pre_class_survey_results.Rmd

---
title: "Survey Results"
author: "Brian Wright"
date: "8/25/2021"
output: 
  html_document: 
    toc: yes
    toc_float:
      toc_collapsed: true
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

```{r,include=FALSE}
library(tidyverse)

```

```{r, include=FALSE}
data <- read_csv("~/git_3001/DS-3001/pre_class_survey_data.csv")

#View(data)
```

## Let's take a look at the numeric responses 
```{r}
str(data)

column_index <- tibble(colnames(data))
column_index

hist(data$`On a scale of 1 to 10 rank your comfort with the R programming language, generally`, main = "R Programming")


hist(data$`On a scale from 1 to 10 rank your comfort with markdown or Rmarkdown generally.`, main = "Markdown")

hist(data$`On scale from 1 to 10 rank your comfort with using tidyverse packages`, main = "Tidyverse")


hist(data$`On a scale from 1 to 10 rank your experience using machine learning models.`, main = "Machine Learning")



```

## Now let's check out the factors(what's a better appoarch) 
```{r}
x_table <- table(data$`What is your major?`)

vis <- ggplot(data, aes(y=`What is your major?`))#flipped the cartesian coordinates for better viewing

column_index
data[[8]]

factor_viz <- function(x,y,z){
  vis <- ggplot(x, aes(y=x[[y]]))+
    geom_bar()+
    labs(x="Count",y=z)+
    theme_minimal()
  vis
}

#What would the function look like for major and language
factor_viz(data,8,"Major")

factor_viz(data,9,"Language")

factor_viz(data,10,"Data Scientist?")

factor_viz(data,13,"Pizza Pineapple?")

```