Chapter 3 Help functions

library (tidyverse) 

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## v readr   1.3.1     v forcats 0.4.0

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library (broom) 
library (gganimate)  
library (car)

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library (swemaps) # devtools::install_github('reinholdsson/swemaps')
library (ggpubr)

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library (Cubist)

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library(PerformanceAnalytics)

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library(factoextra)

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## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ

library(clValid)

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## Loading required package: cluster

nuts <- read_csv ("nuts.csv", col_types = cols(), locale = readr::locale (encoding = "UTF-8"), na = c("..", "NA"))

## Warning: Missing column names filled in: 'X1' [1]

options(tibble.print_max = Inf)

nuts %>%
  knitr::kable(
  booktabs = TRUE,
  caption = 'NUTS (Nomenclature des Unités Territoriales Statistiques) is the EU\'s hierarchical regional division.') 

Table 3.1: NUTS (Nomenclature des Unités Territoriales Statistiques) is the EU’s hierarchical regional division.

X1	Länskod	Län	NUTS1	NUTS2	NUTS3
1	1	Stockholms	SE1 Östra Sverige	SE11 Stockholm	SE110
2	3	Uppsala	SE1 Östra Sverige	SE12 Östra Mellansverige	SE121
3	4	Södermanlands	SE1 Östra Sverige	SE12 Östra Mellansverige	SE122
4	5	Östergötlands	SE1 Östra Sverige	SE12 Östra Mellansverige	SE123
5	6	Jönköpings	SE2 Södra Sverige	SE21 Småland med öarna	SE211
6	7	Kronobergs	SE2 Södra Sverige	SE21 Småland med öarna	SE212
7	8	Kalmar	SE2 Södra Sverige	SE21 Småland med öarna	SE213
8	9	Gotlands	SE2 Södra Sverige	SE21 Småland med öarna	SE214
9	10	Blekinge	SE2 Södra Sverige	SE22 Sydsverige	SE221
10	12	Skåne	SE2 Södra Sverige	SE22 Sydsverige	SE224
11	13	Hallands	SE2 Södra Sverige	SE23 Västsverige	SE231
12	14	Västra Götalands	SE2 Södra Sverige	SE23 Västsverige	SE232
13	17	Värmlands	SE3 Norra Sverige	SE31 Norra Mellansverige	SE311
14	18	Örebro	SE1 Östra Sverige	SE12 Östra Mellansverige	SE124
15	19	Västmanlands	SE1 Östra Sverige	SE12 Östra Mellansverige	SE125
16	20	Dalarnas	SE3 Norra Sverige	SE31 Norra Mellansverige	SE312
17	21	Gävleborgs	SE3 Norra Sverige	SE31 Norra Mellansverige	SE313
18	22	Västernorrlands	SE3 Norra Sverige	SE32 Mellersta Norrland	SE321
19	23	Jämtlands	SE3 Norra Sverige	SE32 Mellersta Norrland	SE322
20	24	Västerbottens	SE3 Norra Sverige	SE33 Övre Norrland	SE331
21	25	Norrbottens	SE3 Norra Sverige	SE33 Övre Norrland	SE332

map_ln_n <- map_ln %>%
  mutate(lnkod_n = as.numeric(lnkod)) 
  
relative_dev <- function (x){  
  return (x / x[1])  
}  

tot_dev <- function (x){  
  scales::percent ((tail(x, 1) / x[1]) - 1)  
}  

perc_women <- function(x){  
  ifelse (length(x) == 2, scales::percent (x[2] / (x[1] + x[2])), NA)
}  

perc_sal <- function(x){  
  ifelse (length(x) == 2, scales::percent (x[2] / x[1]), NA)
}  

readfile <- function (file1){  
  read_csv (file1, col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>%  
    gather (starts_with("19"), starts_with("20"), key = "year", value = salary) %>%  
    drop_na() %>%  
    mutate (year2 = parse_number (year)) %>%  
    mutate (heading = file1) %>%  
    mutate (relsalary = relative_dev (salary))  
}