The code provided is a R script that loads and processes data from a CSV file named ’test.csv’. The data appears to be related to sales of different products.

Here’s a breakdown of what the code does:

1. It loads the necessary libraries, including readr for reading the CSV file and dplyr for data manipulation.
2. It reads the CSV file into a data frame using read_csv.
3. It applies the mutate function from dplyr to the data frame, creating new columns by concatenating existing column names with _x, _y, or other suffixes. For example, it creates a new column named ‘OTS_SM1_x1’ by concatenating ‘OTS_SM1’ and ‘_x1’.
4. It applies the transmute function from dplyr to the data frame, removing all but one of the columns that were created in the previous step.
5. The resulting data frame is printed to the console.

The output of this script would be a simplified version of the original data, with only some of the most important variables and relationships preserved.

Here’s an excerpt from the dplyr documentation that might help explain how to achieve similar results:

library(dplyr)

# Read in the CSV file
df <- read_csv("test.csv")

# Create new columns by concatenating existing column names with _x or _y suffixes
df %>% 
  mutate(OTS_SM1_x1 = OTS_SM0_1 + OTS_SM1_1,
         OTS_SM1_x2 = OTS_SM0_2 + OTS_SM1_2,
         # ... and so on for all columns that need to be concatenated

# Remove all but one of the new columns created in the previous step
df %>% 
  transmute(OTS_SM1_x1, OTS_SM1_x2, # select only these two columns
           OTS_SM0_1, OTS_SM0_2) # and these two more, if needed

Note that this is just one possible way to simplify the data using dplyr. The actual implementation will depend on the specific requirements of your project.

Last modified on 2023-12-01