# How to calculate values to fill descriptive statistics table in one shot

I have this dataset > dput(head(data, 130)) structure(list(ID = 1:130, Gender = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… Read More How to calculate values to fill descriptive statistics table in one shot

# How do I run a mixed linear regression model on several outcomes variables and get presentable results?

I finally gave up and admitted I need help. I have this data set with 3 different groups, measured at 2 time points and 49 outcome variables. I would like to do a mixed linear regression analysis on each outcome variable for within group change between time points. As shown in table below: Id rand… Read More How do I run a mixed linear regression model on several outcomes variables and get presentable results?

# How to use `label_date_short`?

How I am using wrongly label_date_short from scales package? library(tidyverse) library(scales) date_taille <- tibble( Taille = rep(c("taille_hiver", "taille_ete"), times = 2), Date_taille = c("2016-08-01", "2016-02-01", "2018-08-01", "2018-02-01") %>% as.Date() ) ggplot(date_taille) + aes(x = Date_taille, y = Taille) + geom_point() + scale_x_date(date_breaks = "month", date_labels = label_date_short()) #or label_date() #> Error in format(x, format =… Read More How to use `label_date_short`?

# "Multi-step" regression with broom and dplyr in R

I am looking for a way to perform "multi-step" regression with broom and dplyr in R. I use "multi-step" as a placeholder for regression analyses in which you integrate in the final regression model elements of previous regression models, such as the fit or the residuals. An example for such a "multi-step" regression would be… Read More "Multi-step" regression with broom and dplyr in R