Introduction

library(ggplot2)

Theme & language setup

ggplot(economics) +
  geom_line(aes(date, unemploy), color = closest_mnb_color("blue")) +
  theme_mnb_ts(base_size = 16) + 
  labs(subtitle = "Munkanélküliség az USA-ban") + 
  scale_y_number()

options(lang = "en")

ggplot(economics) +
  geom_line(aes(date, unemploy), color = closest_mnb_color("blue")) +
  theme_mnb_ts(base_size = 16) + 
  labs(subtitle = "Unemployment in the USA") + 
  scale_y_number()

Working with colors

mnb_colors()
#> [1] "#18223e" "#6fa0be" "#f8c567" "#b2242a" "#7aa140" "#da3232" "#e57b2b"
#> [8] "#787975" "#b9e1eb"

We developed a function to simple rewrite user specified colors to the ones that matches our corporate style.

closest_mnb_color("red")
#> [1] "#da3232"

Tables

data(mtcars)

mtcars |> 
  head() |> 
  tibble::rowid_to_column("model type") |> # long colname
  gt_mnb(title = "MTcars dataset", comment = "This is a built-in dataset")

MTcars dataset
Model type	MPG	CYL	DISP	HP	DRAT	WT	QSEC	VS	AM	GEAR	CARB
1	21.00	6	160	110	3.90	2.62	16.46	0	1	4	4
2	21.00	6	160	110	3.90	2.88	17.02	0	1	4	4
3	22.80	4	108	93	3.85	2.32	18.61	1	1	4	1
4	21.40	6	258	110	3.08	3.21	19.44	1	0	3	1
5	18.70	8	360	175	3.15	3.44	17.02	0	0	3	2
6	18.10	6	225	105	2.76	3.46	20.22	1	0	3	1
This is a built-in dataset

MNB color scales

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

economics |> 
  select(date, unemploy, psavert) |> 
  mutate(across(2:3, \(x) x / lag(x) - 1)) |>
  tail(10) |> 
  tidyr::pivot_longer(- 1) |> 
  ggplot(aes(date, name, fill = value)) +
  geom_tile(color = "black") +
  theme_mnb_ts(base_size = 16) + 
  scale_fill_mnb_divergent_b() +
  labs(subtitle = "Macrostatistics of the USA", fill = "Relative m-o-m change")