Visual_R.rmd

---
title: "Visual_R"
author: "Manidhar Vutla"
date: "2 November 2018"
output: html_document
---

**Q1) a. Creating a new variable age_group**
```{r}
nyt1 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt1.csv")

# Dividing Age into different categories
nyt1$age_group <- cut(nyt1$Age, c(0,18,25,35,45,55,65,Inf), right= FALSE, labels = c("<18","18-24","25-34","35-44","45-54","55-64","65+"))

# Summary
summary(nyt1)

```


**Q1) b. (i) Plotting Impressions and CTR for 6 categories**

```{r}

library(ggplot2)

#CTR vs Impression Graph

g1 <- ggplot(nyt1, aes(x=Impressions,y=((Clicks/Impressions)),color= age_group)) +geom_point(size = 2) + labs(title="CTR vs Impressions")

g1 + labs(y="CTR")

```






``` {r}
#Impressions

library(ggplot2)

 

b <- ggplot(nyt1, aes(Impressions, fill=age_group))
b+ geom_bar() + labs(title="Impressions")

```

```{r}

#CTR  

ctr <- ifelse(nyt1$Impressions !=0, nyt1$Clicks/nyt1$Impressions,NA)

nyt1$CTR <- ctr

c <- ggplot(data = nyt1,aes(CTR, fill = age_group, color = age_group))
c +  geom_histogram(position = "dodge", binwidth = 0.1) +  labs(x = "CTR",title="CTR")

```
**Q1)b. (ii) New Variable to segment by click behaviour**

``` {r}
#Plot to visualise clicks by different age groups

ggplot(nyt1, aes(y=age_group, x=Clicks, color=age_group)) + geom_line(size=2) + theme_bw()


```
**Less than 18 year segment recorded highest number of clicks by <18 followed by 55+ , 35-44 and other segments.** 
**So, I categorised with the new variable "Click_Behavior". (Zero, Low, Medium and High) as shown.**


```{r}
nyt1$Click_Behavior <- ifelse(is.na(nyt1$CTR), 'NA', ifelse(nyt1$CTR==0, 'Zero',ifelse(nyt1$CTR>0 & nyt1$CTR<=0.2, 'Low', 
                                     ifelse(nyt1$CTR>0.2 & nyt1$CTR<=0.4, 'Medium',
                                            'High'))))
summary(nyt1$Click_Behavior)
```

**Q1) b. (iii) Visual Comparisions**

```{r}
library(ggplot2)
segment <- ifelse(nyt1$Age < 18,c("<18"), c(">=18")) 


nyt1$Segment <- segment

d<- ggplot(nyt1, aes(y=CTR, x=Impressions, color=factor(Gender))) + geom_point(data=subset(nyt1, Segment=='<18'),size=2) + labs(title="<18 Male vs Female", y="CTR", color="Gender") 

d + geom_point(data=subset(nyt1, Segment=='>=18'),size=2) + labs(title="<18 Male vs Female and >=18 Male and Female Clicks", y="CTR", color="Gender") +scale_color_manual(labels = c("Female", "Male"), values = c("blue", "red")) + facet_grid(.~ Segment)

```

**Q1) C. Potential Metrics and Weekly Trend**
```{r}
#As we estimated female under 18 recorded highest number of clicks, taking their mean would estimate their age on an average. (Taking age>0)

mean(nyt1[nyt1$Age>0 & nyt1$Age<18 & nyt1$Gender==0,"Age"])
```

``` {r}
var(nyt1[nyt1$Age>0 & nyt1$Age<18 & nyt1$Gender==0, "Age"])

```
*15 years is the average female age with variance 3 recording highest number of clicks. The max value is 17.*

``` {r}
mean(nyt1[nyt1$Age>=18 & nyt1$Gender==1,"Age"])

```

``` {r}

median(nyt1[nyt1$Age>=18 & nyt1$Gender==1,"Age"])

```

*As Men recorded highest number of clicks in the segment >=18 years are 42 years old on an average. Median is 41 (distribution will be skewed to the right) *

```{r}

#R Code for visualizing weekly trend

nyt1 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt1.csv")
nyt2 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt2.csv")
nyt3 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt3.csv")
nyt4 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt4.csv")
nyt5 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt5.csv")
nyt6 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt6.csv")
nyt7 <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\nyt7.csv")


nyt1$day <- "day1"
nyt2$day <- "day2"
nyt3$day <- "day3"
nyt4$day <- "day4"
nyt5$day <- "day5"
nyt6$day <- "day6"
nyt7$day <- "day7"


nyt <- rbind(nyt1, nyt2, nyt3, nyt4, nyt5, nyt6, nyt7)

levels(nyt$day) <- list('day1' = 'day1',
                        'day2' = 'day2',
                        'day3' = 'day3',
                        'day4' = 'day4',
                        'day5' = 'day5',
                        'day6' = 'day6',
                        'day7' = 'day7')

nyt$Gender <- ifelse(nyt$Gender == 0, 'Female','Male')





week <- ggplot(nyt, aes(x=day, y=Clicks))
week + geom_bar(stat = "identity") + facet_grid(.~ Gender) 
```

<ul><h3>Patterns Observed</h3>

	<li>Less than 18 segment record the highest CTR among all the age groups. Female under 18 have the highest clicks than females who are more than 18 years old. Males who are above 18 years record more clicks than who are less than 18 years and Males record more number of clicks than female in the above 18 segment having an average age of 42 years.</li>

<li>However, the CTR rate for most of male and female is less than 0.2.</li> 

<li>In Weekly trend, it is clear that overall female clicks are higher than male. It is also evident that Day 6 records highest number of clicks in female and male graph. In all the other days, almost equal number of clicks were recorded in female and male respectively.</li>


##Q2) Dataset Chosen: IPL Dataset(Cricket Dataset)

*IPL Dataset (Cricket Dataset) has details of all the matches in all seasons with team1, team2 , toss_winner, toss_decision, winner, win_by_runs, win_by_wickets and other details.*

**Q2) a. Visualising the number of matches played and victories per team**

``` {r}

matches <- read.csv("C:\\Users\\welcome\\Desktop\\Stuff\\MEngg\\Sem-1\\DS\\matches.csv")
library(dplyr)

matches %>% summarise(match_count = n())

```


```{r}


library(dplyr)
library(ggplot2)

#wins - groups by winner(team_name) with number of victories

wins = matches %>% group_by(winner) %>% summarise(win_count = n())

ggplot(wins, aes(x=winner,y=win_count)) + geom_bar(fill="#0073C2FF", stat="identity") + geom_text(aes(label=win_count), hjust= -0.3) +  coord_flip()

```
<br>*Plot was made to compare between Toss wins and Match wins. How likely toss winning and match winning are.*

```{r}


toss_win <- matches %>% group_by(toss_winner) %>% summarise(toss_count=n())
wins <- wins [-1,]

total <- data.frame(team=toss_win$toss_winner, tosswin=toss_win$toss_count,matchwin=wins$win_count )

ggplot(total, aes(x=tosswin, y=matchwin, group=1, color=team))+ geom_point(size=3) + geom_line(size=2)

```










##Data was made usable

*-	By removing columns with most number of NA (Made whole column null in the data frame)*
*-	Rows having NULL values (or) empty strings are removed.*


##Interpretations

*-	Mumbai Indians recorded highest number of victories while kochi tuskers and Pune Warriors record lowest number of victories in all seasons*

*-	Chennai Super Kings, Kolkata Knight Riders toss winning and match winning is most likely followed by Rising Pune Supergaint.*