DV_Final Project

Video Presentation: https://youtu.be/r0bgZfXPkMI

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
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data <- read.csv("SMU Data/DV/CBBTCUSD.csv")
summary(data)

##      DATE              CBBTCUSD      
##  Length:1828        Min.   :  211.2  
##  Class :character   1st Qu.:  737.6  
##  Mode  :character   Median : 4738.7  
##                     Mean   : 5035.9  
##                     3rd Qu.: 8256.0  
##                     Max.   :19650.0

data$date <- as.Date(data$DATE)
# Plot
data %>%
  #tail(100) %>%
  ggplot(aes(x=date, y=CBBTCUSD)) +
    geom_line() +
    geom_point() +
  geom_smooth(method = lm)

## `geom_smooth()` using formula 'y ~ x'

library(hrbrthemes)

#library(extrafont)
#font_import()
#loadfonts(device = "win")

data %>%
  tail(10) %>%
  ggplot( aes(x=date, y=CBBTCUSD)) +
    geom_line( color="grey") +
    geom_point(shape=21, color="black", fill="#69b3a2", size=6) +
    theme_ipsum() +
    ggtitle("Evolution of bitcoin price")

data %>% 
  ggplot( aes(x=date, y=CBBTCUSD)) +
    geom_line(color="#69b3a2") +
    ylim(0,22000) +
    annotate(geom="text", x=as.Date("2017-01-01"), y=20089, 
             label="Bitcoin price reached 20k $\nat the end of 2017") +
    annotate(geom="point", x=as.Date("2017-12-17"), y=20089, size=10, shape=21, fill="transparent") +
    geom_hline(yintercept=5000, color="orange", size=1) +
    theme_ipsum()

library(tidyverse)

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## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
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library(hrbrthemes)
library(kableExtra)

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options(knitr.table.format = "html")
library(babynames)
library(viridis)

## Loading required package: viridisLite

library(DT)
library(plotly)

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library(dplyr)
library(lubridate)

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## Attaching package: 'lubridate'

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library(patchwork)
library(viridis)

# Load dataset from github
dataETH <- read.csv("SMU Data/DV/CBETHUSD.csv")
dataLTC <- read.csv("SMU Data/DV/CBLTCUSD.csv")
dataBCH <- read.csv("SMU Data/DV/CBBCHUSD.csv")
dataBTC <- read.csv("SMU Data/DV/CBBTCUSD.csv")

dataETH$date <- as.Date(dataETH$DATE)
dataLTC$date <- as.Date(dataLTC$DATE)
dataBCH$date <- as.Date(dataBCH$DATE)
dataBTC$date <- as.Date(dataBTC$DATE)

dataETH['Coin'] = 'ETH'
dataLTC['Coin'] = 'LTC'
dataBCH['Coin'] = 'BCH'
dataBTC['Coin'] = 'BTC'

#change column name to value

names(dataETH)[2] <- "Value"
names(dataLTC)[2] <- "Value"
names(dataBCH)[2] <- "Value"
names(dataBTC)[2] <- "Value"

#by coin


allcoins <- rbind(dataETH, dataLTC, dataBCH, dataBTC)
a1 <- allcoins

#[order(as.Date(allcoins$DATE, format="%Y-%m-%d")),]

# Plot
a1 %>%
  ggplot(aes(x=date, y=Value, group=Coin, color=Coin)) +
    geom_area() +
    scale_fill_viridis(discrete = TRUE) +
    theme(legend.position="none") +
    ggtitle("Crypto Trend") +
    theme_ipsum() +
    theme(
      legend.position="none",
      panel.spacing = unit(1, "lines"),
      strip.text.x = element_text(size = 8),
      plot.title = element_text(size=10)
    ) +
    facet_wrap(~Coin)

#Referencing from https://bookdown.org/content/b298e479-b1ab-49fa-b83d-a57c2b034d49/evolution.html