the assignment uses inconsistent data types

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ORA-00932: inconsistent datatypes: expected CHAR got NUMBER

I am trying to SUBSTR the first 3 character in use this query -> CASE WHEN (RMSTMP_PNG.ota_activity_lotinfo.KEY = 'TestProgram') THEN TO_CHAR(SUBSTR(RMSTMP_PNG.ota_activity_lotinfo.VALUE,1,3)) ELSE 0 END AS Test1 But it return me this ORA-00932: inconsistent datatypes: expected CHAR got NUMBER 00932. 00000 - "inconsistent datatypes: expected %s got %s". May i know what wrong in my query? I tried to use To_NUMBER or To CHAR, it also return me same error. Appreciate if someone could help on this issue. Below is my query:

CASE WHEN (RMSTMP_PNG.ota_activity_lotinfo.KEY = 'TestProgram') THEN TO_CHAR(SUBSTR(RMSTMP_PNG.ota_activity_lotinfo.VALUE,1,3)) ELSE 0 END AS Test1

Your case statement returns both a character (then to_char(...)) and a number (else 0). Try modifying the else condition to return a string:

or the first condition to return a number:

• Hi pmdba, Its working now. Thanks a lot. I encounterd one problem. can you help me on this? appreciate so much if you could help me on this . dba.stackexchange.com/questions/276176/… –  Yong Commented Sep 28, 2020 at 8:47

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ORA-00932 : inconsistent datatypes | ORA-00932 Error Resolution

Ora-00932 : inconsistent datatypes :.

In my previous articles, I have given the proper  idea of different oracle errors , which are frequently come. In this article, I will try to explain another common error, which has been searched on google approximately 9 k times per month. ORA-00932 is another simple error, which is commonly come in database when user tries to use inconsistent datatype. The datatype is most important in Oracle database management system. When user tries to execute the operation with two different datatypes, which are incompatible, this error will come.

Why this error will come?

The main cause of ORA-00932 error is using two different incompatible datatypes for executing query without converting it using function. I will try to produce this error using different scenarios.

Incompatible datatype:

As I explained that, this error will come because of use of incompatible datatype. Let’s take an example for the same

CREATE TABLE T_error (creation_date DATE); INSERT INTO T_error values(125); commit;

SQL Error: ORA-00932: inconsistent datatypes: expected DATE got NUMBER 00932. 00000 –  “inconsistent datatypes: expected %s got %s” *Cause: *Action:

Modify system tables:

When user tries to modify the system table then this error will come.

Using Long Datatype:

As you all know that user can use long datatype once in the table. When user tries to use like operator with long datatype column then this error will occur. Let’s try to produce this error

CREATE TABLE t_long (name long); INSERT INTO t_long VALUES(‘Amit’); commit; select * from t_long where name like ‘Ami%’;

ORA-00932: inconsistent datatypes: expected CHAR got LONG 00932. 00000 –  “inconsistent datatypes: expected %s got %s” *Cause: *Action: Error at Line: 8 Column: 27

Using Long Datatype with upper function (any functions) :

When user tries to use the long datatype with upper function this error will come.

select upper(name) from t_long;

ORA-00932: inconsistent datatypes: expected CHAR got LONG 00932. 00000 –  “inconsistent datatypes: expected %s got %s” *Cause: *Action: Error at Line: 8 Column: 13

Resolution of this error:

I have explained different scenarios of this error. In this section I will try to explain the solution of the error.

Use Conversion functions:

It is proposed to use the conversion functions while using the inconsistent datatype. Means if user is entering the number value to date function then TO_DATE function needs to be used by the user.Just like that user needs to try different Sql conversion functions like TO_NUMBER,TO_CHAR

Query should be :

INSERT INTO T_error VALUES(to_date(’12-MAY-10′,’DD-MON-YYYY’));

Do not try to modify System tables :

Don’t try to modify the system tables as user can not modify oracle in built system tables.

 For Long datatype:

When user is using long datatype following needs to be done :

1.Do not use like operator in SQL with long datatype .

2.Do not use functions with long datatype.

3.Try to convert long datatype column to varchar2 type.

These are above possible solutions of resolving the error ORA-00932.Hope you like this article.

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Stats and R

Top 10 errors in r and how to fix them, introduction, 1. unmatched parentheses, curly braces, square brackets or quotes, 2. using a function that is not installed or loaded, 3. typos in function, variable, dataset, object or package names, 4. missing, incorrect or misspelled arguments in functions, 5. wrong, inappropriate or inconsistent data types, 6. forgetting the + sign in ggplot2, 7. misunderstanding between = and ==, 8. undefined columns selected, 9. problem when importing or using the wrong data file, $ operator is invalid for atomic vectors, object of type ‘closure’ is not subsettable, nas introduced by coercion, removed … rows containing non-finite values (stat_bin()).

If you are just starting with R, you will often encounter errors in your code which prevent it to run. I remember when I was just starting to use R, errors in my code were so frequent that I almost gave up learning this programming language. I even recall that I went back to Excel a few times to finish my analyses because I could not find what was causing the issue.

Fortunately, I forced myself to continue despite the difficulties of the beginning. And today, even if I still encounter errors almost every time I write R code, with experience and practice, it takes less and less time to fix them. If you are also struggling at the beginning, rest assured, it is normal: everyone experiences some frustration when learning a new programming language (and this is the case not only with R).

In this post, I highlight the 10 most common errors in R and how to fix them . Of course, errors depend on your code and your analyses, so it is impossible to cover all of them (and Google does it way better than me). However, I would like to focus on some common syntax mistakes that are frequent when learning R, and which can sometimes take a long time to be fixed before realizing that the solution is right in front of our eyes.

This collection is based on my personal experience and the errors encountered by my students when I teach R. This list being non-exhaustive, feel free to comment (at the end of the post) with errors you often face when using R.

For each error, I provide examples and solutions to fix them. I also mention a couple of warnings (which are, strictly speaking, not errors) at the end of the post.

One rather trivial but still quite frequent error is a missing parenthesis, curly brace, square bracket or quotation mark.

This type of error is applicable to many programming languages. In R, for instance:

These errors are easy to detect when the code is basic, but can become much harder to spot with a more complex code, for instance: 1

Thankfully, if you use RStudio, 2 a closing parenthesis, curly brace, square bracket or quotation mark will automatically be written when you open one.

Bear in mind that when installing a package, you must use (single or double) quotation marks around the package’s name:

Instead, write one of the two following options:

The solution of course is to simply match all opening parentheses, curly braces, square brackets and quotation marks with their closing counterparts:

Also, make sure:

• to correctly place commas:
• you do not mix single and double quotation marks for the same element:

Note that c('Group 1', "Group 2") does not throw an error but for consistency, it is not recommended to mix single and double quotes within the same vector.

If you encounter the following error: “Error in … : could not find function ‘…’”, for example:

it means you are trying to use a function belonging to a package which is not yet installed or loaded.

To solve this error, you have to install the package (if it is not installed yet) and load it with the install.packages() and library() functions, respectively:

If you are not sure about the usage of these two functions, see more details about installing and loading a package in R .

Another common mistake is to misspell a function, a variable, a dataset, an object or a package’s name, for example:

Make sure that you correctly spell all your functions, variables, datasets, objects and packages:

Note that R is case sensitive ; mean() is considered different than Mean() for R!

If you are sure that you correctly spelled an object, a function or a dataset but you still have an error stating that “object ‘…’ is not found”, make sure that you defined your object/function/dataset before calling it!

It often happens that a student asks me to come to his/her computer because he/she runs the exact same code than me, but cannot make it work. Most of the time, if his/her code is indeed exactly the same than mine, he/she simply has not executed a object/function/dataset before running the code which includes that object/function/dataset. In other words, he/she simply tries to use an undefined object or variable.

Remember that writing code in a R script (contrarily to the console) does not mean it is compiled. You actually have to run it (by clicking on the Run button or using the keyboard shortcut) in order the code to be executed and used later. If you are still struggling with this, see the basics of R and RStudio .

Most R functions require arguments. For example, the rnorm() function requires at least the number of observations, specified via the argument n .

Your code will not run if you do not specify compulsory arguments, or if incorrectly specify an argument. Moreover, the result might not be what you expect if you misspell an argument:

The last piece of code does not throw an error, but the result is not what we want.

To solve these errors, make sure to specify at least all compulsory arguments of the function, and the correct ones:

• In rnorm() , it is the standard deviation, sd , which can be specified in addition to the number of observations n (instead of the variance var ).
• Removing NA is done with na.rm (instead of narm ).

If you do not know the arguments of a function by heart, you can always check the documentation with ?function_name or help(function_name) , for example:

There are several data types in R , the main ones being:

You know that some operations and analyses are possible and appropriate only with some specific types of data.

For example, it is not appropriate to compute the mean of a factor or character variable:

Likewise, although it is technically possible, it makes little sense to draw a barplot of a quantitative continuous variable because in most cases, the frequency will be 1 for each value:

(By the way, if your data is not already displayed in the form of a table, do not forget to add table() inside the barplot() function.)

Make sure to use the appropriate operation and type of analysis depending on the variable(s) of interest.

For example:

• for factor variables, it is more appropriate to compute frequencies and/or relative frequencies, and draw barplots
• for quantitative continuous variables, it is more appropriate to compute the mean, median, etc. and draw histograms, boxplots, etc.
• for logical variables, the mean, 3 a frequency table and a barplot are appropriate
• for character variables, word clouds are the most appropriate (unless the variable can be considered as a factor variable because there are not too many different levels)

We now illustrate the examples in R: 4

For the interested reader, see the most common descriptive statistics in R for different types of data.

Note that, as for descriptive statistics, the choice of the statistical test depends on the variable’s type. See this flowchart to help you in selecting the most appropriate statistical test depending on the number of variables and their types.

An error linked to the one mentioned above is inconsistent data type. See it in practice with the following example:

As you can see, vector x is numerical, whereas vector y is in the form of character. This is due to the fact that the last element of y is surrounded with quotation marks (and thus considered as a string instead of a numerical value), so the entire vector takes the character form.

This can happen when you import a dataset into R and one or several elements of a variable are not encoded correctly. This leads to the entire variable to be considered as a character variable by R.

To avoid this, it is a good practice to check the structure of your dataset (with str() ) after importing it to make sure all your variables have the desired format. If not, you can either correct the values in the initial file or change the format in R (with as.numeric() ).

If you just learned to use the ggplot2 package for your visualizations (and I highly recommend it!), a common mistake is to forget the + sign.

You know that a visualization made with ggplot2 is constructed by adding several layers:

For all your graphics with ggplot2, do not forget to add a + sign after each layer except the last one .

Assignment in R can be done in three ways, from the most to the least common:

The second method, that is = , should not be confused with == .

Indeed, assigning an object (with any of the three above methods) is used to save something in R. For example, if we want to save the vector (1, 3, 7) and rename that vector x , we can write:

When executing this piece of code, you will see that the vector x of size 3 appears in the tab “Environment” (the top right panel if you use the default view of RStudio):

From now on, we can use that vector simply by calling it by its name:

By no means, you can assign an object with == :

So you are wondering, when would we need to use == ? Actually, it is used when you want to use an equal sign.

I understand that it may be abstract and confusing at the moment, so let’s suppose the following two scenarios as examples (which are the two most common cases when we use == ):

• we want to check whether an assigned object or variable respects some conditions, and
• we want to subset a dataframe based on one or several conditions.

For these examples, suppose a sample of 5 children:

Let’s now write different pieces of code for these two scenarios to illustrate them:

• We want to check whether the variable Age is equal to the vector (1, 2, 3, 4, 5) :

With this code, we ask whether the first element of the variable Age is equal to 1, the second element of the variable Age is equal to 2, and so on. The answer is of course FALSE , FALSE , TRUE , FALSE and FALSE since only the third child has an age equal to 3 years.

• We want to know which of our 5 sampled children are girls:

The results show that the first, second and fifth children are girls, while the third and fourth children are not girls.

If you write any of these two lines:

You actually overwrite the Age and Gender variables, such that our 5 children will have an age from 1 to 5 (1 year for the first child, up to 5 years for the fifth child) and all of them will be girls.

• Now suppose we want to subset our dataframe based on a condition, namely, we want to extract only the children who are 7 years old:

If you do not want to use the subset function, you can also use square brackets:

As you can see in the previous examples, we do not want to assign anything. Instead, we are asking “is this variable or vector equal to something else?”. For that specific need, we use == .

So to sum up, for technical reasons and in order to distinguish between the two concepts, R uses = for assignments, and == for the equality sign. Make sure to understand the difference between the two to avoid any errors.

If you are used to subset dataframes with square brackets, [] , instead of the subset() or filter() functions, you may have faced the error “Error in [.data.frame(…) : undefined columns selected”.

This occurs when R does not understand the column you want to use while subsetting the dataset.

Considering the same sample of 5 children introduced earlier, the following code will throw an error:

because it does not specify the column dimension.

Remember that dataframes in R have two dimensions:

• the rows (one for each experimental unit), and
• the columns (one for each variable)

and in that particular order (so row first, then column)!

Since dataframes have two dimensions, R expects two dimensions when you call dat[] .

In particular, it expects the first and then the second dimension, separated by a comma :

This code means that we are extracting all rows where Age is equal to 7 (first dimension, i.e. before the comma), for all variables of the dataset (since we did not specify any column after the comma).

For the interested reader, see more ways to subset and manipulate data in R .

Importing a dataset in R can be quite challenging for beginners, mainly due to the misunderstanding about the working directory.

When importing a file, R will not search for the file in all your folders of your computer. Instead, it will look only in one specific folder. If your dataset is not inside that folder, it will result in an error such as “cannot open file ‘…’: No such file or directory”:

To fix this, you must specify the path to the folder where your dataset is located. In other words, you need to tell R in which folder you want it to work, hence the name working directory.

Setting the working directory can be done with the setwd() function or via the “Files” tab in the lower right panel of RStudio:

Alternatively, you can move the dataset in the folder where R is currently working (this can be found with getwd() ). See more details on importing a file into R and about the working directory .

Another related problem is to use the wrong file. This error is different than the previous ones in the sense that you will not encounter an error but your analyses will still be wrong.

It may sound trivial, but make sure to import and use the correct data file! This is particularly the case if you have files for different points in time and which have a common structure (for example weekly or monthly data files with the exact same variables). It happened to me that I reported results for the wrong week (fortunately, without much consequence).

Also, make sure that you actually use all the rows you want to include in your analyses. It happened to me that, in order to test a model (and avoid long computing times), I extracted a random sample of the original dataset, and almost forgot about this sampling when running my final analyses.

It is thus a good practice to remind you to remove sampling and filters after you have tested your code (and before interpreting the final results).

10. Problem when using the $ operator

For the last error of this top 10, I would like to focus on two related errors:

• “$ operator is invalid for atomic vectors”, and
• “object of type ‘closure’ is not subsettable”.

I gather them in one single section because they are linked to each other in the sense that they both involve the $ operator.

To understand this error, we first must recall that an atomic vector is a one -dimensional object (usually created with c() ). This is different than dataframes or matrices which are two -dimensional (i.e., rows form the first dimension and columns correspond to the second dimension).

The error “$ operator is invalid for atomic vectors” occurs when we try to access an element of an atomic vector using the dollar operator ( $ ):

The $ operator cannot be used to extract elements in atomic vectors. Instead, we must use double brackets [[]] notation:

Remember that the $ operator can be used with dataframes, so we can also fix this error by first converting the atomic vector to a dataframe, 5 and then access an element by its name with the $ operator:

Another error (which I must admit is quite obscure and confusing when learning R) is the following: “object of type ‘closure’ is not subsettable”.

This error occurs when we try to subset or access some elements of a function. An example with the well-known mean() function:

In R, we can subset lists, vectors, matrices, dataframes, but not functions. So it throws an error because it is impossible to subset an object of type “closure”, and a function is of that type:

Most of the times, you will not encounter this error when using a basic function such as the mean() function (because it is unlikely that your goal is really to subset a function…).

Indeed, you will most likely face this error when trying to subset a dataset named data , but this dataset is not defined in the environment (because it has not been imported or created properly for instance).

To understand the concept, see the following examples:

So far so good. Now suppose we made a mistake when creating the dataset:

You will notice that a comma is missing between variables x and y . As a result, the dataset named data is not created and thus not defined.

Therefore, if we now try to access the variable x from that dataset data , R will actually try to subset the function named data instead of the dataset named data !

This happens because, I repeat, the dataset data does not exist, so R looks for an object named data and find a function with that name:

Warnings are different than errors in the sense that they alert you about something, but it does not prevent you from running the code. It is a good practice to read these warnings as they may give you valuable information.

There are too many warnings to mention them all, but I would like to focus on two common ones:

• “NAs introduced by coercion”, and
• “Removed … rows containing non-finite values (stat_bin())”.

This warning occurs when you try to convert a vector which includes at least one non-numerical value to a numeric vector:

You do not need to fix it since it is only a warning and not an error. R is simply informing you that at least one element in the initial vector was converted to NA because it could not be converted to a numeric value.

This warning occurs when you use ggplot2 to draw plots. For instance:

Again, as it is a warning you do not need to fix it. It is simply informing you that there are some missing values ( NA ) in the variable of interest and that these missing values are removed to construct the plot.

Thanks for reading.

I hope that this collection of errors prevented you from making some coding mistakes, or that it helped you in debugging your code.

If you still cannot fix your error, I would recommend to read the documentation of the function (if you struggle with a function in particular), or look online for the solution. Bear in mind that if you encounter an error, it is very likely that someone else posted the answer online (Stack Overflow is usually a good resource).

R has a steep learning curve, in particular if you are not familiar with another programming language. Nonetheless, with practice and time, you will make less and less coding errors, but more importantly, you will be more and more proficient in typing the right keywords in search engines, resulting in less time spent looking for the solution.

As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion.

There are 2 mistakes in that piece of code, feel free to try to fix them as an exercise. ↩︎

And I strongly recommend using RStudio and not just R. See the differences here . ↩︎

Note that mean() applied to a logical variable gives the proportion of TRUE . ↩︎

par(mfrow = c(1, 2)) is used to put two plots next to each other. ↩︎

Note that we also need to take the transpose of the vector x in order to have it as 1 row, 3 columns. ↩︎

Related articles

• RStudio addins, or how to make your coding life easier?
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Handling Inconsistent Data

Handling inconsistent data in R is a crucial step in data preprocessing and cleaning. Inconsistent data can include missing values, outliers, errors, and inconsistencies in formats. In R Programming Language Properly addressing these issues ensures that your data is reliable and suitable for analysis. Here are common techniques for handling inconsistent data in R.

Inconsistent Data

Inconsistent data is data that is inconsistent, conflicted, or incompatible within a dataset or across many datasets. Data inconsistencies can occur for a variety of reasons, including mistakes in data entry, data processing, or data integration. These discrepancies might show as disagreements in data element values, formats, or interpretations. Inconsistent data can lead to faulty analysis, untrustworthy outcomes, and data management challenges.

1. Identifying Missing Values

• Missing Data : Missing values in R are typically represented as NA (Not Available) or NaN (Not-a-Number) for numeric data.
• Detection Methods : The is.na() function is commonly used to detect missing values in R. Alternatively, you can use complete.cases() to identify complete cases (rows without any missing values) in a data frame.

2. Handling Missing Values

Imputation: Imputation is the process of filling in missing values. Common imputation methods include mean, median, mode imputation, or more advanced methods like k-Nearest Neighbors (KNN) imputation.

Removal: Rows or columns with excessive missing values can be removed using functions like na.omit() or by filtering based on the presence of missing values.

3. Detecting and Handling Outliers

Outlier Detection: Outliers are extreme values that deviate significantly from the majority of data points. Common methods include the IQR method and the Z-score method.

Handling Outliers: Outliers can be addressed by removing them, transforming the data, or using robust statistical methods that are less sensitive to outliers.

4. Standardizing Data Formats

Data format consistency is essential, especially for date, time, and categorical variables. Use functions like as.Date() or as.factor() to standardize formats.

Date variables should adhere to a consistent format to ensure accurate analysis and visualization.

5. Dealing with Duplicate Data

Duplicate rows can distort analysis results. Use functions like duplicated() to identify and functions like unique() or subsetting to remove duplicates.

Ensure that you understand the criteria for identifying duplicates, as it may depend on specific columns.

6. Handling Inconsistent Categorical Data

Categorical variables may have inconsistent spellings or categories. The recode() function or manual recoding can help standardize categories.

Ensure that categorical variables are correctly encoded as factors for proper analysis.

7. Regular Expressions

Regular expressions (regex) are powerful tools for pattern matching and replacement in text data. The gsub() function is commonly used for global pattern substitution.

Understanding regular expressions allows you to perform advanced text cleaning operations.

8. Data Transformation

Data transformation involves converting or scaling data to meet specific requirements. This can include unit conversions, logarithmic scaling, or standardization of numeric variables.

Transformation may be necessary to make data suitable for modeling or analysis.

9. Data Validation

Data validation involves checking data against predefined rules or criteria. It ensures that data adheres to specific requirements or constraints.

Validation checks can prevent incorrect or inconsistent data from entering your analysis.

10. Documentation

Maintaining detailed documentation of data cleaning steps is crucial. It allows you and others to understand the transformations applied, the reasoning behind them, and ensures reproducibility.

Documentation is essential for transparency and collaboration, particularly in data analysis projects involving multiple team members.

Handling inconsistent data is often an iterative process that involves exploration, cleansing, and validation. The goal is to ensure that your data is accurate, reliable, and suitable for the intended analysis or modeling tasks. Different datasets may require different approaches, and domain knowledge plays a significant role in understanding the context of data inconsistencies.

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DataFrames type assignment inconsistency

I ran into this strange behavior and I’m not sure if it’s a problem with DataFrames or my interpretation with how it works.

When i generate my dataframe with an integer array and a float array, i get two float columns. But if i generate my dataframe with an integer array, float array and string array, i get an integer, float and string column.

Dataframes changes the type of data it assigned to the first column!

I’m still investigating, but I’m wondering if someone understands this behavior.

This is because your intermediate array [x,[1.0,2.0,3.0,4.0,5.0]] uses a Type promotion in its construction. Rather than Vector{Union{Vector{Int64}, Vector{Float64}} it just becomes Vector{Vector{Float64}}

However it can’t do that with a vector of strings, so it just reverts to Vector{Vector{Any}} .

Any[x,[1.0,2.0,3.0,4.0,5.0]] works, as does Vector{<:Number}[x,[1.0,2.0,3.0,4.0,5.0]]

Thanks very much!

Is DataFrames attempting to avoid using Any for some (possibly related to efficiency) reason ?

No, this is outside of DataFrames. Julia evaluates the argument [x,[1.0,2.0,3.0,4.0,5.0]] first, then DataFrames deals with it.

DataFrames has lots of constructors you can use, though. So if that one doesn’t suit your needs there are other ones that might be better suited.

Thx again, that’s very helpful. I’ve just started using DataFrames and one of the things that I found a bit tricky is setting up a dataframe dynamically based on data I’m reading from a file, i.e. without knowing number of columns or column types ahead of time.

I would suggest making an empty DataFrame, df = DataFrame() and then adding columns iteratively with

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zsh: inconsistent error handling in assignments

This is zsh -f :

Why is local messing up error handling?

• variable-substitution
• process-substitution

From the zsh manual regarding the typeset builtin (which local is a special case of):

Unlike parameter assignment statements, typeset 's exit status on an assignment that involves a command substitution does not reflect the exit status of the command substitution. Therefore, to test for an error in a command substitution, separate the declaration of the parameter from its initialization: # WRONG typeset var1=$(exit 1) || echo "Trouble with var1" # RIGHT typeset var1 && var1=$(exit 1) || echo "Trouble with var1"

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Why do I get an "Incompatible types at assignment" error in verilog?

I have the following Verilog code, why do I get the "Incompatible types at assignment"-Error for the assignment "pwmData = 4'b1000;"? I got the error in Active-HDL 9.2.

pwmData[3:0] defines a 4-element array of 1-bit entries.

If you want to create a 4-bit register (this is not the same as a 4x 1-bit array), than the range goes on the other side:

reg [3:0] pwmData;

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