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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>tidy data and tidy tools</title>
<meta charset="utf-8" />
<meta name="author" content="Eric Leung" />
<meta name="date" content="2020-05-17" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# tidy data and tidy tools
## Overview of tools of effective data analysis
### Eric Leung
### 2020-05-17
---
# Overview
- Tidy data
- Core and useful tidyverse functions
- Useful packages
---
# Tidy data overview
![Tidy data](https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)
*Source*: https://r4ds.had.co.nz/tidy-data.html
---
# Tidy data are easier to work with inside the tidyverse
> "Tidy datasets are all alike, but every messy dataset is messy in its own
way."
>
> –– Hadley Wickham
---
# Objective
Take built-in phyloseq object and convert it to a tidy data set. Then wrangle
the data using tidy principles and tidyverse packages<sup>*</sup>.
.footnote[[*] The "tidyverse" packages are a suite of packages that adhere to
tidy data principles]
---
# Load packages
```r
library(phyloseq)
# install.packages("remotes")
# remotes::install_github("mikemc/speedyseq")
library(speedyseq) # Optional
```
---
# phyloseq data example
```r
# Load data example
data("GlobalPatterns")
GlobalPatterns
```
```
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 19216 taxa and 26 samples ]:
## sample_data() Sample Data: [ 26 samples by 7 sample variables ]:
## tax_table() Taxonomy Table: [ 19216 taxa by 7 taxonomic ranks ]:
## phy_tree() Phylogenetic Tree: [ 19216 tips and 19215 internal nodes ]:
## taxa are rows
```
--
Let's convert this into a tidy data set.
---
# `psmelt()` converts to tidy data
```r
psm_gp <- psmelt(GlobalPatterns) # Create tidy data
```
--
```r
library(tibble)
psm_gp <- tibble(psm_gp) # Make nicer data frame
```
--
```r
head(psm_gp, 5) # View first five rows
```
```
## # A tibble: 5 x 17
## OTU Sample Abundance X.SampleID Primer Final_Barcode Barcode_truncated_p…
## <chr> <chr> <dbl> <fct> <fct> <fct> <fct>
## 1 549656 AQC4cm 1177685 AQC4cm ILBC_… ACAGCT AGCTGT
## 2 279599 LMEpi24M 914209 LMEpi24M ILBC_… ACACTG CAGTGT
## 3 549656 AQC7cm 711043 AQC7cm ILBC_… ACAGTG CACTGT
## 4 549656 AQC1cm 554198 AQC1cm ILBC_… ACAGCA TGCTGT
## 5 360229 M31Tong 540850 M31Tong ILBC_… ACACGA TCGTGT
## # … with 10 more variables: Barcode_full_length <fct>, SampleType <fct>,
## # Description <fct>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
## # Family <chr>, Genus <chr>, Species <chr>
```
---
# Quick note on the tidyverse
- The {tidyverse} package is a suite of packages
- Home website https://www.tidyverse.org/
- Running `library(tidyverse)` will load the main tidyverse packages
- The following code loads the appropriate packages within the tidyverse when
needed
---
# Let's clean the `Descriptions` column
```r
library(dplyr)
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
head(5)
```
```
## # A tibble: 5 x 4
## OTU Sample SampleType Description
## <chr> <chr> <fct> <fct>
## 1 549656 AQC4cm Freshwater (creek) "Allequash Creek, 3-4 cm depth"
## 2 279599 LMEpi24M Freshwater "Lake Mendota Minnesota, 24 meter epilimni…
## 3 549656 AQC7cm Freshwater (creek) "Allequash Creek, 6-7 cm depth"
## 4 549656 AQC1cm Freshwater (creek) "Allequash Creek, 0-1cm depth"
## 5 360229 M31Tong Tongue "M3, Day 1, tongue, whole body study "
```
---
# Just keep stool samples
```r
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
filter(SampleType == "Feces") %>%
head(5)
```
```
## # A tibble: 5 x 4
## OTU Sample SampleType Description
## <chr> <chr> <fct> <fct>
## 1 331820 M11Fcsw Feces "M1, Day 1, fecal swab, whole body study "
## 2 331820 M31Fcsw Feces "M3, Day 1, fecal swab, whole body study"
## 3 189047 TS29 Feces "Twin #2"
## 4 158660 M11Fcsw Feces "M1, Day 1, fecal swab, whole body study "
## 5 244304 M11Fcsw Feces "M1, Day 1, fecal swab, whole body study "
```
--
The descriptions have lots of information in them that we might want to
visualize later on with ggplot2.
--
Difficult to plot when the data is locked in text form.
**Note**: some of the following code may not be "best" design decision
---
# Load core data wrangling libraries
```r
library(tidyr)
```
--
```r
# Column must be character, `Description` is currently a factor
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
filter(SampleType == "Feces") %>%
* mutate(Description = as.character(Description)) %>%
* separate_rows(Description, sep = ", ") %>%
head(5)
```
```
## # A tibble: 5 x 4
## OTU Sample SampleType Description
## <chr> <chr> <fct> <chr>
## 1 331820 M11Fcsw Feces "M1"
## 2 331820 M11Fcsw Feces "Day 1"
## 3 331820 M11Fcsw Feces "fecal swab"
## 4 331820 M11Fcsw Feces "whole body study "
## 5 331820 M31Fcsw Feces "M3"
```
---
# Let's move values into columns
```r
# Create `value` column to keep value
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
filter(SampleType == "Feces") %>%
mutate(Description = as.character(Description)) %>%
separate_rows(Description, sep = ", ") %>%
* mutate(value = 1) %>%
* pivot_wider(names_from = Description,
* values_from = value) %>%
head(5)
```
```
## # A tibble: 5 x 11
## OTU Sample SampleType M1 `Day 1` `fecal swab` `whole body study ` M3
## <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 331820 M11Fcsw Feces 1 1 1 1 NA
## 2 331820 M31Fcsw Feces NA 1 1 NA 1
## 3 189047 TS29 Feces NA NA NA NA NA
## 4 158660 M11Fcsw Feces 1 1 1 1 NA
## 5 244304 M11Fcsw Feces 1 1 1 1 NA
## # … with 3 more variables: whole body study <dbl>, Twin #2 <dbl>, Twin #1 <dbl>
```
---
# We can reverse our pivot
```r
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
filter(SampleType == "Feces") %>%
mutate(Description = as.character(Description)) %>%
separate_rows(Description, sep = ", ") %>%
mutate(value = 1) %>%
pivot_wider(names_from = Description,
values_from = value) %>%
* pivot_longer(cols = !c(OTU, Sample, SampleType),
* names_to = "Description",
* values_to = "Value") %>%
head(5)
```
```
## # A tibble: 5 x 5
## OTU Sample SampleType Description Value
## <chr> <chr> <fct> <chr> <dbl>
## 1 331820 M11Fcsw Feces "M1" 1
## 2 331820 M11Fcsw Feces "Day 1" 1
## 3 331820 M11Fcsw Feces "fecal swab" 1
## 4 331820 M11Fcsw Feces "whole body study " 1
## 5 331820 M11Fcsw Feces "M3" NA
```
---
# Future steps
- With the code here, you could further process the data to filter on specific
features.
- For example, you could focus on stool samples from a specific twins or a whole
body study.
- The {stringr} package within the tidyverse will prove useful for checking the
contents of strings
---
# Functions reviewed
- **Note**: the following functions are in the form `<package>::<function>`
- **`dplyr::select()`** = select, subset, and remove columns from data frame
- **`dplyr::filter()`** = remove or keep specific rows based on information in
data frame
- **`dplyr::mutate()`** = convert or transform column values
- **`tidyr::separate_rows()`** = take column values and separate them into
multiple rows
- **`tidyr:pivot_wider()`** and **`tidyr::pivot_longer()`** = transform data
frame from long to wide data
---
# Useful packages
- janitor = miscellaneous cleaning
- unheadr = weird nested headers
- tidycells = multiple tables spaced out in Excel
---
# janitor for cleaning columns and creating frequency tables
```r
library(janitor)
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
filter(SampleType == "Feces") %>%
mutate(Description = as.character(Description)) %>%
separate_rows(Description, sep = ", ") %>%
* clean_names() %>%
head(5)
```
```
## # A tibble: 5 x 4
## otu sample sample_type description
## <chr> <chr> <fct> <chr>
## 1 331820 M11Fcsw Feces "M1"
## 2 331820 M11Fcsw Feces "Day 1"
## 3 331820 M11Fcsw Feces "fecal swab"
## 4 331820 M11Fcsw Feces "whole body study "
## 5 331820 M31Fcsw Feces "M3"
```
---
# janitor for creating frequency tables
```r
psm_gp %>%
select(OTU, Sample, SampleType, Description) %>%
* tabyl(SampleType) %>%
* adorn_totals()
```
```
## SampleType n percent
## Feces 76864 0.15384615
## Freshwater 38432 0.07692308
## Freshwater (creek) 57648 0.11538462
## Mock 57648 0.11538462
## Ocean 57648 0.11538462
## Sediment (estuary) 57648 0.11538462
## Skin 57648 0.11538462
## Soil 57648 0.11538462
## Tongue 38432 0.07692308
## Total 499616 1.00000000
```
---
# unheadr for nested headers
**Before**
| scientific\_name | common\_name | red\_list\_status | mass\_kg |
| :---------------------- | :--------------------------- | :---------------- | -------: |
| Asia | NA | NA | NA |
| CERCOPITHECIDAE | NA | NA | NA |
| Trachypithecus obscurus | Dusky Langur | NT | 7.13 |
| Presbytis sumatra | Black Sumatran Langur | EN | 6.00 |
---
# unheadr for nested headers
**After**
| scientific\_name | common\_name | red\_list\_status | mass\_kg | family |
| :---------------------- | :--------------------------- | :---------------- | -------: | :-------------- |
| Trachypithecus obscurus | Dusky Langur | NT | 7.13 | CERCOPITHECIDAE |
| Presbytis sumatra | Black Sumatran Langur | EN | 6.00 | CERCOPITHECIDAE |
---
# tidycells for odd cell arrangements
![](https://r-rudra.github.io/tidycells/articles/ext/marks.png)
---
# Resources
- R for Data Science https://r4ds.had.co.nz
- Ted's list of underrated tidyverse functions
https://hugo-portfolio-example.netlify.app/projects/tidyverse_functions/
- {janitor} R package https://github.com/sfirke/janitor
- {unheadr} R package https://github.com/luisDVA/unheadr
- {tidycells} https://r-rudra.github.io/tidycells/
- Blog post on cleaning messy data
https://rfortherestofus.com/2019/12/how-to-clean-messy-data-in-r/
</textarea>
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