Synopsis

This report will make a Leaflet plot, showing the population distribution of each province for China

Data

The data used in this project will show below. I will present all of the rows since it is such a tiny dataframe.
name lat lng Population
1 Aomen 21.33 115.07 55.23
2 Lasa 29.39 91.08 300.21
3 Xining 36.38 101.48 562.67
4 Yinchuan 38.27 106.16 630.14
5 Hong Kong 21.23 115.12 709.76
6 Haikou 20.02 110.20 867.15
7 Tianjin 39.02 117.12 1293.82
8 Beijing 39.55 116.24 1961.20
9 Wulumuqi 43.45 87.36 2181.33
10 Shanghai 31.14 121.29 2301.39
11 Taibei 25.03 121.30 2316.20
12 Huhehaote 40.48 111.41 2470.63
13 Lanzhou 36.04 103.51 2557.53
14 Changchun 43.54 125.19 2746.22
15 Chongqing 29.35 106.33 2884.00
16 Guiyang 26.35 106.42 3476.65
17 Fuzhou 26.05 119.18 3552.00
18 Taiyuan 37.54 112.33 3571.21
19 Xi’an 34.17 108.57 3732.74
20 Ha’erbing 45.44 126.36 3831.22
21 Shengyang 41.48 123.25 4374.63
22 Nanchang 28.40 115.55 4456.74
23 Kumming 25.04 102.42 4596.60
24 Nanning 22.48 108.19 4602.66
25 Hangzhou 30.16 120.10 5442.00
26 Wuhan 30.35 114.17 5723.77
27 Hefei 31.52 117.17 5950.10
28 Changsha 28.12 112.59 6568.37
29 Shijiazhuang 38.02 114.30 7185.42
30 Nanjing 32.03 118.46 7865.99
31 Xuzhou 34.15 117.11 7865.99
32 Chengdu 30.40 104.04 8041.82
33 Zhengzhou 34.46 113.40 9402.36
34 Jinan 36.40 117.00 9579.31
35 Guangzhou 23.08 113.14 10430.03

Plot

In this section, I will make a Leaflet plot. You will see a NASA-Earth-At-Night style tiles and some cilcles, centers of which represents the capital of that province and radius of which represents the poplation of that province.

Since I used assProviderTiles() to load NASA-Earth-At-Night tile instead of addTiles(), you need to wait for a short minute for the tile appearing.

library(leaflet)
library(grDevices)
colorPal <- colorRampPalette(c("snow", "lightyellow"))
pop_cities %>% leaflet() %>% addProviderTiles("NASAGIBS.ViirsEarthAtNight2012") %>% 
  addCircles(weight = 1, radius = pop_cities$Population*30, popup = pop_cities$name, 
             color = colorPal(dim(pop_cities)[1]))