R-Programming_EDA_Week1_IP
RuthNguli
2022-03-18
Defining the Question
To identify which individuals are most likely to click on an online cryptography course ads.
Metric for success
The metric of success will be attained on identifying individuals who click on the ads.
Understanding the business context
Cryptography is an indispensable tool for protecting information in computer systems. A cryptography course teaches how the cryptographic system a works and its real world application.
Experimental Design
Define the question, the metric for success, the context, experimental design taken.
Read and explore the given dataset.
Cleaning Data
Perform Exploratory Data Cleaning (Univariate & Bivariate)
Conclusion
Recommendations
1. Reading data
# read data from url: http://bit.ly/IPAdvertisingData
# load data
#
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()ads <- read.csv("http://bit.ly/IPAdvertisingData")
# preview head of the data
#
head(ads)# previewing the tail of the data
#
tail(ads)# checking column names
#
colnames(ads)## [1] "Daily.Time.Spent.on.Site" "Age"
## [3] "Area.Income" "Daily.Internet.Usage"
## [5] "Ad.Topic.Line" "City"
## [7] "Male" "Country"
## [9] "Timestamp" "Clicked.on.Ad"# Checking the data has appropriate data types
#
str(ads)## 'data.frame': 1000 obs. of 10 variables:
## $ Daily.Time.Spent.on.Site: num 69 80.2 69.5 74.2 68.4 ...
## $ Age : int 35 31 26 29 35 23 33 48 30 20 ...
## $ Area.Income : num 61834 68442 59786 54806 73890 ...
## $ Daily.Internet.Usage : num 256 194 236 246 226 ...
## $ Ad.Topic.Line : chr "Cloned 5thgeneration orchestration" "Monitored national standardization" "Organic bottom-line service-desk" "Triple-buffered reciprocal time-frame" ...
## $ City : chr "Wrightburgh" "West Jodi" "Davidton" "West Terrifurt" ...
## $ Male : int 0 1 0 1 0 1 0 1 1 1 ...
## $ Country : chr "Tunisia" "Nauru" "San Marino" "Italy" ...
## $ Timestamp : chr "2016-03-27 00:53:11" "2016-04-04 01:39:02" "2016-03-13 20:35:42" "2016-01-10 02:31:19" ...
## $ Clicked.on.Ad : int 0 0 0 0 0 0 0 1 0 0 ...Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
The advertising data has 1000 rows and 10 columns Column Male and Clicked.on.Ad are represented as integer however should be converter to factor as they are represnting categorical variables
Data Cleaning
# change data types of Male and Clicked.on.Ad columns from int to factor
#
ads$Male <- as.factor(ads$Male)
ads$Clicked.on.Ad <- as.factor(ads$Clicked.on.Ad)
# confirming if the changes have made successfully
#
str(ads)## 'data.frame': 1000 obs. of 10 variables:
## $ Daily.Time.Spent.on.Site: num 69 80.2 69.5 74.2 68.4 ...
## $ Age : int 35 31 26 29 35 23 33 48 30 20 ...
## $ Area.Income : num 61834 68442 59786 54806 73890 ...
## $ Daily.Internet.Usage : num 256 194 236 246 226 ...
## $ Ad.Topic.Line : chr "Cloned 5thgeneration orchestration" "Monitored national standardization" "Organic bottom-line service-desk" "Triple-buffered reciprocal time-frame" ...
## $ City : chr "Wrightburgh" "West Jodi" "Davidton" "West Terrifurt" ...
## $ Male : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 1 2 2 2 ...
## $ Country : chr "Tunisia" "Nauru" "San Marino" "Italy" ...
## $ Timestamp : chr "2016-03-27 00:53:11" "2016-04-04 01:39:02" "2016-03-13 20:35:42" "2016-01-10 02:31:19" ...
## $ Clicked.on.Ad : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...#Extracting and creating Date column from timestamp
#
ads$Date <- as.Date(ads$Timestamp)
glimpse(ads)## Rows: 1,000
## Columns: 11
## $ Daily.Time.Spent.on.Site <dbl> 68.95, 80.23, 69.47, 74.15, 68.37, 59.99, 88.~
## $ Age <int> 35, 31, 26, 29, 35, 23, 33, 48, 30, 20, 49, 3~
## $ Area.Income <dbl> 61833.90, 68441.85, 59785.94, 54806.18, 73889~
## $ Daily.Internet.Usage <dbl> 256.09, 193.77, 236.50, 245.89, 225.58, 226.7~
## $ Ad.Topic.Line <chr> "Cloned 5thgeneration orchestration", "Monito~
## $ City <chr> "Wrightburgh", "West Jodi", "Davidton", "West~
## $ Male <fct> 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, ~
## $ Country <chr> "Tunisia", "Nauru", "San Marino", "Italy", "I~
## $ Timestamp <chr> "2016-03-27 00:53:11", "2016-04-04 01:39:02",~
## $ Clicked.on.Ad <fct> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, ~
## $ Date <date> 2016-03-27, 2016-04-04, 2016-03-13, 2016-01-~# Checking missing values
#
library(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.0, built: 2021-05-26)
## ## Copyright (C) 2005-2022 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##missmap(ads)
Our dataset does not have any missing values
# Checking for any duplicates
#
sum(duplicated(ads))## [1] 0There are no duplicates
# Checking for outliers
#
non_char <- ads %>% select(Daily.Time.Spent.on.Site, Age, Area.Income, Daily.Internet.Usage, Male,Clicked.on.Ad, Date)
boxplot(non_char)
There a few identifiable outliers in Area.Income column, we leave
them as they represent real data.
# Checking the summarry of dataset
#
summary(ads)## Daily.Time.Spent.on.Site Age Area.Income Daily.Internet.Usage
## Min. :32.60 Min. :19.00 Min. :13996 Min. :104.8
## 1st Qu.:51.36 1st Qu.:29.00 1st Qu.:47032 1st Qu.:138.8
## Median :68.22 Median :35.00 Median :57012 Median :183.1
## Mean :65.00 Mean :36.01 Mean :55000 Mean :180.0
## 3rd Qu.:78.55 3rd Qu.:42.00 3rd Qu.:65471 3rd Qu.:218.8
## Max. :91.43 Max. :61.00 Max. :79485 Max. :270.0
## Ad.Topic.Line City Male Country
## Length:1000 Length:1000 0:519 Length:1000
## Class :character Class :character 1:481 Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Timestamp Clicked.on.Ad Date
## Length:1000 0:500 Min. :2016-01-01
## Class :character 1:500 1st Qu.:2016-02-17
## Mode :character Median :2016-04-07
## Mean :2016-04-09
## 3rd Qu.:2016-05-31
## Max. :2016-07-24Bivariate analysis
# Attaching ads data to R
#
attach(ads)
# Plotting a histogram of Daily Time spent on site
hist(Daily.Time.Spent.on.Site, col='pink')
Most time spent on site is between 65 and 80
#Plotting a histogram of Age
#
hist(Age, col='pink')
Most participants are aged between 25 and 40 years old
# plotting a histogramof Area income
#
hist(Area.Income, col="pink")
Income is skewed to the left. Most participants had income between
50,000 and 70,000
# A bar plot of Male Partipation
#
barplot(table(Male), col="pink", main="Bar Plot of Male distribution")
Male participants were slightly fewer than those not male
# A bar plot of Clicked on Ads
#
barplot(table( Clicked.on.Ad), col="pink", main="A Bar plot of Clicked on Ads")
There is equal distribution between those who clicked and those
didn’t click on AD
# Plotting a Histogram of Daily internet usage
#
hist(Daily.Internet.Usage, col="pink")
## Bivariate Analysis
colnames(ads)## [1] "Daily.Time.Spent.on.Site" "Age"
## [3] "Area.Income" "Daily.Internet.Usage"
## [5] "Ad.Topic.Line" "City"
## [7] "Male" "Country"
## [9] "Timestamp" "Clicked.on.Ad"
## [11] "Date"# A Boxplot of clicked on ad vs Time spent on ad
#
plot(Daily.Time.Spent.on.Site ~ Clicked.on.Ad, data = ads, col="pink", main="A Box Plot of Daily time spent on site vs Clicked on Ad")
* Most people who clicked on ads did not spent much time on site*
# A scatter-plot of Age Vs Clicked on ads
#
ggplot(ads,aes(Age,Clicked.on.Ad, colour= Clicked.on.Ad))+
geom_point(size=3)
Those who clicked on ads are aged between 20 and 60.
# A Scatter plot of Date Vs Daily time spent on site
#
ads %>% ggplot(aes(Date,Daily.Time.Spent.on.Site, colour = Clicked.on.Ad))+
geom_point(size=3, alpha = 0.5)+geom_smooth(method=lm, se= F)## `geom_smooth()` using formula 'y ~ x'
Majority of those who clicked on ad through Jan to July spent less
time online
# A scatter plot of Age VS time spent on site
#
ads %>% ggplot(aes(Age,Daily.Time.Spent.on.Site, colour = Clicked.on.Ad))+
geom_point(size=3, alpha = 0.5)+geom_smooth(method=lm, se= F)## `geom_smooth()` using formula 'y ~ x'
Most people who clicked on ads are aged between 30 -50 years and
they spent much less time online
# A scatter plot of random selected countries VS Ages
#
filter(ads, Country %in% c("Cuba","Tunisia","Korea","Peru","Thailand","Greece","Senrgal","Ukraine","Australia")) %>%
ggplot(aes(Age,Country , colour=Clicked.on.Ad)) +
geom_point()
Most Countries the people who click on ads are 30yrs and above
# A scatter plot of Age VS time spent on site
#
ads %>% ggplot(aes(Area.Income,Daily.Time.Spent.on.Site, colour = Clicked.on.Ad))+
geom_point(size=3, alpha = 0.5)+geom_smooth(method=lm, se= F)## `geom_smooth()` using formula 'y ~ x'
Num <- ads %>% select(Daily.Time.Spent.on.Site, Age, Area.Income, Daily.Internet.Usage)
corr <- cor(Num)
corr## Daily.Time.Spent.on.Site Age Area.Income
## Daily.Time.Spent.on.Site 1.0000000 -0.3315133 0.3109544
## Age -0.3315133 1.0000000 -0.1826050
## Area.Income 0.3109544 -0.1826050 1.0000000
## Daily.Internet.Usage 0.5186585 -0.3672086 0.3374955
## Daily.Internet.Usage
## Daily.Time.Spent.on.Site 0.5186585
## Age -0.3672086
## Area.Income 0.3374955
## Daily.Internet.Usage 1.0000000Daily internet usage is positively corr to daily time spent online 0.52 , Age is negatively corr to time spent online -0.37
Conclusion
Ads are mostly clicked: Those aged between 30 and 60 years Those who spent less time online Ads are clicked through out the months.
Recommendations
Its Recommend that to target age group between 30 and 60, have a well detailed and well explained advert as those who click on ads do spent much time on site and make sure advert run through out the year