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special_zasypani_PK.R
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###### INICIALIZACE ######
library(tidyr)
library(dplyr)
library(ggplot2)
library(shiny)
library(skimr)
library(stringr)
library(tidyr)
library(tools)
library(dplyr)
library(ggplot2)
library(zoo)
library(gridExtra)
library(psych)
library(gdata)
library(tibble)
require(data.table)
library(tidyr)
library(tools)
library(dplyr)
library(ggplot2)
library(zoo)
library(gridExtra)
library(psych)
library(gdata)
library(tibble)
library(corrplot)
require(data.table)
library(cluster)
library(factoextra)
#library(ggsankey)
library(clusterCrit)
library(forcats)
# library(DT)
##### ?TEN? DAT ######
# setwd("E:/Projekty_a_granty/TACR_Strix/")
setwd("d:/5_papers/2023_zasypane_geotextilie/ruzne_zasypani/")
### Na?ti dataset s pr?b?hy odtok? a koncentrac?
qsres <- read.csv("zasypani_odtoky.csv", stringsAsFactors = F, sep = ";", dec = ".", header = T)
#all_in <- read.csv("xlab_data_clean.csv", stringsAsFactors = F)
grain = read.csv("d:/5_papers/2023_zasypane_geotextilie/_dta_work/grain_size_export.csv", stringsAsFactors = F, sep = ";", dec = ",", header = T)
####READ GRAIN SIZE RES DTA#####
qsres$TIMESTAMP = as.POSIXct(strptime(qsres$datum, "%Y-%m-%d"))
#all_in$TIMESTAMP = as.POSIXct(strptime(all_in$datum, "%Y-%m-%d"))
#all_in_sel = subset(all_in, all_in$datum == "2020-11-02" | all_in$datum == "2020-11-05")
#qsres = rbind(qsres, all_in_sel)
qsres$poc_stav = substr(qsres$poc_stav, 1, 4)
qsres <- qsres %>% filter(!is.na(doba_s))
# Add a new column with corresponding names as runoff
qsres <- qsres %>%
mutate(opat_typ_grain = recode(opat_typ,
"uhor" = "BSOIL",
"H4VP" = "BRECP",
"H4MZ" = "FRECP",
"H4MP" = "4MFIL",
"H1MP" = "1MFIL",
"H3MP" = "3MFIL"))
qsres <- qsres %>%
mutate(poc_stav = recode(poc_stav,
"such" = "dry",
"mokr" = "wet"))
qsres <- qsres %>%
mutate(poc_stav_int = paste(intenzita, poc_stav, sep = "_"))
qsres <- qsres %>%
mutate(poc_stav_int = recode(poc_stav_int,
"60_dry" = "1 60 dry",
"60_wet" = "2 60 wet",
"120_wet" = "3 120 wet",
"160_wet" = "4 160 wet"))
xx = str(qsres)
qsresnF = qsres
qsres <- qsres %>% mutate(lokalita = as.factor(lokalita),
datum = as.Date(datum,tryFormats = c("%d.%m.%Y")),
simcislo = as.factor(simcislo),
# plocha = as.factor(plocha),
sklon_st = str_replace(sklon_st,"20","22"),
sklon_st = as.factor(sklon_st),
opat_typ = as.factor(opat_typ),
poc_stav = as.factor(poc_stav),
intenzita = as.factor(intenzita),
repetice = as.factor(repetice),
system = as.factor(system),
ID = as.factor(ID))
#NAvalue = as.factor(NAvalue))
#qsstat <- read.csv("W_special_zasypani_stat.csv", stringsAsFactors = F)
# select total value at the end
qsresnF <- qsresnF %>%
mutate(max_ID = paste(opat_typ, poc_stav, intenzita, sep = "_"))
#qsresnF <- qsresnF %>% mutate(opatreni = ifelse(opat_typ %in% c('UHO2','uhor' ), FALSE, TRUE))
maxrun = data.frame()
names = unique(qsresnF$max_ID)
for (id in names){
print(id)
sl <- qsresnF[qsresnF$max_ID == id,]
max_row <- sl %>% filter(t2_min == max(t2_min, na.rm = TRUE))
maxrun <- rbind(maxrun, max_row)
}
grain <- grain %>%
mutate(poc_stav_en = recode(init_state,
"D1" = "dry",
"D2" = "dry",
"D3" = "dry",
"W1" = "wet",
"W2" = "wet",
"W3" = "wet"
))
grain <- grain %>%
mutate(poc_stav_int = paste(intensity, poc_stav_en, sep = "_"))
grain <- grain %>%
mutate(poc_stav_int = recode(poc_stav_int,
"60_dry" = "1 60 dry",
"60_wet" = "2 60 wet",
"120_wet" = "3 120 wet",
"160_wet" = "4 160 wet"))
summary_table <- grain %>%
group_by(poc_stav_int, aggregates, particlesize, variant_runoff, init_state_num) %>%
summarize(
median_percentage = median(percentage)
) %>%
ungroup()
summary_table$opat_typ_grain = summary_table$variant_runoff
summary_table <- summary_table %>%
mutate(grain_id = paste(opat_typ_grain, poc_stav_int, init_state_num, particlesize, sep = "_"))
sdata_wide <- summary_table %>%
pivot_wider(names_from = aggregates, values_from = c(median_percentage, particlesize))
sdata_wide$ratio = sdata_wide$median_percentage_without - sdata_wide$median_percentage_with
sdata_wide <- sdata_wide %>%
mutate(init_state_char = recode(init_state_num,
"5" = "05",
"15" = "15",
"30" = "30",
))
sdata_wide <- sdata_wide %>%
mutate(g_ID = paste(poc_stav_int, init_state_char, sep = "_"))
###### - plot data
qsresnF_sel = qsresnF#[qsresnF$opat_typ_grain != "BSOIL",]
summary_table_sel = summary_table#[summary_table$opat_typ_grain != "BSOIL",]
summary_table_sel <- summary_table_sel %>% drop_na()
sdata_wide_sel = sdata_wide#[sdata_wide$opat_typ_grain == "BSOIL",]
scaleFactorq <- max(qsresnF_sel$prutok2_l.min) / max(qsresnF_sel$smyv1_g.l)
scaleFactor <- median(qsresnF_sel$Qcum_l) / median(qsresnF_sel$S2cum_g)
scaleFactor_grain <- max(qsresnF_sel$Qcum_l) / 60
xxplot = ggplot() + #(qsresnF_sel$cas_do_odtok + (qsresnF_sel$t1_min + qsresnF_sel$t2_min)/2)))+
geom_line (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$Qcum_l), method="loess", col="black") +
geom_point (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$Qcum_l), method="loess", col="black") +
geom_line(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$S2cum_g * scaleFactor), method="loess", col="grey", show.legend = TRUE) +
geom_point(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$S2cum_g * scaleFactor), method="loess", col="grey") +
geom_point(data = summary_table_sel, mapping = aes(x = summary_table_sel$init_state_num, y = summary_table_sel$median_percentage*scaleFactor_grain, color = as.character(summary_table_sel$particlesize), shape = as.character(summary_table_sel$aggregates))) +
# stat_summary(mapping = aes(x = grain$init_state_num , y = grain$percentage, colour = grain$aggregates, shape = as.factor(grain$particlesize)), fun = median, geom="point", show.legend = FALSE, vjust=-1, size = 10)+
scale_y_continuous(name="\u03A3 l", sec.axis=sec_axis(~./scaleFactor, name="g/l")) +
facet_grid (opat_typ_grain ~ poc_stav_int) +
ylim (0, 350) +
theme_bw()
plot(xxplot)
xxplot_Q = ggplot() + #(qsresnF_sel$cas_do_odtok + (qsresnF_sel$t1_min + qsresnF_sel$t2_min)/2)))+
geom_line (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$Qcum_l), method="loess", col="black") +
geom_point (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$Qcum_l), method="loess", col="black") +
facet_grid (poc_stav_int ~ opat_typ_grain) +
labs(title = "Runoff", x = "Time", y = "Cumulative runoff [l]")+
theme_bw()
plot(xxplot_Q)
xxplot_S = ggplot() + #(qsresnF_sel$cas_do_odtok + (qsresnF_sel$t1_min + qsresnF_sel$t2_min)/2)))+
geom_line(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$S2cum_g * scaleFactor), method="loess", col="grey", show.legend = TRUE) +
geom_point(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$S2cum_g * scaleFactor), method="loess", col="grey") +
facet_grid (poc_stav_int ~ opat_typ_grain, scales = "free_y") +
ylim (0, 1000) +
labs(title = "Soil loss", x = "Time", y = "Cumulative soil loss [g]")+
theme_bw()
plot(xxplot_S)
scaleFactor_grain2 <- max(qsresnF_sel$Qcum_l) / 80
xxplot2 <- ggplot() +
geom_line(data = qsresnF_sel, mapping = aes(x = t2_min, y = prutok1_l.min), col = "black") +
geom_point(data = qsresnF_sel, mapping = aes(x = t2_min, y = prutok1_l.min), col = "black") +
geom_line(data = qsresnF_sel, mapping = aes(x = t2_min, y = sedflux1_g.min * scaleFactorq), col = "grey", show.legend = TRUE) +
geom_point(data = qsresnF_sel, mapping = aes(x = t2_min, y = sedflux1_g.min * scaleFactorq), col = "grey") +
geom_point(data = summary_table_sel, mapping = aes(x = init_state_num, y = median_percentage * scaleFactor_grain2, color = as.character(particlesize), shape = as.character(aggregates))) +
facet_grid(opat_typ_grain ~ poc_stav_int) + #, scales = "free_y") +
scale_y_continuous(
name = "q - black [l/min]",
sec.axis = sec_axis(~./scaleFactor_grain2, name = "flux - gray [g/min], PSD - colours [% Undersize]")
) +
theme_bw() +
labs(title = "Runoff, Sediment flux and Particle Size Distribution", x = "Time")
# Print the plot
print(xxplot2)
ratioplot = ggplot() +
geom_point(data = sdata_wide_sel, aes(x = sdata_wide_sel$g_ID, y = ratio, colour = as.character(sdata_wide_sel$particlesize_with)), shape = 1, size = 4) +
geom_point(data = sdata_wide_sel, aes(x = sdata_wide_sel$g_ID,y = sdata_wide_sel$median_percentage_without, colour = as.character(sdata_wide_sel$particlesize_with)), shape = 2, size = 1) +
geom_jitter(data = sdata_wide_sel, aes(x = sdata_wide_sel$g_ID, y = sdata_wide_sel$median_percentage_with, colour = as.character(sdata_wide_sel$particlesize_with)), shape = 0, size = 1.5) +
theme_bw()+
labs(title = "Particle Size Distribution", x = "data seqence", y = "[%]")+
#ylim (0, 15) +
facet_grid(opat_typ_grain ~ ., scales = "free_y")+
theme(axis.text.x = element_text(angle = 80, hjust = 1))
plot(ratioplot)
yyplot = ggplot() + #(qsresnF_sel$cas_do_odtok + (qsresnF_sel$t1_min + qsresnF_sel$t2_min)/2)))+
geom_line (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$prutok1_l.min), method="loess", col="black") +
geom_point (data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$prutok1_l.min), method="loess", col="black") +
geom_line(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$smyv1_g.l * scaleFactorq), method="loess", col="grey", show.legend = TRUE) +
geom_point(data = qsresnF_sel, mapping = aes(x = qsresnF_sel$t2_min, y=qsresnF_sel$smyv1_g.l * scaleFactorq), method="loess", col="grey") +
geom_point(data = sdata_wide_sel, mapping = aes(x = sdata_wide_sel$init_state_num, y = sdata_wide_sel$ratio*scaleFactor_grain, colour = as.character(sdata_wide_sel$particlesize_with))) +
# stat_summary(mapping = aes(x = grain$init_state_num , y = grain$percentage, colour = grain$aggregates, shape = as.factor(grain$particlesize)), fun = median, geom="point", show.legend = FALSE, vjust=-1, size = 10)+
#scale_y_continuous(name="\u03A3 l", sec.axis=sec_axis(~./scaleFactor, name="\u03A3 g")) +
#scale_y_continuous(name="\u03A3 l", sec.axis=sec_axis(name="\u03A3 g")) +
facet_grid (opat_typ_grain ~ poc_stav_int) +
ylim (0, 25) +
theme_bw()
plot(yyplot)
### ?ten? dat z anal?z dmt
#sxdmt <- read.csv("DMT_strix_komplet_leden2020_2.csv", sep = ";", dec = ",")
#str(sxdmt)
#sxdmt <- sxdmt %>% mutate(# repetice = as.factor(repetice),
# opatreni = as.logical(opatreni),
# V = abs(V_objem_odnosu))
# ### Pokud u? jsou data na?ten? z p?edchoz?ho skriptu
# qsres <- QSdata_all
# qsstat <- QSstat_all
### P?ipojen? dat z dmt
#qsstat <- qsstat %>% left_join(y = select(sxdmt, ID:V), by = "ID") %>%
# mutate(ID = as.factor(ID),
# NAvalues = as.factor(NAvalues))
#qsres <- mutate(qsres, V = 0)
#### APLIKACE PRO ZOBRAZOV?N? PR?B?H? ODTOKU A DAL??CH VELI?IN ####
# Define UI for application that plots the runoff variables from selected experimental settings
ui <- fluidPage(
# Sidebar layout with a input and output definitions
sidebarLayout(
# Inputs
sidebarPanel(width = 3, style = "padding: 10px;",
h4("Mapování proměnných v grafu"),
wellPanel(style = "padding: 5px;",
# Select variable on X axis
selectInput(inputId = "x",
label = "Proměnné na ose x:",
choices = c("čas t1" = "t1_min", "čas t2" = "t2_min", "Interval" = "interval",
"Typ opatření" = "opat_typ", "Poč.stav (č.sim)" = "simcislo", "Sklon (?)" = "sklon_st", "Intenzita (mm/h)" = "intenzita",
"Opatření ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice",
"Pr?tok t1 (l/min)" = "prutok1_l.min", "Konc.sedimentu t1 (g/l)" = "smyv1_g.l",
"L?tkov? tok t1 (g/min)" = "sedflux1_g.min", "Odteklé množství t2 (l)" = "Qcum_l",
"Celkový smyv 1 t2 (g)" = "S1cum_g", "Celkový smyv 2 t2 (g)" = "S2cum_g",
"Pom?r odnosu 1 t2 (%)" = "S1cumRel", "Pom?r odnosu 2 t2 (%)" = "S2cumRel"), #, "Objem DMT" = "V"),
selected = "t1_min"),
# Select variable on Y axis
selectInput(inputId = "y",
label = "Proměnné na ose y:",
choices = c("čas t1" = "t1_min", "čas t2" = "t2_min", "Interval" = "interval",
"Typ opatření" = "opat_typ", "Poč.stav (č.sim)" = "simcislo", "Sklon (?)" = "sklon_st", "Intenzita (mm/h)" = "intenzita",
"Opatření ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice",
"Průtok t1 (l/min)" = "prutok1_l.min", "Konc.sedimentu t1 (g/l)" = "smyv1_g.l",
"Látkový tok t1 (g/min)" = "sedflux1_g.min", "Odtekl? mno?stv? t2 (l)" = "Qcum_l",
"Celkový smyv 1 t2 (g)" = "S1cum_g", "Celkový smyv 2 t2 (g)" = "S2cum_g",
"Poměr odnosu 1 t2 (%)" = "S1cumRel", "Poměr odnosu 2 t2 (%)" = "S2cumRel",
"V_objem_odnosu", "V abs" = "V", "hmotnost_smyvu", "DEM1_mean", "DEM1_std", "DEM2_mean", "DEM2_std", "DEMd_mean", "DEMd_std",
"slp1_mean", "slp2_mean", "slp1_std", "slp2_std", "slpd_mean", "slpd_std",
"asp1_mean", "asp2_mean", "aspd_mean", "plc1_mean", "plc2_mean", "plcd_mean", "prc1_mean", "prc2_mean", "prcd_mean"),
selected = "prutok1_l.min"),
# Select setting for color
selectInput(inputId = "color",
label = "Barva podle:",
choices = c("Typ opatření" = "opat_typ", "Sklon (°)" = "sklon_st", "Po?.stav (č.sim)" = "simcislo", "Intenzita (mm/h)" = "intenzita",
"Opat?en? ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice", "ID simulace" = "ID"),
selected = "opat_typ"),
# Select setting for linetype
selectInput(inputId = "ltype",
label = "Typ čáry podle:",
choices = c("Typ opatření" = "opat_typ", "Sklon (°)" = "sklon_st", "Poč.stav (č.sim)" = "simcislo", "Intenzita (mm/h)" = "intenzita",
"Opatření ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice", "ID simulace" = "ID"),
selected = "opatreni"),
# Select setting for rows
selectInput(inputId = "row",
label = "Rozlišit v řádcích:",
choices = c("Typ opatření" = "opat_typ", "Sklon (°)" = "sklon_st", "Poč.stav (č.sim)" = "simcislo", "Intenzita (mm/h)" = "intenzita",
"Opatření ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice", "ID simulace" = "ID", "."),
selected = "."),
# Select setting for columns
selectInput(inputId = "col",
label = "Rozlišit?it ve sloupcích:",
choices = c("Typ opatření" = "opat_typ", "Sklon (°)" = "sklon_st", "Poč.stav (č.sim)" = "simcislo", "Intenzita (mm/h)" = "intenzita",
"Opatření ANO/NE" = "opatreni", "Systém" = "system", "Repetice" = "repetice", "ID simulace" = "ID", "."),
selected = "sklon_st"),
# Specify scales types in facets
checkboxGroupInput(inputId = "scales",
label = "Rozvolni rozsah os:",
choices = c("x" = 1, "y" = 2),
selected = 2,
inline = T),
# Highlight missing datapoints
checkboxInput(inputId = "mis",
label = "Zvýrazni chybějící datové body:",
value = T)
),
# h3 header for filtering
h4("Filtr datasetu"),
wellPanel(style = "padding: 5px;",
# Select levels of opat_typ
checkboxGroupInput(inputId = "lev_opat_typ",
label = "Typ opatření:",
choices = levels(qsres$opat_typ),
selected = levels(qsres$opat_typ),
inline = T),
# Select levels of sklon_st
checkboxGroupInput(inputId = "lev_sklon",
label = "Sklon plochy:",
choices = levels(qsres$sklon_st),
selected = levels(qsres$sklon_st),
inline = T),
# Select initial conditions
checkboxGroupInput(inputId = "lev_simcislo",
label = "Počáteční stav:",
choices = c("s" = 1, "m" = 2, "s+rýhy" = 3, "m+rýhy" = 4),
selected = levels(qsres$simcislo),
inline = T),
# Select rainfall intensity
checkboxGroupInput(inputId = "lev_intenzita",
label = "Intenzita deště:",
choices = levels(qsres$intenzita),
selected = levels(qsres$intenzita),
inline = T),
# Select repetition numbers
checkboxGroupInput(inputId = "lev_rep",
label = "Repetice:",
choices = levels(qsres$repetice),
selected = levels(qsres$repetice),
inline = T),
# Select plots with/out measures
checkboxGroupInput(inputId = "lev_opatreni",
label = "Plochy s/bez opat?en?:",
choices = c(T, F),
selected = c(T, F),
inline = T),
# Select location of measurements
checkboxGroupInput(inputId = "lev_loc",
label = "Lokalita:",
choices = levels(qsres$lokalita),
selected = levels(qsres$lokalita),
inline = T)
),
# Action button for applying the filter
actionButton(inputId = "filter_dataset",
label = "Filtruj a zobraz dataset")
),
mainPanel(width = 9,
# Outputs
mainPanel(
tabsetPanel(type = "tabs",
# Tab 1: Line plot of the time series
tabPanel("Průběhy veličin",
br(), # Visual separation
plotOutput(outputId = "timeseries",
hover = "plot1_hover",
height = "800px"),
verbatimTextOutput(outputId = "bpoint_info1")
),
# Tab 2: Scatterplot of the cumulative statistics
tabPanel("Bodov? statistiky",
br(), # Visual separation
plotOutput(outputId = "scatterplot",
hover = "plot2_hover",
height = "800px"),
verbatimTextOutput(outputId = "bpoint_info2")
),
# Tab 3: Summary statistics of preselected variables and settings
tabPanel("Summary",
br(),
verbatimTextOutput(outputId = "summary") # summary of filtered dataset
)
), style = "width: 100%"
)
)
)
)
# Define server function required to create the scatterplot
server <- function(input, output) {
# Turn off the inline histogramms in skimr summary
skim_with(numeric = list(hist = NULL))
# Create reactive dataframes - filtered subsets for generating the plots
fres <- eventReactive(
eventExpr = input$filter_dataset,
valueExpr = {
req(input$lev_opat_typ)
qsres %>% filter(opat_typ %in% input$lev_opat_typ,
sklon_st %in% input$lev_sklon,
simcislo %in% input$lev_simcislo,
intenzita %in% input$lev_intenzita,
repetice %in% input$lev_rep,
opatreni %in% input$lev_opatreni,
lokalita %in% input$lev_loc)})
fstat <- eventReactive(
eventExpr = input$filter_dataset,
valueExpr = {
req(input$lev_opat_typ)
qsstat %>% filter(opat_typ %in% input$lev_opat_typ,
sklon_st %in% input$lev_sklon,
simcislo %in% input$lev_simcislo,
intenzita %in% input$lev_intenzita,
repetice %in% input$lev_rep,
opatreni %in% input$lev_opatreni,
lokalita %in% input$lev_loc)})
# Create reactive filtered subset of predefined columns for generating the summary
ffres <- reactive(fres() %>%
ungroup() %>%
select(opat_typ, opatreni, sklon_st, simcislo, intenzita, repetice, NAvalue,
prutok1_l.min, smyv1_g.l, sedflux1_g.min, Qcum_l, S1cum_g, S2cum_g, S1cumRel, S2cumRel))
# ffstat <- reactive(fstat() %>%
# ungroup() %>%
# select(interval, opat_typ, plocha, simcislo, opatreni, prutok_l.min, smyv_g.l, Qcum_l, Scum_g, sedflux_g.min, ScumRel))
myplot1 <- eventReactive(
eventExpr = fres(),
valueExpr = {
ggplot(data = fres(), aes_string(x = input$x, y = input$y, group = "ID", col = input$color, linetype = input$ltype)) +
geom_line() +
geom_point() +
facet_grid(paste(input$row, "~", input$col, sep = " "),
scales = c("fixed", "free_x", "free_y", "free")[which(sum(as.numeric(input$scales)) == c(0,1,2,3))]) +
theme(legend.position = "bottom")
})
myplot2 <- eventReactive(
eventExpr = fstat(),
valueExpr = {
ggplot(data = fstat(), aes_string(x = input$x, y = input$y, col = input$color, shape = input$ltype)) +
geom_point(size = 3) +
facet_grid(paste(input$row, "~", input$col, sep = " "),
scales = c("fixed", "free_x", "free_y", "free")[which(sum(as.numeric(input$scales)) == c(0,1,2,3))]) +
theme(legend.position = "bottom")
})
output$timeseries <- renderPlot({
if (input$mis == T) {
myplot1() + geom_point(aes(shape = NAvalue), size = 3, col = "red") + guides(shape = F)
} else{
myplot1()
}
})
output$bpoint_info1 <- renderPrint({
nearPoints(fres(), coordinfo = input$plot1_hover) %>%
select(ID) %>% ungroup() %>% as.data.frame()
})
output$scatterplot <- renderPlot({myplot2()})
output$bpoint_info2 <- renderPrint({
nearPoints(fstat(), coordinfo = input$plot2_hover) %>%
select(ID) %>% ungroup() %>% as.data.frame()
})
output$summary <- renderPrint({
print(skim(ungroup(ffres())))
})
}
# Create a Shiny app object
shinyApp(ui = ui, server = server)
xxplot = ggplot(qsresnF, aes(x = qsresnF$t1_min, y=qsresnF$Qcum_l), colour = "red") + #(qsresnF$cas_do_odtok + (qsresnF$t1_min + qsresnF$t2_min)/2)))+
geom_line() +
# geom_jitter(aes(x = qsresnF$t1_min, y=qsresnF$S2cum_g * scaleFactor), method="loess", col="red") +
# stat_summary(aes(x = grain$init_state_num , y = grain$percentage, colour = grain$aggregates, shape = as.factor(grain$particlesize)), fun = median, geom="point", show.legend = FALSE, vjust=-1, size = 10)+
# scale_y_continuous(name="l/min", sec.axis=sec_axis(~./scaleFactor, name="g/l")) +
facet_grid (qsresnF$opat_typ_grain ~ qsresnF$poc_stav_int)
plot(xxplot)
grain_data_long <- grain %>%
pivot_longer(cols = particlesize),
names_to = "particlesize",
values_to = "value")
gplot = ggplot() + #grain, aes(x = grain$init_state_num , y = grain$percentage, colour = grain$aggregates, shape = as.factor(particlesize))) +
stat_summary(aes(x = grain$init_state_num , y = grain$percentage, colour = grain$aggregates, shape = as.factor(grain$particlesize)), fun = median, geom="point", show.legend = TRUE, vjust=-1, size = 10)
plot(gplot)
scale_x_log10(name = "Undersize of particles [\u03BCm]", breaks = c(2.13e+00, 9.86e+00, 5.18e+01), labels = c("2", "10", "50"))#, fill = Vodni_tok)) + geom_bar() + facet_grid(size ~ ., scales = "free"))
max_out = maxrun[,c(5, 6, 11, 15, 29, 35, 37)]
write.csv(max_out, "max_runoff.csv")
eDTA1 <- eDTA %>% mutate(opat_typ = ifelse(poc_stav %in% c("such\xe1" ), "bare_dry", "bare_wet"))
eDTA = eDTA[c(3, 5, )]
x <- subset(qsres, "opatreni" = "FALSE")
vyber_R <- select(qsres, lokalita, simcislo, repetice, sklon_st, intenzita, ID, t1_min, t2_min, prutok1_l.min, Qcum_l)
x = qsres [qsres$opatreni == "FALSE",]
xx = x [x$simcislo == "1",]
xxx = xx [xx$lokalita == "Jir",]
vyber_R <- select(xxx, lokalita, simcislo, repetice, sklon_st, intenzita, ID, t1_min, t2_min, prutok1_l.min, Qcum_l)
write.csv(vyber_R, "vyber_R.csv")
mean(vyber_R$sklon_st == 30 & vyber_R$t2_min == "2.5", vyber_R$prutok1_l.min)