Data visualization

for incomplete datasets in R

Hanne Oberman

PhD candidate at Utrecht University

Missingness

Case study

set.seed(123)
library(palmerpenguins)
library(mice)
library(ggmice)
library(ggplot2)

Penguin populations

Code 
ggplot(penguins, aes(x = year, fill = sex, group = sex)) +
  geom_bar(position = "dodge") + 
  scale_x_continuous(breaks = c(2007, 2008, 2009)) +
  theme_classic()

Penguin populations

Code 
ggplot(penguins, aes(x = year, fill = sex, group = sex)) +
  geom_bar(position = "dodge") + 
  facet_wrap(~species) + 
  scale_x_continuous(breaks = c(2007, 2008, 2009)) +
  theme_classic()

Incomplete data

str(penguins)
tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
 $ species          : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ island           : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ bill_length_mm   : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
 $ bill_depth_mm    : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
 $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
 $ body_mass_g      : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
 $ sex              : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
 $ year             : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...

Incomplete data

visdat::vis_dat(penguins)

Response indicator

is.na(penguins)
       species island bill_length_mm bill_depth_mm flipper_length_mm
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       body_mass_g   sex  year
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Response indicator

naniar::vis_miss(penguins)

Missingness rate

colSums(is.na(penguins))
          species            island    bill_length_mm     bill_depth_mm 
                0                 0                 2                 2 
flipper_length_mm       body_mass_g               sex              year 
                2                 2                11                 0

Missingness rate

VIM::aggr(penguins, numbers = TRUE, prop = FALSE)

Missing data pattern

md.pattern(penguins)
    species island year bill_length_mm bill_depth_mm flipper_length_mm
333       1      1    1              1             1                 1
9         1      1    1              1             1                 1
2         1      1    1              0             0                 0
          0      0    0              2             2                 2
    body_mass_g sex   
333           1   1  0
9             1   0  1
2             0   0  5
              2  11 19

Missing data pattern

plot_pattern(penguins)

Imputation workflow

Imputation models

pred <- quickpred(penguins, mincor = 0.4)
meth <- make.method(penguins)
plot_pred(pred, method = meth, square = FALSE)

Correlation

plot_corr(penguins, square = FALSE)

Scatter plot

ggmice(penguins, aes(sex, flipper_length_mm)) +
  geom_point(size = 2)

Faceted distribution

ggmice(penguins, aes(flipper_length_mm)) +
  geom_histogram(fill = "white") +
  facet_grid(factor(
    is.na(sex), 
    levels = c(TRUE, FALSE), 
    labels = c("missing sex", "observed sex")
    ) ~ .)

Faceted scatter plot

ggmice(penguins, aes(sex, flipper_length_mm)) +
  geom_point(size = 2) +
  facet_wrap(~species)

Faceted distribution

ggmice(penguins, aes(flipper_length_mm)) +
  geom_histogram(fill = "white") +
  facet_grid(factor(
    is.na(sex), 
    levels = c(TRUE, FALSE), 
    labels = c("missing sex", "observed sex")
    ) ~ species)

Adjust imputation models

pred["sex", c("species", "flipper_length_mm")] <- 1
meth["sex"] <- "pmm"
plot_pred(pred, method = meth)

Impute

imp <- mice(
  penguins, 
  pred = pred, 
  method = meth, 
  m = 20,
  print = FALSE)
plot_trace(imp)

Stripplot

ggmice(imp, aes(x = .imp, y = flipper_length_mm)) +
  geom_point() +
  labs(x = "Imputation number")

Stripplot

ggmice(imp, aes(x = .imp, y = sex)) +
  geom_jitter(width = 0.05) +
  labs(x = "Imputation number")

Scatter plot

ggmice(imp, aes(sex, flipper_length_mm)) +
  geom_point() +
  facet_grid(~species)

Scatter plot with boxplot

ggmice(imp, aes(sex, flipper_length_mm)) +
  geom_point() +
  geom_boxplot() +
  facet_grid(~species)

Faceted distribution

ggmice(imp, aes(sex, group = .imp)) +
  geom_histogram(fill = "white", stat="count") +
  facet_grid(~ species)

Populations after imputation

Code 
ggplot(complete(imp), aes(x = year, fill = sex, group = sex)) +
  geom_bar(position = "dodge") +
  facet_wrap( ~ species) +
  scale_x_continuous(breaks = c(2007, 2008, 2009)) +
  theme_classic()

Thank you!

Hanne Oberman, hanneoberman.github.io/presentations