01-data_visualisation.Rmd

# (PART\*) Whole game {-}

# Data visualisation

**Learning objectives:**

:::: {style="display: flex;"}

::: {}

-   Produce a simple plot with `ggplot2`
-   Use different **`aes`thetic mappings** and **`geom_*()` functions** to produce more complex plots
-   Visualize **distributions and relationships** in data
-   Produce **small multiples** with `facet()`
-   [Make the plot to the right](https://r4ds.hadley.nz/data-visualize_files/figure-html/unnamed-chunk-7-1.png)

:::

::: {}

![](https://r4ds.hadley.nz/data-visualize_files/figure-html/unnamed-chunk-7-1.png)

:::

::::


## Loading Packages in R {-}

-   `install.packages("Package_name")` to **install** a package.
    -   Before 1st use, but only once
    -   Packages you need may already be installed
-   `library(Package_name)` to **load** a package.
    -   In general, do this at start of session
-   Alternatively: `ggplot2::ggplot()`. 
    -   "ggplot2" = package, "ggplot()" = function

```{r 02-library}
library(tidyverse)
```

We'll also need...

```{r 02-otherpkgs, message=FALSE, warning=FALSE}
library(palmerpenguins)
library(ggthemes)
```

## Peeking at the `palmerpenguins` {-}

```{r 02-dataset}
palmerpenguins::penguins
```

## Creating a ggplot: ggplot() {-}

-   Call `ggplot()` function. 
-   Arguments: 
    -   **data** to use
    -   how to **map** them to **aesthetics**

```{r 02-plot01, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
)
```

## Creating a ggplot: geom_\*() {-}

```{r 02-plot02}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point()
```

(warning = 2 penguins in dataset w/ missing body mass / flipper length)

## Adding Aesthetics and Layers {-}

- **Aesthetics** = visual properties of the plot.
- More aesthetics may tell the data's story better.
- Types of **aesthetic mapping**:
    -   coordinates: x and y
    -   linewidth
    -   shape
    -   color
    -   fill
    -   alpha (transparency)
    -   stroke
    -   linetype
    -   group
    -   show.legend
    -   others, sometimes specific to geom

```{r 02-plot03, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(
    x = flipper_length_mm, 
    y = body_mass_g, 
    color = species
  )
) +
  geom_point()
```


## Adding Layers {-}

- Additional `geom_*()` layers may help as well

```{r 02-plot04, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
) +
  geom_point() +
  geom_smooth(method = "lm")
```

- `geom_*()` functions available:
    -   point (scatterplot)
    -   line
    -   smooth (curve)
    -   histogram / bar or (stat_count) / col
    -   boxplot
    -   map
    -   text / label
    -   ...

## Global vs. Local Aesthetics {-}

:::: {style="display: flex;"}
::: {style="margin-right: 0.5em; width: 50%;"}
**Global aesthetics:** 

-   in `ggplot()` 
-   apply to all layers

```{r 02-plot05, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(
    x = flipper_length_mm, 
    y = body_mass_g, 
    color = species
  )
) +
  geom_point() +
  geom_smooth(method = "lm")
```
:::

::: {style="margin-left: 0.5em; width: 50%;"}
**Local aesthetics:** 

-   in `geom_*()` 
-   apply only to that layer.

```{r 02-plot06, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(
    x = flipper_length_mm, 
    y = body_mass_g
  )
) +
  geom_point(
    aes(color = species)) +
  geom_smooth(method = "lm")
```
:::
::::

## Improving Accessibility {-}

- Mapping shape and color may help colorblind readers
- Colorblind-friendly palette would help, too

```{r 02-plot07, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(aes(color = species, shape = species)) +
  geom_smooth(method = "lm") +
  scale_color_colorblind()

```

## Why Stop There? {-}

Use `labs()` to add understandable titles and axes.

```{r 02-plot08, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(aes(color = species, shape = species)) +
  geom_smooth(method = "lm") +
  labs(
    title = "Body mass and flipper length",
    subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
    x = "Flipper length (mm)", y = "Body mass (g)",
    color = "Species", shape = "Species"
  ) +
  scale_color_colorblind()
```

## Exercises {-}

**1. How many rows are in `penguins`? How many columns?**

```{r 02-ex01-01}
penguins
```

See first line. 344 rows x 8 columns.

**2. What does the `bill_depth_mm` variable in the `penguins` data frame describe? Read the help for `?penguins` to find out.**

Call `?penguins` for definition from package documentation

**3. Make a scatterplot of `bill_depth_mm` vs. `bill_length_mm`. That is, make a scatterplot with `bill_depth_mm` on the y-axis and `bill_length_mm` on the x-axis. Describe the relationship between these two variables.**

```{r 02-ex03-01, message=FALSE, warning=FALSE}
ggplot(penguins) +
  geom_point(aes(x = bill_depth_mm, y = bill_length_mm))
```

Positive, linear relationship? We'll see more about this in later exercises.

**4. What happens if you make a scatterplot of species vs. bill_depth_mm? What might be a better choice of geom?**

```{r 02-ex04-01}
ggplot(penguins) +
  geom_point(aes(x = species, y = bill_depth_mm))
```

Dotplot for every species. Boxplot would tell us more.

```{r 02-ex04-02}
ggplot(penguins) +
  geom_boxplot(aes(x = species, y = bill_depth_mm))
```

This better compares bill depths across species.

**5. Why does the following give an error and how would you fix it?**

```{r 02-ex05-01, eval=FALSE}
ggplot(data = penguins) + 
  geom_point()
```

The error reads `! geom_point() requires the following missing aesthetics: x and y`. Call needs x variable and y variable.

**6. What does the `na.rm` argument do in `geom_point()`? What is the default value of the argument? Create a scatterplot where you successfully use this argument set to `TRUE`.**

From function documentation, "If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed."

```{r 02-ex06-01}
ggplot(penguins) +
  geom_point(aes(x = species, y = bill_depth_mm), na.rm = TRUE)
```

**7. Add the following caption to the plot you made in the previous exercise: "Data come from the `palmerpenguins` package." Hint: Take a look at the documentation for `labs()`.**

`caption` argument adds a caption.

```{r 02-ex07-01, message=FALSE, warning=FALSE}
ggplot(penguins) +
  geom_boxplot(aes(x = species, y = bill_depth_mm)) +
  labs(
    title = "Distribution of Penguin Bill Depths by Species",
    subtitle = "For penguins at Palmer Station Antarctica",
    x = "Species",
    y = "Bill depth (in millimeters)",
    caption = "Data come from the `palmerpenguins` package."
  )
```

**8.Recreate the following visualization. What aesthetic should `bill_depth_mm` be mapped to? And should it be mapped at the global level or at the geom level?**

![](https://r4ds.hadley.nz/data-visualize_files/figure-html/unnamed-chunk-17-1.png)

```{r 02-ex08-01, message=FALSE, warning=FALSE}
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = bill_depth_mm)) +
  geom_smooth(method = "loess")
```

**9. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.**

```{r 02-ex09-02, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
) +
  geom_point() +
  geom_smooth(se = FALSE)

# THINK BEFORE SCROLLING DOWN
# 
# 
# 
# 
# 
# 
# 
```

**10. Will these two graphs look different? Why/why not?**

```{r 02-ex10-01, eval=FALSE, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point() +
  geom_smooth()

ggplot() +
  geom_point(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  ) +
  geom_smooth(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  )
```

Only difference is mapping globally vs. locally, and mapped same in both geoms, so they should look the same.

```{r 02-ex10-02, echo=FALSE, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point() +
  geom_smooth()

ggplot() +
  geom_point(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  ) +
  geom_smooth(
    data = penguins,
    mapping = aes(x = flipper_length_mm, y = body_mass_g)
  )
```

## `ggplot2` calls {-}

- For brevity, "data =" and "mapping =" often dropped 
- But things still work...

```{r 02-twin02, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) + 
  geom_point()
```

## Visualizing Distributions: Barplots {-}

Quickly check out variable distributions.

Barplots help with categorical variables.

```{r 02-categorical, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = species)) +
  geom_bar()
```

## Visualizing Distributions: Histograms {-}

Histograms aid with numerical variables

```{r 02-numerical, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 200)
```

## Visualizing Distributions: Density plots {-}

Density plots also help

```{r 02-numerical2, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = body_mass_g)) +
  geom_density()
```

## (more) Exercises {-}

**1. Make a bar plot of species of penguins, where you assign species to the y aesthetic. How is this plot different?**

```{r 02-ex11, message=FALSE, warning=FALSE}
ggplot(penguins, aes(y = species)) +
  geom_bar()
```

Bars horizontal instead of vertical.

**2. How are the following two plots different? Which `aesthetic`, `color` or `fill`, is more useful for changing the color of bars?**

```{r 02-ex12, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = species)) +
  geom_bar(color = "red")

ggplot(penguins, aes(x = species)) +
  geom_bar(fill = "red")
```

For `geom_bar()`, "color" affects borders. "Fill" affects inside.

**3. What does the `bins` argument in geom_histogram() do?**

The `bins` argument is helpful when you don't have a particular bin width in mind, but you do want to narrow things down to a particular number of bins.

**4. Make a histogram of the `carat` variable in the `diamonds` dataset that is available when you load the tidyverse package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?**

Let's try 1, 0.1, and 0.01.

```{r 02-ex14-01, message=FALSE, warning=FALSE}
ggplot(diamonds) +
  geom_histogram(aes(x = carat), binwidth = 1)
```

```{r 02-ex14-02, message=FALSE, warning=FALSE}
ggplot(diamonds) +
  geom_histogram(aes(x = carat), binwidth = 0.1)
```

```{r 02-ex14-03, message=FALSE, warning=FALSE}
ggplot(diamonds) +
  geom_histogram(aes(x = carat), binwidth = 0.01)
```

Here, a `binwidth` of 0.1 seems to be a good compromise between showing granularity in the data and not overwhelming ourselves with too many bars.

## Visualizing relationships {-}

`ggplot2` is also incredible at displaying relationships between variables.

## Numerical Variable and Categorical Variable {-}

Boxplots help here (or color-coded density plots).

```{r 02-relationship-boxplot, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = species, y = body_mass_g)) +
  geom_boxplot()
```

## Two Categorical Variables {-}

Try a bar plot.

```{r 02-relationship-bar, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar()
```

To get proportions, set `position` argument to "fill"

```{r 02-relationship-barprop, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "fill")
```

## Two Numerical Variables {-}

Scatterplots shine here.

```{r 02-relationship-scatter, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()
```

## Three or More Variables {-}

Map each to different variable

```{r 02-relationship-scatter-multi, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = island))
```

Or use `facet_wrap()` to create "small multiples"

```{r 02-relationship-facet, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = species)) +
  facet_wrap(~island)
```

## (Even More) Exercises {-}

**1. The `mpg` data frame that is bundled with the `ggplot2` package contains 234 observations collected by the US Environmental Protection Agency on 38 car models. Which variables in `mpg` are categorical? Which variables are numerical? (Hint: Type `?mpg` to read the documentation for the dataset.) How can you see this information when you run `mpg?`**

Running `glimpse(mpg)` gets us most of the way there. We can assume all of the character-based variables ("\<chr\>") are categorical. Since we know we're dealing with cars, we can also recognize that year and cyl (number of cylinders), while technically numerical, are effectively categorical as there is a limited number of possible results. That leaves displ (engine displacement), cty (mpg in city), and hwy (mpg on a highway) as purely numerical.

**2. Make a scatterplot of `hwy` vs. `displ` using the `mpg` data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?**

```{r 02-ex16-01, message=FALSE, warning=FALSE}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ))
```

```{r 02-ex16-02, message=FALSE, warning=FALSE}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, color = cty))
```

```{r 02-ex16-03, message=FALSE, warning=FALSE}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, size = cty))
```

```{r 02-ex16-04, message=FALSE, warning=FALSE}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, color = cty, size = year))
```

```{r 02-ex16-05}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, color = cty, size = year, shape = manufacturer))
```

First off, shape doesn't accept numerical variables. When a categorical variable is mapped to size, a warning suggests this is ill-advised. With color, numerical variables are given a gradient palette while categoricals get varying colors (We've seen this behavior before [think penguin species], so it's not plotted here).

**3. In the scatterplot of `hwy` vs. `displ`, what happens if you map a third variable to `linewidth`?**

```{r 02-ex17-01}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, color = cty, size = year, linewidth = manufacturer))
```

It's ignored because there are no lines in a scatterplot. (Also, if you use a categorical variable, it advises you against it).

**4. What happens if you map the same variable to multiple aesthetics?**

```{r 02-ex18-01, message=FALSE, warning=FALSE}
ggplot(mpg) +
  geom_point(aes(x = hwy, y = displ, color = cty, size = cty))
```

Both work, but it can be kind of redundant. It can be useful for colorblind users, though (as we saw before with color + shape).

**5. Make a scatterplot of `bill_depth_mm` vs. `bill_length_mm` and color the points by `species`. What does adding coloring by species reveal about the relationship between these two variables? What about faceting by species?**

```{r 02-ex19-01, message=FALSE, warning=FALSE}
ggplot(penguins) +
  geom_point(aes(x = bill_depth_mm, y = bill_length_mm, color = species))
```

```{r 02-ex19-02, message=FALSE, warning=FALSE}
ggplot(penguins) +
  geom_point(aes(x = bill_depth_mm, y = bill_length_mm, color = species))+
  facet_wrap(~species)
```

Color-coding helps spot the relationship of bill sizes by species (compare to Exercise 3 in the first block of exercises). Faceting helps the eye notice those relationships a little better by taking the "noise" of the other species out of the picture.

**6. Why does the following yield two separate legends? How would you fix it to combine the two legends?**

```{r 02-ex20-01}
ggplot(
  data = penguins,
  mapping = aes(
    x = bill_length_mm, y = bill_depth_mm, 
    color = species, shape = species
  )
) +
  geom_point() +
  labs(color = "Species")
```

Adding an argument to `labs` about our shape as well as our color should solve that.

```{r 02-ex20-02, message=FALSE, warning=FALSE}
ggplot(
  data = penguins,
  mapping = aes(
    x = bill_length_mm, y = bill_depth_mm
  )
) +
  geom_point(aes(color = species, shape = species)) +
  labs(color = "Species", shape = "Species")
```

**7. Create the two following stacked bar plots. Which question can you answer with the first one? Which question can you answer with the second one?**

```{r 02-ex21-01, message=FALSE, warning=FALSE}
ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "fill")
ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "fill")
```

The first tells us which species make up certain proportions of an island's overall population. The second tells us how a species' overall population is split among the islands.

## Saving Your Plots {-}

`ggsave()` lets you get your plots out of R for sharing.

```{r 02-ggsave, eval=FALSE}
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()
ggsave(filename = "penguin-plot.png", width = 800, units = "px")
#> Saving 800 x 1983 px image
#> Warning message:
#> Removed 2 rows containing missing values (`geom_point()`). 
```

Setting `width` and/or `height` helps with reproducibility.

## (Yet More) Exercises {-}

**1. Run the following lines of code. Which of the two plots is saved as `mpg-plot.png`? Why?**

```{r 02-ex22-01, warning=FALSE, message=FALSE, eval=FALSE}
ggplot(mpg, aes(x = class)) +
  geom_bar()
ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point()
ggsave("mpg-plot.png")
```

It will run the second. In looking at `?ggplot2::ggsave`, we see that it defaults to the most recent plot run.

**2. What do you need to change in the code above to save the plot as a PDF instead of a PNG? How could you find out what types of image files would work in `ggsave()`?**

Changing filename in `ggsave()` to end in `.pdf` instead of `.png` will change the type. Looking at the function documentation, we see there are options for: "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" and "wmf" (windows only).

## Common Problems {-}

Mistakes happen. To everyone.

Even this guy! ![https://en.wikipedia.org/wiki/Hadley_Wickham](images/hadley.jpg)

Maybe it's a mismatched `()` or `""`.

Or a `+` in the wrong spot.

But more than likely, you misspelled something.

[![](images/horst-spelling.png)](https://allisonhorst.com/everything-else)
[Art by Allison Horst](https://allisonhorst.com/everything-else/)

-   Read the error message for hints
-   Google error message
-   Look up documentation
-   Ask R friends
-   **Don't give up ... setbacks are temporary**

## Summary {-}

<h3> TADA! </h3>

<h3> Things you now know how to do: </h3>

:::: {style="display: flex;"}

::: {}

-   Produce a simple plot with `ggplot2`
-   Use different **`aes`thetic mappings** and **`geom_*()` functions** to produce more complex plots
-   Visualize **distributions and relationships** in data
-   Produce **small multiples** with `facet()`
-   [Make the plot to the right](https://r4ds.hadley.nz/data-visualize_files/figure-html/unnamed-chunk-7-1.png)

:::

::: {}

![](https://r4ds.hadley.nz/data-visualize_files/figure-html/unnamed-chunk-7-1.png)

:::

::::

### Resources: {-}

-   [R4DS book (1e)](https://r4ds.had.co.nz/data-visualisation.html)
-   [R for Data Science: Exercise Solutions (1e)](https://jrnold.github.io/r4ds-exercise-solutions/)
-   [A Layered Grammar of Graphics](http://vita.had.co.nz/papers/layered-grammar.pdf)
-   [{ggplot2}](https://cran.r-project.org/web/packages/ggplot2/index.html)
-   [ggplot2 extensions - gallery](https://exts.ggplot2.tidyverse.org/gallery/)
-   [R Graphics Cookbook](https://r-graphics.org/)
-   [Gina Reynolds' ggplot flipbook](https://evamaerey.github.io/ggplot_flipbook/ggplot_flipbook_xaringan.html)


## Meeting Videos {-}

### Cohort 5 {-}

`r knitr::include_url("https://www.youtube.com/embed/0NdiQuuM0vw")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:11:14	Jon Harmon (jonthegeek):	dslc.io/r4ds
00:12:06	Saeed Shafiei Sabet:	Hi everyone!
00:12:29	Sandra Muroy:	Hi Saeed!
00:13:05	Becki R. (she/her):	Hello!
00:13:30	Sandra Muroy:	Hi Becki!
00:22:41	Saeed Shafiei Sabet:	Can also by using ggplot2 do some 3D surface plots?
00:24:01	shamsuddeen:	https://ggplot2.tidyverse.org/reference/geom_contour.html
00:24:12	shamsuddeen:	2D contours of a 3D surface
00:25:34	Saeed Shafiei Sabet:	Thanks @shamsuddeen ;)
00:25:50	Jon Harmon (jonthegeek):	ggplot2 is 2D. There are other packages for 3D visualization, I'll try to link some in your question on the Slack once we're done!
00:26:29	Saeed Shafiei Sabet:	@Jon Thanks a lot! :)
00:26:34	shamsuddeen:	I guess this package provides 3D plotting https://www.rayshader.com/index.html
00:27:24	Jon Harmon (jonthegeek):	Yup, that's the one I was going to recommend: https://cran.r-project.org/web/packages/rayshader/index.html
00:28:28	Jon Harmon (jonthegeek):	I found it super helpful to figure out how to read some of these things as words:
%>% = "and then"
~ = "by" (usually)
00:28:30	shamsuddeen:	Looks at some practical examples of the package here: https://www.tylermw.com/3d-ggplots-with-rayshader/
00:29:54	Saeed Shafiei Sabet:	Thank you!
00:36:02	docksbox@pm.me:	https://jrnold.github.io/r4ds-exercise-solutions/
00:40:57	Jon Harmon (jonthegeek):	?ggplot2::mpg will show all the details of the dataset
00:41:50	Sandra Muroy:	thanks Jon :)
00:42:40	Jon Harmon (jonthegeek):	hwy = "highway miles per gallon", cty = "city miles per gallon" in that set, so usually that's what you'd want on y.
00:43:38	Becki R. (she/her):	Did I hear correctly that the dependent variable goes on the y-axis?
00:44:04	Jon Harmon (jonthegeek):	Generally, yes. But it's whatever you specify as "y" in the "aes" call.
00:44:16	Becki R. (she/her):	ok thanks
00:49:24	Jon Harmon (jonthegeek):	The "labs" function is for all of the labels for your plot.
00:51:26	Jon Harmon (jonthegeek):	https://twitter.com/search?q=%23tidytuesday&src=typed_query
00:51:48	Hector:	Is there any specific use for the ggtitle() function in contrast with labs() ?
00:52:09	Njoki Njuki Lucy:	what is there a difference between stat="count" and stat="identity"? I understand stat = "count returns count per each level.
00:53:23	Jon Harmon (jonthegeek):	@Hector: ggtitle is equivalent to labs() for just the title and subtitle parts. It's just to make it easier to focus on those specific bits.
00:53:56	Jon Harmon (jonthegeek):	@Njoki: "count" means "how many entries have this value?", vs "identity" means "what value is in this cell?"
00:54:59	Hector:	Thank you!
00:55:04	Njoki Njuki Lucy:	thank you.
00:56:26	Jon Harmon (jonthegeek):	"color" = outside, "fill" = inside
00:58:53	docksbox@pm.me:	labs()
01:04:36	Ryan Metcalf:	Could it be stated that “labs” is a more eloquent way of labeling than explicitly calling each field directly? Less lines of code maybe?
01:05:47	Jon Harmon (jonthegeek):	I'm not sure I'd say "eloquent," but it's just another option. They provide the separate functions in case you're looking for them, basically.
01:07:23	Jon Harmon (jonthegeek):	Sorry about that!
01:08:23	Susie Neilson:	This was a great presentation - thank you so much Federica!
01:12:31	docksbox@pm.me:	example would be the use of a map data
01:12:51	Saeed Shafiei Sabet:	Thanks Federica :)
01:13:43	docksbox@pm.me:	great thanks!
01:13:46	Fodil:	thank you everyone was very interesting.
01:13:51	Becki R. (she/her):	Thanks, Federica!
01:13:56	Njoki Njuki Lucy:	Thank you.
01:13:56	Saeed Shafiei Sabet:	Thank you
01:13:58	Saeed Shafiei Sabet:	bye
```
</details>

### Cohort 6 {-}

`r knitr::include_url("https://www.youtube.com/embed/5IMg8AC-1IA")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:16:33	Daniel Adereti:	do I have to run library(tidyverse) everytime I start my R?
00:17:15	Zaynaib Giwa, @zaynaib:	You only need to call it once when you open R Studio
00:21:14	Shannon:	To double check that you have it installed, you can click on the 'Packages' tab in the lower right pane. If it's installed, you'll see it listed next to a checkbox. Once you load the package with the library() function, you will see a checkmark in the box.
00:28:20	Freya Watkins (she/her):	displ 's class is dbl (double) which is a floating point number for decimal precision. int (integer) is for full rounded values (I think)
00:28:49	Zaynaib Giwa, @zaynaib:	cute cat
00:28:49	Vrinda Kalia:	Yes, I was just typing that up @Freya I think you are right
00:30:26	Aalekhya Reddam:	In Line 88 is it a - or = between data and mpg?
00:30:57	Matthew Efoli:	I think it is an equal to sign
00:30:58	Aalekhya Reddam:	Sorry that was my screen lagging!
00:38:43	Shannon:	Another option for finding number of rows and number of columns, that I used, is nrow(mpg) and ncol(mpg). Not as much info as glimpse(), but more concise.
00:41:42	Freya Watkins (she/her):	@Shannon, dim(mpg) also gives both dimensions in one command (number of rows, followed by number of columns) :)
00:42:35	Shannon:	@Freya, perfect! thanks!
00:49:45	Zaynaib Giwa, @zaynaib:	I have to leave a bit early. But it was nice meeting everyone. See you next week.
00:50:02	Shannon:	See you next week!
00:50:30	Matthew Efoli:	See you next week Zaynaib
00:50:34	Aalekhya Reddam:	I have to head out too, see you all next week! :)
00:50:57	Shannon:	👋
00:51:06	Matthew Efoli:	Have a nice weekend Aalekhya!
00:51:20	Freya Watkins (she/her):	missing the closing bracket I think
00:53:58	Daniel Adereti:	See you next week Aalekhya and Zaynaib!
00:54:27	Daniel Adereti:	We have 10 more minutes, so we may have to carry over the chapter to next week, I think
00:55:10	Matthew Efoli:	@Daniel okay
00:56:31	Shannon:	That works for me. I'd have more time to look over the second half of the chapter. :)
00:56:52	Marielena Soilemezidi:	Yes, same for me! :)
00:56:52	Vrinda Kalia:	Yes, this sounds good to me as well!
00:59:17	Shannon:	Nice work presenting, Adeyemi! Thank you!
00:59:20	Marielena Soilemezidi:	Daniel, could you share the link of the spreadsheet here?
00:59:40	Daniel Adereti:	https://docs.google.com/spreadsheets/d/1zy2nXNkvcdqWuF8rQ5ApWRkVQG_UJt0azu3h_mEnY2E/edit#gid=0
00:59:43	Marielena Soilemezidi:	Because I couldn't find it in Slack for some reason
00:59:47	Marielena Soilemezidi:	thank you!!
01:00:35	Daniel Adereti:	👍
01:08:40	Vrinda Kalia:	Maybe try “stroke = class” ?
01:10:01	Freya Watkins (she/her):	try stroke = 5. it should modify the width of the border so I think needs a numeric argument
01:10:08	Daniel Adereti:	maybe this? ggplot(data = mpg)+
  geom_point(mapping = aes(x = displ, y = hwy),
             shape = 17,
             stroke = 3)
01:10:32	Vrinda Kalia:	I see, it needs a numeric argument
01:11:37	Vrinda Kalia:	Thank you so much for leading the discussion, Adeyemi!
01:11:55	Matthew Efoli:	Thank you so much Adeyemi!
01:11:56	Freya Watkins (she/her):	Thank you, Adeyemi! Great job :)
01:11:57	anacuric:	thank you!
01:11:58	Marielena Soilemezidi:	Thank you guys! And thanks Adeyemi for presenting!! :)
01:12:00	Shannon:	Thank you everyone!
01:12:01	Adeyemi Olusola:	thank you
01:12:02	Marielena Soilemezidi:	See you next week!
01:12:16	Matthew Efoli:	see you next week!
```
</details>

`r knitr::include_url("https://www.youtube.com/embed/sEYMNIEvG5Q")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:06:29	Adeyemi Olusola:	Good day Daniel
00:06:58	Daniel Adereti:	Hello Olusola!
00:07:06	Daniel Adereti:	Looking forward to today!
00:07:21	Adeyemi Olusola:	Yeah yeah.
00:12:52	Amélie Gourdon-Kanhukamwe:	Hi everyone, I am in my shared office at work, so cannot really speak (have been meaning to join from the start, but shared office = loads of distraction). (I am based in London UK).
00:14:12	Shannon:	Hi Amelie, nice to 'meet' you :)
00:20:49	Aalekhya Reddam:	I may have missed it but what is the difference between adding a period and not adding one for facet_grid?
00:22:41	Shannon:	I think I missed that too, I second that question.
00:26:04	Freya Watkins (she/her):	The point should have removed one of the dimensions - either vertical or horizontal
00:29:57	Shannon:	So, having a point or no point would have the same result? Ex: ( ~ drv) is the same as( . ~drv)? Or (drv ~) is the same as (drv ~ .) ?
00:32:11	Aalekhya Reddam:	That’s what I understood too Shannon! The location of the ~ determines orientation and I guess the “.” is a placeholder
00:32:48	Shannon:	I think that's how I'm understanding it, too
00:39:09	Freya Watkins (she/her):	Yes I believe that's right @Shannon, @Aalekhya - I think in the previous plot there was an extra dimension (drv ~ cyl) which had rows and columns, then replacing drv with a point (. ~ cyl) just returns a plot faceted with columns for cyl. Then (~ cyl) returns the same plot as (. ~ cyl)
00:39:44	Aalekhya Reddam:	Ah okay, thank you Freya!
00:39:52	Shannon:	Okay, thanks Freya!
00:58:00	Shannon:	That last one drove me crazy, I couldn't figure it out! I tried position = jitter and tied to adjust stroke. Looks like the solution is two geom_point layers. Thanks for solving that one!
01:10:45	Aalekhya Reddam:	I have another meeting and have to head out, thank you for a great lesson Adeyemi! See you all next week
01:10:57	Vrinda Kalia:	Thank you so much, Adeyemi! I appreciate your thoroughness. I need to leave for a 1pm call. See you all next week!
01:11:06	Shannon:	See you next week!
01:11:13	Daniel Adereti:	Hello guys, will we like to conclude the chapter ourselves next time so we move to the next chapter with Matthew?
01:11:20	Shannon:	Thank you Adeyemi!
01:11:30	Marielena Soilemezidi:	See you guys! Thank you Adeyemi!!
01:11:32	Matthew Efoli:	Thank you Adeyemi
01:11:35	Freya Watkins (she/her):	Thanks Adeyemi! Really helpful, thanks for all the extra time and effort on the exercises
01:11:42	Amélie Gourdon-Kanhukamwe:	Thank you!
01:11:58	Freya Watkins (she/her):	I'm happy to finish the rest myself and move on to next chapter next time :)
01:12:43	Matthew Efoli:	I have to go. I will be taking the next chapter
01:12:47	Freya Watkins (she/her):	Bye! Thanks Daniel
01:12:52	Matthew Efoli:	bye
```
</details>


### Cohort 7 {-}

`r knitr::include_url("https://www.youtube.com/embed/eqQJ5tmEGco")`

<details>
<summary> Meeting chat log </summary>

```
00:08:24	Oluwafemi Oyedele:	Let wait for about 5 minute for others to join
01:02:00	Aditi S:	Thank you! I have to leave now but hope to join again next week.
01:05:04	Oluwafemi Oyedele:	geom_jitter is very useful if you want to avoid over-plotting
```
</details>


### Cohort 8 {-}

`r knitr::include_url("https://www.youtube.com/embed/RRxtbv5QCA4")`

<details>
<summary> Meeting chat log </summary>

```
00:11:18	shamsuddeen:	https://www.youtube.com/playlist?list=PL3x6DOfs2NGhS_PhklqT6PwK1Fh7blgP2
00:12:09	Mary Emeraghi:	Hello everyone
00:13:16	Mary Emeraghi:	Hello Shamsuddeen. Please kindly share the link to the GitHub note page
00:13:41	shamsuddeen:	https://happygitwithr.com/
00:13:48	shamsuddeen:	https://www.youtube.com/playlist?list=PL3x6DOfs2NGhS_PhklqT6PwK1Fh7blgP2
00:14:22	shamsuddeen:	https://r4ds.github.io/bookclub-r4ds/
01:24:50	Bidemi Agbozuadu:	Thank you very much! Nice presentation
01:25:32	shamsuddeen:	https://docs.google.com/spreadsheets/d/1reByMPb5Og3OHbRgplzV5Jkz_o6CkEYz_adBGfHTFwg/edit#gid=0
```
</details>