Sharon’s list contains many neat tricks, some of which less well-known base functions, others features of more niche packages. Here’s the ones I am definitely adding to my R tricks overview and want to highlight here as well:

Categorize values into interval cut()

Convert numbers that came in as strings with commas to R numbers with readr::parse_number(mydf$mycol)

Create a searchable, sortable HTML table in 1 line of code with DT::datatable(mydf, filter = 'top')

Display a fraction between 0 and 1 as a percentage with scales::percent(myfraction)

Generate a vector of 1:length(myvec) with seq_along(myvec)

And as if one list was not enough, scrolling through her Twitter feed, I found another R tips and tricks list by Sharon:

Most of my data visualizations I create using R programming — as you might have noticed from the content of my website.

Though I am colorblind myself, I love to work with colors and color palettes in my visualizations. And I’ve come across quite some neat tricks in my time.

This last trick, I learned in this recent blog post I came across, by Chisato. She explored all colors() base R incorporates, using the new ggforce and ggraph packages (thank you Thomas Lin Petersen!). Her exploration resulted in some nice visual overviews, which you can view in more detail in the original blog here.

Amy’s list includes many references to the best practices used by some of the leading data journalism companies, such as the BBC, or professional data companies like Salesforce and IBM.

As I’m worried that this great repository may not stand the test of time on the current Google Docs location, here are the base URLs once more:

MIT researchers have spent years developing the new drag-and-drop analytics tools they call Northstar.

Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze, and evaluate machine learning (ML) pipelines.

Northstar starts as a blank, white interface. Users upload datasets into the system, which appear in a “datasets” box on the left. Any data labels will automatically populate a separate “attributes” box below. There’s also an “operators” box that contains various algorithms, as well as the new AutoML tool. All data are stored and analyzed in the cloud.

You can read more about the tool’s functionalities in this MIT news article, which includes several promising GIFs:

Moreover, on the Northstar website you can find this longer video explaining the tool in detail.

While Northstar looks insanely cool and promising, I do worry about putting such power in the hands of people who may not have much experience with statistics and/or machine learning. We all know how easily errors and bias may slip into data-driven processes, so I am curious to see how these next-gen kind of tools will be deployed and used.

In a recent post, Claus shared the link to a GitHub repository where he hosts some of the R programming code with which Claus made the graphics for his dataviz book. The repository is named practical ggplot2, after the R package Clause used to make many of his visuals.

Check it out, the page contains some pearls and the code behind them, which will help you learn to create fabulous visualizations yourself. Some examples:

Here’s the original tweet in case you want to see the responses.

On twitter, I came across the tweet below showing some great GIF visualizations on the dangers of taking small samples.

Created by Andrew Stewart, and tweeted by John Holbein, the visuals show samples taken from a normal distributed variable with a mean of 10 and a standard deviation of 2. In the left section, Andrew took several samples of 20. In the right section, the sample size was increased to 500.

Just look at how much the distribution and the estimated mean change for small samples!

Andrew shared his code via Github, so I was able to download and tweak it a bit to make my own version.

Andrew’s version seems to be concerned with potential Type 1 errors when small samples are taken. A type 1 error occurs when you reject your null hypothesis (you reject “there is no effect”) while you should not have (“there is actually no effect”).

You can see this in the distributions Andrew sampled from in the tweet above. The data for conditions A (red) and B (blue) are sampled from the same distribution, with mean 10 and standard deviation 2. While there should thus be no difference between the groups, small samples may cause researchers to erroneously conclude that there is a difference between conditions A and B due to the observed data.

We could use Andrew’s basic code and tweak it a bit to simulate a setting in which Type 2 errors could occur. A type 2 error occurs when you do not reject your null hypothesis (you maintain “there is no effect”) whereas there is actually an effect, which you thus missed.

To illustrate this, I adapted Andrew’s code: I sampled data for condition B using a normal distribution with a slightly higher mean value of 11, as opposed to the mean of 10 for condition A. The standard deviation remained the same in both conditions (2).

Next, I drew 10 data samples from both conditions, for various sample sizes: 10, 20, 50, 100, 250, 500, and even 1000. After drawing these samples for both conditions, I ran a simple t-test to compare their means, and estimate whether any observed difference could be considered significant (at the alpha = 0.05 level [95%]).

In the end, I visualized the results in a similar fashion as Andrew did. Below are the results.

As you can see, only in 1 of our 10 samples with size 10 were we able to conclude that there was a difference in means. This means that we are 90% incorrect.

After increasing the sample size to 100, we strongly decrease our risk of Type 2 errors. Now we are down to 20% incorrect conclusions.

At this point though, I decided to rework Andrew’s code even more, to clarify the message.

I was not so much interested in the estimated distribution, which currently only distracts. Similarly, the points and axes can be toned down a bit. Moreover, I’d like to be able to see when my condition samples have significant different means, so let’s add a 95% confidence interval, and some text. Finally, let’s increase the number of drawn samples per sample size to, say, 100, to reduce the influence that chance may have on our Type 2 error rate estimations.

Let’s rerun the code and generate some GIFs!

The below demonstrates that small samples of only 10 observations per condition have only about a 11% probability of detecting the difference in means when the true difference is 1 (or half the standard deviation [i.e., 2]). In other words, there is a 89% chance of a Type 2 error occuring, where we fail to reject the null hypothesis due to sampling error.

Doubling the sample size to 20, more than doubles our detection rate. We now correctly identify the difference 28% of the time.

With 50 observations the Type 2 error rate drops to 34%.

Finally, with sample sizes of 100+ our results become somewhat reliable. We are now able to correctly identify the true difference over 95% of the times.

With a true difference of half the standard deviation, further increases in the sample size start to lose their added value. For instance, a sample size of 250 already uncovers the effect in all 100 samples, so doubling to 500 would not make sense.

I hope you liked the visuals. If you are interested in these kind of analysis, or want to estimate how large of a sample you need in your own study, have a look at power analysis. These analysis can help you determine the best setup for your own research initiatives.

If you’d like to reproduce or change the graphics above, here is the R code. Note that it is strongly inspired by Andrew’s original code.

# setup -------------------------------------------------------------------
# The new version of gganimate by Thomas Lin Pedersen - @thomasp85 may not yet be on CRAN so use devtools
# devtools::install_github('thomasp85/gganimate')
library(ggplot2)
library(dplyr)
library(glue)
library(magrittr)
library(gganimate)
# main function to create and save the animation --------------------------
save_created_animation = function(sample_size,
samples = 100,
colors = c("red", "blue"),
Amean = 10, Asd = 2,
Bmean = 11, Bsd = 2,
seed = 1){
### generate the data
# set the seed
set.seed(seed)
# set the names of our variables
cnames <- c("Score", "Condition", "Sample")
# create an empty data frame to store our simulated samples
df <- data.frame(matrix(rep(NA_character_, samples * sample_size * 2 * length(cnames)), ncol = length(cnames), dimnames = list(NULL, cnames)), stringsAsFactors = FALSE)
# create an empty vector to store whether t.test identifies significant difference in means
result <- rep(NA_real_, samples)
# run a for loop to iteratively simulate the samples
for (i in seq_len(samples)) {
# draw random samples for both conditions
a <- rnorm(sample_size, mean = Amean, sd = Asd)
b <- rnorm(sample_size, mean = Bmean, sd = Bsd)
# test whether there the difference in the means of samples is significant
result[i] = t.test(a, b)$p.value < 0.05
# add the identifiers for both conditions, and for the sample iteration
a <- cbind(a, rep(glue("A\n(μ={Amean}; σ={Asd})"), sample_size), rep(i, sample_size))
b <- cbind(b, rep(glue("B\n(μ={Bmean}; σ={Bsd})"), sample_size), rep(i, sample_size))
# bind the two sampled conditions together in a single matrix and set its names
ab <- rbind(a, b)
colnames(ab) <- cnames
# push the matrix into its reserved spot in the reserved dataframe
df[((i - 1) * sample_size * 2 + 1):((i * (sample_size * 2))), ] <- ab
}
### prepare the data
# create a custom function to calculate the standard error
se <- function(x) sd(x) / sqrt(length(x))
df %>%
# switch data types for condition and score
mutate(Condition = factor(Condition)) %>%
mutate(Score = as.numeric(Score)) %>%
# calculate the mean and standard error to be used in the error bar
group_by(Condition, Sample) %>%
mutate(Score_Mean = mean(Score)) %>%
mutate(Score_SE = se(Score)) ->
df
# create a new dataframe storing the result per sample
df_result <- data.frame(Sample = unique(df$Sample), Result = result, stringsAsFactors = FALSE)
# and add this result to the dataframe
df <- left_join(df, df_result, by = "Sample")
# identify whether not all but also not zero samples identified the difference in means
# if so, store the string "only ", later to be added into the subtitle
result_mention_adj <- ifelse(sum(result) != 0 & sum(result) < length(result), "only ", "")
### create a custom theme
textsize <- 16
my_theme <- theme(
text = element_text(size = textsize),
axis.title.x = element_text(size = textsize),
axis.title.y = element_text(size = textsize),
axis.text.y = element_text(hjust = 0.5, vjust = 0.75),
axis.text = element_text(size = textsize),
legend.title = element_text(size = textsize),
legend.text = element_text(size = textsize),
legend.position = "right",
plot.title = element_text(lineheight = .8, face = "bold", size = textsize),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(color = "grey", size = 0.5, linetype = "solid"),
axis.ticks = element_line(color = "grey")
)
# store the chosen colors in a named vector for use as palette,
# and add the colors for (in)significant results
COLORS = c(colors, "black", "darkgrey")
names(COLORS) = c(levels(df$Condition), "1", "0")
### create the animated plot
df %>%
ggplot(aes(y = Score, x = Condition, fill = Condition, color = Condition)) +
geom_point(aes(y = Score), position = position_jitter(width = 0.25), alpha = 0.20, stroke = NA, size = 1) +
geom_errorbar(aes(ymin = Score_Mean - 1.96 * Score_SE, ymax = Score_Mean + 1.96 * Score_SE), width = 0.10, size = 1.5) +
geom_text(data = . %>% filter(as.numeric(Condition) == 1),
aes(x = levels(df$Condition)[1], y = Result * 10 + 5,
label = ifelse(Result == 1, "Significant!", "Insignificant!"),
col = as.character(Result)), position = position_nudge(x = -0.5), size = 5) +
transition_states(Sample, transition_length = 1, state_length = 2) +
guides(fill = FALSE) +
guides(color = FALSE) +
scale_x_discrete(limits = rev(levels(df$Condition)), breaks = rev(levels(df$Condition))) +
scale_y_continuous(limits = c(0, 20), breaks = seq(0, 20, 5)) +
scale_color_manual(values = COLORS) +
scale_fill_manual(values = COLORS) +
coord_flip() +
theme_minimal() +
my_theme +
labs(x = "Condition") +
labs(y = "Dependent variable") +
labs(title = glue("When drawing {samples} samples of {sample_size} observations per condition")) +
labs(subtitle = glue("The difference in means is identified in {result_mention_adj}{sum(result)} of {length(result)} samples")) +
labs(caption = "paulvanderlaken.com | adapted from github.com/ajstewartlang") ->
ani
### save the animated plot
anim_save(paste0(paste("sampling_error", sample_size, sep = "_"), ".gif"),
animate(ani, nframes = samples * 10, duration = samples, width = 600, height = 400))
}
# call animation function for different sample sizes ----------------------
# !!! !!! !!!
# the number of samples is set to 100 by default
# if left at 100, each function call will take a long time!
# add argument `samples = 10` to get quicker results, like so:
# save_created_animation(10, samples = 10)
# !!! !!! !!!
save_created_animation(10)
save_created_animation(20)
save_created_animation(50)
save_created_animation(100)
save_created_animation(250)
save_created_animation(500)