Data Management

project <-read_csv("JHU_HighGroStocks_v15_2021_02_28.csv")

####Select data for figure and pivot to long

stock_score <- 
  project %>%  
  select(c(Stock,SA_Authors_Score,WallSt_Score,Quant_Score,Value_Grade,Growth,Profitability,Momentum))

stock_score <- pivot_longer(stock_score,-Stock,names_to="stat",values_to = "value")

#####NOTE: I did not standardize/normalize the data, I think you may have?

Making the Figure

###Pipe data and make figure
stock_score %>% 
  #####Select just a few stocks so the heat map isn't huge
  filter(Stock %in% c("ZM","SQ","SHOP","DT","CRWD","BILI")) %>%
  #####Here is the key function for releveling the factor
    mutate(stat=fct_relevel(stat,c("Value_Grade","Profitability","Quant_Score","SA_Authors_Score","WallSt_Score","Momentum","Growth"))) %>% 
  #####Make the figure - this is all your code
  ggplot(aes(x=stat,y=Stock,fill=value)) +
  labs(x="Seeking Alpha Rating Type",y="High Growth Stock", 
       title = "Top Growth Stocks and Ratings ", 
       subtitle="Source Seeking Alpha data  2_28_21") +
  labs(fill = "Rating")+
  scale_fill_viridis(discrete=FALSE) +
  geom_tile(color = "white")+
  theme(axis.text.x=element_text(angle=90,vjust=.5))