Multinomial logistic regression is used to model a nominal outcome variable. Because of the nominal categorical nature of the outcome variable, the output table becomes complex, especially when the researcher includes a great number of predictors. Visualization can help researchers quickly summarize the patterns of results. Using tidyverse R package (ggplot2 is embedded in this package), we can visualize the regression results table.
library(readr)
library(tidyverse)

mydata <- read_csv("Reg_results.csv")

coef_plot <-mydata %>%
  mutate(Group = case_when(
    Group == "moderate" ~ "Moderate",
    Group == "negative" ~ "Negative",
    Group == "neg_pos" ~ "Negative & Positive"),
    Covariate = case_when(
      Covariate == "AGEGRP5" ~ "Age - 65+",
      Covariate == "AGEGRP1" ~  "Age - 25 - 34",
      Covariate == "RELIG" ~ "Religiosity",
      Covariate == "RELIG_KID" ~ "Child \n Religious Faith",
      Covariate == "PCFREQ2"~ "Computer Use",
      Covariate == "EDU3GRP2"~ "College or More",
      Covariate == "EDU3GRP1" ~ "High School",
      Covariate == "EMP3GRP1" ~ "Currently Working"))%>%
  ggplot(aes(y = Covariate, x = estimate, pch = Group,label = OR)) +
  geom_point(aes(y = Covariate, x=estimate), color= "#FF6666") +  
  geom_errorbarh(aes(xmax = Upper, xmin = Lower,height = .12), color ="#FF6666",size = 0.6) +  
  geom_vline(xintercept =0, linetype = "dashed") +
  scale_shape_manual(values = c(0,2,19)) +
  geom_text(size = 3, nudge_x = 2,vjust = -0.25) + 
  facet_grid(.~Group) +
  scale_x_continuous(name ="Regression Coefficients with Odds Ratio", limits=c(-5,5)) +
  theme(legend.position = "bottom") 

coef_plot

Pullman, A., Chen, M., Zou, D., Hives, B., & Liu, Y. (2018). Researching multiple publics through latent profile analysis: Similarities and differences in science and technology attitudes in China, Japan, South Korea and the United States. Public Understanding of Science, 28(2), 130-145. DOI: 10.1177/0963662518791902