tidyverse

My single-argument function outputs a dataframe library(tidyverse) myfun <-function(x) {mtcars %>% filter_(x) %>% group_by(cyl) %>% summarise(mean(disp), mean(drat)) %>% mutate(group=x)} When feeding a single-argument into this function, it outputs, as expected, a dataframe: myfun('mpg>15') cyl mean(disp) mean(drat) group 4 105 4.07 mpg>15 6 183 3.59 mpg>15 8 105 3.20 mpg>15 How to apply such a function to a list of arguments so that the output is one dataframe combining all the results over the list. For example, I'd like to apply myfun to a list c('mpg>15', 'drat>4.2') and, as the result, to

I'm having issues getting approx() to work inside of a mutate_at(). I did manage to get what I want using a very long mutate() function, but for future reference I was wondering if there was a more graceful and less copy-pasting mutate_at() way to do this. The overarching problem is merging a dataset with data from 1 year intervals to one with 3 year intervals, and interpolating years with no data in the dataset with 3 year intervals. There are missing values in between the years, and one year that requires some form of extrapolation. library("tidyverse") demodf <- data.frame(groupvar =

all. I want to obtain the p-value from a correlation matrix using dplyr and/or broom packages and testing multiple variables at the same time . I'm aware of other methods, but dplyr seems easier and more intuitive for me. In addition, dplyr will need to correlate each variable to obtain the specific p-value, what makes the process easier and faster. I checked other links, but they did not work for this question ( example 1 , example 2 , example 3 ) When I use this code, the correlation coefficients are reported. However, the P-values are not. agreg_base_tipo_a %>% dplyr::select(S2.RT, BIS

I want to aggregate Date by group. However, each observation can belong to several groups (e.g. observation 1 belongs to group A and B). I could not find a nice way to achieve this with data.table . Currently I created for each of the possible groups a logical variable which takes the value TRUE if the observation belongs to that group. I am looking for a better way to do this than presented below. I would also like to know how I could achieve this with the tidyverse . library(data.table) # Data set.seed(1) TF <- c(TRUE, FALSE) time <- rep(1:4, each = 5) df <- data.table(time = time, x = rnorm

This is indeed a duplicate for this question r-split-string-using-tidyrseparate , but I cannot use the MWE for my purpose, because I do not know how to adjust the regular Expression. I basically want the same thing, but split the variable after the last underscore. Reason: I have data where some columns show up several times for the same factor/type. I figured I can melt the data separate the value variable before the type string and spread it out again to a wide format with less columns. My Problem is that my variable names have different several underscores and I would like to learn how to

I am very, very new to any type of coding language. I am used to Pivot tables in Excel, and trying to replicate a pivot I have done in Excel in R. I have spent a long time searching the internet/ YouTube, but I just can't get it to work. I am looking to produce a table in which I the left hand side column shows a number of locations, and across the top of the table it shows different pages that have been viewed. I want to show in the table the number of views per location which each of these pages. The data frame 'specificreports' shows all views over the past year for different pages on an

mutate_at() shows an evaluation error when used with group_by() and when imputing a numerical vector for column position as the first (.vars) argument. Issue shows up when using R 3.4.2 and dplyr 0.7.4 version Works fine when using R 3.3.2 and dplyr 0.5.0 Works fine if .vars is character vector (column name) Example: # Create example dataframe Id <- c('10_1', '10_2', '11_1', '11_2', '11_3', '12_1') Month <- c(2, 3, 4, 6, 7, 8) RWA <- c(0, 0, 0, 1.579, NA, 0.379) dftest = data.frame(Id, Month, RWA) # Define column to fill NAs nacol = c('RWA') # Fill NAs with last period dftest_2 <- dftest %>%

I wanted to get all unique pairwise combinations of a unique string column of a dataframe using the tidyverse (ideally). Here is a dummy example: library(tidyverse) a <- letters[1:3] %>% tibble::as_tibble() a #> # A tibble: 3 x 1 #> value #> <chr> #> 1 a #> 2 b #> 3 c tidyr::crossing(a, a) %>% magrittr::set_colnames(c("words1", "words2")) #> # A tibble: 9 x 2 #> words1 words2 #> <chr> <chr> #> 1 a a #> 2 a b #> 3 a c #> 4 b a #> 5 b b #> 6 b c #> 7 c a #> 8 c b #> 9 c c Is there a way to remove 'duplicate' combinations here. That is have the output be the following in this example: # A tibble: