Introduction to diffdf
Craig Gower & Kieran Martin
2024-07-12
Source:vignettes/diffdf-basic.Rmd
diffdf-basic.RmdThe purpose of diffdf is to provide
proc compare like functionality to R for use in second line
programming. In particular we focus on raising warnings if any
differences are found whilst providing in-depth diagnostics to highlight
where these differences have occurred.
Basic usage
Here we show the basic functionality of diffdf using a
dummy data set.
library(diffdf)
LENGTH = 30
suppressWarnings(RNGversion("3.5.0"))
set.seed(12334)
test_data <- tibble::tibble(
ID = 1:LENGTH,
GROUP1 = rep( c(1,2) , each = LENGTH/2),
GROUP2 = rep( c(1:(LENGTH/2)), 2 ),
INTEGER = rpois(LENGTH , 40),
BINARY = sample( c("M" , "F") , LENGTH , replace = T),
DATE = lubridate::ymd("2000-01-01") + rnorm(LENGTH, 0, 7000),
DATETIME = lubridate::ymd_hms("2000-01-01 00:00:00") + rnorm(LENGTH, 0, 200000000),
CONTINUOUS = rnorm(LENGTH , 30 , 12),
CATEGORICAL = factor(sample( c("A" , "B" , "C") , LENGTH , replace = T)),
LOGICAL = sample( c(TRUE , FALSE) , LENGTH , replace = T),
CHARACTER = stringi::stri_rand_strings(LENGTH, rpois(LENGTH , 13), pattern = "[ A-Za-z0-9]")
)
test_data
#> # A tibble: 30 × 11
#> ID GROUP1 GROUP2 INTEGER BINARY DATE DATETIME CONTINUOUS
#> <int> <dbl> <int> <int> <chr> <date> <dttm> <dbl>
#> 1 1 1 1 41 M 2003-06-22 2000-11-28 20:40:53 21.6
#> 2 2 1 2 41 F 2016-12-03 1994-08-30 19:05:02 26.5
#> 3 3 1 3 41 M 2016-05-08 1992-09-11 11:30:18 16.1
#> 4 4 1 4 32 M 2015-06-02 2007-11-12 11:28:29 23.5
#> 5 5 1 5 55 F 1986-04-15 1998-08-04 01:27:49 21.2
#> 6 6 1 6 33 M 1994-05-25 2001-12-05 08:24:35 46.9
#> 7 7 1 7 40 F 2009-02-08 1986-11-02 18:13:03 28.2
#> 8 8 1 8 44 F 2020-07-21 1998-08-22 05:23:24 27.7
#> 9 9 1 9 51 F 1967-05-25 2003-01-03 22:09:29 22.0
#> 10 10 1 10 40 M 2044-03-11 1996-04-19 11:10:12 40.9
#> # ℹ 20 more rows
#> # ℹ 3 more variables: CATEGORICAL <fct>, LOGICAL <lgl>, CHARACTER <chr>
diffdf( test_data , test_data)
#> No issues were found!As you would expect no differences are found. We now look to
introduce various types differences into the data in order to show how
diffdf highlights them. Note that for the purposes of this
vignette we have used the suppress_warnings argument to
stop errors being raised; it is recommended however that this option is
not used in production code as it may mask problems.
Missing Columns
test_data2 <- test_data
test_data2 <- test_data2[,-6]
diffdf(test_data , test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE that are not in COMPARE !!
#> All rows are shown in table below
#>
#> =========
#> COLUMNS
#> ---------
#> DATE
#> ---------Missing Rows
test_data2 <- test_data
test_data2 <- test_data2[1:(nrow(test_data2) - 2),]
diffdf(test_data, test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are rows in BASE that are not in COMPARE !!
#> All rows are shown in table below
#>
#> ===============
#> ..ROWNUMBER..
#> ---------------
#> 29
#> 30
#> ---------------Different Values
test_data2 <- test_data
test_data2[5,2] <- 6
diffdf(test_data , test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> Not all Values Compared Equal
#> All rows are shown in table below
#>
#> =============================
#> Variable No of Differences
#> -----------------------------
#> GROUP1 1
#> -----------------------------
#>
#>
#> All rows are shown in table below
#>
#> ========================================
#> VARIABLE ..ROWNUMBER.. BASE COMPARE
#> ----------------------------------------
#> GROUP1 5 1 6
#> ----------------------------------------Different Types
test_data2 <- test_data
test_data2[,2] <- as.character(test_data2[,2])
diffdf(test_data , test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE and COMPARE with different modes !!
#> All rows are shown in table below
#>
#> ================================
#> VARIABLE MODE.BASE MODE.COMP
#> --------------------------------
#> GROUP1 numeric character
#> --------------------------------
#>
#> There are columns in BASE and COMPARE with different classes !!
#> All rows are shown in table below
#>
#> ==================================
#> VARIABLE CLASS.BASE CLASS.COMP
#> ----------------------------------
#> GROUP1 numeric character
#> ----------------------------------Different Labels
test_data2 <- test_data
attr(test_data$ID , "label") <- "This is a interesting label"
attr(test_data2$ID , "label") <- "what do I type here?"
diffdf(test_data , test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE and COMPARE with differing attributes !!
#> All rows are shown in table below
#>
#> ========================================================================
#> VARIABLE ATTR_NAME VALUES.BASE VALUES.COMP
#> ------------------------------------------------------------------------
#> ID label This is a interesting label what do I type here?
#> ------------------------------------------------------------------------Different Factor Levels
test_data2 <- test_data
levels(test_data2$CATEGORICAL) <- c(1,2,3)
diffdf(test_data , test_data2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE and COMPARE with differing attributes !!
#> All rows are shown in table below
#>
#> ============================================================
#> VARIABLE ATTR_NAME VALUES.BASE VALUES.COMP
#> ------------------------------------------------------------
#> CATEGORICAL levels c("A", "B", "C") c("1", "2", "3")
#> ------------------------------------------------------------
#>
#> Not all Values Compared Equal
#> All rows are shown in table below
#>
#> ================================
#> Variable No of Differences
#> --------------------------------
#> CATEGORICAL 30
#> --------------------------------
#>
#>
#> First 10 of 30 rows are shown in table below
#>
#> ===========================================
#> VARIABLE ..ROWNUMBER.. BASE COMPARE
#> -------------------------------------------
#> CATEGORICAL 1 C 3
#> CATEGORICAL 2 C 3
#> CATEGORICAL 3 A 1
#> CATEGORICAL 4 C 3
#> CATEGORICAL 5 A 1
#> CATEGORICAL 6 A 1
#> CATEGORICAL 7 A 1
#> CATEGORICAL 8 A 1
#> CATEGORICAL 9 C 3
#> CATEGORICAL 10 B 2
#> -------------------------------------------Grouping Variables
A key feature of diffdf that enables easier diagnostics
is the ability to specify which variables form a unique row i.e. which
rows should be compared against each other based upon a key. By default
if no key is specified diffdf will use the row numbers as
the key however in general this isn’t recommended as it means two
identical datasets simply sorted differently can lead to
incomprehensible error messages as every observation is flagged as
different. In diffdf keys can be specified as character
vectors using the keys argument.
test_data2 <- test_data
test_data2$INTEGER[c(5,2,15)] <- 99L
diffdf( test_data , test_data2 , keys = c("GROUP1" , "GROUP2") , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> Not all Values Compared Equal
#> All rows are shown in table below
#>
#> =============================
#> Variable No of Differences
#> -----------------------------
#> INTEGER 3
#> -----------------------------
#>
#>
#> All rows are shown in table below
#>
#> =========================================
#> VARIABLE GROUP1 GROUP2 BASE COMPARE
#> -----------------------------------------
#> INTEGER 1 2 41 99
#> INTEGER 1 5 55 99
#> INTEGER 1 15 44 99
#> -----------------------------------------Misc
Accessing problem rows
As an additional utility diffdf comes with the function
diffdf_issuerows() which can be used to subset your dataset
against the issue object to return just the rows that are flagged as
containing issues.
iris2 <- iris
for (i in 1:3) iris2[i,i] <- 99
diff <- diffdf( iris , iris2, suppress_warnings = TRUE)
diffdf_issuerows( iris , diff)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
diffdf_issuerows( iris2 , diff)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 99.0 3.5 1.4 0.2 setosa
#> 2 4.9 99.0 1.4 0.2 setosa
#> 3 4.7 3.2 99.0 0.2 setosaBear in mind that the vars option can be used to just
subset down to issues associated with particular variables.
diffdf_issuerows( iris2 , diff , vars = "Sepal.Length")
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 99 3.5 1.4 0.2 setosa
diffdf_issuerows( iris2 , diff , vars = c("Sepal.Length" , "Sepal.Width"))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 99.0 3.5 1.4 0.2 setosa
#> 2 4.9 99.0 1.4 0.2 setosaAre there issues ?
Sometimes it can be useful to use the comparison result to fuel
further checks or programming logic. To assist with this
diffdf offers two pieces of functionality namely the
suppress_warnings argument (which has already been shown)
and the diffdf_has_issues() helper function which simply
returns TRUE if differences have been found else FALSE.
iris2 <- iris
for (i in 1:3) iris2[i,i] <- 99
diff <- diffdf( iris , iris2, suppress_warnings = TRUE)
diffdf_has_issues(diff)
#> [1] TRUE
if ( diffdf_has_issues(diff)){
#<Further programming steps / logic>
}Tolerance
You can use the tolerance argument of
diffdf to define how sensitive the comparison should be to
decimal place inaccuracies. This important as very often floating point
numbers will not compare equal due to machine rounding as they cannot be
perfectly represented in binary. By default tolerance is set to
sqrt(.Machine$double.eps)
dsin1 <- data.frame(x = 1.1e-06)
dsin2 <- data.frame(x = 1.1e-07)
diffdf(dsin1 , dsin2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> Not all Values Compared Equal
#> All rows are shown in table below
#>
#> =============================
#> Variable No of Differences
#> -----------------------------
#> x 1
#> -----------------------------
#>
#>
#> All rows are shown in table below
#>
#> ===========================================
#> VARIABLE ..ROWNUMBER.. BASE COMPARE
#> -------------------------------------------
#> x 1 1.1e-06 1.1e-07
#> -------------------------------------------
diffdf(dsin1 , dsin2 , tolerance = 0.001 , suppress_warnings = T)
#> No issues were found!Strictness
By default, the function will note a difference between integer and
double columns, and factor and character columns. It can be useful in
some contexts to prevent this from occuring. We can do so with the
strict_numeric = FALSE and
strict_factor = FALSE arguments.
dsin1 <- data.frame(x = as.integer(c(1,2,3)))
dsin2 <- data.frame(x = as.numeric(c(1,2,3)))
diffdf(dsin1 , dsin2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE and COMPARE with different classes !!
#> All rows are shown in table below
#>
#> ==================================
#> VARIABLE CLASS.BASE CLASS.COMP
#> ----------------------------------
#> x integer numeric
#> ----------------------------------
diffdf(dsin1 , dsin2 , suppress_warnings = T, strict_numeric = FALSE)
#> NOTE: Variable x in base was casted to numeric
#> No issues were found!
dsin1 <- data.frame(x = as.character(c(1,2,3)), stringsAsFactors = FALSE)
dsin2 <- data.frame(x = as.factor(c(1,2,3)))
diffdf(dsin1 , dsin2 , suppress_warnings = T)
#> Differences found between the objects!
#>
#> A summary is given below.
#>
#> There are columns in BASE and COMPARE with different modes !!
#> All rows are shown in table below
#>
#> ================================
#> VARIABLE MODE.BASE MODE.COMP
#> --------------------------------
#> x character numeric
#> --------------------------------
#>
#> There are columns in BASE and COMPARE with different classes !!
#> All rows are shown in table below
#>
#> ==================================
#> VARIABLE CLASS.BASE CLASS.COMP
#> ----------------------------------
#> x character factor
#> ----------------------------------
diffdf(dsin1 , dsin2 , suppress_warnings = T, strict_factor = FALSE)
#> NOTE: Variable x in compare was casted to character
#> No issues were found!