When to use quotation marks vs backticks

There are some inconsistencies across different functions, sometimes you just need to try them out. Always check that your work did what you expect!

Type	Example	Features	Use
character string	`Liver`	word	quotation marks - single or double
character string	`New York`	phrase	quotation marks - single or double
atypical	`col 1`	has spaces and numbers	`backticks`
atypical	`col.1`	has punctuation	`backticks`
atypical	`1`	just a number	`backticks`
atypical	`1st col`	starts with number	`backticks`

Function	Use
tibble()	Quotation marks okay, use backticks if atypical
rename()	Quotation marks okay, use backticks if atypical
filter()	Quotation marks not tolerated, use backticks if atypical
summarize()	Quotation marks not tolerated, use backticks if atypical
mutate()	Quotation marks okay, use backticks if atypical
readr()	Quotation marks required
tidyselect functions (ex.starts_with())	Quotation marks required
recode()	Quotation marks okay, use backticks if atypical
separate() and unite()	See examples below
case_when()	Quotation marks required
stringr functions (ex. str_detect())	Quotation marks required
joins	Quotation marks okay, use backticks if atypical
pivot_longer() / pivot_wider()	Quotation marks okay, use backticks if atypical
modeling functions	Quotation marks not tolerated, use backticks if atypical

Examples

`tibble()` for naming variables

we suggest that you avoid nonstandard variable names - standard names do not need any quotation marks or backticks around the name within tibble
backticks are typically for nonstandard variable names (aka column names):
- those with spaces name with space
- those with punctuation name!
- those that are just numbers 12
- those that start with numbers 1name
single or double quotation marks are typically used for character strings for the values within the data

For example, in the iris data set:

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Sepal.Length is a column/variable name that would often need backticks, while the Species setosa is character string value for the variable Species, and it would need quotation marks.

Let’s check out how some functions work with this.

In the tibble() function when specifying names we need to use backticks when we have spaces or punctuation or variable names that are just numeric characters (this also works with single quotation marks or double quotation marks, but backticks are more common practice).

This is not required if we have a typical type of name without spaces or punctuation. However if we do use quotation marks on such a name is is not a problem either.

# the preferred method

ex_data <- tibble(
  `Number!` = seq(from = 1, to = 5),
  `Var with space` = c("A", "B", "C", "D", "E"),
  `2022` = sample(seq(from = 1, to = 5), size = 5),
  `2021` = sample(seq(from = 1, to = 5), size = 5),
  typical = seq(from = 1, to = 5),
  `typical2` = seq(from = 1, to = 5)
)

ex_data

## # A tibble: 5 × 6
##   `Number!` `Var with space` `2022` `2021` typical typical2
##       <int> <chr>             <int>  <int>   <int>    <int>
## 1         1 A                     3      5       1        1
## 2         2 B                     5      2       2        2
## 3         3 C                     1      4       3        3
## 4         4 D                     4      1       4        4
## 5         5 E                     2      3       5        5

# this works

ex_data <- tibble(
  "Number!" = seq(from = 1, to = 5),
  "Var with space" = c("A", "B", "C", "D", "E"),
  "2022" = sample(seq(from = 1, to = 5), size = 5),
  "2021" = sample(seq(from = 1, to = 5), size = 5),
  typical = seq(from = 1, to = 5),
  "typical2" = seq(from = 1, to = 5)
)
ex_data

## # A tibble: 5 × 6
##   `Number!` `Var with space` `2022` `2021` typical typical2
##       <int> <chr>             <int>  <int>   <int>    <int>
## 1         1 A                     4      3       1        1
## 2         2 B                     1      5       2        2
## 3         3 C                     2      2       3        3
## 4         4 D                     3      4       4        4
## 5         5 E                     5      1       5        5

`rename()`

We suggest that you avoid nonstandard variable names
No quotation marks or backticks are required for standard names
backticks are typically for nonstandard variable names (aka column names):
- those with spaces name with space
- those with punctuation name!
- those that are just numbers 12
- those that start with numbers 1name
single or double quotation marks are typically used for character strings for the values within the data

No quotation marks are backticks are required for standard names. Underscores are OK for standard names:

iris %>%
  rename(Sepal_Length = Sepal.Length) %>%
  head()

##   Sepal_Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Here is an example with nonstandard names:

ex_data %>%
  rename(Number = `Number!`)

## # A tibble: 5 × 6
##   Number `Var with space` `2022` `2021` typical typical2
##    <int> <chr>             <int>  <int>   <int>    <int>
## 1      1 A                     4      3       1        1
## 2      2 B                     1      5       2        2
## 3      3 C                     2      2       3        3
## 4      4 D                     3      4       4        4
## 5      5 E                     5      1       5        5

ex_data %>%
  rename(Number = "Number!")

## # A tibble: 5 × 6
##   Number `Var with space` `2022` `2021` typical typical2
##    <int> <chr>             <int>  <int>   <int>    <int>
## 1      1 A                     4      3       1        1
## 2      2 B                     1      5       2        2
## 3      3 C                     2      2       3        3
## 4      4 D                     3      4       4        4
## 5      5 E                     5      1       5        5

# not necessary but not problematic to put new typical name in quotation marks
ex_data %>%
  rename("Number" = "Number!")

## # A tibble: 5 × 6
##   Number `Var with space` `2022` `2021` typical typical2
##    <int> <chr>             <int>  <int>   <int>    <int>
## 1      1 A                     4      3       1        1
## 2      2 B                     1      5       2        2
## 3      3 C                     2      2       3        3
## 4      4 D                     3      4       4        4
## 5      5 E                     5      1       5        5

Here is another example…

# This works because Sepal.Length already exists as a column name. However `new name!` needs backticks because it doesn't exist yet and R needs to know what it is (not a not equal to conditional for example), as R could interpret it differently.

iris %>%
  rename(`new name!` = Sepal.Length) %>%
  head()

##   new name! Sepal.Width Petal.Length Petal.Width Species
## 1       5.1         3.5          1.4         0.2  setosa
## 2       4.9         3.0          1.4         0.2  setosa
## 3       4.7         3.2          1.3         0.2  setosa
## 4       4.6         3.1          1.5         0.2  setosa
## 5       5.0         3.6          1.4         0.2  setosa
## 6       5.4         3.9          1.7         0.4  setosa

`filter()`

It is best to avoid using quotation marks for column names!

Even though this is an atypical column name, this will not work like you would expect.

Here we see values less than 5 for Sepal.Length.

filter(iris, "Sepal.Length" > 5) %>% head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Instead stick to just using the column name as it is - filter only works on columns that already exists, so it knows what to look for.

This works!

filter(iris, Sepal.Length > 5) %>% head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          5.4         3.9          1.7         0.4  setosa
## 3          5.4         3.7          1.5         0.2  setosa
## 4          5.8         4.0          1.2         0.2  setosa
## 5          5.7         4.4          1.5         0.4  setosa
## 6          5.4         3.9          1.3         0.4  setosa

`summarize()`

Only backticks will work here. Otherwise, the variable gets interpreted as a character string if we use quotation marks.

ex_data %>%
  summarize(mean = mean(`2022`))

## # A tibble: 1 × 1
##    mean
##   <dbl>
## 1     3

# will not work
ex_data %>%
  summarize(mean = mean("2022"))

## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `mean = mean("2022")`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA

## # A tibble: 1 × 1
##    mean
##   <dbl>
## 1    NA

# will not work
ex_data %>%
  summarize(mean = mean("Number!"))

## Warning: There was 1 warning in `summarize()`.
## ℹ In argument: `mean = mean("Number!")`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA

## # A tibble: 1 × 1
##    mean
##   <dbl>
## 1    NA

`mutate()`

Only these work…

ex_data %>% mutate(`Number!` = `Number!` + 2)

## # A tibble: 5 × 6
##   `Number!` `Var with space` `2022` `2021` typical typical2
##       <dbl> <chr>             <int>  <int>   <int>    <int>
## 1         3 A                     4      3       1        1
## 2         4 B                     1      5       2        2
## 3         5 C                     2      2       3        3
## 4         6 D                     3      4       4        4
## 5         7 E                     5      1       5        5

ex_data %>% mutate("Number!" = `Number!` + 2)

## # A tibble: 5 × 6
##   `Number!` `Var with space` `2022` `2021` typical typical2
##       <dbl> <chr>             <int>  <int>   <int>    <int>
## 1         3 A                     4      3       1        1
## 2         4 B                     1      5       2        2
## 3         5 C                     2      2       3        3
## 4         6 D                     3      4       4        4
## 5         7 E                     5      1       5        5

ex_data %>% mutate(Typical = `Number!` + 2)

## # A tibble: 5 × 7
##   `Number!` `Var with space` `2022` `2021` typical typical2 Typical
##       <int> <chr>             <int>  <int>   <int>    <int>   <dbl>
## 1         1 A                     4      3       1        1       3
## 2         2 B                     1      5       2        2       4
## 3         3 C                     2      2       3        3       5
## 4         4 D                     3      4       4        4       6
## 5         5 E                     5      1       5        5       7

`readr` functions

The readr package expects quotation marks not backticks.

This is true for URLs or paths.

# Use this
vacc <- read_csv(file = "http://jhudatascience.org/intro_to_r/data/vaccinations.csv")

tidyselect helper functions like `contains()`

These require quotation marks.

select(iris, contains("et")) %>% head(n = 2)

##   Petal.Length Petal.Width
## 1          1.4         0.2
## 2          1.4         0.2

select(iris, ends_with("Width")) %>% head(n = 2)

##   Sepal.Width Petal.Width
## 1         3.5         0.2
## 2         3.0         0.2

`recode()`

Don’t need quotation marks for existing values.

ToothGrowth %>%
  mutate(supp = recode(supp,
    VC = "Ascorbic_Acid",
    OJ = "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1  Orange_juice 30
## 2 Ascorbic_Acid 30

However it tolerates this.

ToothGrowth %>%
  mutate(supp = recode(supp,
    "VC" = "Ascorbic_Acid",
    "OJ" = "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1  Orange_juice 30
## 2 Ascorbic_Acid 30

Backticks or quotation marks work for atypical column names.

ToothGrowth %>%
  mutate(supp = paste0(supp, "!")) %>% # making atypical
  mutate(supp = recode(supp,
    `VC!` = "Ascorbic_Acid",
    `OJ!` = "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1 Ascorbic_Acid 30
## 2  Orange_juice 30

ToothGrowth %>%
  mutate(supp = paste0(supp, "!")) %>% # making atypical
  mutate(supp = recode(supp,
    "VC!" = "Ascorbic_Acid",
    "OJ!" = "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1 Ascorbic_Acid 30
## 2  Orange_juice 30

`separate` and `unite`

Here we have a very simple dataset. We are separating values based on the ‘.’ character.

df <- tibble(x = c(NA, "x.y", "x.z", "y.z"))
df

## # A tibble: 4 × 1
##   x    
##   <chr>
## 1 <NA> 
## 2 x.y  
## 3 x.z  
## 4 y.z

When naming the column to separate (x), quotation marks are optional. Use backticks if atypical.

df %>% separate(x, into = c("A", "B"))

## # A tibble: 4 × 2
##   A     B    
##   <chr> <chr>
## 1 <NA>  <NA> 
## 2 x     y    
## 3 x     z    
## 4 y     z

df %>% separate("x", into = c("A", "B"))

## # A tibble: 4 × 2
##   A     B    
##   <chr> <chr>
## 1 <NA>  <NA> 
## 2 x     y    
## 3 x     z    
## 4 y     z

However, note that the values supplied for into and sep arguments must be in quotation marks.

# Will not work
df %>% separate(x, into = c(A, B))

## Error: object 'A' not found

unite is easier. Quotation marks are optional for the new column which you are creating (new_column). Backticks are required if atypical.

df2<- df %>% separate(x, into = c("A", "B"))
df2 %>% unite("new_column", A:B, remove = FALSE)

## # A tibble: 4 × 3
##   new_column A     B    
##   <chr>      <chr> <chr>
## 1 NA_NA      <NA>  <NA> 
## 2 x_y        x     y    
## 3 x_z        x     z    
## 4 y_z        y     z

df2 %>% unite(new_column, A:B, remove = FALSE)

## # A tibble: 4 × 3
##   new_column A     B    
##   <chr>      <chr> <chr>
## 1 NA_NA      <NA>  <NA> 
## 2 x_y        x     y    
## 3 x_z        x     z    
## 4 y_z        y     z

They are also optional for the columns you’re uniting together to form the new column (A:B). Backticks are required if atypical.

df2 %>% unite(new_column, A:B, remove = FALSE)

## # A tibble: 4 × 3
##   new_column A     B    
##   <chr>      <chr> <chr>
## 1 NA_NA      <NA>  <NA> 
## 2 x_y        x     y    
## 3 x_z        x     z    
## 4 y_z        y     z

df2 %>% unite(new_column, "A":"B", remove = FALSE)

## # A tibble: 4 × 3
##   new_column A     B    
##   <chr>      <chr> <chr>
## 1 NA_NA      <NA>  <NA> 
## 2 x_y        x     y    
## 3 x_z        x     z    
## 4 y_z        y     z

`case_when()`

Need quotation marks for conditionals and new values.

ToothGrowth %>%
  mutate(supp = case_when(
    supp == "VC" ~ "Ascorbic_Acid",
    supp == "OJ" ~ "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1 Ascorbic_Acid 30
## 2  Orange_juice 30

Only quotation marks work for atypical values. Backticks do not.

ToothGrowth %>%
  mutate(supp = paste0(supp, "!")) %>% # making atypical
  mutate(supp = case_when(
    supp == "VC!" ~ "Ascorbic_Acid",
    supp == "OJ!" ~ "Orange_juice"
  )) %>%
  count(supp)

##            supp  n
## 1 Ascorbic_Acid 30
## 2  Orange_juice 30

`stringr` functions

When working with strings we need to use quotation marks.

x <- c("cat", "dog", "mouse")
# this will not work:
# x <- c(`cat`, `dog`, `mouse`)

When looking for patterns we need to use quotation marks because we are using it as a character string and quotation marks designate this. Backticks will not work.

x <- c("cat", "dog", "mouse")
# this will not work:
# x <- c(`cat`, `dog`, `mouse`)
str_detect(pattern = "t", string = x)

## [1]  TRUE FALSE FALSE

# this will not work:
# str_detect(pattern = `t`, string = x)

`join` functions

By default, joins don’t use column names. The two supplied data objects are not in quotation marks:

band_members %>% inner_join(band_instruments)

## Joining with `by = join_by(name)`

## # A tibble: 2 × 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass

If you supply the by = argument, you can use no quotation marks, quotation marks, or backticks.

band_members %>% inner_join(band_instruments, by = join_by(name))

## # A tibble: 2 × 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass

band_members %>% inner_join(band_instruments, by = join_by("name"))

## # A tibble: 2 × 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass

band_members %>% inner_join(band_instruments, by = join_by(`name`))

## # A tibble: 2 × 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass

Use backticks if the column name is atypical.

band_instruments_2 <- rename(band_instruments, `1.name` = name)
band_members %>% inner_join(band_instruments_2, by = join_by(name == `1.name`))

## # A tibble: 2 × 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 John  Beatles guitar
## 2 Paul  Beatles bass

`pivot_longer` and `pivot_wider`

These functions are a bit trickier because they have multiple arguments.

It is okay if the columns you are pivoting are without quotation marks, with quotation marks, or with backticks. See religion in the example below. Here, the exclamation point is not part of the column name, but rather indicating negation (all columns except religion).

# These work!
relig_income %>%
  pivot_longer(!religion, names_to = "income", values_to = "count")

## # A tibble: 180 × 3
##    religion income             count
##    <chr>    <chr>              <dbl>
##  1 Agnostic <$10k                 27
##  2 Agnostic $10-20k               34
##  3 Agnostic $20-30k               60
##  4 Agnostic $30-40k               81
##  5 Agnostic $40-50k               76
##  6 Agnostic $50-75k              137
##  7 Agnostic $75-100k             122
##  8 Agnostic $100-150k            109
##  9 Agnostic >150k                 84
## 10 Agnostic Don't know/refused    96
## # ℹ 170 more rows

relig_income %>%
  pivot_longer(!"religion", names_to = "income", values_to = "count")

## # A tibble: 180 × 3
##    religion income             count
##    <chr>    <chr>              <dbl>
##  1 Agnostic <$10k                 27
##  2 Agnostic $10-20k               34
##  3 Agnostic $20-30k               60
##  4 Agnostic $30-40k               81
##  5 Agnostic $40-50k               76
##  6 Agnostic $50-75k              137
##  7 Agnostic $75-100k             122
##  8 Agnostic $100-150k            109
##  9 Agnostic >150k                 84
## 10 Agnostic Don't know/refused    96
## # ℹ 170 more rows

relig_income %>%
  pivot_longer(!`religion`, names_to = "income", values_to = "count")

## # A tibble: 180 × 3
##    religion income             count
##    <chr>    <chr>              <dbl>
##  1 Agnostic <$10k                 27
##  2 Agnostic $10-20k               34
##  3 Agnostic $20-30k               60
##  4 Agnostic $30-40k               81
##  5 Agnostic $40-50k               76
##  6 Agnostic $50-75k              137
##  7 Agnostic $75-100k             122
##  8 Agnostic $100-150k            109
##  9 Agnostic >150k                 84
## 10 Agnostic Don't know/refused    96
## # ℹ 170 more rows

# Use backticks if atypical
relig_income %>%
  pivot_longer(c(`$10-20k`, `<$10k`), names_to = "income", values_to = "count")

## # A tibble: 36 × 11
##    religion       `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k` `$100-150k`
##    <chr>              <dbl>     <dbl>     <dbl>     <dbl>      <dbl>       <dbl>
##  1 Agnostic              60        81        76       137        122         109
##  2 Agnostic              60        81        76       137        122         109
##  3 Atheist               37        52        35        70         73          59
##  4 Atheist               37        52        35        70         73          59
##  5 Buddhist              30        34        33        58         62          39
##  6 Buddhist              30        34        33        58         62          39
##  7 Catholic             732       670       638      1116        949         792
##  8 Catholic             732       670       638      1116        949         792
##  9 Don’t know/re…        15        11        10        35         21          17
## 10 Don’t know/re…        15        11        10        35         21          17
## # ℹ 26 more rows
## # ℹ 4 more variables: `>150k` <dbl>, `Don't know/refused` <dbl>, income <chr>,
## #   count <dbl>

However, the new column names you are providing must be in quotation marks. Here, these are supplied in the names_to and values to arguments. Pay attention to “income” and “count”.

# This works
relig_income %>%
  pivot_longer(!religion, names_to = "income", values_to = "count")

## # A tibble: 180 × 3
##    religion income             count
##    <chr>    <chr>              <dbl>
##  1 Agnostic <$10k                 27
##  2 Agnostic $10-20k               34
##  3 Agnostic $20-30k               60
##  4 Agnostic $30-40k               81
##  5 Agnostic $40-50k               76
##  6 Agnostic $50-75k              137
##  7 Agnostic $75-100k             122
##  8 Agnostic $100-150k            109
##  9 Agnostic >150k                 84
## 10 Agnostic Don't know/refused    96
## # ℹ 170 more rows

# will not work
relig_income %>%
  pivot_longer(!religion, names_to = income, values_to = count)

## Error: object 'income' not found

When using pivot_wider(), quotation marks are optional for the names_from and values_from arguments.

# These work!
fish_encounters %>%
  pivot_wider(names_from = station, values_from = seen)

## # A tibble: 19 × 12
##    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE   MAW
##    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> <int>
##  1 4842        1     1      1     1       1     1     1     1     1     1     1
##  2 4843        1     1      1     1       1     1     1     1     1     1     1
##  3 4844        1     1      1     1       1     1     1     1     1     1     1
##  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA    NA
##  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA
##  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA    NA
##  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
##  8 4850        1     1     NA     1       1     1     1    NA    NA    NA    NA
##  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 11 4855        1     1      1     1       1    NA    NA    NA    NA    NA    NA
## 12 4857        1     1      1     1       1     1     1     1     1    NA    NA
## 13 4858        1     1      1     1       1     1     1     1     1     1     1
## 14 4859        1     1      1     1       1    NA    NA    NA    NA    NA    NA
## 15 4861        1     1      1     1       1     1     1     1     1     1     1
## 16 4862        1     1      1     1       1     1     1     1     1    NA    NA
## 17 4863        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 18 4864        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 19 4865        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA

fish_encounters %>%
  pivot_wider(names_from = "station", values_from = "seen")

## # A tibble: 19 × 12
##    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE   MAW
##    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> <int>
##  1 4842        1     1      1     1       1     1     1     1     1     1     1
##  2 4843        1     1      1     1       1     1     1     1     1     1     1
##  3 4844        1     1      1     1       1     1     1     1     1     1     1
##  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA    NA
##  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA
##  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA    NA
##  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
##  8 4850        1     1     NA     1       1     1     1    NA    NA    NA    NA
##  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 11 4855        1     1      1     1       1    NA    NA    NA    NA    NA    NA
## 12 4857        1     1      1     1       1     1     1     1     1    NA    NA
## 13 4858        1     1      1     1       1     1     1     1     1     1     1
## 14 4859        1     1      1     1       1    NA    NA    NA    NA    NA    NA
## 15 4861        1     1      1     1       1     1     1     1     1     1     1
## 16 4862        1     1      1     1       1     1     1     1     1    NA    NA
## 17 4863        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 18 4864        1     1     NA    NA      NA    NA    NA    NA    NA    NA    NA
## 19 4865        1     1      1    NA      NA    NA    NA    NA    NA    NA    NA

`glm()`

You don’t need quotation marks for variables when modeling with glm().

car_data <- mtcars
fit_cars <- glm(mpg ~ cyl + disp + hp + wt * gear, data = car_data)

Use backticks if there is an atypical name.

car_data <- rename(car_data, `mpg!` = mpg)
fit_cars <- glm(`mpg!` ~ cyl + disp + hp + wt * gear, data = car_data)

Do not use quotation marks.

# will not work
fit_cars <- glm("mpg!" ~ cyl + disp + hp + wt * gear, data = car_data)

## Error in terms.formula(formula, data = data): invalid term in model formula

When to use quotation marks vs backticks

In general

Column names

Examples

`tibble()` for naming variables

`rename()`

`filter()`

`summarize()`

`mutate()`

`readr` functions

tidyselect helper functions like `contains()`

`recode()`

`separate` and `unite`

`case_when()`

`stringr` functions

`join` functions

`pivot_longer` and `pivot_wider`

`glm()`

When to use quotation marks vs backticks

In general

Column names

Examples

tibble() for naming variables

rename()

filter()

summarize()

mutate()

readr functions

tidyselect helper functions like contains()

recode()

separate and unite

case_when()

stringr functions

join functions

pivot_longer and pivot_wider

glm()

`tibble()` for naming variables

`rename()`

`filter()`

`summarize()`

`mutate()`

`readr` functions

tidyselect helper functions like `contains()`

`recode()`

`separate` and `unite`

`case_when()`

`stringr` functions

`join` functions

`pivot_longer` and `pivot_wider`

`glm()`