Some package suggestions
By all means, don’t chose something from the list below if you don’t want to! But there are some packages which might be good to think about if your imagination is flagging. I’ll also provide a bit of an example of what they can do.
gghighlight
gghighlight allows you to easily highlight specific lines and points within a ggplot.
An example of a new geom from gghighlight
ggforce
The ggforce package that contains a number of additional geoms and stats for ggplot2 giving added functionality and really fun plots.
An example of a new geom from ggforce
patchwork
patchwork is a truly amazing package that allows you to combine multiple ggplots into multipaneled figures. I’ll admit, this is one of the workhorse packages I use almost every day. But - it doesn’t have a cheatsheet! Can you write one?
An example of multipaneled patchwork plots
vroom
Reading comma separated files and the like seems fast to you now - but that’s only because we’ve been reading files with kilobytes of data in them at most. What about when you get to REAL big data - files with gigabytes or more of data in them? That’s where vroom comes in. It can read over 1GB of data per second! But with great power, comes great responsibility in terms of how you structure loading files - and there are ways to load MANY files with vroom as well. So, try writing a cheat sheet on large file(s) handling with vroom!
vroom::vroom("mtcars.tsv",
col_types = list(cyl = "i", gear = "f",hp = "i", disp = "_",
drat = "_", vs = "l", am = "l", carb = "i")
)
#> # A tibble: 32 x 10
#> model mpg cyl hp wt qsec vs am gear carb
#> <chr> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl> <fct> <int>
#> 1 Mazda RX4 21 6 110 2.62 16.5 FALSE TRUE 4 4
#> 2 Mazda RX4 Wag 21 6 110 2.88 17.0 FALSE TRUE 4 4
#> 3 Datsun 710 22.8 4 93 2.32 18.6 TRUE TRUE 4 1
#> # … with 29 more rowssnakecase
snakecase is an all in one converter of complicated strings to a number of more standardized formats - snakecase, camelcase, and others. In addition to specific parsers, there’s also a general parser, to come up with your own conversion case, and it handles transliterrating other languages on the fly.
library(snakecase)
string <- c("lowerCamelCase")
to_any_case(string)## [1] "lower_camel_case"#from German!
to_any_case("Doppelgänger",
transliterations = "german",
case = "upper_camel")## [1] "Doppelgaenger"scatterD3
Do you love awesome interactive web plots? Then scatterD3 is for you!
Play with this scatterD3 plot!
visdat
We’ve used visdat for basically one function, but there’s so so soooo much more! Build a cheat sheet to help folk explore their data!
skimr
As with visdat, we’ve used skimr for one function only, but, again, so much more!
Data summary
| Name | Piped data |
| Number of rows | 272 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| eruptions | 0 | 1 | 3.49 | 1.14 | 1.6 | 2.16 | 4 | 4.45 | 5.1 | ▇▂▂▇▇ |
| waiting | 0 | 1 | 70.90 | 13.59 | 43.0 | 58.00 | 76 | 82.00 | 96.0 | ▃▃▂▇▂ |
Extra Credit: data validation
Combine the skimr and visdat packages along with any others you find that are useful to create a comprehensive data validation cheat sheet for when you first load a new data set. This is a powerful beast, and is worth up to 25% extra credit.
worms
Taxonomy is hard. worms makes it easy. Names change. We often want a lot of different information from a taxonomy. One place that is standardized to all marine species is the World Register of Marine Species.
rnaturalearth
rnaturalearth provides an interface to the Natural Earth project - a collection of vector and raster files to make amazing maps.
## OGR data source with driver: ESRI Shapefile
## Source: "/private/var/folders/hr/2f_q28hx74382lr9359h3jkc309s_w/T/Rtmp5Uk0PA", layer: "ne_50m_lakes"
## with 275 features
## It has 35 fields
## Integer64 fields read as strings: scalerank ne_id## OGR data source with driver: ESRI Shapefile
## Source: "/private/var/folders/hr/2f_q28hx74382lr9359h3jkc309s_w/T/Rtmp5Uk0PA", layer: "ne_50m_rivers_lake_centerlines"
## with 462 features
## It has 32 fields
## Integer64 fields read as strings: ne_idLakes and rivers of the US
rayshader
rayshadr allows you to make all sorts of hauntingly beautiful photo-realistic 3D plots - maps, ggplots, and more!
rayshader
rnoaa
rnoaa is an astouding package that lets you access many of the different data sets available from NOAA. There is no way you can do a comprehensive cheat sheet here. BUT - if you want to make a cheat sheet to fully explore ONE data set, and show us how to use it to make it useful, that would be perfect!
Sea Ice at the North Pole from 2000-2010
rtweet
Wanna dig into the stew of Twitter and analyze it? What do you find there? What’s awesome? What’s terrible? Check out rtweet
How often does Prof. Byrnes tweet?
the lost pies of magrittr
The magrittr package is far more than just %>%. There are a multitude of pipes we don’t talk about, different ways of creating repeatable workflows with . %>% and more. Write a cheat sheet that embraces the full functionality of magrittr!