descriptive statistics.Rmd

---
title: "descriptive statistics"
author: "Jessica Spencer"
date: "2/25/2021"
output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source("cleaning_for_numerics.R")
```

```{r}
make_plots =  function(dat,breakdown){
  for (col in dat){
    #mfrow=c(2,1)
  #  print(col)
    title_1 = paste(names(col), " for All Participants")
    ggplot(clean_dat, aes(x=col)) + geom_histogram(binwidth = 1,color="darkblue", fill="lightblue") + xlim(1930, 2005) +ggtitle(title_1) + xlab(col)
  title_2 = col+"by" + breakdown
  ggplot(clean_dat, aes(x=col, color=breakdown, fill=bucketed_gender)) + geom_histogram(binwidth = 1,position="dodge", alpha=0.5) + xlim(1930, 2005) +ggtitle(title_2) + xlab(col)+theme(legend.position="top")
  }
}
```

##Descriptive Statistics

```{r}
#age stuff
#View(clean_dat)
clean_dat$yob = strtoi(clean_dat$birth.year)
#View(clean_dat$yob)
ggplot(clean_dat, aes(x=yob)) + geom_histogram(binwidth = 1,color="darkblue", fill="lightblue") + xlim(1930, 2005) +ggtitle("Year of Birth for All Participants") + xlab("Year of Birth")

ggplot(clean_dat, aes(x=yob, color=bucketed_gender, fill=bucketed_gender)) + geom_histogram(binwidth = 1,position="dodge", alpha=0.5) + xlim(1930, 2005) +ggtitle("Year of Birth by Gender for Participants") + xlab("Year of Birth")+theme(legend.position="top")

ggplot(clean_dat, aes(x=yob, color=bucketed_gender, fill=bucketed_gender)) + geom_histogram(binwidth = 1,position="dodge", alpha=0.5) + xlim(1930, 2005) +ggtitle("Year of Birth by Gender for Participants") + xlab("Year of Birth")+theme(legend.position="top")

```


```{r}
ggplot(clean_dat, aes(x=age)) + geom_histogram(binwidth = 1,color="darkblue", fill="lightblue") + xlim(15, 90) +ggtitle("Age for All Participants") + xlab("Age")
ggplot(clean_dat, aes(x=main.chair)) + geom_histogram(binwidth = 1,color="darkblue", fill="lightblue") + xlim(0, 50) +ggtitle("Number of Main Chair Tours for All Participants") + xlab("Number")
```

```{r}
library(Amelia)
library(mlbench)
missmap(clean_dat, col=c("blue", "red"), legend=FALSE)
```

Instrument Breakdown
```{r}
ins = poi %>% group_by(violin,viola) %>% summarize(count = n())
ins
```