datasaurus

datasaurus

• 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?

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

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

Solution

datasaurus_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?

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(datasaurus_dozen$dataset) %>% length()

## [1] 13

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

datasaurus_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

datasaurus_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

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

Solution

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

Solution

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 datasaurus_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(datasaurus_dozen, aes(x = x, y = y)) +
  geom_point()

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

Solution

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

• expand now by getting one dataset per facet

Solution

ggplot(datasaurus_dozen, 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

Solution

ggplot(datasaurus_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

Animation

• install the gganimate package, its dependencies will be automatically installed.

Solution

install.packages("gganimate")

• add the dataset variable to the frame argument in the aes() function call

Solution

library(gganimate)

p <- ggplot(datasaurus_dozen, aes(x = x, y = y, frame = dataset)) +
  geom_point() +
  theme_gray(20) +
  theme(legend.position = "none")

gganimate(p, title_frame = TRUE, "./img/dino.gif")

## Executing: 
## convert -loop 0 -delay 100 Rplot1.png Rplot2.png Rplot3.png
##     Rplot4.png Rplot5.png Rplot6.png Rplot7.png Rplot8.png
##     Rplot9.png Rplot10.png Rplot11.png Rplot12.png Rplot13.png
##     'dino.gif'

## Output at: dino.gif

Solution

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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