Open Government Data Platform India

Screen Shot 2014-09-14 at 1.23.02 PM

I found many datasets in this site but many of them are not useful. Some of them are just junk and others are not useful for predictive analytics.

But I found one that I actually used. The y-axis labels are smudged but that can be fixed.

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The data is JSON which I parsed.

library(RJSONIO)
library(ggplot2)
library(reshape2)
library(grid)
this.dir <- dirname(parent.frame(2)$ofile) 
setwd(this.dir)

airlines   = fromJSON("json")
df <- sapply(airlines$data,unlist)
df <- data.frame(t(df))
colnames(df) <- c( (airlines[[1]][[1]])[[2]], (airlines[[1]][[2]])[[2]], (airlines[[1]][[3]])[[2]], (airlines[[1]][[4]])[[2]], (airlines[[1]][[5]])[[2]], (airlines[[1]][[6]])[[2]], (airlines[[1]][[7]])[[2]], (airlines[[1]][[8]])[[2]], (airlines[[1]][[9]])[[2]],(airlines[[1]][[10]])[[2]] )

df.melted <- melt(df, id = "YEAR")
print(class(df.melted$value))
df.melted$value<-as.numeric(df.melted$value) 
df.melted$value <- format(df.melted$value, scientific = FALSE)
print(ggplot(data = df.melted, aes(x = YEAR, y = value, color = variable)) +geom_point() + theme(axis.text.x = element_text(angle = 90,hjust = 0.9))  + theme(axis.text.y = element_text(angle = 360, hjust = 1, size=7.5, vjust=1))+ theme(plot.margin =unit(c(3,1,0.5,1), "cm")) + ylab("")  + theme(legend.text=element_text(size=6)))

This is the sample data.

 head(df)
     YEAR INTERNATIONAL  ACM (IN NOS) DOMESTIC ACM (IN NOS) TOTAL ACM (IN NOS)
1 1995-96                       92515                314727             407242
2 1996-97                       94884                324462             419346
3 1997-98                       98226                317531             415757
4 1998-99                       99563                325392             424955
5 1999-00                       99701                368015             467716
6 2000-01                      103211                386575             489786
  INTERNATIONAL PAX (IN NOS) DOMESTIC PAX (IN NOS) TOTAL PAX (IN NOS)
1                   11449756              25563998           37013754
2                   12223660              24276108           36499768
3                   12782769              23848833           36631602
4                   12916788              24072631           36989419
5                   13293027              25741521           39034548
6                   14009052              28017568           42026620
  INTERNATIONAL FREIGHT (IN MT) DOMESTIC FREIGHT (IN MT) TOTAL FREIGHT (IN MT)
1                        452853                   196516                649369
2                        479088                   202122                681210
3                        488175                   217405                705580
4                        474660                   224490                699150
5                        531844                   265570                797414
6                        557772                   288373                846145

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