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install.packages("ggplot2")
library(ggplot2)

ggplot(data = data) +
  aes(x = num_orders) +
  geom_density(adjust = 1, fill = "#0c4c8a") +
  theme_minimal()

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ggplot(data = data) +
  aes(x = num_orders, y = num_orders) +
  geom_violin(scale = "area", adjust = 1, fill = "#0c4c8a") +
  theme_minimal()

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ggplot(data = data) +
  aes(x = checkout_price, y = base_price) +
  geom_point(color = "#1f9e89") +
  theme_minimal()

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library(dplyr)
joined_data <- left_join(data,fc,by="center_id")

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data %>% 
  select(center_type,num_orders) %>% 
  filter(center_type=="TYPE_A") %>%
  summarise(avg_A=mean(num_orders))

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data %>% 
  unite_(.,"email_home",c("emailer_for_promotion","homepage_featured")) %>% 
  head()

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str_c("x", "y")
> "xy" #output

str_c("x", "y", "z")
>"xyz"  #output

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x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)

> "App" "Ban" "Pea"   #output

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center <- factor(data$center_type) 
levels(center) 
fct_count(center) #counts the number of categories

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install.packages("lubridate")
library(lubridate)
dates <- c("January 11,2019" , "September 12, 2018", "April 1, 2019")
dates <- mdy(dates)