# NOT RUN { datasets <- readxl_example("datasets.xlsx") read_excel(datasets) # Specify sheet either by position or by name read_excel(datasets, 2) read_excel(datasets, "mtcars") # Skip rows and use default column names read_excel(datasets, skip = 148, col_names = FALSE) # Recycle a single column type read_excel(datasets, col_types = "text") # Specify some col_types and guess others read_excel(datasets, col_types = c("text", "guess", "numeric", "guess", "guess")) # Accomodate a column with disparate types via col_type = "list" df <- read_excel(readxl_example("clippy.xlsx"), col_types = c("text", "list")) df df$value sapply(df$value, class) # Limit the number of data rows read read_excel(datasets, n_max = 3) # Read from an Excel range using A1 or R1C1 notation read_excel(datasets, range = "C1:E7") read_excel(datasets, range = "R1C2:R2C5") # Specify the sheet as part of the range read_excel(datasets, range = "mtcars!B1:D5") # Read only specific rows or columns read_excel(datasets, range = cell_rows(102:151), col_names = FALSE) read_excel(datasets, range = cell_cols("B:D")) # Get a preview of column names names(read_excel(readxl_example("datasets.xlsx"), n_max = 0)) # exploit full .name_repair flexibility from tibble # "universal" names are unique and syntactic read_excel( readxl_example("deaths.xlsx"), range = "arts!A5:F15", .name_repair = "universal" ) # specify name repair as a built-in function read_excel(readxl_example("clippy.xlsx"), .name_repair = toupper) # specify name repair as a custom function my_custom_name_repair <- function(nms) tolower(gsub("[.]", "_", nms)) read_excel( readxl_example("datasets.xlsx"), .name_repair = my_custom_name_repair ) # specify name repair as an anonymous function read_excel( readxl_example("datasets.xlsx"), sheet = "chickwts", .name_repair = ~ substr(.x, start = 1, stop = 3) ) # }
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