prop1 <- sum(GE_survey$`Q17_What department are you in` == "HSS") / nrow(GE_survey)
  1. nrow() and ncol(): Suppose you have a dataset my_data that contains information about customers, with each row representing a different customer and each column representing a different attribute (e.g., ID, name, age, etc.). You can use nrow(my_data) to find out how many customers are in the dataset and ncol(my_data) to find out how many attributes each customer record has.

  2. length(): If you have a vector my_vector containing the sales figures for each month of the year, you can use length(my_vector) to find out how many months of sales data you have.

  3. dim(): Suppose you have a matrix my_matrix that represents the results of a survey, with each row representing a different question and each column representing a different respondent. You can use dim(my_matrix) to find out how many questions were asked in the survey and how many respondents answered the survey.

  4. dimnames(): If my_matrix has row and column names indicating the questions and respondent IDs, respectively, you can use dimnames(my_matrix) to access and manipulate these names.

  5. attributes(): If you have a data frame my_df that contains information about products, including their names, prices, and quantities, you can use attributes(my_df) to view or modify the attributes of the data frame, such as its column names or class.

These functions are commonly used in data analysis and manipulation tasks to understand and work with the structure of your data.

03-07 21:20