There are a number of ways in R to make charts and maps interactive. This note uses the highcharter package and is based on this example. It is the base graphics library used in Fingertips.

library(highcharter)
library(dplyr)
library(viridisLite)
library(forecast)
library(treemap)

thm <- 
  hc_theme(
    colors = c("#1a6ecc", "#434348", "#90ed7d"),
    chart = list(
      backgroundColor = "transparent",
      style = list(fontFamily = "Source Sans Pro")
    ),
    xAxis = list(
      gridLineWidth = 1
    )
  )

Highcharter has a similar syntax to ggplot2. If we use the built in mtcars dataset, we can create a simple interactive scatter plot.

mtcars
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 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
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## 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
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## 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
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## 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
## 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
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
hchart(mtcars, "scatter", hcaes(x = disp, y = mpg, group = am))

Treemaps

data(GNI2014)

head(GNI2014)
##   iso3          country     continent population    GNI
## 3  BMU          Bermuda North America      67837 106140
## 4  NOR           Norway        Europe    4676305 103630
## 5  QAT            Qatar          Asia     833285  92200
## 6  CHE      Switzerland        Europe    7604467  88120
## 7  MAC Macao SAR, China          Asia     559846  76270
## 8  LUX       Luxembourg        Europe     491775  75990
## Static version
tm <- treemap(GNI2014, index = c("continent", "iso3"),
              vSize = "GNI", vColor = "population",
              type = "value", palette = magma(12))

## Interactive version

    hctreemap(tm, allowDrillToNode = TRUE) %>% 
   hc_title(text = "Gross National Income World Data") %>% 
   hc_tooltip(pointFormat = "<b>{point.name}</b>:<br>
                             Pop: {point.valuecolor:,.0f}<br>
                             GNI: {point.value:,.0f}") %>% 
  hc_exporting(enabled = TRUE) # enable export
devtools::install_github("Bart6114/sparklines")
library(sparklines)
data <- readr::read_csv("~/Documents/R_projects/testflex/kistats.csv")
## Parsed with column specification:
## cols(
##   id = col_integer(),
##   date = col_datetime(format = ""),
##   users = col_integer(),
##   sessions = col_integer(),
##   avgTimeonPage = col_double(),
##   pageviews = col_integer(),
##   webname = col_character()
## )
sparkdata <- data %>%
  filter(stringr::str_detect(webname, "Fingertips|chimat")) %>%
  group_by(webname, date) %>%
  summarise(pages = sum(pageviews)) 

tail(sparkdata)
## Source: local data frame [6 x 3]
## Groups: webname [1]
## 
##                                      webname       date pages
##                                        <chr>     <dttm> <int>
## 1 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-16   784
## 2 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-17  1512
## 3 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-18   739
## 4 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-19  1000
## 5 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-20  1928
## 6 www.chimat.org.uk_http://www.chimat.org.uk 2017-03-21  1806
x <- filter(sparkdata, grepl("chimat", webname))
y <- filter(sparkdata, grepl("Fingertips", webname))

The sparkline packages enables the creation of sparklines.

Site Sparkline Boxplot
Chimat
Fingertips