Introduction

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.

1. Unmatched parentheses, curly braces, square brackets or quotes

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:

## missing a closing parenthesis:
mean(c(1, 7, 13)
     
## missing a curly brace before "else":
x <- 7 
if(x > 10) {
  print("x > 10")
 else {
  print("x <= 10")
 }
  
## missing a square bracket:
summary(ggplot2::diamonds[, "price")

## missing a closing quote in 2nd element:
grp <- c("Group 1", "Group 2) 
grp

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

for (i in y) {
  for (j in x) {
    p <- ggboxplot(dat,
      x = colnames(dat[j]), y = colnames(dat[i]),
      color = colnames(dat[j]),
      legend = "none",
      palette = "npg",
      add = "jitter"
    )
    print(
      p + stat_compare_means(aes(label = paste0(..method.., ", p-value = ", ..p.format..),
        method = method1, label.y = max(dat[, i], na.rm = TRUE)
      )
      + stat_compare_means(comparisons = my_comparisons, method = method2, label = "p.format")
    )
  }

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:

## NOT correct:
install.packages(ggplot2)
## Error in install.packages : object 'ggplot2' not found
## correct:
install.packages("ggplot2")

## also correct:
install.packages("ggplot2")

mean(c(1, 7, 13))
## [1] 7
x <- 7
if (x > 10) {
  print("x > 10")
} else {
  print("x <= 10")
}
## [1] "x <= 10"
summary(ggplot2::diamonds[, "price"])
##      price      
##  Min.   :  326  
##  1st Qu.:  950  
##  Median : 2401  
##  Mean   : 3933  
##  3rd Qu.: 5324  
##  Max.   :18823
grp <- c("Group 1", "Group 2")
grp
## [1] "Group 1" "Group 2"

## NOT correct (misplaced comma):
c("Group 1," "Group 2")
## Error: unexpected string constant in "c("Group 1," "Group 2""
## also NOT correct (missing comma):
c("Group 1" "Group 2")
## Error: unexpected string constant in "c("Group 1" "Group 2""
## correct:
c("Group 1", "Group 2")

## NOT correct:
c("Group 1')

## correct:
c("Group 1")

## also correct:
c('Group 1')

2. Using a function that is not installed or loaded

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.

## install package:
install.packages("ggplot2")

## load package:
library(ggplot2)

3. Typos in function, variable, dataset, object or package names

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

## typo in function name:
maen(c(1, 7, 13))
## Error in maen(c(1, 7, 13)) : could not find function "maen"
## typo in variable name:
summary(ggplot2::diamonds[, "detph"])
## Error: Column `detph` doesn't exist
## typo in dataset name:
data <- data.frame(
  x = rnorm(10),
  y = rnorm(10)
)
summary(dta[, 2])
## Error in summary(dta[, 2]) : object 'dta' not found
## typo in object name:
test <- c(1, 7, 13)
mean(tset)
## Error in mean(tset) : object 'tset' not found
## typo in package name:
library("tydiverse")
## Error in library("tydiverse") : there is no package called ‘tydiverse’

mean(c(1, 7, 13))
## [1] 7
summary(ggplot2::diamonds[, "depth"])
##      depth      
##  Min.   :43.00  
##  1st Qu.:61.00  
##  Median :61.80  
##  Mean   :61.75  
##  3rd Qu.:62.50  
##  Max.   :79.00
data <- data.frame(
  x = rnorm(10),
  y = rnorm(10)
)
data[, 2]
##  [1]  1.3048697  2.2866454 -1.3888607 -0.2787888 -0.1333213  0.6359504
##  [7] -0.2842529 -2.6564554 -2.4404669  1.3201133
test <- c(1, 7, 13)
mean(test)
## [1] 7
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

4. Missing, incorrect or misspelled arguments in functions

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:

## missing compulsory argument:
rnorm()
## Error in rnorm() : argument "n" is missing, with no default
## incorrect argument:
rnorm(n = 3, var = 1)
## Error in rnorm(n = 3, var = 1) : unused argument (var = 1)
## misspelled argument:
mean(c(1, 7, 13, NA), narm = TRUE)
## [1] NA

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

rnorm(n = 3, sd = 1)
## [1] -0.3066386 -1.7813084 -0.1719174
mean(c(1, 7, 13, NA), na.rm = TRUE)
## [1] 7

?rnorm()

## or:
help(rnorm)

5. Wrong, inappropriate or inconsistent data types

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

• Numeric
• Character
• Factor
• Logical

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:

gender <- factor(c("female", "female", "male", "female", "male"))

mean(gender)
## Warning in mean.default(gender): argument is not numeric or logical: returning
## NA
## [1] NA

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:

barplot(table(rnorm(10)))

(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.)

## factor:
table(gender)
## gender
## female   male 
##      3      2
prop.table(table(gender))
## gender
## female   male 
##    0.6    0.4
barplot(table(gender))

## quantitative continuous:
x <- rnorm(100)

summary(x)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -2.99309 -0.74143  0.01809 -0.08570  0.58937  2.70189
par(mfrow = c(1, 2)) ## combine two plots
hist(x)
boxplot(x)

## logical:
x <- c(TRUE, FALSE, FALSE, TRUE, TRUE)

mean(x)
## [1] 0.6
table(x)
## x
## FALSE  TRUE 
##     2     3
barplot(table(x))

x <- c(2.4, 3.7, 5.1, 9.8)
class(x)
## [1] "numeric"
y <- c(2.4, 3.7, 5.1, "9.8")
class(y)
## [1] "character"

6. Forgetting the + sign in ggplot2

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:

## load package:
library(ggplot2)

## first layer, the dataset:
ggplot(data = diamonds) +
  ## second layer, the aesthetics:
  aes(x = cut, y = price) +
  ## third layer, the type of plot:
  geom_boxplot() +
  ## add more layers:
  theme_minimal()

7. Misunderstanding between = and ==

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

1. <-
2. =
3. assign()

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:

x <- c(1, 3, 7)

## or:
x = c(1, 3, 7)

## or:
assign("x", c(1, 3, 7))

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:

x
## [1] 1 3 7

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

## NOT correct if we want to assign c(1, 3, 7) to x:
x == c(1, 3, 7)

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 ==):

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

For these examples, suppose a sample of 5 children:

## create dataframe:
dat <- data.frame(
  Name = c("Mary", "Linda", "James", "John", "Patricia"),
  Age = c(7, 10, 3, 9, 7),
  Gender = c("Girl", "Girl", "Boy", "Boy", "Girl")
)

## print dataframe:
dat
##       Name Age Gender
## 1     Mary   7   Girl
## 2    Linda  10   Girl
## 3    James   3    Boy
## 4     John   9    Boy
## 5 Patricia   7   Girl

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):

dat$Age == 1:5
## [1] FALSE FALSE  TRUE FALSE FALSE

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:

dat$Gender == "Girl"
## [1]  TRUE  TRUE FALSE FALSE  TRUE

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:

## this overwrites Age and Gender:
dat$Age = 1:5
dat$Gender = "Girl"

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:

subset(dat, Age == 7)
##       Name Age Gender
## 1     Mary   7   Girl
## 5 Patricia   7   Girl

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

dat[dat$Age == 7, ]
##       Name Age Gender
## 1     Mary   7   Girl
## 5 Patricia   7   Girl

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.

8. Undefined columns selected

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:

dat[dat$Age == 7]
## Error in `[.data.frame`(dat, dat$Age == 7) : undefined columns selected

because it does not specify the column dimension.

dat[dat$Age == 7, ]
##       Name Age Gender
## 1     Mary   7   Girl
## 5 Patricia   7   Girl

9. Problem when importing or using the wrong data file

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 setwd()). 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:

1. “$ operator is invalid for atomic vectors”, and
2. “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.

## define atomic vector:
x <- c(1, 3, 7)

## set names:
names(x) <- LETTERS[1:3]

## print vector:
x
## A B C 
## 1 3 7
## access value of element C:
x$C
## Error in x$C : $ operator is invalid for atomic vectors

x[["C"]]
## [1] 7

## convert atomic vector to dataframe:
x <- as.data.frame(t(x))

## print x:
x
##   A B C
## 1 1 3 7
## access value of element C:
x$C
## [1] 7

mean[1:3]
## Error in mean[1:3] : object of type 'closure' is not subsettable

typeof(mean)
## [1] "closure"

## create dataset:
data <- data.frame(
  x = rnorm(10),
  y = rnorm(10)
)

## print variable x:
data$x
##  [1]  1.12288964  1.43985574 -1.09711377 -0.11731956  1.20149840 -0.46972958
##  [7] -0.05246948 -0.08610730 -0.88767902 -0.44468400

## create dataset (with mistake):
data <- data.frame(x = rnorm(10)
                   y = rnorm(10))

data$x
## Error in data$x : object of type 'closure' is not subsettable

class(data)
## [1] "function"

Warnings

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:

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

x <- c(1, 3, 7, "Emma")

as.numeric(x)
## Warning: NAs introduced by coercion
## [1]  1  3  7 NA

ggplot(airquality) +
  aes(x = Ozone) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 37 rows containing non-finite values (`stat_bin()`).