Colouring selected columns in a facetted column chart

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Colouring selected columns in a facetted column chart

phil-3
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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Re: Colouring selected columns in a facetted column chart

Eric Berger
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 <[hidden email]> 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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Re: Colouring selected columns in a facetted column chart

phil-3
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 <[hidden email]> 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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>> and provide commented, minimal, self-contained, reproducible code.

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Re: Colouring selected columns in a facetted column chart

Eric Berger
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 <[hidden email]> 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 <[hidden email]> 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
> >>
> >> ______________________________________________
> >> [hidden email] 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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Re: Colouring selected columns in a facetted column chart

Rui Barradas
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 <[hidden email]> 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 <[hidden email]> 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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>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>
>
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Re: Colouring selected columns in a facetted column chart

phil-3
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$TIME %in% c("2019-Q1", "2019-Q2")) + 1L
> col <- factor(col, labels = c("navyblue", "red"))
>
>
> ggplot(t1) +
>   geom_col(aes(x = TIME, y = GDPgr, fill = col), show.legend = FALSE) +
>   scale_fill_manual(values = levels(col)) +
>   facet_wrap(~ Country, ncol = 3) +
>   theme(axis.text.x = element_text(angle = 50, hjust = 1))
>
>
> Hope this helps,
>
> Rui Barradas
>
> Às 04:21 de 25/08/19, Eric Berger escreveu:
>> This seems to work
>> ggplot(t1) +
>>    geom_col(aes(x=TIME,y=GDPgr,fill=col),show.legend=FALSE) +
>>       scale_fill_manual(values=c("navyblue","red")) +
>>           facet_wrap(~Country,ncol=3)
>>
>> HTH,
>> Eric
>>
>>
>> On Sun, Aug 25, 2019 at 5:45 AM <[hidden email]> wrote:
>>
>>> Resubmitted as recommended:
>>>
>>> I am having difficulty with a chart using ggplot. It is a facetted
>>> column chart showing GDP growth rates by country. The columns are
>>> coloured navyblue, except that I want to colour the most recent
>>> columns,
>>> for 2019-Q1 and 2019-Q2, red. For some countries data are available
>>> up
>>> to 2019-Q2 while for others data are only available up to 2019-Q1. My
>>> code and data frame are shown below and it almost works, but not
>>> quite.
>>> For some reason the red bars for Germany, Korea, Norway, Sweden and
>>> United Kingdom are slightly off. Any help will be much appreciated.
>>>
>>> Here is my reprex:
>>>
>>> library(tidyverse)
>>> t1 <- read.table("t1.txt",header=TRUE,sep="\t")
>>> col <- rep("navyblue",nrow(t1))
>>> for (i in 1:nrow(t1)) {
>>>     if((t1$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 <[hidden email]> 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
>>>>>
>>>>> ______________________________________________
>>>>> [hidden email] 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]]
>>
>> ______________________________________________
>> [hidden email] 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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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.