Replacing String Mismatches with Identical and Correct Names

In this article, we will explore a common problem in data analysis: replacing string mismatches with identical and correct names. We’ll use a real-world example to illustrate the issue and provide a step-by-step solution using R.

The Issue at Hand

Suppose you are working with a dataset of species received from different sources. The first column contains the names of species, but the names from the same species are not identical due to differences in formatting or conventions used by the source. For instance, consider a small fraction of the original list:

In this example, the correct name for each position should be used instead of the mismatched ones. The next correct name is for positions D1, F3, and G1.

Our goal is to replace these mismatches with the identical and correct names.

Step 1: Data Preparation

To begin, we need to prepare our data in a suitable format for analysis. We’ll use R’s dplyr, stringr, and tidyr packages to achieve this.

pacman::p_load(dplyr, stringr, tidyr)

We load the necessary libraries and create a new dataset called dat.

library(dplyr)
library(stringr)
library(tidyr)

# Create the dataset
data("iris", package = "datasets")  # Not used in this example

dat <- data.frame(Species = c("A. thaliana","Arabidopsis.thaliana","ARABIDOPSIS Thaliana",
                               "Pisum.sativum","P. Sativum","PISUM SATIVUM",
                               "B. Vulgaris", "BETA VULGARIS", "Beta.vulgaris",
                               "Secale.cereale", "S. CEREALE", "SECALE CEREALE"),
                  Position = c("C1", "C2", "C3", "D1", "D2", "D3",
                               "F1", "F2", "F3", "G1", "G2", "G3"))

# Convert to data.frame
dat <- as.data.frame(dat)

Step 2: Extracting the Common Name Pattern

Next, we’ll extract the common name pattern from each species using regular expressions. The str_extract function from the stringr package is used for this purpose.

# Extract the common name pattern
dat %>% 
  mutate(Species2 = str_extract(Species, pattern = "(^[A-Z]{1}[a-z]+\\.[a-z]+$)", group = 1),
         P = str_extract(Species, pattern ="\\D"))

This step creates two new columns in our dataset: Species2 and P. The Species2 column contains the common name pattern for each species, while the P column contains any non-alphanumeric characters (digits or special characters) that were present in the original species names.

Step 3: Grouping by Position

We’ll group our data by position to ensure that we’re applying the correct correction for each one.

# Group by position and fill Species2 down-up
dat %>% 
  group_by(Position) %>% 
  fill(Species2, .direction = "downup") %>% 
  ungroup() %>% 
  select(-P)

This step groups our data by Position, fills the Species2 column from the row above for each position if its value is not already present in the current group (the .direction = "downup" argument ensures this), and then ungroups the data.

Step 4: Selecting the Final Result

Finally, we’ll select only the final corrected species name column.

# Final result
final_dat <- dat %>% 
  select(Species2)

The resulting dataset contains the correct names for each position:

Conclusion

In this article, we explored the problem of replacing string mismatches with identical and correct names in a dataset. We used R’s dplyr, stringr, and tidyr packages to achieve this. The key steps involved extracting the common name pattern from each species using regular expressions, grouping by position, filling the corrected value down-up for each group, and finally selecting only the final result.

By following these steps, you should be able to correctly replace string mismatches with identical and correct names in your dataset.

Last modified on 2024-04-02