Update Introduction to dplyr

95aea55c · Arham Akheel · GitHub · dce82d50 · 95aea55c
Unverified Commit 95aea55c authored Mar 13, 2018 by Arham Akheel Committed by GitHub Mar 13, 2018
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Introduction to dplyr Introduction to dplyr +111 -5

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--- a/Introduction to dplyr
+++ b/Introduction to dplyr
-wine = read.csv('wine.csv', stringsAsFactors = F, encoding = 'UTF-8')
+################################################################################
-install.packages('dplyr')
+## This code is property of Data Science Dojo
-install.packages('ggplot2')
+## 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)
-wine = wine[,-1]
-wine = wine %>% select(-c(description)) 
+#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