---
title: "350.wk2.data"
author: "William Revelle"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(width=100)
```
# Some comments about characters and numbers        



Most of the data we analyze is `numeric`.  But sometimes we will have variables that are `character`.  This will lead to some interesting problems.

Consider the following dataset created by a fellow student.

First, we need to remember to make `psych` active.

```{r}
library(psych)
library(psychTools)
```


```{r}
fn <- "https://personality-project.org/courses/350/datasets/hp.csv"
hp <- read.file(fn)
headTail(hp) #just show the first and last 4 lines
summary(hp)  #this the R way of summarizing
describe(hp)  #this is the psych way of describing 

```

That some of the data are `character` means that the `cor` function will not work.  `describe and `lowerCor` converts the character data to numeric using the `char2numeric` function and then does normal operations on the data.

But this leads to some confusion, in that characters are converted to numeric values in alphabetical order.  Thus, `female' becomes 1 and `male` becomes 2, but `man` becomes 1 and `woman` becomes 2.  

To let you it has automatically done this conversion, it adds and * to the variable name.  Thus  sex, and Gen are renamed as  sex* and Gen*.

Look at the correlations.  sex* and Gen* are negative correlated. 

```{r}
lowerCor(hp)
```
This is easy to see if we show the data after we convert it using char2numeric.


```{r}
converted <- char2numeric(hp)
headTail(converted)
```

