Title: Analysis of Household Income and Expenditure
by Muhammad Salman Shah
Chapter 2
library(ggplot2)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlangmydata<-read.csv(file.choose())
mydata## Province Region Income Expenditure
## 1 punjab rural 140400 147596
## 2 kpk urban 90000 201762
## 3 balochis rural 356000 297956
## 4 punjab urban 156000 144994
## 5 kpk urban 282000 357188
## 6 sindh rural 144000 159570
## 7 kpk rural 248000 260594
## 8 punjab rural 96000 105800
## 9 sindh urban 205800 223508
## 10 sindh rural 97200 111516
## 11 sindh rural 225000 153132
## 12 sindh rural 140000 143244
## 13 balochis urban 108000 160854
## 14 sindh rural 300000 252520
## 15 punjab urban 840000 505670
## 16 sindh rural 168000 185957
## 17 punjab urban 120000 124792
## 18 kpk rural 120000 104450
## 19 sindh rural 120000 126920
## 20 sindh urban 696000 635876
## 21 punjab urban 355200 406982
## 22 balochis urban 204000 222508
## 23 punjab rural 168000 136252
## 24 punjab urban 110000 190060
## 25 punjab rural 347850 371358
## 26 punjab rural 785000 257448
## 27 kpk urban 387000 436252
## 28 balochis rural 384000 412890
## 29 sindh rural 114000 120922
## 30 punjab rural 708000 730310
## 31 sindh urban 252000 274720
## 32 sindh rural 107000 119594
## 33 kpk rural 216000 217326
## 34 punjab urban 130000 141528
## 35 balochis rural 360000 291650
## 36 punjab rural 113400 186606
## 37 sindh urban 180000 194978
## 38 punjab urban 162900 184344
## 39 punjab rural 320100 182656
## 40 punjab rural 306000 412520
## 41 punjab urban 422000 383792
## 42 punjab urban 216000 245056
## 43 punjab urban 432000 253670
## 44 sindh rural 66840 69650
## 45 balochis rural 78000 64710
## 46 kpk urban 312000 331242
## 47 punjab rural 190000 239750
## 48 punjab rural 240550 225730
## 49 balochis rural 396000 435854
## 50 punjab rural 1485600 717746
## 51 balochis urban 876000 871950
## 52 kpk rural 224200 266207
## 53 kpk rural 144000 155314
## 54 balochis urban 60000 84384
## 55 sindh rural 100000 116582
## 56 sindh rural 288000 316324
## 57 kpk rural 81000 215212
## 58 punjab rural 265000 153674
## 59 sindh rural 456000 379700
## 60 punjab urban 194400 195520
## 61 punjab rural 94500 80382
## 62 balochis rural 185000 212480
## 63 punjab rural 108000 122088
## 64 punjab rural 243000 151628
## 65 sindh urban 480000 581546
## 66 punjab urban 346800 343620
## 67 punjab rural 182000 142620
## 68 punjab urban 240000 266950
## 69 kpk urban 300000 277206
## 70 sindh urban 720000 795430
## 71 kpk urban 768000 529754
## 72 sindh urban 318000 293584
## 73 punjab rural 461750 144896
## 74 balochis urban 720000 206700
## 75 kpk rural 204000 152602
## 76 sindh urban 534000 527306
## 77 kpk urban 204000 197155
## 78 sindh rural 107400 105366
## 79 punjab urban 480000 405452
## 80 punjab rural 420000 431536
## 81 punjab urban 152400 177318
## 82 kpk rural 222000 240798
## 83 sindh urban 540000 507476
## 84 punjab rural 24000 75442
## 85 balochis rural 165000 189944
## 86 punjab rural 14700 83120
## 87 sindh rural 678000 642300
## 88 punjab rural NA 207056
## 89 kpk rural 175000 301399
## 90 punjab urban 144000 115818
## 91 punjab urban 124800 102112
## 92 balochis rural 168000 189422
## 93 punjab rural 48000 55432
## 94 sindh urban 264000 309326
## 95 punjab rural 26400 144462
## 96 punjab urban 138000 139582
## 97 kpk rural 288000 319070
## 98 balochis rural 150000 174004
## 99 punjab urban 288000 247110
## 100 punjab rural 100800 101848
## 101 sindh rural 177000 198564
## 102 sindh rural 65000 112924
## 103 punjab rural 96000 111700
## 104 punjab urban 315000 289386
## 105 kpk rural 288000 405936
## 106 sindh rural 336000 388940
## 107 sindh rural 60500 64230
## 108 sindh rural 660000 641710
## 109 balochis rural 70000 87728
## 110 sindh rural 204000 190298
## 111 sindh rural 128300 145644
## 112 punjab rural 167800 128668
## 113 sindh rural 232400 192470
## 114 punjab urban 426000 410800
## 115 balochis urban 456000 262476
## 116 sindh rural 247200 189652
## 117 sindh rural 180000 169644
## 118 sindh rural 345000 220048
## 119 punjab urban 420000 329838
## 120 sindh urban 264000 285542
## 121 punjab rural 42000 75674
## 122 balochis rural 250000 177714
## 123 kpk urban 264000 313924
## 124 punjab urban 1044000 553256
## 125 sindh urban 660000 396156
## 126 punjab rural 92000 106840
## 127 sindh rural 81600 87504
## 128 kpk rural 108000 193628
## 129 kpk urban 288000 321940
## 130 sindh rural 157500 140568
## 131 sindh rural 60900 67968
## 132 punjab urban 288000 317408
## 133 kpk rural 334000 249706
## 134 balochis rural 324000 267368
## 135 kpk rural 600000 547977
## 136 sindh rural 254000 211846
## 137 punjab urban 192000 220248
## 138 sindh rural 126000 139160
## 139 kpk rural 20000 108050
## 140 sindh rural 775000 324882
## 141 sindh rural 77600 90050
## 142 balochis rural 194800 141120
## 143 sindh rural 48000 38720
## 144 sindh rural 150000 197440
## 145 sindh rural 153600 170410
## 146 punjab rural 227500 95070
## 147 sindh rural 90000 89116
## 148 sindh rural 456000 477020
## 149 sindh urban 286800 215100
## 150 punjab rural 109050 214584
## 151 kpk urban 252000 305914
## 152 punjab rural 248000 223186
## 153 punjab urban 280000 245220
## 154 punjab rural 91300 186546
## 155 punjab urban 240000 248508
## 156 kpk rural 37500 130644
## 157 punjab urban 528000 445268
## 158 sindh rural 60000 65324
## 159 kpk rural 105600 231576
## 160 punjab urban 22800 113906
## 161 sindh urban 276000 322362
## 162 punjab rural 394000 445232
## 163 sindh urban 246000 287770
## 164 punjab urban 435600 472460
## 165 punjab rural 255668 241870
## 166 sindh rural 129000 124766
## 167 punjab rural 165600 161056
## 168 sindh rural 97000 111698
## 169 punjab rural 230270 118084
## 170 kpk urban 85050 89968
## 171 kpk urban 234000 344800
## 172 punjab rural 183700 188798
## 173 punjab rural 187900 193591
## 174 kpk rural 180000 194485
## 175 punjab urban 513600 440572
## 176 sindh urban 660000 558180
## 177 punjab urban 108000 266270
## 178 punjab urban 456000 412570
## 179 punjab urban 268800 298722
## 180 punjab rural 156000 145524
## 181 sindh rural 180000 254968
## 182 sindh rural 88200 78652
## 183 sindh rural 499600 412390
## 184 punjab urban 1020000 750980
## 185 sindh rural 336200 204820
## 186 kpk rural 336000 395670
## 187 balochis rural 264000 272980
## 188 punjab rural 779110 359890
## 189 punjab urban 389000 427426
## 190 sindh rural 157850 108250
## 191 sindh rural 353000 58676
## 192 kpk urban 173000 269813
## 193 punjab rural 484000 382640
## 194 kpk rural 272000 157085
## 195 kpk urban 336000 393576
## 196 punjab rural 62000 72742
## 197 punjab urban 240000 281610
## 198 sindh rural 102900 114046
## 199 punjab rural 486030 208870
## 200 punjab urban 236000 278744
## 201 sindh urban 220000 195552
## 202 balochis urban 138400 149800
## 203 kpk rural 144000 118577
## 204 punjab urban 300000 266940
## 205 punjab rural 156000 164756
## 206 punjab rural 168000 191532
## 207 punjab urban 326400 335250
## 208 sindh rural 120000 137702
## 209 punjab rural 79200 347980
## 210 punjab urban 168000 198012
## 211 balochis rural 144000 168690
## 212 kpk urban 192000 242376
## 213 sindh urban 180000 160644
## 214 punjab rural 200700 141330
## 215 balochis rural 324000 306504
## 216 punjab rural 36000 38206
## 217 punjab rural 331750 195802
## 218 sindh rural 76500 76000
## 219 punjab rural 38000 96160
## 220 punjab rural 354400 177500
## 221 balochis rural 177000 188818
## 222 punjab rural 200400 162402
## 223 balochis rural 280000 342884
## 224 punjab urban 258000 174348
## 225 balochis rural 260000 301480
## 226 punjab rural 133120 119070
## 227 punjab rural 39500 155480
## 228 punjab rural 228000 81930
## 229 punjab rural 52000 67748
## 230 balochis rural 144000 101808
## 231 kpk urban 374400 475900
## 232 sindh rural 66000 74314
## 233 kpk rural 314000 310556
## 234 kpk urban 420000 674720
## 235 punjab rural 78800 116182
## 236 kpk rural 216000 215958
## 237 punjab urban 288000 270816
## 238 punjab rural 857700 553650
## 239 sindh rural 700000 584304
## 240 balochis rural 168000 195210
## 241 sindh urban 144000 135868
## 242 punjab rural 30000 76306
## 243 sindh rural 120000 112593
## 244 kpk rural 11890 151688
## 245 balochis rural 180000 208242
## 246 punjab rural 141650 146506
## 247 punjab rural 205000 232222
## 248 kpk rural 205500 113924
## 249 sindh urban 660000 881440
## 250 punjab rural 25500 75172
## 251 punjab rural 40500 41394
## 252 punjab rural 432000 428478
## 253 kpk rural 155500 191190
## 254 sindh rural 216000 238440
## 255 punjab rural 60000 198088
## 256 sindh urban 360000 366230
## 257 punjab rural 242200 248250
## 258 punjab urban 504000 529376
## 259 kpk rural 168000 265273
## 260 punjab rural 67000 66330
## 261 punjab rural 144000 110870
## 262 sindh rural 124000 135114
## 263 balochis rural 96000 88732
## 264 punjab urban 60000 66818
## 265 punjab rural 672000 537200
## 266 kpk rural 88000 186414
## 267 punjab rural 200400 183470
## 268 sindh rural 393800 223300
## 269 sindh urban 600000 457952
## 270 punjab rural 122200 123749
## 271 sindh urban 144000 151244
## 272 balochis urban 240000 281076
## 273 kpk rural 320000 342767
## 274 punjab urban 210000 209752
## 275 kpk rural 308000 276834
## 276 punjab urban 186000 148322str(mydata)## 'data.frame': 276 obs. of 4 variables:
## $ Province : Factor w/ 4 levels "balochis","kpk",..: 3 2 1 3 2 4 2 3 4 4 ...
## $ Region : Factor w/ 2 levels "rural","urban": 1 2 1 2 2 1 1 1 2 1 ...
## $ Income : int 140400 90000 356000 156000 282000 144000 248000 96000 205800 97200 ...
## $ Expenditure: int 147596 201762 297956 144994 357188 159570 260594 105800 223508 111516 ...qplot(mydata$Income,mydata$Expenditure)## Warning: Removed 1 rows containing missing values (geom_point).
qplot(mydata$Income,mydata$Expenditure)## Warning: Removed 1 rows containing missing values (geom_point).
qplot(Income,Expenditure,data = mydata)## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
plot(mydata$Income, mydata$Expenditure, type="l")
points(mydata$Income, mydata$Expenditure)
plot(mydata$Income, mydata$Expenditure/2, col="red")
points(mydata$Income, mydata$Expenditure/2,col="red")
qplot(mydata$Income,mydata$Expenditure,geom = "line")## Warning: Removed 1 rows containing missing values (geom_path).
qplot(Income,Expenditure,data = mydata,geom = "line")## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
qplot(Income,Expenditure,data = mydata,geom=c("line","point"))## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+geom_point()## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
barplot(mydata$Income, names.arg=mydata$Expenditure)
table(mydata$Income)##
## 11890 14700 20000 22800 24000 25500 26400 30000 36000
## 1 1 1 1 1 1 1 1 1
## 37500 38000 39500 40500 42000 48000 52000 60000 60500
## 1 1 1 1 1 2 1 4 1
## 60900 62000 65000 66000 66840 67000 70000 76500 77600
## 1 1 1 1 1 1 1 1 1
## 78000 78800 79200 81000 81600 85050 88000 88200 90000
## 1 1 1 1 1 1 1 1 2
## 91300 92000 94500 96000 97000 97200 100000 100800 102900
## 1 1 1 3 1 1 1 1 1
## 105600 107000 107400 108000 109050 110000 113400 114000 120000
## 1 1 1 4 1 1 1 1 5
## 122200 124000 124800 126000 128300 129000 130000 133120 138000
## 1 1 1 1 1 1 1 1 1
## 138400 140000 140400 141650 144000 150000 152400 153600 155500
## 1 1 1 1 9 2 1 1 1
## 156000 157500 157850 162900 165000 165600 167800 168000 173000
## 3 1 1 1 1 1 1 7 1
## 175000 177000 180000 182000 183700 185000 186000 187900 190000
## 1 2 6 1 1 1 1 1 1
## 192000 194400 194800 200400 200700 204000 205000 205500 205800
## 2 1 1 2 1 4 1 1 1
## 210000 216000 220000 222000 224200 225000 227500 228000 230270
## 1 4 1 1 1 1 1 1 1
## 232400 234000 236000 240000 240550 242200 243000 246000 247200
## 1 1 1 4 1 1 1 1 1
## 248000 250000 252000 254000 255668 258000 260000 264000 265000
## 2 1 2 1 1 1 1 4 1
## 268800 272000 276000 280000 282000 286800 288000 300000 306000
## 1 1 1 2 1 1 7 3 1
## 308000 312000 314000 315000 318000 320000 320100 324000 326400
## 1 1 1 1 1 1 1 2 1
## 331750 334000 336000 336200 345000 346800 347850 353000 354400
## 1 1 3 1 1 1 1 1 1
## 355200 356000 360000 374400 384000 387000 389000 393800 394000
## 1 1 2 1 1 1 1 1 1
## 396000 420000 422000 426000 432000 435600 456000 461750 480000
## 1 3 1 1 2 1 4 1 2
## 484000 486030 499600 504000 513600 528000 534000 540000 600000
## 1 1 1 1 1 1 1 1 2
## 660000 672000 678000 696000 700000 708000 720000 768000 775000
## 4 1 1 1 1 1 2 1 1
## 779110 785000 840000 857700 876000 1020000 1044000 1485600
## 1 1 1 1 1 1 1 1barplot(table(mydata$Income))
qplot(factor(Income),data = mydata)
ggplot(mydata,aes(factor(Income)))+geom_bar()
hist(mydata$Income)
hist(mydata$Expenditure)
hist(mydata$Income,breaks = 10)
qplot(mydata$Income)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
plot(mydata$Income,mydata$Expenditure)
qplot(mydata$Province,mydata$Income,geom = "boxplot")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
qplot(Province,Income,data = mydata,geom = "boxplot")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=Province,y=Income))+geom_boxplot()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
qplot(interaction(mydata$Province, mydata$Region), mydata$Income,geom="boxplot")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
qplot(interaction(Province,Region),Income,data = mydata,geom = "boxplot")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=interaction(Province,Region),Income))+geom_boxplot()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
#chapter#3
ggplot(mydata,aes(x=Province))+geom_bar()
ggplot(mydata,aes(x=Province,y=Income,fill=Region))+geom_bar(stat = "identity")## Warning: Removed 1 rows containing missing values (position_stack).
ggplot(mydata,aes(x=reorder(Province,Income),y=Income,fill=Region))+geom_bar(stat = "identity")+scale_fill_manual(values = c("#669933", "#FFCC66"))+xlab("Province")## Warning: Removed 1 rows containing missing values (position_stack).
ggplot(mydata,aes(x=Province,y=Expenditure,fill=Region))+geom_bar(stat = "identity",position = "identity")
ggplot(mydata,aes(x=Province,y=Income))+geom_bar(stat = "identity",width = 1)## Warning: Removed 1 rows containing missing values (position_stack).
ggplot(mydata,aes(x=Province,y=Income,fill=Region))+geom_bar(stat = "identity",width = 0.5,position = "dodge")## Warning: Removed 1 rows containing missing values (geom_bar).
ggplot(mydata,aes(x=Province,y=Income,fill=Region))+geom_bar(stat = "identity",width = 0.5,position = position_dodge(0.7))## Warning: Removed 1 rows containing missing values (geom_bar).
ggplot(mydata,aes(x=Province,y=Expenditure,fill=Region))+geom_bar(stat = "identity")
ggplot(mydata,aes(x=Province,y=Expenditure,fill=Region))+geom_bar(stat = "identity")+guides(fill=guide_legend(reverse = TRUE))
ggplot(mydata,aes(x=Province,y=Expenditure,fill=Region))+geom_bar(stat = "identity",colour="black")+guides(fill=guide_legend(reverse = TRUE))+scale_fill_brewer(palette = "Pastel1")
mydata## Province Region Income Expenditure
## 1 punjab rural 140400 147596
## 2 kpk urban 90000 201762
## 3 balochis rural 356000 297956
## 4 punjab urban 156000 144994
## 5 kpk urban 282000 357188
## 6 sindh rural 144000 159570
## 7 kpk rural 248000 260594
## 8 punjab rural 96000 105800
## 9 sindh urban 205800 223508
## 10 sindh rural 97200 111516
## 11 sindh rural 225000 153132
## 12 sindh rural 140000 143244
## 13 balochis urban 108000 160854
## 14 sindh rural 300000 252520
## 15 punjab urban 840000 505670
## 16 sindh rural 168000 185957
## 17 punjab urban 120000 124792
## 18 kpk rural 120000 104450
## 19 sindh rural 120000 126920
## 20 sindh urban 696000 635876
## 21 punjab urban 355200 406982
## 22 balochis urban 204000 222508
## 23 punjab rural 168000 136252
## 24 punjab urban 110000 190060
## 25 punjab rural 347850 371358
## 26 punjab rural 785000 257448
## 27 kpk urban 387000 436252
## 28 balochis rural 384000 412890
## 29 sindh rural 114000 120922
## 30 punjab rural 708000 730310
## 31 sindh urban 252000 274720
## 32 sindh rural 107000 119594
## 33 kpk rural 216000 217326
## 34 punjab urban 130000 141528
## 35 balochis rural 360000 291650
## 36 punjab rural 113400 186606
## 37 sindh urban 180000 194978
## 38 punjab urban 162900 184344
## 39 punjab rural 320100 182656
## 40 punjab rural 306000 412520
## 41 punjab urban 422000 383792
## 42 punjab urban 216000 245056
## 43 punjab urban 432000 253670
## 44 sindh rural 66840 69650
## 45 balochis rural 78000 64710
## 46 kpk urban 312000 331242
## 47 punjab rural 190000 239750
## 48 punjab rural 240550 225730
## 49 balochis rural 396000 435854
## 50 punjab rural 1485600 717746
## 51 balochis urban 876000 871950
## 52 kpk rural 224200 266207
## 53 kpk rural 144000 155314
## 54 balochis urban 60000 84384
## 55 sindh rural 100000 116582
## 56 sindh rural 288000 316324
## 57 kpk rural 81000 215212
## 58 punjab rural 265000 153674
## 59 sindh rural 456000 379700
## 60 punjab urban 194400 195520
## 61 punjab rural 94500 80382
## 62 balochis rural 185000 212480
## 63 punjab rural 108000 122088
## 64 punjab rural 243000 151628
## 65 sindh urban 480000 581546
## 66 punjab urban 346800 343620
## 67 punjab rural 182000 142620
## 68 punjab urban 240000 266950
## 69 kpk urban 300000 277206
## 70 sindh urban 720000 795430
## 71 kpk urban 768000 529754
## 72 sindh urban 318000 293584
## 73 punjab rural 461750 144896
## 74 balochis urban 720000 206700
## 75 kpk rural 204000 152602
## 76 sindh urban 534000 527306
## 77 kpk urban 204000 197155
## 78 sindh rural 107400 105366
## 79 punjab urban 480000 405452
## 80 punjab rural 420000 431536
## 81 punjab urban 152400 177318
## 82 kpk rural 222000 240798
## 83 sindh urban 540000 507476
## 84 punjab rural 24000 75442
## 85 balochis rural 165000 189944
## 86 punjab rural 14700 83120
## 87 sindh rural 678000 642300
## 88 punjab rural NA 207056
## 89 kpk rural 175000 301399
## 90 punjab urban 144000 115818
## 91 punjab urban 124800 102112
## 92 balochis rural 168000 189422
## 93 punjab rural 48000 55432
## 94 sindh urban 264000 309326
## 95 punjab rural 26400 144462
## 96 punjab urban 138000 139582
## 97 kpk rural 288000 319070
## 98 balochis rural 150000 174004
## 99 punjab urban 288000 247110
## 100 punjab rural 100800 101848
## 101 sindh rural 177000 198564
## 102 sindh rural 65000 112924
## 103 punjab rural 96000 111700
## 104 punjab urban 315000 289386
## 105 kpk rural 288000 405936
## 106 sindh rural 336000 388940
## 107 sindh rural 60500 64230
## 108 sindh rural 660000 641710
## 109 balochis rural 70000 87728
## 110 sindh rural 204000 190298
## 111 sindh rural 128300 145644
## 112 punjab rural 167800 128668
## 113 sindh rural 232400 192470
## 114 punjab urban 426000 410800
## 115 balochis urban 456000 262476
## 116 sindh rural 247200 189652
## 117 sindh rural 180000 169644
## 118 sindh rural 345000 220048
## 119 punjab urban 420000 329838
## 120 sindh urban 264000 285542
## 121 punjab rural 42000 75674
## 122 balochis rural 250000 177714
## 123 kpk urban 264000 313924
## 124 punjab urban 1044000 553256
## 125 sindh urban 660000 396156
## 126 punjab rural 92000 106840
## 127 sindh rural 81600 87504
## 128 kpk rural 108000 193628
## 129 kpk urban 288000 321940
## 130 sindh rural 157500 140568
## 131 sindh rural 60900 67968
## 132 punjab urban 288000 317408
## 133 kpk rural 334000 249706
## 134 balochis rural 324000 267368
## 135 kpk rural 600000 547977
## 136 sindh rural 254000 211846
## 137 punjab urban 192000 220248
## 138 sindh rural 126000 139160
## 139 kpk rural 20000 108050
## 140 sindh rural 775000 324882
## 141 sindh rural 77600 90050
## 142 balochis rural 194800 141120
## 143 sindh rural 48000 38720
## 144 sindh rural 150000 197440
## 145 sindh rural 153600 170410
## 146 punjab rural 227500 95070
## 147 sindh rural 90000 89116
## 148 sindh rural 456000 477020
## 149 sindh urban 286800 215100
## 150 punjab rural 109050 214584
## 151 kpk urban 252000 305914
## 152 punjab rural 248000 223186
## 153 punjab urban 280000 245220
## 154 punjab rural 91300 186546
## 155 punjab urban 240000 248508
## 156 kpk rural 37500 130644
## 157 punjab urban 528000 445268
## 158 sindh rural 60000 65324
## 159 kpk rural 105600 231576
## 160 punjab urban 22800 113906
## 161 sindh urban 276000 322362
## 162 punjab rural 394000 445232
## 163 sindh urban 246000 287770
## 164 punjab urban 435600 472460
## 165 punjab rural 255668 241870
## 166 sindh rural 129000 124766
## 167 punjab rural 165600 161056
## 168 sindh rural 97000 111698
## 169 punjab rural 230270 118084
## 170 kpk urban 85050 89968
## 171 kpk urban 234000 344800
## 172 punjab rural 183700 188798
## 173 punjab rural 187900 193591
## 174 kpk rural 180000 194485
## 175 punjab urban 513600 440572
## 176 sindh urban 660000 558180
## 177 punjab urban 108000 266270
## 178 punjab urban 456000 412570
## 179 punjab urban 268800 298722
## 180 punjab rural 156000 145524
## 181 sindh rural 180000 254968
## 182 sindh rural 88200 78652
## 183 sindh rural 499600 412390
## 184 punjab urban 1020000 750980
## 185 sindh rural 336200 204820
## 186 kpk rural 336000 395670
## 187 balochis rural 264000 272980
## 188 punjab rural 779110 359890
## 189 punjab urban 389000 427426
## 190 sindh rural 157850 108250
## 191 sindh rural 353000 58676
## 192 kpk urban 173000 269813
## 193 punjab rural 484000 382640
## 194 kpk rural 272000 157085
## 195 kpk urban 336000 393576
## 196 punjab rural 62000 72742
## 197 punjab urban 240000 281610
## 198 sindh rural 102900 114046
## 199 punjab rural 486030 208870
## 200 punjab urban 236000 278744
## 201 sindh urban 220000 195552
## 202 balochis urban 138400 149800
## 203 kpk rural 144000 118577
## 204 punjab urban 300000 266940
## 205 punjab rural 156000 164756
## 206 punjab rural 168000 191532
## 207 punjab urban 326400 335250
## 208 sindh rural 120000 137702
## 209 punjab rural 79200 347980
## 210 punjab urban 168000 198012
## 211 balochis rural 144000 168690
## 212 kpk urban 192000 242376
## 213 sindh urban 180000 160644
## 214 punjab rural 200700 141330
## 215 balochis rural 324000 306504
## 216 punjab rural 36000 38206
## 217 punjab rural 331750 195802
## 218 sindh rural 76500 76000
## 219 punjab rural 38000 96160
## 220 punjab rural 354400 177500
## 221 balochis rural 177000 188818
## 222 punjab rural 200400 162402
## 223 balochis rural 280000 342884
## 224 punjab urban 258000 174348
## 225 balochis rural 260000 301480
## 226 punjab rural 133120 119070
## 227 punjab rural 39500 155480
## 228 punjab rural 228000 81930
## 229 punjab rural 52000 67748
## 230 balochis rural 144000 101808
## 231 kpk urban 374400 475900
## 232 sindh rural 66000 74314
## 233 kpk rural 314000 310556
## 234 kpk urban 420000 674720
## 235 punjab rural 78800 116182
## 236 kpk rural 216000 215958
## 237 punjab urban 288000 270816
## 238 punjab rural 857700 553650
## 239 sindh rural 700000 584304
## 240 balochis rural 168000 195210
## 241 sindh urban 144000 135868
## 242 punjab rural 30000 76306
## 243 sindh rural 120000 112593
## 244 kpk rural 11890 151688
## 245 balochis rural 180000 208242
## 246 punjab rural 141650 146506
## 247 punjab rural 205000 232222
## 248 kpk rural 205500 113924
## 249 sindh urban 660000 881440
## 250 punjab rural 25500 75172
## 251 punjab rural 40500 41394
## 252 punjab rural 432000 428478
## 253 kpk rural 155500 191190
## 254 sindh rural 216000 238440
## 255 punjab rural 60000 198088
## 256 sindh urban 360000 366230
## 257 punjab rural 242200 248250
## 258 punjab urban 504000 529376
## 259 kpk rural 168000 265273
## 260 punjab rural 67000 66330
## 261 punjab rural 144000 110870
## 262 sindh rural 124000 135114
## 263 balochis rural 96000 88732
## 264 punjab urban 60000 66818
## 265 punjab rural 672000 537200
## 266 kpk rural 88000 186414
## 267 punjab rural 200400 183470
## 268 sindh rural 393800 223300
## 269 sindh urban 600000 457952
## 270 punjab rural 122200 123749
## 271 sindh urban 144000 151244
## 272 balochis urban 240000 281076
## 273 kpk rural 320000 342767
## 274 punjab urban 210000 209752
## 275 kpk rural 308000 276834
## 276 punjab urban 186000 148322ggplot(mydata,aes(x=interaction(Province,Region),y=Income))+geom_bar(stat = "identity")+geom_text(aes(label=Income),vjust=-0.2)## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
ggplot(mydata,aes(x=interaction(Province,Region),y=Income))+geom_bar(stat = "identity")+geom_text(aes(label=Income),vjust=-0.2)+ylim(0,max(mydata$Income)*1.05)## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (geom_text).
ggplot(mydata,aes(x=interaction(Province,Region),y=Income))+geom_bar(stat = "identity")+geom_text(aes(y=Income+0.1,label=Income))## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (geom_text).
ggplot(mydata,aes(x=Province,y=Income,fill=Region))+geom_bar(stat = "identity",position = "dodge")+geom_text(aes(label=Income),vjust=1.5,colour="white",position = position_dodge(0.9),size=3)## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_text).
ggplot(mydata,aes(x=Income,y=Province))+geom_point() ## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=reorder(Province,Income)))+geom_point(size=3)+theme_bw()+theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank(),panel.grid.major.y = element_line(colour = "grey60",linetype = "dashed"))## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=reorder(Province,Income),y=Income))+geom_point(size=3)+theme_bw()+theme(axis.text.x = element_text(angle=60,hjust=1),panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank(),panel.grid.major.x = element_line(colour = "grey60",linetype = "dashed"))## Warning: Removed 1 rows containing missing values (geom_point).
#chapter#4 Line graph
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
mydata1<-mydata
mydata1$Income<-factor(mydata1$Income)
ggplot(mydata1,aes(x=Income,y=Expenditure,group=1))+geom_line()
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+ylim(0,max(mydata$Expenditure))## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+expand_limits(y=0)## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+geom_point()## Warning: Removed 1 rows containing missing values (geom_path).## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+geom_point()+scale_y_log10()## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
library(plyr)
mydata$Region## [1] rural urban rural urban urban rural rural rural urban rural rural
## [12] rural urban rural urban rural urban rural rural urban urban urban
## [23] rural urban rural rural urban rural rural rural urban rural rural
## [34] urban rural rural urban urban rural rural urban urban urban rural
## [45] rural urban rural rural rural rural urban rural rural urban rural
## [56] rural rural rural rural urban rural rural rural rural urban urban
## [67] rural urban urban urban urban urban rural urban rural urban urban
## [78] rural urban rural urban rural urban rural rural rural rural rural
## [89] rural urban urban rural rural urban rural urban rural rural urban
## [100] rural rural rural rural urban rural rural rural rural rural rural
## [111] rural rural rural urban urban rural rural rural urban urban rural
## [122] rural urban urban urban rural rural rural urban rural rural urban
## [133] rural rural rural rural urban rural rural rural rural rural rural
## [144] rural rural rural rural rural urban rural urban rural urban rural
## [155] urban rural urban rural rural urban urban rural urban urban rural
## [166] rural rural rural rural urban urban rural rural rural urban urban
## [177] urban urban urban rural rural rural rural urban rural rural rural
## [188] rural urban rural rural urban rural rural urban rural urban rural
## [199] rural urban urban urban rural urban rural rural urban rural rural
## [210] urban rural urban urban rural rural rural rural rural rural rural
## [221] rural rural rural urban rural rural rural rural rural rural urban
## [232] rural rural urban rural rural urban rural rural rural urban rural
## [243] rural rural rural rural rural rural urban rural rural rural rural
## [254] rural rural urban rural urban rural rural rural rural rural urban
## [265] rural rural rural rural urban rural urban urban rural urban rural
## [276] urban
## Levels: rural urbantg<-ddply(mydata,c("Region","Income"),summarise,length=mean(Expenditure))
ggplot(tg,aes(x=Income,y=length,colour=Region))+geom_line() ## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(tg,aes(x=Income,y=length,linetype=Region))+geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(tg,aes(x=factor(Income),y=length,colour=Region,group=Region))+geom_line()
ggplot(tg,aes(x=Income,y=length))+geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(tg,aes(x=Income,y=length,shape=Region))+geom_line()+geom_point(size=4)## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(tg,aes(x=Income,y=length,fill=Region))+geom_line()+geom_point(size=4,shape=21)## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(tg,aes(x=Income,y=length,shape=Region))+geom_line(position = position_dodge(0.2))+geom_point(position = position_dodge(0.2),size=2)## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line(linetype="dashed",size=1,colour="blue")## Warning: Removed 1 rows containing missing values (geom_path).
tg<-ddply(mydata,c("Region","Income"),summarise,length=mean(Expenditure))
ggplot(tg,aes(x=Income,y=length,colour=Region))+geom_line()+scale_color_brewer(palette = "set1")## Warning in pal_name(palette, type): Unknown palette set1## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(tg,aes(x=Income,y=length,group=Region))+geom_line(colour="darkgreen",size=1.5)## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(tg,aes(x=Income,y=length,colour=Region))+geom_line(linetype="dashed")+geom_point(shape=22,size=3,fill="white") ## Warning: Removed 1 rows containing missing values (geom_path).## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+geom_point(size=4,shape=22,colour="darkblue",fill="pink")## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_line()+geom_point(size=4,shape=21,colour="white")## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
tg<-ddply(mydata,c("Region","Income"),summarise,length=mean(Expenditure))
pd<-position_dodge(0.2)
ggplot(tg,aes(x=Income,y=length,fill=Region))+geom_line(position = pd)+geom_point(shape=21,size=3,position = pd)+scale_fill_manual(values = c("black","white"))## Warning: Removed 1 rows containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_area()## Warning: Removed 1 rows containing missing values (position_stack).
ggplot(mydata,aes(x=Income,y=Expenditure,fill=Province))+geom_area()## Warning: Removed 1 rows containing missing values (position_stack).
mydata1<-ddply(mydata,"Income",transform,percent=Expenditure/sum(Expenditure)*100)
ggplot(mydata1,aes(x=Income,y=percent,fill=Province))+geom_area(colour="black",size=0.2,alpha=0.4)+scale_fill_brewer(palette = "Blues",breaks=rev(mydata$Province))## Warning: Removed 1 rows containing missing values (position_stack).
#Chapter#5
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point(shape=21)## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point(size=2)## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure,colour=Region))+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure,shape=Region))+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure,shape=Region,colour=Region))+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure,shape=Region,colour=Region))+geom_point()+scale_shape_manual(values = c(1,2))+scale_colour_brewer(palette="Set1")## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point(shape=3)## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure,shape=Region))+geom_point(size=3)+scale_shape_manual(values=c(1,4))## Warning: Removed 1 rows containing missing values (geom_point).
sp<-ggplot(mydata,aes(x=Income,y=Expenditure))
sp+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
sp+geom_point(alpha=.1)## Warning: Removed 1 rows containing missing values (geom_point).
sp+geom_point(alpha=.5)## Warning: Removed 1 rows containing missing values (geom_point).
sp1<-ggplot(mydata,aes(x=Province,y=Income))
sp1+geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
sp1 + geom_point(position=position_jitter(width=.5, height=0))## Warning: Removed 1 rows containing missing values (geom_point).
sp1+geom_boxplot(aes(group=Region))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
sp<-ggplot(mydata,aes(x=Income,y=Expenditure))
sp + geom_point() + stat_smooth(method=lm)## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
sp + geom_point() + stat_smooth(method=lm,level = 0.99)## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
sp + geom_point() + stat_smooth(method=lm,se=FALSE)## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
sp + geom_point(colour="grey60") + stat_smooth(method=lm,se=FALSE,colour="black")## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
sp + geom_point(colour="grey60") + stat_smooth(method=loess)## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point(position = position_jitter(width = 0.3,height=0.06),alpha=0.4,shape=21,size=1.5)+stat_smooth(method = glm,family=binomial)## Warning: Ignoring unknown parameters: family## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata, aes(x=Income, y=Expenditure, colour=Region)) +geom_point() + geom_line(mydata=mydata)## Warning: Ignoring unknown parameters: mydata
## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_path).
model <- lm(Income ~ Province, mydata)
ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point() + geom_rug()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point() + geom_rug(position = "jitter",size=.2)## Warning: Removed 1 rows containing missing values (geom_point).
##chapter#6 ##Histogram
ggplot(mydata,aes(x=Income))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
w<-mydata$Income
ggplot(NULL,aes(x=w))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
h<-ggplot(mydata,aes(x=Income))
library(MASS)
ggplot(mydata,aes(x=Income))+geom_histogram(fill="white",colour="black")+facet_grid(Province~.)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
ggplot(mydata,aes(x=Income))+geom_histogram(fill="white",colour="black")+facet_grid(.~Province)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
ggplot(mydata,aes(x=Income))+geom_histogram(fill="blue",colour="black")+facet_grid(Region~.)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
ggplot(mydata,aes(x=Income))+geom_histogram(fill="blue",colour="black")+facet_grid(Region~.,scales = "free")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
ggplot(mydata,aes(x=Expenditure,fill="Region"))+geom_histogram(position = "identity",alpha=0.4)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##density curve
ggplot(mydata,aes(x=Expenditure))+geom_density()
ggplot(mydata,aes(x=Income))+geom_density()## Warning: Removed 1 rows containing non-finite values (stat_density).
ggplot(mydata,aes(x=Expenditure))+geom_line(stat = "density")+expand_limits(y=0)
ggplot(mydata,aes(x=Expenditure))+geom_line(stat = "density",adjust=0.25,colours="red")+geom_line(stat = "density")+geom_line(stat = "density",adjust=2,colour="blue")## Warning: Ignoring unknown parameters: colours
ggplot(mydata,aes(x=Expenditure))+geom_density(fill="blue",alpha=.2)+xlim(6,14)## Warning: Removed 276 rows containing non-finite values (stat_density).
ggplot(mydata,aes(x=Expenditure))+geom_density(fill="blue",colour=NA,alpha=.2)+geom_line(stat="density")+xlim(6,14)## Warning: Removed 276 rows containing non-finite values (stat_density).## Warning: Removed 276 rows containing non-finite values (stat_density).
ggplot(mydata,aes(x=Expenditure,y=..density..))+geom_histogram(fill="cornsilk",colour="grey60",size=.2)+geom_density()+xlim(8,14)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 276 rows containing non-finite values (stat_bin).
## Warning: Removed 276 rows containing non-finite values (stat_density).
mydata1<-mydata
mydata1$Province<-factor(mydata1$Province)
ggplot(mydata1,aes(x=Expenditure,colour=Province))+geom_density()
ggplot(mydata1,aes(x=Expenditure,fill=Province))+geom_density(alpha=.5)
ggplot(mydata1,aes(x=Expenditure,))+geom_density()+facet_grid(Province~.)
##frequency polygon
ggplot(mydata,aes(x=Expenditure))+geom_freqpoly()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##Boxplot
ggplot(mydata,aes(x=factor(Region),y=Income))+geom_boxplot()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=factor(Region),y=Income))+geom_boxplot(width=0.4)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=factor(Region),y=Income))+geom_boxplot(outlier.size = 1.5,outlier.shape = 21)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=1,y=Income))+geom_boxplot()+scale_x_continuous(breaks = NULL)+theme(axis.title.x = element_blank())## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=factor(Region),y=Income))+geom_boxplot(notch = TRUE)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata,aes(x=factor(Region),y=Income))+geom_boxplot()+stat_summary(fun.y = "mean",geom = "point",shape=23,size=3,fill="white")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing non-finite values (stat_summary).
##Violin Plot
p<-ggplot(mydata,aes(x=Region,y=Income))
p+geom_violin()## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
p+geom_violin(trim = FALSE)## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
p+geom_violin(scale = "count")## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
p+geom_violin(adjust=.22)## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
##Dot Plot
p<-ggplot(mydata,aes(x=Income,y=Expenditure))
p+geom_point()+stat_density2d()## Warning: Removed 1 rows containing non-finite values (stat_density2d).## Warning: Removed 1 rows containing missing values (geom_point).
p+stat_density2d(aes(colour=..level..))## Warning: Removed 1 rows containing non-finite values (stat_density2d).
p+stat_density2d(aes(fill=..density..),geom = "raster",contour = FALSE)## Warning: Removed 1 rows containing non-finite values (stat_density2d).
p+geom_point()+stat_density2d(aes(alpha=..density..),geom = "tile",contour = FALSE)## Warning: Removed 1 rows containing non-finite values (stat_density2d).
## Warning: Removed 1 rows containing missing values (geom_point).
p+stat_density2d(aes(fill=..density..),geom = "raster",contour = FALSE,h=c(.5,5))## Warning: Removed 1 rows containing non-finite values (stat_density2d).
##chapter#7
p<-ggplot(mydata,aes(x=Income,y=Expenditure))+geom_point()
p+annotate("text",x=3,y=15,label="Group1")+annotate("text",x=5,y=18,label="Group2")## Warning: Removed 1 rows containing missing values (geom_point).
p+annotate("text",x=3,y=15,label="Group1",family="serif",fontface="italic",colour="darkred",size=3)+annotate("text",x=5,y=18,label="Group2",family="serif",fontface="italic",colour="darkred",size=3)## Warning: Removed 1 rows containing missing values (geom_point).
p+annotate("text",x=3,y=15,label="Group1",alpha=.1)+annotate("text",x=5,y=18,label="Group2",alpha=.1)## Warning: Removed 1 rows containing missing values (geom_point).
p + annotate("text", x=-Inf, y=Inf, label="Upper left", hjust=-.2, vjust=2) +annotate("text", x=mean(range(mydata$Income)), y=-Inf, vjust=-0.4,label="Bottom middle")## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_text).
p + annotate("text", x=2, y=0.3, parse=TRUE,label="frac(1, sqrt(2 * pi)) * e ^ {-x^2 / 2}")## Warning: Removed 1 rows containing missing values (geom_point).
p + annotate("text", x=0, y=0.05, parse=TRUE, size=4,label="'Function: ' * y==frac(1, sqrt(2*pi)) * e^{-x^2/2}")## Warning: Removed 1 rows containing missing values (geom_point).
p<-ggplot(mydata,aes(x=Income,y=Expenditure,colour=Region))+geom_point()
p+geom_hline(yintercept = 35)+geom_vline(xintercept = 6)## Warning: Removed 1 rows containing missing values (geom_point).
p + geom_abline(intercept=37.4, slope=1.75)## Warning: Removed 1 rows containing missing values (geom_point).
hw_means <- ddply(mydata, "Region", summarise, Expenditure=mean(Expenditure))
p + geom_hline(aes(yintercept=Expenditure, colour=Region), mydata=hw_means,linetype="dashed", size=1)## Warning: Ignoring unknown parameters: mydata## Warning: Removed 1 rows containing missing values (geom_point).
pg <- ggplot(mydata, aes(x=Province, y=Income)) + geom_point()
pg + geom_vline(xintercept = 2)## Warning: Removed 1 rows containing missing values (geom_point).
pg + geom_vline(xintercept = which(levels(mydata$Province)=="ctrl"))## Warning: Removed 1 rows containing missing values (geom_point).
p <- ggplot(subset(mydata, Region=="Region"), aes(x=Expenditure, y=Income)) +geom_line()
p + annotate("segment", x=8, xend=11, y=6, yend=11)
p + annotate("segment", x=8, xend=11, y=6, yend=11, colour="blue",size=2, arrow=arrow()) +annotate("segment", x=10, xend=12, y=-8, yend=15,arrow=arrow(ends="both", angle=90, length=unit(.2,"cm")))
pg<-mydata
pg$Region<-"no"
pg$Region[pg$Province=="trt2"] <- "yes"
ggplot(pg, aes(x=Province, y=Expenditure, fill=Region)) + geom_boxplot() +scale_fill_manual(values=c("grey85", "#FFDDCC"), guide=FALSE)
ggplot(mydata, aes(x=Province, y=Income, fill=Region)) + geom_boxplot() +scale_fill_manual(values=c("grey85", "grey85", "#FFDDCC"), guide=FALSE)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Province, y=Income, fill=Region)) +geom_bar(position="dodge",stat = "identity") +geom_errorbar(aes(ymin=Income-Expenditure, ymax=Income+Expenditure),position="dodge", width=.2)## Warning: Removed 1 rows containing missing values (geom_bar).## Warning: Removed 1 rows containing missing values (geom_errorbar).
ggplot(mydata, aes(x=Province, y=Income, fill=Region)) +geom_bar(position="dodge",stat = "identity") +geom_errorbar(aes(ymin=Income-Expenditure, ymax=Income+Expenditure),position="dodge", width=.2)## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_errorbar).
p <- ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point() + facet_grid(. ~ Province)###chapter#8 Axes
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() + coord_flip()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() + coord_flip()+scale_x_discrete(limits=rev(levels(mydata$Region)))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p<-ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot()
p## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p+ylim(0,max(mydata$Income))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ylim(0, 10)## <ScaleContinuousPosition>
## Range:
## Limits: 0 -- 10scale_y_continuous(limits=c(0, 10))## <ScaleContinuousPosition>
## Range:
## Limits: 0 -- 10p + ylim(0, 10) + scale_y_continuous(breaks=NULL)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_y_continuous(breaks=NULL) + ylim(0, 10)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 276 rows containing non-finite values (stat_boxplot).
p + scale_y_continuous(limits=c(0, 10), breaks=NULL)## Warning: Removed 276 rows containing non-finite values (stat_boxplot).
p + scale_y_continuous(limits = c(5, 12))## Warning: Removed 276 rows containing non-finite values (stat_boxplot).
p + coord_cartesian(ylim = c(6, 14))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + expand_limits(y=0)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() + scale_y_reverse()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() + ylim(8, 10)## Warning: Removed 276 rows containing non-finite values (stat_boxplot).
sp <- ggplot(mydata, aes(x=Income,y=Expenditure)) + geom_point()
sp + coord_fixed()## Warning: Removed 1 rows containing missing values (geom_point).
sp + coord_fixed() +scale_y_continuous(breaks=seq(0, 420, 30)) +scale_x_continuous(breaks=seq(0, 420, 30))## Warning: Removed 1 rows containing missing values (geom_point).
sp + coord_fixed(ratio=1/2) +scale_y_continuous(breaks=seq(0, 420, 30)) +scale_x_continuous(breaks=seq(0, 420, 15))## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot()## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() +scale_y_continuous(breaks=c(4, 4.25, 4.5, 5, 6, 8))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p <- ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot()
p + theme(axis.text.y = element_blank())## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + theme(axis.ticks = element_blank(), axis.text.y = element_blank())## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_y_continuous(breaks=NULL)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
hwp <- ggplot(mydata, aes(x=Province, y=Income)) +geom_point()
hwp + scale_y_continuous(breaks=c(50, 56, 60, 66, 72),labels=c("Tiny", "Really\nshort", "Short","Medium", "Tallish"))## Warning: Removed 1 rows containing missing values (geom_point).
bp <- ggplot(mydata, aes(x=Region, y=Income)) + geom_boxplot() +scale_x_discrete(breaks=c("ctrl", "trt1", "trt2"),labels=c("Control", "Treatment 1", "Treatment 2"))
bp## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
bp + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
bp + theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
bp + theme(axis.text.x = element_text(family="Times", face="italic",colour="darkred", size=rel(0.9)))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
hwp <- ggplot(mydata, aes(x=Province, y=Income, colour=Region)) +geom_point()
hwp## Warning: Removed 1 rows containing missing values (geom_point).
hwp + xlab("Total income") + ylab("Total Expenditure")## Warning: Removed 1 rows containing missing values (geom_point).
hwp + labs(x = "Total Income", y = "Total Expenditure")## Warning: Removed 1 rows containing missing values (geom_point).
p <- ggplot(mydata, aes(x=Region, y=Expenditure)) + geom_boxplot()
p
p + theme(axis.title.x=element_blank())
p + xlab("")
hwp <- ggplot(mydata, aes(x=Province, y=Income)) + geom_point()
hwp + theme(axis.title.x=element_text(face="italic", colour="darkred", size=14))## Warning: Removed 1 rows containing missing values (geom_point).
p <- ggplot(mydata, aes(x=Province, y=Income)) + geom_point()
p + theme(axis.line = element_line(colour="black"))## Warning: Removed 1 rows containing missing values (geom_point).
p + theme_bw() +theme(panel.border = element_blank(),axis.line = element_line(colour="black"))## Warning: Removed 1 rows containing missing values (geom_point).
p + theme_bw() +theme(panel.border = element_blank(),axis.line = element_line(colour="black", size=4))## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(mydata, aes(x=Income, y=Expenditure, label=rownames(mydata)))+geom_text(size=3)## Warning: Removed 1 rows containing missing values (geom_text).
ggplot(mydata, aes(x=Income,y=Expenditure)) + geom_line()## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata, aes(x=Income,y=Expenditure)) + geom_line() +scale_y_log10(breaks=c(2,10,50,250))## Warning: Removed 1 rows containing missing values (geom_path).
ggplot(mydata, aes(x=Income, fill=Province)) +geom_histogram(binwidth=15, origin=-7.5, colour="black", size=.25) +guides(fill=guide_legend(reverse=TRUE)) +coord_polar() +scale_x_continuous(limits=c(0,360), breaks=seq(0, 360, by=45),minor_breaks=seq(0, 360, by=15)) +scale_fill_brewer()## Warning: `origin` is deprecated. Please use `boundary` instead.## Warning: Removed 276 rows containing non-finite values (stat_bin).
##Chapter#9
p <- ggplot(mydata, aes(x=Province, y=Income)) + geom_point()
p + ggtitle("Total Income and Expenditure in a year")## Warning: Removed 1 rows containing missing values (geom_point).
p + ggtitle("Total Income and Expenditure in a year")+theme(plot.title = element_text(vjust = -2.5))## Warning: Removed 1 rows containing missing values (geom_point).
p <- ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point()
p + theme(axis.title.x=element_text(size=16, lineheight=.9, family="Times",face="bold.italic", colour="red"))## Warning: Removed 1 rows containing missing values (geom_point).## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
p + ggtitle("Total Income and Expenditure in a year") +theme(plot.title=element_text(size=rel(1.5), lineheight=.9, family="Times",face="bold.italic", colour="red"))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
p <- ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point()
p + theme_grey()## Warning: Removed 1 rows containing missing values (geom_point).
p + theme_bw()## Warning: Removed 1 rows containing missing values (geom_point).
p + theme_grey(base_size=16, base_family="Times")## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database
## Warning: font family not found in Windows font database## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x,
## x$y, : font family not found in Windows font database
p <- ggplot(mydata, aes(x=Income, y=Expenditure, colour=Region)) + geom_point()
p + theme(panel.grid.major = element_line(colour="red"),panel.grid.minor = element_line(colour="red", linetype="dashed", size=0.2),panel.background = element_rect(fill="lightblue"),panel.border = element_rect(colour="blue", fill=NA, size=2))## Warning: Removed 1 rows containing missing values (geom_point).
p + ggtitle("Plot title here") +theme(axis.title.x = element_text(colour="red", size=14),axis.text.x = element_text(colour="blue"),axis.title.y = element_text(colour="red", size=14, angle = 90),axis.text.y = element_text(colour="blue"),plot.title = element_text(colour="red", size=20, face="bold"))## Warning: Removed 1 rows containing missing values (geom_point).
p + theme(legend.background = element_rect(fill="grey85", colour="red", size=1),legend.title = element_text(colour="blue", face="bold", size=14),legend.text = element_text(colour="red"),legend.key = element_rect(colour="blue", size=0.25))## Warning: Removed 1 rows containing missing values (geom_point).
p + facet_grid(Region ~ .) + theme(strip.background = element_rect(fill="pink"),strip.text.y = element_text(size=14, angle=-90, face="bold"))## Warning: Removed 1 rows containing missing values (geom_point).
p + theme(axis.title.x = element_text(colour="red")) + theme_bw()## Warning: Removed 1 rows containing missing values (geom_point).
p + theme_bw() + theme(axis.title.x = element_text(colour="red", size=12))## Warning: Removed 1 rows containing missing values (geom_point).
p <- ggplot(mydata, aes(x=Income, y=Expenditure)) + geom_point()
p + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())## Warning: Removed 1 rows containing missing values (geom_point).
##chapter#10
p <- ggplot(mydata, aes(x=Province, y=Income, fill=Region)) + geom_boxplot()
p## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + guides(fill=FALSE)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_fill_discrete(guide=FALSE)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + theme(legend.position="none")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p <- ggplot(mydata, aes(x=Province, y=Income, fill=Region)) + geom_boxplot() +scale_fill_brewer(palette="Pastel2")
p + theme(legend.position="top")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + theme(legend.position=c(1,0), legend.justification=c(1,0))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + theme(legend.position=c(1,1), legend.justification=c(1,1))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p <- ggplot(mydata, aes(x=Province, y=Income, fill=Region)) + geom_boxplot()
p## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_fill_discrete(limits=c("trt1", "trt2", "ctrl"))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_fill_grey(start=.5, end=1, limits=c("trt1", "trt2", "ctrl"))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_fill_brewer(palette="Pastel2", limits=c("trt1", "trt2", "ctrl"))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + guides(fill=guide_legend(reverse=TRUE))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + labs(fill="Area")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
hw <- ggplot(mydata, aes(x=Income, y=Expenditure, colour=Region)) +geom_point(aes(size=Expenditure)) + scale_size_continuous(range=c(1,4))
hw## Warning: Removed 1 rows containing missing values (geom_point).
hw + labs(colour="Rural/Urbar", size="Expenditure\n(Rupees)")## Warning: Removed 1 rows containing missing values (geom_point).
hw1 <- ggplot(mydata, aes(x=Income, y=Expenditure, shape=Region, colour=Region)) +geom_point()
hw1## Warning: Removed 1 rows containing missing values (geom_point).
hw1 + labs(shape="Rural/Urban", colour="rural/Urban")## Warning: Removed 1 rows containing missing values (geom_point).
p + scale_fill_discrete(labels=c("punjab", "sindh ", "kp","balochistan"))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
p + scale_fill_discrete(labels=c("Urban", "Rural","Type 2\ntreatment")) +theme(legend.text=element_text(lineheight=.8),legend.key.height=unit(1, "cm"))## Warning: Removed 1 rows containing non-finite values (stat_boxplot).