Introduction to dplyr

<div class="my-footer"><a href="https://www.reisanar.com/" target="_blank" rel="noopener"><span>@reisanar</span></a></div>

---

# Data Transformation <br>and Exploration

**Rei Sanchez-Arias, Ph.D.**

]

---

layout: false
name: common-tasks
class: center middle section-title section-title-2 animated fadeIn
<div class="my-footer"><a href="https://www.reisanar.com/" target="_blank" rel="noopener"><span>@reisanar</span></a></div>

# Motivation

---

---

# Common tasks

<small>
- Often you will need to create some *new variables* or *summaries*, or maybe you just want to *rename *the variables or *reorder* the observations to make the data a little easier to work with.

- We will focus on how to use the `dplyr` package, another core member of the `tidyverse`.

**Prerequisites:** Install the `nycflights13` and `tidyverse` packages
</small>

```r
library(tidyverse)
library(nycflights13)
```
---

# Data from `nycflights13`

This dataset contains flights departing New York City (NYC) in 2013. It contains all 336,776 flights that departed from NYC in 2013. 
]
.pull-right[
<figure><img src="https://raw.githubusercontent.com/reisanar/figs/master/flights13.png"><figcaption></figcaption></figure>
]

The data comes from the [US Bureau of Transportation Statistics](https://www.bts.gov/browse-statistical-products-and-data), and is documented in `?flights`

---

layout: false
name: dplyr
class: center middle section-title section-title-2 animated fadeIn
background-image: url("https://raw.githubusercontent.com/reisanar/figs/master/dplyr-logo.png")
background-position: right
background-size: contain
<div class="my-footer"><a href="https://www.reisanar.com/" target="_blank" rel="noopener"><span>@reisanar</span></a></div>

# Introducing<br>`dplyr`

---

---

# The `tidyverse`

The `tidyverse` is an opinionated [collection of R packages](https://www.tidyverse.org/packages/) designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

Definition of **tidy data**: (see [paper](https://vita.had.co.nz/papers/tidy-data.pdf) by Hadley Wickham)

- Each variable is a column

- Each observation is a row

- Each type of observational unit is a table

---

# Core `tidyverse` packages

.smaller[
           | 
-----------------|--------------------------------------------------------
[`ggplot2`](https://ggplot2.tidyverse.org/) | data visualization
[`dplyr`](https://dplyr.tidyverse.org/) | data manipulation
[`tidyr`](https://tidyr.tidyverse.org/)| tidy up data
[`readr`](https://readr.tidyverse.org/)| data import
[`purrr`](https://purrr.tidyverse.org/)| functional programming
[`tibble`](https://tibble.tidyverse.org/)| dataframes reimagined

]

<figure><img src="https://raw.githubusercontent.com/reisanar/figs/master/tidyverse-packages.png"><figcaption></figcaption></figure>
]

.smaller[
           | 
-----------------|--------------------------------------------------------
[`stringr`](https://stringr.tidyverse.org/) | working with strings
[`forcats`](https://forcats.tidyverse.org/)|  working with factors

]

---

# `dplyr` basics

.left-column[
<figure><img src="https://raw.githubusercontent.com/reisanar/figs/master/dplyr-new-old.png"><figcaption></figcaption></figure>

]

2. **Reorder** the rows: `arrange()`

3. **Pick variables** by their names: `select()`

4. **Create new variables** with functions of existing variables: `mutate()`

5. **Collapse** many values down to a single summary: `summarize()`

6. Operate on a **group-by-group** basis: `group_by()`

]

---

# Filtering _rows_

`filter()` allows you to subset observations based on their values.  For example, we can select all flights on January 1st with:
]

```r
filter(flights, month == 1, day == 1)
```

]

.tiny[
<div id="htmlwidget-ce14499fed40274eac04" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-ce14499fed40274eac04">{"x":{"filter":"none","vertical":false,"fillContainer":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79","80","81","82","83","84","85","86","87","88","89","90","91","92","93","94","95","96","97","98","99","100"],[622,749,646,628,559,652,624,656,655,701,639,732,645,746,607,739,624,558,725,635,752,601,602,724,659,658,629,729,628,732,657,656,533,559,646,608,613,544,715,606,656,644,643,732,724,557,651,629,656,559,739,517,659,655,712,743,606,602,709,600,627,557,554,723,637,743,558,734,655,659,558,752,702,717,743,558,629,745,727,611,555,615,643,558,632,623,653,719,659,733,741,615,542,752,554,623,711,728,658,600],[630,710,645,630,559,655,630,700,700,700,640,645,647,746,607,745,630,600,730,635,750,600,610,730,700,700,630,730,630,729,700,659,529,600,645,600,610,545,713,610,705,636,645,735,725,600,655,630,700,600,739,515,700,655,715,749,610,605,700,600,630,600,558,725,645,730,600,737,700,700,600,759,700,720,730,600,630,745,730,600,600,615,646,600,608,627,700,721,705,736,745,615,540,755,600,610,715,732,700,600]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>dep_time<\/th>\n      <th>sched_dep_time<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":5,"scrollX":true,"lengthChange":false,"dom":"tp","columnDefs":[{"className":"dt-right","targets":[1,2]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]

---

# Comparisons

.small[
`dplyr` functions never modify their inputs, so if you want to save the result, you will need to use the assignment operator, `<-`
]

```r
jan1 <- filter(flights, month == 1, day == 1)
```
]

.small[
<br>
To use filtering effectively, you have to know how to select the observations that you want using comparison operators:

`>`, `>=`, `<`, `<=`,

`!=` (not equal), and

`==` (equal).
]
]

<figure><img src="http://r4ds.had.co.nz/diagrams/transform-logical.png"><figcaption></figcaption></figure>
]
---

# Flights in November _OR_ December

The following code finds all flights that departed in November OR December:

```r
filter(flights, month == 11 | month == 12)
```

A useful short-hand is `x %in% y`. This will select every row where `x` is one of the values in `y`. We could use it to rewrite the code above:

```r
nov_dec <- filter(flights, month %in% c(11, 12))
```

---

# Arrange rows with `arrange()`

`arrange()` works similarly to `filter()` except that instead of selecting rows, it **changes their order**.

It takes a data frame and a set of column names to order by.

```r
arrange(flights, year, month, day)
```

Use `desc()` to re-order by a column in descending order:

```r
arrange(flights, desc(arr_delay))
```

---

# Select columns with `select()`

`select()` allows you to rapidly zoom in on a useful subset using the names of the **variables**.

```r
# Select columns by name
select(flights, year, month, day)
```
]

<br>
There are a number of helper functions you can use within `select()`:

`starts_with("abc")`: matches names that begin with `"abc"`.

`ends_with("xyz")`: matches names that end with `"xyz"`.

`contains("ijk")`: matches names that contain `"ijk"`.

---

# Add new variables with `mutate()`

To _add new columns_ that are functions of existing columns. That is the job of `mutate()`. `mutate()` always adds new columns at the end of your dataset.

```r
# create a smaller dataset with less columns
flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
```

---

# Example: using `mutate()`

```r
mutate(flights_sml,
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60
)
```
]

```r
mutate(flights_sml,
  gain = arr_delay - dep_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)
```
]

]

.small[_A random sample of 100 observations is shown below:_]
.tiny[
<div id="htmlwidget-3423915c721449f87749" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-3423915c721449f87749">{"x":{"filter":"none","vertical":false,"fillContainer":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79","80","81","82","83","84","85","86","87","88","89","90","91","92","93","94","95","96","97","98","99","100"],[2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013,2013],[9,12,10,5,5,11,5,6,11,10,8,8,9,5,6,2,4,7,5,1,1,6,7,7,10,11,6,11,3,6,4,10,8,2,2,1,11,6,3,10,7,1,9,9,3,5,9,7,7,6,8,12,7,8,9,1,7,10,7,1,7,2,8,4,2,8,10,11,4,5,8,3,6,1,12,7,4,6,1,4,1,4,2,8,10,1,7,5,8,2,11,9,11,3,1,10,4,4,3,7],[9,29,28,7,16,21,26,4,11,22,19,16,7,13,30,25,5,29,10,28,30,19,18,11,23,17,17,18,28,21,23,21,5,4,12,3,19,13,28,4,19,21,18,10,28,29,11,16,26,26,11,23,7,27,22,18,25,30,21,28,27,25,27,18,19,22,17,25,17,25,11,8,30,25,13,11,2,29,3,8,19,11,5,5,10,5,7,31,4,1,8,10,18,14,30,22,9,17,17,24],[10,-5,-2,-3,-1,0,111,-10,0,-3,89,-4,-10,-2,-8,-3,-6,28,-6,-4,-5,-4,2,87,-7,-7,50,1,54,5,68,0,-8,-2,-5,37,-8,81,-7,4,-4,-8,-3,-5,-4,-12,25,0,-8,5,-3,2,-1,-10,-4,-6,89,-5,19,3,5,-5,-5,-4,34,87,-3,-7,-11,-3,-3,3,-5,-4,6,126,11,-3,5,-6,-5,1,55,-1,-2,-5,65,-5,6,20,3,10,3,-6,-3,-5,23,3,-6,27],[-9,-8,-13,-22,-4,-10,81,-20,-4,-19,49,-16,-39,-25,-27,-27,-2,25,-6,-11,11,-10,50,62,-9,-8,49,-5,52,9,94,10,-26,-22,-22,31,-15,87,-12,0,0,-20,-20,-30,-34,-20,20,1,-2,-3,-10,2,104,-35,-23,-19,89,1,8,2,1,12,4,-13,39,88,-9,-19,-17,-26,-10,-5,-2,5,1,111,1,-18,26,-24,-22,8,55,-22,-2,1,79,-12,9,2,0,-16,8,-25,-16,5,22,1,-4,54],[199,1096,200,762,1605,1028,258,1089,427,719,733,2475,1183,1400,2133,888,762,725,725,2475,1065,404,569,264,184,1089,733,213,1068,944,214,725,200,2475,2402,4963,1389,1167,762,229,431,2475,799,937,719,1598,264,762,719,2227,1092,746,963,502,1504,209,708,872,1020,944,1990,1076,335,1035,1076,213,746,541,533,628,1008,1400,2248,2434,711,301,963,2133,1023,445,746,1569,746,1400,733,733,479,187,544,200,2133,2565,1504,820,944,799,431,200,1183,1096],[37,174,46,101,220,138,42,146,69,111,109,320,140,179,261,138,113,113,120,351,155,65,89,47,31,146,127,45,155,135,47,123,35,322,332,628,200,168,106,54,63,343,110,122,107,192,50,103,111,294,159,122,138,73,195,47,105,144,152,138,263,176,50,140,159,54,115,86,89,83,157,186,290,381,122,52,155,274,173,67,116,235,114,182,102,132,81,39,84,39,307,322,225,117,137,123,61,34,176,166],[-19,-3,-11,-19,-3,-10,-30,-10,-4,-16,-40,-12,-29,-23,-19,-24,4,-3,0,-7,16,-6,48,-25,-2,-1,-1,-6,-2,4,26,10,-18,-20,-17,-6,-7,6,-5,-4,4,-12,-17,-25,-30,-8,-5,1,6,-8,-7,0,105,-25,-19,-13,0,6,-11,-1,-4,17,9,-9,5,1,-6,-12,-6,-23,-7,-8,3,9,-5,-15,-10,-15,21,-18,-17,7,0,-21,0,6,14,-7,3,-18,-3,-26,5,-19,-13,10,-1,-2,2,27],[0.616666666666667,2.9,0.766666666666667,1.68333333333333,3.66666666666667,2.3,0.7,2.43333333333333,1.15,1.85,1.81666666666667,5.33333333333333,2.33333333333333,2.98333333333333,4.35,2.3,1.88333333333333,1.88333333333333,2,5.85,2.58333333333333,1.08333333333333,1.48333333333333,0.783333333333333,0.516666666666667,2.43333333333333,2.11666666666667,0.75,2.58333333333333,2.25,0.783333333333333,2.05,0.583333333333333,5.36666666666667,5.53333333333333,10.4666666666667,3.33333333333333,2.8,1.76666666666667,0.9,1.05,5.71666666666667,1.83333333333333,2.03333333333333,1.78333333333333,3.2,0.833333333333333,1.71666666666667,1.85,4.9,2.65,2.03333333333333,2.3,1.21666666666667,3.25,0.783333333333333,1.75,2.4,2.53333333333333,2.3,4.38333333333333,2.93333333333333,0.833333333333333,2.33333333333333,2.65,0.9,1.91666666666667,1.43333333333333,1.48333333333333,1.38333333333333,2.61666666666667,3.1,4.83333333333333,6.35,2.03333333333333,0.866666666666667,2.58333333333333,4.56666666666667,2.88333333333333,1.11666666666667,1.93333333333333,3.91666666666667,1.9,3.03333333333333,1.7,2.2,1.35,0.65,1.4,0.65,5.11666666666667,5.36666666666667,3.75,1.95,2.28333333333333,2.05,1.01666666666667,0.566666666666667,2.93333333333333,2.76666666666667],[-30.8108108108108,-1.03448275862069,-14.3478260869565,-11.2871287128713,-0.818181818181818,-4.34782608695652,-42.8571428571429,-4.10958904109589,-3.47826086956522,-8.64864864864865,-22.0183486238532,-2.25,-12.4285714285714,-7.70949720670391,-4.36781609195402,-10.4347826086957,2.12389380530973,-1.5929203539823,0,-1.1965811965812,6.19354838709677,-5.53846153846154,32.3595505617978,-31.9148936170213,-3.87096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class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>year<\/th>\n      <th>month<\/th>\n      <th>day<\/th>\n      <th>dep_delay<\/th>\n      <th>arr_delay<\/th>\n      <th>distance<\/th>\n      <th>air_time<\/th>\n      <th>gain<\/th>\n      <th>hours<\/th>\n      <th>gain_per_hour<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":6,"scrollX":true,"lengthChange":false,"dom":"tp","columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7,8,9,10]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]

]

---

# Grouped summaries with `summarise()`

`summarise()` _collapses_ a data frame to a single row:

```r
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))
```

```
## # A tibble: 1 × 1
##   delay
##   <dbl>
## 1  12.6
```

The `summarise()` function is useful when we pair it with `group_by()`.

This way, the analysis can be done for individual groups.

---

layout: false
name: pipe
class: center middle section-title section-title-2 animated fadeIn
background-image: url("https://raw.githubusercontent.com/reisanar/figs/master/pipe.png")
background-position: right
background-size: contain
<div class="my-footer"><a href="https://www.reisanar.com/" target="_blank" rel="noopener"><span>@reisanar</span></a></div>

# The pipe<br> operator

---

---

# The pipe

.pull-left[
<figure><img src="https://raw.githubusercontent.com/reisanar/figs/master/mario.png"><figcaption></figcaption></figure>
]

.pull-right[
<figure><img src="https://raw.githubusercontent.com/reisanar/figs/master/flappybird.png"><figcaption></figcaption></figure>
]

.box-inv-2.medium.sp-after[
The pipe operator is given by **`%>%`** <br>(from the `magrittr` [package](https://magrittr.tidyverse.org/reference/pipe.html))
]

---

# Example

The pipe sends the output of the LHS function to the first argument of the RHS function.

```r
# pipe example
sum(1:8) %>%
  sqrt() %>% 
  log()
```

```
## [1] 1.791759
```
]

```r
# the log of the square root
# of the sum of the elements 
# of the vector [1 ... 8]
log(sqrt(sum(1:8)))
```

```
## [1] 1.791759
```
]
---

# Example for data exploration

Imagine that we want to explore the relationship between the distance and average delay for each location.

There are three steps to prepare this data:

1. **Group** flights by destination.

2. **Summarize** to compute distance, average delay, and number of flights.

3. **Filter** to remove noisy points and Honolulu airport, which is almost twice as far away as the next closest airport.

---

# Power of the pipe `%>%` operator

```r
# create dataframe of "delays"
delays <- flights %>% 
  group_by(dest) %>% 
  summarize(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")
```
]
<br>
.smaller[
If you use RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.
]
]

.pull-right[
.small[
You can read it as a series of imperative statements: **group**, then **summarize**, then **filter**. A good way to pronounce `%>%` when reading code is "then".<br><br>

The `n()` function is implemented specifically for each data source and can be used from within `summarize()`, `mutate()` and `filter()`. It returns the number of observations in the current group.
]
]

---

# Transformations

.small[
**Why `na.rm = TRUE` ?** Aggregation functions obey the usual rule of missing values: if there is any missing value in the input, the output will be a missing value! ]

```r
flights %>% 
  group_by(year, month, day) %>% 
  summarize(mean = mean(dep_delay, na.rm = TRUE))
```
]

.tiny[
<br>
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