State.Uts GSDP.Curr.2011.12.Cr. GSDP.Curr.2012.13.Cr.
Length:34 Min. : 0 Min. : 0
Class :character 1st Qu.: 18878 1st Qu.: 21622
Mode :character Median : 154496 Median : 176118
Mean : 253867 Mean : 289017
3rd Qu.: 362894 3rd Qu.: 408951
Max. :1280369 Max. :1459629
GSDP.Curr.2013.14.Cr. GSDP.Curr.2014.15.Cr. GSDP.Curr.2015.16.Cr.
Min. : 0 Min. : 0 Min. : 0
1st Qu.: 23409 1st Qu.: 24063 1st Qu.: 27281
Median : 197700 Median : 219822 Median : 226561
Mean : 327657 Mean : 359945 Mean : 399774
3rd Qu.: 461099 3rd Qu.: 510885 3rd Qu.: 573925
Max. :1649647 Max. :1779138 Max. :1966225
GSDP.Curr.2016.17.Cr. GSDP.Curr.2017.18.Cr. GSDP.Curr.2018.19.Cr.
Min. : 0 Min. : 0 Min. : 0
1st Qu.: 30286 1st Qu.: 33183 1st Qu.: 37324
Median : 258592 Median : 282724 Median : 313719
Mean : 451229 Mean : 501745 Mean : 556773
3rd Qu.: 656200 3rd Qu.: 744109 3rd Qu.: 850826
Max. :2198185 Max. :2352782 Max. :2567897
GSDP.Curr.2019.20.Cr. GSDP.Curr.2020.21.Cr. GSDP.Curr.2021.22.Cr.
Min. : 9719 Min. : 29214 Min. : 37494
1st Qu.: 43984 1st Qu.: 54565 1st Qu.: 130277
Median : 377405 Median : 461261 Median : 638342
Mean : 624831 Mean : 657890 Mean : 787722
3rd Qu.: 948356 3rd Qu.: 985542 3rd Qu.:1182571
Max. :2734552 Max. :2711685 Max. :2179655
NA's :1 NA's :2 NA's :15
X..Growth2012.13 X..Growth2013.14 X..Growth2014.15 X..Growth2015.16
Min. :-10.02 Min. :-5.77 Min. : 0.00 Min. :-5.450
1st Qu.: 11.43 1st Qu.:12.31 1st Qu.: 7.12 1st Qu.: 8.672
Median : 13.46 Median :13.30 Median : 9.81 Median :10.605
Mean : 12.19 Mean :12.90 Mean :10.81 Mean :10.603
3rd Qu.: 14.22 3rd Qu.:15.29 3rd Qu.:11.99 3rd Qu.:14.018
Max. : 20.71 Max. :23.10 Max. :33.11 Max. :21.680
X..Growth2016.17 X..Growth2017.18 X..Growth2018.19 X..Growth2019.20
Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. : 0.000
1st Qu.:10.01 1st Qu.:10.29 1st Qu.: 9.065 1st Qu.: 7.260
Median :11.82 Median :11.74 Median :10.940 Median : 9.210
Mean :11.99 Mean :11.84 Mean :10.491 Mean : 8.976
3rd Qu.:13.53 3rd Qu.:12.91 3rd Qu.:13.053 3rd Qu.:10.570
Max. :20.10 Max. :25.54 Max. :14.420 Max. :22.000
NA's :1
X..Growth2020.21 X..Growth2021.22
Min. :-5.3800 Min. : 2.90
1st Qu.:-0.9025 1st Qu.:11.97
Median : 1.4850 Median :14.56
Mean : 2.1694 Mean :14.27
3rd Qu.: 5.1025 3rd Qu.:18.06
Max. :15.7900 Max. :19.74
NA's :2 NA's :15
'data.frame': 34 obs. of 22 variables:
$ State.Uts : chr "Andhra Pradesh " "Arunachal Pradesh" "Assam" "Bihar" ...
$ GSDP.Curr.2011.12.Cr.: num 379402 11063 143175 247144 158074 ...
$ GSDP.Curr.2012.13.Cr.: num 411404 12547 156864 282368 177511 ...
$ GSDP.Curr.2013.14.Cr.: num 464272 14581 177745 317101 206833 ...
$ GSDP.Curr.2014.15.Cr.: num 524976 17959 195723 342951 221118 ...
$ GSDP.Curr.2015.16.Cr.: num 604229 18509 227959 371602 225163 ...
$ GSDP.Curr.2016.17.Cr.: num 684416 19902 254382 421052 262802 ...
$ GSDP.Curr.2017.18.Cr.: num 786135 22475 283165 468746 282283 ...
$ GSDP.Curr.2018.19.Cr.: num 873721 25331 309336 527976 318101 ...
$ GSDP.Curr.2019.20.Cr.: num 966099 27885 377405 594016 344955 ...
$ GSDP.Curr.2020.21.Cr.: num 1014374 29695 381004 618628 350270 ...
$ GSDP.Curr.2021.22.Cr.: num 1201736 NA 433925 NA NA ...
$ X..Growth2012.13 : num 8.43 13.41 9.56 14.25 12.3 ...
$ X..Growth2013.14 : num 12.8 16.2 13.3 12.3 16.5 ...
$ X..Growth2014.15 : num 13.08 23.17 10.11 8.15 6.91 ...
$ X..Growth2015.16 : num 15.1 3.06 16.47 8.35 1.83 ...
$ X..Growth2016.17 : num 13.27 7.53 11.59 13.31 16.72 ...
$ X..Growth2017.18 : num 14.86 12.93 11.31 11.33 7.41 ...
$ X..Growth2018.19 : num 11.14 12.71 9.24 12.64 12.69 ...
$ X..Growth2019.20 : num 10.57 10.08 22 12.51 8.44 ...
$ X..Growth2020.21 : num 5 6.49 0.95 4.14 1.54 4.69 1.61 -0.46 -1.56 NA ...
$ X..Growth2021.22 : num 18.5 NA 13.9 NA NA ...
GSDP.Curr.2011.12.Cr. GSDP.Curr.2012.13.Cr.
GSDP.Curr.2011.12.Cr. 1.0000000 0.9997533
GSDP.Curr.2012.13.Cr. 0.9997533 1.0000000
GSDP.Curr.2013.14.Cr. 0.9995519 0.9998343
GSDP.Curr.2014.15.Cr. 0.9990817 0.9994978
GSDP.Curr.2015.16.Cr. 0.9983741 0.9987517
GSDP.Curr.2016.17.Cr. 0.9972931 0.9978962
GSDP.Curr.2017.18.Cr. 0.9958589 0.9965453
GSDP.Curr.2018.19.Cr. 0.9947496 0.9955635
GSDP.Curr.2013.14.Cr. GSDP.Curr.2014.15.Cr.
GSDP.Curr.2011.12.Cr. 0.9995519 0.9990817
GSDP.Curr.2012.13.Cr. 0.9998343 0.9994978
GSDP.Curr.2013.14.Cr. 1.0000000 0.9995831
GSDP.Curr.2014.15.Cr. 0.9995831 1.0000000
GSDP.Curr.2015.16.Cr. 0.9990560 0.9996497
GSDP.Curr.2016.17.Cr. 0.9983911 0.9991815
GSDP.Curr.2017.18.Cr. 0.9970468 0.9983347
GSDP.Curr.2018.19.Cr. 0.9960813 0.9975521
GSDP.Curr.2015.16.Cr. GSDP.Curr.2016.17.Cr.
GSDP.Curr.2011.12.Cr. 0.9983741 0.9972931
GSDP.Curr.2012.13.Cr. 0.9987517 0.9978962
GSDP.Curr.2013.14.Cr. 0.9990560 0.9983911
GSDP.Curr.2014.15.Cr. 0.9996497 0.9991815
GSDP.Curr.2015.16.Cr. 1.0000000 0.9997076
GSDP.Curr.2016.17.Cr. 0.9997076 1.0000000
GSDP.Curr.2017.18.Cr. 0.9990861 0.9995315
GSDP.Curr.2018.19.Cr. 0.9983313 0.9989436
GSDP.Curr.2017.18.Cr. GSDP.Curr.2018.19.Cr.
GSDP.Curr.2011.12.Cr. 0.9958589 0.9947496
GSDP.Curr.2012.13.Cr. 0.9965453 0.9955635
GSDP.Curr.2013.14.Cr. 0.9970468 0.9960813
GSDP.Curr.2014.15.Cr. 0.9983347 0.9975521
GSDP.Curr.2015.16.Cr. 0.9990861 0.9983313
GSDP.Curr.2016.17.Cr. 0.9995315 0.9989436
GSDP.Curr.2017.18.Cr. 1.0000000 0.9998057
GSDP.Curr.2018.19.Cr. 0.9998057 1.0000000
---
title: "GSDP_Statewise"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
theme: darkly
storyboard: true
social: menu
source_code: embed
---
```{r setup, include=FALSE}
libraries=c("MASS","lattice","flexdashboard","DT","ggplot2","dplyr","tidyr","corrplot","data.table")
lapply(libraries,require,character.only=TRUE)
```
# EXPLORING DATASET {.tabset}
## Column {data-width=1500}
### Chart A
```{r}
Gsdp=read.csv(file.path("D:\\Downloads\\GSDP_Current_State_wise - Copy.csv"),header = TRUE)
data=na.omit(data)
summary(Gsdp)
str(Gsdp)
attach(Gsdp)
```
# Data Analysis{.tabset}
### HISTOGRAM
```{r}
hist(Gsdp$GSDP.Curr.2015.16.Cr. ,breaks = 50,col = "blue")
abline(v=mean(Gsdp$GSDP.Curr.2015.16.Cr.),col='yellow',lwd=6)
hist(Gsdp$X..Growth2020.21 ,breaks = 100,col = "blue")
```
### BOXPLOT
```{r}
boxplot(GSDP.Curr.2011.12.Cr.~X..Growth2021.22)
```
### SCATERPLOT
```{r}
plot(X..Growth2013.14,GSDP.Curr.2011.12.Cr., main="Gsdp of 2011-12 & 2013-14 ")
```
# RELATION {.tabset}
```{r}
newdata=Gsdp[c(2:9)]
cor(newdata)
```
### CORPLOT
```{r}
corrplot(cor(newdata),method = "number",shade.col = NA,tl.col = "black",tl.srt = 20)
```
# GGPLOT {.tabset}
### GGPLOT 1
```{r}
ggplot(Gsdp)+
geom_point(mapping = aes(x=X..Growth2020.21,y=X..Growth2021.22,color="blue"))+
ggtitle(label = "Gsdp of 2020-21 & 2021-22")
```
### GGPLOT 2
```{r}
ggplot(Gsdp)+
geom_point(mapping = aes(x=X..Growth2013.14,y=GSDP.Curr.2011.12.Cr.,color="yellow"))+
ggtitle(label = "Gsdp of 2013-14 & 2011-12")
```
# DOWNLOAD
```{r}
datatable(Gsdp,extensions = 'Buttons',options = list(dom="Bftrip",Buttons=c('copy','print','csv','pdf')))
```