Pleasant Surprise

While trying to test the boundaries of what fastpages actually supports, I figured I’d try out installing and setting up an R Notebook as well. Luckily enough, it does indeed support compiling and building R kernels as well.

The first step will be to install an R kernel for the notebook which can be done using:

conda install -c r r-essentials

This can be ran either from inside a notebook by prepending a ! in a cell such as !conda install -c r r-essentials or simply run it at the console if you’re in linux and in the project directory.

This is mostly an exibition post about how this can be done so we’re just going to show off some R stuff.

# I miss this selecting over Python's Pandas:
mtcars[order(mtcars$gear, mtcars$mpg), ]

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Cadillac Fleetwood	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
Camaro Z28	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Chrysler Imperial	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4
Merc 450SLC	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3
AMC Javelin	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2
Dodge Challenger	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2
Merc 450SE	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3
Merc 450SL	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Pontiac Firebird	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Toyota Corona	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
Merc 280C	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Volvo 142E	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Fiat X1-9	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1
Honda Civic	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
Fiat 128	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
Toyota Corolla	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1
Maserati Bora	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8
Ford Pantera L	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4
Ferrari Dino	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6
Porsche 914-2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2
Lotus Europa	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2

mtcars[order(mtcars$gear, mtcars$mpg), ] %>%
    ggplot(aes(disp, hp, colour = cyl)) + 
    geom_point()

crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)

# Equivalent to crimes %>% tidyr::pivot_longer(Murder:Rape)
 vars <- lapply(names(crimes)[-1], function(j) {
data.frame(state = crimes$state, variable = j, value = crimes[[j]])
})
crimes_long <- do.call("rbind", vars)

states_map <- map_data("state")
ggplot(crimes_long, aes(map_id = state)) +
    geom_map(aes(fill = value), map = states_map) +
    expand_limits(x = states_map$long, y = states_map$lat) +
    facet_wrap( ~ variable)

I did also try to use ggvis as well but it just wont display properly so that’s unfortunately out.