Group-10 CSE-2

library(readr) library(ggplot2) library(ggthemes) library(dplyr) library(plyr) library(tidyr) library(janitor) library(nortest)
data = read_csv("C:\\Users\\Sarthak\\Desktop\\startup_funding.csv", na=c("N/A", "Undisclosed", "NaN", "undisclosed", "unknown"))
print(spec(data))
#Data Cleaning
data$`Industry Vertical`[data$`Industry Vertical` == 'ECommerce'] = "eCommerce" data$`Industry Vertical`[data$`Industry Vertical` == 'E-Commerce'] = "eCommerce" data$`Industry Vertical`[data$`Industry Vertical` == 'E-commerce'] = "eCommerce" data$`Industry Vertical`[data$`Industry Vertical` == 'ecommerce'] = "eCommerce" data$`Industry Vertical`[data$`Industry Vertical` == 'Ecommerce'] = "eCommerce" data$`Investors Name`[data$`Investors Name` == 'Undisclosed investors'] = "Undisclosed Investors"
data$`Investors Name`[data$`Investors Name` == 'Undisclosed Investor'] = "Undisclosed Investors"
data$`Investors Name`[data$`Investors Name` == 'undisclosed investors'] = "Undisclosed Investors"

#Number of Investment Per Year
data$`Date dd/mm/yyyy` = as.Date(data$`Date dd/mm/yyyy`,format("%d/%m/%Y")) data$year = as.numeric(format(data$`Date dd/mm/yyyy`,"%Y")) data$year[data$year == "15"] = 2015
yeartable = table(data$year) print(yeartable)
df = as.data.frame(yeartable) print(df)
ggplot(df, aes(x= Var1, y= Freq, group=1)) + geom_line() + ylab("Number of investments") + xlab("Year") + theme_economist()

# Most favored cities by investors by_city = data %>% group_by(`City Location`) %>%
filter(!(`City Location` == "nan")) %>% dplyr::summarize(cnt=n()) %>% arrange(desc(cnt)) %>%
head(10)

print(as.data.frame(by_city))
ggplot(as.data.frame(by_city), aes(x = "", y = cnt, fill = `City Location`)) + geom_col() + coord_polar(theta = "y") +
geom_text(aes(label = cnt), position = position_stack(vjust = 0.46))


#Industry Vertical wise Investments
ind = data %>%
group_by(`Industry Vertical`)%>% filter(!(`Industry Vertical` == "nan")) %>% dplyr::summarise(n = n()) %>% drop_na(`Industry Vertical`)%>% arrange(desc(n)) %>%
head(n = 10)

ind %>%
ggplot(aes(x = reorder(`Industry Vertical` , n) , y = n)) + geom_bar(stat='identity',colour="white", fill = c("red")) + theme_solarized() +
labs(x = 'Industry Verticals', y = 'Number of Startups Funded', title = 'Industry Verticals wise Startups Funding') +
coord_flip()

#Top Disclosed investors
investors = data %>% group_by(`Investors Name`)%>%
filter((!`Investors Name` == 'N/A') && !`Investors Name` == "Undisclosed Investors")
%>%
dplyr::summarise(n=n()) %>% arrange(desc(n)) %>% head(10)

print(investors) investors %>%
ggplot(aes(x = reorder(`Investors Name` , n) , y = n)) + geom_bar(stat='identity',colour="black", fill = c("#E75227")) +
labs(x = 'Industry Name', y = 'Number of Startups Funded', title = 'Top Disclosed Investors') +
coord_flip() + theme_minimal()
data$`Amount in USD` = as.numeric(gsub(",","",data$`Amount in USD`))

#Amount invested by year amount = data %>% group_by(year)%>%
dplyr::summarise(n=sum(`Amount in USD`,na.rm=TRUE))%>% drop_na()

print(amount)

ggplot(as.data.frame(amount), aes(x=year, y=n)) + geom_line() + labs(title="Amount invested by year", y="Amount in USD") + theme_economist()
# check if industry vertical influences Amount invested
data = na.omit(data)
consumer = data$`Amount in USD`[data$`Industry Vertical` == "Consumer Internet"] technology = data$`Amount in USD`[data$`Industry Vertical` == "Technology"]
res = t.test(consumer, technology, var.equal = FALSE) if(res$p.value > 0.05){
print("Accept NULL hypothesis")
}else{
print("Reject NULL hypothesis")
}