Creating layered facets as a percentage in tmap interactive plot

[SMUCH97]

Below is a photo of a map that shows the number of a particular category of a population age bracket. As you can see by clicking on any given area it will show you how many of those age brackets are in that area. However, is there a way to show these numbers in relation to each other as a percentage.

e.g. Aa en Hunze will show:

pop 0-14 is 16%
pop 15-24 is 9%
pop 25-44 is 19%
pop 45-65 is 34%
pop 65+ is 22%

All total to 100% of the given population in Aa en Hunze? But when you click on a separate area it will also give you its own individual percentage composition.

Below is some example code you can copy and paste to create the interactive map:

library(spData)
library(spDataLarge)

data(NLD_muni)

tmap_mode("view")

tm_shape(NLD_muni) +
  tm_fill(c("pop_0_14", "pop_15_24", "pop_25_44", "pop_45_64", "pop_65plus"),
          style="jenks", id = "name", legend.show = FALSE) +
  tm_borders()  +
  tm_facets(as.layers = T)

Additionally, it would also be great to try fix the palette in a more appropriate way. Because currently, the colour palette is representative of the last age bracket in the string 'pop_65plus'. Therefore, it does not show the true representation - ideally it would be suitable if it showed it in terms of the highest number of the overall population across the dataset.

[jlacko]

I hope I understand your question right; what I gather from it is you are facing two issues:

1. add percentage sign to the population variables (the numbers seem to add to 100, with a only minor rounding issues / a few cases of 99)
2. plot the map based on total population (hint: Amsterdam is expected to be the darkest, followed by Rotterdam)

The first issue can be resolved by using a scoped mutate on your data object / note that the numeric values will become strings, which will mess up plotting, but strings are OK for popups - so use them within a popup.vars call.

The second issue is easy; just base the tm_fill() call on the population column, and feed the percentages via popup.vars.

I hope this helps.

library(spData)
library(spDataLarge)

data(NLD_muni)

library(tmap)
library(dplyr)

tmap_mode("view")

NLD_mod <- NLD_muni %>% 
  mutate(across(starts_with("pop_"), ~ paste0(., "%"))) # add % sign to values

tm_shape(NLD_mod) +
  tm_fill("population", # a single variable to be plotted
          # + multiplate variables for popup to be shown
          popup.vars = c("pop_0_14", "pop_15_24", "pop_25_44", "pop_45_64", "pop_65plus"),
          style = "jenks", id = "name", legend.show = FALSE) +
  tm_borders()  +
  tm_facets(as.layers = T)

[SMUCH97]

Hello, thank you for your response.

1. I had not meant just adding the percentage sign to the value. The numbers actually add up to 101 in the given example - so the percentages may seem to look like they are normal. Instead what this is doing is just adding the % sign, and not actually showing the true reflection of the composition for that particular area.

The true percentage values would actually be:

pop 0-14 as 15.84%
pop 15-24 as 8.91%
pop 25 -44 as 18.81%
pop 45 to 64 as 33.66%
pop 65+ as 22.77%

To explain this better. I am attempting to do this on another dataset with the same data structure. So for example your following code would produce this:

Instead, the desired goal would NOT show the value of males and females as 1995 and 3922. But instead have males as 33.72% and females as 66.28%.

2. This fixes this yes. Thank you for this. In my main dataset all i will need to do is mutate a new column of the totals of the variables I am trying to show.

Thank you for your help

[jlacko]

Oki, you will have to tweak the mutate call on your main dataset. Something like male_pct = male / (male + female), perhaps with some rounding and pasting of a percentage sign. As I don't have access to the UK dataset it is difficult for me to be specific.

The key aspect should be 1) calculate the percentages as separate columns (with the percentage sign pasted or not) and 2) base the tm_fill() call on the main population variable, with the calculated percentages in popup.vars argument / so that they show up on a click, but are not used in the chart itself.

[SMUCH97]

Sorry I do not quite understand. Here is some code you can copy and paste.

The variables in question are "Male" "Female" "Gender Not Stated"

# creating two line example data 

data <- structure(list(lad19cd = c("E06000001", "E06000052"), objectid = c(1L, 
                                                                           49L), lad19nm = c("Hartlepool", "Cornwall"), bng_e = c(447160L, 
                                                                                                                                  212497L), bng_n = c(531474L, 64493L), long = c(-1.27018, -4.64254
                                                                                                                                  ), lat = c(54.67614, 50.450218), st_areasha = c(93712619.817559, 
                                                                                                                                                                                  3548934061.31533), st_lengths = c(71011.9339493, 1241482.9365225
                                                                                                                                                                                  ), `35 TO 39` = c(16L, 135L), white = c(181L, 848L), Male = c(104L, 
                                                                                                                                                                                                                                                492L), `RTB-IBD` = c(155L, 745L), `40 TO 44` = c(25L, 100L), 
                       `45 TO 49` = c(19L, 128L), `Ethnicity Not Stated` = c(54L, 
                                                                             597L), `RTB-GLD` = c(41L, 570L), `16 TO 24` = c(12L, 131L
                                                                             ), `50 TO 54` = c(24L, 138L), `55 TO 59` = c(17L, 143L), 
                       Female = c(132L, 962L), COMPARE = c(34L, 2L), `30 TO 34` = c(21L, 
                                                                                    142L), `>=80` = c(10L, 24L), `65 TO 69` = c(22L, 115L), `60 TO 64` = c(28L, 
                                                                                                                                                           115L), `not stated` = c(1L, 5L), INTERVAL = c(1L, 56L), `70 TO 74` = c(18L, 
                                                                                                                                                                                                                                  88L), `RTB-STR` = c(4L, 16L), `25 TO 29` = c(14L, 124L), 
                       `Gender Not Stated` = 2:3, `75 TO 79` = c(12L, 70L), `RTB-INC` = 1:2, 
                       RDC = c(2L, 8L), mixed = c(2L, 4L), `RTB-GEN` = c(NA, 58L
                       ), asian = c(NA, 3L), `<=15` = c(NA, 4L), geometry = structure(list(
                         structure(list(list(structure(c(448986.02, 447072.101, 
                                                         443444.7028, 442588.2966, 440052.7012, 448476.6765, 450118.9667, 
                                                         450132.9512, 454039.03, 451736.05, 452570.529, 448986.02, 
                                                         536729.68, 537135.9959, 533133.0017, 528536.0976, 527819.7026, 
                                                         525831.9601, 525889.424, 525915.536, 528341.61, 532034.85, 
                                                         533906.857, 536729.68), .Dim = c(12L, 2L)))), class = c("XY", 
                                                                                                                 "MULTIPOLYGON", "sfg")), structure(list(list(structure(c(234998.8202, 
                                                                                                                                                                          234199.2005, 231675.7996, 226969.1972, 228841.9968, 226421.5025, 
                                                                                                                                                                          221174.0748, 219693.78, 220379.35, 219444.56, 214460.5, 
                                                                                                                                                                          212656.93, 209007, 205248, 204039, 202073.6, 192911.3, 
                                                                                                                                                                          189055.445, 185723.3, 184224.4, 175678.578, 175640.08, 
                                                                                                                                                                          172566.344, 169790.439, 168442.779, 163047.156, 158149.197, 
                                                                                                                                                                          154807.334, 150551.418, 147582.929, 143206.63, 140842.76, 
                                                                                                                                                                          137285.3, 135221.85, 135876.9, 134157.3994, 136594.2, 
                                                                                                                                                                          145706.03, 148210.311, 152517.724, 153594.542, 161765.89, 
                                                                                                                                                                          165363.8, 166815.81, 165929.6035, 169446.09, 171313.46, 
                                                                                                                                                                          173195.3, 177869.8, 181183.8524, 178400.24, 179260.2, 
                                                                                                                                                                          181955.21, 185101.85, 187807.85, 189018.71, 193211.19, 
                                                                                                                                                                          195934.7, 201605.8, 201741.19, 203898.05, 207591.75, 
                                                                                                                                                                          212215.7, 216120.09, 219355.15, 225603.6, 226774.45, 
                                                                                                                                                                          235955.42, 243532.247, 242824.77, 243911.78, 243028.6, 
                                                                                                                                                                          241872.3, 241450.8, 242606.17, 242283.75, 242585.29, 
                                                                                                                                                                          243305.19, 243570.27, 243752.23, 243628.5275, 236483.503, 
                                                                                                                                                                          236862.2976, 234998.8202, 85587.6273, 90329.4028, 98084.0031, 
                                                                                                                                                                          101116.0951, 110157.1971, 117556.2992, 117412.7424, 114860.61, 
                                                                                                                                                                          106454.29, 101066.96, 97814.5, 94004.29, 90871.3, 89188, 
                                                                                                                                                                          83911.2, 81276.2, 80080.1, 75965.452, 75629.3, 65319.3, 
                                                                                                                                                                          59125.553, 54647.032, 51879.281, 51475.164, 47480.287, 
                                                                                                                                                                          43544.349, 41887.089, 38259.782, 41399.896, 40983.218, 
                                                                                                                                                                          38502.49, 36067.55, 35259.5, 31695.19, 26719, 25091.146, 
                                                                                                                                                                          21508.7, 23826.2, 31062.812, 30547.462, 29159.056, 26447.31, 
                                                                                                                                                                          22515.3, 18843.11, 16533.7005, 11553.24, 11722.33, 16326.86, 
                                                                                                                                                                          16176.5, 21532.0955, 25186.62, 29758.8, 33564.69, 30937.38, 
                                                                                                                                                                          33634.05, 37739.51, 38942.78, 41252.69, 40955.9, 46485.13, 
                                                                                                                                                                          51499.1, 52498.96, 50935.2, 51230, 50188.29, 52316.6, 
                                                                                                                                                                          54063.23, 53817.89, 50574.037, 57101.11, 61854.2, 64425.91, 
                                                                                                                                                                          64075.22, 65591.63, 66286.69, 67931.3, 68882.1, 68711.36, 
                                                                                                                                                                          68553.3, 68240.53, 70929.8645, 78673.7992, 82399.9026, 
                                                                                                                                                                          85587.6273), .Dim = c(84L, 2L)))), class = c("XY", "MULTIPOLYGON", 
                                                                                                                                                                                                                       "sfg"))), class = c("sfc_MULTIPOLYGON", "sfc"), precision = 0, bbox = structure(c(xmin = 134157.3994, 
                                                                                                                                                                                                                                                                                                         ymin = 11553.24, xmax = 454039.03, ymax = 537135.9959), class = "bbox"), crs = structure(list(
                                                                                                                                                                                                                                                                                                           input = "OSGB 1936 / British National Grid", wkt = "PROJCRS[\"OSGB 1936 / British National Grid\",\n    BASEGEOGCRS[\"OSGB 1936\",\n        DATUM[\"OSGB 1936\",\n            ELLIPSOID[\"Airy 1830\",6377563.396,299.3249646,\n                LENGTHUNIT[\"metre\",1]]],\n        PRIMEM[\"Greenwich\",0,\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        ID[\"EPSG\",4277]],\n    CONVERSION[\"British National Grid\",\n        METHOD[\"Transverse Mercator\",\n            ID[\"EPSG\",9807]],\n        PARAMETER[\"Latitude of natural origin\",49,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8801]],\n        PARAMETER[\"Longitude of natural origin\",-2,\n            ANGLEUNIT[\"degree\",0.0174532925199433],\n            ID[\"EPSG\",8802]],\n        PARAMETER[\"Scale factor at natural origin\",0.9996012717,\n            SCALEUNIT[\"unity\",1],\n            ID[\"EPSG\",8805]],\n        PARAMETER[\"False easting\",400000,\n            LENGTHUNIT[\"metre\",1],\n            ID[\"EPSG\",8806]],\n        PARAMETER[\"False northing\",-100000,\n            LENGTHUNIT[\"metre\",1],\n            ID[\"EPSG\",8807]]],\n    CS[Cartesian,2],\n        AXIS[\"(E)\",east,\n            ORDER[1],\n            LENGTHUNIT[\"metre\",1]],\n        AXIS[\"(N)\",north,\n            ORDER[2],\n            LENGTHUNIT[\"metre\",1]],\n    USAGE[\n        SCOPE[\"Engineering survey, topographic mapping.\"],\n        AREA[\"United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore.\"],\n        BBOX[49.75,-9,61.01,2.01]],\n    ID[\"EPSG\",27700]]"), class = "crs"), n_empty = 0L)), row.names = 1:2, class = "data.frame")

# making n/a into zeros
data[is.na(data)] <- 0

# converting from dataframe to sf so it can be used on tmap
final <- sf::st_sf(data,sf_column_name = 'geometry')

View(final)

[jlacko]

I am a terrible explainer...
Consider this code instead, hopefully it will do better than my convoluted words:

library(sf)
library(tmap)
library(dplyr)

# pre-process your data
chrt_src <- final %>% 
  mutate(population = Male + Female + `Gender Not Stated`,
         male_pct = 100 * Male / population,
         female_pct = 100 * Female / population,
         gns_pct = 100 * `Gender Not Stated` / population) %>% 
  mutate(across(ends_with("_pct"), ~ paste0(round(.), "%"))) # round the figures & add % sign at end
  
# draw the tmap map
tm_shape(chrt_src) +
  tm_fill("population", # a single variable to be plotted
          # + multiplate variables for popup to be shown
          popup.vars = c("male_pct", "female_pct", "gns_pct"),
          style = "jenks", id = "name", legend.show = FALSE) +
  tm_borders()  +
  tm_facets(as.layers = T)

[SMUCH97]

@jlacko

chrt_src <- final %>% 
  mutate(population = Male + Female + `Gender Not Stated`,
         male_pct = 100 * Male / population,
         female_pct = 100 * Female / population,
         gns_pct = 100 * `Gender Not Stated` / population) %>% 
  mutate(across(ends_with("_pct"), ~ paste0(round(.), "%"))) # round the figures & add % sign at end
  
# draw the tmap map
tm_shape(chrt_src) +
  tm_fill("population", # a single variable to be plotted
          # + multiplate variables for popup to be shown
          popup.vars = c("male_pct", "female_pct", "gns_pct"),
          style = "jenks", id = "name", legend.show = FALSE) +
  tm_borders()  +
  tm_facets(as.layers = T)

Ok great. I understand now this makes sense. Initially I had thought there may be a way without mutating and essentially plotting a new variable.

Thank you for your help.

[jlacko]

Glad to be of service! I have found the *new column" approach to leaflet popups to give me the most control. I often find myself including stuff like hyperlinks and HTML formatting, which are impossible otherwise.