Using tidyr::nest() with purrr:map() we can do the above steps for each small package use case tibble from every respondent. We will explore several examples of how to sum across columns in R, including summing across a matrix, summing across multiple columns in a dataframe. dplyr summarize by string Ask Question 2 I have a dataframe that has numeric and string values, for example: mydf <- ame (id c (1, 2, 1, 2, 3, 4), value c (32, 12, 43, 6, 50, 20), text c ('A', 'B', 'A', 'B', 'C', 'D')) The value of id variable always corresponds to text variable, e.g., id 1 will always be text 'A'. #> 1 use case 1 use case 3 use case 1 use case 2 Method 1: Apply Function to Multiple Columns multiply values in col1 and col2 by 2 df > mutate (across (c (col1, col2), function(x) x2)) Method 2: Calculate One Summary Statistic for Multiple Columns calculate mean of col1 and col2 df > summarise (across (c (col1, col2), mean, na. Mutate(use_cases = as.integer(str_remove(use_cases, "use case "))) %T>% preview() %>% Separate_rows(use_cases, sep = " ?") %T>% preview() %>% ![]() I want to remove the lower test score (grouped by studentid and testname) but I want to keep all of the other variables that I don't need to group by. Ignore the chunk in here, I’m just setting up a way to preview each step… `%T>%` %` summarise (max) but keep all columns tidyverse uvapnut February 11, 2020, 5:48pm 1 I am a total beginner, and struggling to understand how to format the code to do what I want. The tl dr is that in problems like these I like to nest the problematic columns, work on them as if they were a small little data frame using map() and then unnest them back into the parent row or table. Okay, so I have to admit up front that I'm "cheating" a bit by solving your problem but not answering your question Maybe I'm missing something…Īlso as a rule of thumb I'm trying to stay away from any superseded functions for future-proofing and let's say manually grouping by isn't an option because I'm dealing with 20+ columns with repeated values in the rows and just need to collapse rows across two columns into 1 cell.Ĭontext/background: I'm working with survey responses from Google Forms and one of the questions was a check matrix/grid, and the way Google turns that into a spreadsheet is by making each row in the grid a column in the CSV and then the values the user selected become a concatenated list of values in the cell. # Two functions, continued by_species %>% summarise_at(vars(Petal.Width, Sepal.After checking out the colwise and grouping vignettes, I still have no idea how to perform a group_by all columns except two and then summarize those two columns into one.Ġ, "a", "b", list("x" = 1, "y" = 2, "z" = 3), "c"Īll the documentation keeps pointing me to using across() inside summarise() and I looked into using with those and at least that way I can group_by(id) and then use across(!contains("3"), head, n = 1L) to avoid grouping by all the other columns except q3 and v3, but it doesn't look like I can use a two-parameter function that would operate on q3 an v3. 2.54 ))īy_species %>% mutate_all(funs(rg = diff( range (.))))īy_species %>% summarise_all(funs(med = median))īy_species %>% summarise_all(funs(Q3 = quantile), probs = 0.75 )īy_species %>% summarise_all( c ( "min", "max" )) funs has names or whenever multiple # functions are used. # Note that output variable name must now include function name, in order to # keep things distinct. Were going to learn some of the most common dplyr functions: select(), filter(), mutate(), groupby(), and summarize(). * 0.4 ))īy_species %>% summarise_all(funs( min, max )) Using dplyr summariseat with column index Ask Question Asked Viewed 9k times Part of R Language Collective 24 I noticed that when supplying column indices to dplyr::summarizeat the column to be summarized is determined excluding the grouping column (s). # You can provide an expression or multiple functions with the funs() helper. Were going to learn some of the most common dplyr functions: select(), filter(), mutate(), groupby(), and summarize(). Those are evaluated only once: by_species %>% summarise_all(mean, trim = 1 )īy_species %>% summarise_at(vars(Petal.Width), mean, trim = 1 ) ![]() ![]() # You can also specify columns with column names or column positions: by_species %>% summarise_at( c ( "Sepal.Width", "Petal.Width" ), mean)īy_species %>% summarise_at( c ( 1, 3 ), mean) # summarise_at() can use select() helpers with the vars() function: by_species %>% summarise_at(vars(Petal.Width), mean)īy_species %>% summarise_at(vars(matches( "Width" )), mean) by_species %>% summarise_if( is.numeric, mean) # Use the _at and _if variants for conditional mapping. ![]() # One function by_species %>% summarise_all(n_distinct)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |