Basic examples

We first build two similar data.frames to compare.

df1 <- data.frame(id = paste0("person", 1:3),
                  a = c("a", "b", "c"),
                  b = c(1, 3, 4),
                  c = c("f", "e", "d"),
                  row.names = paste0("rn", 1:3),
                  stringsAsFactors = FALSE)
df2 <- data.frame(id = paste0("person", 3:1),
                  a = c("c", "b", "a"),
                  b = c(1, 3, 4),
                  d = paste0("rn", 1:3),
                  row.names = paste0("rn", c(1,3,2)),
                  stringsAsFactors = FALSE)

To compare these datasets, simply pass them to the comparedf() function:

comparedf(df1, df2)

Compare Object

Function Call: 
comparedf(x = df1, y = df2)

Shared: 3 non-by variables and 3 observations.
Not shared: 2 variables and 0 observations.

Differences found in 2/3 variables compared.
0 variables compared have non-identical attributes.

Use summary() to get a more detailed summary

summary(comparedf(df1, df2))

Summary of data.frames
version	arg	ncol	nrow
x	df1	4	3
y	df2	4	3

Summary of overall comparison
statistic	value
Number of by-variables	0
Number of non-by variables in common	3
Number of variables compared	3
Number of variables in x but not y	1
Number of variables in y but not x	1
Number of variables compared with some values unequal	2
Number of variables compared with all values equal	1
Number of observations in common	3
Number of observations in x but not y	0
Number of observations in y but not x	0
Number of observations with some compared variables unequal	2
Number of observations with all compared variables equal	1
Number of values unequal	4

Variables not shared
version	variable	position	class
x	c	4	character
y	d	4	character

Other variables not compared
No other variables not compared

Observations not shared
No observations not shared

Differences detected by variable
var.x	var.y	n
id	id	2
a	a	2
b	b	0

Differences detected
var.x	var.y	..row.names..	values.x	values.y	row.x	row.y
id	id	1	person1	person3	1	1
id	id	3	person3	person1	3	3
a	a	1	a	c	1	1
a	a	3	c	a	3	3

Non-identical attributes
No non-identical attributes

By default, the datasets are compared row-by-row. To change this, use the by= or by.x= and by.y= arguments:

summary(comparedf(df1, df2, by = "id"))

Summary of data.frames
version	arg	ncol	nrow
x	df1	4	3
y	df2	4	3

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	2
Number of variables compared	2
Number of variables in x but not y	1
Number of variables in y but not x	1
Number of variables compared with some values unequal	1
Number of variables compared with all values equal	1
Number of observations in common	3
Number of observations in x but not y	0
Number of observations in y but not x	0
Number of observations with some compared variables unequal	2
Number of observations with all compared variables equal	1
Number of values unequal	2

Variables not shared
version	variable	position	class
x	c	4	character
y	d	4	character

Other variables not compared
No other variables not compared

Observations not shared
No observations not shared

Differences detected by variable
var.x	var.y	n	NAs
a	a	0	0
b	b	2	0

Differences detected
var.x	var.y	id	values.x	values.y	row.x	row.y
b	b	person1	1	4	1	3
b	b	person3	4	1	3	1

Non-identical attributes
No non-identical attributes

A larger example

Let’s muck up the mockstudy data.

data(mockstudy)
mockstudy2 <- muck_up_mockstudy()

We’ve changed row order, so let’s compare by the case ID:

summary(comparedf(mockstudy, mockstudy2, by = "case"))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	9
Number of variables compared	7
Number of variables in x but not y	4
Number of variables in y but not x	3
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	4
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1762

Variables not shared
version	variable	position	class
x	age	2	integer
x	arm	3	character
x	fu.time	6	integer
x	fu.stat	7	integer
y	fu_time	11	integer
y	fu stat	12	integer
y	Arm	13	character

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor
ast	12	integer	ast	8	numeric

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
sex	sex	1495	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249

Non-identical attributes
var.x	var.y	name
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Column name comparison options

It is possible to change which column names are considered “the same variable”.

Ignoring case

For example, to ignore case in variable names (so that Arm and arm are considered the same), pass tol.vars = "case".

You can do this using comparedf.control()

summary(comparedf(mockstudy, mockstudy2, by = "case", control = comparedf.control(tol.vars = "case")))

or pass it through the ... arguments.

summary(comparedf(mockstudy, mockstudy2, by = "case", tol.vars = "case"))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	10
Number of variables compared	8
Number of variables in x but not y	3
Number of variables in y but not x	2
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	5
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1762

Variables not shared
version	variable	position	class
x	age	2	integer
x	fu.time	6	integer
x	fu.stat	7	integer
y	fu_time	11	integer
y	fu stat	12	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor
ast	12	integer	ast	8	numeric

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	1495	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Treating dots and underscores the same (equivalence classes)

It is possible to treat certain characters or sets of characters as the same by passing a character vector of equivalence classes to the tol.vars= argument.

In short, each string in the vector is split into single characters, and the resulting set of characters is replaced by the first character in the string. For example, passing c("._") would replace all underscores with dots in the column names of both datasets. Similarly, passing c("aA", "BbCc") would replace all instances of "A" with "a" and all instances of "b", "C", or "c" with "B". This is one way to ignore case for certain letters. Otherwise, it’s possible to combine the equivalence classes with ignoring case, by passing (e.g.) c("._", "case").

Passing a single character as an element this vector will replace that character with the empty string. For example, passing c(" “,”.") would remove all spaces and dots from the column names.

For mockstudy, let’s treat dots, underscores, and spaces as the same, and ignore case:

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case") # dots=underscores=spaces, ignore case
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	10
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	7
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1762

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor
ast	12	integer	ast	8	numeric

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	1495	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Manually specifying columns to match together

If you pass a named vector to the tol.vars= argument, comparedf() will line up the names of that vector to the column names of x and the values of that vector to the column names of y. In this way, you can manually specify which non-identically-named columns to compare.

For mockstudy, let’s specify our variables manually in this way:

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c(arm = "Arm", fu.stat = "fu stat", fu.time = "fu_time")
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	10
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	7
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1762

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor
ast	12	integer	ast	8	numeric

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	1495	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Column comparison options

Logical tolerance

Use the tol.logical= argument to change how logicals are compared. By default, they’re expected to be equal to each other.

Numeric tolerance

To allow numeric differences of a certain tolerance, use the tol.num= and tol.num.val= options. tol.num.val= determines the maximum (unsigned) difference tolerated if tol.num="absolute" (default), and determines the maximum (unsigned) percent difference tolerated if tol.num="percent".

Also note the option int.as.num=, which determines whether integers and numerics should be compared despite their class difference. If TRUE, the integers are coerced to numeric. Note that mockstudy$ast is integer, while mockstudy2$ast is numeric:

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE            # compare integers and numerics
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	11
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	4
Number of variables compared with all values equal	7
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1765

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	1495	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	3	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249
ast	ast	86205	27	36	6	3
ast	ast	105271	100	36	3	2
ast	ast	110754	35	36	1	1

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Suppose a tolerance of up to 10 is allowed for ast:

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE,           # compare integers and numerics
                tol.num.val = 10             # allow absolute differences <= 10
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	11
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	4
Number of variables compared with all values equal	7
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1495
Number of observations with all compared variables equal	0
Number of values unequal	1763

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	1495	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	1	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1741 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
sex	sex	76170	Male	Male	26	20
sex	sex	76240	Male	Male	27	21
sex	sex	76431	Female	Female	28	22
sex	sex	76712	Male	Male	29	23
sex	sex	76780	Female	Female	30	24
sex	sex	77066	Female	Female	31	25
sex	sex	77316	Male	Male	32	26
sex	sex	77355	Male	Male	33	27
sex	sex	77591	Male	Male	34	28
sex	sex	77851	Male	Male	35	29
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249
ast	ast	105271	100	36	3	2

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Factor tolerance

By default, factors are compared to each other based on both the labels and the underlying numeric levels. Set tol.factor="levels" to match only the numeric levels, or set tol.factor="labels" to match only the labels.

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE,           # compare integers and numerics
                tol.num.val = 10,            # allow absolute differences <= 10
                tol.factor = "labels"        # match only factor labels
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	11
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	8
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	268
Number of observations with all compared variables equal	1227
Number of values unequal	268

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
var.x	pos.x	class.x	var.y	pos.y	class.y
race	5	character	race	3	factor

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	0	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	1	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (256 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249
ast	ast	105271	100	36	3	2

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Also note the option factor.as.char=, which determines whether factors and characters should be compared despite their class difference. If TRUE, the factors are coerced to characters. Note that mockstudy$race is a character, while mockstudy2$race is a factor:

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE,           # compare integers and numerics
                tol.num.val = 10,            # allow absolute differences <= 10
                tol.factor = "labels",       # match only factor labels
                factor.as.char = TRUE        # compare factors and characters
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	12
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	4
Number of variables compared with all values equal	8
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	1339
Number of observations with all compared variables equal	156
Number of values unequal	1553

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
No other variables not compared

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	0	0
race	race	1285	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	1	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (1531 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
race	race	76170	Caucasian	caucasian	26	20
race	race	76240	Caucasian	caucasian	27	21
race	race	76431	Caucasian	caucasian	28	22
race	race	76712	Caucasian	caucasian	29	23
race	race	76780	Caucasian	caucasian	30	24
race	race	77066	Caucasian	caucasian	31	25
race	race	77316	Caucasian	caucasian	32	26
race	race	77591	Caucasian	caucasian	34	28
race	race	77851	Caucasian	caucasian	35	29
race	race	77956	Caucasian	caucasian	36	30
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249
ast	ast	105271	100	36	3	2

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Character tolerance

Use the tol.char= argument to change how character variables are compared. By default, they are compared as-is, but they can be compared after ignoring case or trimming whitespace or both.

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE,           # compare integers and numerics
                tol.num.val = 10,            # allow absolute differences <= 10
                tol.factor = "labels",       # match only factor labels
                factor.as.char = TRUE,       # compare factors and characters
                tol.char = "case"            # ignore case in character vectors
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	12
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	3
Number of variables compared with all values equal	9
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	268
Number of observations with all compared variables equal	1227
Number of values unequal	268

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
No other variables not compared

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	0	0
race	race	0	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	266	266
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	1	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected (256 not shown)
var.x	var.y	case	values.x	values.y	row.x	row.y
ps	ps	86205	0	NA	6	3
hgb	hgb	88714	NA	-9	192	186
hgb	hgb	88955	NA	-9	204	198
hgb	hgb	89549	NA	-9	229	223
hgb	hgb	89563	NA	-9	231	225
hgb	hgb	89584	NA	-9	237	231
hgb	hgb	89591	NA	-9	238	232
hgb	hgb	89595	NA	-9	239	233
hgb	hgb	89647	NA	-9	243	237
hgb	hgb	89665	NA	-9	244	238
hgb	hgb	89827	NA	-9	255	249
ast	ast	105271	100	36	3	2

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Date tolerance

Use the tol.date= argument to change how dates are compared. By default, they’re expected to be equal to each other.

Other data type tolerances

Use the tol.other= argument to change how other objects are compared. By default, they’re expected to be identical().

Specifying tolerances for each variable

You can also provide a list of tolerance functions to comparedf():

comparedf.control(tol.char = list(
  "none",      # the default
  x1 = "case", # be case-insensitive for the variable "x1"
  x2 = function(x, y) tol.NA(x, y, x != y | y == "NA") # a custom-defined tolerance
))

User-defined tolerance functions

Details

The comparedf.control() function accepts functions for any of the tolerance arguments in addition to the short-hand character strings. This allows the user to create custom tolerance functions to suit his/her needs.

Any custom tolerance function must accept two vectors as arguments and return a logical vector of the same length. The TRUEs in the results should correspond to elements which are deemed “different”. Note that the numeric and date tolerance functions should also include a third argument for tolerance size (even if it’s not used).

CAUTION: the results should not include NAs, since the logical vector is used to subset the input data.frames. The tol.NA() function is useful for considering any NAs in the two vectors (but not both) as differences, in addition to other criteria.

The tol.NA() function is used in all default tolerance functions to help handle NAs.

Example 1

Suppose we want to ignore any dates which are later in the second dataset than the first. We define a custom tolerance function.

my.tol <- function(x, y, tol)
{
  tol.NA(x, y, x > y)
}

date.df1 <- data.frame(dt = as.Date(c("2017-09-07", "2017-08-08", "2017-07-09", NA)))
date.df2 <- data.frame(dt = as.Date(c("2017-10-01", "2017-08-08", "2017-07-10", "2017-01-01")))
n.diffs(comparedf(date.df1, date.df2)) # default finds any differences

[1] 3

n.diffs(comparedf(date.df1, date.df2, tol.date = my.tol)) # our function identifies only the NA as different...

[1] 1

n.diffs(comparedf(date.df2, date.df1, tol.date = my.tol)) # ... until we change the argument order

[1] 3

Example 2

(Continuing our mockstudy example)

Suppose we’re okay with NAs getting replaced by -9.

tol.minus9 <- function(x, y, tol)
{
  idx1 <- is.na(x) & !is.na(y) & y == -9
  idx2 <- tol.num.absolute(x, y, tol) # find other absolute differences
  return(!idx1 & idx2)
}

summary(comparedf(mockstudy, mockstudy2, by = "case",
                tol.vars = c("._ ", "case"), # dots=underscores=spaces, ignore case
                int.as.num = TRUE,           # compare integers and numerics
                tol.num.val = 10,            # allow absolute differences <= 10
                tol.factor = "labels",       # match only factor labels
                factor.as.char = TRUE,       # compare factors and characters
                tol.char = "case",           # ignore case in character vectors
                tol.num = tol.minus9         # ignore NA -> -9 changes
))

Summary of data.frames
version	arg	ncol	nrow
x	mockstudy	14	1499
y	mockstudy2	13	1495

Summary of overall comparison
statistic	value
Number of by-variables	1
Number of non-by variables in common	12
Number of variables compared	12
Number of variables in x but not y	1
Number of variables in y but not x	0
Number of variables compared with some values unequal	2
Number of variables compared with all values equal	10
Number of observations in common	1495
Number of observations in x but not y	4
Number of observations in y but not x	0
Number of observations with some compared variables unequal	2
Number of observations with all compared variables equal	1493
Number of values unequal	2

Variables not shared
version	variable	position	class
x	age	2	integer

Other variables not compared
No other variables not compared

Observations not shared
version	case	observation
x	88989	9
x	90158	8
x	99508	7
x	112263	5

Differences detected by variable
var.x	var.y	n	NAs
arm	Arm	0	0
sex	sex	0	0
race	race	0	0
fu.time	fu_time	0	0
fu.stat	fu stat	0	0
ps	ps	1	1
hgb	hgb	0	0
bmi	bmi	0	0
alk.phos	alk.phos	0	0
ast	ast	1	0
mdquality.s	mdquality.s	0	0
age.ord	age.ord	0	0

Differences detected
var.x	var.y	case	values.x	values.y	row.x	row.y
ps	ps	86205	0	NA	6	3
ast	ast	105271	100	36	3	2

Non-identical attributes
var.x	var.y	name
arm	Arm	label
sex	sex	label
sex	sex	levels
race	race	class
race	race	label
race	race	levels
bmi	bmi	label

Extract Differences

Differences can be easily extracted using the diffs() function. If you only want to determine how many differences were found, use the n.diffs() function.

cmp <- comparedf(mockstudy, mockstudy2, by = "case", tol.vars = c("._ ", "case"), int.as.num = TRUE)
n.diffs(cmp)

[1] 1765

head(diffs(cmp))

  var.x var.y  case values.x values.y row.x row.y
1   sex   sex 76170     Male     Male    26    20
2   sex   sex 76240     Male     Male    27    21
3   sex   sex 76431   Female   Female    28    22
4   sex   sex 76712     Male     Male    29    23
5   sex   sex 76780   Female   Female    30    24
6   sex   sex 77066   Female   Female    31    25

Differences can also be summarized by variable.

diffs(cmp, by.var = TRUE)

         var.x       var.y    n NAs
1          arm         Arm    0   0
2          sex         sex 1495   0
3      fu.time     fu_time    0   0
4      fu.stat     fu stat    0   0
5           ps          ps    1   1
6          hgb         hgb  266 266
7          bmi         bmi    0   0
8     alk.phos    alk.phos    0   0
9          ast         ast    3   0
10 mdquality.s mdquality.s    0   0
11     age.ord     age.ord    0   0

To report differences from only a few variables, one can pass a list of variable names to diffs().

diffs(cmp, vars = c("ps", "ast"), by.var = TRUE)

  var.x var.y n NAs
5    ps    ps 1   1
9   ast   ast 3   0

diffs(cmp, vars = c("ps", "ast"))

     var.x var.y   case values.x values.y row.x row.y
1496    ps    ps  86205        0       NA     6     3
1763   ast   ast  86205       27       36     6     3
1764   ast   ast 105271      100       36     3     2
1765   ast   ast 110754       35       36     1     1

Appendix

Stucture of the Object

(This section is just as much for my use as for yours!)

obj <- comparedf(mockstudy, mockstudy2, by = "case")

There are two main objects in the "comparedf" object, each with its own print method.

The frame.summary contains:

the substituted-deparsed arguments
information about the number of columns and rows in each dataset
the by-variables for each dataset (which may not be the same)
the attributes for each dataset (which get counted in the print method)
a data.frame of by-variables and row numbers of observations not shared between datasets
the number of shared observations

print(obj$frame.summary)

  version        arg ncol nrow   by        attrs       unique n.shared
1       x  mockstudy   14 1499 case 3 attributes 4 unique obs     1495
2       y mockstudy2   13 1495 case 3 attributes 0 unique obs     1495

The vars.summary contains:

variable name, column number, and class vector (with possibly more than one element) for each x and y. These are all NA if there isn’t a match in both datasets.
values, a list-column of the text string "by-variable" for the by-variables, NULL for columns that aren’t compared, or a data.frame containing:
- The by-variables for differences found
- The values which are different for x and y
- The row numbers for differences found
attrs, a list-column of NULL if there are no attributes, or a data.frame containing:
- The name of the attributes
- The attributes for x and y, set to NA if non-existant
- The actual attributes (if show.attr=TRUE).

print(obj$vars.summary)

         var.x pos.x         class.x       var.y pos.y         class.y           values        attrs
1         case     1         integer        case     1         integer      by-variable 0 attributes
2          sex     4          factor         sex     2          factor 1495 differences 2 attributes
3         race     5       character        race     3          factor     Not compared 3 attributes
4           ps     8         integer          ps     4         integer    1 differences 0 attributes
5          hgb     9         numeric         hgb     5         numeric  266 differences 0 attributes
6          bmi    10         numeric         bmi     6         numeric    0 differences 1 attributes
7     alk.phos    11         integer    alk.phos     7         integer    0 differences 0 attributes
8          ast    12         integer         ast     8         numeric     Not compared 0 attributes
9  mdquality.s    13         integer mdquality.s     9         integer    0 differences 0 attributes
10     age.ord    14 ordered, factor     age.ord    10 ordered, factor    0 differences 0 attributes
11         age     2         integer        <NA>    NA              NA     Not compared 0 attributes
12         arm     3       character        <NA>    NA              NA     Not compared 0 attributes
13     fu.time     6         integer        <NA>    NA              NA     Not compared 0 attributes
14     fu.stat     7         integer        <NA>    NA              NA     Not compared 0 attributes
15        <NA>    NA              NA     fu_time    11         integer     Not compared 0 attributes
16        <NA>    NA              NA     fu stat    12         integer     Not compared 0 attributes
17        <NA>    NA              NA         Arm    13       character     Not compared 0 attributes

The comparedf function

Ethan Heinzen, Ryan Lennon, Andrew Hanson

Introduction