Understanding the Problem and Identifying the Solution

The problem presented in the question is related to combining data frames that share the same rows. The solution provided involves using the Reduce function from the purrr package, which applies a function to all items in a list. In this case, the function used is merge, which combines two data frames based on their common columns.

The problem arises when trying to merge multiple data frames that share the same rows but have different column names or structures. The solution provided suggests adding an id column before merging the data frames. This id column serves as a key for identifying and matching rows across different data frames.

Breaking Down the Solution

Step 1: Creating the Data Frames

The first step in solving this problem is to create the individual data frames that will be merged later on. In this case, we have three data frames (R1, R2, and All) each containing measurements of a certain value.

# Create the data frames
files <- dir("mypath", recursive = TRUE, full.names = TRUE, pattern=".tif$")
All <- list()
for (file in files) {
  Fun <- function(f) {
    df <- stack(f)
    return(df)
  }
  All[[file]] <- Fun(file)
}

Step 2: Merging the Data Frames

The next step is to merge these individual data frames into a single frame that includes all measurements. We use the Reduce function from the purrr package, which applies the merge function to all items in the list of data frames.

# Merge the data frames
res <- Reduce(function(...) {
  merge(..., all = TRUE)
}, Map(`[&lt;-`, All, "id", value = substring(names(All), 2)))

Step 3: Ordering by id

The output of this merging process can be messy because it does not preserve the original order of rows with identical values in the id column. We can fix this issue by ordering the merged data frame according to the id values.

# Order the merged data frame
res[order(res$id), -3]

Understanding the Data Frames

Let’s dive deeper into understanding what each data frame represents and how they are structured:

• All: This data frame contains all the measurements for a given value. It includes columns x, y, R1, R2 (for some values), which correspond to different identifiers.

Data Frame R1

The first data frame (R1) has two columns: x and y. The values in these columns represent measurements of a certain value. There is also an additional column R1, which corresponds to the same identifier but for a specific measurement.

# Create DataFrame R1
dfR1 <- structure(list(
  x = c(696060, 696090, 696120, 696150, 
        696180, 696210, 696240, 696270, 
        696300, 696330),
  y = c(-3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450),
  R1 = c(66, 71, 69, 65, 67, 68, 67, 68, 69, 0)
))

• All: This data frame contains all the measurements for a given value. It includes columns x, y, and additional columns corresponding to different identifiers.

# Create DataFrame All
dfAll <- structure(list(
  x = c(696060, 696090, 696120, 696150, 
        696180, 696210, 696240, 696270, 
        696300, 696330),
  y = c(-3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450),
  R1 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)
))

• R2: This data frame has similar structure to R1 but includes an additional column R2, which corresponds to a different identifier.

# Create DataFrame R2
dfR2 <- structure(list(
  x = c(696060, 696090, 696120, 696150, 
        696180, 696210, 696240, 696270, 
        696300, 696330),
  y = c(-3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450, -3327450, -3327450, 
        -3327450, -3327450),
  R2 = c(66, 71, 69, 65, 67, 68, 67, 68, 69, 0)
))

Conclusion

By following the steps outlined in this solution, you can merge multiple data frames that share the same rows but have different column names or structures. The key is to identify a common identifier (in this case, id) that corresponds to each measurement, and then use this identifier to match rows across different data frames.

Last modified on 2024-07-18