Army Analysis
Load the csv dataset from the working directory
df<-read.csv("Data_Task.csv")Keep only the abd_lgth which is greater than 0.5 month
months_in_army<-subset(df, abd_lgth>0.5, select=abd_lgth)Represent the descriptive statistics like Mean, Median, and the Quartiles and a Histogram
summary(months_in_army)## abd_lgth
## Min. : 0.5333
## 1st Qu.: 2.7500
## Median : 7.0000
## Mean : 14.3780
## 3rd Qu.: 18.0000
## Max. :137.0000hist(as.matrix(months_in_army), main="Histogram of Months in the Army", xlab = "Months", ylab="Number of Former Combatants", col="green", breaks=100, ylim=c(0,50), xlim=c(0.5,140))
Create the allegiance_comb variable
df$allegiance_comb<-df$allegiance_freq
for (i in 1:length(df$allegiance_comb)) {
if (is.na(df$ix48_allegiance[i])) {df$allegiance_comb[i]=df$allegiance_freq[i]}
else if (df$ix48_allegiance[i]==0) {
df$allegiance_comb[i]=0
} else
{df$allegiance_comb[i]=df$allegiance_freq[i]}
}Create the stay_comb variable
df$stay_comb<-df$stay_freq
for (i in 1:length(df$stay_comb)) {
if (is.na(df$ix51_stay[i])) {df$stay_comb[i]=df$stay_freq[i]}
else if (df$ix51_stay[i]==0) {
df$stay_comb[i]=0
} else
{df$stay_comb[i]=df$stay_freq[i]}
}Keep a dataset with the “id”, important" “allegiance_comb”, and “stay_comb” columns
df_reduced<-subset(df, select=c(id,important, allegiance_comb, stay_comb ))Now we need to do apply “case-wise deletion” meaning that if there is any missing value in the data we remove them
df_completed<-df_reduced[complete.cases(df_reduced),]
## As we can see we have only 294 Soldiers with non missing values in any of the three fields
nrow(df_completed)## [1] 294Now we can calculate the mean of each column
apply(df_completed[,c(-1)], 2, mean)## important allegiance_comb stay_comb
## 1.7244898 1.2823129 0.4931973Now we can calculate the Average of each Soldier based on the three columns which is the index
###Calculate the mean
Average<-(apply(df_completed[,c(-1)], 1, mean))
### Represent the results in a data frame
data.frame(df_completed, Average)## id important allegiance_comb stay_comb Average
## 5 5 3 0 0 1.0000000
## 7 7 0 0 0 0.0000000
## 9 9 2 3 0 1.6666667
## 11 11 2 0 0 0.6666667
## 15 15 0 0 0 0.0000000
## 17 17 0 0 0 0.0000000
## 20 20 3 3 0 2.0000000
## 21 21 2 3 0 1.6666667
## 22 22 0 0 0 0.0000000
## 23 23 3 3 0 2.0000000
## 24 24 3 3 1 2.3333333
## 27 27 2 3 0 1.6666667
## 28 28 3 3 2 2.6666667
## 29 29 0 3 0 1.0000000
## 30 30 3 3 0 2.0000000
## 32 32 0 1 0 0.3333333
## 35 35 3 1 0 1.3333333
## 37 37 0 0 0 0.0000000
## 39 39 3 0 0 1.0000000
## 40 40 2 3 0 1.6666667
## 41 41 0 1 0 0.3333333
## 44 44 1 3 0 1.3333333
## 45 45 3 3 0 2.0000000
## 47 47 0 0 0 0.0000000
## 48 48 3 3 0 2.0000000
## 51 51 3 0 3 2.0000000
## 57 57 1 1 0 0.6666667
## 58 58 3 3 2 2.6666667
## 59 59 1 1 0 0.6666667
## 60 60 3 3 0 2.0000000
## 61 61 0 2 0 0.6666667
## 62 62 0 0 0 0.0000000
## 63 63 0 0 0 0.0000000
## 64 64 0 1 0 0.3333333
## 65 65 0 0 0 0.0000000
## 66 66 0 3 0 1.0000000
## 67 67 3 3 0 2.0000000
## 69 69 2 3 0 1.6666667
## 71 71 3 3 0 2.0000000
## 74 74 2 0 0 0.6666667
## 75 75 0 1 0 0.3333333
## 77 77 3 0 3 2.0000000
## 78 78 3 0 0 1.0000000
## 79 79 0 3 0 1.0000000
## 80 80 0 1 0 0.3333333
## 85 85 0 0 0 0.0000000
## 87 87 3 0 0 1.0000000
## 89 89 3 2 0 1.6666667
## 91 91 3 3 0 2.0000000
## 92 92 1 0 0 0.3333333
## 96 96 3 0 3 2.0000000
## 98 98 0 3 0 1.0000000
## 100 100 0 3 0 1.0000000
## 101 101 3 0 0 1.0000000
## 102 102 2 2 0 1.3333333
## 106 106 2 3 0 1.6666667
## 107 107 0 3 0 1.0000000
## 121 122 1 0 3 1.3333333
## 127 128 2 0 0 0.6666667
## 132 133 3 0 0 1.0000000
## 133 134 0 3 0 1.0000000
## 136 137 2 3 0 1.6666667
## 137 138 3 3 0 2.0000000
## 138 139 0 0 0 0.0000000
## 141 142 0 3 0 1.0000000
## 143 144 0 0 0 0.0000000
## 144 145 3 3 0 2.0000000
## 145 146 0 0 0 0.0000000
## 148 149 3 3 0 2.0000000
## 150 151 0 0 0 0.0000000
## 151 152 2 0 0 0.6666667
## 152 153 3 3 0 2.0000000
## 154 155 3 3 0 2.0000000
## 157 158 3 0 0 1.0000000
## 158 159 2 1 0 1.0000000
## 159 160 3 3 1 2.3333333
## 160 161 3 3 0 2.0000000
## 162 163 3 0 0 1.0000000
## 163 164 3 2 0 1.6666667
## 165 166 0 3 0 1.0000000
## 166 167 3 3 3 3.0000000
## 168 169 3 2 0 1.6666667
## 170 171 0 3 0 1.0000000
## 171 172 1 3 0 1.3333333
## 173 174 0 1 0 0.3333333
## 174 175 2 3 0 1.6666667
## 175 176 3 0 0 1.0000000
## 176 177 3 3 3 3.0000000
## 177 178 0 0 0 0.0000000
## 178 179 0 3 0 1.0000000
## 180 181 2 1 2 1.6666667
## 181 182 1 0 0 0.3333333
## 182 183 3 0 0 1.0000000
## 183 184 3 3 0 2.0000000
## 187 188 0 0 0 0.0000000
## 188 189 1 0 0 0.3333333
## 189 190 0 3 0 1.0000000
## 190 191 0 0 0 0.0000000
## 195 196 1 1 0 0.6666667
## 196 197 2 0 1 1.0000000
## 197 198 0 1 0 0.3333333
## 198 199 0 0 0 0.0000000
## 202 203 3 1 1 1.6666667
## 203 204 3 0 0 1.0000000
## 209 210 0 1 0 0.3333333
## 210 211 2 0 0 0.6666667
## 214 215 0 0 0 0.0000000
## 216 217 3 2 1 2.0000000
## 218 219 2 3 0 1.6666667
## 220 221 3 0 0 1.0000000
## 221 222 2 0 0 0.6666667
## 222 223 0 0 0 0.0000000
## 224 225 3 3 3 3.0000000
## 227 228 2 0 0 0.6666667
## 228 229 0 0 0 0.0000000
## 231 232 0 0 2 0.6666667
## 233 234 0 1 0 0.3333333
## 234 235 0 0 0 0.0000000
## 235 236 0 0 0 0.0000000
## 236 237 3 0 3 2.0000000
## 237 238 3 0 0 1.0000000
## 238 239 0 0 0 0.0000000
## 240 241 0 3 0 1.0000000
## 241 242 1 0 0 0.3333333
## 242 243 1 3 0 1.3333333
## 247 248 2 3 0 1.6666667
## 249 250 2 3 0 1.6666667
## 252 253 0 3 0 1.0000000
## 253 254 0 0 0 0.0000000
## 256 257 3 0 3 2.0000000
## 257 258 2 0 0 0.6666667
## 258 259 2 0 0 0.6666667
## 260 261 0 0 0 0.0000000
## 261 262 1 3 0 1.3333333
## 263 264 0 3 1 1.3333333
## 264 265 3 0 0 1.0000000
## 265 266 1 0 0 0.3333333
## 266 267 0 0 0 0.0000000
## 267 268 3 1 0 1.3333333
## 272 273 0 1 1 0.6666667
## 275 276 1 0 1 0.6666667
## 280 281 3 3 0 2.0000000
## 281 282 0 3 0 1.0000000
## 284 285 0 0 0 0.0000000
## 285 286 1 1 0 0.6666667
## 286 287 2 3 0 1.6666667
## 287 288 2 3 0 1.6666667
## 288 289 2 3 2 2.3333333
## 289 300 2 0 0 0.6666667
## 290 301 3 3 0 2.0000000
## 303 314 0 0 3 1.0000000
## 310 321 1 3 3 2.3333333
## 315 326 3 2 0 1.6666667
## 331 342 2 0 0 0.6666667
## 333 344 2 0 0 0.6666667
## 337 348 0 3 0 1.0000000
## 340 351 3 2 0 1.6666667
## 341 352 1 0 0 0.3333333
## 345 356 3 2 0 1.6666667
## 346 357 0 0 0 0.0000000
## 348 359 3 1 0 1.3333333
## 349 360 0 0 1 0.3333333
## 354 365 3 3 0 2.0000000
## 360 371 3 0 3 2.0000000
## 361 372 0 0 0 0.0000000
## 364 375 0 1 0 0.3333333
## 367 378 0 0 2 0.6666667
## 368 379 0 0 1 0.3333333
## 372 383 0 0 0 0.0000000
## 374 385 0 3 0 1.0000000
## 375 386 0 1 1 0.6666667
## 376 387 0 0 0 0.0000000
## 377 388 3 0 0 1.0000000
## 378 389 0 0 0 0.0000000
## 380 391 2 2 0 1.3333333
## 381 392 2 0 0 0.6666667
## 382 393 1 3 0 1.3333333
## 384 395 1 0 0 0.3333333
## 387 398 3 3 2 2.6666667
## 389 400 2 0 0 0.6666667
## 394 405 2 3 0 1.6666667
## 397 408 2 3 0 1.6666667
## 400 411 3 3 0 2.0000000
## 403 414 0 0 3 1.0000000
## 404 415 3 3 3 3.0000000
## 407 418 1 2 0 1.0000000
## 411 422 3 0 0 1.0000000
## 413 424 3 0 0 1.0000000
## 414 425 3 1 1 1.6666667
## 421 432 3 0 3 2.0000000
## 423 434 3 3 0 2.0000000
## 425 436 2 0 3 1.6666667
## 434 445 2 0 2 1.3333333
## 435 446 3 0 0 1.0000000
## 438 449 2 0 0 0.6666667
## 444 455 3 0 0 1.0000000
## 445 456 3 3 2 2.6666667
## 447 458 1 2 0 1.0000000
## 451 462 2 3 0 1.6666667
## 456 467 3 3 0 2.0000000
## 457 468 3 3 0 2.0000000
## 458 469 3 3 0 2.0000000
## 463 474 2 3 0 1.6666667
## 467 478 0 0 0 0.0000000
## 468 479 0 0 0 0.0000000
## 473 484 2 3 0 1.6666667
## 477 488 3 0 3 2.0000000
## 480 491 3 0 0 1.0000000
## 483 494 0 1 0 0.3333333
## 486 497 3 0 2 1.6666667
## 498 509 3 3 1 2.3333333
## 500 511 3 3 3 3.0000000
## 504 515 2 3 0 1.6666667
## 506 517 0 0 0 0.0000000
## 509 520 0 2 3 1.6666667
## 511 522 3 0 0 1.0000000
## 512 523 3 2 0 1.6666667
## 516 527 3 1 0 1.3333333
## 521 532 2 0 2 1.3333333
## 522 533 3 0 3 2.0000000
## 523 534 3 0 0 1.0000000
## 526 537 2 0 0 0.6666667
## 528 539 1 0 3 1.3333333
## 538 549 3 1 3 2.3333333
## 539 550 3 1 0 1.3333333
## 540 551 3 1 0 1.3333333
## 541 552 2 1 0 1.0000000
## 547 558 3 1 0 1.3333333
## 549 560 0 0 0 0.0000000
## 550 561 2 0 0 0.6666667
## 551 562 3 1 0 1.3333333
## 552 563 3 3 1 2.3333333
## 553 564 0 2 0 0.6666667
## 556 567 3 2 0 1.6666667
## 565 576 3 0 3 2.0000000
## 566 577 0 0 0 0.0000000
## 567 578 2 2 2 2.0000000
## 568 579 3 3 0 2.0000000
## 569 580 2 3 0 1.6666667
## 573 584 3 3 0 2.0000000
## 587 598 3 0 0 1.0000000
## 590 601 0 0 0 0.0000000
## 591 602 0 3 0 1.0000000
## 593 604 3 1 0 1.3333333
## 599 610 2 0 0 0.6666667
## 600 611 0 0 3 1.0000000
## 601 612 0 3 0 1.0000000
## 602 613 3 3 0 2.0000000
## 607 618 1 0 3 1.3333333
## 609 620 3 0 0 1.0000000
## 610 621 3 0 0 1.0000000
## 616 627 3 0 0 1.0000000
## 631 642 2 0 3 1.6666667
## 634 645 3 0 1 1.3333333
## 637 648 1 1 0 0.6666667
## 638 649 3 3 1 2.3333333
## 640 651 3 1 0 1.3333333
## 642 653 3 3 1 2.3333333
## 643 654 3 0 0 1.0000000
## 648 659 3 3 3 3.0000000
## 652 663 3 3 0 2.0000000
## 653 664 1 3 0 1.3333333
## 660 671 0 2 0 0.6666667
## 668 679 3 3 3 3.0000000
## 669 680 0 0 0 0.0000000
## 671 682 3 2 0 1.6666667
## 678 689 3 1 2 2.0000000
## 679 690 2 0 0 0.6666667
## 680 691 3 0 3 2.0000000
## 684 695 0 0 2 0.6666667
## 685 696 2 3 0 1.6666667
## 686 697 3 0 0 1.0000000
## 690 701 3 2 3 2.6666667
## 696 707 1 0 0 0.3333333
## 698 709 2 2 0 1.3333333
## 705 716 3 0 3 2.0000000
## 710 721 0 2 1 1.0000000
## 711 722 0 2 0 0.6666667
## 712 723 3 3 0 2.0000000
## 713 724 3 0 0 1.0000000
## 714 725 0 0 0 0.0000000
## 716 727 0 0 0 0.0000000
## 720 731 3 0 0 1.0000000
## 721 732 2 3 0 1.6666667
## 722 733 3 3 0 2.0000000
## 723 734 3 0 0 1.0000000
## 724 735 3 0 3 2.0000000
## 726 737 3 0 0 1.0000000
## 730 741 3 0 0 1.0000000
## 732 743 3 0 0 1.0000000
## 734 745 2 0 0 0.6666667
## 735 746 2 2 0 1.3333333
## 738 749 3 0 3 2.0000000
## 741 753 3 3 0 2.0000000Now we calculate the mean and the variance of the index
##The average
mean(apply(df_completed[,c(-1)], 1, sum))## [1] 3.5##The variance
var(apply(df_completed[,c(-1)], 1, sum))## [1] 5.308874##The standard deviationn
sqrt(var(apply(df_completed[,c(-1)], 1, sum)))## [1] 2.304099Given that the Soldier has stayed more than 0.5 months what is the Proportion of Kill_soldier
kill<-subset(df, abd_lgth>0.5 & !is.na(kill_soldier), select=kill_soldier)
###Frequencies
table(kill)## kill
## 0 1
## 260 71###Percentages
table(kill)/dim(kill)[1]## kill
## 0 1
## 0.7854985 0.2145015barplot(table(kill)/dim(kill)[1], main="Kill Soldier", col="red", ylim = c(0,1))
Given that the Soldier has stayed more than 0.5 months what is the Proportion of beat_family
beat<-subset(df, abd_lgth>0.5 & !is.na(beat_family), select=beat_family)
###Frequencies
table(beat)## beat
## 0 1
## 273 58###Percentages
table(beat)/dim(beat)[1]## beat
## 0 1
## 0.8247734 0.1752266barplot(table(beat)/dim(beat)[1], main="Beat Family", col="red", ylim = c(0,1))
Given that the Soldier has stayed more than 0.5 months what is the Proportion of beat_civilian
beat_c<-subset(df, abd_lgth>0.5 & !is.na(beat_civilian), select=beat_civilian)
###Frequencies
table(beat_c)## beat_c
## 0 1
## 237 94###Percentages
table(beat_c)/dim(beat_c)[1]## beat_c
## 0 1
## 0.7160121 0.2839879barplot(table(beat_c)/dim(beat_c)[1], main="Beat Civilian", col="red", ylim = c(0,1))
Given that the Soldier has stayed more than 0.5 months what is the Proportion of kill_family
kill_f<-subset(df, abd_lgth>0.5 & !is.na(kill_family), select=kill_family)
###Frequencies
table(kill_f)## kill_f
## 0 1
## 298 33###Percentages
table(kill_f)/dim(kill_f)[1]## kill_f
## 0 1
## 0.90030211 0.09969789barplot(table(kill_f)/dim(kill_f)[1], main="Kill Family", col="red", ylim = c(0,1))
Given that the Soldier has stayed more than 0.5 months what is the Proportion of kill_civilian
kill_c<-subset(df, abd_lgth>0.5 & !is.na(kill_civilian), select=kill_civilian)
###Frequencies
table(kill_c)## kill_c
## 0 1
## 246 85###Percentages
table(kill_c)/dim(kill_c)[1]## kill_c
## 0 1
## 0.7432024 0.2567976barplot(table(kill_c)/dim(kill_c)[1], main="Kill Civilian", col="red", ylim = c(0,1))
Given that the Soldier has stayed more than 0.5 months what is the Proportion of forced_sex
forced_s<-subset(df, abd_lgth>0.5 & !is.na(forced_sex), select=forced_sex)
###Frequencies
table(forced_s)## forced_s
## 0 1
## 320 11###Percentages
table(forced_s)/dim(forced_s)[1]## forced_s
## 0 1
## 0.96676737 0.03323263barplot(table(forced_s)/dim(forced_s)[1], main="Forced Sex", col="red", ylim = c(0,1))
All plots in one
forced_sex<-table(forced_s)/dim(forced_s)[1]
kill_civilian<-table(kill_c)/dim(kill_c)[1]
kill_family<-table(kill_f)/dim(kill_f)[1]
beat_civilian<-table(beat_c)/dim(beat_c)[1]
beat_family<-table(beat)/dim(beat)[1]
kill_soldier<-table(kill)/dim(kill)[1]
counts<-rbind(forced_sex, kill_civilian, kill_family, beat_civilian, beat_family, kill_soldier)
barplot(counts, main="Bar Plot", xlab="No Yes", legend=rownames(counts), beside=TRUE, ylim = c(0,1), col=c("darkblue","red", "green", "black", "grey", "yellow"))
A Barplot only with the percentage of 1
barplot(counts[,2], main="Barplot", col=rainbow(6), las=2, ylim=c(0,0.3), cex.names=0.8)
A Barplot only with the percentage of 1
par(mar=c(5,8,4,2))
barplot(counts[,2], main="Barplot", col=rainbow(6), las=2, xlim=c(0,0.3), horiz = TRUE )