BasicStatsCode.Rmd

---
title: "Basic Stats"
author: "Lilly Winfree, Danielle Robinson"
date: "May 26"
output:
  pdf_document: default
  html_document: default
  ioslides_presentation: default
  beamer_presentation: default
  slidy_presentation: default
---

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

##Let's learn how to read in data and then do some basic statistical analysis.
Read in data:

```{r}
data1<-read.table("example_lengths_for_R.csv", header=TRUE, sep=',')
```

Reminder! Name your variables carefully!

Note: When we read in the data, we need to specify if there is a **header** (or column titles) and how the data is separated, or **sep**. This data is a csv so it is separated by ',' - sometimes data can be separated by '|' for example.

We can look at the data quickly using **summary()**, **nrow()** to see the row numbers, **colnames()** for column names:

```{r}
summary(data1)
nrow(data1)
colnames(data1)
```

We can subset to different columns with **$** and only see select rows with **[:]**

```{r}
data1$coverlip.code[1:10]
data1$treatment[1:10]
summary(data1$values)
summary(data1$values[data1$treatment=='Control'])
```

Let's plot our data!

```{r}
plot(data1$treatment, data1$values, xlab='treatment', ylab='length values')
```

Note that **xlab** and **ylab** mean x and y labels.

We can also use **hist()** to make a histogram:
```{r}
hist(data1$values[data1$treatment=='Control'], xlab='Length Values', 
     main='Histogram of Length for Control Group')
```

Note that **main** means title here.

Now let's look at how to do some basic stats:
```{r}
mean(data1$values[data1$treatment=='Treatment A'])
mean(data1$values[data1$treatment=='Control'])
mean(data1$values[data1$treatment=='Treatment B'])
median(data1$values[data1$treatment=='Control'])
var(data1$values[data1$treatment=='Control'])
min(data1$values[data1$treatment=='Control'])
max(data1$values[data1$treatment=='Control'])
```

Notice how summary() gave us the same info as the above functions

OK let's do a t-test now! We can use **t.test** to compare the means between 2 of the groups
```{r}
t.test(data1$values[data1$treatment=='Control'], data1$values[data1$treatment=='Treatment B'])
```

What does this tell us?
How do we change the code to look at the other groups?

We can also do an ANOVA with this data using **aov** and plot the ANOVA information:
```{r}
results<-aov(values ~ treatment, data=data1)
summary(results)
```

See ANOVA doc for more info on how an ANOVA works.