Module_05_Pivoting_Data.Rmd

---
title: 'Module 5:  Reshaping Data with Tidyr'
author: "Brian P Steves"
date: "`r Sys.Date()`"
output:
  html_document:
    keep_md: yes
---



Module 5: Reshaping data with Tidyr
====================
# Pivoting Data

  The terms for this have changed several times within the tidyverse (melt, reshape, spread, gather).  Currently the tidyr packages uses the terms pivot_longer and pivot_wider.  I think this is in reference to pivot tables found in Excel.


Recall, that in Module 1 we talked about wide and narrow data formats.  This is how you can switch between them when necessary.


# Pivot_wide

I generally prefer to have my data in the narrow (long) format and sometimes need to transform that data into the wide format.  This is where the pivot_wide() function comes in.
pivot_wider has serveral parameters..
names_from = the column you want to become column names
values_from = the column you want to use for the cells
values_fill = a value to use for missing data
values_fn = a function to apply to values

```{r, message=FALSE, warning=FALSE}
library(dplyr)
library(tidyr)
mtcars %>% select(cyl, mpg, hp) %>% arrange(hp) %>% pivot_wider(names_from=cyl, values_from=mpg, values_fn=mean)

```


## Pivot_longer


```{r}
iris
head(iris)
iris_long <- iris %>% pivot_longer(Sepal.Length:Petal.Width, names_to="Measurement", values_to="Size_mm")
iris_long
```
Bonus, we can split out the leaf type from the measurement dimension in the "measurement" column we just created. Luckily the two are already delineated by a "." and the separate() function will find that and use it.
```{r}
iris_long %>% separate(Measurement, c("Leaf Type", "Dimension"))

```

```



## Homework

Determine if your data set(s) are narrow or wide.  Use tidyr's pivot_wide() and pivot_long() functions to transform your data.

For example, if your data is narrow, transform a subset of your data into a more useful and more readable wide table.

Likewise, if your data is wide, transform it to narrow format