Analysis of binary variable

[adcar]

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.

[andresrcs]

Hi, welcome!

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[adcar]

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 ()

[andresrcs]

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:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[valeri]

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.

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)}

[adcar]

thank you very much @valeri.