dplyr

问题 I am trying to do an inventory calculation in R which requires a row wise calculation for each Mat-Plant combination. Here's a test data set - df <- structure(list(Mat = c("A", "A", "A", "A", "A", "A", "B", "B" ), Plant = c("P1", "P1", "P1", "P2", "P2", "P2", "P1", "P1"), Day = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L), UU = c(0L, 10L, 0L, 0L, 0L, 120L, 10L, 0L), CumDailyFcst = c(11L, 22L, 33L, 0L, 5L, 10L, 20L, 50L)), .Names = c("Mat", "Plant", "Day", "UU", "CumDailyFcst"), class = "data.frame", row

问题 This question already has answers here : Pairwise subtraction in a dataframe R (2 answers) Closed 7 months ago . I have a table with 6 columns Z1 to Z6, and I want to calculate the absolute value of the difference between each of these columns. So far, I enumerate all the differences in a mutate command: FactArray <- FactArray %>% mutate(diff12 = abs(Z1-Z2), diff13 = abs(Z1-Z3), diff14 = abs(Z1-Z4), diff15 = abs(Z1-Z5), diff16 = abs(Z1-Z6), diff23 = abs(Z2-Z3), diff24 = abs(Z2-Z4), diff25 =

问题 I'm trying to follow some rules about when to group data to chart. How would I go from this data frame: # A tibble: 11 x 8 assay year qtr invalid valid total_assays hfr predicted_inv <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 test_case 2016. 1. 2. 36. 38. 0.0350 1.33 2 test_case 2016. 2. 1. 34. 35. 0.0350 1.23 3 test_case 2016. 3. 0. 25. 25. 0.0350 0.875 4 test_case 2016. 4. 2. 23. 25. 0.0350 0.875 5 test_case 2017. 1. 1. 29. 30. 0.0350 1.05 6 test_case 2017. 2. 2. 24. 26. 0.0350 0.910

问题 I'm trying to follow some rules about when to group data to chart. How would I go from this data frame: # A tibble: 11 x 8 assay year qtr invalid valid total_assays hfr predicted_inv <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 test_case 2016. 1. 2. 36. 38. 0.0350 1.33 2 test_case 2016. 2. 1. 34. 35. 0.0350 1.23 3 test_case 2016. 3. 0. 25. 25. 0.0350 0.875 4 test_case 2016. 4. 2. 23. 25. 0.0350 0.875 5 test_case 2017. 1. 1. 29. 30. 0.0350 1.05 6 test_case 2017. 2. 2. 24. 26. 0.0350 0.910

问题 I am attempting to work through Hadley Wickham's R for Data Science and have gotten tripped up on the following question: "How could you use arrange() to sort all missing values to the start? (Hint: use is.na())" I am using the flights dataset included in the nycflights13 package. Given that arrange() sorts all unknown values to the bottom of the dataframe, I am not sure how one would do the opposite across the missing values of all variables. I realize that this question can be answered with

问题 I am attempting to work through Hadley Wickham's R for Data Science and have gotten tripped up on the following question: "How could you use arrange() to sort all missing values to the start? (Hint: use is.na())" I am using the flights dataset included in the nycflights13 package. Given that arrange() sorts all unknown values to the bottom of the dataframe, I am not sure how one would do the opposite across the missing values of all variables. I realize that this question can be answered with

问题 I'm trying to develop a deeper understanding of using the dot (".") with dplyr and using the .data pronoun with dplyr . The code I was writing that motivated this post, looked something like this: cat_table <- tibble( variable = vector("character"), category = vector("numeric"), n = vector("numeric") ) for(i in c("cyl", "vs", "am")) { cat_stats <- mtcars %>% count(.data[[i]]) %>% mutate(variable = names(.)[1]) %>% rename(category = 1) cat_table <- bind_rows(cat_table, cat_stats) } # A tibble:

问题 I'm trying to develop a deeper understanding of using the dot (".") with dplyr and using the .data pronoun with dplyr . The code I was writing that motivated this post, looked something like this: cat_table <- tibble( variable = vector("character"), category = vector("numeric"), n = vector("numeric") ) for(i in c("cyl", "vs", "am")) { cat_stats <- mtcars %>% count(.data[[i]]) %>% mutate(variable = names(.)[1]) %>% rename(category = 1) cat_table <- bind_rows(cat_table, cat_stats) } # A tibble:

问题 I'm trying to develop a deeper understanding of using the dot (".") with dplyr and using the .data pronoun with dplyr . The code I was writing that motivated this post, looked something like this: cat_table <- tibble( variable = vector("character"), category = vector("numeric"), n = vector("numeric") ) for(i in c("cyl", "vs", "am")) { cat_stats <- mtcars %>% count(.data[[i]]) %>% mutate(variable = names(.)[1]) %>% rename(category = 1) cat_table <- bind_rows(cat_table, cat_stats) } # A tibble:

问题 Let's say I have a list of suburb names, crime rate and their council names on a separate table. I know that left_join(table1, table2, by=Suburb) will return the table with newly added rows due to the multiple matches for council. The problem is that suburbs 3 and 4 overlap into two councils. Is there a way to only get the left join to only return the first match only rather than creating new rows to facilitate for the extra ones? In addition, on Table 2, is there a function to only keep the