[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 14:26:11 CEST 2019
Thank you so much for your help.
Philip
On 2019-08-25 03:54, Rui Barradas wrote:
> Hello,
>
> The code in Eric's answer works, but maybe it's better to redo the
> 'col' code.
> It's much simpler to create a factor with appropriate labels. Then,
> the values argument in scale_fill_manual can be set more naturally, it
> can depend on col.
>
> (I have also added a theme to make the axis labels more readable, they
> were over each other. Remove it if not needed.)
>
>
> col <- (t1$TIME %in% c("2019-Q1", "2019-Q2")) + 1L
> col <- factor(col, labels = c("navyblue", "red"))
>
>
> ggplot(t1) +
> geom_col(aes(x = TIME, y = GDPgr, fill = col), show.legend = FALSE) +
> scale_fill_manual(values = levels(col)) +
> facet_wrap(~ Country, ncol = 3) +
> theme(axis.text.x = element_text(angle = 50, hjust = 1))
>
>
> Hope this helps,
>
> Rui Barradas
>
> Às 04:21 de 25/08/19, Eric Berger escreveu:
>> This seems to work
>> ggplot(t1) +
>> geom_col(aes(x=TIME,y=GDPgr,fill=col),show.legend=FALSE) +
>> scale_fill_manual(values=c("navyblue","red")) +
>> facet_wrap(~Country,ncol=3)
>>
>> HTH,
>> Eric
>>
>>
>> On Sun, Aug 25, 2019 at 5:45 AM <phil using philipsmith.ca> wrote:
>>
>>> Resubmitted as recommended:
>>>
>>> I am having difficulty with a chart using ggplot. It is a facetted
>>> column chart showing GDP growth rates by country. The columns are
>>> coloured navyblue, except that I want to colour the most recent
>>> columns,
>>> for 2019-Q1 and 2019-Q2, red. For some countries data are available
>>> up
>>> to 2019-Q2 while for others data are only available up to 2019-Q1. My
>>> code and data frame are shown below and it almost works, but not
>>> quite.
>>> For some reason the red bars for Germany, Korea, Norway, Sweden and
>>> United Kingdom are slightly off. Any help will be much appreciated.
>>>
>>> Here is my reprex:
>>>
>>> library(tidyverse)
>>> t1 <- read.table("t1.txt",header=TRUE,sep="\t")
>>> col <- rep("navyblue",nrow(t1))
>>> for (i in 1:nrow(t1)) {
>>> if((t1$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
>>>>>
>>>>> ______________________________________________
>>>>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
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