Tidyverse_Visualization_Assignemnt_problem2

#upload libraries
library(ggplot2)
library(datasets)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(skimr)
library(dplyr)
library(knitr)
library(directlabels)
library(cowplot)

## 
## Attaching package: 'cowplot'
## 
## The following object is masked from 'package:lubridate':
## 
##     stamp

library(ggrepel)
library(dplyr)
library(forcats)

#upload the fastfood_sales document
d <- read.csv("data_fastfood_sales.csv")

#check the data file
head(d)

##      restaurant average_sales us_sales num_company_stores num_franchised_stores
## 1        Subway        416.86 10800.00                  0                 25908
## 2     Mcdonalds       2670.32 37480.67                842                 13194
## 3     Starbucks        945.27 13167.61               8222                  5708
## 4 Dunkin Donuts        733.13  9192.00                  0                 12538
## 5     Pizza Hut        900.00  5510.84                 96                  7426
## 6   Burger King       1387.81 10028.32                 50                  7196
##   unit_count
## 1      25908
## 2      14036
## 3      13930
## 4      12538
## 5       7522
## 6       7266

dim(d)

## [1] 19  6

colnames(d)

## [1] "restaurant"            "average_sales"         "us_sales"             
## [4] "num_company_stores"    "num_franchised_stores" "unit_count"

#take out the required variables in the dataset
restaurant = d[, "restaurant"]
avg_sales = d[, "average_sales"]
us_sales = d[, "us_sales"]
num_comp_stores = d[, "num_company_stores"]
num_franch_stores = d[, "num_franchised_stores"]
unit_count = d[, "unit_count"]


avg_sales <- d$average_sales * 1000
aes(reorder(Name, Value), Value)

## Aesthetic mapping: 
## * `x` -> `reorder(Name, Value)`
## * `y` -> `Value`

d$average_sales

##  [1]  416.86 2670.32  945.27  733.13  900.00 1387.81 1500.00 1610.00 1000.00
## [10]  815.00 1200.00 1250.00 1130.00  968.45  796.97  360.72 1940.00 1543.00
## [19] 1440.19

plotdata <- d %>% select(average_sales, restaurant) %>% 
  group_by(restaurant) %>% mutate(avg_sale = average_sales * 1000, digits = 0)%>% mutate(round(avg_sale, digits = 0)) %>% 
   mutate(as = reorder(avg_sale, restaurant))

## Warning: There were 19 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `as = reorder(avg_sale, restaurant)`.
## ℹ In group 1: `restaurant = "Arbys"`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 18 remaining warnings.

# Create factor column with decreasing order TRUE
plotdata$restaurant <- factor(plotdata$restaurant, levels = plotdata$restaurant[order(plotdata$avg_sale, decreasing = FALSE)])

p <- ggplot(plotdata, aes( y = round(avg_sale, digits = 0), x= restaurant))
p + geom_bar(stat = "identity", position = 'dodge')+
  coord_flip()+
  ylab("Average sales per unit store (in thousands)") +
  xlab("Restaurant")+
   scale_y_continuous(labels = scales::label_dollar()) +
  theme_classic() +
    geom_text(aes(label = scales::dollar(round(average_sales,digits = 0)), hjust = 0))