DataAnalysis - RQ1.Rmd

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
dataset <- read.csv("pre-processing data-rq1/dataset.csv")
dataset_2 <- read.csv("pre-processing data-rq1/dataset_2.csv")
dataset$group = ordered(dataset$group,levels = c("idle", "low", "medium", "high"))
levels(dataset$group)
library(dplyr)

group_by(dataset, group) %>%
  summarise(
    count = n(),
    mean = mean(energy, na.rm = TRUE),
    sd = sd(energy, na.rm = TRUE)
  )
```


```{r}
library("ggpubr")
library("ggplot2")
library("dplyr")

# p <- ggboxplot(dataset, x = "group", y = "energy", 
          # color = "group", palette = c("#00AFBB", "#E7B800", "#FC4E07", "#04B45F"),
          # order = c("idle", "low", "medium", "high"),
          # ylab = "Energy Consumption (J)", xlab = "Frequency of messages", ylim = c(0, 70))
# ggpar(p, legend.position = "none")

ticks <- c("Idle", "Low", "Medium", "High")

dataset$group <- as.factor(dataset$group)

dataset$group <- factor(dataset$group,
                levels =  c("idle", "low", "medium", "high"))

ggplot(data = dataset, aes(x = group, y = energy)) + geom_boxplot() + theme_pubr() + theme(legend.position = "none") + labs(x="Frequency of messages", y="Energy consumption (J)") + scale_x_discrete(labels= ticks)


ggsave("./dataset-rq1/boxplot_rq1_1.pdf", scale = 1.5, height = 5, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/boxplot_rq1_1.pdf", outfile="./dataset-rq1/boxplot_rq1_1.pdf")
```


```{r}
res.aov <- aov(energy ~ group, data = dataset)
summary(res.aov)
```
```{r}
TukeyHSD(res.aov)
```
```{r}
plot(res.aov, 1)
```
```{r}
library(car)
library(carData)
leveneTest(energy ~ group, data = dataset)
```
```{r}
plot(res.aov, 2)
```
```{r}
# Extract the residuals
aov_residuals <- residuals(object = res.aov )
# Run Shapiro-Wilk test
shapiro.test(x = aov_residuals )
```
```{r}
kruskal.test(energy ~ group, data = dataset)
```
```{r}
library(dplyr)
group_by(dataset, group) %>%
  summarise(p.value = shapiro.test(energy)$p.value)
```
```{r}

idle = dataset[dataset$group== "idle", ]
low = dataset[dataset$group== "low", ]
medium = dataset[dataset$group== "medium", ]
high = dataset[dataset$group== "high",]
library("car")
qqPlot(idle$energy)
qqPlot(low$energy)
qqPlot(medium$energy)
qqPlot(high$energy)

```
```{r}
library(FSA)
hist(idle$energy)
hist(low$energy)
hist(medium$energy)
hist(high$energy)
```

```{r}
library(FSA)

Summarize(energy ~ group,
          data = dataset)
```

```{r}
PT = dunnTest(energy ~ group,
              data=dataset,
              method="bh")

PT
```

```{r}
library(effsize)

cliff.delta(idle$energy, low$energy)
cliff.delta(idle$energy, medium$energy)
cliff.delta(idle$energy, high$energy)
cliff.delta(medium$energy, low$energy)
cliff.delta(low$energy, high$energy)
cliff.delta(medium$energy, high$energy)
```
```{r}
library(e1071)
skewness(idle$energy)
skewness(low$energy)
skewness(medium$energy)
skewness(high$energy)
skewness(dataset$energy)

d <- density(idle$energy) # returns the density data 
plot(d, main = "Density of Energy consumption for Treatment- Idle") # plots the results

idle$subject <- revalue(idle$subject, c("telegram"="Telegram", "whatsapp"="WhatsApp"))
ggplot(data = idle, aes(x = energy, fill=subject)) + geom_density(alpha=0.4) + theme_pubr() + labs(x="Energy consumption (J)", y="Density") + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical", plot.title = element_text(hjust = 0.5)) + ggtitle("Idle")

ggsave("./density-615.pdf", scale = 1.5, height = 4, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/density-615.pdf", outfile="./dataset-rq1/density-615.pdf")
```
```{r}
d <- density(low$energy) # returns the density data 
plot(d, main = "Density of Energy consumption for Treatment- Low") # plots the results

low$subject <- revalue(low$subject, c("telegram"="Telegram", "whatsapp"="WhatsApp"))
ggplot(data = low, aes(x = energy, fill=subject)) + geom_density(alpha=0.4) + theme_pubr() + labs(x="Energy consumption (J)", y="Density") + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical", plot.title = element_text(hjust = 0.5)) + ggtitle("Low frequency")

ggsave("./density-616.pdf", scale = 1.5, height = 4, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/density-616.pdf", outfile="./dataset-rq1/density-616.pdf")

```
```{r}
d <- density(medium$energy) # returns the density data 
plot(d, main = "Density of Energy consumption for Treatment- Medium") # plots the results

medium$subject <- revalue(medium$subject, c("telegram"="Telegram", "whatsapp"="WhatsApp"))
ggplot(data = medium, aes(x = energy, fill=subject)) + geom_density(alpha=0.4) + theme_pubr() + labs(x="Energy consumption (J)", y="Density") + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical", plot.title = element_text(hjust = 0.5)) + ggtitle("Medium frequency")

ggsave("./density-617.pdf", scale = 1.5, height = 4, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/density-617.pdf", outfile="./dataset-rq1/density-617.pdf")

```
```{r}
d <- density(high$energy) # returns the density data 
plot(d, main = "Density of Energy consumption for Treatment- High") # plots the results

high$subject <- revalue(high$subject, c("telegram"="Telegram", "whatsapp"="WhatsApp"))
ggplot(data = high, aes(x = energy, fill=subject)) + geom_density(alpha=0.4) + theme_pubr() + labs(x="Energy consumption (J)", y="Density") + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical", plot.title = element_text(hjust = 0.5)) + ggtitle("High frequency")

ggsave("./dataset-rq1/density-618.pdf", scale = 1.5, height = 4, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/density-618.pdf", outfile="./dataset-rq1/density-618.pdf")
```


```{r}
str(dataset_2)
dataset_2$group <- factor(dataset_2$group, 
                  levels = c("idle", "low", "medium", "high"))
dataset_2$subject <- factor(dataset_2$subject, 
                  levels = c("telegram", "whatsapp"))
```

```{r}
library(plyr)
ggboxplot(dataset_2, x = "group", y = "energy", color = "subject",
          palette = c("#00AFBB", "#E7B800"), ylim = c(0,70))

ticks <- c("Idle", "Low", "Medium", "High")

dataset_2$group <- as.factor(dataset_2$group)
dataset_2$subject <- as.factor(dataset_2$subject)

dataset_2$group <- factor(dataset_2$group,
                levels =  c("idle", "low", "medium", "high"))
dataset_2$subject <- factor(dataset_2$subject,
                levels =  c("telegram", "whatsapp"))
dataset_2$subject <- revalue(dataset_2$subject, c("telegram"="Telegram", "whatsapp"="WhatsApp"))

ggplot(data = dataset_2, aes(x = group, y = energy, fill = subject)) + geom_boxplot() + theme_pubr() + theme(legend.position = "none") + labs(x="Frequency of messages", y="Energy consumption (J)") + scale_x_discrete(labels= ticks) + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical") 

ggsave("./dataset-rq1/boxplot_rq1_2.pdf", scale = 1.5, height = 5, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/boxplot_rq1_2.pdf", outfile="./dataset-rq1/boxplot_rq1_2.pdf")
```
```{r}

summary(dataset_2)

```
```{r}

idle_whatsapp = dataset_2[(dataset_2$group== 'idle') & (dataset_2$subject == 'whatsapp'), ]
idle_telegram = dataset_2[(dataset_2$group== 'idle') & (dataset_2$subject == 'telegram'), ]
low_whatsapp = dataset_2[(dataset_2$group== 'low') & (dataset_2$subject == 'whatsapp'), ]
low_telegram = dataset_2[(dataset_2$group== 'low') & (dataset_2$subject == 'telegram'), ]
medium_whatsapp = dataset_2[(dataset_2$group== 'medium') & (dataset_2$subject == 'whatsapp'), ]
medium_telegram = dataset_2[(dataset_2$group== 'medium') & (dataset_2$subject == 'telegram'), ]
high_whatsapp = dataset_2[(dataset_2$group== 'high') & (dataset_2$subject == 'whatsapp'), ]
high_telegram = dataset_2[(dataset_2$group== 'high') & (dataset_2$subject == 'telegram'), ]

```

```{r}
analysis <- function(x){
  print(round(sd(x),3))
  print(round(mean(x),3))
  print(round(median(x),3))
  print(round(min(x),3))
  print(round(max(x),3))
  print("end")
}

analysis(idle_telegram$energy)
analysis(idle_whatsapp$energy)
```
```{r}
analysis(low_telegram$energy)
analysis(low_whatsapp$energy)
```
```{r}
analysis(medium_telegram$energy)
analysis(medium_whatsapp$energy)
```
```{r}
analysis(high_telegram$energy)
analysis(high_whatsapp$energy)
```
```{r}
hist(dataset$energy, xlab ="Energy Consumption", breaks = 40, main = "Histogram of Energy consumption")

ggplot(data = dataset, aes(x=energy)) + geom_histogram(color="black", fill="white", binwidth=2)+ theme_pubr() + labs(x="Energy consumption (J)", y="Frequency")

ggsave("./dataset-rq1/fig-611.pdf", scale = 1.5, height = 7, width = 10, unit = "cm", device=cairo_pdf)
embed_fonts("./dataset-rq1/fig-611.pdf", outfile="./fig-611.pdf")

x <- density(dataset$energy) # returns the density data 
plot(x, main = "Density of Energy consumption") 

# ggplot(data = dataset, aes(x = energy)) + geom_density(alpha=0.4, fill="lightgray") + theme_pubr() + labs(x="Energy consumption (J)", y="Density") + theme(legend.title=element_blank(), legend.position = c(0.9, 0.9), legend.box = "vertical")

```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.