{"action":"created","comment":{"url":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/pulls/comments/223100229","pull_request_review_id":162153328,"id":223100229,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIyMzEwMDIyOQ==","diff_hunk":"@@ -196,7 +194,7 @@ test_that(\"random columns don't get imputed\", {\n                           prep(training = d_train) %>%\n                           bake(newdata = d_test))\n \n-  expect_true(any(missingness(d_imputed, return_df = FALSE) != 0))\n+  expect_false(any(missingness(d_imputed, return_df = FALSE) != 0))","path":"tests/testthat/test-hcai-impute.R","position":32,"original_position":32,"commit_id":"a3a306647e2041c6ab58f910580a06a40a41b230","original_commit_id":"a3a306647e2041c6ab58f910580a06a40a41b230","user":{"login":"mmastand","id":15145347,"node_id":"MDQ6VXNlcjE1MTQ1MzQ3","avatar_url":"https://avatars0.githubusercontent.com/u/15145347?v=4","gravatar_id":"","url":"https://api.github.com/users/mmastand","html_url":"https://github.com/mmastand","followers_url":"https://api.github.com/users/mmastand/followers","following_url":"https://api.github.com/users/mmastand/following{/other_user}","gists_url":"https://api.github.com/users/mmastand/gists{/gist_id}","starred_url":"https://api.github.com/users/mmastand/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mmastand/subscriptions","organizations_url":"https://api.github.com/users/mmastand/orgs","repos_url":"https://api.github.com/users/mmastand/repos","events_url":"https://api.github.com/users/mmastand/events{/privacy}","received_events_url":"https://api.github.com/users/mmastand/received_events","type":"User","site_admin":false},"body":"I think this test was there just to remind us that this was a problem.","created_at":"2018-10-05T18:26:41Z","updated_at":"2018-10-05T18:29:59Z","html_url":"https://github.com/HealthCatalyst/healthcareai-r/pull/1271#discussion_r223100229","pull_request_url":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/pulls/1271","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/pulls/comments/223100229"},"html":{"href":"https://github.com/HealthCatalyst/healthcareai-r/pull/1271#discussion_r223100229"},"pull_request":{"href":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/pulls/1271"}}},"pull_request":{"url":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/pulls/1271","id":220547084,"node_id":"MDExOlB1bGxSZXF1ZXN0MjIwNTQ3MDg0","html_url":"https://github.com/HealthCatalyst/healthcareai-r/pull/1271","diff_url":"https://github.com/HealthCatalyst/healthcareai-r/pull/1271.diff","patch_url":"https://github.com/HealthCatalyst/healthcareai-r/pull/1271.patch","issue_url":"https://api.github.com/repos/HealthCatalyst/healthcareai-r/issues/1271","number":1271,"state":"open","locked":false,"title":"Rex 1254 bugnumericknnimpute","user":{"login":"glenrs","id":29958218,"node_id":"MDQ6VXNlcjI5OTU4MjE4","avatar_url":"https://avatars3.githubusercontent.com/u/29958218?v=4","gravatar_id":"","url":"https://api.github.com/users/glenrs","html_url":"https://github.com/glenrs","followers_url":"https://api.github.com/users/glenrs/followers","following_url":"https://api.github.com/users/glenrs/following{/other_user}","gists_url":"https://api.github.com/users/glenrs/gists{/gist_id}","starred_url":"https://api.github.com/users/glenrs/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/glenrs/subscriptions","organizations_url":"https://api.github.com/users/glenrs/orgs","repos_url":"https://api.github.com/users/glenrs/repos","events_url":"https://api.github.com/users/glenrs/events{/privacy}","received_events_url":"https://api.github.com/users/glenrs/received_events","type":"User","site_admin":false},"body":"@mmastand, this issues raises warnings and messages for issues #1254 and #998.\r\n\r\nWhen errors were thrown it was difficult to see the warning message, so I decided to make these messages. In R these messages are displayed nicely. Let me know what you think.\r\n\r\nSummary of changes:\r\n- For issue #1254 I raised messages because errors are thrown with character columns\r\n- For issue #998 I raised a warning to alert the user. In this issue, `recipes` succeeds, but just doesn't impute. Sometimes it is masked when `collapse_rare_factors = TRUE`.\r\n\r\nI also changed a test. I didn't agree with the comment. It said that imputation shouldn't work with random variables. I don't think this would be an issue. After I changed the introduced column to factor type the imputation worked.. When you are looking at the tests you might not agree with what I have done. Let me know what you think. Thank you!\r\n\r\n``` r\r\nlibrary(healthcareai)\r\n#> healthcareai version 2.2.0\r\n#> Please visit https://docs.healthcare.ai for full documentation and vignettes. Join the community at https://healthcare-ai.slack.com\r\n\r\nprep_data(pima_diabetes, outcome = age, impute=list(nominal_method=\"knnimpute\"))\r\n#> Training new data prep recipe...\r\n#> `knnimpute` depends on another library that does not support character columns yet. If `knnimpute` fails please convert all character columns to factors for bag imputation.\r\n#> Error in gower_work(x = x, y = y, pair_x = pair_x, pair_y = pair_y, n = n, : STRING_ELT() can only be applied to a 'character vector', not a 'integer'\r\nprep_data(pima_diabetes, outcome = age, impute=list(numeric_method=\"knnimpute\"))\r\n#> Training new data prep recipe...\r\n#> `knnimpute` depends on another library that does not support character columns yet. If `knnimpute` fails please convert all character columns to factors for bag imputation.\r\n#> Error in gower_work(x = x, y = y, pair_x = pair_x, pair_y = pair_y, n = n, : STRING_ELT() can only be applied to a 'character vector', not a 'integer'\r\nprep_data(pima_diabetes, outcome = age, impute=list(nominal_method=\"bagimpute\"))\r\n#> Training new data prep recipe...\r\n#> Warning in hcai_impute(., numeric_method = ip$numeric_method,\r\n#> nominal_method = ip$nominal_method, : `bagimpute` depends on another\r\n#> library that does not support character columns yet. If `bagimpute` does\r\n#> not impute missing values, please convert all character columns to factors.\r\n#> If `collapse_rare_factors` is TRUE, the values that are not imputed might\r\n#> be contained in \"other\".\r\n#> healthcareai-prepped data. Recipe used to prepare data:\r\n#> Data Recipe\r\n#> \r\n#> Inputs:\r\n#> \r\n#>       role #variables\r\n#>    outcome          1\r\n#>  predictor          9\r\n#> \r\n#> Training data contained 768 data points and 376 incomplete rows. \r\n#> \r\n#> Operations:\r\n#> \r\n#> Sparse, unbalanced variable filter removed no terms [trained]\r\n#> Mean Imputation for patient_id, pregnancies, ... [trained]\r\n#> Bagged tree imputation for weight_class, diabetes [trained]\r\n#> Adding levels to: other, missing [trained]\r\n#> Collapsing factor levels for weight_class, diabetes [trained]\r\n#> Adding levels to: other, missing [trained]\r\n#> Dummy variables from weight_class and diabetes [trained]\r\n#> Current data:\r\n#> # A tibble: 768 x 16\r\n#>    patient_id pregnancies plasma_glucose diastolic_bp skinfold insulin\r\n#>         <int>       <int>          <dbl>        <dbl>    <dbl>   <dbl>\r\n#>  1          1           6            148         72       35      156.\r\n#>  2          2           1             85         66       29      156.\r\n#>  3          3           8            183         64       29.2    156.\r\n#>  4          4           1             89         66       23       94 \r\n#>  5          5           0            137         40       35      168 \r\n#>  6          6           5            116         74       29.2    156.\r\n#>  7          7           3             78         50       32       88 \r\n#>  8          8          10            115         72.4     29.2    156.\r\n#>  9          9           2            197         70       45      543 \r\n#> 10         10           8            125         96       29.2    156.\r\n#> # ... with 758 more rows, and 10 more variables: pedigree <dbl>,\r\n#> #   age <int>, weight_class_morbidly.obese <dbl>,\r\n#> #   weight_class_normal <dbl>, weight_class_overweight <dbl>,\r\n#> #   weight_class_other <dbl>, weight_class_missing <dbl>,\r\n#> #   diabetes_Y <dbl>, diabetes_other <dbl>, diabetes_missing <dbl>\r\n```\r\n\r\nCreated on 2018-10-04 by the [reprex\r\npackage](http://reprex.tidyverse.org) 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