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The tidyverse ecosystem is steadily growing and adapting to the needs of its users. As part of this evolution, existing tools are being replaced by new and better methods. As useful as this flexibility is to the strength of the system, sometimes it can be hard to keep track of all the changes. This blogpost will deal with two new developments: the ‘curly-curly’ operator for tidy evaluation and the new ‘pivot’ functions for data reshaping.

We will need the following libraries; in particular make sure that you have installed at least tidyr version 1.0:

libs <- c('dplyr', 'tidyr',          # wrangling
'knitr','kableExtra',      # table styling
'ggplot2','gridExtra',     # plots
'leaflet')                 # interactive maps
invisible(lapply(libs, library, character.only = TRUE))

For this post’s dataset we’ll be doing something different than sampling the usual in-built tables. We will work with the famous NYC Squirrel Census - cataloguing the squirrel population of New York’s Central Park in October 2018. The squirrel census is a great example for a citizen science project that’s both accessible fun and useful scientific contribution.

Talking about fun community projects: the specific dataset we’re using has been prepared by the TidyTuesday project, a weekly social exercise to test our tidyverse skills and provide inspiration. TidyTuesday is run by the R for Data Science Online Learning Community. All datasets are available on github:

squirrels <- read_csv(str_c(
"https://raw.githubusercontent.com/",
"rfordatascience/tidytuesday/master/",
"data/2019/2019-10-29/nyc_squirrels.csv"))
squirrels %>%
select(lat, long, date, running, climbing, eating) %>%
kable() %>%
kable_styling()
lat long date running climbing eating
40.79408 -73.95613 10142018 FALSE FALSE FALSE
40.79485 -73.95704 10062018 TRUE FALSE FALSE
40.76672 -73.97683 10102018 FALSE TRUE FALSE
40.76970 -73.97572 10182018 FALSE TRUE FALSE
40.79753 -73.95931 10182018 FALSE FALSE FALSE

Among other features, the squirrels data includes the latitude and longitude of the sighting, the date, and flags that indicate whether this squirrel was spotted running, climbing, or eating. True to tidy form, each row corresponds to one squirrel. There are many more features in the full dataset, but we will focus on the ones above in the following examples.

First, we will cover tidy evaluation with the {{ }} operator aka ‘curly-curly’.

Introduced as part of rlang version 0.4.0 back in June, curly-curly replaces the approach of quoting with enquo and unquoting with !! (aka ‘bang-bang’). Instead of two operations it provides a compact shorthand for simple cases. Here is a brief example for counting groups of distinct feature values:

# new style
count_groups <- function(df, groupvar){
df %>%
group_by({{ groupvar }}) %>%
count()
}

count_groups(squirrels, climbing) %>%
kable() %>%
kable_styling()
climbing n
FALSE 2365
TRUE 658

In this example, {{ groupvar }} splices the value of groupvar into the group_by call, rather than its name. This is equivalent to the quote-unquote style of !! enquo(groupvar):

# old style
count_groups_old <- function(df, groupvar){
df %>%
group_by(!! enquo(groupvar)) %>%
count()
}

count_groups_old(squirrels, climbing) %>%
kable() %>%
kable_styling()
climbing n
FALSE 2365
TRUE 658

The advantage of {{ }} lies in its relative cognitive ease: think of it as inserting the value of the variable into the expression. No intermediate quoting or unquoting needed.

Next, we will look at how to reshape data with the new functions pivot_wider and pivot_longer, which are replacing the previous spread and gather tools. Most importantly: pivot_wider is the inverse function to pivot_longer, and vice versa.

As the name suggests, pivot_wider makes a tibble wider by turning a single categorical column into multiple columns, one for each category.

Let’s look at this aggregated data frame of sightings of climbing squirrels per day:

squirrels %>%
count(date, climbing) %>%
kable() %>%
kable_styling()
date climbing n
10062018 FALSE 253
10062018 TRUE 84
10072018 FALSE 283
10072018 TRUE 122

Pivoting to a wider format allows us to compare the numbers of climbing and non-climbing squirrels directly next to each other:

# new style with pivot_wider
squirrels %>%
count(date, climbing) %>%
pivot_wider(names_from = climbing, values_from = n, names_prefix = "climbing_") %>%
kable() %>%
kable_styling()
date climbing_FALSE climbing_TRUE
10062018 253 84
10072018 283 122
• The names_from argument indicates which column supplies the new column names. The values of this column are being picked from the feature defined via the values_from keyword. Here, we pick the names from the binary feature climbing and the values from the count column n.

• Among the additional keywords, names_prefix allows us to assign meaningful names to the new columns (which would otherwise simply be the rather generic ‘FALSE’ and ‘TRUE’ here). Another useful keyword is values_fill, which specifies a global replacement for any missing values.

• The new pivot_wider replaces the old spread function, which had comparable yet possibly more confusing parameters. For comparison, here is the same result with spread:

# old style with spread
squirrels %>%
count(date, climbing) %>%
spread(key = climbing, value = n) %>%
kable() %>%
kable_styling()
date FALSE TRUE
10062018 253 84
10072018 283 122

The syntax is very similar, which should make it easy for those familiar with spread to switch to pivot_wider. Note, that spread had no names_prefix argument.

In most tutorials, pivot_wider is somewhat overshadowed by pivot_longer; but I use it frequently to quickly compute proportions for grouped columns. For instance, here are the top 3 days for spotting squirrels climbing:

squirrels %>%
count(date, climbing) %>%
pivot_wider(names_from = climbing, values_from = n, names_prefix = "climbing_") %>%
mutate(climbing_percentage = climbing_TRUE/(climbing_TRUE + climbing_FALSE)*100) %>%
arrange(desc(climbing_percentage)) %>%
kable() %>%
kable_styling()
date climbing_FALSE climbing_TRUE climbing_percentage
10072018 283 122 30.12346
10062018 253 84 24.92582
10082018 220 65 22.80702

Let’s move on to pivot_longer. Being the inverse function to pivot_wider, this tool (often) reduces the number of columns by turning some of the columns into a single new (typically) categorical feature alongside an (often times) numerical feature. The different levels of the categorical column now describe the numerical column in a unique way (similarly to the wider column structure previously). This is all a bit abstract, so let’s do a quick example:

Imagine we’ve extracted both the proportion of climbing squirrels and non-climbing squirrels for each day with the help of pivot_wider. (Of course, one number determines the other but let’s use it as a simple illustration).

squirrels %>%
count(date, climbing) %>%
pivot_wider(names_from = climbing, values_from = n, names_prefix = "climbing_") %>%
mutate(prop_true = climbing_TRUE/(climbing_TRUE + climbing_FALSE)*100,
prop_false = climbing_FALSE/(climbing_TRUE + climbing_FALSE)*100) %>%
kable() %>%
kable_styling()
date climbing_FALSE climbing_TRUE prop_true prop_false
10062018 253 84 24.92582 75.07418
10072018 283 122 30.12346 69.87654

Now we will use pivot_longer to turn those two prop_ columns into a categorical climbing and a numerical percentage feature. Here, we drop the climbing_FALSE and climbing_TRUE columns, so the pivoting doesn’t change the number of columns but it changes the structure of the dataset:

squirrels %>%
count(date, climbing) %>%
pivot_wider(names_from = climbing, values_from = n, names_prefix = "climbing_") %>%
mutate(prop_true = climbing_TRUE/(climbing_TRUE + climbing_FALSE),
prop_false = climbing_FALSE/(climbing_TRUE + climbing_FALSE)) %>%
select(date, prop_true, prop_false) %>%
pivot_longer(cols = c("prop_true", "prop_false"),
names_to = "climbing", values_to = "percentage") %>%
kable() %>%
kable_styling()
date climbing percentage
10062018 prop_true 0.2492582
10062018 prop_false 0.7507418
10072018 prop_true 0.3012346
10072018 prop_false 0.6987654
• The cols argument specifies the columns that will be pivoted. Then, names_to gives the name of the new feature that will hold the (categorical) names of the original columns. And values_to is the new feature that hold their values (here the percentages).

• Note, that as with the older gather method, the new column names have to be passed as strings.

• The resulting data structure is often much better suited for plotting with ggplot2. The categorical feature can directly become a colour, fill, or faceting variable.

Finally, let’s combine curly-curly and pivoting into a comprehensive example. Here, we build a function that takes as arguments the name of a data frame and the name of a logical column, here a squirrel action, and then extracts the percentage of this action per coordinate bin. This is it:

extract_percentage <- function(df, col) {

df %>%
mutate(lat = round(lat*5, 2)/5,
long = round(long*5, 2)/5) %>%
count(lat, long, {{ col }}) %>%
pivot_wider(names_from = {{ col }}, values_from = n,
values_fill = list(n = 0)) %>%
mutate(true = TRUE/(TRUE + FALSE)*100,
false = FALSE/(TRUE + FALSE)*100) %>%
select(lat, long, true, false) %>%
pivot_longer(cols = c("true", "false"),
names_to = "action", values_to = "percentage") %>%
filter(action == "true")
}

extract_percentage(squirrels, climbing) %>%
kable() %>%
kable_styling()
lat long action percentage
40.764 -73.974 true 50.00000
40.766 -73.978 true 21.73913
40.766 -73.976 true 19.23077
• The coordinates are rounded the nearest 0.02 degrees to provide the sample size for summary statistics.

• We only keep the percentage of positive sightings for each action.

We’re now using this function to extract the proportions of squirrels that were observed eating, climbing, or running. Then we visualise those proportions on an interactive map of Manhattan centred on Central Park. The map is constructed using the wonderful leaflet package. Such a map could be used to find locations in the park that might be more promising than others for spotting certain squirrel shenanigans.

loc <- extract_percentage(squirrels, eating)

pal <- colorNumeric(palette = "RdBu", domain = seq(0,100), reverse = TRUE)

leaflet(loc) %>%
setView(lng = median(loc$long), lat = median(loc$lat), zoom = 13) %>%
data = extract_percentage(squirrels, eating),
group = "Eating",
color = ~ pal(percentage),
radius = 6, fillOpacity = 0.7, stroke = FALSE) %>%
data = extract_percentage(squirrels, climbing),
group = "Climbing",
color = ~ pal(percentage),
radius = 6, fillOpacity = 0.7, stroke = FALSE) %>%
data = extract_percentage(squirrels, running),
group = "Running",
color = ~ pal(percentage),
radius = 6, fillOpacity = 0.7, stroke = FALSE) %>%
options = layersControlOptions(collapsed = FALSE)) %>%
addLegend("bottomright", pal = pal, values = ~ percentage,
title = "Percentage", labFormat = labelFormat(suffix = "%"),) %>%
addScaleBar("bottomleft")
• This is an interactive map that is fully movable and zoomable, provided you are connected to the internet. If you aren’t connected to the internet: how did you reach this website? Teach me your dark and mysterious skills. Speaking of dark: I chose a dark background map because I like the way it contrasts with the colours of the circles. Looks especially sweet in dark mode. Click on the subgroups in the upper right corner to see the spatial distributions of their percentages.

• The code is pretty self explanatory, and I will save a more detailed exploration of leaflet for a future blog post. The different sub-datasets for each action are added as CircleMarkers using our èxtract_percentages function. For the colour-coding we define a diverging palette. Each dataset is a layer with a group attribute which is used to define the switch between them.

• Among the things we see in the data, there is a tendency for sightings of squirrels eating to be located in the inner part of the park vs the edges. A lot of climbing happens on the east side; and there’s more running in the north. Feel free to supply fitting stereotypes or jokes about Manhattan here - I’m blissfully unaware of any.

• For a more serious analysis there would the caveat that some of the spatial bins will have small numbers of squirrels overall, which leads to large uncertainties for those percentages. This is something that would need to be taken into account in a deeper analysis.

More resources: