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Introduction to Data Manipulation with dplyr/Introduction to dplyr.R
################################################################################
## 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")
library(dplyr)
library(ggplot2)
#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
wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count))
#Creating a new variable which contains the top 10 countries
selected_countries = wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count)) %>% top_n(10) %>% select(country)
selected_countries
#Changing the format from data frame to vector as.character referencing the country column
selected_countries = as.character(selected_countries$country)
class(selected_countries)
#Subsetting data selecting top ten countries and their points from wine
select_points=wine %>% filter(country %in% selected_countries) %>% select(country, points) %>% arrange(country)
#Scatterplot with smooth line
ggplot(wine, aes(points,price)) + geom_point() + geom_smooth()
#Boxplot between country and points, reordered by median of points. Center aligning the Title of the boxplot
ggplot(select_points, aes(x=reorder(country,points,median),y=points)) + geom_boxplot(aes(fill=country)) + xlab("Country") + ylab("Points") + ggtitle("Distribution of Top 10 Wine Producing Countries") + theme(plot.title = element_text(hjust = 0.5))
#Filter by countries that do not appear on the selected_countries dataset
wine %>% filter(!(country %in% selected_countries)) %>% group_by(country) %>% summarize(median=median(points)) %>% arrange(desc(median))
#Creating a new variable called top using country and points to rate them based on points
top=wine %>% group_by(country) %>% summarize(median=median(points)) %>% arrange(desc(median))
class(top)
#Changing the format from data frame to vector as.character referencing the country column
top=as.character(top$country)
top
#Using intersect function to select the common values in both datasets
both=intersect(top,selected_countries)
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
topwine = wine %>% group_by(variety) %>% summarize(number=n()) %>% arrange(desc(number)) %>% top_n(10)
topwine=as.character(topwine$variety)
topwine
#Plot based on variety and points using group by and summarize
wine %>% filter(variety %in% topwine) %>% group_by(variety)%>% summarize(median=median(points)) %>% ggplot(aes(reorder(variety,median),median)) + geom_col(aes(fill=variety)) + xlab('Variety') + ylab('Median Point') + scale_x_discrete(labels=abbreviate)
#Creating top 15 percent cheapest wines with high rating using intersect function
top15percent=wine %>% arrange(desc(points)) %>% filter(points > quantile(points, prob = 0.85))
cheapest15percent=wine %>% arrange(price) %>% head(nrow(top15percent))
goodvalue = intersect(top15percent,cheapest15percent)
goodvalue
#Feature Engineering
wine = read.csv('wine.csv', stringsAsFactors = FALSE, encoding = 'UTF-8')
save(wine, file = "wine.rda")
load("wine.rda")
#Omiting one column from the wine dataset
wine = wine[,-c(3)]
View(wine)
#Using transmute and mutate functions to append a new column
wine1 = wine %>% mutate(PPratio = points/price)
wine2 = wine %>% transmute(PPratio = points/price)
#Aggregation by country using group by and summarize
wine %>% group_by(country) %>% summarize(total = n())
#Missing country values
wine[wine$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
#####End of Code####
################################################################################
## 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")
library(dplyr)
library(ggplot2)
setwd("C:/Users/Arham/Desktop/Files/Introduction to dplyr")
#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
wine %>%
group_by(country)%>%
summarize(count=n()) %>%
arrange(desc(count))
#Creating a new variable which contains the top 10 countries
selected_countries = wine %>%
group_by(country) %>%
summarize(count=n()) %>%
arrange(desc(count)) %>%
top_n(10) %>%
select(country)
selected_countries
#Changing the format from data frame to vector as.character referencing the country column
selected_countries = as.character(selected_countries$country)
class(selected_countries)
#Subsetting data selecting top ten countries and their points from wine
select_points=wine %>%
filter(country %in% selected_countries) %>%
select(country, points) %>%
arrange(country)
#Scatterplot with smooth line
ggplot(wine, aes(points,price)) +
geom_point() +
geom_smooth()
#Boxplot between country and points, reordered by median of points. Center aligning the Title of the boxplot
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))
#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
top=wine %>%
group_by(country) %>%
summarize(median=median(points)) %>%
arrange(desc(median))
class(top)
#Changing the format from data frame to vector as.character referencing the country column
top=as.character(top$country)
top
#Using intersect function to select the common values in both datasets
both=intersect(top,selected_countries)
both
#Using setdiff to select the non-overlapping values in both datasets
not = setdiff(top, selected_countries)
not
#Creating a subset based on variety using group by and summarize
topwine = wine %>%
group_by(variety) %>%
summarize(number=n()) %>%
arrange(desc(number)) %>%
top_n(10)
topwine=as.character(topwine$variety)
topwine
#Plot based on variety and points using group by and summarize
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)
#Creating top 15 percent cheapest wines with high rating using intersect function
top15percent=wine %>%
arrange(desc(points)) %>%
filter(points > quantile(points, prob = 0.85))
cheapest15percent=wine %>%
arrange(price) %>%
head(nrow(top15percent))
goodvalue = intersect(top15percent,cheapest15percent)
goodvalue
#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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