Hello! I am novice to R.
I am going through statistical analysis with R for my very first time. I hope you will help me.
I need to analyze a binary variable through 3 categories: I want the binary variable to be expressed as frequency on y-axis and the 3 categories on x-axis and have the trend line.
The binary variable is 1=dead, 0=alive, so for each category R should calculate the frequency of "1" over "0" and plot it. In MedCacl it was straightforward.
thank you for your attention.
Hello! I am novice to R.
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thank you for your reply.
it is not about homework, I just wanted to switch from medcalc to R.
i have a dataset with 3527 observation, each characterized by almost one hundred variables. I have already done the analysis with medcalc, I just want to reproduce it with R.
So observations are split into three temporal categories, and mortality (expressed as 0 or 1) needs to go on y-axis as frequency (read also as rate or incidence).
what i have tried is with ggplot:
b <- ggplot(dat, aes(x = eras, y = mean(INHOSPITAL)))
b + geom_point ()
To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue, that includes sample data on a copy/paste friendly format? Please have a look at this guide, to see how to create one:
as it is your first post, I'll try to guess what you are trying to achieve. Given the
iris dataset, I have created an
iris2 data frame which has a
status column which should resemble your binary variable. The 3 categories are in the
library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(ggplot2) iris2 <- iris %>% group_by(Species) %>% mutate(status = ifelse(Sepal.Length > mean(Sepal.Length), 1, 0)) %>% ungroup() iris2 %>% group_by(Species) %>% summarize(survived = mean(status)) %>% ggplot(aes(x=Species, y=survived)) + geom_col() + theme_classic() + scale_y_continuous(labels = scales::percent_format(accuracy = 1))
Created on 2019-11-27 by the reprex package (v0.3.0)
thank you very much @valeri.
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