Narrative lab assignment

---
title: "Narrative lab assignment"
author: "Ishani Wijeratne"
date: "11/27/2017"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}

library(flexdashboard)
library(plotly)
library(ggthemes)
library(ggplot2)
library(gridExtra)
library(GGally)
library(reshape2)

```



```{r}
wdi <- read.csv('WDIData.csv', header = TRUE, stringsAsFactors = FALSE)
wdi <- as.data.frame(wdi)
colnames(wdi)[1] <- 'Country.Name'
colnames(wdi)[5:length(colnames(wdi))] <- gsub(pattern = 'X', replacement = '', x = colnames(wdi)[5:length(colnames(wdi))])

indicator <- c('Wage and salaried workers, total (% of total employment)',
               'Wage and salaried workers, male (% of male employment)', 
               'Wage and salaried workers, female (% of female employment)',
               'Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)',
               'Employers, female (% of female employment)')
               
               
country <- c('United States', 'Sri Lanka', 'Russian Federation', 'Australia')

wdi.reduced <- wdi %>% 
                select(1,3,45:59) %>%
                filter(Indicator.Name %in% indicator, Country.Name %in% country) %>%
                melt(id = c('Country.Name', 'Indicator.Name'))
```


***

WDI (World Development Indicators) DATA for development around the world in women

The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

Agriculture & Rural Development, Aid Effectiveness, Climate Change, Economy & Growth, Education, Energy & Mining, Environment, External Debt, Financial Sector, Gender, Health, Infrastructure, Labor & Social Protection, Poverty, Private Sector, Public Sector, Science & Technology, Social Development, Trade, Urban Development(April, July, September, December(1960 - 2016))

In this presentation I am focusing on the improvement of women in the working envionment around the world.


### Wage and salaried workers, total (% of total employment)

```{r, message=FALSE, warning=FALSE,fig.width = 40, fig.height = 12}

single_line <- function (indicator_name, country_name) {
  temp <- wdi.reduced %>%
            filter(Indicator.Name == indicator_name, Country.Name == country_name)
  min_x <- min(as.numeric(as.character(temp$variable)))
  max_x <- max(as.numeric(as.character(temp$variable)))
  title_name <- paste(temp$Indicator.Name[1], '-', temp$Country.Name[1])
  
  p <- ggplot(temp, aes(x = as.numeric(levels(variable))[variable], y = value)) +
    geom_point() +
    geom_line() +
    labs(title = title_name, x = 'year', y = '%') +
    scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
    theme_economist()  
  p
}

usa_gdp <- single_line('Wage and salaried workers, total (% of total employment)', 'United States')
sl_gdp <- single_line('Wage and salaried workers, total (% of total employment)', 'Sri Lanka')
rf_gdp <- single_line('Wage and salaried workers, total (% of total employment)', 'Russian Federation')
aus_gdp <- single_line('Wage and salaried workers, total (% of total employment)', 'Australia')

grid.arrange(usa_gdp, sl_gdp, rf_gdp, aus_gdp, ncol = 2, nrow = 2)

```


***

Wage and salaried workers, total (% of total employment)

1.  The total number of workers has increased as a whole in all the countries I chose to analyze.




### Wage and salaried workers, male (% of male employment)

```{r, message=FALSE, warning=FALSE,fig.width = 40, fig.height = 12}

single_line <- function (indicator_name, country_name) {
  temp <- wdi.reduced %>%
            filter(Indicator.Name == indicator_name, Country.Name == country_name)
  min_x <- min(as.numeric(as.character(temp$variable)))
  max_x <- max(as.numeric(as.character(temp$variable)))
  title_name <- paste(temp$Indicator.Name[1], '-', temp$Country.Name[1])
  
  p <- ggplot(temp, aes(x = as.numeric(levels(variable))[variable], y = value)) +
    geom_point() +
    geom_line() +
    labs(title = title_name, x = 'year', y = '%') +
    scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
    theme_economist()  
  p
}

usa_gdp <- single_line('Wage and salaried workers, male (% of male employment)', 'United States')
sl_gdp <- single_line('Wage and salaried workers, male (% of male employment)', 'Sri Lanka')
rf_gdp <- single_line('Wage and salaried workers, male (% of male employment)', 'Russian Federation')
aus_gdp <- single_line('Wage and salaried workers, male (% of male employment)', 'Australia')

grid.arrange(usa_gdp, sl_gdp, rf_gdp, aus_gdp, ncol = 2, nrow = 2)

```

***

Wage and salaried workers, male (% of male employment)

1.Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work. 


2.In all the countries there was an increase in the % of male salaried employment.


### Wage and salaried workers, female (% of female employment)

```{r, message=FALSE, warning=FALSE,fig.width = 40, fig.height = 12}

single_line <- function (indicator_name, country_name) {
  temp <- wdi.reduced %>%
            filter(Indicator.Name == indicator_name, Country.Name == country_name)
  min_x <- min(as.numeric(as.character(temp$variable)))
  max_x <- max(as.numeric(as.character(temp$variable)))
  title_name <- paste(temp$Indicator.Name[1], '-', temp$Country.Name[1])
  
  p <- ggplot(temp, aes(x = as.numeric(levels(variable))[variable], y = value)) +
    geom_point() +
    geom_line() +
    labs(title = title_name, x = 'year', y = '%') +
    scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
    theme_economist()  
  p
}

usa_gdp <- single_line('Wage and salaried workers, female (% of female employment)', 'United States')
sl_gdp <- single_line('Wage and salaried workers, female (% of female employment)', 'Sri Lanka')
rf_gdp <- single_line('Wage and salaried workers, female (% of female employment)', 'Russian Federation')
aus_gdp <- single_line('Wage and salaried workers, female (% of female employment)', 'Australia')

grid.arrange(usa_gdp, sl_gdp, rf_gdp, aus_gdp, ncol = 2, nrow = 2)

```

***

Wage and salaried workers, female (% of female employment)

1. In developed countries the female work percentage has always been high compared to the under developed countries. But even then we can clearly see the increase in percentage.


### Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)


```{r, message=FALSE, warning=FALSE,fig.width = 40, fig.height = 12}

single_line <- function (indicator_name, country_name) {
  temp <- wdi.reduced %>%
            filter(Indicator.Name == indicator_name, Country.Name == country_name)
  min_x <- min(as.numeric(as.character(temp$variable)))
  max_x <- max(as.numeric(as.character(temp$variable)))
  title_name <- paste(temp$Indicator.Name[1], '-', temp$Country.Name[1])
  
  p <- ggplot(temp, aes(x = as.numeric(levels(variable))[variable], y = value)) +
    geom_point() +
    geom_line() +
    labs(title = title_name, x = 'year', y = '%') +
    scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
    theme_economist()  
  p
}

usa_gdp <- single_line('Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)', 'United States')
sl_gdp <- single_line('Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)', 'Sri Lanka')
rf_gdp <- single_line('Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)', 'Russian Federation')
aus_gdp <- single_line('Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)', 'Australia')

grid.arrange(usa_gdp, sl_gdp, rf_gdp, aus_gdp, ncol = 2, nrow = 2)

```

***
Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)

1. This shows how much particiaption we have in women in wage employment in the non agricultural sector.


### Employers, female (% of female employment)


```{r, message=FALSE, warning=FALSE,fig.width = 40, fig.height = 12}

single_line <- function (indicator_name, country_name) {
  temp <- wdi.reduced %>%
            filter(Indicator.Name == indicator_name, Country.Name == country_name)
  min_x <- min(as.numeric(as.character(temp$variable)))
  max_x <- max(as.numeric(as.character(temp$variable)))
  title_name <- paste(temp$Indicator.Name[1], '-', temp$Country.Name[1])
  
  p <- ggplot(temp, aes(x = as.numeric(levels(variable))[variable], y = value)) +
    geom_point() +
    geom_line() +
    labs(title = title_name, x = 'year', y = '%') +
    scale_x_continuous(breaks = round(seq(min_x, max_x, by = 1),0)) +
    theme_economist()  
  p
}

usa_gdp <- single_line('Employers, female (% of female employment)', 'United States')
sl_gdp <- single_line('Employers, female (% of female employment)', 'Sri Lanka')
rf_gdp <- single_line('Employers, female (% of female employment)', 'Russian Federation')
aus_gdp <- single_line('Employers, female (% of female employment)', 'Australia')

grid.arrange(usa_gdp, sl_gdp, rf_gdp, aus_gdp, ncol = 2, nrow = 2)

```


***

Employers, female (% of female employment)

1. No data for US but it is interesting to see how much Sri lanka has changed as a developing country to have more percentage of female employers.