tidyverse

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

I would like to filter a dataframe using filter() and str_detect() matching for multiple patterns without multiple str_detect() function calls. In the example below I would like to filter the dataframe df to show only rows containing the letters a f and o . df <- data.frame(numbers = 1:52, letters = letters) df %>% filter( str_detect(.$letters, "a")| str_detect(.$letters, "f")| str_detect(.$letters, "o") ) # numbers letters #1 1 a #2 6 f #3 15 o #4 27 a #5 32 f #6 41 o I have attempted the following df %>% filter( str_detect(.$letters, c("a", "f", "o")) ) # numbers letters #1 1 a #2 15 o #3 32

I'm trying to import a dataset to RStudio, however I am stuck with Chinese characters, as they become messy codes. Here is the code: library(tidyverse) df <- read_csv("中文,英文\n英文,德文") df # A tibble: 1 x 2 `\xd6\xd0\xce\xc4` `Ӣ\xce\xc4` <chr> <chr> 1 "<U+04E2>\xce\xc4" "<U+00B5>\xc2\xce\xc4" When I use the base function read.csv, it works well. I guess I must do something wrong with encoding. But there are no encoding option in read_csv, how can I do this? This is because that the characters are marked as UTF-8 whereas the actual encoding is the system default (you can get by stringi::stri_enc

What is the best way to fit different model formulae that vary by the row of a data frame with the list-columns data structure in tidyverse? In R for Data Science, Hadley presents a terrific example of how to use the list-columns data structure and fit many models easily ( http://r4ds.had.co.nz/many-models.html#gapminder ). I am trying to find a way to fit many models with slightly different formulae. In the below example adapted from his original example, what is the best way to fit a different model for each continent? library(gapminder) library(dplyr) library(tidyr) library(purrr) library

If I add a new row to the ìris dataset with: iris <- as_tibble(iris) > iris %>% add_row(.before=0) # A tibble: 151 × 5 Sepal.Length Sepal.Width Petal.Length Petal.Width Species <dbl> <dbl> <dbl> <dbl> <chr> 1 NA NA NA NA <NA> <--- Good! 2 5.1 3.5 1.4 0.2 setosa 3 4.9 3.0 1.4 0.2 setosa It works. So, why can't I add a new row on top of each "subset" with: iris %>% group_by(Species) %>% add_row(.before=0) Error: is.data.frame(df) is not TRUE If you want to use a grouped operation, you need do like JasonWang described in his comment, as other functions like mutate or summarise expect a result

I'm trying to conduct a chi square test on several groups within the dplyr frame . The problem is, group_by() %>% summarise() doesn't seem to do trick. Simulated data (same structure as problematic data, but random, so p.values should be high) set.seed(1) data.frame(partido=sample(c("PRI", "PAN"), 100, 0.6), genero=sample(c("H", "M"), 100, 0.7), GM=sample(c("Bajo", "Muy bajo"), 100, 0.8)) -> foo I want to compare several groups defined by GM to see if there are changes in the p.values for the crosstab of partido and genero, conditional to GM. The obvious dplyr way should be: foo %>% group_by