R Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

This article illustrates how to handle the “Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)” in the R programming language.

Table of contents:

1) Constructing Example Data

2) Example 1: Reproduce the Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

3) Example 2: Fix the Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

4) Video & Further Resources

Let’s dive right in:

Constructing Example Data

The following data is used as basement for this R programming tutorial.

set.seed(56389)                # Create numeric example vector
x <- c(NA, rnorm(100))
head(x)                        # Head of example vector
# [1]          NA -1.72955699  1.57449671 -1.52165311  0.48225317  0.09359781

The previous output of the RStudio console shows the structure of our example data – It is a random numeric vector that contains one NA value at the first index position.

Example 1: Reproduce the Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

The following R programming syntax shows how to replicate the error message “NA/NaN/Inf in foreign function call (arg 1)” when using the kmeans function in R.

Let’s assume that we want to apply the kmeans function to group our example vector into three clusters. Then, we may try to use the following R code:

kmeans(x, 3)                   # Trying to apply kmeans function
# Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

Unfortunately, the RStudio console returns the “Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)”.

The reason for this is that our input vector contains an NA value and the kmeans function cannot handle missing data.

Note that we have reproduced this error message based on a vector containing NA values. However, the same error message occurs when our input data contains non-numeric values that are not properly formatted as numbers.

So how can we solve this problem in R?

Example 2: Fix the Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)

This example illustrates how to debug the “Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)”.

For this, we have to remove all NA values from our vector using the is.na function:

kmeans(x[!is.na(x)], 3)        # Remove NA values
# K-means clustering with 3 clusters of sizes 26, 45, 29
# 
# Cluster means:
#          [,1]
# 1 -1.21408608
# 2  0.06647359
# 3  1.16156234
# 
# Clustering vector:
#   [1] 1 3 1 2 2 1 3 3 2 2 2 1 2 2 3 3 2 2 3 2 2 2 1 1 2 1 2 2 3 2 3 1 2 2 1 3 2 1 2 1 3 1 1 2 3 2 2 2 1 3 1 2 3 2 3 1 1 3 1 2 1 1 3 2 2 1 2 3 2 2 1 1 2 3 2 2 2 2 3 3 1 3 2 3 3 2 3 2 3 3 3 1 3 2 2 3 1 2 2 2
# 
# Within cluster sum of squares by cluster:
# [1] 12.663139  4.399086  4.661347
#  (between_SS / total_SS =  78.1 %)
# 
# Available components:
# 
# [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"         "iter"         "ifault"

As you can see, the previous R code produced a valid output without any error or warning messages.

Video & Further Resources

Do you want to learn more about errors in R? Then you may want to watch the following video of my YouTube channel. In the video, I’m explaining the examples of this tutorial:

The YouTube video will be added soon.

Besides the video, you might want to have a look at the other tutorials that I have published on www.statisticsglobe.com:

To summarize: You have learned in this tutorial how to deal with the “Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)” in R.

Please let me know in the comments section, in case you have additional questions.

6 Comments. Leave new

TARUN
December 1, 2021 5:53 pm

yes sir tried it but still getting the same error in it even though I tried to remove the values from the columns of the dataset
Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In storage.mode(x) <- "double" : NAs introduced by coercion

Reply
- Joachim
  December 2, 2021 8:11 am
  
  Hey Tarun,
  
  Could you please share the code you have used?
  
  Regards,
  Joachim
  
  Reply
eda
April 24, 2022 1:47 pm

How can I fix this error ?
HiperOptikcluster Error in do_one(nmeth) In addition: Warning message:
In storage.mode(x)
this is my data
1 CR000358 2 CR000536 3 CR000592 4 CR000704 5 CR000991 6 CR001029 7 CR001101 8 CR001101 9 CR001109 10 CR001404 11 CR001404 12 CR001511 13 CR001530 14 CR001571 15 CR001614 16 CR001872 17 CR001998 18 CR002183 19 CR002183 20 CR002183 21 CR002183 22 CR002183 23 CR002183 24 CR002183 25 CR002183 26 CR002183 27 CR002183 28 CR002183 29 CR002223 30 CR002223 31 CR002223 32 CR002223 33 CR002223 34 CR002223 35 CR002223 36 CR002223 37 CR002223 38 CR002484 39 CR003012 40 CR003416 41 CR000005 42 CR000231 43 CR000287 44 CR000441 45 CR000870 46 CR000877 47 CR001083 48 CR001101 49 CR001101 50 CR001101 51 CR001161 52 CR001384 53 CR001397 54 CR001397 55 CR001456 56 CR001544 57 CR001555 58 CR001624 59 CR001974 60 CR002027 61 CR002183 62 CR002183 63 CR002183 64 CR002183 65 CR002183 66 CR002183 67 CR002183 68 CR002183 69 CR002183 70 CR002183 71 CR002183 72 CR002183 73 CR002183 74 CR002183 75 CR002183 76 CR002223 77 CR002223 78 CR002223 79 CR002223 80 CR002223 81 CR002223 82 CR002285 83 CR002301 84 CR002385 85 CR002450 86 CR002497 87 CR003180 88 CR003180 89 CR003416 90 CR003416 91 CR003416 92 CR003416 93 CR003520 94 CR003570 95 CR003632 96 CR004235 97 CR004235 98 CR004427 99 CR004671 100 CR004784 101 CR004842 102 CR004952 103 CR005100 104 CR005103 105 CR005326 106 CR005326 107 CR005569 108 CR005617 109 CR005698 110 CR005875 111 CR006033 ProductCode ProductProductivityName <- kmeans(HiperOptik,9,nstart = 100)
: NA/NaN/Inf in foreign function call (arg 1)
2,3 1 20.07.2019 SC34056 ISTANBUL Aksesuar
35,5 1 10.03.2019 SC34073 ISTANBUL Güneş Gözlüğü
10,2 1 13.08.2019 SC34062 ISTANBUL Güneş Gözlüğü
58 1 19.03.2019 SC34002 ISTANBUL Optik Çerçeve
68,6 1 3.05.2019 SC34024 ISTANBUL Güneş Gözlüğü
38,1 1 10.05.2019 SC34045 ISTANBUL Güneş Gözlüğü
0,7 1 7.06.2019 SC34026 ISTANBUL Aksesuar
1,3 2 19.08.2019 SC34026 ISTANBUL Aksesuar
2,3 1 29.07.2019 SC34016 ISTANBUL Aksesuar
34,6 1 5.05.2019 SC34069 ISTANBUL Güneş Gözlüğü
40,5 1 6.05.2019 SC34026 ISTANBUL Güneş Gözlüğü
19,8 1 9.08.2019 SC34023 ISTANBUL Lens
11,9 2 21.07.2019 SC34009 ISTANBUL Cam
23,7 1 27.03.2019 SC34016 ISTANBUL Optik Çerçeve
10,7 2 3.05.2019 SC34024 ISTANBUL Cam
33,1 2 1.08.2019 SC34018 ISTANBUL Cam
0,7 1 4.08.2019 SC34005 ISTANBUL Aksesuar
2,3 1 9.08.2019 SC34017 ISTANBUL Aksesuar
2,5 1 27.08.2019 SC34017 ISTANBUL Bakım Solüsyon
6,3 1 13.03.2019 SC34017 ISTANBUL Optik Çerçeve
6,3 1 12.04.2019 SC34017 ISTANBUL Optik Çerçeve
6,3 1 25.07.2019 SC34017 ISTANBUL Optik Çerçeve
10,8 1 19.08.2019 SC34017 ISTANBUL Güneş Gözlüğü
13,2 1 15.06.2019 SC34017 ISTANBUL Güneş Gözlüğü
13,9 1 21.06.2019 SC34017 ISTANBUL Güneş Gözlüğü
22,9 1 29.06.2019 SC34017 ISTANBUL Güneş Gözlüğü
62,4 1 4.03.2019 SC34017 ISTANBUL Güneş Gözlüğü
70,8 1 25.04.2019 SC34017 ISTANBUL Güneş Gözlüğü
1,7 1 27.05.2019 SC34017 ISTANBUL Aksesuar
2 1 27.05.2019 SC34017 ISTANBUL Aksesuar
2,9 1 28.04.2019 SC34017 ISTANBUL Bakım Solüsyon
6,3 1 3.03.2019 SC34017 ISTANBUL Optik Çerçeve
6,3 1 23.03.2019 SC34017 ISTANBUL Optik Çerçeve
6,3 1 8.08.2019 SC34017 ISTANBUL Optik Çerçeve
7,5 1 18.08.2019 SC34017 ISTANBUL Bakım Solüsyon
11,5 1 18.08.2019 SC34017 ISTANBUL Güneş Gözlüğü
15,7 1 28.04.2019 SC34017 ISTANBUL Optik Çerçeve
15,6 1 16.08.2019 SC34017 ISTANBUL Güneş Gözlüğü
317,5 2 13.06.2019 SC34024 ISTANBUL Cam
0,7 1 14.04.2019 SC34072 ISTANBUL Aksesuar
59,5 8 20.05.2019 SC34039 ISTANBUL Lens
24,9 2 17.05.2019 SC34047 ISTANBUL Cam
14,6 1 11.08.2019 SC34011 ISTANBUL Lens
0,7 1 3.08.2019 SC34026 ISTANBUL Aksesuar
38,1 1 5.06.2019 SC34037 ISTANBUL Güneş Gözlüğü
38,2 1 7.05.2019 SC34006 ISTANBUL Optik Çerçeve
42,4 1 23.06.2019 SC34012 ISTANBUL Güneş Gözlüğü
0,7 1 12.07.2019 SC34026 ISTANBUL Aksesuar
2,3 1 23.04.2019 SC34026 ISTANBUL Aksesuar
15,6 1 24.06.2019 SC34026 ISTANBUL Güneş Gözlüğü
65,4 1 1.08.2019 SC34029 ISTANBUL Güneş Gözlüğü
2,3 1 29.08.2019 SC34048 ISTANBUL Aksesuar
0 2 5.07.2019 SC34031 ISTANBUL Cam
35,9 2 5.07.2019 SC34031 ISTANBUL Cam
0,7 1 6.04.2019 SC34010 ISTANBUL Aksesuar
5,9 1 5.06.2019 SC34043 ISTANBUL Güneş Gözlüğü
17,2 1 8.07.2019 SC34070 ISTANBUL Lens
2,5 1 29.05.2019 SC34017 ISTANBUL Bakım Solüsyon
29,8 4 18.05.2019 SC34020 ISTANBUL Lens
39,6 1 10.03.2019 SC34028 ISTANBUL Optik Çerçeve
0,7 1 31.03.2019 SC34017 ISTANBUL Aksesuar
0,7 1 31.03.2019 SC34017 ISTANBUL Aksesuar
0,7 1 31.07.2019 SC34017 ISTANBUL Aksesuar
6,2 1 22.07.2019 SC34017 ISTANBUL Optik Çerçeve
10,8 1 15.04.2019 SC34017 ISTANBUL Güneş Gözlüğü
11,5 1 17.07.2019 SC34017 ISTANBUL Güneş Gözlüğü
14,4 1 25.03.2019 SC34017 ISTANBUL Güneş Gözlüğü
21 1 3.05.2019 SC34017 ISTANBUL Optik Çerçeve
24,6 1 21.06.2019 SC34017 ISTANBUL Güneş Gözlüğü
25 1 7.07.2019 SC34017 ISTANBUL Optik Çerçeve
27,7 1 3.06.2019 SC34017 ISTANBUL Güneş Gözlüğü
39,6 1 2.07.2019 SC34017 ISTANBUL Optik Çerçeve
72,6 1 28.08.2019 SC34017 ISTANBUL Optik Çerçeve
81,7 1 28.07.2019 SC34017 ISTANBUL Güneş Gözlüğü
85,8 1 19.08.2019 SC34017 ISTANBUL Optik Çerçeve
0,7 1 28.04.2019 SC34017 ISTANBUL Aksesuar
2,5 1 1.08.2019 SC34017 ISTANBUL Bakım Solüsyon
19,7 1 27.07.2019 SC34017 ISTANBUL Optik Çerçeve
32,9 1 7.03.2019 SC34017 ISTANBUL Optik Çerçeve
53,9 1 4.05.2019 SC34017 ISTANBUL Güneş Gözlüğü
79,2 1 28.08.2019 SC34017 ISTANBUL Güneş Gözlüğü
14,4 1 17.03.2019 SC34019 ISTANBUL Güneş Gözlüğü
0,7 1 31.08.2019 SC34024 ISTANBUL Aksesuar
52,9 2 9.08.2019 SC34035 ISTANBUL Cam
42,4 1 7.08.2019 SC34046 ISTANBUL Güneş Gözlüğü
0,7 1 18.07.2019 SC34042 ISTANBUL Aksesuar
2 1 22.04.2019 SC34012 ISTANBUL Bakım Solüsyon
32,7 3 22.04.2019 SC34012 ISTANBUL Lens
1,9 1 31.03.2019 SC34072 ISTANBUL Bakım Solüsyon
7,3 1 6.05.2019 SC34072 ISTANBUL Güneş Gözlüğü
34,9 1 13.08.2019 SC34072 ISTANBUL Güneş Gözlüğü
36,3 1 7.04.2019 SC34072 ISTANBUL Güneş Gözlüğü
65,4 1 28.06.2019 SC34020 ISTANBUL Güneş Gözlüğü
14,4 1 10.07.2019 SC34031 ISTANBUL Güneş Gözlüğü
8,8 1 12.08.2019 SC34071 ISTANBUL Güneş Gözlüğü
82,3 1 2.08.2019 SC34084 ISTANBUL Optik Çerçeve
113,9 2 2.08.2019 SC34084 ISTANBUL Cam
10,1 1 22.07.2019 SC34028 ISTANBUL Güneş Gözlüğü
27,7 1 1.03.2019 SC34052 ISTANBUL Güneş Gözlüğü
1,2 2 7.03.2019 SC34037 ISTANBUL Cam
4,6 2 22.04.2019 SC34062 ISTANBUL Cam
28 2 14.04.2019 SC34002 ISTANBUL Cam
4,1 2 20.05.2019 SC34011 ISTANBUL Cam
20,7 1 23.06.2019 SC34045 ISTANBUL Güneş Gözlüğü
50,6 1 21.06.2019 SC34011 ISTANBUL Güneş Gözlüğü
64,6 1 3.04.2019 SC34011 ISTANBUL Güneş Gözlüğü
14,9 1 4.05.2019 SC34027 ISTANBUL Güneş Gözlüğü
5,6 1 28.05.2019 SC34007 ISTANBUL Cam
0 2 10.08.2019 SC34009 ISTANBUL Cam
88,6 1 23.06.2019 SC34056 ISTANBUL Güneş Gözlüğü
39,7 4 19.07.2019 SC34006 ISTANBUL Lens

Reply
- Joachim
  April 25, 2022 8:49 am
  
  Hi Eda,
  
  The kmeans function takes a “numeric matrix of data, or an object that can be coerced to such a matrix” as input. However, your data contains many non-numeric columns.
  
  To which part of your data should the kmeans function be applied?
  
  Regards,
  Joachim
  
  Reply
Jennifer
August 13, 2023 2:55 pm

Hi Joachim,

I am using the package Pursuit to construct projection pursuit index, using the function “res <- PP_Optimizer(data=data, findex="friedmantukey")", but then having the error "Error in PP_Index(data = Proj, class = class, vector.proj = Aa, findex = findex, : NA/NaN/Inf in foreign function call (arg 3)".

One of the variables in my data is binary categorical data, with all values 1. Is the the reason causing the above error? How should I fix it?

Many Thanks!!

Reply
- Cansu (Statistics Globe)
  August 14, 2023 7:35 am
  Hello Jennifer,
  
  The fact that one of your variables is a binary categorical variable with only a single value could be a potential cause of this issue. In projection pursuit, we are interested in finding informative projections of the data. If a variable contains no variation (i.e., all values are the same), it will not contribute any useful information to the projection and might be causing numerical instability or other issues in the calculations.
  
  Here are a few steps to address and potentially resolve the error:
  
  Remove Constant Variables: Before running the projection pursuit function, remove any variables that are constant (i.e., have the same value for all observations). You can do this in R using the apply function. For example, to keep only variables with some variation:
  data <- data[, apply(data, 2, var) > 0]
  If the problem persists then:
  
  Check for NA/NaN/Inf Values: Ensure that there are no NA, NaN, or Inf values in your data. You can use the is.na, is.nan, and is.infinite functions to identify such values. If you find any, decide on an appropriate method to handle them (e.g., imputation or removal).
  
  Standardize the Data: Some algorithms behave better when the input data is standardized (mean of 0 and standard deviation of 1 for each variable). You can use the scale function in R for this purpose:
  data <- scale(data)
  I hope one of these solutions helps.
  
  Best,
  Cansu
  Reply