# Select all but one column (e.g., listing_id) airbnb_listings %>% select(-listing_id) # Select all columns within a range airbnb_listings %>% select(country : year_listed) # Reorder columns using relocate() airbnb_listings %>% relocate(city, country) # Rename a column using renam...
select(df, V4:V6) select(df, num_range("V", 4:6)) # Drop variables select(iris, -starts_with("Petal")) select(iris, -ends_with("Width")) select(iris, -contains("etal")) select(iris, -matches(".t.")) select(iris, -Petal.Length, -Petal.Width) # Rename variables: # * sele...
在我看来，在利用dplyr时，实现这一点的最简化和最通用的方法是使用dplyr::select_if，但要比@wjchulme建议的方法更直接（尽管这是一个不错的技巧）：dplyr
dplyr: A grammar of data manipulation. Contribute to tidyverse/dplyr development by creating an account on GitHub.
tibble::rownames_to_column('CarName') %>% select_se(c('CarName', 'cyl', 'gear', 'hp', 'wt')) %>% add_group_indices(groupingVars = groupingVars, indexColumn = 'groupID') %>% add_group_sub_indices(groupingVars = groupingVars, arrangeTerms = c('desc(hp)', 'wt'), orderColumn...
select(iris,ends_with("Length")) # Select every column. select(iris,everything()) # Select columns whose name matches a regular expression select(iris,matches(".t.")) # Select columns whose names are in a group of names select(iris,one_of(c("Species","Genus"))) # Select columns ...
a numeric vector of column positions, or a column specification withselect()semantics generated by the newcolumns()helper. In addition,summarise_if()andmutate_if()take a predicate function or a logical vector (these verbs currently require local sources). All these functions can now take ordinary...
Simply pass the column name(s) to the function: Can I combine arrange() with other dplyr functions? Yes, arrange() is often combined with functions like filter(), mutate(), and select() for comprehensive data manipulation: How do I sort rows in descending order? To sort rows in ...
# Rename one or more variables in a dataframe df <- df %>% rename("INCOME" = "income") df <- df %>% rename("INCOME" = "income", "AGE" = "age") The main “verbs” of dplyr are now introduced. Let’s begin with the select() verb which filters a dataframe by column. # S...
Select every column. select(iris, matches(".t.")) Select columns whose name matches a regular expression. select(iris, num_range("x", 1:5)) Select columns named x1, x2, x3, x4, x5. select(iris, one_of(c("Species", "Genus"))) ...