Data wrangling with dplyr

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# Data wrangling with dplyr
### Danielle Navarro     @djnavarro
### <a href="https://djnavarro.github.io/chdss2018/" class="uri">https://djnavarro.github.io/chdss2018/</a> 11 December 2018

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# Data wrangling

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### .orange[**What?**] Manipulate data
### .orange[**Why?**] Because data are messy
### .orange[**How?**] With tidyverse! (esp. dplyr)

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[@allison_horst](https://twitter.com/allison_horst)

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# Tidyverse

```r
install.packages("tidyverse")
library(tidyverse)
```

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[tidyverse.org](https://www.tidyverse.org/)

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.font4[The "sampling frames" experiment]
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# Property sampling: the robot only detects plaxium spheres
 
<img src="images/data10_property.jpg", width="70%">

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# Category sampling: the robot only tests small spheres
 
<img src="images/data10_category.jpg", width="70%">

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# Small sample size: Elicit judgments after two observations
 
<img src="images/data2_property.jpg", width="70%">

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# Medium sample size: Elicit judgments after six observations
 
<img src="images/data6_property.jpg", width="70%">

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# Large sample size: Elicit judgments after twelve observations
 
<img src="images/data12_property.jpg", width="70%">

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# Seven test items that vary in size: Smallest...
 
<img src="images/test2.jpg", width="70%">

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# Seven test items that vary in size: Largest...
 
<img src="images/test8.jpg", width="70%">

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.font4[Getting started]
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# What do we need?

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- General resources: https://djnavarro.github.io/chdss2018/
- The data: http://compcogscisydney.org/psyr/data/frames_ex2.csv
- Save data here: [your_project]/analysis/data/frames_ex2.csv
- R packages:

```r
install.packages("tidyverse")
install.packages("skimr")
install.packages("lsr")
```
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# An R Markdown document!

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- File > New File > R Markdown...
- Save it: [your_project]/analysis/docs/analysis.Rmd
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# Delete everything except the top bit

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- At the top is the YAML header
- Leave the knitr thing alone
- This is the whole document...
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````
---
title: "Exploring the data"
author: "Danielle Navarro"
date: "4 December 2018"
output: html_document
---

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

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# Start writing!

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- Load packages in the first chunk
- Write **descriptions for humans!**
- Document, document, document
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````
## Getting started

The first thing to do in our analysis is load the R
packages that we'll use to do the work:

```{r}
library(here)
library(tidyverse)
library(skimr)
```

The next step is to load the data set itself. The data 
from the sampling frames experiment are stored in the 
"frames_ex2.csv" file:

```{r}
loc <- here("analysis","data","frames_ex2.csv")
frames <- read_csv(file = loc)
```
````

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# Click the knit button!

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# Tidy the code chunk

.pull.left[.pad1[.font2[
- Note the output messages
- Helpful the first time...
- ... but suppress if you like
- Give it a name (like packageload)
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````
## Getting started

The first thing to do in our analysis is load the R
packages that we'll use to do the work:

```{r packageload, message=FALSE}
library(here)
library(tidyverse)
library(skimr)
```
````
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# Tidy the code chunk

.pull.left[.pad1[.font2[
- Note the output messages
- Helpful the first time...
- ... but suppress if you like
- Give it a name (like packageload)

]]]

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# Import the data

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- Verbal description
- Specify the file location
- Import data with read_csv()
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````
The next step is to load the data set itself. The data 
from the sampling frames experiment are stored in the 
"frames_ex2.csv" file:

```{r dataimport}
loc <- here("analysis","data","frames_ex2.csv")
frames <- read_csv(file = loc)
```
````

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# Import the data

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- Verbal description
- Specify the file location
- Import data with read_csv()
- And click "knit!"
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# Inspect the data set

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- Data as a "tibble" (data frame)
- Printing it shows the structure
- The glimpse() function is nice
- Name *every* code chunk!
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````
The data set has been imported as a tibble. Let's take 
a quick look at the data. Each row in the data set is
a single response, and each column is a variable:

```{r printframes}
frames
```

An alternative way to view the a tibble that sometimes
looks nicer is to use the `glimpse()` function:

```{r glimpseframes}
glimpse(frames)
```
````

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# Inspect the data set

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- Data as a "tibble" (data frame)
- Printing it shows the structure
- The glimpse() function is nice
- Name *every* code chunk!
]]]

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# "Skim" the data

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- Use skimr::skim
- Means, std dev., quantiles
- Also cute text histograms
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````
Finally, as a quick first pass, we can use the `skim()`
function to get a simple overview of each variable:

```{r skimframes}
skim(frames)
```
````

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# "Skim" the data

.pull.left[.pad1[.font2[
- Use skimr::skim
- Means, std dev., quantiles
- Also cute text histograms
]]]
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---

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.font4[A tiny warning about R]
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# R is tricksy

- Suppose `x = 10`
- Suppose I call `plot(x)`
- What does R "pass" to `plot`? 
- I said: the number `10`. But I lied.
- It actually sends the "unevaluated expression" `x` that evaluates to the number `10`. 
- Functions can capture & modify the expression *before* it gets evaluated 
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```r
# simple example
textify <- function(...) {
 y <- deparse(match.call())
 return(y)
}

# let's try it
txt <- textify(what,the,f**k)

# hm.
print(txt)
```

```
## [1] "textify(what, the, f^k)"
```

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# Tidyverse packages do it a lot

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- dplyr
- ggplot2
- readr
- tidyr
- purrr
- etc

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# Beware!

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.font4[Introducing the pipe, %>%]
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# The pipe, %>%

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- Take the frames data...
- Do one thing...
- Then another...
- And then one more...
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```{}
frames %>%
  do_one_thing(.) %>%
  then_another(.) %>%
  and_then_one_more(.)
```

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# Regular code

```{}
do_one_thing(frames) 
```

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# The piped version

```{}
frames %>% do_one_thing() 
```

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# Regular code

```{}
then_another(
  do_one_thing(
    frames
  ) 
)
```

]]]
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# The piped version

```{}
frames %>% 
  do_one_thing() %>%
  then_another()
```

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# Regular code

```{}
and_then_one_more(
  then_another(
    do_one_thing(
      frames
    ) 
  )
)
```

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# The piped version

```{}
frames %>% 
  do_one_thing() %>%
  then_another() %>%
  and_then_one_more()
```

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# Regular code

```{}
then_this(
  and_then_one_more(
    then_another(
      do_one_thing(
        frames
      ) 
    )
  )
)
```

]]]
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# The piped version

```{}
frames %>% 
  do_one_thing() %>%
  then_another() %>%
  and_then_one_more() %>%
  then_this()
```

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# Piping is a good abstraction

```{}
data %>% 
  tidy() %>%
  describe() %>%
  visualise() %>%
  analyse()
```

- We "pipe" our data through operations
- The data set *flows* through our analysis
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---

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.font4[group_by, summarise, ungroup]
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```r
frames %>%
  group_by( GROUP ) %>%
  summarise( EXPRESSION ) %>%
  ungroup()
```

- use `group_by` to define groups
- use `summarise` to... summarise
- use `ungroup` to remove grouping

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## Group &
## Summarise
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```r
frames %>%
  group_by(test_item, sample_size, n_obs, condition) %>%
  summarise(response = mean(response))
```

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## Group &
## Summarise
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```r
frames %>%
  group_by(test_item, sample_size, n_obs, condition) %>%
  summarise(response = mean(response))
```

```
## # A tibble: 42 x 5
## # Groups: test_item, sample_size, n_obs [?]
## test_item sample_size n_obs condition response
## <int> <chr> <int> <chr> <dbl>
## 1 1 large 12 category 7.60
## 2 1 large 12 property 7.16
## 3 1 medium 6 category 7.32
## 4 1 medium 6 property 6.66
## 5 1 small 2 category 6.07
## 6 1 small 2 property 5.78
## 7 2 large 12 category 7.51
## 8 2 large 12 property 7.20
## 9 2 medium 6 category 7.17
## 10 2 medium 6 property 6.95
## # ... with 32 more rows
```

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## Group,
## Summarise,
## Ungroup 
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```r
frames %>%
  group_by(test_item, sample_size, n_obs, condition) %>%
  summarise(response = mean(response)) %>%
* ungroup()
```

```
## # A tibble: 42 x 5
## test_item sample_size n_obs condition response
## <int> <chr> <int> <chr> <dbl>
## 1 1 large 12 category 7.60
## 2 1 large 12 property 7.16
## 3 1 medium 6 category 7.32
## 4 1 medium 6 property 6.66
## 5 1 small 2 category 6.07
## 6 1 small 2 property 5.78
## 7 2 large 12 category 7.51
## 8 2 large 12 property 7.20
## 9 2 medium 6 category 7.17
## 10 2 medium 6 property 6.95
## # ... with 32 more rows
```

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# More summarise
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```r
frames %>% 
  group_by(test_item) %>%
  summarise(
*   mean_resp = mean(response),
*   sd_resp = sd(response),
*   count = n()
  ) %>%
  ungroup()
```

```
## # A tibble: 7 x 4
## test_item mean_resp sd_resp count
## <int> <dbl> <dbl> <int>
## 1 1 6.77 2.56 675
## 2 2 6.88 2.10 675
## 3 3 5.71 2.41 675
## 4 4 4.48 2.68 675
## 5 5 3.76 2.81 675
## 6 6 3.43 2.99 675
## 7 7 3.26 3.11 675
```

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.font4[arrange, filter, select, mutate]
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```r
average_response <- frames %>%
 group_by(test_item, sample_size, n_obs, condition) %>%
 summarise(response = mean(response)) %>%
 ungroup()

*average_response %>%
* filter(sample_size == "small")
```

- First section creates the summary tibble
- Second section applies a filter...
- ... only retains cases where sample_size = "small"

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## Filter
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```r
average_response %>%
  filter(sample_size == "small")
```

```
## # A tibble: 14 x 5
## test_item sample_size n_obs condition response
## <int> <chr> <int> <chr> <dbl>
*## 1 1 small 2 category 6.07
*## 2 1 small 2 property 5.78
## 3 2 small 2 category 6.26
## 4 2 small 2 property 6.21
## 5 3 small 2 category 5.87
## 6 3 small 2 property 5.24
## 7 4 small 2 category 5.11
## 8 4 small 2 property 4.68
## 9 5 small 2 category 4.55
## 10 5 small 2 property 3.99
## 11 6 small 2 category 4.16
## 12 6 small 2 property 3.68
## 13 7 small 2 category 3.98
## 14 7 small 2 property 3.67
```

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## Arrange
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```r
average_response %>%
  filter(sample_size == "small") %>%
* arrange(condition)
```

```
## # A tibble: 14 x 5
## test_item sample_size n_obs condition response
## <int> <chr> <int> <chr> <dbl>
*## 1 1 small 2 category 6.07
*## 2 2 small 2 category 6.26
*## 3 3 small 2 category 5.87
*## 4 4 small 2 category 5.11
*## 5 5 small 2 category 4.55
*## 6 6 small 2 category 4.16
*## 7 7 small 2 category 3.98
## 8 1 small 2 property 5.78
## 9 2 small 2 property 6.21
## 10 3 small 2 property 5.24
## 11 4 small 2 property 4.68
## 12 5 small 2 property 3.99
## 13 6 small 2 property 3.68
## 14 7 small 2 property 3.67
```

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## Select
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```r
average_response %>%
  filter(sample_size == "small") %>%
  arrange(condition) %>%
* select(test_item, condition, response)
```

```
## # A tibble: 14 x 3
## test_item condition response
## <int> <chr> <dbl>
## 1 1 category 6.07
## 2 2 category 6.26
## 3 3 category 5.87
## 4 4 category 5.11
## 5 5 category 4.55
## 6 6 category 4.16
## 7 7 category 3.98
## 8 1 property 5.78
## 9 2 property 6.21
## 10 3 property 5.24
## 11 4 property 4.68
## 12 5 property 3.99
## 13 6 property 3.68
## 14 7 property 3.67
```

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# Store output!
]]

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```r
*average_response_small <- average_response %>%
 filter(sample_size == "small") %>%
 arrange(condition) %>%
 select(test_item, condition, response)

average_response_small
```

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# Mutate
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```r
average_response_small <- average_response_small %>%
* mutate(generalisation = response/9)

average_response_small
```

```
## # A tibble: 14 x 4
## test_item condition response generalisation
## <int> <chr> <dbl> <dbl>
## 1 1 category 6.07 0.674
## 2 2 category 6.26 0.696
## 3 3 category 5.87 0.652
## 4 4 category 5.11 0.568
## 5 5 category 4.55 0.506
## 6 6 category 4.16 0.462
## 7 7 category 3.98 0.442
## 8 1 property 5.78 0.643
## 9 2 property 6.21 0.690
## 10 3 property 5.24 0.583
## 11 4 property 4.68 0.521
## 12 5 property 3.99 0.443
## 13 6 property 3.68 0.409
## 14 7 property 3.67 0.407
```

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# Select
]]

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```r
average_response_small <- average_response_small %>%
 mutate(generalisation = response/9) %>%
* select(-response)

average_response_small
```

```
## # A tibble: 14 x 3
## test_item condition generalisation
## <int> <chr> <dbl>
## 1 1 category 0.674
## 2 2 category 0.696
## 3 3 category 0.652
## 4 4 category 0.568
## 5 5 category 0.506
## 6 6 category 0.462
## 7 7 category 0.442
## 8 1 property 0.643
## 9 2 property 0.690
## 10 3 property 0.583
## 11 4 property 0.521
## 12 5 property 0.443
## 13 6 property 0.409
## 14 7 property 0.407
```

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[@allison_horst](https://twitter.com/allison_horst)

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# Long
]]

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```r
average_response_small
```

```
## # A tibble: 14 x 3
## test_item condition generalisation
## <int> <chr> <dbl>
*## 1 1 category 0.674
## 2 2 category 0.696
## 3 3 category 0.652
## 4 4 category 0.568
## 5 5 category 0.506
## 6 6 category 0.462
## 7 7 category 0.442
*## 8 1 property 0.643
## 9 2 property 0.690
## 10 3 property 0.583
## 11 4 property 0.521
## 12 5 property 0.443
## 13 6 property 0.409
## 14 7 property 0.407
```

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# Spread
# to wide
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```r
wide_avrs <- average_response_small %>%
* spread(key = condition, value = generalisation)
```

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.column.bg-main1[.content.vmiddle.center[
# Spread
# to wide
]]

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```r
wide_avrs <- average_response_small %>%
 spread(key = condition, value = generalisation)

wide_avrs
```

```
## # A tibble: 7 x 3
## test_item category property
## <int> <dbl> <dbl>
*## 1 1 0.674 0.643
## 2 2 0.696 0.690
## 3 3 0.652 0.583
## 4 4 0.568 0.521
## 5 5 0.506 0.443
## 6 6 0.462 0.409
## 7 7 0.442 0.407
```

]]]
]]

---
class: split-20 bg-main1

.column.bg-main1[.content.vmiddle.center[
# Gather
# to long
]]

.column.bg-main3[.content.vtop.center[
.pull.left[.pad1[.font2[

```r
wide_avrs %>% 
* gather(key = "condition", value = "generalisation", category, property)
```

]]]
]]

---
class: split-20 bg-main1

.column.bg-main1[.content.vmiddle.center[
# Gather
# to long
]]

.column.bg-main3[.content.vtop.center[
.pull.left[.pad1[.font2[

```r
wide_avrs %>% 
* gather(key = "condition", value = "generalisation", category, property)
```

]]]
]]

---
class: split-20 bg-main1

.column.bg-main1[.content.vmiddle.center[
# Spread by
# two cols?
]]

.column.bg-main3[.content.vtop.center[
.pull.left[.pad1[.font2[

```r
frames %>% 
  group_by(test_item, sample_size, condition) %>%
  summarise(response = mean(response))
```

```
## # A tibble: 42 x 4
## # Groups: test_item, sample_size [?]
## test_item sample_size condition response
## <int> <chr> <chr> <dbl>
## 1 1 large category 7.60
## 2 1 large property 7.16
## 3 1 medium category 7.32
## 4 1 medium property 6.66
## 5 1 small category 6.07
## 6 1 small property 5.78
## 7 2 large category 7.51
## 8 2 large property 7.20
## 9 2 medium category 7.17
## 10 2 medium property 6.95
## # ... with 32 more rows
```

]]]
]]

---
class: split-20 bg-main1

.column.bg-main1[.content.vmiddle.center[
# Unite...
]]

.column.bg-main3[.content.vtop.center[
.pull.left[.pad1[.font2[

```r
frames %>% 
  group_by(test_item, sample_size, condition) %>%
  summarise(response = mean(response)) %>%
* unite(col = "cond_ss", condition, sample_size)
```

```
## # A tibble: 42 x 3
## # Groups: test_item [7]
## test_item cond_ss response
## <int> <chr> <dbl>
## 1 1 category_large 7.60
## 2 1 property_large 7.16
## 3 1 category_medium 7.32
## 4 1 property_medium 6.66
## 5 1 category_small 6.07
## 6 1 property_small 5.78
## 7 2 category_large 7.51
## 8 2 property_large 7.20
## 9 2 category_medium 7.17
## 10 2 property_medium 6.95
## # ... with 32 more rows
```

]]]
]]

---
class: split-20 bg-main1

.column.bg-main1[.content.vmiddle.center[
# Then spread
]]

.column.bg-main3[.content.vtop.center[
.pull.left[.pad1[.font2[

```r
frames %>% 
  group_by(test_item, sample_size, condition) %>%
  summarise(response = mean(response)) %>%
  unite(col = "cond_ss", condition, sample_size) %>%
* spread(key = "cond_ss", value = response)
```

```
## # A tibble: 7 x 7
## # Groups: test_item [7]
## test_item category_large category_medium category_small property_large
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 7.60 7.32 6.07 7.16
## 2 2 7.51 7.17 6.26 7.20
## 3 3 6.39 5.98 5.87 5.23
## 4 4 5.39 4.97 5.11 3.07
## 5 5 4.72 4.22 4.55 2.26
## 6 6 4.43 3.85 4.16 1.91
## 7 7 4.18 3.61 3.98 1.90
## # ... with 2 more variables: property_medium <dbl>, property_small <dbl>
```

]]]
]]

---

---

## thank u, next