library(tidyverse)
library(gridExtra)
library(pander)
library(ggpubr)

theme_set(theme_bw(base_size = 13, base_family = "Times") +
            theme(plot.title = element_text(size = 14, hjust = 0.0),
                  panel.grid.minor = element_blank(),
                  panel.grid.major = element_line(colour = "gray", linetype="dashed",size = 0.1),
                  legend.position = "none", 
                  legend.title = element_blank(),
                  legend.text.align = 0))

## FUNCTION: Get legend
get_legend = function(myggplot){
  tmp = ggplot_gtable(ggplot_build(myggplot))
  leg = which(sapply(tmp$grobs, function (x) x$name) == "guide-box")
  legend = tmp$grobs[[leg]]
  return(legend)
}

df.type = c("OBS", "Model")

# Read data
df.tas = NULL
for (itype in df.type) {
  df.tas.tmp = read.table(paste0("data-txt/", itype, "-TAS.txt"), header = FALSE)
  
  colnames(df.tas.tmp) = c("date", "var")
  df.tas.tmp$type = itype
  
  df.tas = rbind(df.tas, df.tas.tmp)
}

# Transfrom date to year-mon-day
f.convdate = function(date) {
  return(data_frame(year = as.integer(substr(date,1,4)),
                    mon  = as.integer(substr(date,6,7)),
                    day  = as.integer(substr(date,9,10))))
}

df.tas = cbind(type = df.tas$type, f.convdate(df.tas$date), var = df.tas$var)

df.tas %>% group_by(type, mon) %>% summarise(var = mean(var, na.rm = TRUE)) %>% 
  spread(mon, var) %>% pander(split.table = 130)

p0 = df.tas %>% spread(type, var) %>%
  ggplot(aes(x = OBS, y = Model)) +
  scale_x_continuous(breaks = seq(0, 35, 5), limits = c(5, 32.5)) +
  scale_y_continuous(breaks = seq(0, 35, 5), limits = c(5, 32.5)) +
  coord_fixed(ratio = 1) +
  labs(x = "Observation (C-degree)", y = "Model (C-degree)")

p1 = p0 +
  geom_point(alpha = 0.08, color = "red") +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "Scatter-plot BASIC") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")

p2 = p0 +
  stat_density_2d(geom = "raster", aes(fill = ..density..), contour = FALSE) +
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 0.015), oob = scales::squish) +
  labs(title = "Heatmap of point Density") +
  theme(legend.position = "right")

p3 = p0 +
  geom_bin2d(bins=36, aes(fill=..count..))+
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 20), oob = scales::squish) +
  labs(title = "Heatmap of point Count over ranges") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")

p4 = p0 +
  geom_hex(bins=36, aes(fill=..count..))+
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 20), oob = scales::squish) +
  labs(title = "Hex-Heatmap of point Count over ranges") +
  theme(legend.position = "right")

p5 = p0 +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha= 1, col = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(0, 0.015), oob = scales::squish) +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "2D Kernel density plot of point Density") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")

p6 = p0 +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = 1, show.legend = F) +
  stat_density_2d(aes(size = ..density.., fill = ..density..), geom = "point", 
                  shape = 21, n = 12, contour = FALSE, col = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(0, 0.015), oob = scales::squish) +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "Modified 2D Kernel density plot of point Density") +
  theme(legend.position = "right")

grid.arrange(p1, p2, p3, p4, p5, p6, ncol = 2, heights = c(3, 3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4), c(5, 6)))

df.stat = df.tas %>% group_by(type, mon) %>% 
  summarise(Mean   = mean(var, na.rm = TRUE),
            Median = quantile(var, 0.50, na.rm = TRUE),
            Q25    = quantile(var, 0.25, na.rm = TRUE),
            Q75    = quantile(var, 0.75, na.rm = TRUE))

df.stat %>% ungroup() %>% 
  gather(key = indices, value = value, -c(type, mon)) %>% spread(key = mon, value = value) %>%
  arrange(indices) %>% pander(emphasize.strong.cols = c(1, 2), split.table = 130)

df.stat.plot = df.stat %>% ungroup() %>% 
  gather(key = indices, value = value, -c(type, mon)) %>% 
  mutate(idx = paste0(indices, "-", type))
df.stat.plot$value = round(df.stat.plot$value, digits = 1)

c.month = c("J", "F", "M", "A","M", "J", "J", "A", "S", "O", "N", "D")

p = df.stat.plot %>% ggplot() +
  geom_tile(aes(x = factor(mon), y = idx, fill = value), color = "black") +
  geom_text(aes(x = factor(mon), y = idx, label = value), color = "black", size = 3) +
  geom_hline(yintercept = c(2.5, 4.5, 6.5), size = 1, color = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(10, 30), oob = scales::squish) +
  scale_x_discrete(labels = c.month) +
  labs(title = "Heatmap for indices comparison",
       x = "Month", y = "") +
  theme(legend.position = "bottom")
p

# Create function for cleaner code later
f.ggplot = function(p, discrete = FALSE) {
  p = p +
    scale_color_manual(values = c("red", "blue")) +
    scale_fill_manual(values = c("red", "blue")) +
    labs(x = "Months", y = "Temperature (C-degree)") +
    theme(legend.position = "bottom")
  if (discrete == TRUE) {
    p = p + scale_x_discrete(breaks = seq(12), labels = c.month)
  } else {
    p = p + scale_x_continuous(breaks = seq(12), labels = c.month)
  }
  return(p)
}

p1 = f.ggplot(df.stat %>% ggplot() +
  geom_col(aes(x = mon, y = Mean, fill = factor(type, levels = c("OBS", "Model"))),
           alpha = 0.6, position = position_dodge(0.8)) +
  geom_errorbar(aes(x = mon , ymin = Q25, ymax = Q75, 
                    group = factor(type, levels = c("OBS", "Model"))),
                width = 0.5, position = position_dodge(0.8)) +
  labs(title = "Climatology of Temperature over period 2000-2002",
       subtitle = "Errorbars present range from Q25 to Q75"))

p2 = f.ggplot(df.stat %>% ggplot() +
  geom_line(aes(x = mon, y = Mean, color = factor(type, levels = c("OBS", "Model")))) +
  geom_ribbon(aes(x = mon, ymin = Q25, ymax = Q75, 
                  fill = factor(type, levels = c("OBS", "Model"))), alpha = 0.2) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The shaded area presents range from Q25 & Q75"))
  
p3 = f.ggplot(df.tas %>% ggplot() +
  stat_summary(aes(x = mon, y = var, color = factor(type, levels = c("OBS", "Model"))),
               geom = "pointrange", position = position_dodge(width = 0.5),
               fun.y = mean, fun.ymax = max, fun.ymin = min, size = 0.5) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The points present for Mean, the lines present range from MIN to MAX"))

p4 = f.ggplot(df.tas %>% ggplot() +
  geom_boxplot(aes(x = factor(mon), y = var, fill = factor(type, levels = c("OBS", "Model"))),
               alpha = 0.6, outlier.color = "purple", outlier.shape = 4, outlier.size = 1) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The boxes present Median, Min, Max, Q25, Q75, and outliers"),
  discrete = TRUE)

grid.arrange(p1, p2, p3, p4, ncol = 2, heights = c(3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4)))

m.breaks = seq(4, 32, 0.5)

p1 = df.tas %>% ggplot() +
  geom_histogram(aes(x = var, y = ..count.., fill = factor(type, levels = c("OBS", "Model"))), 
                 breaks = m.breaks, position = "identity", alpha = 0.3) +
  scale_fill_manual(values = c("red", "blue")) +
  scale_y_continuous(breaks = seq(0, 250, 50)) +
  labs(title = "Histogram", x = "Temperature", y = "Frequency (count)") +
  theme(legend.position = c(0.2, 0.85))

f.hist = function(df, breaks, density = FALSE) {
  h = hist(df, breaks = breaks, plot = FALSE)
  if (density == FALSE) {
    df.h = data_frame(x = h$breaks, y = c(h$count, NA))
  } else {
    df.h = data_frame(x = h$breaks, y = c(h$density, NA))
  }
  
  is.na(df.h$y) = df.h$y == 0.0
  
  return(df.h)
}

df.hist = f.hist(df.tas %>% filter(type == "OBS") %>% .$var, breaks = m.breaks) %>%
  mutate(type = "OBS")
df.hist = rbind(df.hist,
                f.hist(df.tas %>% filter(type == "Model") %>% .$var, breaks = m.breaks) %>%
                  mutate(type = "Model"))

p2 = p1 + 
  geom_step(data = df.hist, aes(x = x, y = y, color = factor(type, levels = c("OBS", "Model"))),
            stat = "identity", size = 0.35) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "Histogram with step lines")

p3 = df.tas %>% ggplot() +
  geom_histogram(aes(x = var, y = ..density.., fill = factor(type, levels = c("OBS", "Model"))), 
                 breaks = m.breaks, position = "identity", alpha = 0.3) +
  geom_density(aes(x = var , fill = factor(type, levels = c("OBS", "Model"))), alpha = 0.3) +
  scale_fill_manual(values = c("red", "blue")) +
  scale_y_continuous(breaks = seq(0, 0.3, 0.05)) +
  labs(title = "Histogram (Density) with adding smooth", x = "Temperature", y = "Density") +
  theme(legend.position = c(0.2, 0.85))

p4 = df.tas %>% ggplot() +
  geom_violin(aes(x = type, y = var, group = type, fill = factor(type, levels = c("OBS", "Model"))), 
              trim = TRUE, alpha = 0.3) +
  geom_boxplot(aes(x = type, y = var, group = type, fill = factor(type, levels = c("OBS", "Model"))), 
               width=0.1, alpha = 0.6) +
  scale_fill_manual(values = c("red", "blue")) + 
  scale_y_continuous(breaks = seq(10, 30, 10)) +
  coord_flip() +
  labs(title = "Violin-plot combined with Box-plot", x = "", y = "Temperature") +
  theme(legend.position = c(0.2, 0.5))

grid.arrange(p1, p2, p3, p4, ncol = 2, heights = c(3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4)))

library(ggridges)
library(viridis)

p1 = df.tas %>% ggplot() +
  geom_density_ridges(aes(x = var, y = factor(mon), fill = factor(mon), col = factor(mon)), 
                      scale = 4, show.legend = FALSE) +
  scale_y_discrete(labels = c.month) +
  scale_fill_viridis(option = "A",alpha = 0.5, discrete = T)+
  scale_color_viridis(option = "C",alpha = 1, discrete = T, begin = 1, end = 0) +
  labs(title = "Kernel density plot using 'ggridges'", 
       x = "Temperature", y = "Months")

p2 = df.tas %>% ggplot() +
  geom_density_ridges(aes(x = var, y = factor(mon), fill = factor(mon), col = factor(mon)), 
                      scale = 4, show.legend = FALSE, alpha = 0.5,
                      stat = "binline", binwidth = 0.5, rel_min_height = 0.005) +
  scale_y_discrete(labels = c.month) +
  scale_fill_viridis(option = "A",alpha = 0.5, discrete = T)+
  scale_color_viridis(option = "C",alpha = 1, discrete = T, begin = 1, end = 0) +
  labs(title = "Kernel density plot using 'ggridges'", 
       x = "Temperature", y = "Months")


grid.arrange(p1, p2, ncol = 2, heights = c(3),
             layout_matrix = rbind(c(1, 2)))

p1 = df.tas %>% ggplot() +
  stat_ecdf(aes(x = var, color = type), geom = "step", pad = F) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "CDF using 'ggplot' auto-function", x = "Temperature (C-degree)", y = "CDF") +
  theme(legend.position = c(0.2, 0.85))

f.getcdf = function (df, step = 1, qth = -999, xth = -999) {
  ## Recreate ecdf data
  df.ecdf = data.frame(x = unique(df),
                        y = ecdf(df)(unique(df))*length(df))
  ## rescale y to 0,1 range
  df.ecdf$y = 
    scale(df.ecdf$y,center = min(df.ecdf$y),scale = diff(range(df.ecdf$y)))
  
  df.ecdf = df.ecdf %>% arrange (by = x)
  return (df.ecdf[c(seq(1, length(df.ecdf$x), step), length(df.ecdf$x)), ])
  
  ## To extract correct value
  # if (xth == -999) {
  #   out = dat_ecdf %>% filter (rank(abs(y - qth)) == min(rank(abs(y - qth))))
  #   out = round (out, digits = 3)
  # } else {
  #   out = dat_ecdf %>% filter (rank(abs(x - xth)) == min(rank(abs(x - xth))))
  #   out = round (out, digits = 3)
  # }
  # 
  # return (out)
}

##
df.cdf = f.getcdf(df.tas %>% filter(type == "OBS") %>% .$var, step = 20) %>% 
  mutate(type = "OBS")
df.cdf = rbind(df.cdf,
               f.getcdf(df.tas %>% filter(type == "Model") %>% .$var, step = 20) %>% 
                 mutate(type = "Model"))

p2 = df.cdf %>% ggplot() +
  geom_step(aes(x = x, y = y, color = type)) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "CDF by manually calculation", x = "Temperature (C-degree)", y = "CDF") +
  theme(legend.position = c(0.2, 0.85))

grid.arrange(p1, p2, ncol = 2, heights = c(3),
             layout_matrix = rbind(c(1, 2)))

library(plotly)
ggplotly(p1)

library(plotly)
ggplotly(p2)

---
title: "Visualization: Temperature (Scatter-plot, Heatmap, Box-plot, PDF, CDF, etc.)"
author: "by NXTTNX"
output:
  html_document: 
    code_download: TRUE
    code_folding: show
    number_sections: TRUE
    theme: "default"
    toc: TRUE
    toc_float: TRUE
    dev: 'svg'  
---

<style>
/* resize the widget container */
.plotly { 
  width: 60% !important;
  margin: auto;
}

/* center the widget */
div.svg-container {
  margin: auto !important;
}
</style>


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.align = "center")
```

\
**Set up environment**

```{r message = FALSE, warning = FALSE}
library(tidyverse)
library(gridExtra)
library(pander)
library(ggpubr)

theme_set(theme_bw(base_size = 13, base_family = "Times") +
            theme(plot.title = element_text(size = 14, hjust = 0.0),
                  panel.grid.minor = element_blank(),
                  panel.grid.major = element_line(colour = "gray", linetype="dashed",size = 0.1),
                  legend.position = "none", 
                  legend.title = element_blank(),
                  legend.text.align = 0))

## FUNCTION: Get legend
get_legend = function(myggplot){
  tmp = ggplot_gtable(ggplot_build(myggplot))
  leg = which(sapply(tmp$grobs, function (x) x$name) == "guide-box")
  legend = tmp$grobs[[leg]]
  return(legend)
}

```


# Data Processing

\
**Use CDO to extract data from "netCDF" to "text" format**

Assume that the data including daily observation and model simulation of temperature over period from 
2000-2002. These datasets are normally in netCDF format, and can be extracted by using CDO as below:

* cdo -L -outputtab,date,value -fldmean -selvar,tas OBS.nc 2> /dev/null > data-txt/OBS-TAS.txt
* cdo -L -outputtab,date,value -fldmean -selvar,tas Model.nc 2> /dev/null > data-txt/Model-TAS.txt

\
**Read data & re-shape data to work with "tidyverse" environment**


```{r message = FALSE, warning = FALSE}
df.type = c("OBS", "Model")

# Read data
df.tas = NULL
for (itype in df.type) {
  df.tas.tmp = read.table(paste0("data-txt/", itype, "-TAS.txt"), header = FALSE)
  
  colnames(df.tas.tmp) = c("date", "var")
  df.tas.tmp$type = itype
  
  df.tas = rbind(df.tas, df.tas.tmp)
}

# Transfrom date to year-mon-day
f.convdate = function(date) {
  return(data_frame(year = as.integer(substr(date,1,4)),
                    mon  = as.integer(substr(date,6,7)),
                    day  = as.integer(substr(date,9,10))))
}

df.tas = cbind(type = df.tas$type, f.convdate(df.tas$date), var = df.tas$var)
```

\
**Quick look at annual cycle (More detailed later)**

```{r message = FALSE, warning = FALSE}
df.tas %>% group_by(type, mon) %>% summarise(var = mean(var, na.rm = TRUE)) %>% 
  spread(mon, var) %>% pander(split.table = 130)
```


# Daily Analysis

\
**Create basic plot contents (template)**
```{r message = FALSE, warning = FALSE}
p0 = df.tas %>% spread(type, var) %>%
  ggplot(aes(x = OBS, y = Model)) +
  scale_x_continuous(breaks = seq(0, 35, 5), limits = c(5, 32.5)) +
  scale_y_continuous(breaks = seq(0, 35, 5), limits = c(5, 32.5)) +
  coord_fixed(ratio = 1) +
  labs(x = "Observation (C-degree)", y = "Model (C-degree)")
```

\
**Scatter-plot BASIC**

Using "alpha", the density of points can estimately seen
```{r message = FALSE, warning = FALSE}
p1 = p0 +
  geom_point(alpha = 0.08, color = "red") +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "Scatter-plot BASIC") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")
```

\
**Heatmap of point Density**
```{r message = FALSE, warning = FALSE}
p2 = p0 +
  stat_density_2d(geom = "raster", aes(fill = ..density..), contour = FALSE) +
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 0.015), oob = scales::squish) +
  labs(title = "Heatmap of point Density") +
  theme(legend.position = "right")
```

\
**Heatmap & Hex-Heatmap of point Count over ranges**

The ranges are defined by "bins"

```{r message = FALSE, warning = FALSE}
p3 = p0 +
  geom_bin2d(bins=36, aes(fill=..count..))+
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 20), oob = scales::squish) +
  labs(title = "Heatmap of point Count over ranges") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")

p4 = p0 +
  geom_hex(bins=36, aes(fill=..count..))+
  geom_abline(intercept = 0, slope = 1) +
  scale_fill_distiller(palette = "YlOrRd", direction = 1, 
                       limits = c(0, 20), oob = scales::squish) +
  labs(title = "Hex-Heatmap of point Count over ranges") +
  theme(legend.position = "right")
```

\
**2D Kernel density plot of point Density**
```{r message = FALSE, warning = FALSE}
p5 = p0 +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha= 1, col = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(0, 0.015), oob = scales::squish) +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "2D Kernel density plot of point Density") +
  stat_cor(aes(label = paste(..r.label.., ..rr.label.., ..p.label.., sep = "~`,`~")),
           method = "pearson", label.x = 5, label.y = 31, na.rm = FALSE, 
           size = 4, color = "blue")

p6 = p0 +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = 1, show.legend = F) +
  stat_density_2d(aes(size = ..density.., fill = ..density..), geom = "point", 
                  shape = 21, n = 12, contour = FALSE, col = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(0, 0.015), oob = scales::squish) +
  geom_abline(intercept = 0, slope = 1) +
  labs(title = "Modified 2D Kernel density plot of point Density") +
  theme(legend.position = "right")
```

\
**Put all plots together using grid.arrange (gridExtra)**
```{r message = FALSE, warning = FALSE, fig.width = 10, fig.height = 12}
grid.arrange(p1, p2, p3, p4, p5, p6, ncol = 2, heights = c(3, 3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4), c(5, 6)))
```


# Monthly Analysis

\
**Calculate some climatology indices for each month over period**
```{r message = FALSE, warning = FALSE}
df.stat = df.tas %>% group_by(type, mon) %>% 
  summarise(Mean   = mean(var, na.rm = TRUE),
            Median = quantile(var, 0.50, na.rm = TRUE),
            Q25    = quantile(var, 0.25, na.rm = TRUE),
            Q75    = quantile(var, 0.75, na.rm = TRUE))
```

\
**Print out as a table**
```{r message = FALSE, warning = FALSE}
df.stat %>% ungroup() %>% 
  gather(key = indices, value = value, -c(type, mon)) %>% spread(key = mon, value = value) %>%
  arrange(indices) %>% pander(emphasize.strong.cols = c(1, 2), split.table = 130)
```

\
**Plot the table as heatmap**
```{r message = FALSE, warning = FALSE}
df.stat.plot = df.stat %>% ungroup() %>% 
  gather(key = indices, value = value, -c(type, mon)) %>% 
  mutate(idx = paste0(indices, "-", type))
df.stat.plot$value = round(df.stat.plot$value, digits = 1)

c.month = c("J", "F", "M", "A","M", "J", "J", "A", "S", "O", "N", "D")

p = df.stat.plot %>% ggplot() +
  geom_tile(aes(x = factor(mon), y = idx, fill = value), color = "black") +
  geom_text(aes(x = factor(mon), y = idx, label = value), color = "black", size = 3) +
  geom_hline(yintercept = c(2.5, 4.5, 6.5), size = 1, color = "black") +
  scale_fill_distiller(palette = "Spectral", limits = c(10, 30), oob = scales::squish) +
  scale_x_discrete(labels = c.month) +
  labs(title = "Heatmap for indices comparison",
       x = "Month", y = "") +
  theme(legend.position = "bottom")
p
```

\
**Create basic plot contents (template)**
```{r message = FALSE, warning = FALSE}

# Create function for cleaner code later
f.ggplot = function(p, discrete = FALSE) {
  p = p +
    scale_color_manual(values = c("red", "blue")) +
    scale_fill_manual(values = c("red", "blue")) +
    labs(x = "Months", y = "Temperature (C-degree)") +
    theme(legend.position = "bottom")
  if (discrete == TRUE) {
    p = p + scale_x_discrete(breaks = seq(12), labels = c.month)
  } else {
    p = p + scale_x_continuous(breaks = seq(12), labels = c.month)
  }
  return(p)
}

```

\
**Plotting annual cycle**
```{r message = FALSE, warning = FALSE}
p1 = f.ggplot(df.stat %>% ggplot() +
  geom_col(aes(x = mon, y = Mean, fill = factor(type, levels = c("OBS", "Model"))),
           alpha = 0.6, position = position_dodge(0.8)) +
  geom_errorbar(aes(x = mon , ymin = Q25, ymax = Q75, 
                    group = factor(type, levels = c("OBS", "Model"))),
                width = 0.5, position = position_dodge(0.8)) +
  labs(title = "Climatology of Temperature over period 2000-2002",
       subtitle = "Errorbars present range from Q25 to Q75"))

p2 = f.ggplot(df.stat %>% ggplot() +
  geom_line(aes(x = mon, y = Mean, color = factor(type, levels = c("OBS", "Model")))) +
  geom_ribbon(aes(x = mon, ymin = Q25, ymax = Q75, 
                  fill = factor(type, levels = c("OBS", "Model"))), alpha = 0.2) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The shaded area presents range from Q25 & Q75"))
  
p3 = f.ggplot(df.tas %>% ggplot() +
  stat_summary(aes(x = mon, y = var, color = factor(type, levels = c("OBS", "Model"))),
               geom = "pointrange", position = position_dodge(width = 0.5),
               fun.y = mean, fun.ymax = max, fun.ymin = min, size = 0.5) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The points present for Mean, the lines present range from MIN to MAX"))

p4 = f.ggplot(df.tas %>% ggplot() +
  geom_boxplot(aes(x = factor(mon), y = var, fill = factor(type, levels = c("OBS", "Model"))),
               alpha = 0.6, outlier.color = "purple", outlier.shape = 4, outlier.size = 1) +
  labs(title = "Climatology of Temperature over period 2000-2002", 
       subtitle = "The boxes present Median, Min, Max, Q25, Q75, and outliers"),
  discrete = TRUE)
```

```{r message = FALSE, warning = FALSE, fig.width = 11, fig.height = 8}
grid.arrange(p1, p2, p3, p4, ncol = 2, heights = c(3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4)))
```


# PDF

\
**Starting with histogram**
```{r message = FALSE, warning = FALSE}
m.breaks = seq(4, 32, 0.5)

p1 = df.tas %>% ggplot() +
  geom_histogram(aes(x = var, y = ..count.., fill = factor(type, levels = c("OBS", "Model"))), 
                 breaks = m.breaks, position = "identity", alpha = 0.3) +
  scale_fill_manual(values = c("red", "blue")) +
  scale_y_continuous(breaks = seq(0, 250, 50)) +
  labs(title = "Histogram", x = "Temperature", y = "Frequency (count)") +
  theme(legend.position = c(0.2, 0.85))
```

\
**Manually get histogram value and plot as step line**
```{r message = FALSE, warning = FALSE}
f.hist = function(df, breaks, density = FALSE) {
  h = hist(df, breaks = breaks, plot = FALSE)
  if (density == FALSE) {
    df.h = data_frame(x = h$breaks, y = c(h$count, NA))
  } else {
    df.h = data_frame(x = h$breaks, y = c(h$density, NA))
  }
  
  is.na(df.h$y) = df.h$y == 0.0
  
  return(df.h)
}

df.hist = f.hist(df.tas %>% filter(type == "OBS") %>% .$var, breaks = m.breaks) %>%
  mutate(type = "OBS")
df.hist = rbind(df.hist,
                f.hist(df.tas %>% filter(type == "Model") %>% .$var, breaks = m.breaks) %>%
                  mutate(type = "Model"))

p2 = p1 + 
  geom_step(data = df.hist, aes(x = x, y = y, color = factor(type, levels = c("OBS", "Model"))),
            stat = "identity", size = 0.35) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "Histogram with step lines")
```

\
**Plot histogram with smoothing density**
```{r message = FALSE, warning = FALSE}
p3 = df.tas %>% ggplot() +
  geom_histogram(aes(x = var, y = ..density.., fill = factor(type, levels = c("OBS", "Model"))), 
                 breaks = m.breaks, position = "identity", alpha = 0.3) +
  geom_density(aes(x = var , fill = factor(type, levels = c("OBS", "Model"))), alpha = 0.3) +
  scale_fill_manual(values = c("red", "blue")) +
  scale_y_continuous(breaks = seq(0, 0.3, 0.05)) +
  labs(title = "Histogram (Density) with adding smooth", x = "Temperature", y = "Density") +
  theme(legend.position = c(0.2, 0.85))
```
\
**Using combination of Violin-plot and Box-Plot**
```{r message = FALSE, warning = FALSE}
p4 = df.tas %>% ggplot() +
  geom_violin(aes(x = type, y = var, group = type, fill = factor(type, levels = c("OBS", "Model"))), 
              trim = TRUE, alpha = 0.3) +
  geom_boxplot(aes(x = type, y = var, group = type, fill = factor(type, levels = c("OBS", "Model"))), 
               width=0.1, alpha = 0.6) +
  scale_fill_manual(values = c("red", "blue")) + 
  scale_y_continuous(breaks = seq(10, 30, 10)) +
  coord_flip() +
  labs(title = "Violin-plot combined with Box-plot", x = "", y = "Temperature") +
  theme(legend.position = c(0.2, 0.5))
```

\
```{r message = FALSE, warning = FALSE, fig.width = 10, fig.height = 7}
grid.arrange(p1, p2, p3, p4, ncol = 2, heights = c(3, 3),
             layout_matrix = rbind(c(1, 2), c(3, 4)))
```

\
**Appling "ggridges" density plot for monthly density**
```{r message = FALSE, warning = FALSE, fig.width = 10, fig.height = 5}
library(ggridges)
library(viridis)

p1 = df.tas %>% ggplot() +
  geom_density_ridges(aes(x = var, y = factor(mon), fill = factor(mon), col = factor(mon)), 
                      scale = 4, show.legend = FALSE) +
  scale_y_discrete(labels = c.month) +
  scale_fill_viridis(option = "A",alpha = 0.5, discrete = T)+
  scale_color_viridis(option = "C",alpha = 1, discrete = T, begin = 1, end = 0) +
  labs(title = "Kernel density plot using 'ggridges'", 
       x = "Temperature", y = "Months")

p2 = df.tas %>% ggplot() +
  geom_density_ridges(aes(x = var, y = factor(mon), fill = factor(mon), col = factor(mon)), 
                      scale = 4, show.legend = FALSE, alpha = 0.5,
                      stat = "binline", binwidth = 0.5, rel_min_height = 0.005) +
  scale_y_discrete(labels = c.month) +
  scale_fill_viridis(option = "A",alpha = 0.5, discrete = T)+
  scale_color_viridis(option = "C",alpha = 1, discrete = T, begin = 1, end = 0) +
  labs(title = "Kernel density plot using 'ggridges'", 
       x = "Temperature", y = "Months")


grid.arrange(p1, p2, ncol = 2, heights = c(3),
             layout_matrix = rbind(c(1, 2)))
  
```


# CDF

\
**Starting CDF with auto function in "ggplot"**
```{r message = FALSE, warning = FALSE}
p1 = df.tas %>% ggplot() +
  stat_ecdf(aes(x = var, color = type), geom = "step", pad = F) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "CDF using 'ggplot' auto-function", x = "Temperature (C-degree)", y = "CDF") +
  theme(legend.position = c(0.2, 0.85))

```

\
**Manually calculate CDF and plot**

This help to extract the correct value

```{r message = FALSE, warning = FALSE}
f.getcdf = function (df, step = 1, qth = -999, xth = -999) {
  ## Recreate ecdf data
  df.ecdf = data.frame(x = unique(df),
                        y = ecdf(df)(unique(df))*length(df))
  ## rescale y to 0,1 range
  df.ecdf$y = 
    scale(df.ecdf$y,center = min(df.ecdf$y),scale = diff(range(df.ecdf$y)))
  
  df.ecdf = df.ecdf %>% arrange (by = x)
  return (df.ecdf[c(seq(1, length(df.ecdf$x), step), length(df.ecdf$x)), ])
  
  ## To extract correct value
  # if (xth == -999) {
  #   out = dat_ecdf %>% filter (rank(abs(y - qth)) == min(rank(abs(y - qth))))
  #   out = round (out, digits = 3)
  # } else {
  #   out = dat_ecdf %>% filter (rank(abs(x - xth)) == min(rank(abs(x - xth))))
  #   out = round (out, digits = 3)
  # }
  # 
  # return (out)
}

##
df.cdf = f.getcdf(df.tas %>% filter(type == "OBS") %>% .$var, step = 20) %>% 
  mutate(type = "OBS")
df.cdf = rbind(df.cdf,
               f.getcdf(df.tas %>% filter(type == "Model") %>% .$var, step = 20) %>% 
                 mutate(type = "Model"))

p2 = df.cdf %>% ggplot() +
  geom_step(aes(x = x, y = y, color = type)) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "CDF by manually calculation", x = "Temperature (C-degree)", y = "CDF") +
  theme(legend.position = c(0.2, 0.85))
```

\
**Plot it out**
```{r message = FALSE, warning = FALSE, fig.width = 8, fig.height = 4}
grid.arrange(p1, p2, ncol = 2, heights = c(3),
             layout_matrix = rbind(c(1, 2)))
```

# Bonus - Plotly

One reason to use the manual calculation is that the auto function still not
working properly with "plotly" for example:

**The CDF by "ggplot" auto-function will be broken as:**

```{r message = FALSE, warning = FALSE, fig.width = 5, fig.height = 4}
library(plotly)
ggplotly(p1)
```

\
**Instead of**

```{r message = FALSE, warning = FALSE, fig.width = 5, fig.height = 4}
library(plotly)
ggplotly(p2)
```
