condutividade, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
group_by(codigo) %>%
summarize(
min =
min(condutividade,
na.rm = TRUE),
q1 =
quantile(condutividade, 0.25,
na.rm = TRUE),
median =
median(condutividade,
na.rm = TRUE),
mean =
mean(condutividade,
na.rm= TRUE),
q3 =
quantile(condutividade, 0.75,
na.rm = TRUE),
max =
max(condutividade,
na.rm = TRUE),
n =
length(condutividade))
)## # A tibble: 7 × 8
## codigo min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 0.01 68.5 80.2 80.4 99.5 125. 34
## 2 87398900 39.7 53.4 58.3 61.1 65.5 103 36
## 3 87398950 40.9 64.7 70.1 76.1 82.5 195. 35
## 4 87398980 43.2 51.7 54.0 56.3 61.0 78.9 28
## 5 87405500 47 85.8 121. 146. 209. 286 33
## 6 87406900 62.7 95.9 142. 163. 216. 354. 35
## 7 87409900 65.7 121. 159. 179. 245. 498. 37# plan_wide_19902020 %>%
# select(codigo, IQA) %>%
# group_by(codigo) %>%
# summarize(
# min =
# min(IQA,
# na.rm = TRUE),
# q1 =
# quantile(IQA, 0.25,
# na.rm = TRUE),
# median =
# median(IQA,
# na.rm = TRUE),
# mean =
# mean(IQA,
# na.rm= TRUE),
# q3 =
# quantile(IQA, 0.75,
# na.rm = TRUE),
# max =
# max(IQA,
# na.rm = TRUE))Tempo para esse code chunk ser rodado: 0.33 s
ggsave("cond_elet.png",
units = c("px"),
width = 4500,
height = 2993,
plot = cond_elet,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p1.png",
plot = cond_elet_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p2.png",
plot = cond_elet_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p3.png",
plot = cond_elet_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 9.35 s
9.1.1 IQA
Tempo para esse code chunk ser
rodado: 2.45 s
Tempo
para esse code chunk ser rodado: 1.38 s
Tempo
para esse code chunk ser rodado: 1.06 s
Tempo
para esse code chunk ser rodado: 0.92 s
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
Tempo
para esse code chunk ser rodado: 3.76 s
(sum_IQA_p1 <- plan_wide_19902020 %>%
select(codigo, iqa, ano_coleta) %>%
filter(ano_coleta>"1990" &
ano_coleta<="2000") %>%
group_by(codigo) %>%
summarize(
min =
min(iqa,
na.rm = TRUE),
q1 =
quantile(iqa, 0.25,
na.rm = TRUE),
median =
median(iqa,
na.rm = TRUE),
mean =
mean(iqa,
na.rm= TRUE),
q3 =
quantile(iqa, 0.75,
na.rm = TRUE),
max =
max(iqa,
na.rm = TRUE),
n =
length(iqa)
)
)## # A tibble: 7 × 8
## codigo min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 27.0 35.7 40.9 40.7 46.2 52.2 101
## 2 87398900 27.8 37.9 42.9 43.0 48.0 58.5 101
## 3 87398950 32.8 36.8 41.4 43.2 48.6 61.9 68
## 4 87398980 29.2 35.8 40.4 40.3 44.8 51.9 30
## 5 87405500 24.8 34.9 41.2 40.3 46.9 57.6 97
## 6 87406900 24.7 31.3 37.8 37.4 44.4 49.0 32
## 7 87409900 23.6 31.9 37.1 38.8 46.2 55.4 65(sum_IQA_p2 <- plan_wide_19902020 %>%
select(codigo, iqa, ano_coleta) %>%
filter(ano_coleta>"2000" &
ano_coleta<="2010") %>%
group_by(codigo) %>%
summarize(
min =
min(iqa,
na.rm = TRUE),
q1 =
quantile(iqa, 0.25,
na.rm = TRUE),
median =
median(iqa,
na.rm = TRUE),
mean =
mean(iqa,
na.rm= TRUE),
q3 =
quantile(iqa, 0.75,
na.rm = TRUE),
max =
max(iqa,
na.rm = TRUE),
n =
length(iqa)
)
)## # A tibble: 7 × 8
## codigo min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 27.8 34.6 40.0 39.5 43.5 48.7 75
## 2 87398900 28.5 35.1 37.6 38.3 40.6 48.5 77
## 3 87398950 21.1 29.4 32.7 32.8 36.8 44.0 30
## 4 87398980 24.5 35.7 39.4 39.5 43.4 52.1 66
## 5 87405500 19.8 28.7 31.5 31.9 35.7 48.8 78
## 6 87406900 17.1 25.3 29.0 29.5 32.8 44.1 79
## 7 87409900 16.2 20.5 26.1 25.0 29.8 33.1 31(sum_IQA_p3 <- plan_wide_19902020 %>%
select(codigo, iqa, ano_coleta) %>%
filter(ano_coleta>"2010" &
ano_coleta<="2020") %>%
# ?as_factor(codigo) %>%
group_by(codigo) %>%
summarize(
min =
min(iqa,
na.rm = TRUE),
q1 =
quantile(iqa, 0.25,
na.rm = TRUE),
median =
median(iqa,
na.rm = TRUE),
mean =
mean(iqa,
na.rm= TRUE),
q3 =
quantile(iqa, 0.75,
na.rm = TRUE),
max =
max(iqa,
na.rm = TRUE),
n =
length(iqa),
NAs =
sum(is.na(iqa))
) %>%
mutate(
"%NA" = NAs/n*100
)
)## # A tibble: 7 × 10
## codigo min q1 median mean q3 max n NAs `%NA`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 87398500 40.2 42.5 45.4 44.2 45.5 47.2 34 29 85.3
## 2 87398900 34.1 38.6 41.2 40.2 42.9 44.4 36 32 88.9
## 3 87398950 36.7 39.5 42.4 41.5 44.4 44.6 35 31 88.6
## 4 87398980 40.0 40.0 40.0 40.0 40.0 40.0 28 27 96.4
## 5 87405500 30.8 31.6 32.5 32.5 33.3 34.1 33 31 93.9
## 6 87406900 22.9 24.4 25.9 25.3 26.5 27.2 35 32 91.4
## 7 87409900 24.1 25.1 27.3 26.9 28.2 29.7 37 32 86.5Tempo para esse code chunk ser rodado: 0.33 s
ggsave("iqa.png",
units = c("px"),
width = 4500,
height = 2993,
plot = iqa,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p1.png",
plot = iqa_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p2.png",
plot = iqa_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p3.png",
plot = iqa_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Tempo
para esse code chunk ser rodado: 10.33 s
9.2 Testando coisas
9.3 Correlação
parametros_IQA %>%
dplyr::select(
-codigo,
-ano_coleta,
-nitrogenio_total
) %>%
# group_by(codigo) %>%
rename(
CE = condutividade,
E_coli = escherichia_coli,
OD = oxigenio_dissolvido,
ST = solidos_totais,
Turb = turbidez,
Temp = temperatura_agua,
Ptot = fosforo_total,
# NTot = nitrogenio_total,
NAmon = nitrogenio_amoniacal,
NTK = nitrogenio_kjeldahl
) %>%
ggcorr(
method = "complete.obs",
# "pearson",
# "pairwise",
name = "Correlação",
label = TRUE,
label_alpha = TRUE,
digits = 3,
low = "#3B9AB2",
mid = "#EEEEEE",
high = "#F21A00",
# palette = "RdYlBu",
layout.exp = 0,
legend.position = "left",
label_round = 3,
# legend.size = 18,
geom = "tile",
nbreaks = 10,
)+
labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
theme_linedraw()+
theme(
legend.position = c(0.15, 0.6),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14),
# legend.spacing = unit(element_text(),
# units = 5)
plot.title = element_text(hjust = 0.5,
size = 16)
)
correlação-parametros-qualidade-agua-gravataí no período 1990-2020
# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
dplyr::select(-codigo) %>%
ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
axisLabels = "show")
correlacao_pIQA <- parametros_IQA %>%
group_by(codigo) %>%
correlation::correlation()
correlacao_pIQA %>%
# glimpse()
filter(
p < 0.001
) %>%
as_tibble() %>%
# filter(
# Parameter1 == "oxigenio_dissolvido"
# ) %>%
arrange(desc(r)) ## # A tibble: 129 × 12
## Group Param…¹ Param…² r CI CI_low CI_high t df_er…³ p
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
## 1 873985… nitrog… nitrog… 1 0.95 1 1 Inf 115 0
## 2 873989… nitrog… nitrog… 1 0.95 1 1 Inf 60 0
## 3 874069… nitrog… nitrog… 1 0.95 1 1 3.88e8 67 0
## 4 874099… nitrog… nitrog… 1.00 0.95 1.00 1.00 7.91e2 71 6.50e-140
## 5 873989… nitrog… nitrog… 0.998 0.95 0.997 0.999 1.81e2 116 2.92e-142
## 6 873989… nitrog… nitrog… 0.991 0.95 0.986 0.994 6.11e1 68 2.83e- 59
## 7 874055… nitrog… nitrog… 0.988 0.95 0.982 0.992 6.64e1 110 1.05e- 88
## 8 874099… nitrog… nitrog… 0.976 0.95 0.934 0.992 1.75e1 15 1.51e- 9
## 9 874055… nitrog… nitrog… 0.932 0.95 0.891 0.958 2.07e1 65 1.83e- 28
## 10 874069… nitrog… nitrog… 0.930 0.95 0.890 0.957 2.08e1 67 4.70e- 29
## # … with 119 more rows, 2 more variables: Method <chr>, n_Obs <int>, and
## # abbreviated variable names ¹Parameter1, ²Parameter2, ³df_errorparametros_IQA %>%
# group_by(codigo) %>%
dplyr::select(
nitrogenio_kjeldahl, condutividade
) %>%
# correlation::cor_test() %>%
plot()
Tempo
para esse code chunk ser rodado: 4.08 s
10 Textando o texto
- § falar do comportamento geral dos dados
- 2º § - xº § -> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.
10.8
Os resultados apontam que para o parâmetro OD
11 Gráficos exemplos boxplot
set.seed(2023)
exemplo_boxplot_df <- data.frame(
PM = c("PM1"),
# letras = letters[seq( from = 1, to = 1 )],
Stat1 = rnorm(100,
mean = 5,
sd = 1.8)
)
(sumario_exemplo_bp <- exemplo_boxplot_df %>%
group_by(PM) %>%
summarize(
max = max(Stat1),
p95 = quantile(Stat1, 0.95),
p80 = quantile(Stat1, 0.8),
median = median(Stat1),
p20 = quantile(Stat1, 0.2),
p05 = quantile(Stat1, 0.05),
min = min(Stat1),
) %>%
t() %>%
row_to_names(row_number = 1) %>%
as.numeric()
)## [1] 9.923430 7.866927 6.683828 4.886935 3.545771 2.277104 1.282177(boxplot_example <- exemplo_boxplot_df %>%
ggplot(
aes(
x = PM,
y = Stat1,
)
)+
stat_summary(
fun.data = f,
geom = 'errorbar',
width = 0.15,
position = position_dodge(width = 0.65),
)+
stat_summary(
fun.data = f,
geom = "boxplot",
width = 0.40,
fill = '#F8F8FF',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
)+
labs(
title = "Elementos do *boxplot*",
x= NULL,
y= NULL
)+
ggbeeswarm::geom_quasirandom(
size = 1.4,
alpha = .3,
width = .07,
)+
scale_y_continuous(
expand = expansion(mult = c(0,0)),
n.breaks = 8,
limits = c(-0.3,12)
)+
annotate(
geom = "text",
x = 1.55,
hjust = "right",
y = sumario_exemplo_bp,
label = c("Valor máximo", "P95", "P80", "Mediana", "P20", "P05", "Valor mínimo"),
# fontface = 3
)+
geom_richtext(
x = 0.56,
y = 9.103998,
label.color = NA,
hjust = "center",
label = "<i>Outliers</i>"
)+
geom_curve(
aes(
x = 0.6, xend = 0.98,
y = 9.103998 , yend = 9.103998 , #Outliers
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
#definindo o [
geom_curve(
aes(
x = 0.74, xend = 0.78,
y = 6.683828, yend = 6.683828,
),
curvature = 0,
size = 1.0,
lineend = "butt"
)+
geom_curve(
aes(
x = 0.74, xend = 0.74,
y = 3.545771, yend = 6.683828,
),
curvature = 0,
size = 1.0,
lineend = "butt"
)+
annotate(
geom = "text",
x = 0.56,
hjust = "center",
y = 5.11,
label = "Amplitude\n(P80-P20)"
)+
geom_curve(
aes(
x = 0.74, xend = 0.78,
y = 3.545771 , yend = 3.545771 ,
),
curvature = 0,
size = 1.0,
lineend = "butt"
)+
# fim do [
geom_curve(
aes(
x = 0.6, xend = 0.90,
y = 7.866927 , yend = 7.866927 , #whisker superior
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
annotate(
geom = "text",
x = 0.56,
hjust = "center",
y = 7.866927,
label = "Whisker\nsuperior"
)+
geom_curve(
aes(
x = 0.6, xend = 0.90,
y = 2.277104 , yend = 2.277104 , #whisker inferior
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
annotate(
geom = "text",
x = 0.56,
hjust = "center",
y = 2.277104,
label = "Whisker\ninferior"
)+
geom_curve(
aes(
x = 1.4, xend = 1.01,
y = 9.92343, yend = 9.92343, #valor máximo
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.45, xend = 1.11,
y = 7.866927 , yend = 7.866927 , #P95
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.45, xend = 1.22,
y = 6.683828 , yend = 6.683828 , #P80
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.45, xend = 1.22,
y = 4.886935 , yend = 4.886935 , #P50
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.45, xend = 1.22,
y = 3.545771, yend = 3.545771, #P20
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.45, xend = 1.11,
y = 2.277104, yend = 2.277104, #P05
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
geom_curve(
aes(
x = 1.4, xend = 1.01,
y = 1.282177, yend = 1.282177, #valor mínimo
),
curvature = 0,
size = 1.0,
arrow = arrow(length = unit(0.05, "npc")),
lineend = "round"
)+
# theme_grafs()+
theme_bw()+
theme(
plot.title =
element_markdown(
hjust = 0.5,
color = "black",
size = 19),
)
)
Tempo
para esse code chunk ser rodado: 3.22 s
ggsave(
filename = "exemplo_boxplot.png",
plot = boxplot_example,
units = c("px"),
width = (4500)/1.5,
height = (2993)/1.5,
path = "./graficos",
dpi = 300,
# type = "cairo"
)Tempo para esse code chunk ser rodado: 2.93 s
set.seed(2021)
data <- tibble(
grupo = factor(
c(rep(
"Grupo 1", 100),
rep("Grupo 2", 250),
rep("Grupo 3", 25)
)
),
valor = c(seq(0, 20, length.out = 100),
c(rep(0, 5),
rnorm(30, 2, .1),
rnorm(90, 5.4, .1),
rnorm(90, 14.6, .1),
rnorm(30, 18, .1),
rep(20, 5)
),
rep(seq(0, 20, length.out = 5), 5))
) %>%
rowwise() %>%
mutate(
valor = if_else(
grupo == "Grupo 2", valor + rnorm(1, 0, .4),
valor
)
)
## function to return median and labels
n_fun <- function(x){
return(
data.frame(
y = median(x) - 1.25,
label = paste0(
"n = ",length(x)
)
)
)
}Tempo para esse code chunk ser rodado: 0.06 s
(tukey_n_boxplot <- ggplot(data,
aes(x = grupo,
y = valor)
)+
stat_boxplot(geom = 'errorbar',
width = 0.15,
position = position_dodge(width = 0.65))+
geom_boxplot(fill = "grey92",
width = 0.40,
position = position_dodge(width = 0.65))+
## use summary function to add text labels
stat_summary(
geom = "text",
fun.data = n_fun,
# family = "Oswald",
size = 5
)+
labs(
title = "Tukey *boxplot*",
x= NULL,
# y="mg/L"
)+
# theme_grafs()+
theme_bw()+
theme(
axis.text.y = element_text(
angle = 90,
# size=15,
# face=2
),
plot.title =
element_markdown(
hjust = 0.5,
color = "black",
size = 19)
)
)
(tukey_boxplot <- ggplot(data, aes(x = grupo,
y = valor)) +
stat_boxplot(geom = 'errorbar',
width = 0.15,
position = position_dodge(width = 0.65))+
geom_boxplot(fill = "grey92",
width = 0.40,
position = position_dodge(width = 0.65)) +
## use either geom_point() or geom_jitter()
geom_point(
## draw bigger points
size = 2,
## add some transparency
alpha = .25,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)
)+
theme_bw()+
labs(
title = "Tukey *boxplot*",
x= NULL,
# y="mg/L"
)+
# theme_grafs()+
theme_bw()+
theme(
axis.text.y = element_text(
angle = 90,
# size=15,
# face=2
),
plot.title =
element_markdown(
hjust = 0.5,
color = "black",
size = 19)
))
Tempo
para esse code chunk ser rodado: 1.31 s
data %>%
group_by(grupo) %>%
summarize(
min = min(valor),
P20 = quantile(valor, 0.20),
q1 = quantile(valor, 0.25),
mediana = median(valor),
q3 = quantile(valor, 0.75),
P80 = quantile(valor, 0.80),
max = max(valor)
) %>%
t() %>%
row_to_names(row_number = 1)## Grupo 1 Grupo 2 Grupo 3
## min " 0.0000000" "-0.6345142" " 0.0000000"
## P20 "4.000000" "5.057189" "4.000000"
## q1 "5.000000" "5.245691" "5.000000"
## mediana "10.00000" "10.01593" "10.00000"
## q3 "15.00000" "14.81205" "15.00000"
## P80 "16.00000" "15.04351" "16.00000"
## max "20.0000" "20.3882" "20.0000"(box_percentile_plot <- ggplot(data,
aes(x = grupo, y = valor)
) +
stat_summary(
fun.data = f,
geom = 'errorbar',
width = 0.15,
position = position_dodge(width = 0.65),
)+
stat_summary(
fun.data = f,
geom = "boxplot",
width = 0.40,
fill = 'grey92',
color = "black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
)+
# geom_boxplot(fill = "grey92") +
## use either geom_point() or geom_jitter()
geom_point(
## draw bigger points
size = 2,
## add some transparency
alpha = .25,
## add some jittering
position = position_jitter(
## control randomness and range of jitter
seed = 1, width = .2
)
)+
labs(
title = "*Box Percentile-Plot*",
x= NULL,
# y="mg/L"
)+
# theme_grafs()+
theme_bw()+
theme(
axis.text.y = element_text(
angle = 90,
# size = 15,
# face = 2
),
plot.title =
element_markdown(
hjust = 0.5,
color = "black",
size = 19)
)
)
grid.arrange(
tukey_boxplot, box_percentile_plot,
ncol = 2
)
fig_tukey_garrett <- plot_grid(tukey_boxplot, box_percentile_plot,
labels = "AUTO")Tempo para esse code chunk ser rodado: 4.2 s
ggsave(
filename = "tukey_n_boxplot.png",
plot = tukey_n_boxplot,
units = c("px"),
width = 4500,
height = 2993,
path = "./graficos",
dpi = 300,
# type = "cairo"
)
ggsave(
filename = "tukey_boxplot.png",
plot = tukey_boxplot,
units = c("px"),
width = 4500,
height = 2993,
path = "./graficos",
dpi = 300,
# type = "cairo"
)
ggsave(
filename = "box_percentile_plot.png",
plot = box_percentile_plot,
units = c("px"),
width = 4500,
height = 2993,
path = "./graficos",
dpi = 300,
# type = "cairo"
)
ggsave(
filename = "fig_tukey_garrett.png",
plot = fig_tukey_garrett,
units = c("px"),
width = 4500,
height = 2993,
path = "./graficos",
dpi = 300,
# type = "cairo"
)Tempo para esse code chunk ser rodado: 4.8 s
---
title: "TCC"
author: "Leonardo Fernandes Wink"
date: "`r format(Sys.time(), '%d/%m/%Y')`"
output:
  html_document: 
    distill::distill_article:
    highlight: haddock
    keep_md: yes
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
    fig_width: 10
    fig_height: 6.66
    fig_caption: yes
    code_download: true
  pdf_document:
    toc: yes
  word_document: 
    toc: yes
    keep_md: yes
  github_document:
    html_preview: true
always_allow_html: yes
editor_options: 
  chunk_output_type: console
fig.align: center
---

```{r Rotina pra toda vez que abrir o documento, echo = FALSE}
# Abrir o GitHub Desktop
# Verificar se há pull pra ser feito
# Abrir o RStudio
```

# Brief explanation

Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).

The river flows in the east-west direction as shown in the image below.

![](images/paste-7AD7027F.png)

The logic behind the sorting in the boxplots is because of 2 main reasons:

1.  The original monitoring point isn't easy to understand (8 digits, like 87409900)
2.  Changing the original nomenclature to PM1, PM2 (...) makes it easier to understand that the last point has water contributions of every other point upstream.

Some features that I want to add:

-   If the parameter is x, then use x's classes (with its own classes background color plotted)

-   Define the timescale, should act just like a filter

```{r p1 example, eval=FALSE}
# plan_wide_19902020 %>%
#   filter(ano_coleta > "1990" &
#          ano_coleta <= "2000")
```

# Anotações de coisas por fazer:

-   Descobrir como colocar as estações no sentido correto montante -\> jusante nos sumários

> 87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

-   ~~Aprender a segmentar o meu dataset por períodos~~
-   aprender a criar uma nova coluna com a segmentação dos períodos
-   maybe use `~facet.grid`
-   aprender a colocar a legenda dentro do gráfico
    -   reduzir o tamanho da legenda
-   ~~corrigir os valores 0 de IQA pra NA~~
-   descobrir como conseguir a equação do lm
-   ~~aprender a pivotar o sumário~~ -\> meu sumário do google docs ta batendo direitinho com o do R
-   descobrir se há outros TCCs com disponibilização de códigos
-   ~~`Namon` tá com com casa decimal `","` e `ptot` tá com `"."`~~
-   correlação forte entre condutividade e Namon/Ptot/DBO

| 1990-2000 | 2000-2010 | 2010-2020 |
|:---------:|:---------:|:---------:|
| 1990-2000 | 2000-2010 | 2010-2020 |

# Instalar os pacotes

```{r instalar pacotes, eval=FALSE}
# install.packages(tidyverse)
```

## acessar os pacotes

```{r Acessar os pacotes, message = FALSE, warning = TRUE}
pacman::p_load(readr, rmarkdown, readxl, janitor,
               pillar, dplyr, tidyverse,
               knitr, kableExtra, see,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally, ggtext, cowplot,
               report)
# pacman::p_load(tibbletime)
# cite_packages()
```

```{r cronometrando quanto tempo cada chunk leva}
knitr::knit_hooks$set(time_it = local({
   now <- NULL
   function(before, options) {
      if (before) {
         # record the current time before each chunk
         now <<- Sys.time()
      } else {
         # calculate the time difference after a chunk
         res <- difftime(Sys.time(), now)
         # return a character string to show the time
         paste("Tempo para esse code chunk ser rodado:", round(res, digits = 2), "s")
      }
   }
}))

knitr::opts_chunk$set(time_it = TRUE)
```

```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
```

### referenciando os pacotes

```{r referenciando os pacotes}
version$version.string
# citation(package = "tidyverse")
```

## importando a planilha

```{r Importando a planilha, echo = FALSE, message = TRUE, warning = FALSE}
plan_wide_19902020 <- read_delim("https://raw.githubusercontent.com/leonardofwink/TCC_gh/main/plan_wide_19902020.tsv",
                                 delim = "\t", 
                                 escape_double = FALSE,
                                 col_types = cols(
                                   Alcalinidade = col_double(),
                                   CODIGO = col_character(), 
                                   COORD_GEO_LAT_GRAU = col_double(),
                                   COORD_GEO_LONG_GRAU = col_double(),
                                   DATA_COLETA = col_date(format = "%d/%m/%Y"),
                                   Nitrato = col_double(), 
                                   Nitrito = col_double(),
                                   SDT = col_double(), 
                                   SST = col_double(),
                                   `Vazao` = col_double(), 
                                   `Vazao rio` = col_double()
                                 ),
                                 locale = locale(
                                   date_names = "pt", 
                                   decimal_mark = ",",
                                   grouping_mark = ""
                                 ),
                                 trim_ws = TRUE
) %>% 
  janitor::clean_names() %>% 
  rename(
    pH = p_h,
    iqa_pH = iqa_p_h,
    iqa_pH_2 = iqa_p_h_2
  ) %>% 
  mutate(
    ponto_monitoramento = case_when(
      codigo == "87398500" ~ "PM1",
      codigo == "87398980" ~ "PM2",
      codigo == "87398900" ~ "PM3",
      codigo == "87398950" ~ "PM4",
      codigo == "87405500" ~ "PM5",
      codigo == "87406900" ~ "PM6",
      codigo == "87409900" ~ "PM7"
    )
  ) %>% 
  select(codigo, ponto_monitoramento, everything()) %>% #reordenando colunas
  arrange(data_coleta, ponto_monitoramento)
# teste <- plan_wide_19902020 %>%
#   dplyr::filter(data_coleta >= as.POSIXct("2010-01-01")) #this works
```

```{r Visualização da planilha importada, echo = FALSE}
paged_table(plan_wide_19902020,
            options = list(rows.print = 15,
                           cols.print = 10))
```

# data wrangling

Como há dados faltantes, no cálculo entre o produto das colunas, o R acaba interpretando como se fosse zero, mas na verdade é `NA`.

```{r data wrangling}
plan_wide_19902020 <- plan_wide_19902020 %>% 
   mutate(iqa = ifelse(iqa == 0, NA, iqa))

parametros_IQA <- plan_wide_19902020 %>%
  select(
    codigo,
    ponto_monitoramento,
    pH,
    oxigenio_dissolvido,
    dbo,
    fosforo_total,
    escherichia_coli,
    nitrogenio_amoniacal,
    nitrogenio_kjeldahl,
    nitrogenio_total,
    turbidez,
    temperatura_agua,
    solidos_totais,
    condutividade,
    ano_coleta
  )

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)
```

```{r futuro calculo de IQA, include = FALSE}
plan_wide_19902020 %>% 
  select(starts_with("iqa_")) %>% 
  mutate(
    teste_iqa_calc = prod() #queria tentar gerar o produtório entre as colunas que já possuem o IQA^2
  )
```

```{r Códigos Git, echo = FALSE, include=FALSE}
# cd myrepo
# ls
# head README.md
# git status
# git add README.md
# git commit -m "A commit from my local computer"
# 
# cd .. # voltar pro diretório acima
# rm -rf myrepo/ #remover/apagar a pasta myrepo
```

```{r Aprendendo Git, echo = FALSE, include=FALSE}
# slides da bia que ajudam mt
# https://beatrizmilz.github.io/slidesR/git_rstudio/11-2021-ENCE.html#20
# aprendendo a sincronizar usando esse guia -> 
# https://happygitwithr-com.translate.goog/push-pull-github.html?_x_tr_sl=auto&_x_tr_tl=pt&_x_tr_hl=pt-BR
# library(usethis)
# usethis::create_github_token() criar um código pra acesso e sincronização between R e github

# gitcreds::gitcreds_set() 
# 
# use_git_config(user.name = "leonardofwink",
#                user.email = "leonardofwink@gmail.com")
# usethis::gh_token_help()

# Como mostrar os dados de um arquivo via Git/GitHub
# git clone https://github.com/leonardofwink/myrepo.git
# cd myrepo #acessa a pasta myrepo
# ls #lista os arquivos da pasta 
# head README.md #mostra as primeiras observações do arquivo

# Como mostrar os dados de um arquivo via R
# head(C:/Users/Léo/myrepo/README.md)

# Adicionar uma linha ao README.md e verificar se o Git percebe a mudança
# echo "A line I wrote on my local computer" >> README.md
# git status
## C:\Users\Léo\myrepo>git status
## On branch main
## Your branch is up to date with 'origin/main'.
## 
## Changes not staged for commit:
##   (use "git add <file>..." to update what will be committed)
##   (use "git restore <file>..." to discard changes in working directory)
##         **modified:   README.md**
## 
## no changes added to commit (use "git add" and/or "git commit -a")
```

# setting theme

```{r setting theme}
theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black"){
  theme(
    plot.title = 
      element_text(
        hjust = 0.5,
        color = coloracao_letra,
        size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    strip.background = element_rect(fill = bg,
                                    linetype = 1,
                                    size = 0.5,
                                    color = "black"),
    strip.text = element_text(size = 17),
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}
```

# setting different timescales

```{r setting periodos, echo = TRUE}
plan_wide_19902020 <- plan_wide_19902020 %>% 
  mutate(
    periodo = if_else(
      ano_coleta <= 2000, 
      "1990-2000",
      if_else(
        ano_coleta <= 2010,
        "2000-2010",
        "2010-2020"
      )
    )
  )
```

# setting sumaries

```{r Sumários, echo = FALSE}
sumario <- function(dados = plan_wide_19902020, parametro = "") {
  dados %>% 
    # filter(ano_coleta>"1990" &
    #          ano_coleta<="2000") %>%
    group_by(ponto_monitoramento) %>%
    summarize(
      min =
        min(parametro,
            na.rm = TRUE),
      p05 = 
        quantile(parametro, 0.05,
                 na.rm = TRUE),
      p20 =
        quantile(parametro, 0.20,
                 na.rm = TRUE),
      median =
        median(parametro,
               na.rm = TRUE),
      mean =
        mean(parametro,
             na.rm= TRUE),
      p80 =
        quantile(parametro, 0.80,
                 na.rm = TRUE),
      p95 =
        quantile(parametro, 0.95,
                 na.rm = TRUE),
      max =
        max(parametro,
            na.rm = TRUE)
      )
}
```

# Funções

## criando função para gerar boxplots com percentil 20 e 80

```{r funcao percentil 80}
f <- function(x) {
  r <- quantile(x, probs = c(0.05, 0.20, 0.50, 0.80, 0.95))
  names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
  return(r)
}
```

## Oxigênio Dissolvido

```{r gerando function graf od, echo = FALSE}
boxplot_od <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = oxigenio_dissolvido, titulo = "Oxigênio Dissolvido"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = -Inf, ymax = 2,
             alpha = 1,
             fill = "#ac5079")+ #>pior classe
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 2, ymax = 4,
             alpha = 1,
             fill = "#eb5661")+ #classe 4
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 4, ymax = 5,
             alpha = 1,
             fill = "#fcf7ab")+ #classe 3
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 5, ymax = 6,
             alpha = 1,
             fill = "#70c18c")+ #classe 2
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin= 6, ymax = Inf,
             alpha = 1,
             fill = "#8dcdeb")+ #classe 1
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.7,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    # facet_wrap(~periodo)+
    labs(
      title = titulo,
      x= NULL,
      y="mg/L"
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 11,
      limits = c(-0.3,21)
    )+
    scale_x_discrete(limits = c("87398500",
                                "87398980",
                                "87398900",
                                "87398950",
                                "87405500",
                                "87406900",
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
      method = "lm",
      se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
      aes(group = 1),
      alpha = .5,
      na.rm = TRUE,
      size = 1
    )+
    theme_grafs()
}
```

## DBO

```{r gerando function graf dbo, echo = FALSE}
boxplot_dbo <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = dbo, titulo = "Demanda Bioquímica de Oxigênio"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=10, ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=5, ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=3, ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=0, ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.7,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    # facet_wrap(~periodo)+
    labs(title = titulo,
         x="Estação",
         y="mg/L",
         caption = "Leonardo Fernandes Wink"
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
}
```

## Ptot

```{r gerando function graf ptot, echo = FALSE}
boxplot_ptot <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = fosforo_total, titulo = "Fósforo total"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
  stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
    labs(title = titulo,
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " ")
                      )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
}
```

## E coli

```{r funcao-graf-ecoli, echo = FALSE}
boxplot_ecoli <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = escherichia_coli, titulo = "*Escherichia coli*"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=3200, ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=800, ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=160, ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   # facet_wrap(~periodo)+
   labs(title = titulo,
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
}
```

## Nitrogênio Amoniacal

```{r gerando function graf namon, echo = FALSE}
boxplot_namon <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = nitrogenio_amoniacal, titulo = "Nitrogênio Amoniacal"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   # facet_wrap(~periodo)+
   labs(title = titulo,
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE),
                                 max(plan_wide_19902020$nitrogenio_amoniacal, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
}
```

## Turbidez

```{r gerando function graf turb, echo = FALSE}
boxplot_turb <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = turbidez, titulo = "Turbidez"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
             xmin=-Inf, xmax=Inf,
             ymin=100, ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf, xmax=Inf,
            ymin=40, ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = titulo,
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()
}

```

## pH

```{r gerando function graf pH, echo = FALSE}
boxplot_pH <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = pH, titulo = "pH"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
    annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = -Inf, ymax = 6,
            alpha = 1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 9, ymax = Inf,
            alpha = 1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 6, ymax = 9,
            alpha = 1,
            fill="#8dcdeb")+ #classe 1
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.7,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    labs(
      title = titulo,
      x= NULL,
      y="mg/L"
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    scale_x_discrete(limits = c("87398500",
                                "87398980",
                                "87398900",
                                "87398950",
                                "87405500",
                                "87406900",
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
      method = "lm",
      se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
      aes(group = 1),
      alpha = .5,
      na.rm = TRUE,
      size = 1
    )+
    theme_grafs()
}
```

## Sólidos Totais

```{r gerando function graf SolTot, echo = FALSE}
boxplot_solidos_totais <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = solidos_totais, titulo = "Sólidos totais"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha = 1,
            fill = "#ac5079")+ #>pior classe
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 0, ymax = 500,
            alpha = 1,
            fill = "#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = titulo,
        x= NULL,
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$solidos_totais, na.rm = TRUE),
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "),
                      trans = "log10")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
}
```

## Condutividade

```{r gerando function graf condutividade-eletrica, echo = FALSE}
boxplot_cond_elet <- function(dados = plan_wide_19902020, eixo_x = codigo, eixo_y = condutividade, titulo = "Condutividade elétrica"){
  ggplot2::ggplot(
    data = dados,
    aes(
      x = {{eixo_x}},
      y = {{eixo_y}}
    )
  )+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha = 1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 0, ymax = 500,
            alpha = 1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
      labs(title = titulo,
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # min(plan_wide_19902020$condutividade, na.rm = TRUE),
                        5,
                        1000
                        # max(plan_wide_19902020$condutividade, na.rm = TRUE)
                      ),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
}
```


# Parâmetros físico-químicos

### Oxigênio Dissolvido

```{r Gráfico OD facetted, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
(od <- plan_wide_19902020 %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 1990-2020",
   )+
   facet_wrap(~periodo)
)
```

```{r Gráfico OD periodo 1, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2000"}
(od_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "1990" &
            ano_coleta <= "2000") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 1990-2000"
   )
)
```

```{r Gráfico OD periodo 2, echo = FALSE, warning=FALSE, message = FALSE}
(od_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2000" &
            ano_coleta <= "2010") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 2000-2010"
   )
)
```

```{r Gráfico OD periodo 3, echo = FALSE, warning=FALSE, message = FALSE}
(od_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2010" &
            ano_coleta <= "2020") %>% 
   boxplot_od(
     titulo = "Oxigênio Dissolvido no período 2010-2020"
   )
)
```

```{r Salvando OD, warning=FALSE, message = FALSE,}
ggsave("od.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = od,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

```{r Gráfico IQA OD periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqaod_p1 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "1990" &
                                ano_coleta <= "2000"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 1990-2000",
        x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Gráfico IQA OD periodo2, echo = FALSE, warning= FALSE, message = FALSE}
(iqaod_p2 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "2000" &
                                ano_coleta <= "2010"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2000-2010",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)

```

```{r Gráfico IQA OD periodo3, echo = FALSE, warning=FALSE, message = FALSE}
(iqaod_p3 <-ggplot(plan_wide_19902020 %>% 
                      filter(ano_coleta > "2010" &
                                ano_coleta <= "2020"),
                   aes(codigo,
                       iqa_od, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2010-2020",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Sumário OD, echo = FALSE, warning=FALSE, message = FALSE,}
(sum_od_p1 <- plan_wide_19902020 %>%
    select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
    filter(ano_coleta > "1990" &
             ano_coleta <= "2000") %>% 
   group_by(codigo) %>% 
   # codigo == "87398500" <- "teste1"
    # %>% 
 summarize(
       max = 
         max(oxigenio_dissolvido, na.rm = TRUE),
       p95 = 
         quantile(oxigenio_dissolvido, 0.95, na.rm = TRUE),
       p80 = 
         quantile(oxigenio_dissolvido, 0.80, na.rm = TRUE),
       median = 
         median(oxigenio_dissolvido, na.rm = TRUE),
       mean = 
         mean(oxigenio_dissolvido, na.rm= TRUE),
       p20 = 
         quantile(oxigenio_dissolvido, 0.20, na.rm = TRUE),
       p05 = 
         quantile(oxigenio_dissolvido, 0.05, na.rm = TRUE),
       min = 
         min(oxigenio_dissolvido, na.rm = TRUE),
       n = 
         length(oxigenio_dissolvido)
 ) %>% 
    pivot_longer(
       !codigo,
       names_to = "par",
       values_to = "valor"
    ) %>% 
    pivot_wider(names_from = codigo,
                values_from = valor) %>% 
   rename(
     "PM1" = "87398500",
     "PM2" = "87398900",
     "PM3" = "87398950",
     "PM4" = "87398980",
     "PM5" = "87405500",
     "PM6" = "87406900",
     "PM7" = "87409900"
   ) 
 )

# teste1 <- parametros_IQA %>% 
#   group_by(codigo) %>% 
#   pivot_longer(
#     !codigo,
#     names_to = "parametro",
#     values_to = "valor"
#   ) %>% 
#   # group_by(parametro)
#   pivot_wider(
#     names_from = codigo,
#     values_from = valor,
#     # .groups = "drop"
#   ) %>% 
#   rename(
#     "PM1" = "87398500",
#     "PM2" = "87398900",
#     "PM3" = "87398950",
#     "PM4" = "87398980",
#     "PM5" = "87405500",
#     "PM6" = "87406900",
#     "PM7" = "87409900"
#   ) %>%
#   select(par, PM1, PM2, PM3, PM4, PM5, PM6, PM7) %>% 
#   filter(
#     par == "pH"
#   ) 
# %>% 
#   unnest(dplyr::everything())


# teste1$PM1[2]

# %>%
#   summarize(
#     max =
#       max(oxigenio_dissolvido, na.rm = TRUE),
#     q3 =
#       quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
#     median =
#       median(oxigenio_dissolvido, na.rm = TRUE),
#     mean =
#       mean(oxigenio_dissolvido, na.rm= TRUE),
#     q1 =
#       quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
#     min =
#       min(oxigenio_dissolvido, na.rm = TRUE),
#     n =
#       length(oxigenio_dissolvido)
#     )
# #     
# 
# sum(sum_od_p1$n)



(sum_od_p2 <- plan_wide_19902020 %>%
      select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
      filter(ano_coleta>"2000" &
                ano_coleta<="2010") %>% 
      group_by(codigo) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

(sum_od_p3 <- plan_wide_19902020 %>%
      select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
      filter(ano_coleta>"2010" &
                ano_coleta<="2020") %>% 
      group_by(codigo) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

#   pivot_wider(id_cols = codigo,
#               names_from = codigo,
#               values_from = oxigenio_dissolvido)
# 
# 
#   group_by(codigo) %>%
#   get_summary_stats(type = "common") %>%
#   pivot_wider(id_cols = variable,
#               names_from = codigo,
#               values_from = variable$oxigenio_dissolvido)
# 
# # install.packages("ggpubr")
# # library(ggpubr)
```

### Demanda Bioquímica de Oxigênio

```{r Gráfico DBO facetted, fig.cap="Demanda Bioquímica de Oxigênio no período 1990-2020"}
(dbo <- ggplot(plan_wide_19902020,
               aes(x = codigo,
                   y = dbo))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
        x="Estação",
        y="mg/L",
        # caption = "Leonardo Fernandes Wink"
        )+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico DBO período1, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p1<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                aes(codigo,
                    dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período2, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p2<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2000" &
                             ano_coleta<="2010"),
                aes(codigo,
                    dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período3, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p3<-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta>"2010" &
                             ano_coleta<="2020"),
                aes(codigo,
                    dbo, na.rm=TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo1, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo1<-ggplot(plan_wide_19902020 %>% 
                    filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                  aes(codigo,
                      iqa_dbo))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo2, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo2<-ggplot(plan_wide_19902020%>% 
                     filter(ano_coleta>"2000" &
                               ano_coleta<="2010"),
                  aes(codigo,
                      iqa_dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2000-2010",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo3, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo3<-ggplot(plan_wide_19902020%>% 
                     filter(ano_coleta>"2010" &
                               ano_coleta<="2020"),
                  aes(codigo,
                      iqa_dbo))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Sumário DBO, warning=FALSE, message = FALSE,}
(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(codigo, dbo, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(dbo, 
           na.rm = TRUE),
     q1 = 
       quantile(dbo, 0.25, 
                na.rm = TRUE),
     median = 
       median(dbo, 
              na.rm = TRUE),
     mean = 
       mean(dbo, 
            na.rm= TRUE),
     q3 = 
       quantile(dbo, 0.75, 
                na.rm = TRUE),
     max = 
       max(dbo, 
           na.rm = TRUE))
)

(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)

(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(codigo, dbo, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(dbo, 
            na.rm = TRUE),
      q1 = 
        quantile(dbo, 0.25, 
                 na.rm = TRUE),
      median = 
        median(dbo, 
               na.rm = TRUE),
      mean = 
        mean(dbo, 
             na.rm= TRUE),
      q3 = 
        quantile(dbo, 0.75, 
                 na.rm = TRUE),
      max = 
        max(dbo, 
            na.rm = TRUE))
)
```

```{r Salvando DBO, warning=FALSE, message = FALSE,}
ggsave("dbo.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = dbo,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Fósforo total

```{r Gráfico fósforo total facetted, fig.cap="Fósforo total no período 1990-2020"}
(ptot <- ggplot(plan_wide_19902020,
                aes(codigo,
                    fosforo_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
  stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
  facet_wrap(~periodo)+
    labs(title = "Fósforo total no período 1990-2020",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " ")
                      )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Fósforo total periodo1, warning = FALSE, message = FALSE}
(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ano_coleta>"1990" &
                             ano_coleta<="2000"),
                 aes(codigo,
                     fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo2, warning = FALSE, message = FALSE}
(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2000" &
                                ano_coleta<="2010"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2000-2010",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo3, warning = FALSE, message = FALSE}
(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                      filter(ano_coleta>"2010" &
                                ano_coleta<="2020"),
                   aes(codigo,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2010-2020",
         x="Estação",
         y="mg/L")+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Sumário Fósforo total, warning=FALSE, message = FALSE,}
(sum_ptot_p1 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(fosforo_total, na.rm = TRUE),
     q1 = 
       quantile(fosforo_total, 0.25, na.rm = TRUE),
     median = 
       median(fosforo_total, na.rm = TRUE),
     mean = 
       mean(fosforo_total, na.rm= TRUE),
     q3 = 
       quantile(fosforo_total, 0.75, na.rm = TRUE),
     max = 
       max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(codigo, fosforo_total, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

```

```{r Salvando Ptot, warning=FALSE, message = FALSE,}
ggsave("ptot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = ptot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Escherichia coli

```{r Gráfico Ecoli facetted, fig.cap="Escherichia-coli-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(ecoli <- boxplot_ecoli(
  titulo = "*Escherichia coli* no período 1990-2020"
)+
  facet_wrap(~periodo)
)

(ecoli <- ggplot(plan_wide_19902020,
                 aes(codigo,
                     escherichia_coli))+
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=3200, ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=800, ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=160, ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf, xmax=Inf,
            ymin=0, ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "*Escherichia coli* no período 1990-2020",
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
)
```

```{r Gráfico Ecoli periodo1, warning = FALSE, message = FALSE}
(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"1990" &
                                 ano_coleta<="2000"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 1990-2000",
         x="Estação",
         y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo2, warning = FALSE, message = FALSE}
(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2000" &
                                 ano_coleta<="2010"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2000-2010",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo3, warning = FALSE, message = FALSE}
(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ano_coleta>"2010" &
                                 ano_coleta<="2020"),
                    aes(codigo,
                        escherichia_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2010-2020",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$escherichia_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$escherichia_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Sumário Ecoli, warning=FALSE, message = FALSE,}
(sum_ecoli_p1 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"1990" &
              ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(escherichia_coli, 
           na.rm = TRUE),
     q1 = 
       quantile(escherichia_coli, 0.25, 
                na.rm = TRUE),
     median = 
       median(escherichia_coli, 
              na.rm = TRUE),
     mean = 
       mean(escherichia_coli, 
            na.rm= TRUE),
     q3 = 
       quantile(escherichia_coli, 0.75, 
                na.rm = TRUE),
     max = 
       max(escherichia_coli, 
           na.rm = TRUE))
)

(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)

(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(codigo, escherichia_coli, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(escherichia_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(escherichia_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(escherichia_coli, 
               na.rm = TRUE),
      mean = 
        mean(escherichia_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(escherichia_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(escherichia_coli, 
            na.rm = TRUE))
)
```

```{r Salvando ecoli, warning=FALSE, message = FALSE,}
ggsave("ecoli.png",
       plot = ecoli,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Nitrogênio amoniacal

```{r Gráfico Nitrogênio total facetted, fig.cap="nitrogenio-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(namon <- plan_wide_19902020 %>% 
  boxplot_namon(
    eixo_y = nitrogenio_amoniacal,
    titulo = "Nitrogênio Amoaniacal no período 1990-2020"
    )+
  facet_wrap(~periodo)
 )
```

```{r Gráfico Nitrogênio line, warning = FALSE, message = FALSE}
periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01",  "%Y-%m-%d")

(nitro_line <- 
  plan_wide_19902020 %>%
  filter(ano_coleta > "1990" &
           ano_coleta <= "2020") %>%
  dplyr::select(codigo, nitrogenio_amoniacal, data_coleta, periodo) %>%
  # group_by(codigo) %>%
  mutate(
    ponto_monitoramento = case_when(
      codigo == "87398500" ~ "PM1",
      codigo == "87398980" ~ "PM2",
      codigo == "87398900" ~ "PM3",
      codigo == "87398950" ~ "PM4",
      codigo == "87405500" ~ "PM5",
      codigo == "87406900" ~ "PM6",
      codigo == "87409900" ~ "PM7"
    )
  ) %>% 
    # pivot_wider(
    #   names_from = codigo,
    #   values_from = nitro_amon,
    #   id_cols = data_coleta
    # ) %>% 
    ggplot(
      aes(x = data_coleta,
          y = nitrogenio_amoniacal,
          # color = codigo
      ))+
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
    #       ymin = 13.3, 
    #       ymax = Inf,
    #       alpha= 0.005,
    #       fill= "#ac5079"),
    # show.legend = FALSE)+ #>pior classe
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
  #       ymin= 3.7,
  #       ymax= 13.3,
  #       alpha= 0.005,
  #       fill= "#fcf7ab"),
  #    show.legend = FALSE)+ #classe 3
  # geom_rect(
  #   aes(xmin = periodo_inicial, 
  #       xmax = periodo_final,
  #       ymin= 0,
  #       ymax= 3.7,
  #       alpha= 0.005,
  #       fill= "blue"
  #         # "#8dcdeb"
  #         ),
  #    show.legend = FALSE)+ #classe 1
  annotate("rect",
           xmin= periodo_inicial,
           xmax= periodo_final,
           ymin=13.3,
           ymax=Inf,
           alpha= 0.7,
           fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin=3.7,
             ymax=13.3,
             alpha= 0.7,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin= -Inf,
             ymax=3.7,
             alpha= 0.7,
             fill="#8dcdeb")+ #classe 1
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
    # geom_smooth(
    #   # aes(color = codigo),
    #   method = "lm",
    #   # formula = y ~ poly(x, 2),
    #   # span = 0.2,
    #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
    #   aes(group = 1),
    #   alpha =.5,
    #   na.rm = TRUE,
    #   size = 0.3,
    #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~ponto_monitoramento,
    nrow = 4,
  )+
    theme_bw()
)
```

```{r Gráfico Nitrogênio total periodo1, warning = FALSE, message = FALSE}
(namon_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   boxplot_namon(
     titulo = "Nitrogênio Amoniacal no período 1990-2000"
   )
 )
```

```{r Gráfico Nitrogênio total periodo2, warning = FALSE, message = FALSE}
(namon_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2000" &
            ano_coleta<="2010") %>% 
   boxplot_namon(
     titulo = "Nitrogênio Amoniacal no período 2000-2010"
   )
 )
```

```{r Gráfico Nitrogênio total periodo3, warning = FALSE, message = FALSE}
(namon_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2010" &
            ano_coleta<="2020") %>% 
   boxplot_namon(
     titulo = "Nitrogênio Amoniacal no período 2010-2020"
   )
 )
```

```{r Gráfico Namon 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
```

```{r Sumário Nitrogênio total, warning=FALSE, message = FALSE,}
(sum_namon_p1 <- plan_wide_19902020 %>%
   select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(nitrogenio_amoniacal, 
           na.rm = TRUE),
     q1 = 
       quantile(nitrogenio_amoniacal, 0.25, 
                na.rm = TRUE),
     median = 
       median(nitrogenio_amoniacal, 
              na.rm = TRUE),
     mean = 
       mean(nitrogenio_amoniacal, 
            na.rm= TRUE),
     q3 = 
       quantile(nitrogenio_amoniacal, 0.75, 
                na.rm = TRUE),
     max = 
       max(nitrogenio_amoniacal, 
           na.rm = TRUE),
      n = 
       length(nitrogenio_amoniacal)
   )
)

(sum_namon_p2 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)

(sum_namon_p3 <- plan_wide_19902020 %>%
    select(codigo, nitrogenio_amoniacal, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(nitrogenio_amoniacal, 
            na.rm = TRUE),
      q1 = 
        quantile(nitrogenio_amoniacal, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitrogenio_amoniacal, 
               na.rm = TRUE),
      mean = 
        mean(nitrogenio_amoniacal, 
             na.rm= TRUE),
      q3 = 
        quantile(nitrogenio_amoniacal, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitrogenio_amoniacal, 
            na.rm = TRUE))
)
```

```{r Salvando namon, warning=FALSE, message = FALSE,}
ggsave("namon.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = namon,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Turbidez

```{r Gráfico Turbidez facetted, fig.cap="turbidez-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(turb <- plan_wide_19902020 %>% 
   boxplot_turb(
     titulo = "Turbidez no período 1990-2020"
   )+
   facet_wrap(~periodo)
 )
```

```{r Gráfico Turbidez line, warning = FALSE, message = FALSE}
(turb_line <- plan_wide_19902020 %>%
   filter(ano_coleta > "1990" &
            ano_coleta <= "2020") %>%
   select(codigo, turbidez, data_coleta, periodo) %>%
  group_by(codigo) %>%
  ggplot(
    aes(x = data_coleta,
        y = turbidez,
        color = codigo
    ))+
    geom_line(
      # aes(color = codigo),
      na.rm = TRUE)+
    geom_point(
      # aes(color = codigo),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
  # geom_smooth(
  #   # aes(color = codigo),
  #   method = "lm",
  #   # formula = y ~ poly(x, 2),
  #   # span = 0.2,
  #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   aes(group = 1),
  #   alpha =.5,
  #   na.rm = TRUE,
  #   size = 0.3,
  #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~codigo,
    nrow = 4,
  )+
  theme_bw()
)
```

```{r Gráfico Turbidez periodo1, warning = FALSE, message = FALSE}
(turb_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   boxplot_turb(
     titulo = "Turbidez no período 1990-2000"
   )
 )
```

```{r Gráfico Turbidez periodo2, warning = FALSE, message = FALSE}
(turb_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2000" &
            ano_coleta<="2010") %>% 
   boxplot_turb(
     titulo = "Turbidez no período 2000-2010"
   )
 )
```

```{r Gráfico Turbidez periodo3, warning = FALSE, message = FALSE}
(turb_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2010" &
            ano_coleta<="2020") %>% 
   boxplot_turb(
     titulo = "Turbidez no período 2010-2020"
   )
 )
```

```{r Gráfico turb 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
```

```{r Sumário Turbidez, warning=FALSE, message = FALSE,}
(sum_turb_p1 <- plan_wide_19902020 %>%
   select(codigo, turbidez, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(turbidez, 
              na.rm = TRUE),
     mean = 
       mean(turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(turbidez, 
           na.rm = TRUE))
)

(sum_turb_p2 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
)

(sum_turb_p3 <- plan_wide_19902020 %>%
    select(codigo, turbidez, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
) 
```

```{r Salvando turb, warning=FALSE, message = FALSE,}
ggsave("turb.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = turb,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### pH

```{r Gráfico pH facetted, fig.cap="pH-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(pH <- boxplot_pH(
  titulo = "pH no período 1990-2020"
)+
  facet_wrap(~periodo)
)
```

```{r Gráfico pH periodo1, warning = FALSE, message = FALSE}
(pH_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "1990" &
            ano_coleta <= "2000") %>% 
   boxplot_pH(
     titulo = "pH no período 1990-2000"
   )
 )
```

```{r Gráfico pH periodo2, warning = FALSE, message = FALSE}
(pH_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2000" &
            ano_coleta <= "2010") %>% 
   boxplot_pH(
     titulo = "pH no período 2000-2010"
   )
 )
```

```{r Gráfico pH periodo3, warning = FALSE, message = FALSE}
(pH_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta > "2010" &
            ano_coleta <= "2020") %>% 
   boxplot_pH(
     titulo = "pH no período 2010-2020"
   )
 )
```

```{r Gráfico pH 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
```

```{r Sumário pH, warning=FALSE, message = FALSE,}
(sum_pH_p1 <- plan_wide_19902020 %>%
   select(codigo, pH, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)

(sum_pH_p2 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 

(sum_pH_p3 <- plan_wide_19902020 %>%
    select(codigo, pH, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
```

```{r Salvando pH, warning=FALSE, message = FALSE,}
ggsave("pH.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = pH,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Sólidos totais

```{r Gráfico SólTot facetted, fig.cap="sólidos-totais-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(SolTot <- plan_wide_19902020 %>% 
   boxplot_solidos_totais(
     titulo = "Sólidos totais no período 1990-2020"
   )+
   facet_wrap(~periodo)
)
```

```{r Gráfico SólTot periodo1, warning = FALSE, message = FALSE}
(SolTot_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"1990" &
          ano_coleta<="2000") %>% 
   boxplot_solidos_totais(
     titulo = "Sólidos totais no período 1990-2000"
   )
 )
```

```{r Gráfico SólTot periodo2, warning = FALSE, message = FALSE}
(SolTot_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2000" &
            ano_coleta<="2010") %>% 
   boxplot_solidos_totais(
     titulo = "Sólidos totais no período 2000-2010"
   )
)
```

```{r Gráfico SólTot periodo3, warning = FALSE, message = FALSE}
(SolTot_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2010" &
            ano_coleta<="2020") %>% 
   boxplot_solidos_totais(
     titulo = "Sólidos totais no período 2010-2020"
   )
)
```

```{r Gráfico SólTot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
```

```{r Sumário Sólidos Totais, warning=FALSE, message = FALSE,}
(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(codigo, solidos_totais, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(solidos_totais, 
           na.rm = TRUE),
     q1 = 
       quantile(solidos_totais, 0.25, 
                na.rm = TRUE),
     median = 
       median(solidos_totais, 
              na.rm = TRUE),
     mean = 
       mean(solidos_totais, 
            na.rm= TRUE),
     q3 = 
       quantile(solidos_totais, 0.75, 
                na.rm = TRUE),
     max = 
       max(solidos_totais, 
           na.rm = TRUE))
)

(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)

(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(codigo, solidos_totais, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)
```

```{r Salvando SolTot, warning=FALSE, message = FALSE,}
ggsave("SolTot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = SolTot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Condutividade elétrica

```{r Gráfico cond_elet facetted, fig.cap="condutividade-eletrica-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(cond_elet <- plan_wide_19902020 %>% 
   boxplot_cond_elet(
     titulo = "Condutividade elétrica no período 1990-2020"
   )+
   facet_wrap(~periodo)
)
```

```{r Gráfico cond_elet periodo1, warning = FALSE, message = FALSE}
(cond_elet_p1 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   boxplot_cond_elet(
     titulo = "Condutividade elétrica no período 1990-2000"
   )
)
```

```{r Gráfico cond_elet periodo2, warning = FALSE, message = FALSE}
(cond_elet_p2 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2000" &
            ano_coleta<="2010") %>% 
   boxplot_cond_elet(
     titulo = "Condutividade elétrica no período 2000-2010"
   )
)
```

```{r Gráfico cond_elet periodo3, warning = FALSE, message = FALSE}
(cond_elet_p3 <- plan_wide_19902020 %>% 
   filter(ano_coleta>"2010" &
            ano_coleta<="2020") %>% 
   boxplot_cond_elet(
     titulo = "Condutividade elétrica no período 2010-2020"
   )
)
```

```{r Gráfico cond_elet 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
```

```{r Sumário cond_elet, warning=FALSE, message = FALSE}
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(codigo, condutividade, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(condutividade, 
              na.rm = TRUE),
     mean = 
       mean(condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(condutividade, 
           na.rm = TRUE))
)

(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE))
)

(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(codigo, condutividade, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(condutividade, 
               na.rm = TRUE),
      mean = 
        mean(condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(condutividade, 
            na.rm = TRUE),
      n = 
        length(condutividade))
)

# plan_wide_19902020 %>% 
#    select(codigo, IQA) %>% 
#    group_by(codigo) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
```

```{r Salvando cond_elet, warning=FALSE, message = FALSE}
ggsave("cond_elet.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = cond_elet,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

```

### IQA

```{r Gráfico IQA facetted, fig.cap="iqa-gravataí no período 1990-2020", echo = FALSE, message=FALSE, warning=FALSE}
(iqa <-ggplot(plan_wide_19902020,
              aes(codigo,
                  iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_summary(
     fun.data = f,
     geom = 'errorbar',
     width = 0.3,
     position = position_dodge(width = 0.65),
   )+
   stat_summary(
     fun.data = f,
     geom = "boxplot",
     width = 0.7,
     fill = '#F8F8FF',
     color = "black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
   )+
   facet_wrap(~periodo)+
   labs(title = "Variação do IQA no período 1990-2020",
        x="Estação",
        y="IQA")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
   # theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p1 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "1990" &
                            ano_coleta <= "2000"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
    annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA no período 1990-2000",
         x="Estação",
         y="")+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
   theme_grafs()+
   theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo2, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "2000" &
                            ano_coleta <= "2010"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank()
   )
)
```

```{r Gráfico IQA periodo3, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ano_coleta > "2010" &
                            ano_coleta <= "2020"),
                 aes(codigo,
                     iqa, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()+
    theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
```

```{r Sumário IQA, warning=FALSE, message = FALSE,}
(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(codigo, iqa, ano_coleta) %>% 
   filter(ano_coleta>"1990" &
            ano_coleta<="2000") %>% 
   group_by(codigo) %>% 
   summarize(
     min = 
       min(iqa, 
           na.rm = TRUE),
     q1 = 
       quantile(iqa, 0.25, 
                na.rm = TRUE),
     median = 
       median(iqa, 
              na.rm = TRUE),
     mean = 
       mean(iqa, 
            na.rm= TRUE),
     q3 = 
       quantile(iqa, 0.75, 
                na.rm = TRUE),
     max = 
       max(iqa, 
           na.rm = TRUE),
     n = 
        length(iqa)
   )
)

(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2000" &
             ano_coleta<="2010") %>% 
    group_by(codigo) %>% 
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa)
      )
)

(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(codigo, iqa, ano_coleta) %>% 
    filter(ano_coleta>"2010" &
             ano_coleta<="2020") %>%
    # ?as_factor(codigo) %>% 
    group_by(codigo) %>%
    summarize(
      min = 
        min(iqa, 
            na.rm = TRUE),
      q1 = 
        quantile(iqa, 0.25, 
                 na.rm = TRUE),
      median = 
        median(iqa, 
               na.rm = TRUE),
      mean = 
        mean(iqa, 
             na.rm= TRUE),
      q3 = 
        quantile(iqa, 0.75, 
                 na.rm = TRUE),
      max = 
        max(iqa, 
            na.rm = TRUE),
      n = 
        length(iqa),
      NAs = 
        sum(is.na(iqa))
      ) %>% 
  mutate(
    "%NA" = NAs/n*100
  )
)

```

```{r Salvando iqa, warning=FALSE, message = FALSE,}
ggsave("iqa.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = iqa,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Testando coisas

```{r Testando coisas, include = FALSE, warning=FALSE, message = FALSE,}
# plan_wide_19902020 %>% 
#    select(codigo, oxigenio_dissolvido, ano_coleta) %>% 
#    ggplot(aes(ano_coleta, oxigenio_dissolvido, 
#       col = codigo))+
#    geom_line()+
#    facet_wrap(~ codigo, ncol = 7)

# df111 <- data.frame(x = c(1:100))
# glimpse(df111)
# df111$y <- 2 + 3 * df111$x + rnorm(100, sd = 40)
# 
# lm_eqn <- function(df111){
#     m <- lm(y ~ x, df111);
#     eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#          list(a = format(unname(coef(m)[1]), digits = 2),
#               b = format(unname(coef(m)[2]), digits = 2),
#              r2 = format(summary(m)$r.squared, digits = 3)))
#     as.character(as.expression(eq));
# } 
# p2 <- p111 +
#   geom_text(x = 25, y = 300,
#             label = lm_eqn(df111),
#             parse = TRUE)
# p2
# 
# 
# lm_eqc <- function(plan_wide_19902020){
#    m <- lm(oxigenio_dissolvido ~ codigo, plan_wide_19902020);
#    eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#                     list(a = format(unname(coef(m)[1]), digits = 2),
#                          b = format(unname(coef(m)[2]), digits = 2),
#                          r2 = format(summary(m)$r.squared, digits = 3)))
#    as.character(as.expression(eq));
# }
# 
# (od_p1 <-ggplot(plan_wide_19902020 %>%
#                    filter(ano_coleta>"1990" &
#                              ano_coleta<="2000"),
#                 aes(codigo,
#                     oxigenio_dissolvido))+
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=-Inf,
#                ymax=2,
#                alpha=1,
#                fill="#ac5079")+ #>pior classe
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=2,
#                ymax=4,
#                alpha=1,
#                fill="#eb5661")+ #classe 4
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=4,
#                ymax=5,
#                alpha=1,
#                fill="#fcf7ab")+ #classe 3
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=5,
#                ymax=6,
#                alpha=1,
#                fill="#70c18c")+ #classe 2
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=6,
#                ymax=Inf,
#                alpha=1,
#                fill="#8dcdeb")+ #classe 1
#       stat_boxplot(geom = 'errorbar',
#                    width=0.3,
#                    position = position_dodge(width = 0.65))+
#       geom_boxplot(fill='#F8F8FF',
#                    color="black",
#                    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                    width= 0.7)+
#       labs(title = "Oxigênio Dissolvido no período 1990-2000",
#            x="Estação",
#            y="mg/L")+
#       # geom_jitter(width = .05,
#       #             alpha=.2,
#       #             size=1.5,
#       #             color="black")+
#       scale_y_continuous(expand = expansion(mult = c(0,0)),
#                          n.breaks = 11,
#                          limits = c(-1,21))+
#       scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
#       geom_smooth(method = "lm",
#                   se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                   aes(group=1),
#                   alpha=.5,
#                   na.rm = TRUE,
#                   size = 1)+
#       # annotate(geom_text(aes(x = "87405500", y = 15),
#       #                    label = lm_eqc(plan_wide_19902020),
#       #                    parse = TRUE,
#       #                    inherit.aes = TRUE,
#       #                    check_overlap = TRUE))+
#       #  geom_line(
#       #     aes(color="red"),
#       #     alpha=.0,
#       # )+
#       # scale_color_manual("Legenda",
#       #                    guide="legend",
#       #                    values = c("Classe 1"="#8dcdeb",
#       #                               "Classe 2"="#70c18c",
#       #                               "Classe 3"="#fcf7ab",
#       #                               "Classe 4"="#eb5661",
#       #                               "Pior Classe"="#ac5079"))+
#    # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#    #                                               lwd=c(2,2,2,2,2),
#    #                                               shape=c(NA,NA,NA,NA,NA),
#    #                                               alpha=1)))+
#       theme(legend.position = "bottom")+
#       theme_classic())

# list1111 <- sessionInfo()
# list1111$loadedOnly

# install.packages("ggpmisc")
# library(ggpmisc)

# summary(lm(plan_wide_19902020$codigo~plan_wide_19902020$dbo))
# 
# 
# p <- ggplot(data, aes(y=number, x=pod)) +
#   geom_boxplot()
# print(p)

# install.packages("GGally")


# fit = lm(plan_wide_19902020$oxigenio_dissolvido~ plan_wide_19902020$codigo)
# summary(fit)
# summary.lm(fit)
# 
# pacman::p_load(esquisse)

# sumario <- function(x, y){
#   x %>% 
#     group_by(codigo) %>%
#     summarise(
#       list(min= ~min(y, na.rm = TRUE), 
#            Q1= ~quantile(y, probs = 0.25),
#            median= ~median(y, na.rm = TRUE), 
#            Q3= ~quantile(y, probs = 0.75),
#            max= ~max(y, na.rm = TRUE)),
#       .groups = "drop"
#       )
# }
```

## Correlação

```{r Correlação, fig.cap="correlação-parametros-qualidade-agua-gravataí no período 1990-2020", time_it = TRUE, warning=FALSE, message = FALSE,}
parametros_IQA %>% 
  dplyr::select(
    -codigo,
    -ano_coleta,
    -nitrogenio_total
    ) %>% 
  # group_by(codigo) %>% 
  rename(
    CE = condutividade,
    E_coli = escherichia_coli,
    OD = oxigenio_dissolvido,
    ST = solidos_totais,
    Turb = turbidez,
    Temp = temperatura_agua,
    Ptot = fosforo_total,
    # NTot = nitrogenio_total,
    NAmon = nitrogenio_amoniacal,
    NTK = nitrogenio_kjeldahl
  ) %>% 
  ggcorr(
    method = "complete.obs",
    # "pearson",
    # "pairwise",
    name = "Correlação",
    label = TRUE,
    label_alpha = TRUE,
    digits = 3,
    low = "#3B9AB2",
    mid = "#EEEEEE",
    high = "#F21A00",
    # palette = "RdYlBu",
    layout.exp = 0,
    legend.position = "left",
    label_round = 3,
    # legend.size = 18,
    geom = "tile",
    nbreaks = 10,
  )+
  labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
  theme_linedraw()+
  theme(
    legend.position = c(0.15, 0.6),
    legend.title = element_text(size = 16),
    legend.text = element_text(size = 14),
    # legend.spacing = unit(element_text(),
                          # units = 5)
    plot.title = element_text(hjust = 0.5,
                              size = 16)
  )

# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
  dplyr::select(-codigo) %>%
  ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
          axisLabels = "show")

correlacao_pIQA <- parametros_IQA %>% 
  group_by(codigo) %>% 
  correlation::correlation()

correlacao_pIQA %>% 
  # glimpse()
  filter(
    p < 0.001
  ) %>% 
  as_tibble() %>% 
  # filter(
  #   Parameter1 == "oxigenio_dissolvido"
  # ) %>% 
  arrange(desc(r)) 
  
parametros_IQA %>% 
  # group_by(codigo) %>% 
  dplyr::select(
    nitrogenio_kjeldahl, condutividade
  ) %>% 
  # correlation::cor_test() %>% 
  plot()
```

# Textando o texto

-   § falar do comportamento geral dos dados
-   2º § - xº § -\> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.

`r sum_od_p1$PM1[1]`

Os resultados apontam que para o parâmetro OD

# Gráficos exemplos boxplot

```{r elementos do boxplot}
set.seed(2023)
exemplo_boxplot_df <- data.frame(
  PM = c("PM1"),
  # letras = letters[seq( from = 1, to = 1 )],
  Stat1 = rnorm(100, 
                mean = 5, 
                sd = 1.8)
)

(sumario_exemplo_bp <- exemplo_boxplot_df %>% 
    group_by(PM) %>% 
    summarize(
      max = max(Stat1),
      p95 = quantile(Stat1, 0.95),
      p80 = quantile(Stat1, 0.8),
      median = median(Stat1),
      p20 = quantile(Stat1, 0.2),
      p05 = quantile(Stat1, 0.05),
      min = min(Stat1),
    ) %>% 
    t() %>% 
    row_to_names(row_number = 1) %>% 
    as.numeric()
)


(boxplot_example <- exemplo_boxplot_df %>% 
    ggplot(
      aes(
        x = PM,
        y = Stat1,
      )
    )+
    stat_summary(
      fun.data = f,
      geom = 'errorbar',
      width = 0.15,
      position = position_dodge(width = 0.65),
    )+
    stat_summary(
      fun.data = f,
      geom = "boxplot",
      width = 0.40,
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    )+
    labs(
      title = "Elementos do *boxplot*",
      x= NULL,
      y= NULL
    )+
    ggbeeswarm::geom_quasirandom(
      size = 1.4,
      alpha = .3,
      width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 8,
      limits = c(-0.3,12)
    )+
    annotate(
      geom = "text",
      x = 1.55,
      hjust = "right",
      y = sumario_exemplo_bp,
      label = c("Valor máximo", "P95", "P80", "Mediana", "P20", "P05", "Valor mínimo"),
      # fontface = 3
    )+
    geom_richtext(
      x = 0.56,
      y = 9.103998,
      label.color = NA,
      hjust = "center",
      label = "<i>Outliers</i>"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.98,
        y = 9.103998 , yend = 9.103998 , #Outliers
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    #definindo o [
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 6.683828, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.74,
        y = 3.545771, yend = 6.683828,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 5.11,
      label = "Amplitude\n(P80-P20)"
    )+
    geom_curve(
      aes(
        x = 0.74, xend = 0.78,
        y = 3.545771 , yend = 3.545771 ,
      ),
      curvature = 0,
      size = 1.0,
      lineend = "butt"
    )+
    # fim do [
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 7.866927 , yend = 7.866927 , #whisker superior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 7.866927,
      label = "Whisker\nsuperior"
    )+
    geom_curve(
      aes(
        x = 0.6, xend = 0.90,
        y = 2.277104  , yend = 2.277104  , #whisker inferior
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    annotate(
      geom = "text",
      x = 0.56,
      hjust = "center",
      y = 2.277104,
      label = "Whisker\ninferior"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 9.92343, yend = 9.92343, #valor máximo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 7.866927 , yend = 7.866927 , #P95
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 6.683828  , yend = 6.683828  , #P80
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 4.886935   , yend = 4.886935   , #P50
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.22,
        y = 3.545771, yend = 3.545771, #P20
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.45, xend = 1.11,
        y = 2.277104, yend = 2.277104, #P05
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    geom_curve(
      aes(
        x = 1.4, xend = 1.01,
        y = 1.282177, yend = 1.282177, #valor mínimo
      ),
      curvature = 0,
      size = 1.0,
      arrow = arrow(length = unit(0.05, "npc")),
      lineend = "round"
    )+
    # theme_grafs()+
    theme_bw()+
    theme(
      plot.title = 
        element_markdown(
          hjust = 0.5,
          color = "black",
          size = 19),
    )
)
```

```{r salvando graf elementos boxplot}
ggsave(
  filename = "exemplo_boxplot.png",
  plot = boxplot_example,
  units = c("px"),
  width = (4500)/1.5,
  height = (2993)/1.5,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)
```

```{r criando exemplo tipos boxplot tukey x garrett}
set.seed(2021)

data <- tibble(
  grupo = factor(
    c(rep(
      "Grupo 1", 100), 
      rep("Grupo 2", 250), 
      rep("Grupo 3", 25)
    )
  ),
  valor = c(seq(0, 20, length.out = 100),
            c(rep(0, 5), 
              rnorm(30, 2, .1), 
              rnorm(90, 5.4, .1), 
              rnorm(90, 14.6, .1), 
              rnorm(30, 18, .1), 
              rep(20, 5)
            ),
            rep(seq(0, 20, length.out = 5), 5))
) %>% 
  rowwise() %>%
  mutate(
    valor = if_else(
      grupo == "Grupo 2", valor + rnorm(1, 0, .4), 
      valor
      )
    )

## function to return median and labels
n_fun <- function(x){
  return(
    data.frame(
      y = median(x) - 1.25, 
      label = paste0(
        "n = ",length(x)
      )
    )
  )
}
```

```{r tukey boxplot}
(tukey_n_boxplot <- ggplot(data, 
                           aes(x = grupo, 
                               y = valor)
)+
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65))+
  ## use summary function to add text labels
  stat_summary(
    geom = "text",
    fun.data = n_fun,
    # family = "Oswald",
    size = 5
  )+
  labs(
    title = "Tukey *boxplot*",
    x= NULL,
    # y="mg/L"
  )+
  # theme_grafs()+
  theme_bw()+
  theme(
    axis.text.y = element_text(
      angle = 90, 
      # size=15,
      # face=2
    ),
    plot.title = 
      element_markdown(
        hjust = 0.5,
        color = "black",
        size = 19)
  )
)


(tukey_boxplot <- ggplot(data, aes(x = grupo, 
                                   y = valor)) +
  stat_boxplot(geom = 'errorbar',
               width = 0.15,
               position = position_dodge(width = 0.65))+
  geom_boxplot(fill = "grey92",
               width = 0.40,
               position = position_dodge(width = 0.65)) +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  theme_bw()+
  labs(
      title = "Tukey *boxplot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    ))
```

```{r garrett boxplot}
data %>% 
  group_by(grupo) %>% 
  summarize(
    min = min(valor),
    P20 = quantile(valor, 0.20),
    q1 = quantile(valor, 0.25),
    mediana = median(valor),
    q3 = quantile(valor, 0.75),
    P80 = quantile(valor, 0.80),
    max = max(valor)
  ) %>% 
  t() %>% 
  row_to_names(row_number = 1)
  
  
(box_percentile_plot <- ggplot(data, 
       aes(x = grupo, y = valor)
       ) +
      stat_summary(
        fun.data = f,
        geom = 'errorbar',
        width = 0.15,
        position = position_dodge(width = 0.65),
      )+
      stat_summary(
        fun.data = f,
        geom = "boxplot",
        width = 0.40,
        fill = 'grey92',
        color = "black",
        outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
      )+
  # geom_boxplot(fill = "grey92") +
  ## use either geom_point() or geom_jitter()
  geom_point(
    ## draw bigger points
    size = 2,
    ## add some transparency
    alpha = .25,
    ## add some jittering
    position = position_jitter(
      ## control randomness and range of jitter
      seed = 1, width = .2
    )
  )+
  labs(
      title = "*Box Percentile-Plot*",
      x= NULL,
      # y="mg/L"
    )+
  # theme_grafs()+
  theme_bw()+
  theme(
        axis.text.y = element_text(
          angle = 90, 
          # size = 15,
          # face = 2
        ),
        plot.title = 
          element_markdown(
            hjust = 0.5,
            color = "black",
            size = 19)
    )
  )
grid.arrange(
  tukey_boxplot, box_percentile_plot, 
  ncol = 2
  )
fig_tukey_garrett <- plot_grid(tukey_boxplot, box_percentile_plot, 
                               labels = "AUTO")
```

```{r salvando graf exemplo boxplot}
ggsave(
  filename = "tukey_n_boxplot.png",
  plot = tukey_n_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "tukey_boxplot.png",
  plot = tukey_boxplot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "box_percentile_plot.png",
  plot = box_percentile_plot,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)

ggsave(
  filename = "fig_tukey_garrett.png",
  plot = fig_tukey_garrett,
  units = c("px"),
  width = 4500,
  height = 2993,
  path = "./graficos",
  dpi = 300,
  # type = "cairo"
)
```
