Creación de gráficos del coronavirus en R

Introducción

Queremos mostrar la evolución de casos de coronavirus en R con gráficos estáticos e interactivos.

Gráficos

Interactivo (escala lineal)

Interactivo (escala logaritmica)

Solución

Usamos los datos del repositorio creado por Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Hay tres series de datos temporales: confirmed, deaths y recovered cases. Primero preparamos los datos y creamos el gráfico usando ggplot2 para la versión estática, y plotly para añadir interactividad. Las series de datos incluyen casos de todo el mundo pero en nuestro ejemplo usamos un subconjunto para Alemania, Francia, Italia, España y el Reino Unido.

# Librerias
library(magrittr)
library(lubridate) 
library(tidyverse)
library(plotly)
library(scales)

# Importación de datos
confirmed <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")
deaths <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv")
recovered <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv")

# Data preparation
AppendMe <- function(dfNames) {
  do.call(rbind, lapply(dfNames, function(x) {
    cbind(get(x), source = x)
  }))
}
df <- AppendMe(c("confirmed", "deaths", "recovered"))
data <- df %>%
  rename(province = `Province/State`, country = `Country/Region`) %>% 
  pivot_longer(
    -c(province, country, Lat, Long, source),
    names_to = "date",
    values_to = "count"
  ) %>% 
mutate(date = mdy(date)) 

# Gráfico escala lineal
p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(n = sum(count)) %>%
  ggplot(aes(date, n, colour = country)) +
  geom_line(linetype = 2) +
  geom_point(size = 1) +
  facet_wrap( ~  source  , scales = "free", nrow = 3) +
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (linear scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_continuous(labels = comma)
p # Estático
ggplotly(p) # Interactivo

# Gráfico escala logaritmica
p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(n = sum(count)) %>%
  ggplot(aes(date, n, colour = country)) +
  geom_line(linetype = 2) +
  geom_point(size = 1) +
  facet_wrap( ~  source, scales = "free",  nrow = 3) +
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (log scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_log10(breaks = c(1, 10, 100, 10000))
  p 
ggplotly(p) 

Para subrayar una serie al pasar sobre ella usamos la función highlight del paquete plotly.

p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(cases = sum(count)) %>%
  highlight_key(~ country ) %>% 
  ggplot(aes(date, cases, colour = country)) +
  geom_line(linetype = 2)+
  geom_point(size = 1) +
  facet_wrap(~  source  , scales = "free", nrow = 3)+
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (linear scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_continuous(labels = comma)
ggplotly(p, tooltip = c("country", "date", "cases")) %>% 
highlight(on = "plotly_hover")

Gráficco here. Pantallazo abajo.

Referencias

• csse_covid_19_time_series
• Novel Coronavirus (COVID-19) Cases Data
• Covid-19 Shiny Dashboard

Plotting coronavirus cases in R

Introduction

We want to show the evolution of the coronavirus cases using R creating static and interactive plots.

Plots

Interactive (linear scale)

Interactive (log scale)

Solution

We use the data repository created by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). There are three time-series: confirmed, deaths and recovered cases. First we will prepare the data and then plot the time-series using ggplot2 for the static version and plotly to add interactivity. The data source includes cases across the world, but in our example we will subset the time-series for Germany, France, Italy, Spain, and the United Kingdom.

# Libraries
library(magrittr)
library(lubridate) 
library(tidyverse)
library(plotly)
library(scales)

# Importing data
confirmed <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")
deaths <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv")
recovered <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv")

# Data preparation
AppendMe <- function(dfNames) {
  do.call(rbind, lapply(dfNames, function(x) {
    cbind(get(x), source = x)
  }))
}
df <- AppendMe(c("confirmed", "deaths", "recovered"))
data <- df %>%
  rename(province = `Province/State`, country = `Country/Region`) %>% 
  pivot_longer(
    -c(province, country, Lat, Long, source),
    names_to = "date",
    values_to = "count"
  ) %>% 
mutate(date = mdy(date)) 

# Plot linear scale
p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(n = sum(count)) %>%
  ggplot(aes(date, n, colour = country)) +
  geom_line(linetype = 2) +
  geom_point(size = 1) +
  facet_wrap( ~  source  , scales = "free", nrow = 3) +
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (linear scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_continuous(labels = comma)
p # Static
ggplotly(p) # Interactive

# Plot log scale
p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(n = sum(count)) %>%
  ggplot(aes(date, n, colour = country)) +
  geom_line(linetype = 2) +
  geom_point(size = 1) +
  facet_wrap( ~  source, scales = "free",  nrow = 3) +
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (log scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_log10(breaks = c(1, 10, 100, 10000))
  p 
ggplotly(p) 

To highlight a series while hovering over it, we use the function highlightfrom the plotly package.

p <- data %>%
  filter(country %in% c("Germany", "France", "Italy",  "Spain", "United Kingdom")) %>% 
  group_by(country, date, source) %>%
  summarise(cases = sum(count)) %>%
  highlight_key(~ country ) %>% 
  ggplot(aes(date, cases, colour = country)) +
  geom_line(linetype = 2)+
  geom_point(size = 1) +
  facet_wrap(~  source  , scales = "free", nrow = 3)+
  theme_bw()+
  labs(title = "Cumulative Covid-19 cases (linear scale)")+
  ylab("")+
  scale_x_date(date_labels = "%b %d")+
  scale_y_continuous(labels = comma)
ggplotly(p, tooltip = c("country", "date", "cases")) %>% 
highlight(on = "plotly_hover")

Plot here. Screenshot below.

References

• csse_covid_19_time_series
• Novel Coronavirus (COVID-19) Cases Data
• Covid-19 Shiny Dashboard

Quantile-quantile (Q-Q) plots with ggplot2

Problem

We'd like to create quantile-quantile (Q-Q) plots using ggplot2.

Solution

• Option 1: one column.
We create two examples using one column of a data frame. In the first example, we previously transform the vector rivers into a data frame. In the second, we use the column Volume from the data frame trees.

library(tidyverse)
ggplot(data.frame(rivers), aes(sample = rivers)) + stat_qq() + stat_qq_line()
ggplot(trees, aes(sample = Volume)) + stat_qq() + stat_qq_line()



• Option 2: multiple columns.
We compare the distribution of Miles/(US) gallon by number of cylinders (cyl).

ggplot(mtcars, aes(sample = mpg, colour = factor(cyl))) +
  stat_qq() +
  stat_qq_line()


• Option 3: multiple panels.

In this first example no transformation is needed because a column of the data frame contains the groups..

ggplot(mtcars, aes(sample = mpg)) +
  facet_wrap( ~ factor(cyl)) +
  stat_qq() +
  stat_qq_line()


In the second example, we convert the data frame from wide format to long format for faceting into multiple panels with facet_wrap. Instead of 31 observations for 3 variables, we will have one column condition containing 93 observaciones for the 3 variables.

gather(trees, condition, measurement, Girth:Volume, factor_key = TRUE) %>%
  ggplot(aes(sample = measurement)) +
  facet_wrap( ~ condition, scales = "free") +
  stat_qq() +
  stat_qq_line()


In the third example, we convert from wide to long, facet by chemical composition, and use a colour for each kiln.

library(HSAUR2)
gather(pottery, condition, measurement, Al2O3:BaO, factor_key = TRUE) %>%
  ggplot(aes(sample = measurement, colour = kiln)) +
  facet_wrap(~ condition, scales = "free") +
  stat_qq() +
  stat_qq_line()

References

• A quantile-quantile plot
• stackoverflow
• Spanish version

Gráficos Q-Q con ggplot2

Problema

En entradas anteriores explicamos como crear un gráfico Q-Q con el paquete stats preinstalado por defecto. En esa ocasión mostraremos cómo crear estos gráficos con ggplot2.

Solución

• Opción 1: única columna.
Creamos dos ejemplos de una sola columna de un data frame. En el primero el vector rivers que transformamos previamente en un data frame, y en el segundo la columna Volume del data frame trees.

library(tidyverse)
ggplot(data.frame(rivers), aes(sample = rivers)) + stat_qq() + stat_qq_line()
ggplot(trees, aes(sample = Volume)) + stat_qq() + stat_qq_line()



• Opción 2: múltiples columnas.
Comparamos las distribuciones de Miles/(US) gallon por número de cilindros (cyl).

ggplot(mtcars, aes(sample = mpg, colour = factor(cyl))) +
  stat_qq() +
  stat_qq_line()


• Opción 3: múltiples paneles.

En este primer ejemplo no es necesario realizar ninguna transformación porque el data frame tiene una columna con los grupos.

ggplot(mtcars, aes(sample = mpg)) +
  facet_wrap( ~ factor(cyl)) +
  stat_qq() +
  stat_qq_line()


En este segundo ejemplo transformamos el data frame de formato ancho a largo para poder crear los paneles con facet_wrap. En lugar de tener 31 observaciones para 3 variables, tendremos una columna condition con las 3 variables y 93 observaciones.

gather(trees, condition, measurement, Girth:Volume, factor_key = TRUE) %>%
  ggplot(aes(sample = measurement)) +
  facet_wrap( ~ condition, scales = "free") +
  stat_qq() +
  stat_qq_line()


En este tercer ejemplo empleamos múltiples colores en función del kiln en cada panel por sustancia química encontrada.

library(HSAUR2)
gather(pottery, condition, measurement, Al2O3:BaO, factor_key = TRUE) %>%
  ggplot(aes(sample = measurement, colour = kiln)) +
  facet_wrap(~ condition, scales = "free") +
  stat_qq() +
  stat_qq_line()

Entradas relacionadas

• Construir un gráfico Q-Q desde cero en R
• Gráfico Q-Q en R

Referencias

• A quantile-quantile plot
• stackoverflow
• Versión en inglés

Loops with ggplot2

Problem

We want to create a loop and save plots for each subset of data using ggplot2. Instead of plotting on the same panel using facet_wrap o facet_grid, we'd like to display and save eachplot separately.

library(tidyverse)
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
p + facet_wrap(vars(Species), scales = "free")

Solution

We create an empty list to store all plots. Then, we start a loop for each unique element of the variable (column) Species. To keep the same title format, we leave the function facet_wrap.

# Loop
plots <- list() # Empty list
p_list <- unique(iris$Species)
for (i in seq_along(p_list)) {
  # Plot for each Species
  p <- iris %>% filter(Species == p_list[i]) %>%
    ggplot(aes(Sepal.Length, Sepal.Width)) +
    geom_point() +
    facet_wrap( ~ Species) # Títulos
  plots[[i]] = p
  print(p)
}

To print the whole plot list or a specific element:

# Print list
print(plots)
# Print an element of the list
print(plots[[1]])

Related posts

• Spanish version: Usar bucles en ggplot2

References

• Loops with ggplot2

Usar bucles en ggplot2

Problema

Queremos crear y guardar gráficos separadamente con ggplot2. Es decir, en lugar de mostrar los gráficos en un mismo panel con facet_wrap o facet_grid, queremos crear gráficos independientes, cada uno en un panel y almacenarlos en una lista.

library(tidyverse)
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
p + facet_wrap(vars(Species), scales = "free")

Solución

Creamos una lista vacía donde almacenaremos los gráficos. Iniciamos un bucle generando uno gráfico con ggplot2 para cada elemento único de la lista de especies. Dejamos la función facet_wrap, aunque cada gráfico está en un único panel, para obtener el título de cada especie.

# Bucle
plots <- list() # Creamos una lista vacía
p_list <- unique(iris$Species)
for (i in seq_along(p_list)) {
  # Gráfico por especie
  p <- iris %>% filter(Species == p_list[i]) %>%
    ggplot(aes(Sepal.Length, Sepal.Width)) +
    geom_point() +
    facet_wrap( ~ Species) # Títulos
  plots[[i]] = p
  print(p)
}

Para volver a imprimir la lista completa de gráficos o uno específico.

# Imprime lista completa
print(plots)
# Imprime uno específico
print(plots[[1]])

Entradas relacionadas

• facet_wrap
• facet_grid

Referencias

• Loops with ggplot2