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New example for #2
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spcanelon committed Sep 23, 2020
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82 changes: 36 additions & 46 deletions 04-dplyr.Rmd
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Expand Up @@ -111,47 +111,60 @@ penguins %>%
]
]


.panel[.panel-name[Group By & Summarize]

.pull-left[
We can summarize the data using `group_by()` and `summarize()` to obtain counts by **species** and **sex**
```{r}
penguins %>%
group_by(species, sex) %>% #<<
summarize(n = n()) #<<
```
.middle[We can use `group_by()` to group our data by **species** and **sex**, and `summarize()` to calculate the average **body_mass_g** for each grouping.]
]

.pull-right[
And because we're just _counting_, we also have the option to use `count()` which simplifies our code!

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]

```{r}
penguins %>%
count(species, sex) #<<
select(species, sex, body_mass_g) %>%
group_by(species, sex) %>% #<<
summarize(mean = mean(body_mass_g)) #<<
```
]
]

.panel[.panel-name[Mutate: Ex. 1]

.panel[.panel-name[Counting 1]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

.pull-left[
We can use `mutate()` to create a new variable **n_species** that adds up all observations per **species**
Counting can be done with `group_by()` and `summarize()`, but it's a little cumbersome.

It involves...
1. using `mutate()` to create an intermediate variable **n_species** that adds up all observations per **species**, and
2. an `ungroup()`-ing step
]

.pull-right[
```{r}
penguins %>%
group_by(species) %>%
mutate(n_species = n()) %>% #<<
ungroup() %>%
mutate(n_species = n()) %>% #<<
ungroup() %>% #<<
group_by(species, sex, n_species) %>%
summarize(n = n())
summarize(n = n())
```
]
]

.pull-right[
**OR** we can use `count()`'s friend `add_count()` to create **n_species**, again because we're just _counting_
.panel[.panel-name[Counting 2]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

.pull-left[
In contrast, `count()` and `add_count()` offer a simplified approach

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[
```{r}
penguins %>%
count(species, sex) %>%
Expand All @@ -161,52 +174,29 @@ penguins %>%
]
]

.panel[.panel-name[Mutate: Ex. 2]

With either approach, we can use `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species**
.panel[.panel-name[Mutate]

.pull-left[
```{r}
penguins %>%
group_by(species) %>%
mutate(n_species = n()) %>%
ungroup() %>%
group_by(species, sex, n_species) %>%
summarize(n = n()) %>%
mutate(prop = n/n_species*100) #<<
```
We can add to our counting example by using `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species**

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[
.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]

```{r}
penguins %>%
count(species, sex) %>%
add_count(species, wt = n,
name = "n_species") %>%
mutate(prop = n/n_species*100) #<<
mutate(prop = n/n_species*100) #<<
```
]
]

.panel[.panel-name[Filter]

Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline

.pull-left[
```{r}
penguins %>%
group_by(species) %>%
mutate(n_species = n()) %>%
ungroup() %>%
group_by(species, sex, n_species) %>%
summarize(n = n()) %>%
mutate(prop = n/n_species*100) %>%
filter(species == "Chinstrap") #<<
```
Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline]

]
.pull-right[
```{r}
penguins %>%
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152 changes: 55 additions & 97 deletions 2020-rladies-chi-tidyverse.html
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Expand Up @@ -1190,12 +1190,12 @@
### You might see

.pull-left[
- Gentoo penguins have higher body mass than Adelie and Chinstrap penguins
- Gentoo penguins have higher body mass than Adélie and Chinstrap penguins
- Higher body mass among male Gentoo penguins compared to female penguins
- Pattern not as discernable when comparing Adelie and Chinstrap penguins
- No `NA`s among Chinstrap penguin data points! `sex` was available for each observation
- Pattern not as discernible when comparing Adélie and Chinstrap penguins
- No _NA_s among Chinstrap penguin data points! **sex** was available for each observation

I wonder what percentage of observations are `NA` for each species? Let's get the tidyverse to help us with this!
I wonder what percentage of observations are _NA_ for each species? Let's get the tidyverse to help us with this!

Next stop, `dplyr`!
]
Expand Down Expand Up @@ -1368,66 +1368,59 @@
]
]


.panel[.panel-name[Group By &amp; Summarize]

.pull-left[
We can summarize the data using `group_by()` and `summarize()` to obtain counts by **species** and **sex**

```r
penguins %&gt;%
* group_by(species, sex) %&gt;%
* summarize(n = n())
## # A tibble: 8 x 3
## # Groups: species [3]
## species sex n
## &lt;fct&gt; &lt;fct&gt; &lt;int&gt;
## 1 Adelie female 73
## 2 Adelie male 73
## 3 Adelie &lt;NA&gt; 6
## 4 Chinstrap female 34
## 5 Chinstrap male 34
## 6 Gentoo female 58
## 7 Gentoo male 61
## 8 Gentoo &lt;NA&gt; 5
```
.middle[We can use `group_by()` to group our data by **species** and **sex**, and `summarize()` to calculate the average **body_mass_g** for each grouping.]
]

.pull-right[
And because we're just _counting_, we also have the option to use `count()` which simplifies our code!

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]


```r
penguins %&gt;%
* count(species, sex)
select(species, sex, body_mass_g) %&gt;%
* group_by(species, sex) %&gt;%
* summarize(mean = mean(body_mass_g))
## # A tibble: 8 x 3
## species sex n
## &lt;fct&gt; &lt;fct&gt; &lt;int&gt;
## 1 Adelie female 73
## 2 Adelie male 73
## 3 Adelie &lt;NA&gt; 6
## 4 Chinstrap female 34
## 5 Chinstrap male 34
## 6 Gentoo female 58
## 7 Gentoo male 61
## 8 Gentoo &lt;NA&gt; 5
## # Groups: species [3]
## species sex mean
## &lt;fct&gt; &lt;fct&gt; &lt;dbl&gt;
## 1 Adelie female 3369.
## 2 Adelie male 4043.
## 3 Adelie &lt;NA&gt; NA
## 4 Chinstrap female 3527.
## 5 Chinstrap male 3939.
## 6 Gentoo female 4680.
## 7 Gentoo male 5485.
## 8 Gentoo &lt;NA&gt; NA
```
]
]

.panel[.panel-name[Mutate: Ex. 1]

.panel[.panel-name[Counting 1]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

.pull-left[
We can use `mutate()` to create a new variable **n_species** that adds up all observations per **species**
Counting can be done with `group_by()` and `summarize()`, but it's a little cumbersome.

It involves...
1. using `mutate()` to create an intermediate variable **n_species** that adds up all observations per **species**, and
2. an `ungroup()`-ing step
]

.pull-right[

```r
penguins %&gt;%
group_by(species) %&gt;%
* mutate(n_species = n()) %&gt;%
ungroup() %&gt;%
* ungroup() %&gt;%
group_by(species, sex, n_species) %&gt;%
summarize(n = n())
summarize(n = n())
## # A tibble: 8 x 4
## # Groups: species, sex [8]
## species sex n_species n
Expand All @@ -1442,11 +1435,19 @@
## 8 Gentoo &lt;NA&gt; 124 5
```
]
]

.pull-right[
**OR** we can use `count()`'s friend `add_count()` to create **n_species**, again because we're just _counting_
.panel[.panel-name[Counting 2]
If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`.

----

.pull-left[
In contrast, `count()` and `add_count()` offer a simplified approach

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[

```r
penguins %&gt;%
Expand All @@ -1468,38 +1469,14 @@
]
]

.panel[.panel-name[Mutate: Ex. 2]

With either approach, we can use `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species**
.panel[.panel-name[Mutate]

.pull-left[
We can add to our counting example by using `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species**

```r
penguins %&gt;%
group_by(species) %&gt;%
mutate(n_species = n()) %&gt;%
ungroup() %&gt;%
group_by(species, sex, n_species) %&gt;%
summarize(count = n()) %&gt;%
* mutate(prop = count/n_species*100)
## # A tibble: 8 x 5
## # Groups: species, sex [8]
## species sex n_species count prop
## &lt;fct&gt; &lt;fct&gt; &lt;int&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Adelie female 152 73 48.0
## 2 Adelie male 152 73 48.0
## 3 Adelie &lt;NA&gt; 152 6 3.95
## 4 Chinstrap female 68 34 50
## 5 Chinstrap male 68 34 50
## 6 Gentoo female 124 58 46.8
## 7 Gentoo male 124 61 49.2
## 8 Gentoo &lt;NA&gt; 124 5 4.03
```

.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]
]
.pull-right[
.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)]


```r
penguins %&gt;%
Expand All @@ -1524,28 +1501,9 @@

.panel[.panel-name[Filter]

Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline

.pull-left[
Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline]

```r
penguins %&gt;%
group_by(species) %&gt;%
mutate(n_species = n()) %&gt;%
ungroup() %&gt;%
group_by(species, sex, n_species) %&gt;%
summarize(count = n()) %&gt;%
mutate(prop = count/n_species*100) %&gt;%
* filter(species == "Chinstrap")
## # A tibble: 2 x 5
## # Groups: species, sex [2]
## species sex n_species count prop
## &lt;fct&gt; &lt;fct&gt; &lt;int&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Chinstrap female 68 34 50
## 2 Chinstrap male 68 34 50
```

]
.pull-right[

```r
Expand Down Expand Up @@ -1901,7 +1859,7 @@

### Now let's make it tidy again!

#### We'll use the help of `pivot_longer()`
We'll use the help of `pivot_longer()`


```r
Expand Down Expand Up @@ -2009,12 +1967,12 @@

.pull-left[
### Let's turn this plot
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-53-1.png" width="504" /&gt;
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-50-1.png" width="504" /&gt;
]

.pull-right[
### Into this one!
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-54-1.png" width="504" /&gt;
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-51-1.png" width="504" /&gt;

.panel[.panel-name[Option 1]

Expand All @@ -2041,7 +1999,7 @@
]

.pull-right[
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-57-1.png" width="504" /&gt;
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-54-1.png" width="504" /&gt;
]
]

Expand All @@ -2058,7 +2016,7 @@
* scale_fill_manual(values = nord::nord_palettes$frost)
```

&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-58-1.png" width="360" /&gt;
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-55-1.png" width="360" /&gt;
]

.pull-right[
Expand All @@ -2071,7 +2029,7 @@
* nord::scale_fill_nord(palette = "frost")
```

&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-59-1.png" width="360" /&gt;
&lt;img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-56-1.png" width="360" /&gt;
]
]

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