#TidyTuesday walk-throughs

# #TidyTuesday walk-throughs
## A flipbook made with Xaringan
### <br><br>Edited by Gina Reynolds, 2019
### <br>

---

# Table of contents

---

## Joel Soroos' *UFOs over North Carolina*

---

# UFO sitings

This work is by Joel Soros who focuses on UFO sitings in North Carolina.

---

## Set up

```r
library(tidyverse)
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-25/ufo_sightings.csv"
ufo_raw <- readr::read_csv(file = url) 
```

---

## First a little data cleaning

---

```r
*ufo_raw
```
]]
.column[.content[

```
  # A tibble: 80,332 x 11
     date_time city_area state country ufo_shape encounter_length
     <chr>     <chr>     <chr> <chr>   <chr>                <int>
   1 10/10/19… san marc… tx    us      cylinder              2700
   2 10/10/19… lackland… tx    <NA>    light                 7200
   3 10/10/19… chester … <NA>  gb      circle                  20
   4 10/10/19… edna      tx    us      circle                  20
   5 10/10/19… kaneohe   hi    us      light                  900
   6 10/10/19… bristol   tn    us      sphere                 300
   7 10/10/19… penarth … <NA>  gb      circle                 180
   8 10/10/19… norwalk   ct    us      disk                  1200
   9 10/10/19… pell city al    us      disk                   180
  10 10/10/19… live oak  fl    us      disk                   120
  # … with 80,322 more rows, and 5 more variables:
  #   described_encounter_length <chr>, description <chr>,
  #   date_documented <chr>, latitude <dbl>, longitude <dbl>
```
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_raw %>%
*     janitor::clean_names()
```
]]
.column[.content[

```r
ufo_raw %>%
      janitor::clean_names() %>%
*     select(date_time, city_area,
*            state, latitude,
*            longitude, encounter_length)
```
]]
.column[.content[

```
  # A tibble: 80,332 x 6
     date_time      city_area       state latitude longitude encounter_length
     <chr>          <chr>           <chr>    <dbl>     <dbl>            <int>
   1 10/10/1949 20… san marcos      tx        29.9    -97.9              2700
   2 10/10/1949 21… lackland afb    tx        29.4    -98.6              7200
   3 10/10/1955 17… chester (uk/en… <NA>      53.2     -2.92               20
   4 10/10/1956 21… edna            tx        29.0    -96.6                20
   5 10/10/1960 20… kaneohe         hi        21.4   -158.                900
   6 10/10/1961 19… bristol         tn        36.6    -82.2               300
   7 10/10/1965 21… penarth (uk/wa… <NA>      51.4     -3.18              180
   8 10/10/1965 23… norwalk         ct        41.1    -73.4              1200
   9 10/10/1966 20… pell city       al        33.6    -86.3               180
  10 10/10/1966 21… live oak        fl        30.3    -83.0               120
  # … with 80,322 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_raw %>%
      janitor::clean_names() %>%
      select(date_time, city_area,
             state, latitude,
             longitude, encounter_length) %>%
* rename(lat = latitude,
*        long = longitude)
```
]]
.column[.content[

```
  # A tibble: 80,332 x 6
     date_time        city_area          state   lat    long encounter_length
     <chr>            <chr>              <chr> <dbl>   <dbl>            <int>
   1 10/10/1949 20:30 san marcos         tx     29.9  -97.9              2700
   2 10/10/1949 21:00 lackland afb       tx     29.4  -98.6              7200
   3 10/10/1955 17:00 chester (uk/engla… <NA>   53.2   -2.92               20
   4 10/10/1956 21:00 edna               tx     29.0  -96.6                20
   5 10/10/1960 20:00 kaneohe            hi     21.4 -158.                900
   6 10/10/1961 19:00 bristol            tn     36.6  -82.2               300
   7 10/10/1965 21:00 penarth (uk/wales) <NA>   51.4   -3.18              180
   8 10/10/1965 23:45 norwalk            ct     41.1  -73.4              1200
   9 10/10/1966 20:00 pell city          al     33.6  -86.3               180
  10 10/10/1966 21:00 live oak           fl     30.3  -83.0               120
  # … with 80,322 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_raw %>%
      janitor::clean_names() %>%
      select(date_time, city_area,
             state, latitude,
             longitude, encounter_length) %>%
  rename(lat = latitude,
         long = longitude) %>%
*     filter(
*        state == "nc",
*        lat > 30,  # remove borders erroneously listed as NC outside of state borders
*        lat < 37,  # remove borders erroneously listed as NC outside of state borders
*        long < -75  # remove borders erroneously listed as NC outside of state borders
*        )
```
]]
.column[.content[

```
  # A tibble: 1,862 x 6
     date_time        city_area      state   lat  long encounter_length
     <chr>            <chr>          <chr> <dbl> <dbl>            <int>
   1 10/10/1968 19:00 brevard        nc     35.2 -82.7              180
   2 10/10/1971 21:00 lexington      nc     35.8 -80.3               30
   3 10/10/1991 22:00 frisco         nc     35.2 -75.6             1800
   4 10/10/1998 20:50 mooresville    nc     35.6 -80.8                2
   5 10/10/2005 23:00 hendersonville nc     35.3 -82.5              600
   6 10/10/2009 20:45 wilmington     nc     34.2 -77.9              600
   7 10/11/2008 13:00 salisbury      nc     35.7 -80.5               20
   8 10/11/2011 21:10 holden beach   nc     33.9 -78.3               30
   9 10/1/1961 22:00  graham         nc     36.1 -79.4               30
  10 10/1/1978 00:00  windsor        nc     36.0 -76.9              300
  # … with 1,852 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_raw %>%
      janitor::clean_names() %>%
      select(date_time, city_area,
             state, latitude,
             longitude, encounter_length) %>%
  rename(lat = latitude,
         long = longitude) %>%
      filter(
         state == "nc",
         lat > 30,  # remove borders erroneously listed as NC outside of state borders
         lat < 37,  # remove borders erroneously listed as NC outside of state borders
         long < -75  # remove borders erroneously listed as NC outside of state borders
         ) %>%
*     mutate(
*        encounter_length = encounter_length/3600,              #convert seconds to hours
*        date_time = as.Date(date_time, format = "%m/%d/%Y")
*        )
```
]]
.column[.content[

```
  # A tibble: 1,862 x 6
     date_time  city_area      state   lat  long encounter_length
     <date>     <chr>          <chr> <dbl> <dbl>            <dbl>
   1 1968-10-10 brevard        nc     35.2 -82.7         0.05    
   2 1971-10-10 lexington      nc     35.8 -80.3         0.00833 
   3 1991-10-10 frisco         nc     35.2 -75.6         0.5     
   4 1998-10-10 mooresville    nc     35.6 -80.8         0.000556
   5 2005-10-10 hendersonville nc     35.3 -82.5         0.167   
   6 2009-10-10 wilmington     nc     34.2 -77.9         0.167   
   7 2008-10-11 salisbury      nc     35.7 -80.5         0.00556 
   8 2011-10-11 holden beach   nc     33.9 -78.3         0.00833 
   9 1961-10-01 graham         nc     36.1 -79.4         0.00833 
  10 1978-10-01 windsor        nc     36.0 -76.9         0.0833  
  # … with 1,852 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_raw %>%
      janitor::clean_names() %>%
      select(date_time, city_area,
             state, latitude,
             longitude, encounter_length) %>%
  rename(lat = latitude,
         long = longitude) %>%
      filter(
         state == "nc",
         lat > 30,  # remove borders erroneously listed as NC outside of state borders
         lat < 37,  # remove borders erroneously listed as NC outside of state borders
         long < -75  # remove borders erroneously listed as NC outside of state borders
         ) %>%
      mutate(
         encounter_length = encounter_length/3600,              #convert seconds to hours
         date_time = as.Date(date_time, format = "%m/%d/%Y")
         ) ->
*ufo
```
]]
.column[.content[

]]

---

## prepping map data

He also uses ggplot2's map_data to plot the North Carolina polygon.

```r
map_borders <- 
  ggplot2::map_data("state", 
           region = "north carolina") 
```

---

# The basic plot

---

```r
*ufo
```
]]
.column[.content[

```r
ufo %>%
* ggplot()
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_2-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
* aes(x = long)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_3-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
* aes(y = lat)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_4-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
* # plot nc borders
* geom_polygon(data = map_borders,
*              mapping = aes(group = group),
*              color = "black",
*              fill = "grey25",
*              size = 1.15)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_10-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
* # plot UFO encounters
* aes(size = encounter_length)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_12-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
  # plot UFO encounters
  aes(size = encounter_length) +
  geom_point(color = "green", aes(group = NULL)) +
  coord_fixed(1.3) +
* scale_size_continuous(breaks = c(1, 10, 100))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_15-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
  # plot UFO encounters
  aes(size = encounter_length) +
  geom_point(color = "green", aes(group = NULL)) +
  coord_fixed(1.3) +
  scale_size_continuous(breaks = c(1, 10, 100)) +
* labs(title = "UFOs over North Carolina\n")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_16-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
  # plot UFO encounters
  aes(size = encounter_length) +
  geom_point(color = "green", aes(group = NULL)) +
  coord_fixed(1.3) +
  scale_size_continuous(breaks = c(1, 10, 100)) +
  labs(title = "UFOs over North Carolina\n") +
* labs(size = "Encounter (hrs)")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_17-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
  # plot UFO encounters
  aes(size = encounter_length) +
  geom_point(color = "green", aes(group = NULL)) +
  coord_fixed(1.3) +
  scale_size_continuous(breaks = c(1, 10, 100)) +
  labs(title = "UFOs over North Carolina\n") +
  labs(size = "Encounter (hrs)") +
* labs(caption = "\nEach dot represents a reported UFO sighting between 1995 and 2014.  \nSource: National UFO Reporting Center  | Visualization: Joel Soroos @soroosj")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_build_ufo_nc_18-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo %>%
  ggplot() +
  aes(x = long) +
  aes(y = lat) +
  # plot nc borders
  geom_polygon(data = map_borders,
               mapping = aes(group = group),
               color = "black",
               fill = "grey25",
               size = 1.15) +
  # plot UFO encounters
  aes(size = encounter_length) +
  geom_point(color = "green", aes(group = NULL)) +
  coord_fixed(1.3) +
  scale_size_continuous(breaks = c(1, 10, 100)) +
  labs(title = "UFOs over North Carolina\n") +
  labs(size = "Encounter (hrs)") +
  labs(caption = "\nEach dot represents a reported UFO sighting between 1995 and 2014.  \nSource: National UFO Reporting Center  | Visualization: Joel Soroos @soroosj") ->
*basic_plot
```
]]
.column[.content[

]]

---
  
## Annotation layer

---

```r
*basic_plot
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_annotation_ufo_1-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
basic_plot +
* annotate(geom = "text",
*       label = "30 hour encounter\nin Deep Gap (2009)",
*       size = 3, hjust = 0, color = "green",
*       family = "Rockwell",
*       x = -84.9, y = 36.1
*       )
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_annotation_ufo_7-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
basic_plot +
  annotate(geom = "text",
        label = "30 hour encounter\nin Deep Gap (2009)",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -84.9, y = 36.1
        ) +
* annotate(geom = "curve",
*          x = -83.2, y = 36.2,
*          xend = -81.7, yend = 36.27,
*       arrow = arrow(length = unit(0.2, "cm")),
*       size = 0.4, color = "green", curvature = -0.4
*       )
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_annotation_ufo_13-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
basic_plot +
  annotate(geom = "text",
        label = "30 hour encounter\nin Deep Gap (2009)",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -84.9, y = 36.1
        ) +
  annotate(geom = "curve",
           x = -83.2, y = 36.2,
           xend = -81.7, yend = 36.27,
        arrow = arrow(length = unit(0.2, "cm")),
        size = 0.4, color = "green", curvature = -0.4
        ) +
* # Gastonia encounter annotation
*    annotate("text",
*       label = "120 hour encounter\nin Gastonia in 1993.",
*       size = 3, hjust = 0, color = "green",
*       family = "Rockwell",
*       x = -83.1, y = 34.5
*       )
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_annotation_ufo_20-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
basic_plot +
  annotate(geom = "text",
        label = "30 hour encounter\nin Deep Gap (2009)",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -84.9, y = 36.1
        ) +
  annotate(geom = "curve",
           x = -83.2, y = 36.2,
           xend = -81.7, yend = 36.27,
        arrow = arrow(length = unit(0.2, "cm")),
        size = 0.4, color = "green", curvature = -0.4
        ) +
  # Gastonia encounter annotation
     annotate("text",
        label = "120 hour encounter\nin Gastonia in 1993.",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -83.1, y = 34.5
        ) +
*    annotate(geom = "curve",
*             x = -82.27, y = 34.65,
*             xend = -81.37, yend = 35.2,
*       arrow = arrow(length = unit(0.2, "cm")),
*       size = 0.4, color = "green", curvature = -0.3
*       )
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_annotation_ufo_26-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
basic_plot +
  annotate(geom = "text",
        label = "30 hour encounter\nin Deep Gap (2009)",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -84.9, y = 36.1
        ) +
  annotate(geom = "curve",
           x = -83.2, y = 36.2,
           xend = -81.7, yend = 36.27,
        arrow = arrow(length = unit(0.2, "cm")),
        size = 0.4, color = "green", curvature = -0.4
        ) +
  # Gastonia encounter annotation
     annotate("text",
        label = "120 hour encounter\nin Gastonia in 1993.",
        size = 3, hjust = 0, color = "green",
        family = "Rockwell",
        x = -83.1, y = 34.5
        ) +
     annotate(geom = "curve",
              x = -82.27, y = 34.65,
              xend = -81.37, yend = 35.2,
        arrow = arrow(length = unit(0.2, "cm")),
        size = 0.4, color = "green", curvature = -0.3
        ) ->
*ufo_plot
```
]]
.column[.content[

]]

---

# Adjusting themes

---

```r
*ufo_plot
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_1-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
* ggdark::dark_mode(.theme =
*                     theme_minimal())
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_3-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
* theme(axis.title = element_blank())
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_4-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
* theme(axis.text = element_blank())
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_5-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
* theme(axis.ticks = element_blank())
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_6-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
* theme(text = element_text(family = "Rockwell",
*                           color = "green"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_8-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(text = element_text(family = "Rockwell",
                            color = "green")) +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 18)) +
  theme(plot.caption = element_text(hjust = 0,
                                    size = 8)) +
* theme(legend.title = element_text(size = 10,
*                                   hjust = 0.5,
*                                   vjust = 0.5))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_15-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(text = element_text(family = "Rockwell",
                            color = "green")) +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 18)) +
  theme(plot.caption = element_text(hjust = 0,
                                    size = 8)) +
  theme(legend.title = element_text(size = 10,
                                    hjust = 0.5,
                                    vjust = 0.5)) +
* theme(legend.text = element_text(size = 9,
*                                  hjust = 0.5,
*                                  vjust = 0.5))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_18-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(text = element_text(family = "Rockwell",
                            color = "green")) +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 18)) +
  theme(plot.caption = element_text(hjust = 0,
                                    size = 8)) +
  theme(legend.title = element_text(size = 10,
                                    hjust = 0.5,
                                    vjust = 0.5)) +
  theme(legend.text = element_text(size = 9,
                                   hjust = 0.5,
                                   vjust = 0.5)) +
* theme(legend.position = c(0.82, 0.18))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_19-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(text = element_text(family = "Rockwell",
                            color = "green")) +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 18)) +
  theme(plot.caption = element_text(hjust = 0,
                                    size = 8)) +
  theme(legend.title = element_text(size = 10,
                                    hjust = 0.5,
                                    vjust = 0.5)) +
  theme(legend.text = element_text(size = 9,
                                   hjust = 0.5,
                                   vjust = 0.5)) +
  theme(legend.position = c(0.82, 0.18)) +
* theme(legend.justification = c(0, 1))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_20-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ufo_plot +
  ggdark::dark_mode(.theme =
                      theme_minimal()) +
  theme(axis.title = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(text = element_text(family = "Rockwell",
                            color = "green")) +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 18)) +
  theme(plot.caption = element_text(hjust = 0,
                                    size = 8)) +
  theme(legend.title = element_text(size = 10,
                                    hjust = 0.5,
                                    vjust = 0.5)) +
  theme(legend.text = element_text(size = 9,
                                   hjust = 0.5,
                                   vjust = 0.5)) +
  theme(legend.position = c(0.82, 0.18)) +
  theme(legend.justification = c(0, 1)) +
* theme(legend.key.size = unit(0.1, 'lines'))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_ufo_theme_21-1.png" width="100%" />
]]

---

---

## Ifeoma Egbogah's *Relationship Between Goals and Caps*

---

```r
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-09/squads.csv"
squads <- readr::read_csv(file = url)
```

---

```r
*squads
```
]]
.column[.content[

```
  # A tibble: 552 x 9
     squad_no country pos   player dob                   age  caps goals
        <int> <chr>   <chr> <chr>  <dttm>              <int> <int> <int>
   1        1 US      GK    Alyss… 1988-04-20 00:00:00    31    43     0
   2        2 US      FW    Mallo… 1998-04-29 00:00:00    21    50    15
   3        3 US      MF    Sam M… 1992-10-09 00:00:00    26    47     9
   4        4 US      DF    Becky… 1985-06-06 00:00:00    34   155     0
   5        5 US      DF    Kelle… 1988-08-04 00:00:00    30   115     2
   6        6 US      MF    Morga… 1993-02-26 00:00:00    26    82     6
   7        7 US      DF    Abby … 1993-05-13 00:00:00    26    37     0
   8        8 US      MF    Julie… 1992-04-06 00:00:00    27    79    18
   9        9 US      MF    Linds… 1994-05-26 00:00:00    25    66     8
  10       10 US      FW    Carli… 1982-07-16 00:00:00    36   271   107
  # … with 542 more rows, and 1 more variable: club <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
* mutate(pos = case_when(pos == "DF" ~ "Defense",
*                        pos == "MF" ~ "Mid fielder",
*                        pos == "GK" ~ "Goal keeper",
*                        pos == "FW" ~ "Forward"))
```
]]
.column[.content[

```
  # A tibble: 552 x 9
     squad_no country pos   player dob                   age  caps goals
        <int> <chr>   <chr> <chr>  <dttm>              <int> <int> <int>
   1        1 US      Goal… Alyss… 1988-04-20 00:00:00    31    43     0
   2        2 US      Forw… Mallo… 1998-04-29 00:00:00    21    50    15
   3        3 US      Mid … Sam M… 1992-10-09 00:00:00    26    47     9
   4        4 US      Defe… Becky… 1985-06-06 00:00:00    34   155     0
   5        5 US      Defe… Kelle… 1988-08-04 00:00:00    30   115     2
   6        6 US      Mid … Morga… 1993-02-26 00:00:00    26    82     6
   7        7 US      Defe… Abby … 1993-05-13 00:00:00    26    37     0
   8        8 US      Mid … Julie… 1992-04-06 00:00:00    27    79    18
   9        9 US      Mid … Linds… 1994-05-26 00:00:00    25    66     8
  10       10 US      Forw… Carli… 1982-07-16 00:00:00    36   271   107
  # … with 542 more rows, and 1 more variable: club <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
* ggplot()
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_6-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
* aes(x = caps)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_7-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
* aes(y = goals)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_8-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
  aes(y = goals) +
* geom_point()
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_9-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
  aes(y = goals) +
  geom_point() +
* geom_smooth(method = lm)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_10-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
  aes(y = goals) +
  geom_point() +
  geom_smooth(method = lm) +
* aes(colour = pos)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_11-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
  aes(y = goals) +
  geom_point() +
  geom_smooth(method = lm) +
  aes(colour = pos) +
  scale_colour_viridis_d(option = "B", guide = F) +
  facet_wrap(~pos) +
  labs(x = "Caps") +
  labs(y = "Goals") +
  labs(title = "Relationship Between Goals and Caps") +
  labs(subtitle = "For ladies who featured in the 2019 FIFA women's world cup\nbased on players positon on the field") +
  labs(caption = "Source: data.world | Visualization: Ifeoma Egbogah") +
* theme(plot.subtitle =
*         element_text(size = 10,
*                      color = "#939184",
*                      margin =
*                        margin(b = 0.1, t = -0.1,
*                               l = 2, unit = "cm"),
*                      face = "bold"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_25-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
squads %>%
  mutate(pos = case_when(pos == "DF" ~ "Defense",
                         pos == "MF" ~ "Mid fielder",
                         pos == "GK" ~ "Goal keeper",
                         pos == "FW" ~ "Forward")) %>%
  ggplot() +
  aes(x = caps) +
  aes(y = goals) +
  geom_point() +
  geom_smooth(method = lm) +
  aes(colour = pos) +
  scale_colour_viridis_d(option = "B", guide = F) +
  facet_wrap(~pos) +
  labs(x = "Caps") +
  labs(y = "Goals") +
  labs(title = "Relationship Between Goals and Caps") +
  labs(subtitle = "For ladies who featured in the 2019 FIFA women's world cup\nbased on players positon on the field") +
  labs(caption = "Source: data.world | Visualization: Ifeoma Egbogah") +
  theme(plot.subtitle =
          element_text(size = 10,
                       color = "#939184",
                       margin =
                         margin(b = 0.1, t = -0.1,
                                l = 2, unit = "cm"),
                       face = "bold")) +
* theme(plot.caption =
*         element_text(size = 7,
*                      hjust = .5,
*                      margin =
*                        margin(t = 0.2,
*                               b = 0,
*                               unit = "cm"),
*                               color = "#939184"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_world_cup_33-1.png" width="100%" />
]]

---

## Christian Burkhart's *Some teachers have it tough*

---

# "Teachers have it Tough" by Christian Burkhart

---

## set up and getting data

```r
library(tidyverse)
```

```r
tidy_tuesday_url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-07/student_teacher_ratio.csv"
student_ratio <- readr::read_csv(tidy_tuesday_url)
```

---

## prepping data

---

```r
*student_ratio
```
]]
.column[.content[

```
  # A tibble: 5,189 x 8
     edulit_ind indicator country_code country  year student_ratio flag_codes
     <chr>      <chr>     <chr>        <chr>   <int>         <dbl> <chr>     
   1 PTRHC_2    Lower Se… MRT          Maurit…  2013         56.6  <NA>      
   2 PTRHC_2    Lower Se… MRT          Maurit…  2014         51.9  <NA>      
   3 PTRHC_2    Lower Se… MRT          Maurit…  2015         53.2  <NA>      
   4 PTRHC_2    Lower Se… MRT          Maurit…  2016         38.2  <NA>      
   5 PTRHC_1    Primary … COD          Democr…  2012         34.7  <NA>      
   6 PTRHC_1    Primary … COD          Democr…  2013         37.1  <NA>      
   7 PTRHC_1    Primary … COD          Democr…  2014         35.3  <NA>      
   8 PTRHC_1    Primary … COD          Democr…  2015         33.2  <NA>      
   9 PTRHC_3    Upper Se… SYR          Syrian…  2013          8.47 <NA>      
  10 PTRHC_02   Pre-Prim… GNQ          Equato…  2012         17.5  <NA>      
  # … with 5,179 more rows, and 1 more variable: flags <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
* select(country, student_ratio, indicator)
```
]]
.column[.content[

```
  # A tibble: 5,189 x 3
     country                          student_ratio indicator                
     <chr>                                    <dbl> <chr>                    
   1 Mauritania                               56.6  Lower Secondary Education
   2 Mauritania                               51.9  Lower Secondary Education
   3 Mauritania                               53.2  Lower Secondary Education
   4 Mauritania                               38.2  Lower Secondary Education
   5 Democratic Republic of the Congo         34.7  Primary Education        
   6 Democratic Republic of the Congo         37.1  Primary Education        
   7 Democratic Republic of the Congo         35.3  Primary Education        
   8 Democratic Republic of the Congo         33.2  Primary Education        
   9 Syrian Arab Republic                      8.47 Upper Secondary Education
  10 Equatorial Guinea                        17.5  Pre-Primary Education    
  # … with 5,179 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
* filter(indicator == "Tertiary Education")
```
]]
.column[.content[

```
  # A tibble: 550 x 3
     country student_ratio indicator         
     <chr>           <dbl> <chr>             
   1 Palau            8.22 Tertiary Education
   2 Sweden          14.0  Tertiary Education
   3 Sweden          13.4  Tertiary Education
   4 Sweden          12.9  Tertiary Education
   5 Sweden          12.6  Tertiary Education
   6 Sweden          12.4  Tertiary Education
   7 Belgium         16.6  Tertiary Education
   8 Belgium         16.3  Tertiary Education
   9 Belgium         17.3  Tertiary Education
  10 Belgium         17.6  Tertiary Education
  # … with 540 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
  filter(indicator == "Tertiary Education") %>%
* group_by(country)
```
]]
.column[.content[

```
  # A tibble: 550 x 3
  # Groups:   country [148]
     country student_ratio indicator         
     <chr>           <dbl> <chr>             
   1 Palau            8.22 Tertiary Education
   2 Sweden          14.0  Tertiary Education
   3 Sweden          13.4  Tertiary Education
   4 Sweden          12.9  Tertiary Education
   5 Sweden          12.6  Tertiary Education
   6 Sweden          12.4  Tertiary Education
   7 Belgium         16.6  Tertiary Education
   8 Belgium         16.3  Tertiary Education
   9 Belgium         17.3  Tertiary Education
  10 Belgium         17.6  Tertiary Education
  # … with 540 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
  filter(indicator == "Tertiary Education") %>%
  group_by(country) %>%
* summarise(student_ratio =
*             mean(student_ratio,
*                  na.rm = TRUE))
```
]]
.column[.content[

```
  # A tibble: 148 x 2
     country             student_ratio
     <chr>                       <dbl>
   1 Afghanistan                 22.7 
   2 Albania                     19.9 
   3 Algeria                     25.2 
   4 Andorra                      4.23
   5 Angola                      25.5 
   6 Antigua and Barbuda          8.18
   7 Armenia                      7.13
   8 Aruba                       10.6 
   9 Austria                      7.37
  10 Azerbaijan                   8.95
  # … with 138 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```
  # A tibble: 146 x 2
     country             student_ratio
     <chr>                       <dbl>
   1 Afghanistan                 22.7 
   2 Albania                     19.9 
   3 Algeria                     25.2 
   4 Andorra                      4.23
   5 Angola                      25.5 
   6 Antigua and Barbuda          8.18
   7 Armenia                      7.13
   8 Aruba                       10.6 
   9 Austria                      7.37
  10 Azerbaijan                   8.95
  # … with 136 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
  filter(indicator == "Tertiary Education") %>%
  group_by(country) %>%
  summarise(student_ratio =
              mean(student_ratio,
                   na.rm = TRUE)) %>%
  ungroup() %>%
  drop_na(student_ratio) %>%
* rename(region = country)
```
]]
.column[.content[

```
  # A tibble: 146 x 2
     region              student_ratio
     <chr>                       <dbl>
   1 Afghanistan                 22.7 
   2 Albania                     19.9 
   3 Algeria                     25.2 
   4 Andorra                      4.23
   5 Angola                      25.5 
   6 Antigua and Barbuda          8.18
   7 Armenia                      7.13
   8 Aruba                       10.6 
   9 Austria                      7.37
  10 Azerbaijan                   8.95
  # … with 136 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
  filter(indicator == "Tertiary Education") %>%
  group_by(country) %>%
  summarise(student_ratio =
              mean(student_ratio,
                   na.rm = TRUE)) %>%
  ungroup() %>%
  drop_na(student_ratio) %>%
  rename(region = country) %>%
* full_join(
*   map_data("world") %>%
*     filter(region != "Antarctica"),
*   by = "region")
```
]]
.column[.content[

```
  # A tibble: 94,703 x 7
     region      student_ratio  long   lat group order subregion
     <chr>               <dbl> <dbl> <dbl> <dbl> <int> <chr>    
   1 Afghanistan          22.7  74.9  37.2     2    12 <NA>     
   2 Afghanistan          22.7  74.8  37.2     2    13 <NA>     
   3 Afghanistan          22.7  74.8  37.2     2    14 <NA>     
   4 Afghanistan          22.7  74.7  37.3     2    15 <NA>     
   5 Afghanistan          22.7  74.7  37.3     2    16 <NA>     
   6 Afghanistan          22.7  74.7  37.3     2    17 <NA>     
   7 Afghanistan          22.7  74.6  37.2     2    18 <NA>     
   8 Afghanistan          22.7  74.4  37.2     2    19 <NA>     
   9 Afghanistan          22.7  74.4  37.1     2    20 <NA>     
  10 Afghanistan          22.7  74.5  37.1     2    21 <NA>     
  # … with 94,693 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
student_ratio %>%
  select(country, student_ratio, indicator) %>%
  filter(indicator == "Tertiary Education") %>%
  group_by(country) %>%
  summarise(student_ratio =
              mean(student_ratio,
                   na.rm = TRUE)) %>%
  ungroup() %>%
  drop_na(student_ratio) %>%
  rename(region = country) %>%
  full_join(
    map_data("world") %>%
      filter(region != "Antarctica"),
    by = "region") ->
*student_ratio_world
```
]]
.column[.content[

]]

---

## Build figure

---

```r
*ggplot(data = student_ratio_world)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_1-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
* aes(x = long)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_2-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
* aes(y = lat)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_3-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
* aes(map_id = region)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_4-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
* geom_map(map = student_ratio_world,
*          col = "grey34",
*          alpha = .6, size = .05)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_7-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
* aes(fill = student_ratio)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_8-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
* guides(fill =
*          guide_legend(title =
*                         "# of students\nper teacher"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_18-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
  guides(fill =
           guide_legend(title =
                          "# of students\nper teacher")) +
* coord_map("gilbert", xlim = c(-180, 180))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_19-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
  guides(fill =
           guide_legend(title =
                          "# of students\nper teacher")) +
  coord_map("gilbert", xlim = c(-180, 180)) +
* labs(title = '"Oh dear, some teachers have it tough"')
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_20-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
  guides(fill =
           guide_legend(title =
                          "# of students\nper teacher")) +
  coord_map("gilbert", xlim = c(-180, 180)) +
  labs(title = '"Oh dear, some teachers have it tough"') +
* labs(subtitle = "How the student-to-teacher ratio varies across the globe*")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_21-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
  guides(fill =
           guide_legend(title =
                          "# of students\nper teacher")) +
  coord_map("gilbert", xlim = c(-180, 180)) +
  labs(title = '"Oh dear, some teachers have it tough"') +
  labs(subtitle = "How the student-to-teacher ratio varies across the globe*") +
* labs(caption = "Visualization: Christian Burkhart for #TidyTuesday\nData:\n*average 2012-2017 Terciary Education")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_map_22-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = student_ratio_world) +
  aes(x = long) +
  aes(y = lat) +
  aes(map_id = region) +
  geom_map(map = student_ratio_world,
           col = "grey34",
           alpha = .6, size = .05) +
  aes(fill = student_ratio) +
  scale_fill_gradient(low = "#55bee9",
                      high = "#f1b545",
                      na.value = "#282828") +
  scale_y_continuous(breaks = c()) +
  scale_x_continuous(breaks = c()) +
  labs(x = "") +
  labs(y = "") +
  guides(fill =
           guide_legend(title =
                          "# of students\nper teacher")) +
  coord_map("gilbert", xlim = c(-180, 180)) +
  labs(title = '"Oh dear, some teachers have it tough"') +
  labs(subtitle = "How the student-to-teacher ratio varies across the globe*") +
  labs(caption = "Visualization: Christian Burkhart for #TidyTuesday\nData:\n*average 2012-2017 Terciary Education") ->
*plot
```
]]
.column[.content[

]]

---

## Adjust theme

---

```r
*plot
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_1-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
* theme(plot.title =
*         element_text(color = "#ffffff",
*                      margin = margin(t = 30,
*                                      b = 10),
*                      size = 12))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_6-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
* theme(plot.subtitle =
*         element_text(color = "#ababab",
*                      margin = margin(b = 20),
*                      size = 10))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_10-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
  theme(plot.subtitle =
          element_text(color = "#ababab",
                       margin = margin(b = 20),
                       size = 10)) +
  theme(plot.caption =
          element_text(color = "#ababab",
                       size = 7, hjust = 1)) +
* theme(plot.background  =
*         element_rect(fill  = "#323232"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_15-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
  theme(plot.subtitle =
          element_text(color = "#ababab",
                       margin = margin(b = 20),
                       size = 10)) +
  theme(plot.caption =
          element_text(color = "#ababab",
                       size = 7, hjust = 1)) +
  theme(plot.background  =
          element_rect(fill  = "#323232")) +
* theme(legend.title =
*         element_text(color = "#6d6d6d",
*                      size = 8))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_18-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
  theme(plot.subtitle =
          element_text(color = "#ababab",
                       margin = margin(b = 20),
                       size = 10)) +
  theme(plot.caption =
          element_text(color = "#ababab",
                       size = 7, hjust = 1)) +
  theme(plot.background  =
          element_rect(fill  = "#323232")) +
  theme(legend.title =
          element_text(color = "#6d6d6d",
                       size = 8)) +
* theme(legend.text =
*         element_text(color = "#6d6d6d",
*                      size = 8))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_21-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
  theme(plot.subtitle =
          element_text(color = "#ababab",
                       margin = margin(b = 20),
                       size = 10)) +
  theme(plot.caption =
          element_text(color = "#ababab",
                       size = 7, hjust = 1)) +
  theme(plot.background  =
          element_rect(fill  = "#323232")) +
  theme(legend.title =
          element_text(color = "#6d6d6d",
                       size = 8)) +
  theme(legend.text =
          element_text(color = "#6d6d6d",
                       size = 8)) +
* theme(legend.background =
*         element_rect(fill  = "#323232"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_23-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
plot +
  theme(plot.title =
          element_text(color = "#ffffff",
                       margin = margin(t = 30,
                                       b = 10),
                       size = 12)) +
  theme(plot.subtitle =
          element_text(color = "#ababab",
                       margin = margin(b = 20),
                       size = 10)) +
  theme(plot.caption =
          element_text(color = "#ababab",
                       size = 7, hjust = 1)) +
  theme(plot.background  =
          element_rect(fill  = "#323232")) +
  theme(legend.title =
          element_text(color = "#6d6d6d",
                       size = 8)) +
  theme(legend.text =
          element_text(color = "#6d6d6d",
                       size = 8)) +
  theme(legend.background =
          element_rect(fill  = "#323232")) +
* theme(panel.background =
*         element_rect(fill  = "#323232"))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_complete_teacher_student_25-1.png" width="100%" />
]]

---

---

## David Carayon's *Revenue generated for the 5 most successful media*

---

# Set up

David uses the tidyverse and #tidytuesday data

```r
library(tidyverse)
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv"

media_franchises <- readr::read_csv(url) %>%
  mutate(revenue = revenue * 1000000000) 
```

---

## Data prep

Identifying top revenue media types

---

```r
*media_franchises
```
]]
.column[.content[

```
  # A tibble: 321 x 7
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 A Song o… Book sales        9.00e8         1996 Novel          George …
   2 A Song o… Box Office        1.00e6         1996 Novel          George …
   3 A Song o… Home Video/Ente…  2.80e8         1996 Novel          George …
   4 A Song o… TV                4.00e9         1996 Novel          George …
   5 A Song o… Video Games/Gam…  1.32e8         1996 Novel          George …
   6 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   7 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   8 Aladdin   Merchandise, Li…  5.00e8         1992 Animated film  Walt Di…
   9 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
  10 Aladdin   Video Games/Gam…  2.20e9         1992 Animated film  Walt Di…
  # … with 311 more rows, and 1 more variable: owners <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
* group_by(original_media)
```
]]
.column[.content[

```
  # A tibble: 321 x 7
  # Groups:   original_media [18]
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 A Song o… Book sales        9.00e8         1996 Novel          George …
   2 A Song o… Box Office        1.00e6         1996 Novel          George …
   3 A Song o… Home Video/Ente…  2.80e8         1996 Novel          George …
   4 A Song o… TV                4.00e9         1996 Novel          George …
   5 A Song o… Video Games/Gam…  1.32e8         1996 Novel          George …
   6 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   7 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   8 Aladdin   Merchandise, Li…  5.00e8         1992 Animated film  Walt Di…
   9 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
  10 Aladdin   Video Games/Gam…  2.20e9         1992 Animated film  Walt Di…
  # … with 311 more rows, and 1 more variable: owners <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
* summarise(total_revenue = sum(revenue))
```
]]
.column[.content[

```
  # A tibble: 18 x 2
     original_media    total_revenue
     <chr>                     <dbl>
   1 Animated cartoon    87107000000
   2 Animated film      102252000000
   3 Animated series    121024000000
   4 Anime               47990000000
   5 Book                82539000000
   6 Cartoon              4065000000
   7 Cartoon character   80026000000
   8 Comic book          95100000000
   9 Comic strip         17285000000
  10 Digital pet         11128000000
  11 Film               111674000000
  12 Greeting card        9054000000
  13 Manga              253791000000
  14 Musical theatre      6155000000
  15 Novel               85860000000
  16 Television series   68723000000
  17 Video game         334900000000
  18 Visual novel         3569000000
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
* top_n(5, total_revenue)
```
]]
.column[.content[

```
  # A tibble: 5 x 2
    original_media  total_revenue
    <chr>                   <dbl>
  1 Animated film    102252000000
  2 Animated series  121024000000
  3 Film             111674000000
  4 Manga            253791000000
  5 Video game       334900000000
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
* pull(original_media)
```
]]
.column[.content[

```
  [1] "Animated film"   "Animated series" "Film"            "Manga"          
  [5] "Video game"
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
*top_5_media
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
top_5_media
*media_franchises
```
]]
.column[.content[

```
  # A tibble: 187 x 7
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   2 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   3 Aladdin   Merchandise, Li…  5.00e8         1992 Animated film  Walt Di…
   4 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
   5 Aladdin   Video Games/Gam…  2.20e9         1992 Animated film  Walt Di…
   6 Angry Bi… Box Office        3.53e8         2009 Video game     Jaakko …
   7 Angry Bi… Home Video/Ente…  2.70e7         2009 Video game     Jaakko …
   8 Angry Bi… Merchandise, Li…  8.00e9         2009 Video game     Jaakko …
   9 Angry Bi… Video Games/Gam…  1.00e8         2009 Video game     Jaakko …
  10 Anpanman  Box Office        6.70e7         1973 Manga          Takashi…
  # … with 177 more rows, and 1 more variable: owners <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
top_5_media
media_franchises %>%
  unique() %>%
  filter(original_media %in% top_5_media) %>%
* mutate(revenue_category =
*          recode(revenue_category,
*                 "Merchandise, Licensing & Retail" =
*                   "Merchandise",
*                 "Video Games/Games" =
*                   "Games"))
```
]]
.column[.content[

```
  # A tibble: 187 x 7
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   2 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   3 Aladdin   Merchandise       5.00e8         1992 Animated film  Walt Di…
   4 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
   5 Aladdin   Games             2.20e9         1992 Animated film  Walt Di…
   6 Angry Bi… Box Office        3.53e8         2009 Video game     Jaakko …
   7 Angry Bi… Home Video/Ente…  2.70e7         2009 Video game     Jaakko …
   8 Angry Bi… Merchandise       8.00e9         2009 Video game     Jaakko …
   9 Angry Bi… Games             1.00e8         2009 Video game     Jaakko …
  10 Anpanman  Box Office        6.70e7         1973 Manga          Takashi…
  # … with 177 more rows, and 1 more variable: owners <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
top_5_media
media_franchises %>%
  unique() %>%
  filter(original_media %in% top_5_media) %>%
  mutate(revenue_category =
           recode(revenue_category,
                  "Merchandise, Licensing & Retail" =
                    "Merchandise",
                  "Video Games/Games" =
                    "Games")) %>%
*     filter(original_media %in% top_5_media)
```
]]
.column[.content[

```
  # A tibble: 187 x 7
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   2 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   3 Aladdin   Merchandise       5.00e8         1992 Animated film  Walt Di…
   4 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
   5 Aladdin   Games             2.20e9         1992 Animated film  Walt Di…
   6 Angry Bi… Box Office        3.53e8         2009 Video game     Jaakko …
   7 Angry Bi… Home Video/Ente…  2.70e7         2009 Video game     Jaakko …
   8 Angry Bi… Merchandise       8.00e9         2009 Video game     Jaakko …
   9 Angry Bi… Games             1.00e8         2009 Video game     Jaakko …
  10 Anpanman  Box Office        6.70e7         1973 Manga          Takashi…
  # … with 177 more rows, and 1 more variable: owners <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
top_5_media
media_franchises %>%
  unique() %>%
  filter(original_media %in% top_5_media) %>%
  mutate(revenue_category =
           recode(revenue_category,
                  "Merchandise, Licensing & Retail" =
                    "Merchandise",
                  "Video Games/Games" =
                    "Games")) %>%
      filter(original_media %in% top_5_media) %>%
*     mutate(categ = paste0(franchise, " (",
*                           revenue_category, ")"))
```
]]
.column[.content[

```
  # A tibble: 187 x 8
     franchise revenue_category revenue year_created original_media creators
     <chr>     <chr>              <dbl>        <int> <chr>          <chr>   
   1 Aladdin   Box Office        7.60e8         1992 Animated film  Walt Di…
   2 Aladdin   Home Video/Ente…  1.00e9         1992 Animated film  Walt Di…
   3 Aladdin   Merchandise       5.00e8         1992 Animated film  Walt Di…
   4 Aladdin   Music             4.47e8         1992 Animated film  Walt Di…
   5 Aladdin   Games             2.20e9         1992 Animated film  Walt Di…
   6 Angry Bi… Box Office        3.53e8         2009 Video game     Jaakko …
   7 Angry Bi… Home Video/Ente…  2.70e7         2009 Video game     Jaakko …
   8 Angry Bi… Merchandise       8.00e9         2009 Video game     Jaakko …
   9 Angry Bi… Games             1.00e8         2009 Video game     Jaakko …
  10 Anpanman  Box Office        6.70e7         1973 Manga          Takashi…
  # … with 177 more rows, and 2 more variables: owners <chr>, categ <chr>
```
]]
---
class: split-40
count: false
.column[.content[

```r
media_franchises %>%
  group_by(original_media) %>%
  summarise(total_revenue = sum(revenue)) %>%
  top_n(5, total_revenue) %>%
  pull(original_media) ->
top_5_media
media_franchises %>%
  unique() %>%
  filter(original_media %in% top_5_media) %>%
  mutate(revenue_category =
           recode(revenue_category,
                  "Merchandise, Licensing & Retail" =
                    "Merchandise",
                  "Video Games/Games" =
                    "Games")) %>%
      filter(original_media %in% top_5_media) %>%
      mutate(categ = paste0(franchise, " (",
                            revenue_category, ")")) ->
*media_franchises_5
```
]]
.column[.content[

]]

---

# A sina plot of revenue versus media category

---

```r
*ggplot(data = media_franchises_5)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_1-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
* aes(x = original_media)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_2-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
* aes(y = revenue)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_3-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
* aes(fill = original_media)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_4-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
* geom_violin(alpha = 0.3)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_5-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
* ggforce::geom_sina(color = "black",
*                    size = 3, shape = 21)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_7-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
* aes(label = categ)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_8-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
* guides(fill = FALSE, color = FALSE)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_14-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
* labs(x = "Original Media", y = "Total revenue")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_15-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Original Media", y = "Total revenue") +
* scale_y_continuous(label = scales::dollar)
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_16-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Original Media", y = "Total revenue") +
  scale_y_continuous(label = scales::dollar) +
* ggthemes::theme_wsj()
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_17-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Original Media", y = "Total revenue") +
  scale_y_continuous(label = scales::dollar) +
  ggthemes::theme_wsj() +
* labs(title = "Revenue generated for\nthe 5 most successful media")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_18-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Original Media", y = "Total revenue") +
  scale_y_continuous(label = scales::dollar) +
  ggthemes::theme_wsj() +
  labs(title = "Revenue generated for\nthe 5 most successful media") +
* theme(title = element_text(size = 16))
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_19-1.png" width="100%" />
]]
---
class: split-40
count: false
.column[.content[

```r
ggplot(data = media_franchises_5) +
  aes(x = original_media) +
  aes(y = revenue) +
  aes(fill = original_media) +
  geom_violin(alpha = 0.3) +
  ggforce::geom_sina(color = "black",
                     size = 3, shape = 21) +
  aes(label = categ) +
  ggrepel::geom_label_repel(
    data = media_franchises_5 %>%
      filter(revenue > 19 * 10^9),
    color = "black", fill = "wheat",
    family = "mono", size = 4) +
  guides(fill = FALSE, color = FALSE) +
  labs(x = "Original Media", y = "Total revenue") +
  scale_y_continuous(label = scales::dollar) +
  ggthemes::theme_wsj() +
  labs(title = "Revenue generated for\nthe 5 most successful media") +
  theme(title = element_text(size = 16)) +
* labs(caption = "\n Data from Wikipedia | plot by @david_carayon")
```
]]
.column[.content[
<img src="tidytuesday_highlights_files/figure-html/output_top_5_20-1.png" width="100%" />
]]

---

# Thanks

Thanks to the plot builders for their willingness to share their work!

---

# The End!

Thanks having a look at this set of #TidyTuesday plots!

The code for this work lives [**here**](https://github.com/EvaMaeRey/tidytuesday_walk_through).

Interested in building your own flipbook? It is fun! The code is still underdevelopment and we'd love to have your feedback. A minimal example is [**here**]( https://evamaerey.github.io/little_flipbooks_library/tidytuesday_minimal_example/tidytuesday_minimal_example#1).