divide.RdSeparate a data.frame into a list of any depth by one or more stratification columns whose levels become the names.
divide(
data,
...,
depth = Inf,
remove = TRUE,
drop = TRUE,
sep = "|"
)Any data.frame.
Selection of columns to split by. See dplyr::select for details.
Depth to split to. Defaults to Inf. See details for more information.
Should the stratfication columns be removed? Defaults to TRUE.
Should unused combinations of stratification variables be dropped? Defaults to TRUE.
String to separate values of each stratification variable by. Defaults to "|". Only used when the number of stratification columns exceeds the desired depth.
For the depth, use positive integers to move from the root and negative integers to move from the leaves. The maximum (minimum) depth will be used for integers larger (smaller) than such.
A list
#Unquoted selection
heart_disease %>%
divide(
Sex
)
#> $Female
#> # A tibble: 97 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 41 Atypical angina 130 204 FALSE 172 No No
#> 2 62 Asymptomatic 140 268 FALSE 160 No Yes
#> 3 57 Asymptomatic 120 354 FALSE 163 Yes No
#> 4 56 Atypical angina 140 294 FALSE 153 No No
#> 5 48 Non-anginal pain 130 275 FALSE 139 No No
#> 6 58 Typical angina 150 283 TRUE 162 No No
#> 7 50 Non-anginal pain 120 219 FALSE 158 No No
#> 8 58 Non-anginal pain 120 340 FALSE 172 No No
#> 9 66 Typical angina 150 226 FALSE 114 No No
#> 10 69 Typical angina 140 239 FALSE 151 No No
#> # … with 87 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>
#> $Male
#> # A tibble: 206 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Typical angina 145 233 TRUE 150 No No
#> 2 67 Asymptomatic 160 286 FALSE 108 Yes Yes
#> 3 67 Asymptomatic 120 229 FALSE 129 Yes Yes
#> 4 37 Non-anginal pain 130 250 FALSE 187 No No
#> 5 56 Atypical angina 120 236 FALSE 178 No No
#> 6 63 Asymptomatic 130 254 FALSE 147 No Yes
#> 7 53 Asymptomatic 140 203 TRUE 155 Yes Yes
#> 8 57 Asymptomatic 140 192 FALSE 148 No No
#> 9 56 Non-anginal pain 130 256 TRUE 142 Yes Yes
#> 10 44 Atypical angina 120 263 FALSE 173 No No
#> # … with 196 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>
#Using select helpers
heart_disease %>%
divide(
matches("^S")
)
#> $Female
#> # A tibble: 97 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 41 Atypical angina 130 204 FALSE 172 No No
#> 2 62 Asymptomatic 140 268 FALSE 160 No Yes
#> 3 57 Asymptomatic 120 354 FALSE 163 Yes No
#> 4 56 Atypical angina 140 294 FALSE 153 No No
#> 5 48 Non-anginal pain 130 275 FALSE 139 No No
#> 6 58 Typical angina 150 283 TRUE 162 No No
#> 7 50 Non-anginal pain 120 219 FALSE 158 No No
#> 8 58 Non-anginal pain 120 340 FALSE 172 No No
#> 9 66 Typical angina 150 226 FALSE 114 No No
#> 10 69 Typical angina 140 239 FALSE 151 No No
#> # … with 87 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>
#> $Male
#> # A tibble: 206 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Typical angina 145 233 TRUE 150 No No
#> 2 67 Asymptomatic 160 286 FALSE 108 Yes Yes
#> 3 67 Asymptomatic 120 229 FALSE 129 Yes Yes
#> 4 37 Non-anginal pain 130 250 FALSE 187 No No
#> 5 56 Atypical angina 120 236 FALSE 178 No No
#> 6 63 Asymptomatic 130 254 FALSE 147 No Yes
#> 7 53 Asymptomatic 140 203 TRUE 155 Yes Yes
#> 8 57 Asymptomatic 140 192 FALSE 148 No No
#> 9 56 Non-anginal pain 130 256 TRUE 142 Yes Yes
#> 10 44 Atypical angina 120 263 FALSE 173 No No
#> # … with 196 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>
#Reduced depth
heart_disease %>%
divide(
Sex,
HeartDisease,
depth = 1
)
#> $`Female|No`
#> # A tibble: 72 × 7
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR ExerciseInduc…¹
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 41 Atypical angina 130 204 FALSE 172 No
#> 2 57 Asymptomatic 120 354 FALSE 163 Yes
#> 3 56 Atypical angina 140 294 FALSE 153 No
#> 4 48 Non-anginal pain 130 275 FALSE 139 No
#> 5 58 Typical angina 150 283 TRUE 162 No
#> 6 50 Non-anginal pain 120 219 FALSE 158 No
#> 7 58 Non-anginal pain 120 340 FALSE 172 No
#> 8 66 Typical angina 150 226 FALSE 114 No
#> 9 69 Typical angina 140 239 FALSE 151 No
#> 10 71 Atypical angina 160 302 FALSE 162 No
#> # … with 62 more rows, and abbreviated variable name ¹ExerciseInducedAngina
#>
#> $`Male|No`
#> # A tibble: 92 × 7
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR ExerciseInduc…¹
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 63 Typical angina 145 233 TRUE 150 No
#> 2 37 Non-anginal pain 130 250 FALSE 187 No
#> 3 56 Atypical angina 120 236 FALSE 178 No
#> 4 57 Asymptomatic 140 192 FALSE 148 No
#> 5 44 Atypical angina 120 263 FALSE 173 No
#> 6 52 Non-anginal pain 172 199 TRUE 162 No
#> 7 57 Non-anginal pain 150 168 FALSE 174 No
#> 8 54 Asymptomatic 140 239 FALSE 160 No
#> 9 49 Atypical angina 130 266 FALSE 171 No
#> 10 64 Typical angina 110 211 FALSE 144 Yes
#> # … with 82 more rows, and abbreviated variable name ¹ExerciseInducedAngina
#>
#> $`Female|Yes`
#> # A tibble: 25 × 7
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR ExerciseInduc…¹
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 62 Asymptomatic 140 268 FALSE 160 No
#> 2 65 Asymptomatic 150 225 FALSE 114 No
#> 3 61 Asymptomatic 130 330 FALSE 169 No
#> 4 51 Asymptomatic 130 305 FALSE 142 Yes
#> 5 62 Asymptomatic 160 164 FALSE 145 No
#> 6 60 Asymptomatic 150 258 FALSE 157 No
#> 7 61 Asymptomatic 145 307 FALSE 146 Yes
#> 8 43 Asymptomatic 132 341 TRUE 136 Yes
#> 9 62 Non-anginal pain 130 263 FALSE 97 No
#> 10 63 Asymptomatic 150 407 FALSE 154 No
#> # … with 15 more rows, and abbreviated variable name ¹ExerciseInducedAngina
#>
#> $`Male|Yes`
#> # A tibble: 114 × 7
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR ExerciseInduc…¹
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 67 Asymptomatic 160 286 FALSE 108 Yes
#> 2 67 Asymptomatic 120 229 FALSE 129 Yes
#> 3 63 Asymptomatic 130 254 FALSE 147 No
#> 4 53 Asymptomatic 140 203 TRUE 155 Yes
#> 5 56 Non-anginal pain 130 256 TRUE 142 Yes
#> 6 48 Atypical angina 110 229 FALSE 168 No
#> 7 58 Atypical angina 120 284 FALSE 160 No
#> 8 58 Non-anginal pain 132 224 FALSE 173 No
#> 9 60 Asymptomatic 130 206 FALSE 132 Yes
#> 10 40 Asymptomatic 110 167 FALSE 114 Yes
#> # … with 104 more rows, and abbreviated variable name ¹ExerciseInducedAngina
#>
#Keep columns in result; change delimiter in names
heart_disease %>%
divide(
Sex,
HeartDisease,
depth = 1,
remove = FALSE,
sep = ","
)
#> $`Female,No`
#> # A tibble: 72 × 9
#> Age Sex ChestPain BP Cholest…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 41 Female Atypical angina 130 204 FALSE 172 No No
#> 2 57 Female Asymptomatic 120 354 FALSE 163 Yes No
#> 3 56 Female Atypical angina 140 294 FALSE 153 No No
#> 4 48 Female Non-anginal pain 130 275 FALSE 139 No No
#> 5 58 Female Typical angina 150 283 TRUE 162 No No
#> 6 50 Female Non-anginal pain 120 219 FALSE 158 No No
#> 7 58 Female Non-anginal pain 120 340 FALSE 172 No No
#> 8 66 Female Typical angina 150 226 FALSE 114 No No
#> 9 69 Female Typical angina 140 239 FALSE 151 No No
#> 10 71 Female Atypical angina 160 302 FALSE 162 No No
#> # … with 62 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
#>
#> $`Male,No`
#> # A tibble: 92 × 9
#> Age Sex ChestPain BP Choleste…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Male Typical angina 145 233 TRUE 150 No No
#> 2 37 Male Non-anginal pain 130 250 FALSE 187 No No
#> 3 56 Male Atypical angina 120 236 FALSE 178 No No
#> 4 57 Male Asymptomatic 140 192 FALSE 148 No No
#> 5 44 Male Atypical angina 120 263 FALSE 173 No No
#> 6 52 Male Non-anginal pain 172 199 TRUE 162 No No
#> 7 57 Male Non-anginal pain 150 168 FALSE 174 No No
#> 8 54 Male Asymptomatic 140 239 FALSE 160 No No
#> 9 49 Male Atypical angina 130 266 FALSE 171 No No
#> 10 64 Male Typical angina 110 211 FALSE 144 Yes No
#> # … with 82 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
#>
#> $`Female,Yes`
#> # A tibble: 25 × 9
#> Age Sex ChestPain BP Cholest…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 62 Female Asymptomatic 140 268 FALSE 160 No Yes
#> 2 65 Female Asymptomatic 150 225 FALSE 114 No Yes
#> 3 61 Female Asymptomatic 130 330 FALSE 169 No Yes
#> 4 51 Female Asymptomatic 130 305 FALSE 142 Yes Yes
#> 5 62 Female Asymptomatic 160 164 FALSE 145 No Yes
#> 6 60 Female Asymptomatic 150 258 FALSE 157 No Yes
#> 7 61 Female Asymptomatic 145 307 FALSE 146 Yes Yes
#> 8 43 Female Asymptomatic 132 341 TRUE 136 Yes Yes
#> 9 62 Female Non-anginal pain 130 263 FALSE 97 No Yes
#> 10 63 Female Asymptomatic 150 407 FALSE 154 No Yes
#> # … with 15 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
#>
#> $`Male,Yes`
#> # A tibble: 114 × 9
#> Age Sex ChestPain BP Choleste…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 67 Male Asymptomatic 160 286 FALSE 108 Yes Yes
#> 2 67 Male Asymptomatic 120 229 FALSE 129 Yes Yes
#> 3 63 Male Asymptomatic 130 254 FALSE 147 No Yes
#> 4 53 Male Asymptomatic 140 203 TRUE 155 Yes Yes
#> 5 56 Male Non-anginal pain 130 256 TRUE 142 Yes Yes
#> 6 48 Male Atypical angina 110 229 FALSE 168 No Yes
#> 7 58 Male Atypical angina 120 284 FALSE 160 No Yes
#> 8 58 Male Non-anginal pain 132 224 FALSE 173 No Yes
#> 9 60 Male Asymptomatic 130 206 FALSE 132 Yes Yes
#> 10 40 Male Asymptomatic 110 167 FALSE 114 Yes Yes
#> # … with 104 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
#>
#Move inward from maximum depth
heart_disease %>%
divide(
Sex,
HeartDisease,
ChestPain,
depth = -1
)
#> $Female
#> $Female$`No|Typical angina`
#> # A tibble: 4 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 58 150 283 TRUE 162 No
#> 2 66 150 226 FALSE 114 No
#> 3 69 140 239 FALSE 151 No
#> 4 60 150 240 FALSE 171 No
#>
#> $Female$`No|Atypical angina`
#> # A tibble: 16 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 41 130 204 FALSE 172 No
#> 2 56 140 294 FALSE 153 No
#> 3 71 160 302 FALSE 162 No
#> 4 41 105 198 FALSE 168 No
#> 5 45 130 234 FALSE 175 No
#> 6 55 135 250 FALSE 161 No
#> 7 54 132 288 TRUE 159 Yes
#> 8 45 112 160 FALSE 138 No
#> 9 63 140 195 FALSE 179 No
#> 10 50 120 244 FALSE 162 No
#> 11 46 105 204 FALSE 172 No
#> 12 34 118 210 FALSE 192 No
#> 13 74 120 269 FALSE 121 Yes
#> 14 49 134 271 FALSE 162 No
#> 15 41 126 306 FALSE 163 No
#> 16 55 132 342 FALSE 166 No
#>
#> $Female$`Yes|Atypical angina`
#> # A tibble: 2 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 58 136 319 TRUE 152 No
#> 2 57 130 236 FALSE 174 No
#>
#> $Female$`No|Non-anginal pain`
#> # A tibble: 34 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 48 130 275 FALSE 139 No
#> 2 50 120 219 FALSE 158 No
#> 3 58 120 340 FALSE 172 No
#> 4 65 140 417 TRUE 157 No
#> 5 46 142 177 FALSE 160 Yes
#> 6 54 135 304 TRUE 170 No
#> 7 65 155 269 FALSE 148 No
#> 8 65 160 360 FALSE 151 No
#> 9 51 140 308 FALSE 142 No
#> 10 53 128 216 FALSE 115 No
#> # … with 24 more rows
#>
#> $Female$`Yes|Non-anginal pain`
#> # A tibble: 1 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 62 130 263 FALSE 97 No
#>
#> $Female$`No|Asymptomatic`
#> # A tibble: 18 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 57 120 354 FALSE 163 Yes
#> 2 53 130 264 FALSE 143 No
#> 3 53 138 234 FALSE 160 No
#> 4 57 128 303 FALSE 159 No
#> 5 35 138 183 FALSE 182 No
#> 6 62 124 209 FALSE 163 No
#> 7 42 102 265 FALSE 122 No
#> 8 58 100 248 FALSE 122 No
#> 9 62 140 394 FALSE 157 No
#> 10 45 138 236 FALSE 152 Yes
#> 11 50 110 254 FALSE 159 No
#> 12 64 180 325 FALSE 154 Yes
#> 13 46 138 243 FALSE 152 Yes
#> 14 64 130 303 FALSE 122 No
#> 15 49 130 269 FALSE 163 No
#> 16 67 106 223 FALSE 142 No
#> 17 71 112 149 FALSE 125 No
#> 18 58 130 197 FALSE 131 No
#>
#> $Female$`Yes|Asymptomatic`
#> # A tibble: 22 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 62 140 268 FALSE 160 No
#> 2 65 150 225 FALSE 114 No
#> 3 61 130 330 FALSE 169 No
#> 4 51 130 305 FALSE 142 Yes
#> 5 62 160 164 FALSE 145 No
#> 6 60 150 258 FALSE 157 No
#> 7 61 145 307 FALSE 146 Yes
#> 8 43 132 341 TRUE 136 Yes
#> 9 63 150 407 FALSE 154 No
#> 10 56 200 288 TRUE 133 Yes
#> # … with 12 more rows
#>
#>
#> $Male
#> $Male$`No|Typical angina`
#> # A tibble: 12 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 63 145 233 TRUE 150 No
#> 2 64 110 211 FALSE 144 Yes
#> 3 40 140 199 FALSE 178 Yes
#> 4 51 125 213 FALSE 125 Yes
#> 5 34 118 182 FALSE 174 No
#> 6 52 118 186 FALSE 190 No
#> 7 52 152 298 TRUE 178 No
#> 8 42 148 244 FALSE 178 No
#> 9 59 178 270 FALSE 145 No
#> 10 69 160 234 TRUE 131 No
#> 11 56 120 193 FALSE 162 No
#> 12 64 170 227 FALSE 155 No
#>
#> $Male$`Yes|Typical angina`
#> # A tibble: 7 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 65 138 282 TRUE 174 No
#> 2 59 170 288 FALSE 159 No
#> 3 59 160 273 FALSE 125 No
#> 4 38 120 231 FALSE 182 Yes
#> 5 61 134 234 FALSE 145 No
#> 6 59 134 204 FALSE 162 No
#> 7 45 110 264 FALSE 132 No
#>
#> $Male$`No|Atypical angina`
#> # A tibble: 25 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 56 120 236 FALSE 178 No
#> 2 44 120 263 FALSE 173 No
#> 3 49 130 266 FALSE 171 No
#> 4 44 130 219 FALSE 188 No
#> 5 48 130 245 FALSE 180 No
#> 6 52 120 325 FALSE 172 No
#> 7 52 134 201 FALSE 158 No
#> 8 54 108 309 FALSE 156 No
#> 9 41 135 203 FALSE 132 No
#> 10 44 120 220 FALSE 170 No
#> # … with 15 more rows
#>
#> $Male$`Yes|Atypical angina`
#> # A tibble: 7 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 48 110 229 FALSE 168 No
#> 2 58 120 284 FALSE 160 No
#> 3 62 120 281 FALSE 103 No
#> 4 66 160 246 FALSE 120 Yes
#> 5 54 192 283 FALSE 195 No
#> 6 57 124 261 FALSE 141 No
#> 7 57 154 232 FALSE 164 No
#>
#> $Male$`No|Non-anginal pain`
#> # A tibble: 34 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 37 130 250 FALSE 187 No
#> 2 52 172 199 TRUE 162 No
#> 3 57 150 168 FALSE 174 No
#> 4 44 130 233 FALSE 179 Yes
#> 5 61 150 243 TRUE 137 Yes
#> 6 59 150 212 TRUE 157 No
#> 7 51 110 175 FALSE 123 No
#> 8 53 130 197 TRUE 152 No
#> 9 54 125 273 FALSE 152 No
#> 10 54 150 232 FALSE 165 No
#> # … with 24 more rows
#>
#> $Male$`Yes|Non-anginal pain`
#> # A tibble: 17 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 56 130 256 TRUE 142 Yes
#> 2 58 132 224 FALSE 173 No
#> 3 64 140 335 FALSE 158 No
#> 4 58 112 230 FALSE 165 No
#> 5 50 140 233 FALSE 163 No
#> 6 60 140 185 FALSE 155 No
#> 7 46 150 231 FALSE 147 No
#> 8 68 180 274 TRUE 150 Yes
#> 9 49 120 188 FALSE 139 No
#> 10 57 128 229 FALSE 150 No
#> 11 64 125 309 FALSE 131 Yes
#> 12 47 108 243 FALSE 152 No
#> 13 70 160 269 FALSE 112 Yes
#> 14 69 140 254 FALSE 146 No
#> 15 49 118 149 FALSE 126 No
#> 16 59 126 218 TRUE 134 No
#> 17 67 152 212 FALSE 150 No
#>
#> $Male$`No|Asymptomatic`
#> # A tibble: 21 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 57 140 192 FALSE 148 No
#> 2 54 140 239 FALSE 160 No
#> 3 43 150 247 FALSE 171 No
#> 4 59 135 234 FALSE 161 No
#> 5 42 140 226 FALSE 178 No
#> 6 65 120 177 FALSE 140 No
#> 7 45 104 208 FALSE 148 Yes
#> 8 66 120 302 FALSE 151 No
#> 9 48 122 222 FALSE 186 No
#> 10 45 115 260 FALSE 185 No
#> # … with 11 more rows
#>
#> $Male$`Yes|Asymptomatic`
#> # A tibble: 83 × 6
#> Age BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
#> <dbl> <dbl> <dbl> <lgl> <dbl> <fct>
#> 1 67 160 286 FALSE 108 Yes
#> 2 67 120 229 FALSE 129 Yes
#> 3 63 130 254 FALSE 147 No
#> 4 53 140 203 TRUE 155 Yes
#> 5 60 130 206 FALSE 132 Yes
#> 6 40 110 167 FALSE 114 Yes
#> 7 60 117 230 TRUE 160 Yes
#> 8 43 120 177 FALSE 120 Yes
#> 9 57 150 276 FALSE 112 Yes
#> 10 55 132 353 FALSE 132 Yes
#> # … with 73 more rows
#>
#>
#No depth returns original data (and warning)
heart_disease %>%
divide(
Sex,
depth = 0
)
#> Warning: Split depth is 0; returning original data.
#> # A tibble: 303 × 9
#> Age Sex ChestPain BP Cholest…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Male Typical angina 145 233 TRUE 150 No No
#> 2 67 Male Asymptomatic 160 286 FALSE 108 Yes Yes
#> 3 67 Male Asymptomatic 120 229 FALSE 129 Yes Yes
#> 4 37 Male Non-anginal pain 130 250 FALSE 187 No No
#> 5 41 Female Atypical angina 130 204 FALSE 172 No No
#> 6 56 Male Atypical angina 120 236 FALSE 178 No No
#> 7 62 Female Asymptomatic 140 268 FALSE 160 No Yes
#> 8 57 Female Asymptomatic 120 354 FALSE 163 Yes No
#> 9 63 Male Asymptomatic 130 254 FALSE 147 No Yes
#> 10 53 Male Asymptomatic 140 203 TRUE 155 Yes Yes
#> # … with 293 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
heart_disease %>%
divide(
Sex,
HeartDisease,
depth = -5
)
#> Warning: Split depth is 0; returning original data.
#> # A tibble: 303 × 9
#> Age Sex ChestPain BP Cholest…¹ Blood…² Maxim…³ Exerc…⁴ Heart…⁵
#> <dbl> <fct> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Male Typical angina 145 233 TRUE 150 No No
#> 2 67 Male Asymptomatic 160 286 FALSE 108 Yes Yes
#> 3 67 Male Asymptomatic 120 229 FALSE 129 Yes Yes
#> 4 37 Male Non-anginal pain 130 250 FALSE 187 No No
#> 5 41 Female Atypical angina 130 204 FALSE 172 No No
#> 6 56 Male Atypical angina 120 236 FALSE 178 No No
#> 7 62 Female Asymptomatic 140 268 FALSE 160 No Yes
#> 8 57 Female Asymptomatic 120 354 FALSE 163 Yes No
#> 9 63 Male Asymptomatic 130 254 FALSE 147 No Yes
#> 10 53 Male Asymptomatic 140 203 TRUE 155 Yes Yes
#> # … with 293 more rows, and abbreviated variable names ¹Cholesterol,
#> # ²BloodSugar, ³MaximumHR, ⁴ExerciseInducedAngina, ⁵HeartDisease
#Larger than maximum depth returns maximum depth (default)
heart_disease %>%
divide(
Sex,
depth = 100
)
#> $Female
#> # A tibble: 97 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 41 Atypical angina 130 204 FALSE 172 No No
#> 2 62 Asymptomatic 140 268 FALSE 160 No Yes
#> 3 57 Asymptomatic 120 354 FALSE 163 Yes No
#> 4 56 Atypical angina 140 294 FALSE 153 No No
#> 5 48 Non-anginal pain 130 275 FALSE 139 No No
#> 6 58 Typical angina 150 283 TRUE 162 No No
#> 7 50 Non-anginal pain 120 219 FALSE 158 No No
#> 8 58 Non-anginal pain 120 340 FALSE 172 No No
#> 9 66 Typical angina 150 226 FALSE 114 No No
#> 10 69 Typical angina 140 239 FALSE 151 No No
#> # … with 87 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>
#> $Male
#> # A tibble: 206 × 8
#> Age ChestPain BP Cholesterol BloodSugar MaximumHR Exerc…¹ Heart…²
#> <dbl> <fct> <dbl> <dbl> <lgl> <dbl> <fct> <fct>
#> 1 63 Typical angina 145 233 TRUE 150 No No
#> 2 67 Asymptomatic 160 286 FALSE 108 Yes Yes
#> 3 67 Asymptomatic 120 229 FALSE 129 Yes Yes
#> 4 37 Non-anginal pain 130 250 FALSE 187 No No
#> 5 56 Atypical angina 120 236 FALSE 178 No No
#> 6 63 Asymptomatic 130 254 FALSE 147 No Yes
#> 7 53 Asymptomatic 140 203 TRUE 155 Yes Yes
#> 8 57 Asymptomatic 140 192 FALSE 148 No No
#> 9 56 Non-anginal pain 130 256 TRUE 142 Yes Yes
#> 10 44 Atypical angina 120 263 FALSE 173 No No
#> # … with 196 more rows, and abbreviated variable names ¹ExerciseInducedAngina,
#> # ²HeartDisease
#>