data viz with ggplot

load packages

We can use the library() function to load the packages we need. The tidyverse package contains tons of useful functions for data wrangling and visualisation (including ggplot). The ozbabynames package contains data from birth records in Australia.

library(tidyverse)
library(ozbabynames)

read the babynames data

ozbabynames <- ozbabynames

glimpse(ozbabynames)

Rows: 252,358
Columns: 5
$ name  <chr> "Charlotte", "Olivia", "Ava", "Amelia", "Mia", "Isla"…
$ sex   <chr> "Female", "Female", "Female", "Female", "Female", "Fe…
$ year  <int> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,…
$ count <int> 577, 550, 464, 442, 418, 392, 378, 353, 351, 339, 334…
$ state <chr> "New South Wales", "New South Wales", "New South Wale…

define the names you want to compare

btw Lady Gaga’s real name is Stefani

artist_names <- c("Billie", "Taylor", "Stefani")

whose name is most popular??

ozbabynames %>%
  filter(name %in% artist_names) %>%
  group_by(name, year) %>% 
  summarise(count = sum(count)) %>%
  ggplot(aes(x = year, 
             y = count,
             colour = name)) +
  geom_line() +
  theme_bw() +
 facet_wrap(~name,
             scales = "free_y") +
  theme(legend.position = "none") +
  labs(title = "artist names plot with free_y")

What does the scales = "free y" do? What happens if you delete it? Is the plot more meaningful with “free y” or without it?

ozbabynames %>%
  filter(name %in% artist_names) %>%
  group_by(name, year) %>% 
  summarise(count = sum(count)) %>%
  ggplot(aes(x = year, 
             y = count,
             colour = name)) +
  geom_line() +
  theme_bw() +
 facet_wrap(~name) +
  theme(legend.position = "none") +
  labs(title = "artist names plot without free_y")

Recreate the plot above with your names

your_names <- c("Jenny", "Kate", "Danielle")

What do you need to change about the code below to make it plot your names?

ozbabynames %>%
  filter(name %in% your_names) %>%
  group_by(name, year) %>% 
  summarise(count = sum(count)) %>%
  ggplot(aes(x = year, 
             y = count,
             colour = name)) +
  geom_line() +
  theme_bw() +
 facet_wrap(~name,
             scales = "free_y") +
  theme(legend.position = "none") +
  labs(title = "our names plot with free_y")

Do you need scales = "free_y"?

ozbabynames %>%
  filter(name %in% your_names) %>%
  group_by(name, year) %>% 
  summarise(count = sum(count)) %>%
  ggplot(aes(x = year, 
             y = count,
             colour = name)) +
  geom_line() +
  theme_bw() +
 facet_wrap(~name) +
  theme(legend.position = "none") +
    labs(title = "our names plot without free_y")

babynames post