# A view of Spanish fertility by age groups (with the help of log scales)

I have been working a lot with the demography library in R, it is a great teaching tool for demography, modeling, life tables, graphic visualization of demographic data, and for many other things (see demography ).
There are a lot of examples available using data from the Human Fertility and Mortality Database.
Here I am using data that I have obtained from Spanish Statistics, a fertility rates time series consisting of 5 years age groups (available from download from here).
It is very nice to plot fertility rates by age groups as one can appreciate the changes in fertility occurred over time (in terms of quantum) and how much each age group contributes to fertility. In the case of Spain,.

``` library(demography) plot(spain,plot.type="time",xlab="Year",lwd=2) legend("topright",legend=c("15-19","20-24","25-29", "30-34","35-39","40-44","45-49"), col=c("red","yellow","lightgreen","green","lightblue", "blue","violet"),bty="n",lty=1,cex=0.8,lwd=2) ```

The very same plot can be obtained through ggplot2 library (given an appropriate theme (see ggplot themes):

```ggplot(ddfert, aes(Year, Female, group= Age,col= Age))+ geom_line()+ scale_color_manual(values= c("red", "yellow", "lightgreen", "green","lightblue", "blue", "violet"))+ scale_x_continuous(labels = c(1975, 1985, 1995, 2005, 2015))+ scale_y_continuous("Fertility Rate") ```

I find it often interesting to plot using a log scale, so that small values don’t get compressed to the end of the graph. In this case it would be sufficient to add to the demography code:
`plot(spain, plot.type="time", xlab= "Year", lwd=2, transform=T)...`
and to ggplot :
`ggplot(ddfert, aes(Year, log(Female), group= Age,col= Age))+...`

## Author: acarioli

is a researcher at the Geography and Environment department of the University of Southampton, WorldPop project team. She is also affiliated researcher at CED, UAB and Dondena Centre. Her interests include spatial econometrics and modeling, bayesian methods, machine learning processes, forecasting, micro-data simulation, and data visualization. Demo-traveler, Mac enthusiast, R zealot and Rladies member.