tech_stocks_covid.R

library(scales)
library(ggrepel)
library(ggthemes)
library(tidyverse)
library(tidyquant)
library(lubridate)

stocks <- tq_get(c('FB', 'NFLX', 'AAPL', 'GOOG', 'AMZN'), 
                 get = 'stock.prices', 
                 from = Sys.Date() - years(1)) %>% 
    select(date, adjusted, symbol)

index_date <- as.Date('2020-01-02')

index_value <- stocks %>% 
    filter(date == index_date) %>% 
    select(symbol, base = adjusted)
    
plot_df <- stocks %>% 
    filter(date >= '2020-01-01') %>% 
    left_join(index_value, by = 'symbol') %>% 
    mutate(adjusted = round(adjusted / base * 100, digits = 2),
           symbol = factor(symbol, levels = c('FB', 'AMZN', 'AAPL', 'NFLX', 'GOOG')))

label_values <- plot_df %>% 
    mutate(date_group = case_when(date == max(date) ~ 'latest',
                                  date == index_date ~ 'first',
                                  TRUE ~ as.character(NA))) %>% 
    filter(!is.na(date_group)) %>% 
    group_by(symbol) %>% 
    mutate(pct_change = (adjusted[date_group == 'latest'] - adjusted[date_group == 'first']) / adjusted[date_group == 'first']) %>% 
    ungroup() %>% 
    filter(date_group == 'latest') %>% 
    mutate(prefix = ifelse(as.numeric(pct_change) > 0, '+', ''),
           label = paste0(symbol, ': ', prefix, scales::percent(pct_change, accuracy = 1), ' YTD'))

plot_df %>% 
    ggplot(aes(x = date, y = adjusted, color = symbol)) +
    geom_line(size = 1) +
    geom_text_repel(data = label_values, aes(label = label), size = 3, nudge_x = 30, show.legend = FALSE) +
    labs(x = '', y = '', color = '',
         title = 'Not all FAANGs are created equal',
         subtitle = 'Netflix and Amazon are weathering COVID-19 far better than the rest of big tech',
         caption = 'Data through Apr 24, 2020. Stock prices indexed to 100 as of Jan 2, 2020') +
    scale_x_date(limits = c(as.Date(NA), as.Date('2020-06-01'))) +
    theme_economist()