tidyverse

I have the following data frame: df <- structure(list(X2 = c("BB_137.HVMSC", "BB_138.combined.HVMSC", "BB_139.combined.HVMSC", "BB_140.combined.HVMSC", "BB_141.HVMSC", "BB_142.combined.HMSC-bm")), .Names = "X2", row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame")) Which looks like this > df # A tibble: 6 x 1 X2 <chr> 1 BB_137.HVMSC 2 BB_138.combined.HVMSC 3 BB_139.combined.HVMSC 4 BB_140.combined.HVMSC 5 BB_141.HVMSC 6 BB_142.combined.HMSC-bm What I want to do is to separate into two columns (with . as separator), by keeping the last field as second column col1 col2 BB_137 HVMSC

Here is my toy dataframe. df <- tibble::tribble( ~var1, ~var2, ~var3, ~var4, ~var5, ~var6, ~var7, "A", "C", 1L, 5L, "AA", "AB", 1L, "A", "C", 2L, 5L, "BB", "AC", 2L, "A", "D", 1L, 7L, "AA", "BC", 2L, "A", "D", 2L, 3L, "BB", "CC", 1L, "B", "C", 1L, 8L, "AA", "AB", 1L, "B", "C", 2L, 6L, "BB", "AC", 2L, "B", "D", 1L, 9L, "AA", "BC", 2L, "B", "D", 2L, 6L, "BB", "CC", 1L) How can I get the combination of a minimum number of variables that uniquely identify the observations in the dataframe i.e which variables together can make the primary key ? The way I approached this problem is to find the

Suppose we have a character vector cols_to_select containing some columns we want to select from a dataframe df , e.g. df <- tibble::data_frame(a=1:3, b=1:3, c=1:3, d=1:3, e=1:3) cols_to_select <- c("b", "d") Suppose also we want to use dplyr::select because it's part of an operation that uses %>% so using select makes the code easy to read. There seem to be a number of ways which this can be achieved, but some are more robust than others. Please could you let me know which is the 'correct' version and why? Or perhaps there is another, better way? dplyr::select(df, cols_to_select) #Fails if

I'm trying to use map() of purrr package to apply filter() function to the data stored in a nested data frame. "Why wouldn't you filter first, and then nest? - you might ask. That will work (and I'll show my desired outcome using such process), but I'm looking for ways to do it with purrr . I want to have just one data frame, with two list-columns, both being nested data frames - one full and one filtered. I can achieve it now by performing nest() twice: once on all data, and second on filtered data: library(tidyverse) df <- tibble( a = sample(x = rep(c('x','y'),5), size = 10), b = sample(c(1

I am using r 3.3.3, dplyr 0.7.4, and Hmisc 4.1-1. I noticed that the order I load packages effects whether or not a dplyr::summaries function wold work or not. I understand that loading packages in a different order would mask certain functions but I am using the package::function() syntax to avoid that issue. The exact issue revolves around labeled variables. I know that there has been issues in the past with tidyverse and variable labels but none seem to address why this particular situation is occurring. First example that works - I load only Hmisc then dplyr and I am able to summaries the

I did mtcars %>% by_row(sum) but got the message: by_row() is deprecated; please use a combination of: tidyr::nest(); dplyr::mutate(); purrr::map() My naive approach is this mtcars %>% group_by(id = row_number()) %>% nest(-id) %>% mutate(hi = map_dbl(data, sum)) Is there a way to do it without creating an "id" column? Is this what you are looking for? mtcars %>% mutate(rowsum = rowSums(.)) Output: mpg cyl disp hp drat wt qsec vs am gear carb rowsum 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 328.980 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 329.795 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

I am trying to assign a column name to a variable using mutate. df <-data.frame(x = sample(1:100, 50), y = rnorm(50)) new <- function(name){ df%>%mutate(name = ifelse(x <50, "small", "big")) } When I run new(name = "newVar") it doesn't work. I know mutate_() could help but I'm struggling in using it together with ifelse . Any help would be appreciated. Using dplyr 0.7.1 and its advances in NSE, you have to UQ the argument to mutate and then use := when assigning. There is lots of info on programming with dplyr and NSE here: https://cran.r-project.org/web/packages/dplyr/vignettes/programming