ggplot2 - A Strong Package Used in Data Visulization

Geom

• geom_point()
• geom_smooth()  
• geom_boxplot() 
• geom_bar()
• geom_histogram()
• geom_density()     
• geom_linerange()
• geom_text()
• geom_curve(x = , xend = , y = , yend = , arrow = arrow(length = unit(0.3, 'cm ')), curvature = 0.5)
• stat_function()

Tips: if the variable is not continues, use bar graphs to display the distribution of categorical variables instead of histogram or density plot.

THEME

• theme(axis.text = element_text(),
       axis.tltle = element_text(),
       plot.title = element_text(),
       legend.position = , 
       legend.title = element_blank(),
       axis.text.x = element_text(size = 10, angle = 90, hjust = 1, face = 'bold')
• ylab(') 
• xlab()
• ggtitle(')

GUIDES & LEGEND

• guides()
• ylim()
• coord_trans() 
• coord_flip()
• scale_y_continuous() 
• scale_y_discrete()
• scale_color()  

Tips: The only difference between coord_trans() and scale_y_continuous() is where the tick marks on the axis are drawn (scale_y_continuous has the tick more evenly)

FACET

• facet_wrap(~, nrow = )
• facet_grid(~, labeller = label_both)

COLOR

• RColorBrewer  

library(RColorBrewer)
display.brewer.all()
g + scale_fill_brewer(palette = ‘Accent’, ‘Dark2’)

• wesanderson  

library(wesanderson)
ag + scale_fill_brewer(‘Zissou1’)
g + scale_fill_manual(values = wes_palette(n = 3, name = ‘Zissou1’, ‘FantasticFox1’))

Data wrangling

We used the make_babynames_dist() function in the mdsr package to add some more convenient variables to our dataset and to filter for only the data that is relevant to people alive in 2014.

BabynamesDist <- make_babynames_dist() 
head(BabynamesDist)

## # A tibble: 6 x 9
##    year sex   name      n   prop alive_prob count_thousands age_today
##   <dbl> <chr> <chr> <int>  <dbl>      <dbl>           <dbl>     <dbl>
## 1  1900 F     Mary  16706 0.0526          0           16.7        114
## 2  1900 F     Helen  6343 0.0200          0            6.34       114
## 3  1900 F     Anna   6114 0.0192          0            6.11       114
## 4  1900 F     Marg…  5304 0.0167          0            5.30       114
## 5  1900 F     Ruth   4765 0.0150          0            4.76       114
## 6  1900 F     Eliz…  4096 0.0129          0            4.10       114
## # … with 1 more variable: est_alive_today <dbl>

We filtered the data so we only have babies with the name Joseph who are male.

joseph <- BabynamesDist %>%
  filter(name == 'Joseph' & sex == 'M')

Data visualization

We ploted the number of Joseph’s born each year who are estimated to be alive on January 1, 2014. We will get that estimate by mutiplying the number of Josephs born in a year by the probability that a person from that year will be alive on January 1, 2014. We added the line for the number of Josephs born each year to our plot

name_plot <- ggplot(data = joseph) + 
  geom_bar(aes(x = year, y = count_thousands * alive_prob),
           stat = 'identity', fill = '#b2d7e9', 
           colour = 'white') + 
  geom_line(aes(x = year, y = count_thousands), 
            lwd = 1) + 
  ylab("Number of People (thousands)") + 
  xlab(NULL) + 
  theme(axis.title = element_text(size = 16, 
                                  face = "bold"))

name_plot

Next we need to add a darker blue bar indicating the median year of birth for a Jospeh alive on January 1, 2014.

median_yob <- with(joseph, wtd.quantile(year, est_alive_today, probs = .5))

name_plot <- name_plot + 
  geom_bar(stat = 'identity', color = 'white', fill = '#008fd5', 
           aes(x = year, y = if_else(year == median_yob,
                           est_alive_today/1000, 0)))
name_plot

Finally we updated the axis labels

name_plot_2 <- name_plot + 
  ggtitle("Age Distribution of American Boys Joseph") + 
  geom_text(x = 1940, y = 30, 
            label = "Number of Josephs \n born each year") + 
  geom_text(x = 1929, y = 12, 
            label = "Number of Josephs \n born each year 
            \n estimated to be alive", colour = "#b2d7e9") + 
  geom_text(x = 2003, y = 37, 
            label = "The median \nliving Joseoph 
            \n is 37 years old", colour = "darkgray")+
  geom_curve(x = 1995, xend = 1974, y = 37, yend = 24, 
             arrow = arrow(length = unit(.3, "cm")), 
             curvature = .5) + ylim(0, 40) + 
  theme(plot.title = element_text(size = 22))

name_plot_2

We can also change the name to “Jessie” and the sex to “female”

name_plot %+% filter(BabynamesDist, name == 'Josephine' & sex == 'F')