[R] Colouring selected columns in a facetted column chart

phii m@iii@g oii phiiipsmith@c@ phii m@iii@g oii phiiipsmith@c@
Sun Aug 25 04:45:32 CEST 2019


Resubmitted as recommended:

I am having difficulty with a chart using ggplot. It is a facetted 
column chart showing GDP growth rates by country. The columns are 
coloured navyblue, except that I want to colour the most recent columns, 
for 2019-Q1 and 2019-Q2, red. For some countries data are available up 
to 2019-Q2 while for others data are only available up to 2019-Q1. My 
code and data frame are shown below and it almost works, but not quite. 
For some reason the red bars for Germany, Korea, Norway, Sweden and 
United Kingdom are slightly off. Any help will be much appreciated.

Here is my reprex:

library(tidyverse)
t1 <- read.table("t1.txt",header=TRUE,sep="\t")
col <- rep("navyblue",nrow(t1))
for (i in 1:nrow(t1)) {
   if((t1$TIME[i]=="2019-Q1" | t1$TIME[i]=="2019-Q2")) {
     col[i] <- "red"}
}
ggplot(t1) +
   geom_col(aes(x=TIME,y=GDPgr),fill=col) +
   facet_wrap(~Country,ncol=3)

Here is my data frame, called "t1.txt", output by dput():

structure(list(TIME = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label = c("2016-Q4", "2017-Q1",
"2017-Q2", "2017-Q3", "2017-Q4", "2018-Q1", "2018-Q2", "2018-Q3",
"2018-Q4", "2019-Q1", "2019-Q2"), class = "factor"), LOCATION = 
structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L
), .Label = c("AUS", "BEL", "CAN", "CHE", "DEU", "DNK", "ESP",
"EU28", "FIN", "FRA", "GBR", "ISR", "ITA", "JPN", "KOR", "NLD",
"NOR", "NZL", "PRT", "SWE", "USA"), class = "factor"), Country = 
structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L), .Label = 
c("Australia",
"Belgium", "Canada", "Denmark", "European Union (28 countries)",
"Finland", "France", "Germany", "Israel", "Italy", "Japan", "Korea",
"Netherlands", "New Zealand", "Norway", "Portugal", "Spain",
"Sweden", "Switzerland", "United Kingdom", "United States"), class = 
"factor"),
     Value = c(440518, 442141, 445739, 448672, 451302, 455680,
     459697, 461024, 462032, 463907, 106675, 107394, 107828, 108003,
     108744, 109037, 109386, 109676, 110081, 110459, 110680, 493742,
     498719, 504100.5, 505745, 507883, 509758.75, 512958, 515639.25,
     515971.75, 516489.5, 499945, 511319, 505254, 500363, 504837,
     508633, 511901, 513630, 517726, 518368, 3301202.652555, 
3323886.876398,
     3345038.332666, 3367136.027609, 3390431.080785, 3404554.778774,
     3419358.570571, 3430321.169276, 3440915.89772, 3458087.265837,
     3465003.441, 48525, 49368, 49430, 49596, 50153, 50352, 50449,
     50507, 50530, 50822, 551760, 556305, 560160, 563998, 568125,
     569542, 570670, 572387, 574640, 576494, 577905, 716743.4074,
     725268.5864, 729321.5731, 735610.6375, 740991.229, 741969.5787,
     744834.6127, 744065.912, 745603.2305, 748468.2276, 747909.2496,
     307789.55, 308323.023, 311759.624, 315651.46, 319056.442,
     322272.592, 323422.356, 325702.534, 329052.641, 332851.725,
     333686.876, 396162.2, 398379, 399893, 401534, 403053.4, 403937.8,
     403977.3, 403434.2, 403190.7, 403697.9, 403794.7, 130406025,
     131558850, 132121450, 133064400, 133475100, 133386850, 133931825,
     133289800, 133836225, 134777725, 135369050, 431473400, 435435200,
     437712100, 444064400, 443599800, 447909300, 450495800, 452561100,
     456769700, 455081000, 459958000, 178453.593134, 179367.793134,
     180964.533134, 182189.893134, 183625.193134, 184793.473134,
     185981.973134, 186425.153134, 187434.343134, 188324.263134,
     189297.773134, 59062, 59348, 59743, 60320, 60737, 61031,
     61655, 61927, 62282, 62800, 784704, 788709, 794220, 798283,
     800232, 803756, 807187, 810942, 815921, 815323, 44303.821,
     44632.068, 44803.721, 45062.631, 45426.14, 45641.37, 45910.934,
     46027.362, 46203.578, 46453.392, 46685.65896, 279431, 281707,
     284169, 285986, 288064, 289861, 291583, 293145, 294768, 296732,
     298147, 1150761, 1151977, 1169243, 1177835, 1181734, 1192111,
     1197931, 1196262, 1209430, 1215583, 1214691, 168268.356822,
     168865.076317, 170078.764694, 171405.16327, 172777.427869,
     174168.535837, 175400.870886, 175089.0314, 175664.228343,
     176651.744992, 496470, 498582, 499885, 502473, 504487, 504785,
     506842, 510346, 511482, 514019, 513029, 4456057.75, 4481314,
     4505262, 4540889.5, 4580616, 4609563.5, 4649533.75, 4683180,
     4695887, 4731820.25, 4755955), GDPgr = c(1, 0.4, 0.8, 0.7,
     0.6, 1, 0.9, 0.3, 0.2, 0.4, 0.3, 0.7, 0.4, 0.2, 0.7, 0.3,
     0.3, 0.3, 0.4, 0.3, 0.2, 0.6, 1, 1.1, 0.3, 0.4, 0.4, 0.6,
     0.5, 0.1, 0.1, 0.9, 2.3, -1.2, -1, 0.9, 0.8, 0.6, 0.3, 0.8,
     0.1, 0.8, 0.7, 0.6, 0.7, 0.7, 0.4, 0.4, 0.3, 0.3, 0.5, 0.2,
     0.2, 1.7, 0.1, 0.3, 1.1, 0.4, 0.2, 0.1, 0, 0.6, 0.6, 0.8,
     0.7, 0.7, 0.7, 0.2, 0.2, 0.3, 0.4, 0.3, 0.2, 0.4, 1.2, 0.6,
     0.9, 0.7, 0.1, 0.4, -0.1, 0.2, 0.4, -0.1, 0.9, 0.2, 1.1,
     1.2, 1.1, 1, 0.4, 0.7, 1, 1.2, 0.3, 0.5, 0.6, 0.4, 0.4, 0.4,
     0.2, 0, -0.1, -0.1, 0.1, 0, 0.2, 0.9, 0.4, 0.7, 0.3, -0.1,
     0.4, -0.5, 0.4, 0.7, 0.4, 0.8, 0.9, 0.5, 1.5, -0.1, 1, 0.6,
     0.5, 0.9, -0.4, 1.1, 0.9, 0.5, 0.9, 0.7, 0.8, 0.6, 0.6, 0.2,
     0.5, 0.5, 0.5, 0.5, 0.5, 0.7, 1, 0.7, 0.5, 1, 0.4, 0.6, 0.8,
     2, 0.5, 0.7, 0.5, 0.2, 0.4, 0.4, 0.5, 0.6, -0.1, 0.8, 0.7,
     0.4, 0.6, 0.8, 0.5, 0.6, 0.3, 0.4, 0.5, 0.5, 0.6, 0.8, 0.9,
     0.6, 0.7, 0.6, 0.6, 0.5, 0.6, 0.7, 0.5, 0.4, 0.1, 1.5, 0.7,
     0.3, 0.9, 0.5, -0.1, 1.1, 0.5, -0.1, -0.1, 0.4, 0.7, 0.8,
     0.8, 0.8, 0.7, -0.2, 0.3, 0.6, 0.7, 0.4, 0.3, 0.5, 0.4, 0.1,
     0.4, 0.7, 0.2, 0.5, -0.2, 0.5, 0.6, 0.5, 0.8, 0.9, 0.6, 0.9,
     0.7, 0.3, 0.8, 0.5)), class = "data.frame", row.names = c(NA,
-224L))



On 2019-08-24 22:39, Eric Berger wrote:
> Hi Phil,
> Please resubmit your question with the data frame contents shown as
> the output from the command
> dput(t1.txt). This will make it easier for people to run your reprex
> and respond to your question.
> 
> Best,
> Eric
> 
> On Sun, Aug 25, 2019 at 5:26 AM <phil using philipsmith.ca> wrote:
> 
>> I am having difficulty with a chart using ggplot. It is a facetted
>> column chart showing GDP growth rates by country. The columns are
>> coloured navyblue, except that I want to colour the most recent
>> columns,
>> for 2019-Q1 and 2019-Q2, red. For some countries data are available
>> up
>> to 2019-Q2 while for others data are only available up to 2019-Q1.
>> My
>> code and data frame are shown below and it almost works, but not
>> quite.
>> For some reason the red bars for Germany, Korea, Norway, Sweden and
>> United Kingdom are slightly off. Any help will be much appreciated.
>> 
>> Here is my reprex:
>> 
>> library(tidyverse)
>> t1 <- read.table("t1.txt",header=TRUE,sep="\t")
>> col <- rep("navyblue",nrow(t1))
>> for (i in 1:nrow(t1)) {
>> if((t1$TIME[i]=="2019-Q1" | t1$TIME[i]=="2019-Q2")) {
>> col[i] <- "red"}
>> }
>> ggplot(t1) +
>> geom_col(aes(x=TIME,y=GDPgr),fill=col) +
>> facet_wrap(~Country,ncol=3)
>> 
>> Here is my data frame, called "t1.txt":
>> 
>> "TIME"  "LOCATION"      "Country"       "Value" "GDPgr"
>> "2016-Q4"       "AUS"   "Australia"     440518  1
>> "2017-Q1"       "AUS"   "Australia"     442141  0.4
>> "2017-Q2"       "AUS"   "Australia"     445739  0.8
>> "2017-Q3"       "AUS"   "Australia"     448672  0.7
>> "2017-Q4"       "AUS"   "Australia"     451302  0.6
>> "2018-Q1"       "AUS"   "Australia"     455680  1
>> "2018-Q2"       "AUS"   "Australia"     459697  0.9
>> "2018-Q3"       "AUS"   "Australia"     461024  0.3
>> "2018-Q4"       "AUS"   "Australia"     462032  0.2
>> "2019-Q1"       "AUS"   "Australia"     463907  0.4
>> "2016-Q4"       "BEL"   "Belgium"       106675  0.3
>> "2017-Q1"       "BEL"   "Belgium"       107394  0.7
>> "2017-Q2"       "BEL"   "Belgium"       107828  0.4
>> "2017-Q3"       "BEL"   "Belgium"       108003  0.2
>> "2017-Q4"       "BEL"   "Belgium"       108744  0.7
>> "2018-Q1"       "BEL"   "Belgium"       109037  0.3
>> "2018-Q2"       "BEL"   "Belgium"       109386  0.3
>> "2018-Q3"       "BEL"   "Belgium"       109676  0.3
>> "2018-Q4"       "BEL"   "Belgium"       110081  0.4
>> "2019-Q1"       "BEL"   "Belgium"       110459  0.3
>> "2019-Q2"       "BEL"   "Belgium"       110680  0.2
>> "2016-Q4"       "CAN"   "Canada"        493742  0.6
>> "2017-Q1"       "CAN"   "Canada"        498719  1
>> "2017-Q2"       "CAN"   "Canada"        504100.5        1.1
>> "2017-Q3"       "CAN"   "Canada"        505745  0.3
>> "2017-Q4"       "CAN"   "Canada"        507883  0.4
>> "2018-Q1"       "CAN"   "Canada"        509758.75       0.4
>> "2018-Q2"       "CAN"   "Canada"        512958  0.6
>> "2018-Q3"       "CAN"   "Canada"        515639.25       0.5
>> "2018-Q4"       "CAN"   "Canada"        515971.75       0.1
>> "2019-Q1"       "CAN"   "Canada"        516489.5        0.1
>> "2016-Q4"       "DNK"   "Denmark"       499945  0.9
>> "2017-Q1"       "DNK"   "Denmark"       511319  2.3
>> "2017-Q2"       "DNK"   "Denmark"       505254  -1.2
>> "2017-Q3"       "DNK"   "Denmark"       500363  -1
>> "2017-Q4"       "DNK"   "Denmark"       504837  0.9
>> "2018-Q1"       "DNK"   "Denmark"       508633  0.8
>> "2018-Q2"       "DNK"   "Denmark"       511901  0.6
>> "2018-Q3"       "DNK"   "Denmark"       513630  0.3
>> "2018-Q4"       "DNK"   "Denmark"       517726  0.8
>> "2019-Q1"       "DNK"   "Denmark"       518368  0.1
>> "2016-Q4"       "EU28"  "European Union (28 countries)"
>> 3301202.652555  0.8
>> "2017-Q1"       "EU28"  "European Union (28 countries)"
>> 3323886.876398  0.7
>> "2017-Q2"       "EU28"  "European Union (28 countries)"
>> 3345038.332666  0.6
>> "2017-Q3"       "EU28"  "European Union (28 countries)"
>> 3367136.027609  0.7
>> "2017-Q4"       "EU28"  "European Union (28 countries)"
>> 3390431.080785  0.7
>> "2018-Q1"       "EU28"  "European Union (28 countries)"
>> 3404554.778774  0.4
>> "2018-Q2"       "EU28"  "European Union (28 countries)"
>> 3419358.570571  0.4
>> "2018-Q3"       "EU28"  "European Union (28 countries)"
>> 3430321.169276  0.3
>> "2018-Q4"       "EU28"  "European Union (28 countries)"
>> 3440915.89772   0.3
>> "2019-Q1"       "EU28"  "European Union (28 countries)"
>> 3458087.265837  0.5
>> "2019-Q2"       "EU28"  "European Union (28 countries)" 3465003.441
>> 0.2
>> "2016-Q4"       "FIN"   "Finland"       48525   0.2
>> "2017-Q1"       "FIN"   "Finland"       49368   1.7
>> "2017-Q2"       "FIN"   "Finland"       49430   0.1
>> "2017-Q3"       "FIN"   "Finland"       49596   0.3
>> "2017-Q4"       "FIN"   "Finland"       50153   1.1
>> "2018-Q1"       "FIN"   "Finland"       50352   0.4
>> "2018-Q2"       "FIN"   "Finland"       50449   0.2
>> "2018-Q3"       "FIN"   "Finland"       50507   0.1
>> "2018-Q4"       "FIN"   "Finland"       50530   0
>> "2019-Q1"       "FIN"   "Finland"       50822   0.6
>> "2016-Q4"       "FRA"   "France"        551760  0.6
>> "2017-Q1"       "FRA"   "France"        556305  0.8
>> "2017-Q2"       "FRA"   "France"        560160  0.7
>> "2017-Q3"       "FRA"   "France"        563998  0.7
>> "2017-Q4"       "FRA"   "France"        568125  0.7
>> "2018-Q1"       "FRA"   "France"        569542  0.2
>> "2018-Q2"       "FRA"   "France"        570670  0.2
>> "2018-Q3"       "FRA"   "France"        572387  0.3
>> "2018-Q4"       "FRA"   "France"        574640  0.4
>> "2019-Q1"       "FRA"   "France"        576494  0.3
>> "2019-Q2"       "FRA"   "France"        577905  0.2
>> "2016-Q4"       "DEU"   "Germany"       716743.4074     0.4
>> "2017-Q1"       "DEU"   "Germany"       725268.5864     1.2
>> "2017-Q2"       "DEU"   "Germany"       729321.5731     0.6
>> "2017-Q3"       "DEU"   "Germany"       735610.6375     0.9
>> "2017-Q4"       "DEU"   "Germany"       740991.229      0.7
>> "2018-Q1"       "DEU"   "Germany"       741969.5787     0.1
>> "2018-Q2"       "DEU"   "Germany"       744834.6127     0.4
>> "2018-Q3"       "DEU"   "Germany"       744065.912      -0.1
>> "2018-Q4"       "DEU"   "Germany"       745603.2305     0.2
>> "2019-Q1"       "DEU"   "Germany"       748468.2276     0.4
>> "2019-Q2"       "DEU"   "Germany"       747909.2496     -0.1
>> "2016-Q4"       "ISR"   "Israel"        307789.55       0.9
>> "2017-Q1"       "ISR"   "Israel"        308323.023      0.2
>> "2017-Q2"       "ISR"   "Israel"        311759.624      1.1
>> "2017-Q3"       "ISR"   "Israel"        315651.46       1.2
>> "2017-Q4"       "ISR"   "Israel"        319056.442      1.1
>> "2018-Q1"       "ISR"   "Israel"        322272.592      1
>> "2018-Q2"       "ISR"   "Israel"        323422.356      0.4
>> "2018-Q3"       "ISR"   "Israel"        325702.534      0.7
>> "2018-Q4"       "ISR"   "Israel"        329052.641      1
>> "2019-Q1"       "ISR"   "Israel"        332851.725      1.2
>> "2019-Q2"       "ISR"   "Israel"        333686.876      0.3
>> "2016-Q4"       "ITA"   "Italy" 396162.2        0.5
>> "2017-Q1"       "ITA"   "Italy" 398379  0.6
>> "2017-Q2"       "ITA"   "Italy" 399893  0.4
>> "2017-Q3"       "ITA"   "Italy" 401534  0.4
>> "2017-Q4"       "ITA"   "Italy" 403053.4        0.4
>> "2018-Q1"       "ITA"   "Italy" 403937.8        0.2
>> "2018-Q2"       "ITA"   "Italy" 403977.3        0
>> "2018-Q3"       "ITA"   "Italy" 403434.2        -0.1
>> "2018-Q4"       "ITA"   "Italy" 403190.7        -0.1
>> "2019-Q1"       "ITA"   "Italy" 403697.9        0.1
>> "2019-Q2"       "ITA"   "Italy" 403794.7        0
>> "2016-Q4"       "JPN"   "Japan" 130406025       0.2
>> "2017-Q1"       "JPN"   "Japan" 131558850       0.9
>> "2017-Q2"       "JPN"   "Japan" 132121450       0.4
>> "2017-Q3"       "JPN"   "Japan" 133064400       0.7
>> "2017-Q4"       "JPN"   "Japan" 133475100       0.3
>> "2018-Q1"       "JPN"   "Japan" 133386850       -0.1
>> "2018-Q2"       "JPN"   "Japan" 133931825       0.4
>> "2018-Q3"       "JPN"   "Japan" 133289800       -0.5
>> "2018-Q4"       "JPN"   "Japan" 133836225       0.4
>> "2019-Q1"       "JPN"   "Japan" 134777725       0.7
>> "2019-Q2"       "JPN"   "Japan" 135369050       0.4
>> "2016-Q4"       "KOR"   "Korea" 431473400       0.8
>> "2017-Q1"       "KOR"   "Korea" 435435200       0.9
>> "2017-Q2"       "KOR"   "Korea" 437712100       0.5
>> "2017-Q3"       "KOR"   "Korea" 444064400       1.5
>> "2017-Q4"       "KOR"   "Korea" 443599800       -0.1
>> "2018-Q1"       "KOR"   "Korea" 447909300       1
>> "2018-Q2"       "KOR"   "Korea" 450495800       0.6
>> "2018-Q3"       "KOR"   "Korea" 452561100       0.5
>> "2018-Q4"       "KOR"   "Korea" 456769700       0.9
>> "2019-Q1"       "KOR"   "Korea" 455081000       -0.4
>> "2019-Q2"       "KOR"   "Korea" 459958000       1.1
>> "2016-Q4"       "NLD"   "Netherlands"   178453.593134   0.9
>> "2017-Q1"       "NLD"   "Netherlands"   179367.793134   0.5
>> "2017-Q2"       "NLD"   "Netherlands"   180964.533134   0.9
>> "2017-Q3"       "NLD"   "Netherlands"   182189.893134   0.7
>> "2017-Q4"       "NLD"   "Netherlands"   183625.193134   0.8
>> "2018-Q1"       "NLD"   "Netherlands"   184793.473134   0.6
>> "2018-Q2"       "NLD"   "Netherlands"   185981.973134   0.6
>> "2018-Q3"       "NLD"   "Netherlands"   186425.153134   0.2
>> "2018-Q4"       "NLD"   "Netherlands"   187434.343134   0.5
>> "2019-Q1"       "NLD"   "Netherlands"   188324.263134   0.5
>> "2019-Q2"       "NLD"   "Netherlands"   189297.773134   0.5
>> "2016-Q4"       "NZL"   "New Zealand"   59062   0.5
>> "2017-Q1"       "NZL"   "New Zealand"   59348   0.5
>> "2017-Q2"       "NZL"   "New Zealand"   59743   0.7
>> "2017-Q3"       "NZL"   "New Zealand"   60320   1
>> "2017-Q4"       "NZL"   "New Zealand"   60737   0.7
>> "2018-Q1"       "NZL"   "New Zealand"   61031   0.5
>> "2018-Q2"       "NZL"   "New Zealand"   61655   1
>> "2018-Q3"       "NZL"   "New Zealand"   61927   0.4
>> "2018-Q4"       "NZL"   "New Zealand"   62282   0.6
>> "2019-Q1"       "NZL"   "New Zealand"   62800   0.8
>> "2016-Q4"       "NOR"   "Norway"        784704  2
>> "2017-Q1"       "NOR"   "Norway"        788709  0.5
>> "2017-Q2"       "NOR"   "Norway"        794220  0.7
>> "2017-Q3"       "NOR"   "Norway"        798283  0.5
>> "2017-Q4"       "NOR"   "Norway"        800232  0.2
>> "2018-Q1"       "NOR"   "Norway"        803756  0.4
>> "2018-Q2"       "NOR"   "Norway"        807187  0.4
>> "2018-Q3"       "NOR"   "Norway"        810942  0.5
>> "2018-Q4"       "NOR"   "Norway"        815921  0.6
>> "2019-Q1"       "NOR"   "Norway"        815323  -0.1
>> "2016-Q4"       "PRT"   "Portugal"      44303.821       0.8
>> "2017-Q1"       "PRT"   "Portugal"      44632.068       0.7
>> "2017-Q2"       "PRT"   "Portugal"      44803.721       0.4
>> "2017-Q3"       "PRT"   "Portugal"      45062.631       0.6
>> "2017-Q4"       "PRT"   "Portugal"      45426.14        0.8
>> "2018-Q1"       "PRT"   "Portugal"      45641.37        0.5
>> "2018-Q2"       "PRT"   "Portugal"      45910.934       0.6
>> "2018-Q3"       "PRT"   "Portugal"      46027.362       0.3
>> "2018-Q4"       "PRT"   "Portugal"      46203.578       0.4
>> "2019-Q1"       "PRT"   "Portugal"      46453.392       0.5
>> "2019-Q2"       "PRT"   "Portugal"      46685.65896     0.5
>> "2016-Q4"       "ESP"   "Spain" 279431  0.6
>> "2017-Q1"       "ESP"   "Spain" 281707  0.8
>> "2017-Q2"       "ESP"   "Spain" 284169  0.9
>> "2017-Q3"       "ESP"   "Spain" 285986  0.6
>> "2017-Q4"       "ESP"   "Spain" 288064  0.7
>> "2018-Q1"       "ESP"   "Spain" 289861  0.6
>> "2018-Q2"       "ESP"   "Spain" 291583  0.6
>> "2018-Q3"       "ESP"   "Spain" 293145  0.5
>> "2018-Q4"       "ESP"   "Spain" 294768  0.6
>> "2019-Q1"       "ESP"   "Spain" 296732  0.7
>> "2019-Q2"       "ESP"   "Spain" 298147  0.5
>> "2016-Q4"       "SWE"   "Sweden"        1150761 0.4
>> "2017-Q1"       "SWE"   "Sweden"        1151977 0.1
>> "2017-Q2"       "SWE"   "Sweden"        1169243 1.5
>> "2017-Q3"       "SWE"   "Sweden"        1177835 0.7
>> "2017-Q4"       "SWE"   "Sweden"        1181734 0.3
>> "2018-Q1"       "SWE"   "Sweden"        1192111 0.9
>> "2018-Q2"       "SWE"   "Sweden"        1197931 0.5
>> "2018-Q3"       "SWE"   "Sweden"        1196262 -0.1
>> "2018-Q4"       "SWE"   "Sweden"        1209430 1.1
>> "2019-Q1"       "SWE"   "Sweden"        1215583 0.5
>> "2019-Q2"       "SWE"   "Sweden"        1214691 -0.1
>> "2016-Q4"       "CHE"   "Switzerland"   168268.356822   -0.1
>> "2017-Q1"       "CHE"   "Switzerland"   168865.076317   0.4
>> "2017-Q2"       "CHE"   "Switzerland"   170078.764694   0.7
>> "2017-Q3"       "CHE"   "Switzerland"   171405.16327    0.8
>> "2017-Q4"       "CHE"   "Switzerland"   172777.427869   0.8
>> "2018-Q1"       "CHE"   "Switzerland"   174168.535837   0.8
>> "2018-Q2"       "CHE"   "Switzerland"   175400.870886   0.7
>> "2018-Q3"       "CHE"   "Switzerland"   175089.0314     -0.2
>> "2018-Q4"       "CHE"   "Switzerland"   175664.228343   0.3
>> "2019-Q1"       "CHE"   "Switzerland"   176651.744992   0.6
>> "2016-Q4"       "GBR"   "United Kingdom"        496470  0.7
>> "2017-Q1"       "GBR"   "United Kingdom"        498582  0.4
>> "2017-Q2"       "GBR"   "United Kingdom"        499885  0.3
>> "2017-Q3"       "GBR"   "United Kingdom"        502473  0.5
>> "2017-Q4"       "GBR"   "United Kingdom"        504487  0.4
>> "2018-Q1"       "GBR"   "United Kingdom"        504785  0.1
>> "2018-Q2"       "GBR"   "United Kingdom"        506842  0.4
>> "2018-Q3"       "GBR"   "United Kingdom"        510346  0.7
>> "2018-Q4"       "GBR"   "United Kingdom"        511482  0.2
>> "2019-Q1"       "GBR"   "United Kingdom"        514019  0.5
>> "2019-Q2"       "GBR"   "United Kingdom"        513029  -0.2
>> "2016-Q4"       "USA"   "United States" 4456057.75      0.5
>> "2017-Q1"       "USA"   "United States" 4481314 0.6
>> "2017-Q2"       "USA"   "United States" 4505262 0.5
>> "2017-Q3"       "USA"   "United States" 4540889.5       0.8
>> "2017-Q4"       "USA"   "United States" 4580616 0.9
>> "2018-Q1"       "USA"   "United States" 4609563.5       0.6
>> "2018-Q2"       "USA"   "United States" 4649533.75      0.9
>> "2018-Q3"       "USA"   "United States" 4683180 0.7
>> "2018-Q4"       "USA"   "United States" 4695887 0.3
>> "2019-Q1"       "USA"   "United States" 4731820.25      0.8
>> "2019-Q2"       "USA"   "United States" 4755955 0.5
>> 
>> ______________________________________________
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