broom

Consider the following data / example. Each dataset contains a number of samples with one observation and one estimate: library(tidyverse) library(broom) data = read.table(text = ' dataset sample_id observation estimate A A1 4.8 4.7 A A2 4.3 4.5 A A3 3.1 2.9 A A4 2.1 2 A A5 1.1 1 B B1 4.5 4.3 B B2 3.9 4.1 B B3 2.9 3 B B4 1.8 2 B B5 1 1.2 ', header = TRUE) I want to calculate a linear model per dataset to remove any linear bias between observation and estimate, and get the fitted values next to the original ones: data %>% group_by(dataset) %>% do(lm(observation ~ estimate, data = .) %>% augment

For data... library(productplots) library(ggmosaic) For code... library(tidyverse) library(broom) I'm trying to create tidy chisq.test output so that I can easily filter or visualize p-values. I'm using the "happy" dataset (which is included with either of the packages listed above) For this example, if I wanted to condition the "happy" variable on all other variables,I would isolate the categorical variables (I'm not going to create factor groupings out of age, year, etc, for this example), and then run a simple function. df<-happy%>%select(-year,-age,-wtssall) lapply(df,function(x)chisq.test

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

When using broom I was used to combine dplyr::group_by and dplyr::do to perform actions on grouped data thanks to @drob. For example, fitting a linear model to cars depending on their gear system: library("dplyr") library("tidyr") library("broom") # using do() mtcars %>% group_by(am) %>% do(tidy(lm(mpg ~ wt, data = .))) # Source: local data frame [4 x 6] # Groups: am [2] # am term estimate std.error statistic p.value # (dbl) (chr) (dbl) (dbl) (dbl) (dbl) # 1 0 (Intercept) 31.416055 2.9467213 10.661360 6.007748e-09 # 2 0 wt -3.785908 0.7665567 -4.938848 1.245595e-04 # 3 1 (Intercept) 46.294478

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