Programming with dplyr: Part 03, working with strings

Reading time ~6 minutes

More on programming with dplyr: converting quosures to strings

In this post, we have programmed a simple function using dplyr’s programming capabilities based on tidyeval; for more intro to programming with dplyr, see here.

In this post, we’ll go one step further and programm a function where a quosure will be turned to a string. Why this? Because quite a number of functions out there except strings as input parameters.



Data example

Say, we have a string where we search for a word stem. However this stem does not appear in its “stem” form, but always with some suffixes. Let our stem be “spd” (the name of the German Social-Democratic party), and (for simplicity), we’ll assume two “instances” of “spd” that occurr with suffix, ie., “spdbt” and “spdde”. (I was just working on a text mining on Tweets of German politiicians, hence the example).

data <- c("spdde", "sdf", "sdf", "fdds", "spdde", "dsf", "spdbt", "df") %>% as_tibble
stem <- "spd"

Task 01: Extract the “spd” stem out of the data

Non-programmatically, ie., interactively, this is quite straight-forward. Some knowledge of Regex is helpful to render the task a bit more general:

data %>% 
  mutate(is_instance = str_detect(string = value, pattern = "spd\\w+"),
         instance = str_match(value, "spd\\w+"))
## # A tibble: 8 x 3
##   value is_instance instance
##   <chr>       <lgl>    <chr>
## 1 spdde        TRUE    spdde
## 2   sdf       FALSE     <NA>
## 3   sdf       FALSE     <NA>
## 4  fdds       FALSE     <NA>
## 5 spdde        TRUE    spdde
## 6   dsf       FALSE     <NA>
## 7 spdbt        TRUE    spdbt
## 8    df       FALSE     <NA>

\\w means “find a word-character” (ie., letter or digit), and + means that at least 1 hit is expected. pattern is the pattern to be looked for and string defines the string where to search for the pattern.

Task 02: Make it a function

Obviously, a function is much more general. Say we have 10 parties we would like to warp through; it becomes tedious to repeat that code many times. We will want a function. How to do that with dplyr?

Let’s define a function with input parameters df for the name of the dataframe, col for the name of the column where the stemming is performend, and stem, the term to be stemmed.

stemm_col <- function(df, col, stem){
  col <- enquo(col)
  col_name <- quo_name(enquo(col))
  stem_name <- quo_name(enquo(stem))

  df %>% 
    mutate(stemmed = str_extract(string = !!col,
                                pattern = paste0(stem_name,"\\w+"))) -> output

See whether it runs:

stemm_col(df = data, col = value, stem = "spd")
## # A tibble: 8 x 2
##   value stemmed
##   <chr>   <chr>
## 1 spdde   spdde
## 2   sdf    <NA>
## 3   sdf    <NA>
## 4  fdds    <NA>
## 5 spdde   spdde
## 6   dsf    <NA>
## 7 spdbt   spdbt
## 8    df    <NA>

Bit by bit explanation

  • col <- enquo(col) – Take the parameter col, and just remember, R, that it is an expression, in this case, the parameter of a function, denoting a column.

  • col_name <- quo_name(enquo(col)) – Now turn col (an expression) to a string, because the function further down (str_extract) expects a string.

  • str_extract(string = !!col,... – Hey R, when you extract the strings, you need to know which string to search. OK, take col which is an expression, and now evaluate it (indicated by !!), in this case, understand that it’s a column. Now go get the values.

Main idea

When working with NSE, it is important to distinguish between expression, strings and evaluation. Using NSE, we can look at the parameters of a function, and grap the parameters before they are evaluated. That’s why the parameters need not be well-behaved, importantly, they do not need to have quotation marks, which saves typing for the user.

From the perspective of the R-interpreter (the machine), the steps of action looks more or less like this:

  1. Look at the parameters of the function

  2. Save the parameters not as strings, not as variables, but as expressions (via enquo)

  3. Convert the expression to a string (via quo_name) or, eventually,

  4. Evaluate it, ie., unquote (let the function to its job) via !! (called bang-bang) or via UQ.

Crashkurs Datenanalyse mit R

# Willkommen zum R-CrashkursNicht jeder liebt Datenanalyse und Statistik... in gleichem Maße! Das ist zumindest meine Erfahrung aus dem U...… Continue reading

Different ways to count NAs over multiple columns

Published on September 08, 2017

Different ways to present summaries in ggplot2

Published on September 08, 2017