San_Francisco_Boba_Tea-Analysis-with-R-Programming

Data Wrangling and Visualization with R Programming

Installing the packages, setting up Rstudio cloud and wrangling packages

install.packages("tidyverse") library(tidyverse) install.packages("dplyr") library("dplyr")

Importing the csv dataset into R

read.csv("San Francisco Boba Tea Shop.csv")

saving the dataset

Boba_tea<- read.csv("San Francisco Boba Tea Shop.csv")

checking the structure and summary of the columns

str(Boba_tea) head(Boba_tea) library("ggplot2")

Updating the tidyverse packages

tidyverse_update() update.packages()

create folder to keep your file

dir.create("Data Cleaning") dir.create("Data Viz") file.copy("Data_VIsuaalization.R", "Data viz")

Cleaning Data after installing packages for cleaning

install.packages("here") library("here") install.packages("skimr") library("skimr") install.packages("janitor") library("janitor") library("dplyr")

Working with pipes

install.packages("dplyr") library(dplyr)

filtering dataset

filtered_boba <- filter(Boba_tea,rating ==5) View(filtered_boba)

sorting and arranging dataset

arrange(filtered_boba, city)

Sorting the rating by mean, max summary

Boba_tea %>% 
  group_by(rating) %>% 
  summarize(mean_rating = mean(rating))  
  

Boba_tea %>% 
  group_by(city) %>% 
  summarize(max_rating = max(rating))  


Boba_tea %>% 
  group_by(city, name) %>% 
  summarize(max_rating = max(rating), mean_rating = mean(rating))  

applying pipes for filtering and sorting the top rated tea shops and top cities

Filtered_BR5 <- Boba_tea %>% filter(rating>4.5) %>% arrange(rating) View(Filtered_BR5)

Filter_id <- Boba_tea %>% filter(rating>2.5) %>% arrange(id) View(Filter_id)

using pipes to find the mean rating per city above 4.0 rating

Filter_Good_Rating_City <- Boba_tea %>% filter(rating>3.5) %>% group_by(city) %>% summarize(mean_rating = mean(rating, na.rm = T),.group="Good & Excellent Rating") View(Filter_Good_Rating_City)

Separate the Longitute and latitude

separate(Boba_tea, lat.long, into = c('lat', 'long'), sep '-')

code for visualization

ggplot(data = Boba_tea) + geom_bar(mapping = aes(x = rating, color = rating))

Tea shop count with minimum of 4.0

Boba_tea %>% filter(rating>=4) %>% ggplot(aes(x=name)) + geom_bar(mapping = aes(x = rating), colour = "red") + labs(title = "Tea Shop Count with at least rating of 4")