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 <p...@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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