# ggscatterstats

#### 2018-08-14

The function ggstatsplot::ggscatterstats is meant to provide a publication-ready scatterplot with all statistical details included in the plot itself to show association between two continuous variables. This function is also helpful during the data exploration phase. We will see examples of how to use this function in this vignette with the ggplot2movies dataset.

To begin with, here are some instances where you would want to use ggscatterstats-

• to check linear association between two continuous variables
• to check distribution of two continuous variables

Note before: The following demo uses the pipe operator (%>%), so in case you are not familiar with this operator, here is a good explanation: http://r4ds.had.co.nz/pipes.html

## Correlation plot with ggscatterstats

To illustrate how this function can be used, we will use the ggplot2movies dataset. This dataset provides information about movies scraped from IMDB. Let’s have a look at the data-

library(ggplot2movies)
library(dplyr)

dplyr::glimpse(x = ggplot2movies::movies)
#> Observations: 58,788
#> Variables: 24
#> $title <chr> "$", "$1000 a Touchdown", "$21 a Day Once a Month"...
#> $year <int> 1971, 1939, 1941, 1996, 1975, 2000, 2002, 2002, 19... #>$ length      <int> 121, 71, 7, 70, 71, 91, 93, 25, 97, 61, 99, 96, 10...
#> $budget <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA... #>$ rating      <dbl> 6.4, 6.0, 8.2, 8.2, 3.4, 4.3, 5.3, 6.7, 6.6, 6.0, ...
#> $votes <int> 348, 20, 5, 6, 17, 45, 200, 24, 18, 51, 23, 53, 44... #>$ r1          <dbl> 4.5, 0.0, 0.0, 14.5, 24.5, 4.5, 4.5, 4.5, 4.5, 4.5...
#> $r2 <dbl> 4.5, 14.5, 0.0, 0.0, 4.5, 4.5, 0.0, 4.5, 4.5, 0.0,... #>$ r3          <dbl> 4.5, 4.5, 0.0, 0.0, 0.0, 4.5, 4.5, 4.5, 4.5, 4.5, ...
#> $r4 <dbl> 4.5, 24.5, 0.0, 0.0, 14.5, 14.5, 4.5, 4.5, 0.0, 4.... #>$ r5          <dbl> 14.5, 14.5, 0.0, 0.0, 14.5, 14.5, 24.5, 4.5, 0.0, ...
#> $r6 <dbl> 24.5, 14.5, 24.5, 0.0, 4.5, 14.5, 24.5, 14.5, 0.0,... #>$ r7          <dbl> 24.5, 14.5, 0.0, 0.0, 0.0, 4.5, 14.5, 14.5, 34.5, ...
#> $r8 <dbl> 14.5, 4.5, 44.5, 0.0, 0.0, 4.5, 4.5, 14.5, 14.5, 4... #>$ r9          <dbl> 4.5, 4.5, 24.5, 34.5, 0.0, 14.5, 4.5, 4.5, 4.5, 4....
#> $r10 <dbl> 4.5, 14.5, 24.5, 45.5, 24.5, 14.5, 14.5, 14.5, 24.... #>$ mpaa        <chr> "", "", "", "", "", "", "R", "", "", "", "", "", "...
#> $Action <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,... #>$ Animation   <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $Comedy <int> 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,... #>$ Drama       <int> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,...
#> $Documentary <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,... #>$ Romance     <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $Short <int> 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,... We will be using cleaned version of this dataset included in the ggstatsplot package itself. library(ggstatsplot) # see the selected data (we have data from 1813 movies) dplyr::glimpse(x = ggstatsplot::movies_wide) #> Observations: 1,813 #> Variables: 14 #>$ title       <fct> 'Til There Was You, 10 Things I Hate About You, 10...
#> $year <int> 1997, 1999, 2002, 2004, 1999, 2001, 1972, 2003, 19... #>$ length      <int> 113, 97, 98, 98, 102, 120, 180, 107, 87, 101, 99, ...
#> $budget <dbl> 23.00, 16.00, 1.10, 37.00, 85.00, 42.00, 4.00, 76.... #>$ rating      <dbl> 4.8, 6.7, 5.6, 6.4, 6.1, 6.1, 7.3, 5.1, 5.4, 5.4, ...
#> $votes <int> 799, 19095, 181, 7859, 14344, 10866, 1754, 9556, 8... #>$ mpaa        <fct> PG-13, PG-13, R, PG-13, R, R, PG, PG-13, R, R, R, ...
#> $Action <int> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,... #>$ Animation   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $Comedy <int> 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,... #>$ Drama       <int> 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,...
#> $Documentary <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,... #>$ Romance     <int> 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,...
#> $Short <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... Now that we have a clean dataset, we can start asking some interesting questions. For example, let’s see if the average rating a movie has depends on its budget.  ggstatsplot::ggscatterstats( data = ggstatsplot::movies_wide, # dataframe from which variables are to be taken x = budget, # predictor/independent variable y = rating, # dependent variable xlab = "Budget (in millions of US dollars)", # label for the x-axis ylab = "Rating on IMDB", # label for the y-axis marginal = TRUE, # show marginal distribution marginal.type = "density", # type of plot for marginal distribution centrality.para = "mean", # if and which type of centrality parameter to be plotted margins = "both", # marginal distribution on both axes xfill = "#CC79A7", # fill for marginal distributions on the x-axis yfill = "#009E73", # fill for marginal distributions on the y-axis xalpha = 0.5, # changing transparency for the x-axis marginals yalpha = 0.75, # changing transparency for the y-axis marginals xsize = 1, # changing size for the x-axis marginals ysize = 1, # changing size for the y-axis marginals type = "pearson", # type of linear association title = "Relationship between movie budget and IMDB rating", caption = "Source: www.imdb.com", messages = FALSE ) There is indeed a significant, positive correlation between the amount of money studio invests in a movie and the ratings given by the audiences. We should also note that this is a really small correlation and only about 1% of variation in ratings is explained by budget. The type (of test) argument also accepts the following abbreviations: "p" (for parametric/pearson’s), "np" (for nonparametric/spearman), "r" (for robust). Important: In contrast to all other functions in this package, the ggscatterstats function returns object that is not further modifiable with ggplot2. This can be avoided by not plotting the marginal distributions (marginal = FALSE). Currently trying to find a workaround this problem. Using ggscatterstats() in R Notebooks or Rmarkdown If you try including a ggscatterstats() plot inside an R Notebook or Rmarkdown code chunk, you’ll notice that the plot doesn’t get output. In order to get a ggscatterstats() to show up in an these contexts, you need to save the ggscatterstats plot as a variable in one code chunk, and explicitly print it using the grid package in another chunk, like this: # include the following code in your code chunk inside R Notebook or Markdown grid::grid.newpage() grid::grid.draw( ggstatsplot::ggscatterstats( data = ggstatsplot::movies_wide, x = budget, y = rating, marginal = TRUE, messages = FALSE ) ) ## Grouped analysis with grouped_ggscatterstats What if we want to do the same analysis do the same analysis for movies with different MPAA (Motion Picture Association of America) film ratings (NC-17, PG, PG-13, R)? In that case, we will have to either write a for loop or use purrr, none of which seem like an exciting prospect. ggstatsplot provides a special helper function for such instances: grouped_ggstatsplot. This is merely a wrapper function around ggstatsplot::combine_plots. It applies ggstatsplot across all levels of a specified grouping variable and then combines list of individual plots into a single plot. Note that the grouping variable can be anything: conditions in a given study, groups in a study sample, different studies, etc. Let’s see how we can use this function to apply ggscatterstats for all MPAA ratings. We will be running parametric tests (Pearson’s r, i.e.). If you set type = "np" or type = "r", results from non-parametric or robust test will be displayed. ggstatsplot::grouped_ggscatterstats( # arguments relevant for ggstatsplot::ggscatterstats data = ggstatsplot::movies_wide, title.prefix = "MPAA Rating", x = budget, y = rating, grouping.var = mpaa, marginal.type = "boxplot", # arguments relevant for ggstatsplot::combine_plots title.text = "Relationship between movie budget and IMDB rating", caption.text = "Source: www.imdb.com", nrow = 4, ncol = 1, labels = c("(a)","(b)","(c)","(d)") ) #> Warning: This function doesn't return a ggplot2 object and is not further modifiable with ggplot2 functions.Warning: This function doesn't return a ggplot2 object and is not further modifiable with ggplot2 functions.Warning: This function doesn't return a ggplot2 object and is not further modifiable with ggplot2 functions.Warning: This function doesn't return a ggplot2 object and is not further modifiable with ggplot2 functions. As seen from the plot, this analysis has revealed something interesting: The relationship we found between budget and IMDB rating holds only for PG-13 and R-rated movies. Indeed, the relationship even reverses for non-rated or NC-17 rated films. Although this is a quick and dirty way to explore large amount of data with minimal effort, it does come with an important limitation: reduced flexibility. For example, if we wanted to add, let’s say, a separate type of marginal distribution plot for each MPAA rating or if we wanted to use different types of correlations across different levels of MPAA ratings (NC-17 has only 6 movies, so a robust correlation would be a good idea), this is not possible. But this can be easily done using purrr. ## Grouped analysis with ggscatterstats + purrr Let’s run the same analysis using purrr::pmap. Note before: Unlike the function call so far, while using purrr::pmap, we will need to quote the arguments. # let's split the dataframe and create a list by mpaa rating mpaa_list <- ggstatsplot::movies_wide %>% base::split(x = ., f = .$mpaa, drop = TRUE)

# this created a list with 4 elements, one for each mpaa rating
# you can check the structure of the file for yourself
# str(mpaa_list)

# checking the length and names of each element
length(mpaa_list)
#> [1] 4
names(mpaa_list)
#> [1] "NC-17" "PG"    "PG-13" "R"

# running function on every element of this list note that if you want the same
# value for a given argument across all elements of the list, you need to
# specify it just once
plot_list <- purrr::pmap(
.l = list(
data = mpaa_list,
x = "budget",
y = "rating",
xlab = "Budget (in millions of US dollars)",
ylab = "Rating on IMDB",
title = list(
"MPAA Rating: NC-17",
"MPAA Rating: PG",
"MPAA Rating: PG-13",
"MPAA Rating: R"
),
type = list("r", "np", "np", "np"),
marginal.type = list("histogram", "boxplot", "density", "violin"),
centrality.para = "mean",
xfill = list("#56B4E9", "#009E73", "#999999", "#0072B2"),
yfill = list("#D55E00", "#CC79A7", "#F0E442", "#D55E00"),
ggtheme = list(
ggplot2::theme_grey(),
ggplot2::theme_classic(),
ggplot2::theme_light(),
ggplot2::theme_minimal()
),
messages = FALSE
),
.f = ggstatsplot::ggscatterstats
)

# combining all individual plots from the list into a single plot using combine_plots function
ggstatsplot::combine_plots(
plotlist = plot_list,
title.text = "Relationship between movie budget and IMDB rating",
caption.text = "Source: www.imdb.com",
caption.size = 16,
title.color = "red",
caption.color = "blue",
nrow = 4,
ncol = 1,
labels = c("(a)","(b)","(c)","(d)")
)

## Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues