Data Visualizations
A vignette illustrating different ways to visualize data
Introduction
This vignette will explore several aspects of data visualization using a lentil phenology data set from Wright et al. 2020. For further details on the lentil population and data visit: https://dblogr.com/academic/lentil_diversity_panel/lentil_diversity_panel.html
Prepare Data
# devtools::install_github("derekmichaelwright/agData")
library(agData)
myCaption <- "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: AGILE"
myColors <- c("steelblue","darkgreen","darkorange")
# Prep data
d1 <- read.csv("data_visualizations_1.csv")
#
myLocations <- c("Morocco", "Nepal", "Italy")
myEntries <- c("Crimson AGL", "ILL 28 AGL", "PI 299366 LSP AGL",
"Laird AGL", "CDC Robin AGL",
"PI 177430 LSP AGL", "Indianhead AGL", "PI 289066 AGL")
d2 <- read.csv("data_visualizations_2.csv") %>%
mutate(Location = factor(Location, levels = myLocations),
Name = factor(Name, levels = myEntries) ) Histograms
For this first example, we will illustrate the different ways to visualize histograms. This data set includes days from sowing to flower (DTF), days from sowing to swollen pod (DTS), and days from sowing to maturity (DTM), for 324 lentil accessions (LDP) grown in Metaponto, Italy in the 2016-2017 growing season.
# Prep data
xx <- d1 %>%
gather(Trait, Value, DTF, DTS, DTM) %>%
mutate(Trait = factor(Trait, levels = c("DTF","DTS","DTM")),
Trait = plyr::mapvalues(Trait, c("DTF","DTS","DTM"),
c("Days to Flower","Days to Swollen Pod","Days to Maturity")))
# Plot
mp <- ggplot(xx, aes(fill = Trait, x = Value)) +
theme_agData(legend.position = "none",
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
scale_fill_manual(values = myColors) +
labs(x = NULL, y = NULL)
# (A)
mp1 <- mp +
geom_histogram(binwidth = 3, color = "black", alpha = 0.7) +
facet_grid(. ~ Trait) +
labs(title = "Lentil Diversity Panel (LDP) Phenology in Italy",
subtitle = "(A) Facetted histograms")
# (B)
mp2 <- mp +
geom_histogram(binwidth = 3, position = "dodge",
color = "black", lwd = 0.4, alpha = 0.7) +
labs(subtitle = "(B) Dodged histograms")
# (C)
mp3 <- mp +
geom_histogram(binwidth = 3, color = "black", alpha = 0.7) +
labs(subtitle = "(C) Stacked histograms (binwidth = 3 days)")
# (D)
mp4 <- mp +
geom_histogram(binwidth = 1, lwd = 0.4, color = "black", alpha = 0.7) +
labs(subtitle = "(D) Stacked histograms (binwidth = 1 day)")
# (E)
mp5 <- mp +
geom_density(alpha = 0.5) +
labs(subtitle = "(E) Density plots", x = "Days After Planting",
caption = myCaption)
#
mp <- ggarrange(mp1, mp2, mp3, mp4, mp5, ncol = 1, nrow = 5,
heights = c(1.1,1,1,1,1.3))
ggsave("data_visualizations_01.png", mp, width = 6, height = 8)Bonus - Histogram + scatterplot
Scatter plots and histograms can be combined to display histograms along with a correlation plot.
# Prep data
library(ggExtra)
# Plot
mp <- ggplot(d1, aes(x = DTF, y = DTM)) +
geom_point(alpha = 0.7) +
theme_agData() +
labs(title = "Lentil Diversity Panel (LDP) Phenology in Italy",
x = "Days to Flower", y = "Days to Maturity",
caption = myCaption)
mp <- ggMarginal(mp, type = "densigram",
xparams = list(fill = alpha("steelblue",0.7)),
yparams = list(fill = alpha("darkorange",0.7)))
ggsave("data_visualizations_02.png", mp, width = 6, height = 4)Boxplots
For the second example, we will use a subset of 8 accessions from the LDP grown in Morocco, Nepal and Italy with 3 replicates in each location. This data can be visualized in a number of different ways.
# Plot
mp <- ggplot(d2, aes(x = Location, y = DTF, fill = Location)) +
theme_agData(legend.position = "none") +
scale_fill_manual(values = myColors) +
labs(x = NULL, y = NULL)
# (A)
mp1 <- mp +
geom_boxplot(alpha = 0.6, coef = 2.5) +
stat_boxplot(geom = "errorbar", width = 0.25, coef = 2.5) +
labs(subtitle = "(A) boxplots")
# (B)
mp2 <- mp +
geom_violin(alpha = 0.6) +
labs(subtitle = "(B) violin plots")
# (C)
mp3 <- mp +
geom_violin(alpha = 0.6) +
geom_boxplot(width = 0.1, fill = "white",
outlier.shape = NA, coef = 0) +
labs(subtitle = "(C) violins + boxplots")
# (D)
mp4 <- mp +
geom_violin(alpha = 0.6) +
geom_beeswarm(aes(shape = Name), alpha = 0.7, cex = 1.5) +
scale_shape_manual(values = 0:7) +
labs(subtitle = "(D) violins + points", caption = myCaption)
#
mp <- ggarrange(mp1, mp2, mp3, mp4, ncol = 2, nrow = 2, align = "hv") %>%
annotate_figure(text_grob("Lentil Diversity Panel (LDP) Subset - Days to Flower", hjust = 0.69))
ggsave("data_visualizations_03.png", mp, width = 6, height = 6, bg = "white")Bonus - Faceted
Additionally, if we want to compare individual genotypes instead of environments, we can change how we present the data.
# Plot
mp <- ggplot(d2, aes(x = Location, y = DTF,
color = Location, shape = Name)) +
geom_beeswarm(alpha = 0.7, cex = 2, lwd = 2) +
facet_wrap(Name ~ ., ncol = 4) +
theme_agData(legend.position = "bottom",
axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
scale_color_manual(name = NULL, values = myColors) +
scale_shape_manual(values = 0:7, guide = F) +
labs(title = "Lentil Diversity Panel (LDP) Subset",
x = NULL, y = "Days to Flower", caption = myCaption)
ggsave("data_visualizations_04.png", mp, width = 7, height = 5)Bonus - G x E
Here we can see the various genotype by environment interactions (G x E).
# prep data
xx <- d2 %>% group_by(Name, Location) %>%
summarise(DTF = mean(DTF, na.rm = T))
myColors <- c("darkred", "darkgoldenrod2", "darkblue", "darkgreen",
"maroon4", "purple4", "black", "steelblue" )
# plot
mp <- ggplot(xx, aes(x = Location, y = DTF,
color = Name, shape = Name, group = Name)) +
geom_point(size = 3, alpha = 0.8) +
geom_line(size = 1.25, alpha = 0.6) +
scale_color_manual(name = NULL, values = myColors) +
scale_shape_manual(name = NULL, values = 0:7) +
theme_agData(legend.position = "bottom") +
coord_cartesian(xlim = c(1.5,2.5)) +
guides(color = guide_legend(nrow = 2, byrow = T)) +
labs(title = "Lentil Diversity Panel (LDP) Subset",
x = NULL, y = "Days to Flower", caption = myCaption)
ggsave("data_visualizations_05.png", mp, width = 7, height = 5)