currency-rates.utf8

First load up the tidyverse

library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Then read in the data, rates.csv and name it rates

# ___ <- read_csv("data/___")
rates <- read_csv(here::here("data/rates.csv"))

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   date = col_date(format = "")
## )

## See spec(...) for full column specifications.

head(rates)

## # A tibble: 6 x 169
##   date         AED   AFN   ALL   AMD   ANG   AOA   ARS   AUD   AWG   AZN   BAM
##   <date>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-06-20  3.67  68.1  119.  481.  1.78  166.  16.1  1.32  1.80   1.7  1.76
## 2 2017-06-21  3.67  68.1  119.  480.  1.78  166.  16.2  1.32  1.79   1.7  1.75
## 3 2017-06-22  3.67  68.1  119.  481.  1.78  166.  16.1  1.33  1.79   1.7  1.75
## 4 2017-06-23  3.67  68.1  118.  479.  1.78  166.  16.2  1.32  1.80   1.7  1.75
## 5 2017-06-24  3.67  68.1  118.  479.  1.78  166.  16.2  1.32  1.80   1.7  1.75
## 6 2017-06-25  3.67  67.9  118.  479.  1.78  166.  16.1  1.32  1.80   1.7  1.75
## # … with 157 more variables: BBD <dbl>, BDT <dbl>, BGN <dbl>, BHD <dbl>,
## #   BIF <dbl>, BMD <dbl>, BND <dbl>, BOB <dbl>, BRL <dbl>, BSD <dbl>,
## #   BTC <dbl>, BTN <dbl>, BWP <dbl>, BYN <dbl>, BZD <dbl>, CAD <dbl>,
## #   CDF <dbl>, CHF <dbl>, CLF <dbl>, CLP <dbl>, CNH <dbl>, CNY <dbl>,
## #   COP <dbl>, CRC <dbl>, CUC <dbl>, CUP <dbl>, CVE <dbl>, CZK <dbl>,
## #   DJF <dbl>, DKK <dbl>, DOP <dbl>, DZD <dbl>, EGP <dbl>, ERN <dbl>,
## #   ETB <dbl>, EUR <dbl>, FJD <dbl>, FKP <dbl>, GBP <dbl>, GEL <dbl>,
## #   GGP <dbl>, GHS <dbl>, GIP <dbl>, GMD <dbl>, GNF <dbl>, GTQ <dbl>,
## #   GYD <dbl>, HKD <dbl>, HNL <dbl>, HRK <dbl>, HTG <dbl>, HUF <dbl>,
## #   IDR <dbl>, ILS <dbl>, IMP <dbl>, INR <dbl>, IQD <dbl>, IRR <dbl>,
## #   ISK <dbl>, JEP <dbl>, JMD <dbl>, JOD <dbl>, JPY <dbl>, KES <dbl>,
## #   KGS <dbl>, KHR <dbl>, KMF <dbl>, KPW <dbl>, KRW <dbl>, KWD <dbl>,
## #   KYD <dbl>, KZT <dbl>, LAK <dbl>, LBP <dbl>, LKR <dbl>, LRD <dbl>,
## #   LSL <dbl>, LYD <dbl>, MAD <dbl>, MDL <dbl>, MGA <dbl>, MKD <dbl>,
## #   MMK <dbl>, MNT <dbl>, MOP <dbl>, MRO <dbl>, MUR <dbl>, MVR <dbl>,
## #   MWK <dbl>, MXN <dbl>, MYR <dbl>, MZN <dbl>, NAD <dbl>, NGN <dbl>,
## #   NIO <dbl>, NOK <dbl>, NPR <dbl>, NZD <dbl>, OMR <dbl>, PAB <dbl>, …

Now plot the data:

on the x axis put date
on the y azis put AUD

ggplot(rates, 
       # aes(x = ___, 
       #     y = ___)) + 
       aes(x = date,
           y = AUD)) +
  geom_line()

Make a subset of the data that includes the following columns:

date
AUD
GBP
JPY
CNY
CAD

rates_sub <- rates %>% 
  # select(___, 
  #        ___, 
  #        ___, 
  #        ___, 
  #        ___, 
  #        ___)
  select(date,
         AUD,
         GBP,
         JPY,
         CNY,
         CAD)

rates_sub

## # A tibble: 24 x 6
##    date         AUD   GBP   JPY   CNY   CAD
##    <date>     <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 2017-06-20  1.32 0.792  111.  6.83  1.33
##  2 2017-06-21  1.32 0.789  111.  6.83  1.33
##  3 2017-06-22  1.33 0.789  111.  6.83  1.32
##  4 2017-06-23  1.32 0.786  111.  6.84  1.33
##  5 2017-06-24  1.32 0.786  111.  6.84  1.33
##  6 2017-06-25  1.32 0.785  111.  6.83  1.33
##  7 2017-06-26  1.32 0.786  112.  6.84  1.33
##  8 2017-06-27  1.32 0.780  112.  6.81  1.32
##  9 2017-06-28  1.31 0.773  112.  6.80  1.30
## 10 2017-06-29  1.30 0.769  112.  6.79  1.30
## # … with 14 more rows

Now pivot the data into long form, where:

names_to is currency
values_to is rate

rates_long <- rates_sub %>%
  pivot_longer(cols = -date,
               names_to = "currency", 
               values_to = "rate")
               # names_to = ___, 
               # values_to = ___)
rates_long

## # A tibble: 120 x 3
##    date       currency    rate
##    <date>     <chr>      <dbl>
##  1 2017-06-20 AUD        1.32 
##  2 2017-06-20 GBP        0.792
##  3 2017-06-20 JPY      111.   
##  4 2017-06-20 CNY        6.83 
##  5 2017-06-20 CAD        1.33 
##  6 2017-06-21 AUD        1.32 
##  7 2017-06-21 GBP        0.789
##  8 2017-06-21 JPY      111.   
##  9 2017-06-21 CNY        6.83 
## 10 2017-06-21 CAD        1.33 
## # … with 110 more rows

Now plot the currency rates

with date on the x axis
with rate on the y axis
And faceted by each currency

ggplot(rates_long, 
       #% aes(x = ___, 
       #%     y = ___)) + 
       aes(x = date,
           y = rate)) +
  geom_line() +
  facet_wrap(~currency, # facetted by currency
             ncol = 1, 
             scales = "free_y")

What do you learn?

Describe the similarities and differences between the currencies.