Using str_detect, str_count, and str_match_all to Analyze Strings in a List
In this article, we will explore how to count and return which strings in a list have been detected using str_detect. We’ll also dive into the str_count and str_match_all functions to achieve our goal.
Introduction to str_detect
str_detect is a powerful function from the stringr package in R that allows us to detect whether a given string contains one or more specified substrings. It returns a logical vector indicating the presence of each substring.
In the example data provided, we have a dataframe df containing food names and their corresponding ingredients. We want to identify which foods contain specific ingredients like “Flour”, “Water”, or “Salt”.
Using str_detect to Identify Key Ingredients
First, let’s use str_detect to create a new column in our dataframe indicating whether each ingredient is present in the list of strings we’re interested in.
library(tidyverse)
strings_to_check <- c("Water", "Salt", "Flour")
df2 <- df %>%
mutate(Key_Ingredient = str_detect(Ingredients, paste(strings_to_check, collapse = "|")))
Understanding str_count
str_count is another useful function from the stringr package that counts the number of occurrences of a specified substring in a given string. It returns the count as an integer.
We can use str_count to count how many times each key ingredient appears in the list of ingredients for each food item.
df2 <- df2 %>%
mutate(Key_Count = str_count(Ingredients, paste(strings_to_check, collapse = "|")))
Understanding str_match_all
Finally, we have str_match, which can be used to match one or more specified substrings in a given string. The function returns a vector of indices where each substring is found.
However, in our case, we want to see if the entire ingredient list matches the pattern of key ingredients. To achieve this, we’ll use str_match_all.
df2 <- df2 %>%
mutate(Key_Used = str_match_all(Ingredients, paste(strings_to_check, collapse = "|")))
Handling Missing Values
It’s also important to note that if an ingredient doesn’t match any of our specified substrings (i.e., it’s not in the list), str_detect, str_count, and str_match_all will return NA. We can handle this by replacing NA values with a specific value, such as “Unknown”, to ensure consistency in our output.
df2 <- df2 %>%
mutate(Key_Used = ifelse(Key_Used == NA, "Unknown", Key_Used))
Example Usage
Let’s create an example dataframe and apply the above steps:
# Create a sample dataframe
data <- data.frame(
Food = c("Appleberry Muffins", "Blue Moon Pancakes", "Crystalized Starfruit",
"Dragonfruit Delight", "Ethereal Eclairs", "Flaming Firefruit",
"Glowing Grapes", "Honeydew Haze", "Iridescent Ice Cream",
"Jellybean Jamboree", "Kiwi Kaleidoscope", "Lunar Lemons",
"Mystic Marshmallows", "Nebula Noodles", "Omega Oranges",
"Phantom Peaches", "Quasar Quince", "Radiant Raspberries",
"Stellar Strawberries", "Twilight Tangerines", "Universal Ugli Fruit",
"Vortex Veggies", "Whirlwind Walnuts", "Xenon Xacuti",
"Yellow Yams of Yore", "Zephyr Zucchini"),
Ingredients = c("Flour, Vanilla Extract, Olive Oil, Milk, Garlic, Carrots, Chicken",
"Baking Powder, Garlic, Eggs, Ice, Sugar, Tofu, Rice",
"Milk, Beef, Tofu, Rice, Salt, Garlic, Mushrooms",
"Rice, Milk, Pork, Yeast, Carrots, Tofu, Mushrooms",
"Pasta, Flour, Water, Mushrooms, Chicken, Vanilla Extract, Yeast",
"Pepper, Yeast, Vanilla Extract, Sugar, Wheat, Olive Oil, Pork",
"Garlic, Nutmeg, Beef, Salt, Tofu, Onions, Baking Powder",
"Salt, Water, Rice, Yeast, Flour, Honey, Mushrooms",
"Water, Salt, Onions, Pasta, Spinach, Pork, Carrots",
"Salt, Eggs, Flour, Baking Powder, Water, Potatoes, Yeast",
"Water, Honey, Salt, Potatoes, Vanilla Extract, Pork, Pasta",
"Salt, Tofu, Olive Oil, Baking Powder, Pork, Vanilla Extract, Cinnamon",
"Salt, Flour, Onions, Water, Chicken, Eggs, Milk",
"Honey, Flour, Pork, Beef, Potatoes, Spinach, Chicken",
"Mushrooms, Water, Salt, Olive Oil, Spinach, Tofu, Potatoes",
"Wheat, Carrots, Baking Powder, Tofu, Eggs, Nutmeg, Potatoes",
"Honey, Tomatoes, Vanilla Extract, Flour, Garlic, Butter, Salt",
"Salt, Yeast, Garlic, Rice, Sugar, Spinach, Baking Powder",
"Flour, Onions, Spinach, Pork, Yeast, Water, Potatoes",
"Potatoes, Eggs, Kale, Beef, Spinach, Vanilla Extract, Milk",
"Cinnamon, Yeast, Potatoes, Flour, Salt, Water, Garlic",
"Milk, Salt, Flour, Olive Oil, Garlic, Water, Spinach",
"Salt, Flour, Beef, Garlic, Milk, Potatoes, Olive Oil",
"Water, Salt, Yeast, Rice, Garlic, Vanilla Extract, Eggs",
"Vanilla Extract, Garlic, Chestnuts, Baking Powder, Tofu, Carrots, Sugar",
"Pork, Honey, Baking Powder, Onions, Sugar, Yeast, Water")
)
# Apply the steps
df2 <- df %>%
mutate(Key_Ingredient = str_detect(Ingredients, paste(strings_to_check, collapse = "|"))) %>%
mutate(Key_Count = ifelse(str_count(Ingredients, paste(strings_to_check, collapse = "|")) == 0, "Unknown", str_count(Ingredients, paste(strings_to_check, collapse = "|"))) %>%
mutate(Key_Used = ifelse(str_match_all(Ingredients, paste(strings_to_check, collapse = "|")) != 0 | is.na(str_match_all(Ingredients, paste(strings_to_check, collapse = "|))), "Unknown", str_match_all(Ingredients, paste(strings_to_check, collapse = "|"))))
Conclusion
In this article, we have explored how to use str_detect, str_count, and str_match_all functions from the stringr package in R to analyze strings in a list. We have also covered how to handle missing values and provide an example usage of these functions.
By following the steps outlined above, you can efficiently analyze your own datasets and gain insights into the presence of specific substrings or patterns within them.
Last modified on 2023-10-13