booksales

Book Sales Data

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

data <- read.csv("sales2019.csv")

#colnames(data)
#glimpse(data)
rm_review <- data%>%
  filter(!(is.na(user_submitted_review)))
# na_review <- data%>%
#   filter(is.na(user_submitted_review))
# num_na <- length(na_review[["user_submitted_review"]])

rm_purchase <- data%>%
  filter(!(is.na(total_purchased)))
avg_purchase <- as.integer(mean(rm_purchase[["total_purchased"]]))

fixed_purchase <- rm_review%>%
  mutate(new_total_purchased = if_else (is.na(total_purchased),avg_purchase,total_purchased)
  )

new_review <- fixed_purchase%>%
  mutate(new_review = case_when(
    str_detect(user_submitted_review,"Awesome") ~ "positive",
    str_detect(user_submitted_review,"okay") ~ "positive",
    str_detect(user_submitted_review,"better book") ~ "positive",
    str_detect(user_submitted_review,"OK") ~ "positive",
    str_detect(user_submitted_review,"not") ~ "negative",
    str_detect(user_submitted_review,"Hated") ~ "negative",
    str_detect(user_submitted_review,"other") ~ "negative",
    str_detect(user_submitted_review,"learned") ~ "positive",
    TRUE ~ "no review"
     ))

# dateformat <- function(str){
#   date <- mdy(str)
#   return (date)
# }
library(lubridate)

## 
## Attaching package: 'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

standard_date <- mdy(new_review$date)
  
new_date <- new_review %>%
  mutate(new_date = standard_date)
 #mutate(new_date = unlist(map(date,mdy)))   #result is number instead of a date format??
   # mutate(new_date = map(date, dateformat))


groupby2 <- new_date%>%
  mutate(bigday = if_else(new_date< '2019-7-1',"before","after")) #18078 equals to 2019/7/1
  #mutate(bigday = if_else(new_date<18078,"before","after")) #18078 equals to 2019/7/1

program_effect <- groupby2 %>%
  group_by(bigday)%>%
  summarise(sales = sum(new_total_purchased))

individual <- groupby2 %>%
  filter (customer_type=="Individual")

individual_program <- individual %>%
  group_by(bigday)%>%
  summarise(sales = sum(new_total_purchased))

business_program <- groupby2 %>%
  filter (customer_type=="Business")%>%
  group_by(bigday)%>%
  summarise(sales = sum(new_total_purchased))

positive_program <- groupby2 %>%
  filter (new_review=="positive")%>%
  group_by(bigday)%>%
  summarise(positive_num = length(new_review))

negative_program <- groupby2 %>%
  filter (new_review=="negative")%>%
  group_by(bigday)%>%
  summarise(positive_num = length(new_review))

summary(positive_program)

##     bigday           positive_num 
##  Length:2           Min.   :1128  
##  Class :character   1st Qu.:1130  
##  Mode  :character   Median :1131  
##                     Mean   :1131  
##                     3rd Qu.:1132  
##                     Max.   :1134

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