Wrangle Data

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Bitcoin Price Data

Wide to long

merge <- readRDS("data/bitcoin_raw.rds")
long  <-  gather(merge, variable, measurement, 
                 Preis:Nachrichtenartikel, factor_key=TRUE)

Drop one day only contained in google data

long %>% 
  filter(date != "2018-02-06") ->
long

Prepare special variable values (min, max, end, quartiles)

mins <- group_by(long, variable) %>% slice(which.min(measurement)) # max value ts
maxs <- group_by(long, variable) %>% slice(which.max(measurement)) # min value ts
ends <- group_by(long, variable) %>% filter(date == max(date))  # last value of ts
quarts <- long %>% group_by(variable) %>% # quartiles for grey bar
  summarize(quart1 = quantile(measurement, 0.25, na.rm = TRUE),
            quart2 = quantile(measurement, 0.75, na.rm = TRUE)) %>%
  right_join(long, by = "variable")

Create index values

long %>% 
  filter(complete.cases(.), variable != "close_ratio") %>% 
  group_by(variable) %>% 
  mutate(max = max(measurement), index = measurement/max) %>% 
  filter(variable == "Preis" | variable == "Google-Suchen") ->
long.index

Bitcoin Region

Prepare for ggplot Map

#generate id
data(World)
World@data$id_name <- seq(1,length(World@data$name)) 
# change spacial data frame to data frame
map_draw <- fortify(World, region = "id_name")

Add World@data

Through the fortify command, some information from the dataset of the spacial data frame ist lost. In this step the World@data part gets merged to the map_draw dataframe by hand.

map_draw$id <- as.numeric(map_draw$id)
data <- World@data
 
#Add the stats to the map data
map_draw <- left_join(map_draw, World@data, by = c('id'='id_name')) %>% 
      left_join(gg_oct17_world, by = c("name" = "Country")) %>% 
      mutate(bitcoin = ifelse(is.na(Bitcoin), 0, Bitcoin))