Startup Fund anlalysis 2017
Santhosh Ramurthy
Nov 21, 2017
Startups Funds usage In india Analysis
Interested in the Indian startup ecosystem just like me? Wanted to know what type of startups are getting funded in the last few years? Wanted to know who are the important investors? Wanted to know the hot fields that get a lot of funding these days? This dataset is a chance to explore the Indian start up scene. Deep dive into funding data and derive insights into the future!
Content This dataset has funding information of the Indian startups from January 2015 to August 2017. It includes columns with the date funded, the city the startup is based out of, the names of the funders, and the amount invested (in USD).
For more information on the values of individual fields, check out the Column Metadata.
Acknowledgements Thanks to trak.in who are generous enough to share the data publicly for free.
Inspiration Possible questions which could be answered are:
How does the funding ecosystem change with time? Do cities play a major role in funding? Which industries are favored by investors for funding? Who are the important investors in the Indian Ecosystem? How much funds does startups generally get in India?:
#The startup fund Anlaysis in india 2017
library("knitr")
library("devtools")
startup <- read.csv("C:\\Users\\ksantoram\\Desktop\\rdataset\\startup_funding.csv")
View(startup)
summary(startup)## SNo Date StartupName
## Min. : 0.0 02/02/2015: 11 Swiggy : 7
## 1st Qu.: 592.8 08/07/2015: 11 UrbanClap: 6
## Median :1185.5 30/11/2016: 11 Jugnoo : 5
## Mean :1185.5 04/10/2016: 10 Medinfi : 5
## 3rd Qu.:1778.2 01/06/2015: 9 NoBroker : 5
## Max. :2371.0 04/5/2016 : 9 Paytm : 5
## (Other) :2311 (Other) :2339
## IndustryVertical SubVertical CityLocation
## Consumer Internet:772 : 936 Bangalore:627
## Technology :313 Online Pharmacy : 9 Mumbai :446
## :171 Food Delivery Platform : 8 New Delhi:381
## eCommerce :171 Online lending platform : 5 Gurgaon :240
## ECommerce : 53 Online Learning Platform: 4 :179
## Healthcare : 30 Data Analytics platform : 3 Pune : 84
## (Other) :862 (Other) :1407 (Other) :415
## InvestorsName InvestmentType AmountInUSD
## Undisclosed Investors : 33 Seed Funding :1271 : 847
## Undisclosed investors : 27 Private Equity:1066 1,000,000: 130
## Indian Angel Network : 24 SeedFunding : 30 500,000 : 91
## Ratan Tata : 24 : 1 100,000 : 55
## Kalaari Capital : 16 Crowd funding : 1 2,000,000: 55
## Group of Angel Investors: 15 Crowd Funding : 1 3,000,000: 50
## (Other) :2233 (Other) : 2 (Other) :1144
## Remarks
## :1953
## Series A : 177
## Series B : 64
## Pre-Series A: 37
## Series C : 28
## Series D : 11
## (Other) : 102##Firstly we would visualize the length and breadth of our dataset
dim(startup)## [1] 2372 10summary(startup)## SNo Date StartupName
## Min. : 0.0 02/02/2015: 11 Swiggy : 7
## 1st Qu.: 592.8 08/07/2015: 11 UrbanClap: 6
## Median :1185.5 30/11/2016: 11 Jugnoo : 5
## Mean :1185.5 04/10/2016: 10 Medinfi : 5
## 3rd Qu.:1778.2 01/06/2015: 9 NoBroker : 5
## Max. :2371.0 04/5/2016 : 9 Paytm : 5
## (Other) :2311 (Other) :2339
## IndustryVertical SubVertical CityLocation
## Consumer Internet:772 : 936 Bangalore:627
## Technology :313 Online Pharmacy : 9 Mumbai :446
## :171 Food Delivery Platform : 8 New Delhi:381
## eCommerce :171 Online lending platform : 5 Gurgaon :240
## ECommerce : 53 Online Learning Platform: 4 :179
## Healthcare : 30 Data Analytics platform : 3 Pune : 84
## (Other) :862 (Other) :1407 (Other) :415
## InvestorsName InvestmentType AmountInUSD
## Undisclosed Investors : 33 Seed Funding :1271 : 847
## Undisclosed investors : 27 Private Equity:1066 1,000,000: 130
## Indian Angel Network : 24 SeedFunding : 30 500,000 : 91
## Ratan Tata : 24 : 1 100,000 : 55
## Kalaari Capital : 16 Crowd funding : 1 2,000,000: 55
## Group of Angel Investors: 15 Crowd Funding : 1 3,000,000: 50
## (Other) :2233 (Other) : 2 (Other) :1144
## Remarks
## :1953
## Series A : 177
## Series B : 64
## Pre-Series A: 37
## Series C : 28
## Series D : 11
## (Other) : 102#Important Information
#Our data has various blank cells in the Industry Vertical Coloumn, Subvertical Coloumn, City Location, Amount in USD. Thus while plotting indivudual data of each data set we would ignore the incomplete Data set or empty cell. Also as there are upto 2000 empty cells in remarks section with a lot of varied information we would ignore analysing each and every remark
#Cleaning the data by removing the , characters to convert it into pure numeric forms from the AMount coloumn and filling the null cells with NA value will help us efficient analysis of the data
startup$Remarks <- NULL
startup[startup == ""] <- NA
startup$AmountInUSD <- as.numeric(gsub(",","",startup$AmountInUSD))
##Let us Describe statistics of our significant variables.
library(psych)
##Now looking up at the major Industrial sectors that have been funded. Firstly we would in general like to see the amount of Investment. Thus calling out the describe function
cleanstartup <- startup[complete.cases(startup$AmountInUSD), ]
describe(cleanstartup$AmountInUSD)## vars n mean sd median trimmed mad min max
## X1 1 1525 12031073 64031175 1070000 3335938 1378818 16000 1.4e+09
## range skew kurtosis se
## X1 1399984000 15.94 309.36 1639670##Creating contingency plots. Here we would clear the entire row in the presence of an empty cell in the row which would help in clear analysis. We would still be left with significant amount of data to analyse the trends
Cleanstartup <- startup[complete.cases(startup), ]
View(Cleanstartup)
dim(Cleanstartup)## [1] 869 9##Thus we have 869 data rows to check that have complete data at hand.
summary(Cleanstartup)## SNo Date StartupName
## Min. : 0 30/11/2016: 10 Swiggy : 4
## 1st Qu.: 336 04/5/2016 : 9 Byju’s : 3
## Median : 690 04/10/2016: 7 Capital Float: 3
## Mean : 697 04/04/2017: 6 Flipkart : 3
## 3rd Qu.:1042 13/02/2017: 6 Fynd : 3
## Max. :1431 17/01/2017: 6 Koovs : 3
## (Other) :825 (Other) :850
## IndustryVertical SubVertical CityLocation
## Consumer Internet:459 Food Delivery Platform : 4 Bangalore:267
## Technology :189 Online lending platform : 4 Mumbai :181
## eCommerce :112 Online Pharmacy : 4 New Delhi:130
## ECommerce : 32 ECommerce Marketplace : 3 Gurgaon : 97
## Logistics : 16 Online Learning Platform: 3 Pune : 37
## Education : 15 Cab Aggregation App : 2 Hyderabad: 35
## (Other) : 46 (Other) :849 (Other) :122
## InvestorsName InvestmentType AmountInUSD
## Undisclosed Investors: 17 Private Equity:476 Min. :1.800e+04
## Undisclosed investors: 16 Seed Funding :392 1st Qu.:3.500e+05
## undisclosed investors: 11 Debt Funding : 1 Median :1.000e+06
## Kalaari Capital : 9 : 0 Mean :1.113e+07
## Brand Capital : 7 Crowd funding : 0 3rd Qu.:5.000e+06
## Indian Angel Network : 7 Crowd Funding : 0 Max. :1.400e+09
## (Other) :802 (Other) : 0Cleanstartup1 <- Cleanstartup
str(Cleanstartup1)## 'data.frame': 869 obs. of 9 variables:
## $ SNo : int 0 3 4 5 6 7 8 9 10 13 ...
## $ Date : Factor w/ 701 levels "01/03/2017","01/04/2017",..: 12 39 39 9 60 83 108 108 108 157 ...
## $ StartupName : Factor w/ 2001 levels "#Fame","121Policy",..: 1734 1965 291 161 455 431 914 1123 1713 292 ...
## $ IndustryVertical: Factor w/ 744 levels "","360-degree view creating platform",..: 701 101 101 101 701 160 160 160 101 101 ...
## $ SubVertical : Factor w/ 1365 levels "","3D printed experimental Human Liver tissue creator",..: 1119 294 491 1087 353 937 944 75 540 285 ...
## $ CityLocation : Factor w/ 72 levels "","Agra","Ahmedabad",..: 5 41 26 5 3 22 5 51 41 5 ...
## $ InvestorsName : Factor w/ 1886 levels "","1Crowd","1Crowd (through crowd funding)",..: 815 896 1088 1336 748 219 834 764 247 717 ...
## $ InvestmentType : Factor w/ 8 levels "","Crowd funding",..: 5 7 7 7 5 5 5 5 5 7 ...
## $ AmountInUSD : num 1.3e+06 5.0e+05 8.5e+05 1.0e+06 2.6e+06 2.0e+07 8.5e+06 1.2e+07 1.0e+06 1.0e+06 ...Cleanstartup1$CityLocation <- as.character(Cleanstartup1$CityLocation)
Cleanstartup1$CityLocation[Cleanstartup1$CityLocation != "Bangalore" & Cleanstartup1$CityLocation != "Mumbai" & Cleanstartup1$CityLocation != "New Delhi" & Cleanstartup1$CityLocation != "Gurgaon" & Cleanstartup1$CityLocation != "Pune" & Cleanstartup1$CityLocation != "Hyderabad"] <- "Others"
View(Cleanstartup1)
table(Cleanstartup1$CityLocation)##
## Bangalore Gurgaon Hyderabad Mumbai New Delhi Others Pune
## 267 97 35 181 130 122 37#Now Bracketing the Vertical Industrial Sector
startup2 <- Cleanstartup1
startup2$IndustryVertical <- as.character(startup2$IndustryVertical)
#clean code
startup2$IndustryVertical[startup2$IndustryVertical != "Consumer Internet" & startup2$IndustryVertical != "Technology" & startup2$IndustryVertical != "ECommerce" & startup2$IndustryVertical != "Logistics" & startup2$IndustryVertical != "Education" & startup2$IndustryVertical != "Healthcare"] <- "OtherSectors"
View(startup2)
table(startup2$CityLocation)##
## Bangalore Gurgaon Hyderabad Mumbai New Delhi Others Pune
## 267 97 35 181 130 122 37table(startup2$IndustryVertical)##
## Consumer Internet ECommerce Education Healthcare
## 459 32 15 14
## Logistics OtherSectors Technology
## 16 144 189dim(startup2)## [1] 869 9#We would also clean the type of Investment;Simply converting from Factor to character is sufficient
startup2$InvestmentType <- as.character(startup2$InvestmentType)
#So Finally we have our dataset CleanData3.df which is a subset of Data1.df after cleaning and bracketing major variables that were significantly less for us to invest our time to analyse them. Thus moving ahead with our CleanData3.df dataset
table(cleanstartup$Date)##
## 01/03/2017 01/04/2017 01/05/2015 01/05/2017 01/06/2015 01/06/2017
## 2 1 2 2 7 0
## 01/07/2015 01/07/2016 01/07/2017 01/08/2015 01/08/2016 01/08/2017
## 3 1 1 2 3 1
## 01/09/2015 01/1/2016 01/10/2015 01/11/2016 01/12/2015 01/12/2016
## 3 0 1 1 4 3
## 01/2/2016 01/3/2016 01/4/2016 01/5/2016 01/6/2016 01/7/2016
## 0 0 1 2 0 0
## 01/8/2016 01/9/2016 02/01/2015 02/01/2017 02/02/2015 02/02/2017
## 2 1 2 1 10 0
## 02/03/2015 02/03/2017 02/05/2017 02/06/2015 02/06/2017 02/07/2015
## 2 4 3 2 0 3
## 02/07/2016 02/08/2016 02/08/2017 02/09/2015 02/10/2015 02/11/2015
## 1 2 2 2 1 2
## 02/11/2016 02/12/2015 02/12/2016 02/2/2016 02/3/2016 02/5/2016
## 2 1 2 5 2 0
## 02/6/2016 03/01/2017 03/02/2015 03/02/2017 03/03/2015 03/03/2017
## 2 1 2 0 1 3
## 03/04/2015 03/04/2017 03/05/2017 03/06/2015 03/07/2015 03/07/2017
## 1 2 2 5 2 1
## 03/08/2015 03/08/2016 03/09/2015 03/10/2015 03/10/2016 03/11/2015
## 3 2 2 1 3 6
## 03/11/2016 03/12/2015 03/2/2016 03/3/2016 03/5/2016 03/6/2016
## 2 2 4 2 3 2
## 04/01/2017 04/02/2016 04/03/2017 04/04/2015 04/04/2017 04/05/2015
## 0 1 1 2 6 3
## 04/05/2017 04/06/2015 04/07/2015 04/07/2016 04/07/2017 04/08/2015
## 0 4 1 4 1 1
## 04/08/2016 04/09/2015 04/1/2016 04/10/2016 04/11/2015 04/11/2016
## 1 3 1 7 2 2
## 04/12/2015 04/3/2016 04/4/2016 04/5/2016 04/9/2016 05/01/2015
## 3 4 1 9 0 3
## 05/01/2017 05/02/2015 05/02/2016 05/03/2015 05/04/2017 05/05/2015
## 0 2 4 2 1 4
## 05/05/2017 05/06/2015 05/06/2017 05/07/2015 05/07/2016 05/07/2017
## 1 3 1 0 3 3
## 05/08/2015 05/08/2016 05/1/2016 05/10/2015 05/10/2016 05/11/2015
## 6 0 3 4 1 2
## 05/11/2016 05/12/2016 05/4/2016 05/5/2016 05/9/2016 06/01/2015
## 2 1 0 1 0 4
## 06/01/2016 06/01/2017 06/02/2015 06/02/2016 06/02/2017 06/03/2015
## 5 1 2 1 1 1
## 06/03/2017 06/04/2015 06/04/2017 06/05/2015 06/06/2017 06/07/2015
## 3 1 2 2 3 1
## 06/07/2016 06/07/2017 06/08/2015 06/08/2016 06/10/2015 06/10/2016
## 3 0 4 1 5 2
## 06/11/2015 06/12/2016 06/4/2016 06/5/2016 06/6/2016 06/9/2016
## 5 1 4 1 1 3
## 07/01/2015 07/01/2016 07/01/2017 07/02/2017 07/03/2017 07/04/2015
## 3 3 1 2 5 3
## 07/04/2017 07/05/2015 07/06/2016 07/06/2017 07/07/2015 07/07/2016
## 1 3 5 2 2 2
## 07/07/2017 07/08/2015 07/09/2015 07/10/2015 07/10/2016 07/11/2016
## 1 2 4 2 3 2
## 07/12/2015 07/12/2016 07/3/2016 07/4/2016 07/5/2016 07/9/2016
## 4 2 5 0 1 4
## 08/01/2016 08/02/2016 08/02/2017 08/03/2017 08/04/2015 08/04/2016
## 3 3 4 3 3 1
## 08/04/2017 08/05/2015 08/05/2017 08/06/2015 08/06/2016 08/06/2017
## 1 3 4 1 5 4
## 08/07/2015 08/07/2016 08/08/2016 08/09/2015 08/10/2015 08/12/2015
## 9 1 3 1 4 1
## 08/12/2016 08/3/2016 08/9/2016 09/01/2015 09/01/2016 09/01/2017
## 2 3 2 1 0 1
## 09/02/2015 09/02/2016 09/02/2017 09/03/2015 09/03/2017 09/04/2015
## 2 0 2 2 2 4
## 09/05/2017 09/06/2015 09/06/2016 09/06/2017 09/08/2016 09/10/2015
## 1 4 1 2 0 5
## 09/11/2015 09/11/2016 09/12/2015 09/12/2016 09/3/2016 09/5/2016
## 6 2 3 3 4 3
## 09/7/2015 09/9/2016 10/01/2017 10/02/2015 10/02/2016 10/02/2017
## 2 1 5 0 3 1
## 10/03/2015 10/03/2017 10/04/2015 10/04/2017 10/05/2017 10/06/2015
## 0 2 1 2 3 1
## 10/06/2016 10/07/2017 10/08/2015 10/08/2016 10/09/2015 10/10/2016
## 1 1 7 2 3 1
## 10/11/2015 10/11/2016 10/12/2015 10/3/2016 10/5/2016 10/7/2015
## 1 1 7 3 5 3
## 10/9/2016 11/01/2016 11/01/2017 11/02/2015 11/03/2015 11/04/2015
## 2 3 0 2 4 1
## 11/04/2016 11/04/2017 11/05/2017 11/06/2015 11/06/2016 11/07/2016
## 3 2 1 2 0 3
## 11/07/2017 11/08/2015 11/08/2016 11/09/2015 11/10/2016 11/11/2015
## 2 5 3 2 2 2
## 11/11/2016 11/12/2015 11/3/2016 11/5/2016 12/01/2015 12/01/2016
## 0 3 0 2 1 3
## 12/01/2017 12/02/2015 12/02/2016 12/03/2015 12/04/2016 12/04/2017
## 2 2 4 1 1 4
## 12/05.2015 12/05/2015 12/05/2017 12/06/2015 12/06/2017 12/07/2016
## 1 2 3 1 5 1
## 12/07/2017 12/08/2015 12/08/2016 12/09/2015 12/10/2015 12/10/2016
## 1 4 4 1 2 0
## 12/11/2015 12/12/2016 12/5/2016 12/9/2016 13/01/2015 13/01/2016
## 1 4 3 4 0 4
## 13/01/2017 13/02/2015 13/02/2017 13/03/2015 13/04.2015 13/04/2015
## 2 2 6 2 1 4
## 13/04/2016 13/04/2017 13/05/2015 13/06/2016 13/06/2017 13/07/2016
## 4 1 0 2 1 4
## 13/08/2015 13/10/2015 13/10/2016 13/11/2015 13/12/2016 13/5/2016
## 3 2 2 0 2 2
## 13/7/2015 13/9/2016 14/01/2015 14/01/2016 14/01/2017 14/02/2017
## 2 1 1 1 1 2
## 14/03/2015 14/03/2017 14/04/2015 14/04/2016 14/04/2017 14/05/2015
## 2 0 1 1 1 2
## 14/06/2016 14/06/2017 14/07/2016 14/07/2017 14/08/2015 14/09/2015
## 3 2 2 2 2 4
## 14/10/2015 14/10/2016 14/11/2016 14/12/2015 14/12/2016 14/3/2016
## 2 3 2 3 3 2
## 14/7/2015 14/9/2016 15/01.2015 15/01/2016 15/02/2016 15/02/2017
## 5 1 1 1 5 2
## 15/03/2017 15/04/2015 15/04/2016 15/04/2017 15/05/2015 15/05/2017
## 2 3 4 1 1 1
## 15/06/2015 15/06/2016 15/06/2017 15/07/2016 15/08/2016 15/09/2015
## 2 1 1 4 0 4
## 15/10/2015 15/10/2016 15/11/2016 15/12/2015 15/12/2016 15/3/2016
## 0 3 2 1 4 3
## 15/7/2015 15/9/2016 16/01/2015 16/01/2016 16/01/2017 16/02/2015
## 3 3 1 0 2 2
## 16/02/2016 16/02/2017 16/03/2015 16/03/2017 16/04/2015 16/05/2017
## 3 2 4 3 2 2
## 16/06/2015 16/06/2016 16/06/2017 16/08/2016 16/09/2015 16/10/2015
## 1 3 3 5 3 0
## 16/11/2015 16/11/2016 16/12/2015 16/12/2016 16/3/2016 16/5/2016
## 3 1 3 2 2 1
## 16/7/2015 16/9/2016 17/01/2017 17/02/2015 17/02/2016 17/02/2017
## 1 2 6 0 3 4
## 17/03/2015 17/03/2017 17/04/2015 17/04/2017 17/05/2017 17/06/2015
## 0 0 1 4 2 1
## 17/06/2016 17/07/2017 17/08/2015 17/08/2016 17/09/2015 17/10/2015
## 3 2 3 2 2 1
## 17/10/2016 17/11/2015 17/11/2016 17/12/2015 17/3/2016 17/5/2016
## 2 6 2 2 4 0
## 17/7/2015 18/01/2015 18/01/2016 18/01/2017 18/02/2015 18/02/2016
## 2 1 1 2 1 3
## 18/02/2017 18/03/2015 18/04/2015 18/04/2016 18/04/2017 18/05/2015
## 1 2 0 1 3 0
## 18/05/2017 18/06/2015 18/07/2016 18/07/2017 18/08/2015 18/08/2016
## 2 2 3 0 4 0
## 18/09/2015 18/10/2016 18/11/2015 18/11/2016 18/12/2015 18/3/2016
## 4 2 3 1 2 1
## 18/5/2016 19/01/2015 19/01/2016 19/01/2017 19/02/2016 19/03/2015
## 4 7 5 1 2 2
## 19/04/2016 19/04/2017 19/05/2015 19/05/2017 19/06/2015 19/06/2017
## 3 1 7 1 2 2
## 19/07/2016 19/07/2017 19/08/2015 19/08/2016 19/09/2015 19/10/2015
## 4 2 2 2 0 2
## 19/10/2016 19/11/2015 19/12/2016 19/3/2016 19/5/2016 19/9/2016
## 3 3 1 1 3 2
## 2/01/2017 20/01/2015 20/01/2016 20/01/2017 20/02/2015 20/02/2017
## 0 2 4 0 2 1
## 20/03/2015 20/04/2015 20/04/2016 20/04/2017 20/05/2015 20/05/2017
## 3 4 1 1 1 0
## 20/06/2015 20/06/2016 20/06/2017 20/07/2016 20/07/2017 20/08/2015
## 2 0 2 2 1 3
## 20/10/2015 20/10/2016 20/11/2015 20/12/2016 20/5/2016 20/7/2015
## 2 0 4 2 1 2
## 20/9/2016 21/01/2015 21/01/2016 21/02/2015 21/02/2017 21/03/2015
## 1 2 4 1 1 0
## 21/03/2017 21/04/2016 21/04/2017 21/05/2015 21/06/2015 21/06/2016
## 3 0 1 1 1 5
## 21/06/2017 21/07/2016 21/07/2017 21/08/2015 21/09/2015 21/10/2015
## 4 2 1 1 3 4
## 21/10/2016 21/11/2015 21/11/2016 21/12/2015 21/12/2016 21/3/2016
## 1 2 2 3 2 1
## 21/7/2015 21/9/2016 22/01//2015 22/01/2015 22/01/2016 22/01/2017
## 1 4 1 2 5 1
## 22/02/2016 22/02/2017 22/03/2017 22/04/2015 22/04/2016 22/05/2015
## 5 1 1 2 2 2
## 22/05/2017 22/06/2015 22/06/2016 22/06/2017 22/08/2016 22/09/2015
## 2 2 1 3 1 5
## 22/11/2016 22/12/2015 22/12/2016 22/3/2016 22/7/2015 22/9/2016
## 4 4 2 4 2 4
## 23/01/2017 23/02/2015 23/02/2016 23/02/2017 23/03/2015 23/03/2017
## 1 4 1 1 1 2
## 23/04/2015 23/05/2017 23/06/2015 23/06/2016 23/06/2017 23/08/2016
## 5 2 3 2 2 2
## 23/09/2015 23/10/2015 23/10/2016 23/11/2015 23/11/2016 23/12/2015
## 3 4 0 2 3 2
## 23/12/2016 23/3/2016 23/5/2016 23/7/2015 23/9/2016 24/01/2015
## 1 4 1 7 1 1
## 24/01/2017 24/02/2015 24/02/2016 24/02/2017 24/03/2015 24/03/2017
## 3 1 4 0 2 2
## 24/04/2015 24/04/2017 24/05/2017 24/06/2015 24/06/2016 24/08/2015
## 1 3 1 3 5 2
## 24/08/2016 24/09/2015 24/10/2016 24/11/2015 24/11/2016 24/12/2015
## 6 4 2 2 1 2
## 24/3/2016 24/5/2016 24/7/2015 25/01/2015 25/01/2016 25/01/2017
## 1 1 1 1 2 3
## 25/02/2015 25/02/2016 25/03/2015 25/04/2016 25/04/2017 25/05/2015
## 4 3 4 2 2 1
## 25/05/2017 25/06/2015 25/07/2017 25/08/2015 25/08/2016 25/09/2015
## 1 2 2 4 2 0
## 25/10/2016 25/11/2015 25/11/2016 25/5/2016 25/7/2015 26/01/2017
## 3 3 0 2 1 2
## 26/02/2015 26/02/2016 26/03/2015 26/04/2016 26/04/2017 26/05/2015
## 1 2 2 3 4 3
## 26/05/2017 26/06/2015 26/06/2017 26/07/2017 26/08/2015 26/08/2016
## 0 2 3 2 3 3
## 26/10/2015 26/10/2016 26/11/2015 26/11/2016 26/12/2016 26/5/2016
## 5 2 3 0 2 2
## 26/9/2016 27/01/2015 27/01/2016 27/02/2015 27/02/2017 27/03/2015
## 0 1 2 2 2 2
## 27/03/2017 27/04/2015 27/04/2016 27/04/2017 27/05/2015 27/06/2016
## 0 3 2 2 3 3
## 27/06/2017 27/07/2017 27/08/2015 27/10/2015 27/10/2016 27/11/2015
## 2 1 0 3 3 1
## 27/12/2016 27/5/2016 27/7/2015 27/9/2016 28/01/2015 28/01/2016
## 1 0 1 1 1 2
## 28/02/2017 28/03/2015 28/03/2017 28/04/2015 28/04/2016 28/04/2017
## 2 3 2 3 2 1
## 28/05/2015 28/06/2016 28/06/2017 28/07/2017 28/08/2015 28/09/2015
## 2 4 2 1 2 1
## 28/10/2015 28/10/2016 28/11/2015 28/11/2016 28/12/2015 28/12/2016
## 2 0 0 2 2 0
## 28/3/2016 28/7/2015 28/9/2016 29/01/2015 29/01/2016 29/02/2016
## 1 4 4 2 5 1
## 29/03/2017 29/04/2015 29/04/2016 29/04/2017 29/05/2017 29/06/2015
## 4 6 2 0 1 3
## 29/06/2016 29/06/2017 29/08/2015 29/08/2016 29/09/2015 29/10/2015
## 3 1 0 4 4 1
## 29/10/2016 29/11/2016 29/12/2015 29/12/2016 29/3/2016 29/7/2015
## 3 1 2 1 4 4
## 29/9/2016 30/01/2015 30/01/2016 30/01/2017 30/03/2015 30/03/2017
## 2 1 0 4 2 0
## 30/05/2015 30/05/2017 30/06/2015 30/06/2016 30/06/2017 30/09/2015
## 1 2 2 3 2 3
## 30/10/2015 30/10/2016 30/11/2015 30/11/2016 30/12/2015 30/12/2016
## 5 1 3 10 0 0
## 30/3/2016 30/5/2016 30/7/2015 30/8/2016 30/9/2016 31/01/2015
## 1 1 5 5 1 1
## 31/01/2017 31/03/2015 31/03/2017 31/05/2017 31/08/2015 31/10/2015
## 1 3 4 2 2 1
## 31/10/2016 31/3/2016 31/5/2016 31/7/2015 31/8/2016
## 1 0 3 3 1table(startup2$IndustryVertical)##
## Consumer Internet ECommerce Education Healthcare
## 459 32 15 14
## Logistics OtherSectors Technology
## 16 144 189table(startup2$CityLocation)##
## Bangalore Gurgaon Hyderabad Mumbai New Delhi Others Pune
## 267 97 35 181 130 122 37#The major Industrial sectors acquiring funding by numbers
table(startup2$InvestmentType)##
## Debt Funding Private Equity Seed Funding
## 1 476 392#The funded startups location
sum(startup2$AmountInUSD)## [1] 9674311608boxplot(startup2$AmountInUSD, horizontal = TRUE, xlab = "Amount in USD", main = "Startup Investment plot")
boxplot(startup2$AmountInUSD ~ startup2$Date, horizontal = TRUE, xlab= "Amount of Investment in USD", ylab = "Year", main = "Year Wise Investment Analysis")
boxplot(startup2$AmountInUSD ~ startup2$IndustryVertical,horizontal = FALSE, ylab= "Amount of Investment in USD", xlab = "Industry Sector", main = "Industrial Sector Wise Investment Analysis", boxwex = 0.6, names = c("Con Int", "Ecomm", "Edu", "HealthCare", "Logist.", "Others", "Tech"))
mytable <- xtabs(startup2$AmountInUSD ~ startup2$IndustryVertical)
plot(mytable, ylab = "AMount of Investment", xlab = "Industry Sector")
boxplot(startup2$AmountInUSD ~ startup2$CityLocation,horizontal = FALSE, ylab= "Amount of Investment in USD", xlab = "City", main = "Startup citiwise location Wise Investment Analysis", boxwex = 0.6, names = c("Bang", "Gurgaon", "Hyd", "Mum", "N. Delhi", "Others", "Pune"))
boxplot(startup2$AmountInUSD ~ startup2$InvestmentType,horizontal = FALSE, ylab= "Amount of Investment in USD", xlab = "Investment Type", main = "Investment type vs Investment Analysis")
#mytable <- xtabs(startup2$AmountInUSD ~ statup2$InvestmentType)
plot(mytable, ylab = "AMount of Investment", xlab = "Type of Investment")
b = barplot(head(sort(table(startup2$CityLocation), decreasing=T),20),col=rainbow(10,0.5), las=2, ylim=c(0,750), xlab="City Name", ylab="No Of StartUps")
text(b,head(sort(table(startup2$CityLocation), decreasing=T),20),head(sort(table(startup2$CityLocation), decreasing=T),20),srt=90, pos=4)
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.