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Crypto Currencies

Plotting Crypto Currencies using the `tidyverse`.

Questions

1. Load object `data/06-plotting-06.rda`
2. Plot Time Series line chart using `ggplot()`
• Use `gather()` reformat the data.frame from wide to long format using `Date` as id variable.
• Use `facet_grid()` to plot to different panels, adjust parameter `scales`.
• Remove x-axis label (`xlab()`) for each plot, set y-axis label to USD.
3. What do we observe? Hint: Zoom to time frame where no `NA's` are present (e.g. from `2017-07-01`) or consider `na.omit()` before step 2.
4. Plot relative performances by indexing each coin to 100 at the start, adjust y-axis as necessary. Use `scale_y_continuous(labels = scales::percent)` to format y-axis as percent.
5. Which coin performed best in percentage terms? Use absolute performance numbers from 4. and show the absolute performance numbers for each coin on the last observation day.
6. Select performances for the first day of each month output the resulting table. Hint Use `filter()` with `floor_date` to filter rows for the start of each month. Bonus: Calculate the MOM returns using `group_by(coin)` and `mutate()` to calculate the difference `price_adj - lag(price_adj)`.

``load(file = "data/06-plotting-06.rda")``

4. Performance Plot

Hint

1. Extract values from `2017-07-01` into a separate data.frame and do a left join by `coin`.
2. Divide prices by prices from `2017-07-01`

5. Performance Table

Absolute performance on last date:

``````##         Date.x   BTCUSD   ETHUSD    XRPUSD
## 115 2017-10-23 2.527511 1.128503 0.8322165``````

Absolute performance on first month of day:

``````## # A tibble: 4 x 4
##   Date.x     BTCUSD ETHUSD XRPUSD
##   <date>      <dbl>  <dbl>  <dbl>
## 1 2017-07-01   1     1      1
## 2 2017-08-01   1.16  0.886  0.769
## 3 2017-09-01   2.09  1.54   1.10
## 4 2017-10-01   1.89  1.20   0.876``````

MOM Performance

``````## # A tibble: 4 x 4
##   Date.x     BTCUSD ETHUSD XRPUSD
##   <date>      <dbl>  <dbl>  <dbl>
## 1 2017-07-01 NA     NA     NA
## 2 2017-08-01  0.161 -0.114 -0.231
## 3 2017-09-01  0.926  0.652  0.328
## 4 2017-10-01 -0.196 -0.333 -0.221``````