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Merged
ytyky merged 2 commits into from
May 6, 2022
Merged

Datatransformation #27

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fb41b86
rewrite data transformation
ytyky May 6, 2022
7bced1c
add missing "+" sign
ytyky May 6, 2022
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44 changes: 34 additions & 10 deletions 03-cleaning.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -14,26 +14,52 @@ USDT<-USDT[(nrow(USDT)-370):(nrow(USDT)-4),]

## Combine the Data

we will combine the price of each cryptocurrency into one table df.close
we combine the price of each cryptocurrency into one table, df.close

```{r}
df.close<-data.frame(cbind(BTC[,1],BTC[,2],BNB[,2],ETH[,2],USDC[,2],USDT[,2]))
colnames(df.close)<-c("Date","BTC","BNB","ETH","USDC","USDT")
head(df.close)
```

Here, we transform the table df.close into the new table df.close1 which will be helpful for plotting.

| Date | BTC | BNB | ETH | USDC | USDT | Price |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| record |

where BTC is Bitcoin, BNB is Binance Coin, ETH

* Date: date of the crypto record
* BTC: price of Bitcoin
* BNB: price of Binance Coin
* ETH: price of Ethereum
* USDC: price of USD Coin
* USDT: price of USD Tether

(all price are in USD)

This table can help us quickly find price of all 5 cryptos we selected given date information.

Then we create a comprehensive tidy data table include all financial information, df.crypto:

| date | cryptocurrency | price | volume | market_cap |
| :----: | :----: | :----: | :----: | :----: |
| record |


```{r}
df.close1<- gather(df.close[,2:6], cryptocurrency, price)
df.close1$price<-as.numeric(df.close1$price)
head(df.close1)
```

## Calculate the Return and Volatility

Firstly, we calculate the return of each date by the formula: (final price/ initial price)-1.
Then we calculate the 15 days volatility of each crypto by calculating the stand deviation of 15 days return, and multiply square root of 365. (For stocks, the volatility is stand deviation multiply square root of 250, but the cryptocurrency can be traded every day, so we choose square root of 365 )
Firstly, we calculate the return of each date by the formula:
$$\frac{final\: pric e - initial\: price}{initial\: price}$$
Then we calculate the 15 days volatility of each crypto by calculating the stand deviation of 15 days return, and multiply square root of 365.

$$sd(15 \: days \: return) \cdot \sqrt{365}$$

(For stocks, the volatility is stand deviation multiply square root of 250, but the cryptocurrency can be traded every day, so we choose square root of 365 )

```{r}
# volatility
Expand Down Expand Up @@ -113,9 +139,9 @@ for (i in 16:nrow(USDT)){
}
```

## Combine All the Value into One table
## Combine All the Value into One Table

Finally, we combine the volatility, return, and market capitalization and volume with the df.close1, creating the final table df.crpyto
Finally, we add the volatility, return, into df.crpyto, plus month and year column seperated from date in case we want to group data. The fianl format of our table df.crypto:

```{r}
df.close1$Date<-rep(df.close$Date,5)
Expand All @@ -132,5 +158,3 @@ df.crypto$year <- strftime(df.crypto$Date, "%Y") # Create year column
df.crypto$month <- strftime(df.crypto$Date, "%m")
head(df.crypto)
```


4 changes: 2 additions & 2 deletions 05-results.Rmd
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Expand Up @@ -93,7 +93,7 @@ ggplot(rbind(df.crypto[df.crypto$cryptocurrency=="USDC",],
facet_wrap(~cryptocurrency,scales="free") +
scale_y_continuous(labels = unit_format(unit = "B", scale = 1e-9)) +
xlab("date") +
ylab("volume in billions")
ylab("volume in billions") +
theme_grey(16)
```

Expand All @@ -111,7 +111,7 @@ ggplot(rbind(df.crypto[df.crypto$cryptocurrency=="USDC",],
geom_bar(position="stack", stat="identity") +
scale_y_continuous(labels = unit_format(unit = "B", scale = 1e-9)) +
ylab("marketcap in billions") +
xlab("date")
xlab("date") +
theme_grey(16)
```

Expand Down