This guided practical will demonstrate that the tidyverse allows to compute summary statistics and visualize datasets efficiently. This dataset is already stored in a tidy tibble, cleaning steps will come in future practicals.

Those kind of questions are optional

datasauRus package

  • check if you have the package datasauRus installed
library(datasauRus)
  • should return nothing. If there is no package called ‘datasauRus’ appears, it means that the package needs to be installed. Use this:
install.packages("datasauRus")

Explore the dataset

Since we are dealing with a tibble, we can just type

datasaurus_dozen

only the first 10 rows are displayed.

dataset x y
dino 55.3846 97.1795
dino 51.5385 96.0256
dino 46.1538 94.4872
dino 42.8205 91.4103
dino 40.7692 88.3333
dino 38.7179 84.8718
dino 35.6410 79.8718
dino 33.0769 77.5641
dino 28.9744 74.4872
dino 26.1538 71.4103
What are the dimensions of this dataset? Rows and columns?
  • base version, using either dim(), ncol() and nrow()

# dim() returns the dimensions of the data frame, i.e number of rows and columns
dim(datasaurus_dozen)
## [1] 1846    3
# ncol() only number of columns
ncol(datasaurus_dozen)
## [1] 3
# nrow() only number of rows
nrow(datasaurus_dozen)
## [1] 1846
  • tidyverse version

nothing to be done, a tibble display its dimensions, starting by a comment (‘#’ character)
Assign the datasaurus_dozen to the ds_dozen name This aims at populating the Global Environment

solution 

ds_dozen <- datasaurus_dozen
Using Rstudio, those dimensions are now also reported within the interface, where?

in the Environment panel -> Global Environment

How many datasets are present?

  • base version

Tip

you want to count the number of unique elements in the column dataset. The function length() returns the length of a vector, such as the unique elements 

unique(ds_dozen$dataset) %>% length()
## [1] 13
  • tidyverse version
# n_distinct counts the unique elements in a given vector.
# we use summarise to return only the desired column named n here.
summarise(ds_dozen, n = n_distinct(dataset))
## # A tibble: 1 x 1
##       n
##   <int>
## 1    13
  • even better way, compute and display the number of lines per dataset

Tip

the function count in dplyr does the group_by() by the specified column + summarise(n = n()) which returns the number of observation per defined group. 

count(ds_dozen, dataset)
## # A tibble: 13 x 2
##    dataset        n
##    <chr>      <int>
##  1 away         142
##  2 bullseye     142
##  3 circle       142
##  4 dino         142
##  5 dots         142
##  6 h_lines      142
##  7 high_lines   142
##  8 slant_down   142
##  9 slant_up     142
## 10 star         142
## 11 v_lines      142
## 12 wide_lines   142
## 13 x_shape      142

Check summary statistics per dataset

Compute the mean of the x & y column. For this, you need to group_by() the appropriate column and then summarise()

Tip

in summarise() you can define as many new columns as you wish. No need to call it for every single variable. 

ds_dozen %>%
  group_by(dataset) %>%
  summarise(mean_x = mean(x),
            mean_y = mean(y))

dataset mean_x mean_y
away 54.26610 47.83472
bullseye 54.26873 47.83082
circle 54.26732 47.83772
dino 54.26327 47.83225
dots 54.26030 47.83983
h_lines 54.26144 47.83025
high_lines 54.26881 47.83545
slant_down 54.26785 47.83590
slant_up 54.26588 47.83150
star 54.26734 47.83955
v_lines 54.26993 47.83699
wide_lines 54.26692 47.83160
x_shape 54.26015 47.83972
Compute both mean and standard deviation (sd) in one go using across()

ds_dozen %>%
  group_by(dataset) %>%
  # across works with first on which columns and second on what to perform on selection
  # 2 possibilities to select columns
  # summarise(across(where(is.double), list(mean = mean, sd = sd)))
  summarise(across(c(x, y), list(mean = mean, sd = sd)))

dataset x_mean x_sd y_mean y_sd
away 54.26610 16.76983 47.83472 26.93974
bullseye 54.26873 16.76924 47.83082 26.93573
circle 54.26732 16.76001 47.83772 26.93004
dino 54.26327 16.76514 47.83225 26.93540
dots 54.26030 16.76774 47.83983 26.93019
h_lines 54.26144 16.76590 47.83025 26.93988
high_lines 54.26881 16.76670 47.83545 26.94000
slant_down 54.26785 16.76676 47.83590 26.93610
slant_up 54.26588 16.76885 47.83150 26.93861
star 54.26734 16.76896 47.83955 26.93027
v_lines 54.26993 16.76996 47.83699 26.93768
wide_lines 54.26692 16.77000 47.83160 26.93790
x_shape 54.26015 16.76996 47.83972 26.93000
What can you conclude?

all mean and sd are the same for the 13 datasets

Plot the datasauRus

Plot the ds_dozen with ggplot such the aesthetics are aes(x = x, y = y)

with the geometry geom_point()

Tip

the ggplot() and geom_point() functions must be linked with a + sign 

ggplot(ds_dozen, aes(x = x, y = y)) +
  geom_point()
Reuse the above command, and now colored by the dataset column

ggplot(ds_dozen, aes(x = x, y = y, colour = dataset)) +
  geom_point()

Too many datasets are displayed.

How can we plot only one at a time?

Tip

You can filter for one dataset upstream of plotting 

ds_dozen %>%
  filter(dataset == "away") %>%
  ggplot(aes(x = x, y = y)) +
  geom_point()
Adjust the filtering step to plot two datasets

Tip

R provides the inline instruction %in% to test if there a match of the left operand in the right one (a vector most probably) 

ds_dozen %>%
  filter(dataset %in% c("away", "dino")) %>%
  # alternative without %in% and using OR (|)
  #filter(dataset == "away" | dataset == "dino") %>%
  ggplot(aes(x = x, y = y, colour = dataset)) +
  geom_point()
Expand now by getting one dataset per facet

ds_dozen %>%
  filter(dataset %in% c("away", "dino")) %>%
  ggplot(aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  facet_wrap(~ dataset)
Remove the filtering step to facet all datasets

ds_dozen %>%
  ggplot(aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  facet_wrap(~ dataset, ncol = 3)
Tweak the theme and use the theme_void and remove the legend

ggplot(ds_dozen, aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  theme_void() +
  theme(legend.position = "none") +
  facet_wrap(~ dataset, ncol = 3)
Are the datasets actually that similar?

No ;) We were fooled by the summary stats

Tip

the R package gifski could be installed on your machine, makes the GIF creation faster. gifski is internally written in rust, and this language needs cargo to run. See this article to get it installed on your machine. First install rust before install the R package gifski. Please note, that the animate() step still takes ~ 3-5 minutes depending on your machine.
Install gganimate, its dependencies will be automatically installed.

install.packages("gganimate")
Use the dataset variable to the transition_states() argument layer

library(gganimate)

ds_dozen %>%
  ggplot(aes(x = x, y = y)) +
  geom_point() +
  # transition will be made using the dataset column
  transition_states(dataset, transition_length = 5, state_length = 2) +
  # for a firework effect!
  shadow_wake(wake_length = 0.05) +
  labs(title = "dataset: {closest_state}") +
  theme_void(14) +
  theme(legend.position = "none") -> ds_anim
# more frames to slow down the animation
ds_gif <- animate(ds_anim, nframes = 500, fps = 10, renderer = gifski_renderer())
ds_gif
anim_save(title_frame = TRUE, "./img/ds.gif")

Visualize the tiny differences in means for both coordinates
  • need to zoom tremendously to see differences. Accumulate all states to better see the motions.

ds_dozen %>%
  group_by(dataset) %>%
  summarise(across(c(x, y), list(mean = mean, sd = sd))) %>% 
  ggplot(aes(x = x_mean, y = y_mean, colour = dataset)) +
  geom_point(size = 25, alpha = 0.6) +
  # zoom in like crazy
  coord_cartesian(xlim = c(54.25, 54.3), ylim = c(47.75, 47.9)) +
  # animate
  transition_states(dataset, transition_length = 5, state_length = 2) +
  # do not remove previous states to pile up dots
  shadow_mark() +
  labs(title = "dataset: {closest_state}") +
  theme_minimal(14) +
  theme(legend.position = "none") -> ds_mean_anim
ds_mean_gif <- animate(ds_mean_anim, nframes = 100, fps = 10)
ds_mean_gif
anim_save("img/ds_mean.gif")

Conclusion

never trust summary statistics alone; always visualize your data | Alberto Cairo

Authors

  • Alberto Cairo, (creator)
  • Justin Matejka
  • George Fitzmaurice
  • Lucy McGowan

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