Analysis of binary variable

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.

Hi, welcome!

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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:

Hi @adcar,

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 Species column.

#> 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
iris2 <- iris %>% 
    group_by(Species) %>% 
    mutate(status = ifelse(Sepal.Length > mean(Sepal.Length), 1, 0)) %>% 

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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