crpto.Rmd

---
fontsize: 22pt
output:
  html_document:
    toc: yes
    toc_float: yes
  word_document:
    toc: yes
  pdf_document:  
    toc: yes
    toc_depth: 3
font-family: Arial
theme: united
title: '**Dark market analysis**'
---
<hr>
<br>


<style>
  
#TOC {
 position: fixed;
  left: 0;
  top: 0;
  width: 250px;
  height: 100%;
  overflow:auto;
}
body {
  max-width: 1200px;
  margin: auto;
  margin-left:150px;
  line-height: 20px;
}
h1.title {
    font-size: 28px;
}
h4 {
    font-size: 20px;
}
h3 {
    font-size: 24px;
}
h2 {
    font-size: 25px;
}
h1 {
    font-size: 26px;
}
.center {
    display: block;
    margin-left: auto;
    margin-right: auto;
    width: 28%;
}
.table {
    width: 40%;
}

.btn {
    border-width: 0 0px 0px 0px;
    font-weight: normal;
    text-transform: ;
}
.btn-default {
    color: #2ecc71;
    background-color: #ffffff;
    border-color: #ffffff;
}
</style>


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

#setwd("/Users/carmenlim/Dropbox/PhD/Cryptomarket/1_Dataset")
library(DT)
library(ggplot2)
library(knitr)
library(dplyr)
library(kableExtra)
library(plotly)
 
cry <- read.csv("cryptonia1.csv")
arg <- read.csv("agartha.csv")
toc <- read.csv("tochka.csv")
```

# 1. Cryptonia market
<hr>
<br>

- Established March/April 2019
- 12,600 drug listings
- 515 potential eliquid and vaping related listings

<br>
<br>

## Data Cleaning
<hr>
<br>

- `r 515 - nrow(cry)` listings were removed (e.g. if the description did not mention about the product to be used for vaping purposes)
- Total number of listings used in descriptive analysis below:  `r nrow(cry)`

<br>
<br>

## Results

<br>
<br>
<br>


### Dataset
<hr>
<br>

```{r, echo = FALSE}
datatable(cry[,c(1:2,4:5,9,10)])
```

<br>
<br>

### Number of vendors
<hr>
<br>

- Range of listings: (1-40)
- number of vendors: 94
- mean number of listings: 4.8 per seller

<br>
<br>

```{r , echo=FALSE}
q<- cry %>%  count(vendor,sort=TRUE)  
q

#w<- cry %>% group_by(vendor) %>% summarize(count=n())
#w

```
<br>
<br>

```{r, echo = FALSE}
ggplot(data = q, mapping= aes (x = n)) +
  geom_histogram(binwidth = 0.5)
```


### Types of products sold  
<hr>
<br>


```{r , echo=FALSE}
p <- cry %>%
  group_by(type.of.product) %>%
  summarise(n = n())

q<- ggplot(data = cry) + 
  geom_bar(mapping = aes(type.of.product)) + 
  ylim(0,200) + 
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```

### Listing categories 
<hr>
<br>

```{r , echo=FALSE}
p <- cry %>%
  group_by(drug.cat) %>%
  summarise(n = n())

q<- ggplot(data = cry) + 
  geom_bar(mapping = aes(drug.cat)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Origin of products
<hr>
<br>


```{r , echo=FALSE}
p <- cry %>%
  group_by(origin) %>%
  summarise(n = n())

q<- ggplot(data = cry) + 
  geom_bar(mapping = aes(origin)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Shipping destination
<hr>
<br>
<br>


```{r , echo=FALSE}
p <- cry %>%
  group_by(destination) %>%
  summarise(n = n())

q<-ggplot(data = cry) + 
  geom_bar(mapping = aes(destination)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```

<br>
<br>
<br>

# 2. Agartha market
<hr>
<br>

- Agartha Market is an Agora clone
- Established March/April 2019
- Most dangerous and deadly drugs are banned. 
- Banned products: Fentanyl, Heroin, Crystal Meth, Crack, MDPV, DNP, Scopalamine or synthetic THC/spice
- There are lots of synthetic stuff in this market
- ~70k drug listings (need to check)
- 1602 potential eliquid and vaping related listings

<br>
<br>


## Data Cleaning
<hr>
<br>

- `r 1602 - nrow(arg)` listings were removed (e.g. if the description did not mention about the product to be used for vaping purposes)
- Total number of listings used in descriptive analysis below:  `r nrow(arg)`

<br>
<br>


## Results

<br>
<br>
<br>


### Dataset
<hr>
<br>

```{r, echo = FALSE}
datatable(arg[,c(2,4,10, 7,5:6)])
```

<br>
<br>


### Number of vendors
<hr>
<br>

- Range of listings: 1-195
- number of vendors: 92
- mean number of listings: 15.9 per seller

<br>
<br>

```{r , echo=FALSE}
q<- arg %>%  count(vendor,sort=TRUE)  
q

```
<br>
<br>

```{r, echo = FALSE}
ggplot(data = q, mapping= aes (x = n)) +
  geom_histogram(binwidth = 0.5)
```


### Types of products sold  
<hr>
<br>


```{r , echo=FALSE}
p <- arg %>%
  group_by(type.of.product) %>%
  summarise(n = n())

q<- ggplot(data = arg) + 
  geom_bar(mapping = aes(type.of.product)) + 
  #ylim(0,200) + 
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Listing categories 
<hr>
<br>

```{r , echo=FALSE}
p <- arg %>%
  group_by(drug.detail) %>%
  summarise(n = n())

q<- ggplot(data = arg) + 
  geom_bar(mapping = aes(drug.detail)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```



### Origin of products
<hr>
<br>

```{r , echo=FALSE}
p <- arg %>%
  group_by(origin) %>%
  summarise(n = n())

q<- ggplot(data = arg) + 
  geom_bar(mapping = aes(origin)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Shipping destination
<hr>
<br>
<br>


```{r , echo=FALSE}
p <- arg %>%
  group_by(destination) %>%
  summarise(n = n())

q<-ggplot(data = arg) + 
  geom_bar(mapping = aes(destination)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```

<br>
<br>
<br>



# 3. Tochka market
<hr>
<br>

- Established 2015
- ~4k drug listings
- 88 potential eliquid and vaping related listings

<br>
<br>




## Data Cleaning
<hr>
<br>

- `r 88 - nrow(toc)` listings were removed (e.g. if the description did not mention about the product to be used for vaping purposes)
- Total number of listings used in descriptive analysis below:  `r nrow(toc)`

<br>
<br>

## Results

<br>
<br>
<br>


### Dataset
<hr>
<br>


```{r, echo = FALSE}
datatable(toc[,c(2,4,8,7,5,6)])
```

<br>
<br>



### Number of vendors
<hr>
<br>

- Range of listings: 1-21
- number of vendors: 20
- mean number of listings: 4.1 per seller

<br>
<br>

```{r , echo=FALSE}
q<- toc %>%  count(vendor,sort=TRUE)  
q

```
<br>
<br>

```{r, echo = FALSE}
ggplot(data = q, mapping= aes (x = n)) +
  geom_histogram(binwidth = 0.5)
```



### Types of products sold  
<hr>
<br>


```{r , echo=FALSE}
p <- toc %>%
  group_by(type.of.product) %>%
  summarise(n = n())

q<- ggplot(data = toc) + 
  geom_bar(mapping = aes(type.of.product)) + 
  #ylim(0,200) + 
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Listing categories 
<hr>
<br>

```{r , echo=FALSE}
p <- toc %>%
  group_by(drug.category) %>%
  summarise(n = n())

q<- ggplot(data = toc) + 
  geom_bar(mapping = aes(drug.category)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```



### Origin of products
<hr>
<br>

```{r , echo=FALSE}
p <- toc %>%
  group_by(origin) %>%
  summarise(n = n())

q<- ggplot(data = toc) + 
  geom_bar(mapping = aes(origin)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```


### Shipping destination
<hr>
<br>
<br>


```{r , echo=FALSE}
p <- toc %>%
  group_by(destination) %>%
  summarise(n = n())

q<-ggplot(data = toc) + 
  geom_bar(mapping = aes(destination)) + 
  coord_flip() +
  xlab(" ")
mytext=paste("No = ", p$n, "\n", sep = "")  
style( ggplotly(q), text=mytext, hoverinfo = "text" )
```

<br>
<br>
<br>