Compare two data frames by keys

Obtain differences between two data frames

compare_df(dfcomp_x, dfcomp_y, keys_x, keys_y = NA, compare_values = FALSE)

Arguments

dfcomp_x	first data frame to compare
dfcomp_y	second data frame to compare
keys_x	keys of the first dataframe
keys_y	(optional) keys of the second dataframe, if missing both data frames will be compared with the keys_x
compare_values	(optional) if TRUE it will not only compare keys, but also will check if the values of non-key matching columns have the same values

Value

Differences and coincident values

Examples

data(heart_disease)
a=heart_disease
b=heart_disease
a=subset(a, age >45)
b=subset(b, age <50)
b$gender='male'
b$chest_pain=ifelse(b$chest_pain ==3, 4, b$chest_pain)
res=compare_df(a, b, c('age', 'gender'))
#> Coincident keys: 110
#> Keys not present in X: 63
#> Keys not present in Y: 222
# Print the keys that didn't match
res
#> $coincident
#>     age gender
#> 67   46   male
#> 68   46   male
#> 69   46   male
#> 70   46   male
#> 71   46   male
#> 72   46   male
#> 73   46   male
#> 74   46   male
#> 75   46   male
#> 76   46   male
#> 77   46   male
#> 78   46   male
#> 79   46   male
#> 80   46   male
#> 81   46   male
#> 82   46   male
#> 83   46   male
#> 84   46   male
#> 85   46   male
#> 86   46   male
#> 87   46   male
#> 88   46   male
#> 89   46   male
#> 90   46   male
#> 91   46   male
#> 92   46   male
#> 93   46   male
#> 94   46   male
#> 95   47   male
#> 96   47   male
#> 97   47   male
#> 98   47   male
#> 99   47   male
#> 100  47   male
#> 101  47   male
#> 102  47   male
#> 103  47   male
#> 104  47   male
#> 105  47   male
#> 106  47   male
#> 107  47   male
#> 108  47   male
#> 109  47   male
#> 110  47   male
#> 111  47   male
#> 112  47   male
#> 113  47   male
#> 114  47   male
#> 115  47   male
#> 116  47   male
#> 117  47   male
#> 118  47   male
#> 119  47   male
#> 121  48   male
#> 122  48   male
#> 123  48   male
#> 124  48   male
#> 125  48   male
#> 126  48   male
#> 127  48   male
#> 128  48   male
#> 129  48   male
#> 130  48   male
#> 131  48   male
#> 132  48   male
#> 133  48   male
#> 134  48   male
#> 135  48   male
#> 136  48   male
#> 137  48   male
#> 138  48   male
#> 139  48   male
#> 140  48   male
#> 141  48   male
#> 142  48   male
#> 143  48   male
#> 144  48   male
#> 145  48   male
#> 146  48   male
#> 147  48   male
#> 148  48   male
#> 149  48   male
#> 150  48   male
#> 151  48   male
#> 152  48   male
#> 153  48   male
#> 154  48   male
#> 155  48   male
#> 156  48   male
#> 157  48   male
#> 158  48   male
#> 159  48   male
#> 160  48   male
#> 161  48   male
#> 162  48   male
#> 165  49   male
#> 166  49   male
#> 167  49   male
#> 168  49   male
#> 169  49   male
#> 170  49   male
#> 171  49   male
#> 172  49   male
#> 173  49   male
#> 174  49   male
#> 175  49   male
#> 176  49   male
#> 177  49   male
#> 178  49   male
#> 179  49   male
#> 
#> $rows_not_in_X
#>    age gender
#> 1   29   male
#> 2   34   male
#> 3   34   male
#> 4   35   male
#> 5   35   male
#> 6   35   male
#> 7   35   male
#> 8   37   male
#> 9   37   male
#> 10  38   male
#> 11  38   male
#> 12  39   male
#> 13  39   male
#> 14  39   male
#> 15  39   male
#> 16  40   male
#> 17  40   male
#> 18  40   male
#> 19  41   male
#> 20  41   male
#> 21  41   male
#> 22  41   male
#> 23  41   male
#> 24  41   male
#> 25  41   male
#> 26  41   male
#> 27  41   male
#> 28  41   male
#> 29  42   male
#> 30  42   male
#> 31  42   male
#> 32  42   male
#> 33  42   male
#> 34  42   male
#> 35  42   male
#> 36  42   male
#> 37  43   male
#> 38  43   male
#> 39  43   male
#> 40  43   male
#> 41  43   male
#> 42  43   male
#> 43  43   male
#> 44  43   male
#> 45  44   male
#> 46  44   male
#> 47  44   male
#> 48  44   male
#> 49  44   male
#> 50  44   male
#> 51  44   male
#> 52  44   male
#> 53  44   male
#> 54  44   male
#> 55  44   male
#> 56  45   male
#> 57  45   male
#> 58  45   male
#> 59  45   male
#> 60  45   male
#> 61  45   male
#> 62  45   male
#> 63  45   male
#> 
#> $rows_not_in_Y
#>     age gender
#> 64   46 female
#> 65   46 female
#> 66   46 female
#> 120  48 female
#> 163  49 female
#> 164  49 female
#> 180  50 female
#> 181  50 female
#> 182  50 female
#> 183  50   male
#> 184  50   male
#> 185  50   male
#> 186  50   male
#> 187  51 female
#> 188  51 female
#> 189  51 female
#> 190  51 female
#> 191  51   male
#> 192  51   male
#> 193  51   male
#> 194  51   male
#> 195  51   male
#> 196  51   male
#> 197  51   male
#> 198  51   male
#> 199  52 female
#> 200  52   male
#> 201  52   male
#> 202  52   male
#> 203  52   male
#> 204  52   male
#> 205  52   male
#> 206  52   male
#> 207  52   male
#> 208  52   male
#> 209  52   male
#> 210  52   male
#> 211  52   male
#> 212  53 female
#> 213  53 female
#> 214  53 female
#> 215  53   male
#> 216  53   male
#> 217  53   male
#> 218  53   male
#> 219  53   male
#> 220  54 female
#> 221  54 female
#> 222  54 female
#> 223  54 female
#> 224  54 female
#> 225  54   male
#> 226  54   male
#> 227  54   male
#> 228  54   male
#> 229  54   male
#> 230  54   male
#> 231  54   male
#> 232  54   male
#> 233  54   male
#> 234  54   male
#> 235  54   male
#> 236  55 female
#> 237  55 female
#> 238  55 female
#> 239  55 female
#> 240  55   male
#> 241  55   male
#> 242  55   male
#> 243  55   male
#> 244  56 female
#> 245  56 female
#> 246  56 female
#> 247  56   male
#> 248  56   male
#> 249  56   male
#> 250  56   male
#> 251  56   male
#> 252  56   male
#> 253  56   male
#> 254  56   male
#> 255  57 female
#> 256  57 female
#> 257  57 female
#> 258  57 female
#> 259  57   male
#> 260  57   male
#> 261  57   male
#> 262  57   male
#> 263  57   male
#> 264  57   male
#> 265  57   male
#> 266  57   male
#> 267  57   male
#> 268  57   male
#> 269  57   male
#> 270  57   male
#> 271  57   male
#> 272  58 female
#> 273  58 female
#> 274  58 female
#> 275  58 female
#> 276  58 female
#> 277  58 female
#> 278  58   male
#> 279  58   male
#> 280  58   male
#> 281  58   male
#> 282  58   male
#> 283  58   male
#> 284  58   male
#> 285  58   male
#> 286  58   male
#> 287  58   male
#> 288  58   male
#> 289  58   male
#> 290  58   male
#> 291  59 female
#> 292  59   male
#> 293  59   male
#> 294  59   male
#> 295  59   male
#> 296  59   male
#> 297  59   male
#> 298  59   male
#> 299  59   male
#> 300  59   male
#> 301  59   male
#> 302  59   male
#> 303  59   male
#> 304  59   male
#> 305  60 female
#> 306  60 female
#> 307  60 female
#> 308  60 female
#> 309  60 female
#> 310  60   male
#> 311  60   male
#> 312  60   male
#> 313  60   male
#> 314  60   male
#> 315  60   male
#> 316  60   male
#> 317  61 female
#> 318  61 female
#> 319  61   male
#> 320  61   male
#> 321  61   male
#> 322  61   male
#> 323  61   male
#> 324  61   male
#> 325  62 female
#> 326  62 female
#> 327  62 female
#> 328  62 female
#> 329  62 female
#> 330  62 female
#> 331  62 female
#> 332  62   male
#> 333  62   male
#> 334  62   male
#> 335  62   male
#> 336  63 female
#> 337  63 female
#> 338  63 female
#> 339  63 female
#> 340  63 female
#> 341  63   male
#> 342  63   male
#> 343  63   male
#> 344  63   male
#> 345  64 female
#> 346  64 female
#> 347  64 female
#> 348  64   male
#> 349  64   male
#> 350  64   male
#> 351  64   male
#> 352  64   male
#> 353  64   male
#> 354  64   male
#> 355  65 female
#> 356  65 female
#> 357  65 female
#> 358  65 female
#> 359  65   male
#> 360  65   male
#> 361  65   male
#> 362  65   male
#> 363  66 female
#> 364  66 female
#> 365  66 female
#> 366  66   male
#> 367  66   male
#> 368  66   male
#> 369  66   male
#> 370  67 female
#> 371  67 female
#> 372  67 female
#> 373  67   male
#> 374  67   male
#> 375  67   male
#> 376  67   male
#> 377  67   male
#> 378  67   male
#> 379  68 female
#> 380  68   male
#> 381  68   male
#> 382  68   male
#> 383  69 female
#> 384  69   male
#> 385  69   male
#> 386  70   male
#> 387  70   male
#> 388  70   male
#> 389  70   male
#> 390  71 female
#> 391  71 female
#> 392  71 female
#> 393  74 female
#> 394  76 female
#> 395  77   male
#> 
# Accessing the keys not present in the first data frame
res[[1]]$rows_not_in_X
#> NULL
# Accessing the keys not present in the second data frame
res[[1]]$rows_not_in_Y
#> NULL
# Accessing the keys which coincide completely
res[[1]]$coincident
#> NULL
# Accessing the rows whose values did not coincide
res[[1]]$different_values
#> NULL