TD datasaurus

`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()

solution

# 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

solution

nothing to be done, a tibble display its dimensions, starting by a comment (‘#’ character)

assign the `datasaurus_dozen` to the `ds_dozen` object. This aims at populating the Global Environment

solution

ds_dozen <- datasaurus_dozen

using Rstudio, those dimensions are now also reported within the interface, where?

solution

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

solution

unique(ds_dozen$dataset) %>% length()

## [1] 13

tidyverse version

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.

solution

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.

solution

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

solution

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 the standard deviation of the `x` & `y` column in a same way

solution

ds_dozen %>%
  group_by(dataset) %>%
  summarise(sd_x = sd(x),
            sd_y = sd(y))

solution

dataset	sd_x	sd_y
away	16.76983	26.93974
bullseye	16.76924	26.93573
circle	16.76001	26.93004
dino	16.76514	26.93540
dots	16.76774	26.93019
h_lines	16.76590	26.93988
high_lines	16.76670	26.94000
slant_down	16.76676	26.93610
slant_up	16.76885	26.93861
star	16.76896	26.93027
v_lines	16.76996	26.93768
wide_lines	16.77000	26.93790
x_shape	16.76996	26.93000

do then all in one go using `summarise_if` so we exclude the `dataset` column and compute the others

solution

ds_dozen %>%
  group_by(dataset) %>%
  summarise_if(is.double, funs(mean = mean, sd = sd))

solution

## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.

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

what can you conclude?

solution

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

solution

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

reuse the above command, and now colored by the `dataset` column

solution

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?

solution

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)

solution

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

solution

ds_dozen %>%
  filter(dataset %in% c("away", "dino")) %>%
  ggplot(aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  facet_wrap(~ dataset)

tweak the theme and use the `theme_void` and remove the legend

solution

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?

solution

no ;) We were fooled by the summary stats

Tip

the R package gifski could be installed on your machine, makes the GIF creation faster.

install `gganimate`, its dependencies will be automatically installed.

solution

install.packages("gganimate")

use the `dataset` variable to the `transition_states()` argument layer

solution

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)
ds_gif
anim_save(title_frame = TRUE, "./img/ds.gif")

solution

visualized as small the differences in means for both coordinates

need to zoom tremendously to see almost nothing. Accumule all states to better see the motions.

solution

ds_dozen %>%
  group_by(dataset) %>%
  summarise_if(is.double, funs(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")

solution

Conclusion

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

Authors

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

from this post

TD datasaurus

Aurelien Ginolhac

2019-09-17

datasauRus package

Explore the dataset

what are the dimensions of this dataset? Rows and columns?

assign the datasaurus_dozen to the ds_dozen object. This aims at populating the Global Environment

using Rstudio, those dimensions are now also reported within the interface, where?

How many datasets are present?

Tip

Tip

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

compute the standard deviation of the x & y column in a same way

do then all in one go using summarise_if so we exclude the dataset column and compute the others

what can you conclude?

Plot the datasauRus

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

Tip

reuse the above command, and now colored by the dataset column

too many datasets are displayed, how can we plot only one at a time?

adjust the filtering step to plot two datasets?

Tip

expand now by getting one dataset per facet

remove the filtering step to facet all datasets

tweak the theme and use the theme_void and remove the legend

are the datasets actually that similar?

Tip

install gganimate, its dependencies will be automatically installed.

use the dataset variable to the transition_states() argument layer

visualized as small the differences in means for both coordinates

Conclusion

`datasauRus` package

assign the `datasaurus_dozen` to the `ds_dozen` object. This aims at populating the Global Environment

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

compute the standard deviation of the `x` & `y` column in a same way

do then all in one go using `summarise_if` so we exclude the `dataset` column and compute the others

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

reuse the above command, and now colored by the `dataset` column

expand now by getting one `dataset` per facet

tweak the theme and use the `theme_void` and remove the legend

install `gganimate`, its dependencies will be automatically installed.

use the `dataset` variable to the `transition_states()` argument layer