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Pass variables by name into a function that calls dplyr?

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1

I'm trying to create a function that will take 2 variables from a dataset, and map their distinct values side by side, after which it will write the out to a csv file. I'll be using dplyr's distinct function for getting the unique values.

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct(var1, var2)
 write.csv(df_distinct, 'var1.csv')


map_table(iris, Species, Petal.Width)

1) map_table(iris, Species, Petal.Width) doesn't produce what I want. It should produce 27 rows of data, instead I'm getting 150 rows of data.

2) How can I name the csv file after the input of var1?
So if var1 = 'Sepal.Length', the name of the file should be 'Sepal.Length.csv'

r function dplyr parameter-passing non-standard-evaluation

share|improve this question

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  non-standard evaluation (NSE) is one well-known hiccup when using dplyr. Here's [one related question from back in 2014](how can i tell select() in dplyr that the string it is seeing is a column name in a data frame); but the solution here is cleaner, so this should probably not be closed-as-duplicate.
  
  – smci
  Mar 9 at 2:24
  

  
  
  
  

add a comment | 

1

I'm trying to create a function that will take 2 variables from a dataset, and map their distinct values side by side, after which it will write the out to a csv file. I'll be using dplyr's distinct function for getting the unique values.

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct(var1, var2)
 write.csv(df_distinct, 'var1.csv')


map_table(iris, Species, Petal.Width)

1) map_table(iris, Species, Petal.Width) doesn't produce what I want. It should produce 27 rows of data, instead I'm getting 150 rows of data.

2) How can I name the csv file after the input of var1?
So if var1 = 'Sepal.Length', the name of the file should be 'Sepal.Length.csv'

r function dplyr parameter-passing non-standard-evaluation

share|improve this question

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  non-standard evaluation (NSE) is one well-known hiccup when using dplyr. Here's [one related question from back in 2014](how can i tell select() in dplyr that the string it is seeing is a column name in a data frame); but the solution here is cleaner, so this should probably not be closed-as-duplicate.
  
  – smci
  Mar 9 at 2:24
  

  
  
  
  

add a comment | 

I'm trying to create a function that will take 2 variables from a dataset, and map their distinct values side by side, after which it will write the out to a csv file. I'll be using dplyr's distinct function for getting the unique values.

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct(var1, var2)
 write.csv(df_distinct, 'var1.csv')


map_table(iris, Species, Petal.Width)

1) map_table(iris, Species, Petal.Width) doesn't produce what I want. It should produce 27 rows of data, instead I'm getting 150 rows of data.

2) How can I name the csv file after the input of var1?
So if var1 = 'Sepal.Length', the name of the file should be 'Sepal.Length.csv'

r function dplyr parameter-passing non-standard-evaluation

share|improve this question

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct(var1, var2)
 write.csv(df_distinct, 'var1.csv')


map_table(iris, Species, Petal.Width)

share|improve this question

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

share|improve this question

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

edited Mar 9 at 2:08

smci

15.4k678109

edited Mar 9 at 2:08

smci

15.4k678109

asked Mar 8 at 6:48

spidermarnspidermarn

664

asked Mar 8 at 6:48

spidermarnspidermarn

664

3 Answers
3

active

oldest

votes

2

If you want to pass the col names without quotes, you need to use non-standard evaluation. (More here)

deparse(substitute()) will get you the name for the file output.

library(dplyr)

map_table <- function(df, var1, var2)

 file_name <- paste0(deparse(substitute(var1)), ".csv") # file name

 var1 <- enquo(var1) # non-standard eval
 var2 <- enquo(var2) # equo() caputures the expression passed, ie: Species

 df_distinct <- df %>% 
 distinct(!!var1, !!var2) # non-standard eval, !! tells dplyr to use Species

 write.csv(df_distinct, file = file_name)



map_table(iris, Species, Petal.Width)

share|improve this answer

answered Mar 8 at 9:05

RLaveRLave

5,21911226

add a comment | 

0

You're trying to pass the columns as objects. Try passing their names instead and then use a select helper:

map_table <- function(df, var1, var2)
 df_distinct <- df %>% select(one_of(c(var1, var2)))%>%
 distinct()
 write.csv(df_distinct, 'var1.csv')


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

answered Mar 8 at 7:04

RohitRohit

902412

add a comment | 

0

1) Ok the answer is to use distinct_ instead of distinct. And the variables being called need to be apostrophized.
2) use apply function to concatenate values/string formatting, and file =

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct_(var1, var2)
 write.csv(df_distinct, file = paste(var1,'.csv'))


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

edited Mar 8 at 7:40

answered Mar 8 at 7:18

spidermarnspidermarn

664

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  There's also a solution without the quotes in the function call.
  
  – RLave
  Mar 8 at 9:09
  
  

  
  
  
  

add a comment | 

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2

If you want to pass the col names without quotes, you need to use non-standard evaluation. (More here)

deparse(substitute()) will get you the name for the file output.

library(dplyr)

map_table <- function(df, var1, var2)

 file_name <- paste0(deparse(substitute(var1)), ".csv") # file name

 var1 <- enquo(var1) # non-standard eval
 var2 <- enquo(var2) # equo() caputures the expression passed, ie: Species

 df_distinct <- df %>% 
 distinct(!!var1, !!var2) # non-standard eval, !! tells dplyr to use Species

 write.csv(df_distinct, file = file_name)



map_table(iris, Species, Petal.Width)

share|improve this answer

answered Mar 8 at 9:05

RLaveRLave

5,21911226

add a comment | 

2

If you want to pass the col names without quotes, you need to use non-standard evaluation. (More here)

deparse(substitute()) will get you the name for the file output.

library(dplyr)

map_table <- function(df, var1, var2)

 file_name <- paste0(deparse(substitute(var1)), ".csv") # file name

 var1 <- enquo(var1) # non-standard eval
 var2 <- enquo(var2) # equo() caputures the expression passed, ie: Species

 df_distinct <- df %>% 
 distinct(!!var1, !!var2) # non-standard eval, !! tells dplyr to use Species

 write.csv(df_distinct, file = file_name)



map_table(iris, Species, Petal.Width)

share|improve this answer

answered Mar 8 at 9:05

RLaveRLave

5,21911226

add a comment | 

If you want to pass the col names without quotes, you need to use non-standard evaluation. (More here)

deparse(substitute()) will get you the name for the file output.

library(dplyr)

map_table <- function(df, var1, var2)

 file_name <- paste0(deparse(substitute(var1)), ".csv") # file name

 var1 <- enquo(var1) # non-standard eval
 var2 <- enquo(var2) # equo() caputures the expression passed, ie: Species

 df_distinct <- df %>% 
 distinct(!!var1, !!var2) # non-standard eval, !! tells dplyr to use Species

 write.csv(df_distinct, file = file_name)



map_table(iris, Species, Petal.Width)

share|improve this answer

answered Mar 8 at 9:05

RLaveRLave

5,21911226

library(dplyr)

map_table <- function(df, var1, var2)

 file_name <- paste0(deparse(substitute(var1)), ".csv") # file name

 var1 <- enquo(var1) # non-standard eval
 var2 <- enquo(var2) # equo() caputures the expression passed, ie: Species

 df_distinct <- df %>% 
 distinct(!!var1, !!var2) # non-standard eval, !! tells dplyr to use Species

 write.csv(df_distinct, file = file_name)



map_table(iris, Species, Petal.Width)

share|improve this answer

answered Mar 8 at 9:05

RLaveRLave

5,21911226

answered Mar 8 at 9:05

RLaveRLave

5,21911226

answered Mar 8 at 9:05

RLaveRLave

5,21911226

0

You're trying to pass the columns as objects. Try passing their names instead and then use a select helper:

map_table <- function(df, var1, var2)
 df_distinct <- df %>% select(one_of(c(var1, var2)))%>%
 distinct()
 write.csv(df_distinct, 'var1.csv')


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

answered Mar 8 at 7:04

RohitRohit

902412

add a comment | 

0

You're trying to pass the columns as objects. Try passing their names instead and then use a select helper:

map_table <- function(df, var1, var2)
 df_distinct <- df %>% select(one_of(c(var1, var2)))%>%
 distinct()
 write.csv(df_distinct, 'var1.csv')


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

answered Mar 8 at 7:04

RohitRohit

902412

add a comment | 

You're trying to pass the columns as objects. Try passing their names instead and then use a select helper:

map_table <- function(df, var1, var2)
 df_distinct <- df %>% select(one_of(c(var1, var2)))%>%
 distinct()
 write.csv(df_distinct, 'var1.csv')


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

answered Mar 8 at 7:04

RohitRohit

902412

map_table <- function(df, var1, var2)
 df_distinct <- df %>% select(one_of(c(var1, var2)))%>%
 distinct()
 write.csv(df_distinct, 'var1.csv')


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

answered Mar 8 at 7:04

RohitRohit

902412

answered Mar 8 at 7:04

RohitRohit

902412

answered Mar 8 at 7:04

RohitRohit

902412

0

1) Ok the answer is to use distinct_ instead of distinct. And the variables being called need to be apostrophized.
2) use apply function to concatenate values/string formatting, and file =

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct_(var1, var2)
 write.csv(df_distinct, file = paste(var1,'.csv'))


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

edited Mar 8 at 7:40

answered Mar 8 at 7:18

spidermarnspidermarn

664

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  There's also a solution without the quotes in the function call.
  
  – RLave
  Mar 8 at 9:09
  
  

  
  
  
  

add a comment | 

0

1) Ok the answer is to use distinct_ instead of distinct. And the variables being called need to be apostrophized.
2) use apply function to concatenate values/string formatting, and file =

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct_(var1, var2)
 write.csv(df_distinct, file = paste(var1,'.csv'))


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

edited Mar 8 at 7:40

answered Mar 8 at 7:18

spidermarnspidermarn

664

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  There's also a solution without the quotes in the function call.
  
  – RLave
  Mar 8 at 9:09
  
  

  
  
  
  

add a comment | 

1) Ok the answer is to use distinct_ instead of distinct. And the variables being called need to be apostrophized.
2) use apply function to concatenate values/string formatting, and file =

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct_(var1, var2)
 write.csv(df_distinct, file = paste(var1,'.csv'))


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

edited Mar 8 at 7:40

answered Mar 8 at 7:18

spidermarnspidermarn

664

map_table <- function(df, var1, var2)
 df_distinct <- df %>% distinct_(var1, var2)
 write.csv(df_distinct, file = paste(var1,'.csv'))


map_table(iris, 'Species', 'Petal.Width')

share|improve this answer

edited Mar 8 at 7:40

answered Mar 8 at 7:18

spidermarnspidermarn

664

answered Mar 8 at 7:18

spidermarnspidermarn

664

answered Mar 8 at 7:18

spidermarnspidermarn

664