summarize

问题 I am currently trying to apply the summarise function in order to isolate the relevant observations from a large data set. A simple reproducible example is given here: df <- data.frame(c(1,1,1,2,2,2,3,3,3), as.logical(c(TRUE,FALSE,TRUE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE)), as.numeric(c(0,5,0,0,0,0,7,0,7))) colnames(df) <- c("ID", "Status", "Price") ID Status Price 1 1 TRUE 0 2 1 FALSE 5 3 1 TRUE 0 4 2 TRUE 0 5 2 TRUE 0 6 2 TRUE 0 7 3 FALSE 7 8 3 TRUE 0 9 3 FALSE 7 I would like to sort the table

问题 I am currently trying to apply the summarise function in order to isolate the relevant observations from a large data set. A simple reproducible example is given here: df <- data.frame(c(1,1,1,2,2,2,3,3,3), as.logical(c(TRUE,FALSE,TRUE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE)), as.numeric(c(0,5,0,0,0,0,7,0,7))) colnames(df) <- c("ID", "Status", "Price") ID Status Price 1 1 TRUE 0 2 1 FALSE 5 3 1 TRUE 0 4 2 TRUE 0 5 2 TRUE 0 6 2 TRUE 0 7 3 FALSE 7 8 3 TRUE 0 9 3 FALSE 7 I would like to sort the table

问题 I have a data frame like this Observations: 2,190,835 Variables: 13 $ patientid <int> 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489, 4489… $ preparationid <dbl> 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1000307, 1… $ doseday <int> 90, 90, 91, 91, 92, 92, 92, 92, 93, 93, 93, 93, 94, 94, 94, 94, 95, 95, 95, 95, 99, 99, 100, 100, 10… $ route <fct> enteral., enteral., enteral., enteral., enteral.,

问题 I’m trying to represent some continuous data via binning. Continuous weighting data of an area should be binned as: VeryHigh, High, Low, VeryLow. The weighting values are based on an interaction between certain Types of events grouped by an Area and so can change depending on the Type selected by the report user. I have included some sample data below and an outline of what’s been done so far. Start with five sets of area data (A-E). Within each is one or more incident Types. Each incident

问题 Let's say I have the following (simplified) tibble containing a group and values in vectors: set.seed(1) (tb_vec <- tibble(group = factor(rep(c("A","B"), c(2,3))), values = replicate(5, sample(3), simplify = FALSE))) # A tibble: 5 x 2 group values <fct> <list> 1 A <int [3]> 2 A <int [3]> 3 B <int [3]> 4 B <int [3]> 5 B <int [3]> tb_vec[[1,2]] [1] 1 3 2 I would like to summarize the values vectors per group by summing them (vectorized) and tried the following: tb_vec %>% group_by(group) %>%

问题 Assuming I have a data frame like term cnt apple 10 apples 5 a apple on 3 blue pears 3 pears 1 How could I filter all partial found strings within this column, e.g. getting as a result term cnt apple 10 pears 1 without indicating to which terms I want to filter (apple|pears), but through a self-referencing manner (i.e. it does check each term against the whole column and removes terms that are a partial match). The number of tokens is not limited, nor the consistency of strings (i.e. "mapples

问题 Assuming I have a data frame like term cnt apple 10 apples 5 a apple on 3 blue pears 3 pears 1 How could I filter all partial found strings within this column, e.g. getting as a result term cnt apple 10 pears 1 without indicating to which terms I want to filter (apple|pears), but through a self-referencing manner (i.e. it does check each term against the whole column and removes terms that are a partial match). The number of tokens is not limited, nor the consistency of strings (i.e. "mapples

来源： https://stackoverflow.com/questions/49546631/moving-from-deprecated-summarize-to-new-summarize-in-dplyr

来源： https://stackoverflow.com/questions/49546631/moving-from-deprecated-summarize-to-new-summarize-in-dplyr

来源： https://stackoverflow.com/questions/27850054/count-occurrences-of-a-string-by-row-in-a-large-data-frame