In MichaelGurkov/LearningMaterials: What the Package Does (One Line, Title Case)

library(Quandl)

Quandl.api_key(Sys.getenv("Q_key"))

library(tidyverse)

library(lubridate)

library(readxl)

library(xts)

codes = tribble(
  ~name,~code,
  "gdp_pc_nominal_lcu","NGDPPC",
  "gdp_pc_real_lcu","NGDPRPC",
  "gdp_pc_nominal_usd","NGDPDPC",
  "gdp_total_nominal_lcu", "NGDP",
  "gdp_total_real_lcu", "NGDP_R",
  "gdp_total_nominal_usd", "NGDPD"
)

df = map2(codes$name,codes$code, function(name,code){

  temp_df = Quandl(paste0("ODA/ISR_", code),type = "raw")

  temp_df = temp_df %>% 
    rename_all(tolower) %>% 
    rename(!!name := 2)


})

df = df %>% 
  reduce(inner_join, by = "date")

df = df %>% 
  pivot_longer(-date) %>% 
  separate(col = name,
           into = c("indicator","population","deflator","currency"),
           sep = "_")

rm(codes)

gdp_components_table = tribble(~component, ~code,
        "consumption","OECD/SNA_TABLE1_ISR_P3_CPC",
        "government","OECD/SNA_TABLE1_ISR_P3S13_CPC",
        "capital","OECD/SNA_TABLE2_ISR_P5S1_CPC",
        "external_balance","OECD/SNA_TABLE1_ISR_B11_CPC")

gdp_components_df = map2(gdp_components$code, gdp_components$component,
                     function(temp_code, temp_name){
                       temp_df = Quandl(temp_code, type = "raw")
                       temp_df = temp_df %>% 
                         mutate(Date = as_date(Date)) %>% 
                         set_names(c("date", temp_name))
                       return(temp_df)
                       }) %>%
  reduce(inner_join, "date") %>% 
  pivot_longer(-date)

rm(gdp_components_table)

gdp_components_df = gdp_components_df %>%
  mutate(name = factor(
    name,
    levels = c("consumption", "government", "capital", "external_balance"),
    labels = c("Private consumption", "Government", "Investment",
              "External Balance (Net Export)")
  ))

df %>% 
  filter(year(date) >= 1995) %>% 
  filter(year(date) < year(Sys.Date())) %>%
  filter(population == "total") %>% 
  filter(currency == "lcu") %>% 
  filter(deflator == "nominal") %>% 
  ggplot(aes(x = as_factor(year(date)), y = value)) +
  geom_col() + 
  scale_y_continuous(labels = scales::comma_format()) + 
  xlab(NULL) + ylab(NULL) + ggtitle("Israel GDP (billions of ILS)") + 
  theme(legend.title = element_blank())

price_converter = df %>% 
  filter(year(date) == 1995) %>% 
  filter(population == "total") %>% 
  filter(currency == "lcu") %>% 
  select(deflator, value)

price_converter = price_converter$value[price_converter$deflator == "real"] /
  price_converter$value[price_converter$deflator == "nominal"]


df %>% 
  filter(year(date) >= 1995) %>% 
  filter(year(date) < year(Sys.Date())) %>%
  filter(population == "total") %>% 
  filter(currency == "lcu") %>% 
  select(-c(population, currency, indicator)) %>% 
  mutate(value = if_else(deflator == "real",
                         value / price_converter, value)) %>%
  pivot_wider(names_from = deflator) %>% 
  mutate(nominal = nominal - real) %>% 
  pivot_longer(-date, names_to = "deflator") %>% 
  ggplot(aes(x = as_factor(year(date)), y = value, fill = deflator)) +
  geom_col() + 
  scale_fill_manual(values = c("lightblue", "gray30")) +
  scale_y_continuous(labels = scales::comma_format()) + 
  xlab(NULL) + ylab(NULL) + ggtitle("Israel GDP (billions of ILS)") + 
  theme(legend.title = element_blank(), legend.position = "right")

rm(price_converter)

df %>%
  filter(year(date) >= 1995) %>%
  filter(year(date) < year(Sys.Date())) %>%
  filter(population == "pc") %>%
  select(-c(population, indicator)) %>%
  group_by(deflator, currency) %>%
  arrange(date) %>%
  mutate(diff = value / lag(value) - 1) %>%
  mutate(
    indicator = case_when(
      deflator == "nominal" & currency == "usd" ~ "USD",
      deflator == "nominal" &
        currency == "lcu" ~ "Nominal",
      deflator == "real" &
        currency == "lcu" ~ "Real"
    )
  ) %>% 
ggplot(aes(
  x = as_factor(year(date)),
  y = diff,
  fill = (diff > 0)
)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap( ~ indicator, scales = "free", ncol = 1) +
  xlab(NULL) + ylab(NULL) + ggtitle("Changes in GDP per capita") +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5))

gdp_components_df %>% 
  filter(date >= as_date("2000-01-01")) %>% 
  group_by(date) %>% 
  mutate(share = value / sum(value),.groups = "drop") %>% 
  ggplot(aes(x = date, y = share, fill = name)) + 
  geom_col() + 
  scale_y_continuous(labels = scales::percent_format()) + 
  xlab(NULL) + ylab(NULL) + ggtitle("GDP components") + 
  scale_fill_viridis_d(direction = -1) + 
  theme(legend.title = element_blank())

MichaelGurkov/LearningMaterials documentation built on July 9, 2022, 5:17 p.m.