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Error in rbind(deparse.level …) numbers of columns of arguments do not match, This issue happens when you try to row-bind two or more data frames together in R using the rbind() function, but the data frames don’t all have the same amount of columns.

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This guide explains in detail how to resolve this issue.

## How to reproduce the Error in rbind(deparse.level …) numbers of columns of arguments do not match?

Suppose we have the two R data frames shown below:

First will create a data frame

```df1 <- data.frame(x=c(11, 14, 14, 15, 23),
y=c(84, 94, 72, 98, 120))
df1
x   y
1 11  84
2 14  94
3 14  72
4 15  98
5 23 120```

Now we  can create a second data frame

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```df2 <- data.frame(x=c(22, 22, 32, 35, 37),
y=c(33, 62, 52, 10, 10),
z=c(22, 37, 47, 58, 85))
df2
x  y  z
1 22 33 22
2 22 62 37
3 32 52 47
4 35 10 58
5 37 10 85```

Now imagine that we try to row-bind these two data frames into a single data frame using rbind:

Let’s try to row-bind the two data frames together

```rbind(df1, df2)
Error in rbind(deparse.level, ...) :  numbers of columns of arguments do not match```

The two data frames don’t have the same number of columns, thus we get an error.

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## How to correct the issue

There are two solutions to this issue:

### Method 1: Using rbind on Common Columns

Using the intersect() method to identify the shared column names between the data frames and then row-binding the data frames solely to those columns is one technique to solve this issue.

Now we can find the common column names

`common <- intersect(colnames(df1), colnames(df2))`

Let’s row-bind only on common column names

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`df3 <- rbind(df1[common], df2[common])`

Let’s view the result

```df3
x   y
1  11  84
2  14  94
3  14  72
4  15  98
5  23 120
6  22  33
7  22  62
8  32  52
9  35  10
10 37  10```

### Method 2: Use bind_rows() from dplyr

Using the bind_rows() function from the dplyr package, which automatically fills in NA values for column names that do not match, is another way to solve this issue:

`library(dplyr) `

Let’s bind together both data frames

`df3 <- bind_rows(df1, df2) `

Now we can view the result

```df3
x   y  z
1  11  84 NA
2  14  94 NA
3  14  72 NA
4  15  98 NA
5  23 120 NA
6  22  33 22
7  22  62 37
8  32  52 47
9  35  10 58
10 37  10 85```

Due to the absence of column z in this data frame, NA values have been filled in for the values from df1.

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