purrr

Here is the data: library(tidyverse) data <- tibble::tribble( ~var1, ~var2, ~var3, ~var4, ~var5, "a", "d", "g", "hello", 1L, "a", "d", "h", "hello", 2L, "b", "e", "h", "k", 4L, "b", "e", "h", "k", 7L, "c", "f", "i", "hello", 3L, "c", "f", "i", "hello", 4L ) and the vectors, I want to use: filter_var <- c("hello") groupby_vars1 <- c("var1", "var2", "var3") groupby_vars2 <- c("var1", "var2") joinby_vars1 <- c("var1", "var2") joinby_vars2 <- c("var1", "var2", "var3") 2nd & 5th, and 3rd & 4th vectors are same, but please assume they are different and retain them as different vectors. Now I want to

I have a situation where I have two data frames I would like to join. The table params describes the param for a unit in terms time and angle ranges. The table data is longer and contains id, time and angle parameters. I would like to join the param value from params when id match and time is in the range between valid_from and valid_to and ang is between angle_begin angle_end in the data table. Below is an example of the tables. params <- data.frame(id = 1:4 ,valid_from = 1 ,valid_to = c(10, 20, 30, 40) ,angle_begin = c(120, 90, 0, 50) ,angle_end = c(180, 170, 160, 150) ,param = c("A", "B",

There are many questions about rolling regression in R, but here I am specifically looking for something that uses dplyr , broom and (if needed) purrr . This is what makes this question different. I want to be tidyverse consistent. Is is possible to do a proper running regression with tidy tools such as purrr:map and dplyr ? Please consider this simple example: library(dplyr) library(purrr) library(broom) library(zoo) library(lubridate) mydata = data_frame('group' = c('a','a', 'a','a','b', 'b', 'b', 'b'), 'y' = c(1,2,3,4,2,3,4,5), 'x' = c(2,4,6,8,6,9,12,15), 'date' = c(ymd('2016-06-01', '2016

I know that there are many related questions here on SO, but I am looking for a purrr solution, please, not one from the apply list of functions or cbind/rbdind (I want to take this opportunity to get to know purrr better). I have a list of dataframes and I would like to add a new column to each dataframe in the list. The value of the column will be the name of the dataframe, i.e. the name of each element in the list. There is something similar here , but it involves the use of a function and mutate_each() , whereas I need just mutate() . To give you an idea of the list (called comentarios ),

I have to following issue using R. In short I want to create multiple new columns in a data frame based on calculations of different column pairs in the data frame. The data looks as follows: df <- data.frame(a1 = c(1:5), b1 = c(4:8), c1 = c(10:14), a2 = c(9:13), b2 = c(3:7), c2 = c(15:19)) df a1 b1 c1 a2 b2 c2 1 4 10 9 3 15 2 5 11 10 4 16 3 6 12 11 5 17 4 7 13 12 6 18 5 8 14 13 7 19 The output is supposed to look like the following: a1 b1 c1 a2 b2 c2 sum_a sum_b sum_c 1 4 10 9 3 15 10 7 25 2 5 11 10 4 16 12 9 27 4 7 13 12 6 18 16 13 31 5 8 14 13 7 19 18 15 33 I can achieve this using dplyr

I am trying to add a column of predictions to a dataframe that has a list column that contains an lm model. I adopted some of the code from this post . I have made a toy example here: library(dplyr) library(purrr) library(tidyr) library(broom) set.seed(1234) exampleTable <- data.frame( ind = c(rep(1:5, 5)), dep = rnorm(25), groups = rep(LETTERS[1:5], each = 5) ) %>% group_by(groups) %>% nest(.key=the_data) %>% mutate(model = the_data %>% map(~lm(dep ~ ind, data = .))) %>% mutate(Pred = map2(model, the_data, predict)) exampleTable <- exampleTable %>% mutate(ind=row_number()) that gives me a