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I am tasked with explaining incredibly complex things to people who do not have a lot of time. Consequently, using visuals has been a life saver.

One day I was visiting a school explaining the Common Eurpoean Framework of Reference for Languages, which, in a nutshell, describes what language learners can do at different levels of proficiency **AND** the number of hours it takes for them to progress to each level.

During the presentation I used the following table in a slide:

Image Courtesy of *Keep Calm and Teach English*

While that image is informative, it is, in my humble opinion, a little hard to comprehend in comparison to this one:

So how do you make the plot above? Glad you asked π

# Step 1: Create the data frame

As the table above shows, there are seven levels what we want to represent (A0 to C2) and a range of hours from 0 β 1200.

```
library(tidyverse)
library(knitr) #To make the table look pretty on HTML
cefr_hours <- tibble(cefr = as_factor(c("A0", "A1", "A2", "B1", "B2", "C1", "C2")),
hours = c(0, 100, 200, 400, 600, 800, 1200))
kable(cefr_hours)
```

cefr | hours |
---|---|

A0 | 0 |

A1 | 100 |

A2 | 200 |

B1 | 400 |

B2 | 600 |

C1 | 800 |

C2 | 1200 |

# Step 2: Expand the data frame

In order to color the sections between the levels, we need to create groups so that `ggplot()`

divides the the plot based on the correct levels. To do that, weβll simply double the data frame.

```
cefr_hours <- tibble(cefr = as_factor(c("A0", "A1", "A2", "B1", "B2", "C1", "C2")),
hours = c(0, 100, 200, 400, 600, 800, 1200))
cefr_hours <- cefr_hours %>%
bind_rows(cefr_hours)
kable(cefr_hours)
```

cefr | hours |
---|---|

A0 | 0 |

A1 | 100 |

A2 | 200 |

B1 | 400 |

B2 | 600 |

C1 | 800 |

C2 | 1200 |

A0 | 0 |

A1 | 100 |

A2 | 200 |

B1 | 400 |

B2 | 600 |

C1 | 800 |

C2 | 1200 |

# Step 3: Create groups

Next, we rearrange the data frame by CEFR level (more on that later) and create a group for each level. To do so, we create a new column called `group`

using `dplyr::mutate`

.

```
cefr_hours <- tibble(cefr = as_factor(c("A0", "A1", "A2", "B1", "B2", "C1", "C2")),
hours = c(0, 100, 200, 400, 600, 800, 1200))
cefr_hours <- cefr_hours %>%
bind_rows(cefr_hours) %>%
arrange(cefr) %>%
mutate(group = ceiling((row_number() - 1) / 2))
kable(cefr_hours)
```

cefr | hours | group |
---|---|---|

A0 | 0 | 0 |

A0 | 0 | 1 |

A1 | 100 | 1 |

A1 | 100 | 2 |

A2 | 200 | 2 |

A2 | 200 | 3 |

B1 | 400 | 3 |

B1 | 400 | 4 |

B2 | 600 | 4 |

B2 | 600 | 5 |

C1 | 800 | 5 |

C1 | 800 | 6 |

C2 | 1200 | 6 |

C2 | 1200 | 7 |

If we donβt use `arrange()`

we get the following mess.

```
cefr_hours <- tibble(cefr = as_factor(c("A0", "A1", "A2", "B1", "B2", "C1", "C2")),
hours = c(0, 100, 200, 400, 600, 800, 1200))
cefr_hours <- cefr_hours %>%
bind_rows(cefr_hours) %>%
mutate(group = ceiling((row_number() - 1) / 2))
kable(cefr_hours)
```

cefr | hours | group |
---|---|---|

A0 | 0 | 0 |

A1 | 100 | 1 |

A2 | 200 | 1 |

B1 | 400 | 2 |

B2 | 600 | 2 |

C1 | 800 | 3 |

C2 | 1200 | 3 |

A0 | 0 | 4 |

A1 | 100 | 4 |

A2 | 200 | 5 |

B1 | 400 | 5 |

B2 | 600 | 6 |

C1 | 800 | 6 |

C2 | 1200 | 7 |

*βWhat about *`ceiling()`

?β

`ceiling()`

?βGood question!

We use `ceiling()`

in order to create the groups. If since we want βA1 to A2β to be one group, we need to return whole numbers. For more on how to use `ceiling()`

please click here.

# Step 4: Remove Unecessary Groups

Since we donβt want the first or last level to be a group unto itself, we use `dplyr::filter()`

to remove the first and the last group by saying `group`

is equal to all rows except for the `min()`

and `max()`

(i.e., the first and the last).

```
cefr_hours <- tibble(cefr = as_factor(c("A0", "A1", "A2", "B1", "B2", "C1", "C2")),
hours = c(0, 100, 200, 400, 600, 800, 1200))
cefr_hours <- cefr_hours %>%
bind_rows(cefr_hours) %>%
arrange(cefr) %>%
mutate(group = ceiling((row_number() - 1) / 2)) %>%
filter(group != min(group), group != max(group))
kable(cefr_hours)
```

cefr | hours | group |
---|---|---|

A0 | 0 | 1 |

A1 | 100 | 1 |

A1 | 100 | 2 |

A2 | 200 | 2 |

A2 | 200 | 3 |

B1 | 400 | 3 |

B1 | 400 | 4 |

B2 | 600 | 4 |

B2 | 600 | 5 |

C1 | 800 | 5 |

C1 | 800 | 6 |

C2 | 1200 | 6 |

# Step 5: Make the plot

From here, it is simply a matter of plugging the data into `ggplot()`

.

```
ggplot(data = cefr_hours, mapping =aes(x= cefr, y=hours, group = group, fill = group)) +
geom_ribbon(aes(ymin = 0, ymax = hours))
```

But, of course, when weβre talking about `ggplot()`

, that means we have no end of options at our disposal.

```
ggplot(data = cefr_hours, mapping =aes(x= cefr, y=hours, group = group, fill = group)) +
geom_ribbon(aes(ymin = 0, ymax = hours)) +
scale_color_brewer(palette = "Blues") +
theme_minimal() + # Set the theme
labs(title = "Hours of Guided Learning Per Level", # Give the plot a title
subtitle = "Source: Cambridge English Assessment", # Give it a subtitle
x = "", # Remove the title on the x axis
y = "") + # Remove the title on the y axis
theme(legend.position = "none", # Delete the legend
axis.text.x = element_text(size = 20), # Set the size to 20
axis.text.y = element_text(size = 20), # Set the size to 20
plot.title = element_text(size = 25)) # Set the size to 25
```

Finally, a special thanks to Jordo82 whose answer to my question enabled me to make this plot.

Happy Coding!

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