The default will always be to terminate execution at the first exception, but add a optional parameter that will override this behavior and count the number of failures

I discovered that data.matrix doesn't convert non-factor strings into numbers like I assumed it did.

What about being able to run functions like within_n_mads() on a group wise basis, for example:

library(dplyr)
library(assertr)

mtcars %>%
  group_by(cyl) %>%
  insist(within_n_mads(2), mpg)

Unless I'm very much mistaken, this isn't currently implemented, but would be extremely powerful for testing datasets which are a mix of categorical and continuous variable.

check_rows :: data.frame df : df -> (df -> [a]) -> ([a] -> ([a] -> Bool)) -> df

check_rows(a, b, c, ...)
a = data frame
b = function that takes data frame and returns nrow vector
c = predicate generating function
... optional "select" rows

apply predicate generating function to return value from b(a)
then map the resultant predicate [c(b(a))] over return value from b(a)
[map(c(b(a)), b(a))]

example:

iris %>%
   check_rows(cosine_dist, within_n_sds(5), -Species) %>%
    ...

Hi what a nice package--

Perhaps you'd like to add to your vignette that it's easy to check that a data frame column contains a column

data.frame(a=c(1,2,3)) %>%
    verify("a" %>% exists) %>%    # ok
    verify("b" %>% exists)        # fails

I am looking for ways to visualise the result of an assertr chain, i.e. with shiny. Therefore, I need a complete list of succeeded and failed assertions. Setting the option error_fun to error_append in chain_end gives error messages for failed assertions as attributes. I think it would be helpful to also get something similar for successful assertions, so at the end of the chain, I have a complete list of all assertions tested and whether they failed or not. I looked at the source code and it seems the just adding a new success_and_error_function is not sufficient to achieve my goal.

It would be helpful to allow for warnings rather than errors. I think the way to do this would be to allow each testing function an argument like warn, but the default go to a package-level option (options(assert_warn=FALSE)) which is false by default.

Investigate this.

Is it possible to output an error list for verify function as assert does?

It will be helpful to have the index and all used variables in the expression/function.

Here is a predicate for verify that tests if there are any duplicate keys a data.frame. Feel free to include it with the package if you'd like.

#' Returns TRUE if no values are duplicated
#'
#' This function tests if values in the sepcified columns have no
#' duplicate values. This the inverse of
#' \code{\link[base]{duplicated}}. This is a convenience function
#' meant to be used as a predicate in an \code{\link{assertr}}
#' verify statment.
#'
#' Warning: Since this uses \code{\link[base]{duplicated}}, the columns
#' are pasted together with '\t', this will fail if any of the
#' specified columns contain '\t' characters.
#'
#' @param ... columns from the data.frame
#' @return A vector of the same length that is TRUE when the values in
#' the specified columns are distinct
#' @seealso \code{\link{duplicated}}
#' @examples
#' z <- data.frame(a=c(1,2,3,4,5,6), b=c(1,1,1,3,3,3), c=c(1,2,3,1,2,3))
#' zz <- z %>% verify(no_duplicates(a))
#' zz <- z %>% verify(no_duplicates(b))    # verification failed! (4 failures)
#' zz <- z %>% verify(no_duplicates(c))    # verification failed! (3 failures)
#' zz <- z %>% verify(no_duplicates(a,b))
#' zz <- z %>% verify(no_duplicates(a,c))
#' zz <- z %>% verify(no_duplicates(b,c))
#' zz <- z %>% verify(no_duplicates(a,b,c))
#'
#' @export
no_duplicates <- function(...){
    args <- list(...)
    args$incomparables=F
    !duplicated.data.frame(args)
}

Implement either parallel execution or Rcpp execution

assert_rows :: data.frame df => df -> (df -> [a]) -> (a -> Bool) -> df

assert_rows(a, b, c, ...)
a = data frame
b = function that takes data frame and returns nrow vector
c = predicate
... optional "select" rows

map the predicate c over return value from b(a)
[map(c), b(a))]

example:

iris %>%
   assert_rows(cosine_dist, within_bounds(0,.5), -Species) %>%
    ...

the error message will read like....
Error: Assertion 'within_bounds' violated at row 4 of iris (value: 32.4)

If assert_rows or insist_rows is called during a chain, and it finds no new errors, and there are existing errors from the earlier parts of the chain, it will return an empty string. This breaks the chain. To continue the chain, these functions should always call either the success function or the error function and return the data set.

Here is a transcript of a session exhibiting the problem. I believe that the output from the first chain is correct, and that that from the second and third is incorrect. Indeed, all three chains should give the same output.

> library(assertr)
> library(magrittr)
> test.df = data.frame(x=c(1,0,2))
> test.df %>% chain_start %>% verify(x>0) %>% chain_end
There is 1 error:

- verification [x > 0] failed! (1 failure)

Error: assertr stopped execution
> test.df %>% chain_start %>% verify(x>0) %>% assert_rows(col_concat, is_uniq, x) %>% chain_end
[1] ""
> test.df %>% chain_start %>% verify(x>0) %>% assert_rows(col_concat, is_uniq, x) %>% assert_rows(col_concat, is_uniq, x) %>% chain_end
Error in UseMethod("select_") : 
  no applicable method for 'select_' applied to an object of class "character"
> 

Patch file below.

First commit adds tests (which fail), second fixes bug (making tests pass). Consider squashing or rewriting them.

From 844789702f93338fabdaad222a4997bd56853cfc Mon Sep 17 00:00:00 2001
From: Peter Wicks Stringfield <[email protected]>
Date: Tue, 21 Mar 2017 16:20:38 -0500
Subject: [PATCH 1/2] add tests for chaining error

---
 tests/testthat/test-assertions.R | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/tests/testthat/test-assertions.R b/tests/testthat/test-assertions.R
index beb085a..84b791f 100644
--- a/tests/testthat/test-assertions.R
+++ b/tests/testthat/test-assertions.R
@@ -24,6 +24,7 @@ mnexmpl.data[12,1] <- NA
 nanmnexmpl.data <- mnexmpl.data
 nanmnexmpl.data[10,1] <- 0/0
 
+test.df <- data.frame(x = c(0,1,2))
 
 # custom error (or success) messages
 yell <- function(message){
@@ -560,3 +561,38 @@ test_that("insist_rows breaks appropriately (using se)", {
 ###########################################
 
 
+########## chaining works ############
+
+# A special error function for these tests, produces the error but no
+# standard output.
+error_no_output <- function (list_of_errors, data=NULL, ...) {
+  stop("assertr stopped execution", call.=FALSE)
+}
+
+test_that("assert_rows works with chaining", {
+  code_to_test <- function () {
+    test.df %>%
+      chain_start %>%
+      # This gives one error.
+      assert(within_bounds(1, Inf), x) %>%
+      # This gives no errors.
+      assert_rows(col_concat, is_uniq, x) %>%
+      assert_rows(col_concat, is_uniq, x) %>%
+      chain_end(error_fun = error_no_output)
+  }
+  expect_error(code_to_test(),
+               "assertr stopped execution")
+})
+
+test_that("insist_rows works with chaining", {
+  code_to_test <- function () {
+    test.df %>%
+      chain_start %>%
+      assert(within_bounds(1, Inf), x) %>%
+      insist_rows(col_concat, function (a_vector) {function (xx) TRUE}, x) %>%
+      insist_rows(col_concat, function (a_vector) {function (xx) TRUE}, x) %>%
+      chain_end(error_fun = error_no_output)
+  }
+  expect_error(code_to_test(),
+               "assertr stopped execution")
+})
-- 
1.9.1


From ff69f4ff6fa4537b97f811403290e4b739f4c1fa Mon Sep 17 00:00:00 2001
From: Peter Wicks Stringfield <[email protected]>
Date: Tue, 21 Mar 2017 16:20:52 -0500
Subject: [PATCH 2/2] fix chaining error

---
 R/assertions.R | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/R/assertions.R b/R/assertions.R
index cbb2173..b43cb48 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -253,7 +253,10 @@ assert_rows_ <- function(data, row_reduction_fn, predicate, ..., .dots,
 
   num.violations <- sum(!log.vec)
   if(num.violations==0)
-    return("")
+    # There are errors, just no new ones, so calling success
+    # is inappropriate, so we must call the error function.
+    # NOT calling either function would break the pipeline.
+    return(error_fun(list(), data=data))
   loc.violations <- which(!log.vec)
 
   error <- make.assertr.assert_rows.error(name.of.row.redux.fn,
@@ -517,7 +520,10 @@ insist_rows_ <- function(data, row_reduction_fn, predicate_generator, ...,
 
   num.violations <- sum(!log.vec)
   if(num.violations==0)
-    return("")
+    # There are errors, just no new ones, so calling success
+    # is inappropriate, so we must call the error function.
+    # NOT calling either function would break the pipeline.
+    return(error_fun(list(), data=data))
   loc.violations <- which(!log.vec)
 
   error <- make.assertr.assert_rows.error(name.of.row.redux.fn,
-- 
1.9.1

After patch:

> library(assertr)
> library(magrittr)
> test.df = data.frame(x=c(1,0,2))
> test.df %>% chain_start %>% verify(x>0) %>% chain_end
There is 1 error:

- verification [x > 0] failed! (1 failure)

Error: assertr stopped execution
> test.df %>% chain_start %>% verify(x>0) %>% assert_rows(col_concat, is_uniq, x) %>% chain_end
There is 1 error:

- verification [x > 0] failed! (1 failure)

Error: assertr stopped execution
> test.df %>% chain_start %>% verify(x>0) %>% assert_rows(col_concat, is_uniq, x) %>% assert_rows(col_concat, is_uniq, x) %>% chain_end
There is 1 error:

- verification [x > 0] failed! (1 failure)

Error: assertr stopped execution
> 

Is it possible to have assert() not return the data frame? I tried setting success_fun = function() {} but it complains
Error in success_fun(data) : unused argument (data)
I want to use assert() to implement a check but do nothing if it passes. Returning the data frame results in it being printed to the screen when I run my code from the command line. Wrapping the check inside capture.output(assert(...), file='/dev/null') causes problems when there is an error.

Some error messages in dplyr have changed with the switch to tidyselect, the tests in assertr fail now: https://github.com/krlmlr/dplyr/blob/r-0.7.5/revdep/problems.md#newly-broken

Generally I prefer to test only messages and output which are under control of the package that tests them, to avoid such breakages.

• mahalanobois
• cosine

Data frames with a tbl_df wrapper (e.g., data imported with the readr package) raise an error in the maha_dist function.

something %>%
  filter(in_set(c("M", "F"))(Gender)) %>%
  something

Throws a "bounds must be checked on a single element"
which is the wrong error, anyway

increase efficiency of in_set by usng any()
maybe make it vectorized

assert_rows(mtcars, num_row_NAs, function(x) if(x==10) FALSE, everything())

Says

Error in vapply(a.column, predicate, logical(1)) : 
  values must be length 1,
 but FUN(X[[1]]) result is length 0

This would help identify misspecied categorical variables. If, for example, in iris a few versicolors were versecolor, the percentage of records with that value would be low.

I haven't thought this through yet. Can I use with within_bounds? Or is it it's own complete function?

This is a feature request - thank you for all your work on assertr so far.

Could there be assertions about dates?

I realise I can write a predicate function or convert to numeric. Just wondering about some built in approach.

Here are a couple of approaches I've used

within_dateRange <- function(x, date_min, date_max) {
  x > date_min & x < date_max
}

assert(within_bounds(as.numeric(as.Date('2015-01-01')), as.numeric(as.Date('2015-02-01'))), Sample_date)

mutate(Sample_date_num = as.numeric(Sample_date)) %>% 
assert(within_bounds(as.numeric(as.Date('2015-01-01')), as.numeric(as.Date('2015-02-01'))), Sample_date_num)

Detect if a data type check is being performed on a column so that assert doesn't have to check every element

Like within_n_sds predicate generator but better and more robust

I tried to write a assert to check that a specific cell (e.g. to check that table from a web scrape via rvest has the right format) has a specific value, but somehow this doesn't work because the predicates assume that they are handled on a single element (and are vectorized by insist) and therefore have no knowledge about the row number :-(

The idea is this:

html %>% html_node(css=".table") %>% html_table() %>% insist(row_is(1, "Start"), X1)

This would check that the first element in column X1 is the string Start.

Here are two versions which do not work:

# Does not work because the innermost function does not know which row it is
row_is <- function(row_n, value){
    if (!is.numeric(row_n)) 
        stop("row_n must be numeric")
    function(a_vector){
        function(x){
            if (is.na(value)){
                is.na(a_vector[row_n])
            } else {
                a_vector[row_n] == value
            }
        }
    }
}
# Does not work because one function too little...    
row_is_not <- function(row_n, value){
    if (!is.numeric(row_n)) 
        stop("row_n must be numeric")
    fun <- function(a_vector){
        res = rep(TRUE, length(a_vector))
        if (is.na(value)){
            res[row_n] <- !is.na(a_vector[row_n])
        } else {
            res[row_n] <- a_vector[row_n] != value
        }
        return(res)
    }
    return(fun)
}

this should also make within_n_sds vectorized. confirm this!

Like the \w+_ functions in dplyr to allow standard evaluation

Thanks for the great package!

I'm wondering if you'd consider adding support for the .data and .env pronouns from the tidyverse's new tidyeval framework. (.data says "look in the dataframe for the column" while .env says something like "look in the environment for the value")

See the dplyr programming vignette has some background.) I think the rlang::eval_tidy function will be key here.

Here's a demo:

library(rlang)
library(assertr)

mtcars %>% verify(mpg > 30)  # verify works!
#> verification [mpg > 30] failed! (28 failures)

mgp <- 40
mtcars %>% verify(mgp > 30)  # fat fingers :(
# No error because mgp exists elsewhere

# I'd like to write:
mtcars %>% verify(.data$mpg > 30)
# and 
mtcars %>% verify(.data$mpg > .env$mgp)


# How it works in dplyr:
mtcars %>% dplyr::mutate(x = mgp)  # no error, but mgp always 40

# but if we're careful, we get an error
mtcars %>% dplyr::mutate(x = .data$mgp)
#> Error in mutate_impl(.data, dots) : 
#>  Evaluation error: Column `mgp`: not found in data.

insist_rows :: data.frame df : df -> (df -> [a]) -> ([a] -> ([a] -> Bool)) -> df

check_rows(a, b, c, ...)
a = data frame
b = function that takes data frame and returns nrow vector
c = predicate generating function
... optional "select" rows

apply predicate generating function to return value from b(a)
then map the resultant predicate [c(b(a))] over return value from b(a)
[map(c(b(a)), b(a))]

example:

iris %>%
   insist_rows(maha_dist, within_n_sds(5), -Species) %>%
    ...

the error message will read like....
Error: Assertion 'within_n_sds' violated at row 4 of iris (value: 32.4)

Like disallowing above a certain percentage of missing values, etc....

because it makes a NOTE and then CRAN will never accept the package :(

this function is meant to be used with the \code{\link{insist}} function to

Read over all of it, actually

Hi thanks for putting together assertr. I am new to using it, sorry if this should be straightforward.

How would you approach writing a custom predicate for duplicates using assertr? It seems that some basic design choices of assertr make this hard.

The function should:

• Test whether specified vars are joinly duplicates
• Tell you something informative on failure (ideal would be the number of unique duplicate values, number of total duplicated rows, and an example set of row numbers with duplicates. Minimum would be the number of unique duplicate values)
• Be extremely fast

My understanding is that this would be hard because assert applies the predicate function to each specified variable, and I would want all of the specified variables passed to the predicate function.

The ideal would be something like:

df <- data.frame(
    country = c("USA", "USA", "Canada", "Canada"), 
    region = c("Alabama", "Alabama", "Alberta", "Quebec")
)
df %>% assert(no_duplicates, country, region)
#Error: 
#country, region in df has 1 unique duplicated value and 2 duplicated rows (e.g., (1, 2))

Or maybe it's just assert(no_duplicates(country, region))?

Thanks.

Would it be possible to add a return_true option to verify and assert that returns just a short "TRUE" if the expr or predicate is TRUE instead of returning the data?

Consider the following data.frame

df  <- mtcars  %>% tibble::rownames_to_column(.)  %>%  dplyr::select(cyl, rowname, vs:carb)

Suppose I want to verify whether all numeric/double columns are really integers. For one column I could do:

verify(df, all(cyl == floor(cyl)))

(See http://stackoverflow.com/a/10114392)

But if I want to verify this for multiple columns (I guess) I would have to use the (IMHO) rather verbose:

verify(df, all(which(sapply(df, is.numeric)) == floor(which(sapply(df, is.numeric)))))

So wouldn't a verify_if, along the lines of dplyr's select_if and mutate_if, be a good idea? :)
Or am I missing something?

verify_if(df, is.numeric, all(cyl == floor(cyl)))

I have a use for this package where I'm iterating over many data frames using map and verifying their inputs. I want to be able to identify which ones are a success and which ones have an error with the error information included.

In the current setup of the package, the error information is not captured. Below is a reproducible example.

library(tidyverse) 
library(assertr)

safe_assert <- safely(assert)
current <- map(seq(20, 40, 10), ~{
  safe_assert(mtcars, within_bounds(0, .x), mpg, error_fun = error_report)})

current

[[1]]
[[1]]$result
NULL

[[1]]$error
<simpleError: assertr stopped execution>

[[2]]
[[2]]$result
NULL

[[2]]$error
<simpleError: assertr stopped execution>

[[3]]
[[3]]$result
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

I think it would be beneficial to include the print information in the stop and warning functions of the error_report function (see below). Have you considered this?

error_report <- function(errors, data=NULL, warn=FALSE, ...){
  if(!is.null(data) && !is.null(attr(data, "assertr_errors")))
    errors <- append(attr(data, "assertr_errors"), errors)
  num.of.errors <- length(errors)
  head <- paste0(sprintf("There %s %d error%s:\n",
                         if (num.of.errors==1) "is" else "are",
                         num.of.errors,
                         if (num.of.errors==1) "" else "s"))
  body <- sapply(errors, function(x) paste0("\n- ", x))
  if(!warn)
    stop(paste0("assertr stopped execution", "\n\n", head, body, "\n"), call.=FALSE)
  warning(paste0("assertr encountered errors", "\n\n", head, body, "\n"), call.=FALSE)
  return(data)
}

Rerunning the same example with the changes to error_report the new output is below.

library(tidyverse) 
library(assertr)

# with change to error_report function()
safe_assert <- safely(assert)
current <- map(seq(20, 40, 10), ~{
  safe_assert(mtcars, within_bounds(0, .x), mpg, error_fun = error_report)})

current

[[1]]
[[1]]$result
NULL

[[1]]$error
<simpleError: assertr stopped execution

There is 1 error:

• Error in "within_bounds(0, .x)": Column 'mpg' violates assertion 'within_bounds(0, .x)' 14 times

[[2]]
[[2]]$result
NULL

[[2]]$error
<simpleError: assertr stopped execution

There is 1 error:

• Error in "within_bounds(0, .x)": Column 'mpg' violates assertion 'within_bounds(0, .x)' 4 times

[[3]]
[[3]]$result
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

If you are open to this, let me know and I can make a pull request. And if you don't want to make the change to the default settings, a solution could be having a global package option to include or not include in the stop and warning functions.

Thanks. This is a great package!

hey @tonyfischetti - We want all rOpenSci pkgs to consistently keep track of changes, following https://github.com/ropensci/onboarding/blob/master/packaging_guide.md#-news

• you already keep a NEWS file, thanks! but looks like it doesn't include news for your latest CRAN version?
• thanks for git tagging! but can you do a git tag for each CRAN release?
• You have for some releases, but could you please use the releases tab on this repo to include the associated NEWS items for every tag/version ? thanks 😄

To ensure that, for example, Mahalanobis distance isn't crazy for each particular observation

Excellent package!

I'm wondering if there currently exists (or if their might be in the future) an option in assert() that could list each violation of a predicate in the given column, rather than simply listing the number times and one example?

https://github.com/dataproofer/Dataproofer

• it looks like we already have all of these

When num_row_NAs gets an object when "data.frame" class is not the first one (e.g. output from dplyr/tibble) it throws an error "data" must be a data.frame (or matrix).

mtcars <- tibble::as_tibble(mtcars)
[1] "tbl_df"     "tbl"        "data.frame"
assertr::num_row_NAs(mtcars)
Error: "data" must be a data.frame (or matrix)

It is due to a line in row-redux.R:

if(!(class(data) %in% c("matrix", "data.frame")))

It can be fixed, e.g., as follows:

if(!(any(class(data) %in% c("matrix", "data.frame"))))

Would it be possible to have an error function that returns the data that failed at least one test?

Right now assertr uses "stop" and a message to halt execution. I should allow the user to specify their own function that will be called when an assertion is violated.

For example, one could make a function that accepts the error string and emails the error message before calling stop

a generalized solution is to make a function that takes an email address and an error string, emails the error string to the email address and then halts execution. Then, a partially applied function can be used as the custom error function...

email.and error <- function(email, message){
  ...
}

mtcars %>% assert(is_set(c(1,0)), vs, on_error=partial(email.and.error("[email protected]")))

Thanks for the great package!

Here's an incorrect message I got when calling the not_na() function through namespace:

df = data_frame(var = c(1, NA))
df %>% assertr::assert(assertr::not_na, var)
#> Error: 
#> Vector 'var' violates assertion '::' 1 time (value [NA] at index 2)

If assertr is loaded beforehand, the message is fine:

library(assertr)
df %>% assert(not_na, var)
#> Error: 
#> Vector 'var' violates assertion 'not_na' 1 time (value [NA] at index 2)

Also, is it possible to have a short cut function such as assertr::assert_not_na(df, var)?