Reading data from csv file
data=read.csv("/Volumes/Apple/Programming/R_DataFiles/RD/Agri_Area.csv")
Print data
print(data)
## Crop Kerala Thiruvananthapuram Kollam Pathanamthitta Alappuzha
## 1 Paddy 196870 2119 1555 2534 31724
## 2 Coconut 790223 72340 51834 15884 33227
## 3 Arecanut 99126 1036 1931 1114 1325
## 4 Tapioca 69405 14585 15147 5220 2715
## 5 Banana 59835 2676 2884 2059 476
## 6 Plantain 57683 7120 5231 1988 2121
## 7 Jack 92969 6853 6687 2883 2714
## 8 Mango 79992 4707 5651 1806 4633
## 9 Pappaya 19076 1787 1416 735 1075
## 10 Cashew 43090 1213 2334 447 1805
## 11 Pepper 85948 2293 3330 1707 616
## 12 Ginger 4986 95 318 295 81
## 13 Turmeric 2603 73 240 83 43
## 14 Tea 30205 962 606 NA NA
## 15 Coffee 84987 0 0 0 0
## 16 Cardamom 39730 0 0 665 0
## 17 Rubber 550840 32160 37240 50880 4500
## Kottayam Idukki Eranakulam Thrissur Palakkad Malappuram Kozhikode
## 1 16272 887 5950 26625 81120 8687 2872
## 2 26849 16546 NA 81602 59976 103391 120683
## 3 1614 2244 4134 6271 8900 17895 10134
## 4 5631 6919 5152 1290 1958 5117 1583
## 5 2948 3486 4993 2165 15736 7762 1938
## 6 2894 3903 4650 5259 9629 4294 3587
## 7 4008 15428 4108 4757 6744 8698 10137
## 8 2864 6224 4491 7021 10006 8570 8380
## 9 1140 1037 1316 1473 1439 2532 2012
## 10 375 1147 433 1661 2051 2313 1981
## 11 3215 42694 1867 1790 2510 2938 3474
## 12 111 540 98 50 1106 53 42
## 13 97 188 246 77 576 326 286
## 14 NA 21970 NA 530 831 NA NA
## 15 0 12740 0 0 4833 0 0
## 16 85 31810 0 0 2760 70 220
## 17 11440 40580 60140 15660 37860 42750 21920
## Wayanad Kannur Kasaragod
## 1 9204 5478 3843
## 2 12403 89238 64335
## 3 13461 9386 19681
## 4 1888 1696 504
## 5 9739 2328 645
## 6 1413 3334 2260
## 7 8632 8551 2769
## 8 5107 7830 2702
## 9 413 1945 756
## 10 716 19769 6845
## 11 12498 4269 2747
## 12 2125 57 15
## 13 192 143 33
## 14 5306 NA NA
## 15 67414 0 0
## 16 4120 0 0
## 17 10790 48050 33910
Summary of data
summary(data)
## Crop Kerala Thiruvananthapuram Kollam
## Arecanut: 1 Min. : 2603 Min. : 0 Min. : 0
## Banana : 1 1st Qu.: 39730 1st Qu.: 962 1st Qu.: 606
## Cardamom: 1 Median : 69405 Median : 2119 Median : 2334
## Cashew : 1 Mean :135739 Mean : 8825 Mean : 8024
## Coconut : 1 3rd Qu.: 92969 3rd Qu.: 6853 3rd Qu.: 5651
## Coffee : 1 Max. :790223 Max. :72340 Max. :51834
## (Other) :11
## Pathanamthitta Alappuzha Kottayam Idukki
## Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 188
## 1st Qu.: 610.5 1st Qu.: 377.2 1st Qu.: 309 1st Qu.: 1147
## Median : 1756.5 Median : 1565.0 Median : 2879 Median : 6224
## Mean : 5518.8 Mean : 5440.9 Mean : 4971 Mean :12255
## 3rd Qu.: 2621.2 3rd Qu.: 3161.2 3rd Qu.: 4414 3rd Qu.:16546
## Max. :50880.0 Max. :33227.0 Max. :26849 Max. :42694
## NA's :1 NA's :1 NA's :1
## Eranakulam Thrissur Palakkad Malappuram
## Min. : 0.0 Min. : 0 Min. : 576 Min. : 0
## 1st Qu.: 339.5 1st Qu.: 530 1st Qu.: 1958 1st Qu.: 1816
## Median : 4108.0 Median : 1790 Median : 4833 Median : 4706
## Mean : 6505.2 Mean : 9190 Mean :14590 Mean : 13462
## 3rd Qu.: 4821.5 3rd Qu.: 6271 3rd Qu.:10006 3rd Qu.: 8690
## Max. :60140.0 Max. :81602 Max. :81120 Max. :103391
## NA's :2 NA's :1
## Kozhikode Wayanad Kannur Kasaragod
## Min. : 0 Min. : 192 Min. : 0 Min. : 0.0
## 1st Qu.: 1259 1st Qu.: 1888 1st Qu.: 1308 1st Qu.: 386.2
## Median : 2442 Median : 5306 Median : 3802 Median : 2481.0
## Mean : 11828 Mean : 9731 Mean :12630 Mean : 8815.3
## 3rd Qu.: 8818 3rd Qu.:10790 3rd Qu.: 8760 3rd Qu.: 4593.5
## Max. :120683 Max. :67414 Max. :89238 Max. :64335.0
## NA's :1 NA's :1 NA's :1
BAR PLOT MULTIPLE OPTIONS
randomcolor=grDevices::colors()[grep('gr(ale)y',grDevices::colors(),invert = T)]
barplot(data$Kollam,names=data$Crop,las=2,col = sample(randomcolor,length(data$Malappuram)),
ylab="Area in Hectares",col.lab='olivedrab',cex.lab=0.9,
lwd=1,col.axis=4,cex.axis = 0.8,font.lab=3,family="Gill Sans",
main = "AREA UNDER CROPS IN 2017-18",cex.main=1.1,col.main=10,font.main=4,
ylim=c(0,60000),font.axis=3)
mtext(side=1,line=4,"Crop name",col='orchid3',font=3,cex=0.8,family='Arial Narrow')
HISTOGRAM WITH DIFFERENT OPTIONS
area=read.csv("/Users/shibukumarapple/Desktop/Paddy_A.csv")
print(area)
## Year Kerala_A1000 Kerala_A India_A Perc
## 1 1955-56 759353 75.94 3152 2.41
## 2 1956-57 781398 78.14 3228 2.42
## 3 1957-58 766774 76.68 3230 2.37
## 4 1958-59 768435 76.84 3317 2.32
## 5 1959-60 768966 76.90 3382 2.27
## 6 1960-61 778926 77.89 3413 2.28
## 7 1961-62 752704 75.27 3469 2.17
## 8 1962-63 802676 80.27 3569 2.25
## 9 1963-64 805083 80.51 3581 2.25
## 10 1964-65 801121 80.11 3646 2.20
## 11 1965-66 802329 80.23 3547 2.26
## 12 1966-67 799438 79.94 3525 2.27
## 13 1967-68 809544 80.95 3644 2.22
## 14 1968-69 873871 87.39 3697 2.36
## 15 1969-70 874059 87.41 3768 2.32
## 16 1970-71 874830 87.48 3759 2.33
## 17 1971-72 875157 87.52 3776 2.32
## 18 1972-73 873704 87.37 3669 2.38
## 19 1973-74 874675 87.47 3829 2.28
## 20 1974-75 881466 88.15 3948 2.23
## 21 1975-76 876022 87.60 3948 2.22
## 22 1976-77 854374 85.44 3851 2.22
## 23 1977-78 840374 84.04 4028 2.09
## 24 1978-79 799238 79.92 4048 1.97
## 25 1979-80 793266 79.33 3942 2.01
## 26 1980-81 801699 80.17 4015 2.00
## 27 1981-82 806871 80.69 4071 1.98
## 28 1982-83 778499 77.85 3826 2.03
## 29 1983-84 740086 74.01 4124 1.79
## 30 1984-85 730379 73.04 4116 1.77
## 31 1985-86 678281 67.83 4114 1.65
## 32 1986-87 663803 66.38 4117 1.61
## 33 1987-88 604082 60.41 3881 1.56
## 34 1988-89 577557 57.76 4173 1.38
## 35 1989-90 583388 58.34 4217 1.38
## 36 1990-91 559450 55.95 4269 1.31
## 37 1991-92 541327 54.13 4265 1.27
## 38 1992-93 537608 53.76 4178 1.29
## 39 1993-94 507832 50.78 4254 1.19
## 40 1994-95 503290 50.33 4281 1.18
## 41 1995-96 471150 47.12 4284 1.10
## 42 1996-97 430826 43.08 4343 0.99
## 43 1997-98 387122 38.71 4345 0.89
## 44 1998-99 352631 35.26 4480 0.79
## 45 1999-00 349774 34.98 4516 0.77
## 46 2000-01 347455 34.75 4471 0.78
## 47 2001-02 322368 32.24 4490 0.72
## 48 2002-03 310521 31.05 4118 0.75
## 49 2003-04 287340 28.73 4259 0.67
## 50 2004-05 289974 29.00 4191 0.69
## 51 2005-06 275742 27.57 4366 0.63
## 52 2006-07 263529 26.35 4381 0.60
## 53 2007-08 228938 22.89 4391 0.52
## 54 2008-09 234265 23.43 4554 0.51
## 55 2009-10 234013 23.40 4192 0.56
## 56 2010-11 213187 21.32 4286 0.50
## 57 2011-12 208161 20.82 4401 0.47
## 58 2012-13 197277 19.73 4275 0.46
## 59 2013-14 199611 19.96 4395 0.45
Histogram
hist(area$India_A,breaks = 10,col=(sample(randomcolor)),border = 34,freq = FALSE,
main="HISTOGRAM OF AREA UNDER PADDY IN INDIA FROM 1956 TO 2014",
cex.main=1,col.main='darkviolet',xlab='Area under Paddy',
col.lab=2,cex.lab=0.8,font.lab=2,font.axis=3,col.axis=4,lwd=2)
# add a normal distribution line in histogram
curve(dnorm(x, mean=mean(area$India_A), sd=sd(area$India_A)), add=TRUE, col="green")
HISTOGRAM WITH FREQUENCY COUNTS
h=hist(area$India_A,breaks = 10,col=(sample(randomcolor)),border = 34,freq = TRUE,
main="HISTOGRAM OF AREA UNDER PADDY IN INDIA FROM 1956 TO 2014",
cex.main=1,col.main='darkviolet',xlab='Area under Paddy',
col.lab=2,cex.lab=0.8,font.lab=2,font.axis=3,col.axis=4,lwd=2)
h
## $breaks
## [1] 3000 3200 3400 3600 3800 4000 4200 4400 4600
##
## $counts
## [1] 1 4 6 7 7 13 15 6
##
## $density
## [1] 8.474576e-05 3.389831e-04 5.084746e-04 5.932203e-04 5.932203e-04
## [6] 1.101695e-03 1.271186e-03 5.084746e-04
##
## $mids
## [1] 3100 3300 3500 3700 3900 4100 4300 4500
##
## $xname
## [1] "area$India_A"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5),cex=1,font=4,col='deeppink')
HISTOGRAM WITH UNEQUAL BREAKS IN BARS
h=hist(area$India_A,col=(sample(randomcolor)),border = 5,freq = TRUE,
main="HISTOGRAM OF AREA UNDER PADDY IN INDIA FROM 1956 TO 2014",
cex.main=1,col.main='chocolate',xlab='Area under Paddy',
col.lab=2,cex.lab=0.8,font.lab=2,font.axis=3,col.axis=4,lwd=2,
breaks=c(3000,3400,4000,4400,4500,5000))
## Warning in plot.histogram(r, freq = freq1, col = col, border = border,
## angle = angle, : the AREAS in the plot are wrong -- rather use 'freq =
## FALSE'
h
## $breaks
## [1] 3000 3400 4000 4400 4500 5000
##
## $counts
## [1] 5 20 28 4 2
##
## $density
## [1] 2.118644e-04 5.649718e-04 1.186441e-03 6.779661e-04 6.779661e-05
##
## $mids
## [1] 3200 3700 4200 4450 4750
##
## $xname
## [1] "area$India_A"
##
## $equidist
## [1] FALSE
##
## attr(,"class")
## [1] "histogram"
text(h$mids,h$counts,labels=h$counts, adj=c(0.5, -0.5),cex=0.8,font=4,col='steelblue')
STUDY THE RELATION BETWEEN TWO VARIABLES
data=read.csv("/Volumes/Apple/Programming/R_DataFiles/RD/Plantain_Agri.csv")
print(data)
## Year Area Production Productivity
## 1 2004-05 54612 416115 7619
## 2 2005-06 55222 445333 8064
## 3 2006-07 53096 435636 8205
## 4 2007-08 51367 391896 7629
## 5 2008-09 50126 399633 7973
## 6 2009-10 47802 338546 7082
## 7 2010-11 49129 353772 7201
## 8 2011-12 48747 330634 6783
## 9 2012-13 48859 351315 7190
## 10 2013-14 54512 362395 6648
x=data$Area
y=data$Production
print(x)
## [1] 54612 55222 53096 51367 50126 47802 49129 48747 48859 54512
print(y)
## [1] 416115 445333 435636 391896 399633 338546 353772 330634 351315 362395
plot(y~x)
Customised scatter diagram
par(mar=c(5,4,3,1)) # set the margin
plot(y~x,xaxt="none",yaxt="none",xlab="",ylab="",col='deeppink',cex=1.5,pch=18)
axis(1,seq(46000,56000,1000),las=2,font=3,cex.axis=0.7,family='Optima',lwd=3,col.ticks = 'red')
axis(2,seq(330000,450000,10000),las=2,font=3,cex.axis=0.7,col='violet',col.axis='darkorange')
abline(h=seq(330000,450000,10000),v=seq(46000,56000,1000),lty=2,col='gray')
mtext(side=1,line=3,"Production of plantain",col='blue',font=2,cex=0.8,family='Arial Narrow')
mtext(side=2,line=3,'Area of plantain',col='darkgreen',font=2,cex=0.8,family='Arial Narrow')
mtext(side=3,line=0.5,'Area Vs Production of plantain- Scatter chart',col='red',cex=1.3,
family='Palatino',font=4)
text(x=49000,y=435000,'Relation between area and production',col='gold3',font=3,cex=0.7)
