Visualizing time series data - I

Lecture 5

Dr. Mine Çetinkaya-Rundel

Duke University
STA 313 - Spring 2024

Warm up

Announcements

  • HW 1 due at 5 pm today

  • Let me know any requests for Project 1 teams by 5 pm today

  • Review Project 1 assignment before tomorrow and start work on Project 1 proposal in lab

Setup

# load packages
library(countdown)
library(tidyverse)
library(janitor)
library(colorspace)
library(broom)
library(fs)
library(MoMAColors)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7, # 7" width
  fig.asp = 0.618, # the golden ratio
  fig.retina = 3, # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300 # higher dpi, sharper image
)

From last time: Bringing together multiple data frames

Data

professions <- read_csv("data/professions.csv")
dates <- read_csv("data/dates.csv")
works <- read_csv("data/works.csv")

Visualizing joined data

The following plot displays some information from each of the three data frames:

Joining with `by = join_by(name)`
Joining with `by = join_by(name)`

Livecoding

Reveal below for code developed during live coding session.

Transform 
scientists <- professions |>
  left_join(dates) |>
  left_join(works)
Joining with `by = join_by(name)`
Joining with `by = join_by(name)`
Transform 
scientists_longer <- scientists |>
  mutate(
    birth_year = case_when(
      name == "Ada Lovelace" ~ 1815,
      name == "Marie Curie" ~ 1867,
      TRUE ~ birth_year
    ),
    death_year = case_when(
      name == "Ada Lovelace" ~ 1852,
      name == "Marie Curie" ~ 1934,
      name == "Flossie Wong-Staal" ~ 2020,
      TRUE ~ death_year
    ),
    status = if_else(is.na(death_year), "alive", "deceased"),
    death_year = if_else(is.na(death_year), 2021, death_year),
    known_for = if_else(name == "Rosalind Franklin", "understanding of the molecular structures of DNA ", known_for)
  ) |>
  pivot_longer(
    cols = contains("year"),
    names_to = "year_type",
    values_to = "year"
  ) |>
  mutate(death_year_fake = if_else(year == 2021, TRUE, FALSE))
Plot 
ggplot(scientists_longer, 
       aes(x = year, y = fct_reorder(name, as.numeric(factor(profession))), group = name, color = profession)) +
  geom_point(aes(shape = death_year_fake), show.legend = FALSE) +
  geom_line(aes(linetype = status), show.legend = FALSE) +
  scale_shape_manual(values = c("circle", NA)) +
  scale_linetype_manual(values = c("dashed", "solid")) +
  scale_color_moma_d("Lupi") +
  scale_x_continuous(expand = c(0.01, 0), breaks = seq(1820, 2020, 50)) +
  geom_text(aes(y = name, label = known_for), x = 2030, show.legend = FALSE, hjust = 0) +
  geom_text(aes(label = profession), x = 1809, y = Inf, hjust = 1, vjust = 1, show.legend = FALSE) +
  coord_cartesian(clip = "off") +
  labs(
    x = "Year", y = NULL,
    title = "10 women in science who changed the world",
    caption = "Source: Discover magazine"
  ) +
  facet_grid(profession ~ ., scales = "free_y", space = "free_y", switch = "x") +
  theme(
    plot.margin = unit(c(1, 24, 1, 5), "lines"),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    plot.caption = element_text(hjust = 2), # manual hack
    strip.background = element_blank(),
    strip.text = element_blank(),
    axis.title.x = element_text(hjust = 0),
    panel.background = element_rect(fill = "#f0f0f0", color = "white"),
    panel.grid.major = element_line(color = "white", linewidth = 0.5)
  )

🔗 vizdata.org