This is assignment, we will try to understand the trend how the children are enrolled in schools in india. The data is downloaded from Government website available for public use

This is how the data looks like:

head(Num.Students)

##      School.Type           India..State..UTs Gender     X2001     X2002
## 1 Primary School                       India  Total 113883060 122397715
## 2 Primary School                       India    Boy        NA  65084379
## 3 Primary School                       India   Girl        NA  57313336
## 4 Primary School Andaman and Nicobar Islands  Total     40022     39625
## 5 Primary School Andaman and Nicobar Islands    Boy        NA     20603
## 6 Primary School Andaman and Nicobar Islands   Girl        NA     19022
##       X2003     X2004     X2005     X2006     X2007     X2008     X2009
## 1 128266291 130763067 130822117 133719922 135470561 135323683 133694932
## 2  68360423  69674543  69789220  71087421  71089547  70604246  69749314
## 3  59905868  61088524  61032897  62632501  64381014  64719437  63945618
## 4     40388     40274     37601     38174     36637     35192     34242
## 5     20981     20852     19303     19582     18811     17996     17553
## 6     19407     19422     18298     18592     17826     17196     16689
##       X2010
## 1 135316946
## 2  70468427
## 3  64848519
## 4     33416
## 5     17114
## 6     16302

Student.Num <- Num.Students[Num.Students$India..State..UTs=="India",]
names(Student.Num)= c("SchoolType","Country","Gender","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010")
library(reshape)
Student.Num <-melt(Student.Num, id = c("SchoolType","Country", "Gender"))
names(Student.Num) = c("SchoolType", "Country", "Gender","Year","Numofstudent")

Lets plot the data to understand the trend.

library(ggplot2)
ggplot(Student.Num, aes(Year,Numofstudent/10^6 ,group = Gender,colour = Gender)) +  geom_line() + facet_grid(facets = SchoolType ~ .)

## Warning: Removed 2 rows containing missing values (geom_path).