Introduction to dplyr 5.48 KB
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################################################################################
## This code is property of Data Science Dojo
## Copyright (C) 2017~2018
##
## Objective: Manipulate and visualize data using R
## Please install "dplyr" package: install.packages("dplyr")
## Please install "ggplot2" package: install.packages("ggplot2")
################################################################################
# Script for following along in Introduction to dplyr
# Copy-paste line by line or use the "Run" button in R Studio
#Set the working directory, example: setwd("directory/dataset folder")

install.packages("dplyr")
install.packages("ggplot2")
                 
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library(dplyr)
library(ggplot2)
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#Reading the dataset from the working directory. 
#Setting string values as characters
#loading the greek characters
wine = read.csv("wine.csv", stringsAsFactors = FALSE, encoding = 'UTF-8')
                 
View (wine)
                 
#Removing columns from dataset
wine = wine[,-c(1,3)]
                 
#Creating a dataset by counting all observations grouped by country and then creating a new variable called count
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wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count))
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#Creating a new variable which contains the top 10 countries
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selected_countries = wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count)) %>% top_n(10) %>% select(country)
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selected_countries
                 
                 
#Changing the format from data frame to vector as.character referencing the country column
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selected_countries = as.character(selected_countries$country)
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class(selected_countries)
                 
#Subsetting data selecting top ten countries and their points from wine
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select_points=wine %>% filter(country %in% selected_countries) %>% select(country, points) %>% arrange(country)
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#Scatterplot with smooth line
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ggplot(wine, aes(points,price)) + geom_point() + geom_smooth()
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#Boxplot between country and points, reordered by median of points. Center aligning the Title of the boxplot
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ggplot(select_points, aes(x=reorder(country,points,median),y=points)) + geom_boxplot(aes(fill=country)) + xlab("Country") + ylab("Points") + ggtitle("Distribution of Top 10 Wine Producing Countries") + theme(plot.title = element_text(hjust = 0.5))
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#Filter by countries that do not appear on the selected_countries dataset
wine %>% filter(!(country %in% selected_countries)) %>% group_by(country) %>%  summarize(median=median(points)) %>% arrange(desc(median))
                 
#Creating a new variable called top using country and points to rate them based on points
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top=wine %>% group_by(country) %>% summarize(median=median(points)) %>% arrange(desc(median))
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class(top)
                 
#Changing the format from data frame to vector as.character referencing the country column
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top=as.character(top$country)
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top
                 
#Using intersect  function to select the common values in both datasets
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both=intersect(top,selected_countries)
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both
                 
#Using setdiff to select the non-overlapping values in both datasets
not = setdiff(top, selected_countries)
not
                 
#Creating a subset based on variety using group by and summarize
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topwine = wine %>% group_by(variety) %>% summarize(number=n()) %>% arrange(desc(number)) %>% top_n(10)
topwine=as.character(topwine$variety)
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topwine
                 
#Plot based on variety and points using group by and summarize
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wine %>% filter(variety %in% topwine) %>% group_by(variety)%>% summarize(median=median(points)) %>% ggplot(aes(reorder(variety,median),median)) + geom_col(aes(fill=variety)) + xlab('Variety') + ylab('Median Point') + scale_x_discrete(labels=abbreviate)
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#Creating top 15 percent cheapest wines with high rating using intersect function
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top15percent=wine %>% arrange(desc(points)) %>% filter(points > quantile(points, prob = 0.85))
cheapest15percent=wine %>% arrange(price) %>% head(nrow(top15percent))
goodvalue = intersect(top15percent,cheapest15percent)
goodvalue
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#Feature Engineering

wine = read.csv('wine.csv', stringsAsFactors = FALSE, encoding = 'UTF-8')

save(wine, file = "wine.rda")
load("wine.rda")

#Omiting one column from the wine dataset
wine = wine[,-c(3)]  

View(wine)

#Using transmute and mutate functions to append a new column
wine1 = wine %>% mutate(PPratio = points/price)
wine2 = wine %>% transmute(PPratio = points/price)

#Aggregation by country using group by and summarize
wine  %>% group_by(country) %>% summarize(total = n())

#Missing country values
wine[wine$country == "",]

#Adding missing values in the dataset
wine$country = ifelse(wine$designation == "Askitikos", "Greece", wine$country)
wine$country = ifelse(wine$designation == "Piedra Feliz", "Chile", wine$country)
wine$country = ifelse(wine$variety == "Red Blend", "Turkey", wine$country)

#Combining Datasets
#Creating a new subset by total number of rows by country
newwine = wine  %>% group_by(country) %>% summarize(total = n()) %>% arrange(desc(total))

#Creating subsets with the head of wine and newwine
subset1=head(wine)
subset2=head(newwine)

#Combining two data frames using full join function
full = full_join(subset1, subset2)
full

#Combining two data frames using inner join function
inner = inner_join(subset1, subset2)
inner

#Combining two data frames using left join function
left = left_join(subset1, subset2)
left

#Combining two data frames using right join function
right = right_join(subset1, subset2)
right
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#####End of Code####