1 Zakład Bioinformatyki, Instytut Informatyki, Uniwersytet w Białymstoku
✉ Correspondence: Jarosław Kotowicz <j.kotowicz@uwb.edu.pl>
Praca z bazami danych ekonomicznych na przykładzie danych udostępnianych przez NBP
Podczytanie biblioteki rnbp
Złoto
Pobranie danych dotyczących ceny złota z ostatnich 93 dni get_last_n_goldprices oraz wyłuskanie tabeli $content.
gold.93 <- get_last_n_goldprices(93)
gold.93.df <- gold.93$content
Obejrzenie jak dane wyglądają (początkowe 12 wierszy)
Wyliczymy cenę uncji złota w PLN oraz w USD.
Wyznaczamy wagę uncji w gramach.
Bieżący kurs dolarów amerykańskich USD (funkcja get_current_exchangerate).
waluta_USD <- get_current_exchangerate("A", "USD")
USD.cena <- (waluta_USD$content[[4]])$mid
Kurs dolara amerykańskiego USD w ciągu ostatnich 93 dni.
waluta_USD.93 <- get_last_n_exchangerates("A", "USD", 93)
USD.cena.93 <-(waluta_USD.93$content)[[4]]$mid
Cena uncji złota w PLN
gold.93.df <- gold.93.df %>%
mutate(cena.ozt.PLN = cena*ozt)
Cana uncji złota w USD (cena.ozt.USD cena według bieżącego kursu i cena.ozt.USD.93 cena według kursu z dnia wyceny złota).
gold.93.df <- gold.93.df %>%
mutate(cena.ozt.USD = cena.ozt.PLN/USD.cena,
cena.ozt.USD.93 = cena.ozt.PLN/USD.cena.93)
Wykres zmian cen złota.
gold.93.df %>% ggplot(aes(x = data, y = cena)) +
geom_point(color = "gold") +
geom_line(aes(x = data, y = cena), stat="identity", color = "gold3")+
labs(title = "Kształtowanie się ceny złota w NBP o okresie ostatnich 93 dni",
x = "Data",
y = "Cena [PLN/g]",
caption = "Opracowanie własne na podstawie danych NBP.") +
theme(plot.title = element_text(hjust=0.5))
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)
Waluty
Pobieranie bieżącego kursu euro
waluta_EUR <- get_current_exchangerate("A", "EUR")
Co właściwie zostało pobrane.
[1] "nbp_api_response"
[1] 3
[1] "content" "path" "response"
waluta_EUR$content %>% length()
[1] 4
waluta_EUR$content %>% names
[1] "table" "currency" "code" "rates"
(waluta_EUR$content)$rates %>% class
[1] "data.frame"
(waluta_EUR$content)$rates %>% dim
[1] 1 3
(waluta_EUR$content)$rates
Tutaj ostateczny kurs średni euro
((waluta_EUR$content)$rates)$mid
[1] 4.5523
Jak to zrobić inaczej przy pomocy operatora odwołania do n-tego elementu listy [[n]]
((waluta_EUR[[1]])[[4]])[[3]]
[1] 4.5523
Można to mieszać
(waluta_EUR$content)[[4]]
((waluta_EUR$content)[[4]])[[3]]
[1] 4.5523
((waluta_EUR$content)[[4]])$mid
[1] 4.5523
Pobieranie n ostatnich kursów waluty
waluta_EUR.93 <- get_last_n_exchangerates("A", "EUR", 93)
(waluta_EUR.93$content)$rate
Wykonanie rysunku zmian kursu euro w okresie ostatnich 93 dni.
(waluta_EUR.93$content)$rate %>%
ggplot(aes(x = effectiveDate, y = mid)) +
geom_point(color = "blue") +
geom_line(aes(x = effectiveDate, y = mid), stat="identity", color = "blue")+
labs(title = "Kształtowanie się ceny Euro w NBP o okresie ostatnich 93 dni",
x = "Data",
y = "Cena euro [PLN]",
caption = "Opracowanie własne na podstawie danych NBP.") +
theme(plot.title = element_text(hjust=0.5))
![](data:image/png;base64,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)
Praca domowa
- Weź trzy waluty (GBP, USD, JPY), pobierz dane z ostatnich 93 dni, stwórz ramkę danych zwierającą daty, i kursy tych walut, narysuj na jednym wykresie zmienę ich kursu.
- Dla złota narysuj wykres zmian ceny w ciągu ostatnich 93 dni, z tym mają być dwie osi Y (na prawym i lewym końcu obszaru rysunku) na lewym brzegu ma być cena w PLN, a na prawym w USD.
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