Some example data.

df <- data.frame(
  patient=sprintf("C%s", seq_len(10)),
  genotype=sample(seq_len(3), 10, replace=TRUE),
  temperature=sample(seq_len(7), 10, replace=TRUE)
)

> head(df, 5)
  patient genotype temperature
1      C1        1           4
2      C2        3           1
3      C3        1           3
4      C4        2           2
5      C5        1           6

A boxplot using ggplot2.

library("tidyverse")

df %>%
  mutate(genotype=as_factor(genotype)) %>%
  ggplot(aes(x=genotype, y=temperature)) +
    geom_boxplot()

Or a stacked barplot as Hamid Ghaedi correctly pointed out.

df %>%
  mutate(across(!patient, as_factor)) %>%
  ggplot(aes(x=genotype, fill=temperature)) +
    geom_bar(position="fill") +
    scale_fill_viridis_d()

Important notice: Box-plot is usually use to show how a quantitative variable is distributed. Especially it does a great job when diffrneces in variance is significant between the groups and you expect to have some outliers ( Like gene expression data). Since you have discretized the tempreature data, using a box-plot for visualization does not make sense anymore, however, you can plot your original tempreature data as box-plot.

Now you have frequency data of tempreature per genotype, you can viusalize the data using a barplot (or prefreably stacked barplot).