1
133446
2025-05-10
##importing data
library(readxl)
df1 <- read_excel("C:\\Users\\Yamama\\OneDrive\\My_Desktop\\project comp\\mydata1.xlsx")
df1<- as.data.frame(df1)
names(df1) #names of the variables## [1] "Gender" "Year" "Cohorts" "Deaths"
## [5] "Marital_Status"dim(df1) #dim of df: 338 observations of 5 variables## [1] 338 5class(df1)## [1] "data.frame"## check the type of the variables
class(df1$Gender)## [1] "character"class(df1$Year)## [1] "numeric"class(df1$Cohorts)## [1] "character"class(df1$Marital_Status)## [1] "character"class(df1$Deaths) #total## [1] "numeric"#class(df2$male_deaths)
#class(df2$female_deaths)
#get the Gender label in the frequency table.
Gender <- df1$Gender
table(Gender)## Gender
## Female Male
## 169 169gender_table <- transform(table(Gender))
gender_table$percent <- round(prop.table(gender_table$Freq)*100, 0)
gender_table## Gender Freq percent
## 1 Female 169 50
## 2 Male 169 50#--------------------------------------------------
#get the Marital_Status label in the frequency table.
Marital_Status <- df1$ Marital_Status
table(Marital_Status)## Marital_Status
## Married Single
## 158 180Marital_Status_table <- transform(table(Marital_Status))
Marital_Status_table$percent <- round(prop.table(Marital_Status_table$Freq)*100, 0)
Marital_Status_table## Marital_Status Freq percent
## 1 Married 158 47
## 2 Single 180 53#**********************************************************************************
#get the Deaths label in the frequency table.
Deaths <- df1$Deaths
table(Deaths)## Deaths
## 0 14 17 18 21 23 24 25 26 27 28 30 31 32 33 35 36 37 38 39
## 10 1 1 2 1 1 2 2 1 2 4 2 5 1 3 2 6 4 2 2
## 40 42 43 44 45 46 47 48 49 50 52 53 54 55 56 57 58 60 61 62
## 5 2 5 1 4 1 3 2 2 8 7 1 5 4 1 4 4 2 4 2
## 63 64 65 66 67 68 69 70 72 73 74 75 76 80 81 83 86 87 88 89
## 1 1 3 5 1 1 2 3 1 2 2 2 1 6 2 1 1 2 2 1
## 90 93 94 96 97 99 100 101 102 103 104 105 110 111 112 116 117 119 122 123
## 1 3 1 1 3 1 2 1 1 1 2 1 1 1 4 1 2 1 1 2
## 125 127 135 136 138 140 145 148 149 150 153 155 156 160 162 164 165 171 175 181
## 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 1 1
## 186 188 194 195 196 197 201 202 203 205 206 208 209 212 216 219 222 223 224 225
## 1 1 1 2 1 1 1 1 1 1 1 2 1 2 2 1 1 2 2 1
## 226 227 229 230 232 233 234 235 236 237 238 241 242 246 247 249 251 252 253 254
## 1 1 2 2 1 1 1 1 1 1 1 3 1 2 1 2 3 1 2 1
## 257 260 262 264 265 268 271 272 273 275 276 277 278 279 281 284 287 289 292 294
## 1 1 1 1 1 1 1 1 1 3 1 1 1 4 1 1 1 1 1 1
## 295 297 301 302 303 304 308 309 313 315 317 318 319 321 329 330 336 337 345 358
## 1 1 1 2 1 2 2 2 2 1 1 1 1 2 2 1 1 1 1 1
## 361 362 363 366 373 380 388 427 436 441 494 546
## 1 1 1 1 1 2 2 1 1 1 1 1Deaths_table <- transform(table(Deaths))
Deaths_table$percent <- round(prop.table(Deaths_table$Freq)*100, 0)
Deaths_table## Deaths Freq percent
## 1 0 10 3
## 2 14 1 0
## 3 17 1 0
## 4 18 2 1
## 5 21 1 0
## 6 23 1 0
## 7 24 2 1
## 8 25 2 1
## 9 26 1 0
## 10 27 2 1
## 11 28 4 1
## 12 30 2 1
## 13 31 5 1
## 14 32 1 0
## 15 33 3 1
## 16 35 2 1
## 17 36 6 2
## 18 37 4 1
## 19 38 2 1
## 20 39 2 1
## 21 40 5 1
## 22 42 2 1
## 23 43 5 1
## 24 44 1 0
## 25 45 4 1
## 26 46 1 0
## 27 47 3 1
## 28 48 2 1
## 29 49 2 1
## 30 50 8 2
## 31 52 7 2
## 32 53 1 0
## 33 54 5 1
## 34 55 4 1
## 35 56 1 0
## 36 57 4 1
## 37 58 4 1
## 38 60 2 1
## 39 61 4 1
## 40 62 2 1
## 41 63 1 0
## 42 64 1 0
## 43 65 3 1
## 44 66 5 1
## 45 67 1 0
## 46 68 1 0
## 47 69 2 1
## 48 70 3 1
## 49 72 1 0
## 50 73 2 1
## 51 74 2 1
## 52 75 2 1
## 53 76 1 0
## 54 80 6 2
## 55 81 2 1
## 56 83 1 0
## 57 86 1 0
## 58 87 2 1
## 59 88 2 1
## 60 89 1 0
## 61 90 1 0
## 62 93 3 1
## 63 94 1 0
## 64 96 1 0
## 65 97 3 1
## 66 99 1 0
## 67 100 2 1
## 68 101 1 0
## 69 102 1 0
## 70 103 1 0
## 71 104 2 1
## 72 105 1 0
## 73 110 1 0
## 74 111 1 0
## 75 112 4 1
## 76 116 1 0
## 77 117 2 1
## 78 119 1 0
## 79 122 1 0
## 80 123 2 1
## 81 125 1 0
## 82 127 1 0
## 83 135 1 0
## 84 136 2 1
## 85 138 1 0
## 86 140 1 0
## 87 145 1 0
## 88 148 1 0
## 89 149 1 0
## 90 150 2 1
## 91 153 1 0
## 92 155 1 0
## 93 156 1 0
## 94 160 1 0
## 95 162 2 1
## 96 164 2 1
## 97 165 1 0
## 98 171 2 1
## 99 175 1 0
## 100 181 1 0
## 101 186 1 0
## 102 188 1 0
## 103 194 1 0
## 104 195 2 1
## 105 196 1 0
## 106 197 1 0
## 107 201 1 0
## 108 202 1 0
## 109 203 1 0
## 110 205 1 0
## 111 206 1 0
## 112 208 2 1
## 113 209 1 0
## 114 212 2 1
## 115 216 2 1
## 116 219 1 0
## 117 222 1 0
## 118 223 2 1
## 119 224 2 1
## 120 225 1 0
## 121 226 1 0
## 122 227 1 0
## 123 229 2 1
## 124 230 2 1
## 125 232 1 0
## 126 233 1 0
## 127 234 1 0
## 128 235 1 0
## 129 236 1 0
## 130 237 1 0
## 131 238 1 0
## 132 241 3 1
## 133 242 1 0
## 134 246 2 1
## 135 247 1 0
## 136 249 2 1
## 137 251 3 1
## 138 252 1 0
## 139 253 2 1
## 140 254 1 0
## 141 257 1 0
## 142 260 1 0
## 143 262 1 0
## 144 264 1 0
## 145 265 1 0
## 146 268 1 0
## 147 271 1 0
## 148 272 1 0
## 149 273 1 0
## 150 275 3 1
## 151 276 1 0
## 152 277 1 0
## 153 278 1 0
## 154 279 4 1
## 155 281 1 0
## 156 284 1 0
## 157 287 1 0
## 158 289 1 0
## 159 292 1 0
## 160 294 1 0
## 161 295 1 0
## 162 297 1 0
## 163 301 1 0
## 164 302 2 1
## 165 303 1 0
## 166 304 2 1
## 167 308 2 1
## 168 309 2 1
## 169 313 2 1
## 170 315 1 0
## 171 317 1 0
## 172 318 1 0
## 173 319 1 0
## 174 321 2 1
## 175 329 2 1
## 176 330 1 0
## 177 336 1 0
## 178 337 1 0
## 179 345 1 0
## 180 358 1 0
## 181 361 1 0
## 182 362 1 0
## 183 363 1 0
## 184 366 1 0
## 185 373 1 0
## 186 380 2 1
## 187 388 2 1
## 188 427 1 0
## 189 436 1 0
## 190 441 1 0
## 191 494 1 0
## 192 546 1 0#**********************************************************************************
#**********************************************************************************
#get Year label in the frequency table.
Year <- df1$Year
table(Year)## Year
## 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## 26 26 26 26 26 26 26 26 26 26 26 26 26Year_table <- transform(table(Year))
Year_table$percent <- round(prop.table(Year_table$Freq)*100, 0)
Year_table## Year Freq percent
## 1 2008 26 8
## 2 2009 26 8
## 3 2010 26 8
## 4 2011 26 8
## 5 2012 26 8
## 6 2013 26 8
## 7 2014 26 8
## 8 2015 26 8
## 9 2016 26 8
## 10 2017 26 8
## 11 2018 26 8
## 12 2019 26 8
## 13 2020 26 8#**********************************************************************************
#get the Cohorts label in the frequency table.
Cohorts <- df1$Cohorts
table(Cohorts)## Cohorts
## 1- 4 years 10-14 years 15-19 years 20-24 years 25-29 years 30-34 years
## 26 26 27 25 26 26
## 35-39 years 40-44years 45-49years 5-9 years 50-54 years 55-59years
## 26 26 26 26 26 26
## 60-64years
## 26Cohorts_table <- transform(table(Cohorts))
Cohorts_table$percent <- round(prop.table(Cohorts_table$Freq)*100, 0)
Cohorts_table## Cohorts Freq percent
## 1 1- 4 years 26 8
## 2 10-14 years 26 8
## 3 15-19 years 27 8
## 4 20-24 years 25 7
## 5 25-29 years 26 8
## 6 30-34 years 26 8
## 7 35-39 years 26 8
## 8 40-44years 26 8
## 9 45-49years 26 8
## 10 5-9 years 26 8
## 11 50-54 years 26 8
## 12 55-59years 26 8
## 13 60-64years 26 8#**********************************************************************************
##Other tables
##Frequency Distribution of gender by deaths
table(df1$Deaths, df1$Gender)##
## Female Male
## 0 5 5
## 14 1 0
## 17 1 0
## 18 2 0
## 21 1 0
## 23 1 0
## 24 2 0
## 25 2 0
## 26 1 0
## 27 2 0
## 28 4 0
## 30 2 0
## 31 4 1
## 32 1 0
## 33 3 0
## 35 2 0
## 36 3 3
## 37 3 1
## 38 1 1
## 39 2 0
## 40 3 2
## 42 1 1
## 43 5 0
## 44 1 0
## 45 1 3
## 46 1 0
## 47 3 0
## 48 2 0
## 49 1 1
## 50 5 3
## 52 4 3
## 53 1 0
## 54 5 0
## 55 3 1
## 56 1 0
## 57 3 1
## 58 4 0
## 60 2 0
## 61 1 3
## 62 1 1
## 63 1 0
## 64 1 0
## 65 3 0
## 66 4 1
## 67 1 0
## 68 1 0
## 69 2 0
## 70 2 1
## 72 0 1
## 73 1 1
## 74 2 0
## 75 2 0
## 76 0 1
## 80 5 1
## 81 2 0
## 83 1 0
## 86 1 0
## 87 1 1
## 88 2 0
## 89 1 0
## 90 1 0
## 93 3 0
## 94 0 1
## 96 1 0
## 97 2 1
## 99 0 1
## 100 1 1
## 101 1 0
## 102 1 0
## 103 0 1
## 104 1 1
## 105 1 0
## 110 0 1
## 111 0 1
## 112 2 2
## 116 0 1
## 117 2 0
## 119 1 0
## 122 0 1
## 123 1 1
## 125 0 1
## 127 1 0
## 135 1 0
## 136 2 0
## 138 1 0
## 140 0 1
## 145 1 0
## 148 0 1
## 149 0 1
## 150 2 0
## 153 0 1
## 155 1 0
## 156 1 0
## 160 1 0
## 162 1 1
## 164 1 1
## 165 1 0
## 171 1 1
## 175 0 1
## 181 0 1
## 186 0 1
## 188 0 1
## 194 0 1
## 195 1 1
## 196 1 0
## 197 1 0
## 201 0 1
## 202 0 1
## 203 0 1
## 205 1 0
## 206 0 1
## 208 0 2
## 209 1 0
## 212 0 2
## 216 0 2
## 219 0 1
## 222 0 1
## 223 0 2
## 224 1 1
## 225 0 1
## 226 0 1
## 227 0 1
## 229 0 2
## 230 0 2
## 232 1 0
## 233 0 1
## 234 0 1
## 235 0 1
## 236 0 1
## 237 0 1
## 238 0 1
## 241 0 3
## 242 0 1
## 246 0 2
## 247 0 1
## 249 1 1
## 251 0 3
## 252 1 0
## 253 0 2
## 254 1 0
## 257 0 1
## 260 0 1
## 262 0 1
## 264 0 1
## 265 0 1
## 268 0 1
## 271 0 1
## 272 0 1
## 273 1 0
## 275 0 3
## 276 0 1
## 277 0 1
## 278 0 1
## 279 0 4
## 281 0 1
## 284 0 1
## 287 1 0
## 289 0 1
## 292 0 1
## 294 0 1
## 295 0 1
## 297 0 1
## 301 0 1
## 302 0 2
## 303 0 1
## 304 0 2
## 308 0 2
## 309 0 2
## 313 0 2
## 315 0 1
## 317 0 1
## 318 0 1
## 319 0 1
## 321 0 2
## 329 0 2
## 330 0 1
## 336 0 1
## 337 0 1
## 345 0 1
## 358 0 1
## 361 0 1
## 362 0 1
## 363 0 1
## 366 0 1
## 373 0 1
## 380 0 2
## 388 0 2
## 427 0 1
## 436 0 1
## 441 0 1
## 494 0 1
## 546 0 1##Frequency Distribution of year by deaths
table(df1$Deaths, df1$Year)##
## 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## 0 2 2 2 2 2 0 0 0 0 0 0 0 0
## 14 0 0 1 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 1 0 0 0 0 0 0 0
## 18 0 0 1 0 0 0 0 1 0 0 0 0 0
## 21 0 0 0 0 1 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 1 0 0 0 0
## 24 0 0 0 0 0 1 1 0 0 0 0 0 0
## 25 0 0 0 0 0 0 1 0 0 0 0 0 1
## 26 0 0 0 1 0 0 0 0 0 0 0 0 0
## 27 0 1 0 0 1 0 0 0 0 0 0 0 0
## 28 0 1 0 0 0 0 0 0 0 0 2 1 0
## 30 0 0 1 0 0 0 0 0 1 0 0 0 0
## 31 0 0 1 1 0 0 0 0 0 0 0 1 2
## 32 0 1 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 1 0 0 0 1 1 0 0 0 0
## 35 0 0 0 0 0 1 0 0 0 1 0 0 0
## 36 1 0 1 0 1 0 0 1 0 0 0 0 2
## 37 1 0 2 0 0 0 0 1 0 0 0 0 0
## 38 0 0 1 0 0 0 0 0 0 0 0 0 1
## 39 0 0 0 0 0 0 1 0 0 1 0 0 0
## 40 0 1 1 1 1 0 0 0 0 0 0 1 0
## 42 0 1 0 0 0 0 0 0 0 0 0 0 1
## 43 1 0 0 0 0 0 0 1 0 1 1 1 0
## 44 0 0 0 0 0 0 0 0 1 0 0 0 0
## 45 1 1 0 0 1 1 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 1 0 0
## 47 0 0 1 0 0 0 0 0 0 1 0 1 0
## 48 0 1 0 0 1 0 0 0 0 0 0 0 0
## 49 0 0 0 0 1 0 0 0 0 0 1 0 0
## 50 1 2 1 0 0 1 0 0 1 2 0 0 0
## 52 1 0 0 0 0 2 2 0 0 0 1 1 0
## 53 0 0 0 0 1 0 0 0 0 0 0 0 0
## 54 0 0 0 2 0 0 1 0 2 0 0 0 0
## 55 0 1 0 1 0 1 0 0 1 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0 1 0
## 57 2 0 0 2 0 0 0 0 0 0 0 0 0
## 58 0 0 0 1 0 0 1 0 1 1 0 0 0
## 60 0 0 0 0 1 1 0 0 0 0 0 0 0
## 61 0 0 0 1 0 0 0 2 1 0 0 0 0
## 62 1 0 0 0 0 0 1 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 1 0 0 0 0 0
## 64 1 0 0 0 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 1 0 1 0 0 1 0 0 0
## 66 0 0 0 0 1 2 0 1 0 0 0 0 1
## 67 0 1 0 0 0 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0 0 0 0 0 0 1
## 69 0 0 0 0 0 0 2 0 0 0 0 0 0
## 70 0 0 0 0 0 0 1 1 1 0 0 0 0
## 72 0 0 0 0 0 0 0 0 0 0 0 1 0
## 73 0 0 0 0 0 0 1 0 0 0 0 1 0
## 74 0 0 0 0 0 0 0 0 0 1 0 1 0
## 75 0 0 0 0 0 0 0 1 0 1 0 0 0
## 76 0 0 1 0 0 0 0 0 0 0 0 0 0
## 80 0 0 1 0 0 1 0 0 0 3 0 1 0
## 81 0 0 0 0 0 0 0 0 0 0 2 0 0
## 83 0 0 0 0 0 0 0 1 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0 0 0 0 1
## 87 1 0 0 0 0 0 0 1 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 1 1 0
## 89 0 0 0 0 0 0 0 0 0 0 0 1 0
## 90 0 0 0 0 0 0 0 0 0 0 0 0 1
## 93 0 0 0 0 0 0 0 0 1 0 1 1 0
## 94 0 0 0 0 0 0 0 0 0 0 1 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 1
## 97 0 0 1 0 0 1 0 0 0 0 1 0 0
## 99 0 0 0 0 0 0 0 0 0 0 0 0 1
## 100 0 0 0 0 0 0 1 0 0 0 0 1 0
## 101 0 0 0 0 0 0 0 0 0 0 1 0 0
## 102 0 0 0 0 0 0 0 0 1 0 0 0 0
## 103 0 1 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 2 0 0 0
## 105 0 1 0 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 1 0 0 0 0
## 111 0 0 0 0 0 0 0 1 0 0 0 0 0
## 112 1 0 0 1 1 0 0 0 1 0 0 0 0
## 116 0 0 0 0 0 0 1 0 0 0 0 0 0
## 117 0 0 0 0 0 1 0 0 0 0 1 0 0
## 119 0 0 0 0 0 0 0 0 0 0 0 0 1
## 122 0 0 0 0 0 0 0 0 0 0 1 0 0
## 123 0 0 0 0 0 1 0 1 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0 0 0 1
## 127 0 1 0 0 0 0 0 0 0 0 0 0 0
## 135 0 0 0 0 0 0 0 0 0 0 0 0 1
## 136 1 0 0 0 0 0 0 0 0 0 1 0 0
## 138 0 0 0 0 0 0 0 0 0 0 0 1 0
## 140 0 0 0 0 0 0 0 0 0 0 0 1 0
## 145 0 0 0 0 1 0 0 0 0 0 0 0 0
## 148 0 0 1 0 0 0 0 0 0 0 0 0 0
## 149 0 0 0 1 0 0 0 0 0 0 0 0 0
## 150 0 0 0 0 0 1 0 1 0 0 0 0 0
## 153 0 0 0 0 0 0 0 0 0 0 1 0 0
## 155 0 0 0 0 0 0 0 0 1 0 0 0 0
## 156 1 0 0 0 0 0 0 0 0 0 0 0 0
## 160 0 0 0 0 0 0 0 0 0 0 0 0 1
## 162 0 0 1 1 0 0 0 0 0 0 0 0 0
## 164 0 0 1 0 1 0 0 0 0 0 0 0 0
## 165 0 0 0 0 0 0 0 0 0 1 0 0 0
## 171 0 0 1 0 0 0 1 0 0 0 0 0 0
## 175 0 1 0 0 0 0 0 0 0 0 0 0 0
## 181 0 0 1 0 0 0 0 0 0 0 0 0 0
## 186 0 1 0 0 0 0 0 0 0 0 0 0 0
## 188 0 0 0 1 0 0 0 0 0 0 0 0 0
## 194 0 0 1 0 0 0 0 0 0 0 0 0 0
## 195 0 1 0 0 0 0 0 0 1 0 0 0 0
## 196 0 0 0 0 0 1 0 0 0 0 0 0 0
## 197 0 0 0 0 1 0 0 0 0 0 0 0 0
## 201 0 0 0 0 0 0 0 1 0 0 0 0 0
## 202 0 0 0 0 0 1 0 0 0 0 0 0 0
## 203 0 0 0 1 0 0 0 0 0 0 0 0 0
## 205 0 0 0 1 0 0 0 0 0 0 0 0 0
## 206 1 0 0 0 0 0 0 0 0 0 0 0 0
## 208 1 0 0 0 0 0 1 0 0 0 0 0 0
## 209 0 0 0 0 0 0 0 0 0 1 0 0 0
## 212 0 0 0 0 1 0 0 0 0 1 0 0 0
## 216 0 0 0 0 0 0 1 0 1 0 0 0 0
## 219 0 1 0 0 0 0 0 0 0 0 0 0 0
## 222 1 0 0 0 0 0 0 0 0 0 0 0 0
## 223 0 0 0 0 0 0 0 0 0 0 0 1 1
## 224 0 2 0 0 0 0 0 0 0 0 0 0 0
## 225 0 0 0 1 0 0 0 0 0 0 0 0 0
## 226 0 0 1 0 0 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 1 0 0 0 0 0 0
## 229 0 0 0 0 1 1 0 0 0 0 0 0 0
## 230 1 0 0 0 0 0 0 1 0 0 0 0 0
## 232 0 0 0 0 0 0 1 0 0 0 0 0 0
## 233 0 0 0 0 0 0 0 0 0 0 1 0 0
## 234 0 0 0 0 0 0 0 1 0 0 0 0 0
## 235 0 0 0 0 1 0 0 0 0 0 0 0 0
## 236 0 0 0 0 0 0 0 0 1 0 0 0 0
## 237 0 0 0 0 0 0 0 0 1 0 0 0 0
## 238 1 0 0 0 0 0 0 0 0 0 0 0 0
## 241 0 0 1 1 0 0 0 0 1 0 0 0 0
## 242 0 0 0 0 1 0 0 0 0 0 0 0 0
## 246 1 1 0 0 0 0 0 0 0 0 0 0 0
## 247 0 0 0 0 0 0 0 0 1 0 0 0 0
## 249 0 0 0 0 0 0 0 1 0 0 1 0 0
## 251 0 0 0 0 0 1 1 0 0 0 0 1 0
## 252 0 0 0 0 0 0 0 1 0 0 0 0 0
## 253 0 0 0 0 0 1 1 0 0 0 0 0 0
## 254 0 0 0 0 0 0 0 0 0 0 0 1 0
## 257 0 0 0 0 0 0 0 1 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0 1 0 0 0 0
## 262 0 0 1 0 0 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 1 0 0 0 0 0 0 0
## 265 0 0 0 0 0 0 0 1 0 0 0 0 0
## 268 0 0 0 0 1 0 0 0 0 0 0 0 0
## 271 0 0 0 0 0 0 0 0 1 0 0 0 0
## 272 0 0 0 0 0 0 0 0 0 1 0 0 0
## 273 1 0 0 0 0 0 0 0 0 0 0 0 0
## 275 0 0 0 0 0 0 0 0 0 1 0 1 1
## 276 0 1 0 0 0 0 0 0 0 0 0 0 0
## 277 0 0 0 0 0 0 0 0 0 1 0 0 0
## 278 0 0 0 0 0 0 1 0 0 0 0 0 0
## 279 0 0 0 1 0 0 0 0 0 1 1 1 0
## 281 0 0 0 1 0 0 0 0 0 0 0 0 0
## 284 0 0 0 0 0 0 0 1 0 0 0 0 0
## 287 0 0 0 0 0 0 0 0 0 0 0 0 1
## 289 0 0 0 0 0 0 0 0 0 0 0 1 0
## 292 0 0 0 0 0 0 0 0 0 0 0 1 0
## 294 0 0 0 0 0 0 0 0 0 0 1 0 0
## 295 0 1 0 0 0 0 0 0 0 0 0 0 0
## 297 0 0 1 0 0 0 0 0 0 0 0 0 0
## 301 0 0 0 0 0 0 1 0 0 0 0 0 0
## 302 0 0 0 1 1 0 0 0 0 0 0 0 0
## 303 1 0 0 0 0 0 0 0 0 0 0 0 0
## 304 0 0 0 0 1 0 0 0 0 1 0 0 0
## 308 0 0 0 0 0 0 0 0 1 0 1 0 0
## 309 0 0 0 0 0 2 0 0 0 0 0 0 0
## 313 0 0 0 0 2 0 0 0 0 0 0 0 0
## 315 0 0 0 0 0 0 0 0 0 1 0 0 0
## 317 0 0 0 0 0 1 0 0 0 0 0 0 0
## 318 1 0 0 0 0 0 0 0 0 0 0 0 0
## 319 0 0 0 1 0 0 0 0 0 0 0 0 0
## 321 0 0 0 0 0 0 1 0 0 0 1 0 0
## 329 0 0 0 0 0 1 0 0 1 0 0 0 0
## 330 0 0 0 0 0 0 0 0 0 0 1 0 0
## 336 0 0 0 0 0 0 0 0 0 0 0 0 1
## 337 0 0 0 0 0 0 1 0 0 0 0 0 0
## 345 0 0 0 0 0 0 0 1 0 0 0 0 0
## 358 0 0 0 0 0 0 0 0 0 0 0 0 1
## 361 0 0 0 0 0 0 0 0 0 0 0 1 0
## 362 0 0 0 0 0 0 0 0 0 0 1 0 0
## 363 0 0 0 1 0 0 0 0 0 0 0 0 0
## 366 0 0 0 0 0 0 0 0 0 1 0 0 0
## 373 0 1 0 0 0 0 0 0 0 0 0 0 0
## 380 0 0 0 0 0 0 0 1 0 0 0 1 0
## 388 0 0 0 0 0 0 0 0 0 1 0 0 1
## 427 0 0 0 0 0 0 0 0 0 0 1 0 0
## 436 1 0 0 0 0 0 0 0 0 0 0 0 0
## 441 0 0 0 0 0 0 0 0 0 0 0 0 1
## 494 0 0 0 0 0 0 0 0 0 0 0 0 1
## 546 0 0 0 0 0 0 0 0 0 0 0 0 1##Frequency Distribution of cohorts by deaths
table(df1$Deaths, df1$Cohorts)##
## 1- 4 years 10-14 years 15-19 years 20-24 years 25-29 years 30-34 years
## 0 10 0 0 0 0 0
## 14 0 1 0 0 0 0
## 17 0 1 0 0 0 0
## 18 0 1 0 0 0 0
## 21 0 0 0 0 0 0
## 23 0 1 0 0 0 0
## 24 0 1 0 0 0 0
## 25 0 0 0 0 0 0
## 26 0 1 0 0 0 0
## 27 0 2 0 0 0 0
## 28 0 1 2 0 0 0
## 30 0 0 1 0 0 0
## 31 0 3 1 0 0 0
## 32 0 0 1 0 0 0
## 33 0 0 2 0 0 0
## 35 0 0 2 0 0 0
## 36 0 2 1 1 0 0
## 37 0 1 1 1 0 1
## 38 0 0 0 0 0 0
## 39 0 1 1 0 0 0
## 40 0 1 1 1 1 0
## 42 1 0 0 0 0 0
## 43 0 0 0 2 0 0
## 44 0 0 0 1 0 0
## 45 0 2 0 1 0 0
## 46 0 0 0 1 0 0
## 47 0 0 0 1 0 0
## 48 0 0 0 1 0 0
## 49 0 0 0 0 0 0
## 50 0 2 0 1 1 2
## 52 0 2 0 0 0 0
## 53 0 0 0 0 0 0
## 54 0 0 0 1 0 1
## 55 0 1 0 0 1 0
## 56 0 0 0 0 1 0
## 57 0 0 0 0 1 0
## 58 0 0 0 1 2 1
## 60 0 0 0 0 0 2
## 61 0 1 0 0 0 0
## 62 0 1 0 0 1 0
## 63 0 0 0 0 1 0
## 64 0 0 0 0 0 0
## 65 0 0 0 0 0 1
## 66 1 0 1 0 2 1
## 67 0 0 0 0 0 0
## 68 0 0 0 0 1 0
## 69 1 0 0 0 0 0
## 70 0 0 0 0 0 0
## 72 0 0 1 0 0 0
## 73 1 0 0 0 0 0
## 74 1 0 0 0 0 1
## 75 0 0 0 0 0 0
## 76 0 0 1 0 0 0
## 80 0 0 1 0 0 1
## 81 0 0 0 0 1 0
## 83 1 0 0 0 0 0
## 86 0 0 0 0 0 1
## 87 1 0 0 0 0 0
## 88 0 0 0 0 0 0
## 89 0 0 0 0 0 0
## 90 0 0 0 0 0 0
## 93 1 0 0 0 0 1
## 94 0 0 1 0 0 0
## 96 0 0 0 0 0 0
## 97 1 0 0 0 0 0
## 99 1 0 0 0 0 0
## 100 1 0 0 0 0 0
## 101 1 0 0 0 0 0
## 102 1 0 0 0 0 0
## 103 0 0 1 0 0 0
## 104 1 0 0 0 0 0
## 105 0 0 0 0 0 0
## 110 1 0 0 0 0 0
## 111 0 0 1 0 0 0
## 112 0 0 2 0 0 0
## 116 0 0 1 0 0 0
## 117 0 0 0 0 0 0
## 119 0 0 0 0 0 0
## 122 1 0 0 0 0 0
## 123 0 0 1 0 0 0
## 125 0 0 0 1 0 0
## 127 0 0 0 0 0 0
## 135 0 0 0 0 0 0
## 136 0 0 0 0 0 0
## 138 0 0 0 0 0 0
## 140 0 0 0 1 0 0
## 145 0 0 0 0 0 0
## 148 0 0 0 0 0 0
## 149 0 0 1 0 0 0
## 150 0 0 0 0 0 0
## 153 0 0 0 1 0 0
## 155 0 0 0 0 0 0
## 156 0 0 0 0 0 0
## 160 0 0 0 0 0 0
## 162 0 0 0 1 0 0
## 164 0 0 1 0 0 0
## 165 0 0 0 0 0 0
## 171 0 0 0 0 0 0
## 175 0 0 0 0 0 0
## 181 0 0 0 0 0 1
## 186 0 0 0 1 0 0
## 188 0 0 0 0 0 0
## 194 0 0 0 0 0 0
## 195 0 0 0 0 0 1
## 196 0 0 0 0 0 0
## 197 0 0 0 0 0 0
## 201 0 0 0 1 0 0
## 202 0 0 0 0 0 0
## 203 0 0 0 0 0 0
## 205 0 0 0 0 0 0
## 206 0 0 1 0 0 0
## 208 0 0 0 1 0 0
## 209 0 0 0 0 0 0
## 212 0 0 0 1 0 0
## 216 0 0 0 0 0 0
## 219 0 0 0 0 0 0
## 222 0 0 0 0 0 1
## 223 0 0 0 0 2 0
## 224 0 0 0 0 0 0
## 225 0 0 0 0 0 1
## 226 0 0 0 0 1 0
## 227 0 0 0 0 0 0
## 229 0 0 0 0 0 1
## 230 0 0 0 0 0 0
## 232 0 0 0 0 0 0
## 233 0 0 0 0 1 0
## 234 0 0 0 0 0 0
## 235 0 0 0 0 0 0
## 236 0 0 0 1 0 0
## 237 0 0 0 0 0 0
## 238 0 0 0 0 0 0
## 241 0 0 0 0 1 0
## 242 0 0 0 0 0 0
## 246 0 0 0 0 2 0
## 247 0 0 0 0 0 0
## 249 0 0 0 0 0 1
## 251 0 0 0 1 0 1
## 252 0 0 0 0 0 0
## 253 0 0 0 0 0 1
## 254 0 0 0 0 0 0
## 257 0 0 0 0 0 0
## 260 0 0 0 0 0 1
## 262 0 0 0 0 0 0
## 264 0 0 0 0 0 1
## 265 0 0 0 0 0 0
## 268 0 0 0 0 0 0
## 271 0 0 0 0 0 0
## 272 0 0 0 0 0 0
## 273 0 0 0 0 0 0
## 275 0 0 0 0 1 1
## 276 0 0 0 0 0 0
## 277 0 0 0 0 0 0
## 278 0 0 0 0 1 0
## 279 0 0 0 0 1 0
## 281 0 0 0 1 0 0
## 284 0 0 0 0 1 0
## 287 0 0 0 0 0 0
## 289 0 0 0 0 0 0
## 292 0 0 0 0 0 0
## 294 0 0 0 0 0 1
## 295 0 0 0 0 0 0
## 297 0 0 0 0 0 0
## 301 0 0 0 0 0 0
## 302 0 0 0 0 1 0
## 303 0 0 0 0 0 0
## 304 0 0 0 1 0 1
## 308 0 0 0 0 0 0
## 309 0 0 0 0 1 0
## 313 0 0 0 0 0 0
## 315 0 0 0 0 0 0
## 317 0 0 0 0 0 0
## 318 0 0 0 0 0 0
## 319 0 0 0 0 0 0
## 321 0 0 0 0 0 0
## 329 0 0 0 0 0 0
## 330 0 0 0 0 0 0
## 336 0 0 0 0 0 0
## 337 0 0 0 0 0 0
## 345 0 0 0 0 0 0
## 358 0 0 0 0 0 0
## 361 0 0 0 0 0 0
## 362 0 0 0 0 0 0
## 363 0 0 0 0 0 0
## 366 0 0 0 0 0 0
## 373 0 0 0 0 0 0
## 380 0 0 0 0 0 0
## 388 0 0 0 0 0 0
## 427 0 0 0 0 0 0
## 436 0 0 0 0 0 0
## 441 0 0 0 0 0 0
## 494 0 0 0 0 0 0
## 546 0 0 0 0 0 0
##
## 35-39 years 40-44years 45-49years 5-9 years 50-54 years 55-59years
## 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0
## 17 0 0 0 0 0 0
## 18 0 0 0 1 0 0
## 21 0 0 0 1 0 0
## 23 0 0 0 0 0 0
## 24 0 0 0 1 0 0
## 25 0 0 0 2 0 0
## 26 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## 28 0 0 0 1 0 0
## 30 0 0 0 1 0 0
## 31 0 0 0 1 0 0
## 32 0 0 0 0 0 0
## 33 0 0 0 1 0 0
## 35 0 0 0 0 0 0
## 36 0 0 0 2 0 0
## 37 0 0 0 0 0 0
## 38 1 0 0 1 0 0
## 39 0 0 0 0 0 0
## 40 0 0 0 1 0 0
## 42 0 0 0 1 0 0
## 43 0 0 0 3 0 0
## 44 0 0 0 0 0 0
## 45 0 0 0 1 0 0
## 46 0 0 0 0 0 0
## 47 0 0 1 1 0 0
## 48 1 0 0 0 0 0
## 49 1 0 0 1 0 0
## 50 0 1 0 1 0 0
## 52 3 0 1 1 0 0
## 53 0 1 0 0 0 0
## 54 1 1 1 0 0 0
## 55 0 0 2 0 0 0
## 56 0 0 0 0 0 0
## 57 0 1 1 1 0 0
## 58 0 0 0 0 0 0
## 60 0 0 0 0 0 0
## 61 1 0 0 2 0 0
## 62 0 0 0 0 0 0
## 63 0 0 0 0 0 0
## 64 0 1 0 0 0 0
## 65 0 1 1 0 0 0
## 66 0 0 0 0 0 0
## 67 0 1 0 0 0 0
## 68 0 0 0 0 0 0
## 69 0 1 0 0 0 0
## 70 1 0 1 1 0 0
## 72 0 0 0 0 0 0
## 73 0 0 1 0 0 0
## 74 0 0 0 0 0 0
## 75 0 1 1 0 0 0
## 76 0 0 0 0 0 0
## 80 1 2 0 0 1 0
## 81 0 0 1 0 0 0
## 83 0 0 0 0 0 0
## 86 0 0 0 0 0 0
## 87 0 0 1 0 0 0
## 88 0 1 0 0 1 0
## 89 1 0 0 0 0 0
## 90 0 1 0 0 0 0
## 93 0 0 0 0 1 0
## 94 0 0 0 0 0 0
## 96 1 0 0 0 0 0
## 97 1 0 0 0 0 1
## 99 0 0 0 0 0 0
## 100 0 0 0 0 1 0
## 101 0 0 0 0 0 0
## 102 0 0 0 0 0 0
## 103 0 0 0 0 0 0
## 104 0 0 0 0 1 0
## 105 0 0 0 0 1 0
## 110 0 0 0 0 0 0
## 111 0 0 0 0 0 0
## 112 0 0 0 0 2 0
## 116 0 0 0 0 0 0
## 117 0 0 0 0 2 0
## 119 0 0 1 0 0 0
## 122 0 0 0 0 0 0
## 123 0 0 0 0 1 0
## 125 0 0 0 0 0 0
## 127 0 0 0 0 0 1
## 135 0 0 0 0 1 0
## 136 0 0 0 0 1 1
## 138 0 0 0 0 0 1
## 140 0 0 0 0 0 0
## 145 0 0 0 0 0 1
## 148 1 0 0 0 0 0
## 149 0 0 0 0 0 0
## 150 0 0 0 0 0 2
## 153 0 0 0 0 0 0
## 155 0 0 0 0 0 1
## 156 0 0 0 0 0 1
## 160 0 0 0 0 0 1
## 162 0 0 0 0 0 1
## 164 0 0 0 0 0 0
## 165 0 0 0 0 0 1
## 171 0 0 1 0 0 1
## 175 1 0 0 0 0 0
## 181 0 0 0 0 0 0
## 186 0 0 0 0 0 0
## 188 1 0 0 0 0 0
## 194 0 1 0 0 0 0
## 195 0 0 0 0 0 0
## 196 0 0 0 0 0 0
## 197 0 0 0 0 0 0
## 201 0 0 0 0 0 0
## 202 1 0 0 0 0 0
## 203 0 0 1 0 0 0
## 205 0 0 0 0 0 0
## 206 0 0 0 0 0 0
## 208 1 0 0 0 0 0
## 209 0 0 0 0 0 0
## 212 1 0 0 0 0 0
## 216 0 0 2 0 0 0
## 219 0 1 0 0 0 0
## 222 0 0 0 0 0 0
## 223 0 0 0 0 0 0
## 224 0 0 1 0 0 0
## 225 0 0 0 0 0 0
## 226 0 0 0 0 0 0
## 227 1 0 0 0 0 0
## 229 0 0 1 0 0 0
## 230 1 1 0 0 0 0
## 232 0 0 0 0 0 0
## 233 0 0 0 0 0 0
## 234 0 1 0 0 0 0
## 235 0 1 0 0 0 0
## 236 0 0 0 0 0 0
## 237 1 0 0 0 0 0
## 238 0 0 1 0 0 0
## 241 0 1 0 0 0 1
## 242 0 0 1 0 0 0
## 246 0 0 0 0 0 0
## 247 0 1 0 0 0 0
## 249 0 0 0 0 0 0
## 251 0 1 0 0 0 0
## 252 0 0 0 0 0 0
## 253 0 1 0 0 0 0
## 254 0 0 0 0 0 0
## 257 0 0 1 0 0 0
## 260 0 0 0 0 0 0
## 262 0 0 0 0 1 0
## 264 0 0 0 0 0 0
## 265 0 0 0 0 1 0
## 268 0 0 0 0 1 0
## 271 0 0 0 0 1 0
## 272 1 0 0 0 0 0
## 273 0 0 0 0 0 0
## 275 1 0 0 0 0 0
## 276 0 0 0 0 0 1
## 277 0 0 1 0 0 0
## 278 0 0 0 0 0 0
## 279 1 2 0 0 0 0
## 281 0 0 0 0 0 0
## 284 0 0 0 0 0 0
## 287 0 0 0 0 0 0
## 289 0 0 1 0 0 0
## 292 0 0 0 0 1 0
## 294 0 0 0 0 0 0
## 295 0 0 0 0 1 0
## 297 0 0 0 0 0 0
## 301 0 0 0 0 1 0
## 302 0 0 0 0 1 0
## 303 0 0 0 0 0 1
## 304 0 0 0 0 0 0
## 308 0 1 0 0 0 1
## 309 0 0 0 0 1 0
## 313 0 0 0 0 0 1
## 315 0 0 0 0 1 0
## 317 0 0 0 0 0 0
## 318 0 0 0 0 1 0
## 319 0 0 0 0 0 1
## 321 0 0 0 0 1 1
## 329 0 0 0 0 0 1
## 330 0 0 1 0 0 0
## 336 1 0 0 0 0 0
## 337 0 0 0 0 0 0
## 345 0 0 0 0 0 1
## 358 0 0 1 0 0 0
## 361 0 0 0 0 0 1
## 362 0 0 0 0 0 1
## 363 0 0 0 0 0 0
## 366 0 0 0 0 0 0
## 373 0 0 0 0 0 0
## 380 0 0 0 0 0 0
## 388 0 1 0 0 0 1
## 427 0 0 0 0 0 0
## 436 0 0 0 0 0 0
## 441 0 0 0 0 1 0
## 494 0 0 0 0 0 1
## 546 0 0 0 0 0 0
##
## 60-64years
## 0 0
## 14 0
## 17 0
## 18 0
## 21 0
## 23 0
## 24 0
## 25 0
## 26 0
## 27 0
## 28 0
## 30 0
## 31 0
## 32 0
## 33 0
## 35 0
## 36 0
## 37 0
## 38 0
## 39 0
## 40 0
## 42 0
## 43 0
## 44 0
## 45 0
## 46 0
## 47 0
## 48 0
## 49 0
## 50 0
## 52 0
## 53 0
## 54 0
## 55 0
## 56 0
## 57 0
## 58 0
## 60 0
## 61 0
## 62 0
## 63 0
## 64 0
## 65 0
## 66 0
## 67 0
## 68 0
## 69 0
## 70 0
## 72 0
## 73 0
## 74 0
## 75 0
## 76 0
## 80 0
## 81 0
## 83 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 0
## 93 0
## 94 0
## 96 0
## 97 0
## 99 0
## 100 0
## 101 0
## 102 0
## 103 0
## 104 0
## 105 0
## 110 0
## 111 0
## 112 0
## 116 0
## 117 0
## 119 0
## 122 0
## 123 0
## 125 0
## 127 0
## 135 0
## 136 0
## 138 0
## 140 0
## 145 0
## 148 0
## 149 0
## 150 0
## 153 0
## 155 0
## 156 0
## 160 0
## 162 0
## 164 1
## 165 0
## 171 0
## 175 0
## 181 0
## 186 0
## 188 0
## 194 0
## 195 1
## 196 1
## 197 1
## 201 0
## 202 0
## 203 0
## 205 1
## 206 0
## 208 0
## 209 1
## 212 0
## 216 0
## 219 0
## 222 0
## 223 0
## 224 1
## 225 0
## 226 0
## 227 0
## 229 0
## 230 0
## 232 1
## 233 0
## 234 0
## 235 0
## 236 0
## 237 0
## 238 0
## 241 0
## 242 0
## 246 0
## 247 0
## 249 1
## 251 0
## 252 1
## 253 0
## 254 1
## 257 0
## 260 0
## 262 0
## 264 0
## 265 0
## 268 0
## 271 0
## 272 0
## 273 1
## 275 0
## 276 0
## 277 0
## 278 0
## 279 0
## 281 0
## 284 0
## 287 1
## 289 0
## 292 0
## 294 0
## 295 0
## 297 1
## 301 0
## 302 0
## 303 0
## 304 0
## 308 0
## 309 0
## 313 1
## 315 0
## 317 1
## 318 0
## 319 0
## 321 0
## 329 1
## 330 0
## 336 0
## 337 1
## 345 0
## 358 0
## 361 0
## 362 0
## 363 1
## 366 1
## 373 1
## 380 2
## 388 0
## 427 1
## 436 1
## 441 0
## 494 0
## 546 1##Frequency Distribution of Marital by deaths
table(df1$Deaths, df1$Marital_Status)##
## Married Single
## 0 5 5
## 14 1 0
## 17 1 0
## 18 1 1
## 21 0 1
## 23 0 1
## 24 1 1
## 25 1 1
## 26 1 0
## 27 1 1
## 28 2 2
## 30 2 0
## 31 2 3
## 32 1 0
## 33 1 2
## 35 0 2
## 36 3 3
## 37 2 2
## 38 1 1
## 39 1 1
## 40 1 4
## 42 1 1
## 43 3 2
## 44 0 1
## 45 2 2
## 46 1 0
## 47 3 0
## 48 0 2
## 49 0 2
## 50 1 7
## 52 6 1
## 53 1 0
## 54 3 2
## 55 2 2
## 56 1 0
## 57 2 2
## 58 2 2
## 60 0 2
## 61 3 1
## 62 0 2
## 63 0 1
## 64 0 1
## 65 1 2
## 66 2 3
## 67 0 1
## 68 1 0
## 69 2 0
## 70 1 2
## 72 0 1
## 73 0 2
## 74 1 1
## 75 1 1
## 76 1 0
## 80 3 3
## 81 1 1
## 83 1 0
## 86 0 1
## 87 0 2
## 88 0 2
## 89 0 1
## 90 0 1
## 93 1 2
## 94 1 0
## 96 0 1
## 97 1 2
## 99 1 0
## 100 2 0
## 101 1 0
## 102 1 0
## 103 1 0
## 104 1 1
## 105 0 1
## 110 1 0
## 111 1 0
## 112 2 2
## 116 0 1
## 117 1 1
## 119 0 1
## 122 0 1
## 123 1 1
## 125 0 1
## 127 1 0
## 135 0 1
## 136 2 0
## 138 0 1
## 140 0 1
## 145 0 1
## 148 0 1
## 149 0 1
## 150 1 1
## 153 1 0
## 155 1 0
## 156 1 0
## 160 0 1
## 162 1 1
## 164 1 1
## 165 1 0
## 171 1 1
## 175 1 0
## 181 1 0
## 186 0 1
## 188 0 1
## 194 0 1
## 195 1 1
## 196 0 1
## 197 0 1
## 201 1 0
## 202 1 0
## 203 0 1
## 205 0 1
## 206 0 1
## 208 1 1
## 209 1 0
## 212 1 1
## 216 0 2
## 219 0 1
## 222 0 1
## 223 0 2
## 224 0 2
## 225 0 1
## 226 1 0
## 227 1 0
## 229 1 1
## 230 1 1
## 232 1 0
## 233 0 1
## 234 0 1
## 235 1 0
## 236 1 0
## 237 1 0
## 238 0 1
## 241 2 1
## 242 1 0
## 246 1 1
## 247 1 0
## 249 1 1
## 251 2 1
## 252 1 0
## 253 1 1
## 254 1 0
## 257 1 0
## 260 1 0
## 262 0 1
## 264 1 0
## 265 1 0
## 268 1 0
## 271 1 0
## 272 0 1
## 273 0 1
## 275 1 2
## 276 0 1
## 277 0 1
## 278 1 0
## 279 3 1
## 281 1 0
## 284 1 0
## 287 0 1
## 289 1 0
## 292 1 0
## 294 0 1
## 295 1 0
## 297 0 1
## 301 1 0
## 302 1 1
## 303 1 0
## 304 1 1
## 308 0 2
## 309 0 2
## 313 1 1
## 315 0 1
## 317 1 0
## 318 0 1
## 319 1 0
## 321 1 1
## 329 1 1
## 330 0 1
## 336 0 1
## 337 0 1
## 345 0 1
## 358 0 1
## 361 1 0
## 362 0 1
## 363 0 1
## 366 0 1
## 373 0 1
## 380 1 1
## 388 0 2
## 427 0 1
## 436 1 0
## 441 0 1
## 494 1 0
## 546 1 0#**********************************************************************************
##**Descriptive Measures [Numeric Variables]:
#**********************************************************************************
##Find the minimum values of deaths using min()
min(df1$Deaths) ## The minimum number of deaths is 0## [1] 0##Find the maximum values of deaths, using max()
max(df1$Deaths) ## The maximum number of death rate is 546## [1] 546##Find the range for deaths, using range() and max()-min()
range(df1$Deaths) #0 546## [1] 0 546max(df1$Deaths)-min(df1$Deaths) ##using max()-min() #546## [1] 546range(df1$Deaths)[2]-range(df1$Deaths)[1] ##using range() #546## [1] 546##calculate the mean of deaths
mean(df1$Deaths) ## On average deaths is about 143.0473## [1] 143.0473##calculate the median of deaths
median(df1$Deaths) ## 97 is the median## [1] 97##calculate the variability using variance (var()) or standard deviation (sd())
##Deaths
var(df1$Deaths) ## The variability is 12618.9## [1] 12618.9sd(df1$Deaths) #or 112.3339## [1] 112.3339## Summary Statistics
summary(df1$Deaths)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 50.0 97.0 143.0 235.8 546.0#Min. 1st Qu. Median Mean 3rd Qu. Max.
#0.0 50.0 97.0 143.0 235.8 546.0 ##*Histogram-----------------------------------------------------------------
hist(df1$Deaths,
xlab="Deaths", ## label of x-axis
ylab="frequency", ## label of y-axis
main="Deaths Frequency Distribution ", ## title
col="red") ##color
hist(df1$Death, xlab="Death", main=" Frequency Distribution of Deaths") #Deaths
#*box Plot----------------------------------------------------------------------
boxplot(df1$Deaths ~ df1$Year,
xlab="Years",
ylab="Deaths",
main=" Deaths by years",
col=2:6)
#**********************************************************************************
##*box Plot----------------------------------------------------------------------
boxplot(df1$Deaths ~ df1$Cohorts,
xlab="Cohorts",
ylab="Deaths",
main=" Deaths by 13 Cohorts",
col=3:7)
##*box Plot----------------------------------------------------------------------
boxplot(df1$Deaths ~ df1$Gender,
xlab="Cohorts",
ylab="Deaths",
main=" Deaths by Gender",
col=3:7)
##*box Plot----------------------------------------------------------------------
boxplot(df1$Deaths ~ df1$Year,
xlab="Years",
ylab="Deaths",
main=" Deaths by years",
col=2:6)
#**********************************************************************************
##*box Plot----------------------------------------------------------------------
boxplot(df1$Deaths ~ df1$Cohorts,
xlab="Cohorts",
ylab="Deaths",
main=" Deaths by 13 Cohorts",
col=3:7)