Bar plot values that are dodged [duplicate] - r

This question already has an answer here:
Position geom_text on dodged barplot
(1 answer)
Closed 8 years ago.
I've a problem plotting a simple bar plot in ggplot2. I'd like to display labels of values above each bar, but am using postion=position_dodge to plot bars along side each other as I have multiple factor variables along my x-axis for a number of data points which refer to individual countries.
Currently using the following code I am getting all values centred over the name of the COUNTRY along my x-axis, whereas I'd like each value centred over each bar along the x-axis:
ggplot(maln_complete_recent_melt, aes(x=COUNTRY, y=percent, fill=location)) + geom_bar(position="dodge", colour="black") +labs(title="Percentage of malnoursihed children according to height for age", fill="DHS Survey") + geom_text(aes(label=round(percent))) + scale_x_discrete(labels=c(paste(maln_complete_recent_melt$COUNTRY, maln_complete_recent_melt$Year, sep="")))
And this is how my plot currently looks:
I'm sure this is something really simple, but having trawled the forums and consulted a number of books on R, I can't find what I'm looking for.
Here's my data for replication:
structure(list(COUNTRY = structure(c(4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L, 4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L, 4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L, 4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L, 4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L, 4L, 10L, 5L, 8L, 14L, 13L,
9L, 2L, 11L, 1L, 6L, 7L, 12L, 3L), class = "factor", .Label = c("Swaziland",
"Namibia", "Zimbabwe", "Comoros", "Kenya", "Tanzania", "Uganda",
"Lesotho", "Mozambique", "Ethiopia", "Rwanda", "Zambia", "Malawi",
"Madagascar")), Year = structure(c(5L, 23L, 20L, 21L, 20L, 14L,
12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L, 5L, 23L, 20L, 21L, 20L,
14L, 12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L, 5L, 23L, 20L, 21L,
20L, 14L, 12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L, 5L, 23L, 20L,
21L, 20L, 14L, 12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L, 5L, 23L,
20L, 21L, 20L, 14L, 12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L, 5L,
23L, 20L, 21L, 20L, 14L, 12L, 18L, 15L, 18L, 14L, 17L, 19L, 22L
), class = "factor", .Label = c("1992", "1993", "1994", "1995",
"1996", "1997", "1998", "1999", "2000", "2000/1", "2001/2", "2003",
"2003/4", "2004", "2005", "2005/6", "2006", "2006/7", "2007",
"2008/9", "2009", "2010", "2011")), location = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L), .Label = c("Urban", "Rural", "Total", "Capital.City",
"Urban.Non.slum", "Urban.Slum"), class = "factor"), percent = c(29.9107142857143,
31.5, 26.4, 30.2, 43.4, 37.8, 29.2, 23.8, 33.1, 23.1, 25.8, 25.5,
39, 27.5, 35.0071736011478, 46.2, 37.1, 41, 50.9, 49.2, 45.7,
31.4, 47.2501049401341, 30, 40.5, 39.5, 47.9, 33.4, 33.8, 44.4,
35.3, 39.2, 50.1, 47.8, 41, 29, 45.3, 28.9, 37.7, 38.1, 45.4,
32, NA, 22, 28.5, 31.7, 46.8, NA, 20.6, NA, 29.2, NA, 16.9, NA,
37.2, 29, 21.6666666666667, 10.7799374501425, 16.0100871430598,
21.2511075992408, 23.7772452013661, 23.8620542603877, 8.15543422154615,
12.5321762341041, 13.5923403336176, 13.0494984481826, 16.4644101423357,
21.5426476162423, 27.4144189089535, 16.2492480072194, 32.9268292682927,
27.8790407698325, 25.0985529949154, 26.2950824206824, 37.9693371617388,
40.5197947988247, 29.3593820697417, 28.5099311066166, 36.0242620139593,
20.7853672439948, 28.5877166151249, 22.8001055045257, 35.5434627654004,
28.0633523545878)), .Names = c("COUNTRY", "Year", "location",
"percent"), row.names = c(NA, -84L), class = "data.frame")


The answer was in the following post: Position geom_text on dodged barplot
Just needed to add the position=position_dodge call into geom_text() as follows:
ggplot(maln_complete_recent_melt, aes(x=COUNTRY, y=percent, fill=location)) + geom_bar(position="dodge", colour="black") +labs(title="Percentage of malnoursihed children according to height for age", fill="DHS Survey") + geom_text(aes(label=round(percent)), position = position_dodge(width=0.9), vjust=-0.5) + scale_x_discrete(labels=c(paste(maln_complete_recent_melt$COUNTRY, maln_complete_recent_melt$Year, sep="")))
Which produced this:


#marty_c answered your question. If you're not required to use this format, though, you might consider facets (using df = maln_complete_recent_melt):
ggplot(df, aes(x=COUNTRY, y=percent, fill=location)) +
geom_histogram(stat="identity") +
labs(title="Percentage of malnoursihed children according to height for age", fill="DHS Survey") +
geom_text(aes(label=round(percent),y=0),vjust=-0.5,size=4)+
scale_x_discrete(labels=c(paste(df$COUNTRY, df$Year, sep="")))+
theme(axis.text.x=element_text(angle=-90, color="black",hjust=0,vjust=0.2))+
facet_grid(location~.)

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0.864173776, 0.899162389, 0.909465125, 0.96350569, 0.978220642,
0.971679886, 0.976158221, 1, 1.025374896, 1.065804414, 1.108567186,
1.166769344, 0.588726028, 0.64526073, 0.733094431, 0.718268082,
0.746291144, 0.799900392, 0.846050389, 0.894179583, 1.015232882,
0.982856394, 1.012948099, 1.041332642, 1.032947106, 1.013566583,
0.980944689, 1, 1.020576612, 1.061740647, 1.117831183, 1.159906251,
0.750587042, 0.769670674, 0.790024355, 0.801712216, 0.817505148,
0.83991247, 0.856517319, 0.878345181, 0.914006005, 0.920044857,
0.949573071, 0.955207703, 0.978810398, 0.985618398, 0.996205139,
1, 1.004364708, 1.017159213, 1.021013703, 1.02682649, 0.825278825,
0.836048671, 0.847570474, 0.858769029, 0.86834942, 0.871868036,
0.875331803, 0.890827568, 0.898928134, 0.915485416, 0.921392822,
0.931246968, 0.945182975, 0.963702812, 0.981800571, 1, 1.013277522,
1.026999204, 1.044176589, 1.067069774, 0.490666665, 0.523850087,
0.54906662, 0.570457925, 0.597126217, 0.632406036, 0.689467717,
0.775073059, 0.828560075, 0.827109078, 0.842215091, 0.887572897,
0.923280339, 0.960610381, 0.988936452, 1, 1.022699304, 1.054533263,
1.098615084, 1.134067127, 0.757140805, 0.809228408, 0.851488047,
0.884918505, 0.889385715, 0.916751643, 0.948479832, 0.960072842,
0.956196673, 0.911566837, 0.884542463, 0.89644222, 0.917048164,
0.929279352, 0.929337342, 1, 1.010128912, 1.026719845, 1.029923385,
1.062349178, 0.786853444, 0.804351028, 0.831286834, 0.859995963,
0.886334727, 0.906191485, 0.937863282, 0.969963165, 1.012104032,
1.038112793, 1.036283847, 1.046222, 1.043339336, 1.02279939,
1.002888566, 1, 0.994233243, 0.998082845, 0.997049083, 0.998951287,
0.740171055, 0.770579402, 0.802054487, 0.833603662, 0.865965514,
0.90147914, 0.937354271, 0.969378485, 0.99123068, 0.992657113,
0.994179737, 0.993983379, 0.992844694, 0.99680058, 0.994574042,
1, 1.003228988, 1.016266499, 1.028341184, 1.04261954, 0.801617134,
0.817716283, 0.834621959, 0.850140657, 0.863935678, 0.880664424,
0.899645623, 0.9226463, 0.944486016, 0.945115307, 0.95522518,
0.964280334, 0.975483583, 0.983073825, 0.988745617, 1, 1.005225593,
1.010468623, 1.020086873, 1.032605559)), row.names = c("CountryA-2000",
"CountryA-2001", "CountryA-2002", "CountryA-2003", "CountryA-2004",
"CountryA-2005", "CountryA-2006", "CountryA-2007", "CountryA-2008",
"CountryA-2009", "CountryA-2010", "CountryA-2011", "CountryA-2012",
"CountryA-2013", "CountryA-2014", "CountryA-2015", "CountryA-2016",
"CountryA-2017", "CountryA-2018", "CountryA-2019", "CountryB-2000",
"CountryB-2001", "CountryB-2002", "CountryB-2003", "CountryB-2004",
"CountryB-2005", "CountryB-2006", "CountryB-2007", "CountryB-2008",
"CountryB-2009", "CountryB-2010", "CountryB-2011", "CountryB-2012",
"CountryB-2013", "CountryB-2014", "CountryB-2015", "CountryB-2016",
"CountryB-2017", "CountryB-2018", "CountryB-2019", "CountryC-2000",
"CountryC-2001", "CountryC-2002", "CountryC-2003", "CountryC-2004",
"CountryC-2005", "CountryC-2006", "CountryC-2007", "CountryC-2008",
"CountryC-2009", "CountryC-2010", "CountryC-2011", "CountryC-2012",
"CountryC-2013", "CountryC-2014", "CountryC-2015", "CountryC-2016",
"CountryC-2017", "CountryC-2018", "CountryC-2019", "CountryD-2000",
"CountryD-2001", "CountryD-2002", "CountryD-2003", "CountryD-2004",
"CountryD-2005", "CountryD-2006", "CountryD-2007", "CountryD-2008",
"CountryD-2009", "CountryD-2010", "CountryD-2011", "CountryD-2012",
"CountryD-2013", "CountryD-2014", "CountryD-2015", "CountryD-2016",
"CountryD-2017", "CountryD-2018", "CountryD-2019", "CountryE-2000",
"CountryE-2001", "CountryE-2002", "CountryE-2003", "CountryE-2004",
"CountryE-2005", "CountryE-2006", "CountryE-2007", "CountryE-2008",
"CountryE-2009", "CountryE-2010", "CountryE-2011", "CountryE-2012",
"CountryE-2013", "CountryE-2014", "CountryE-2015", "CountryE-2016",
"CountryE-2017", "CountryE-2018", "CountryE-2019", "CountryF-2000",
"CountryF-2001", "CountryF-2002", "CountryF-2003", "CountryF-2004",
"CountryF-2005", "CountryF-2006", "CountryF-2007", "CountryF-2008",
"CountryF-2009", "CountryF-2010", "CountryF-2011", "CountryF-2012",
"CountryF-2013", "CountryF-2014", "CountryF-2015", "CountryF-2016",
"CountryF-2017", "CountryF-2018", "CountryF-2019", "CountryG-2000",
"CountryG-2001", "CountryG-2002", "CountryG-2003", "CountryG-2004",
"CountryG-2005", "CountryG-2006", "CountryG-2007", "CountryG-2008",
"CountryG-2009", "CountryG-2010", "CountryG-2011", "CountryG-2012",
"CountryG-2013", "CountryG-2014", "CountryG-2015", "CountryG-2016",
"CountryG-2017", "CountryG-2018", "CountryG-2019", "CountryH-2000",
"CountryH-2001", "CountryH-2002", "CountryH-2003", "CountryH-2004",
"CountryH-2005", "CountryH-2006", "CountryH-2007", "CountryH-2008",
"CountryH-2009", "CountryH-2010", "CountryH-2011", "CountryH-2012",
"CountryH-2013", "CountryH-2014", "CountryH-2015", "CountryH-2016",
"CountryH-2017", "CountryH-2018", "CountryH-2019", "CountryI-2000",
"CountryI-2001", "CountryI-2002", "CountryI-2003", "CountryI-2004",
"CountryI-2005", "CountryI-2006", "CountryI-2007", "CountryI-2008",
"CountryI-2009", "CountryI-2010", "CountryI-2011", "CountryI-2012",
"CountryI-2013", "CountryI-2014", "CountryI-2015", "CountryI-2016",
"CountryI-2017", "CountryI-2018", "CountryI-2019", "CountryJ-2000",
"CountryJ-2001", "CountryJ-2002", "CountryJ-2003", "CountryJ-2004",
"CountryJ-2005", "CountryJ-2006", "CountryJ-2007", "CountryJ-2008",
"CountryJ-2009", "CountryJ-2010", "CountryJ-2011", "CountryJ-2012",
"CountryJ-2013", "CountryJ-2014", "CountryJ-2015", "CountryJ-2016",
"CountryJ-2017", "CountryJ-2018", "CountryJ-2019"), class = c("pdata.frame",
"data.frame"), index = structure(list(Country = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), .Label = c("CountryA",
"CountryB", "CountryC", "CountryD", "CountryE", "CountryF", "CountryG",
"CountryH", "CountryI", "CountryJ"), class = "factor"), Year = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L), .Label = c("2000", "2001",
"2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017",
"2018", "2019"), class = "factor")), class = c("pindex", "data.frame"
), row.names = c(NA, 200L)))

You can use function Between from package plm to calculate the cross sectional averages and add them to your data:
library(plm)
# PanelS is a pdata.frame (otherwise use pdata.frame(your_data, index))
PanelS$A_bar <- Between(PanelS$A)
PanelS$B_bar <- Between(PanelS$B)
PanelS$C_bar <- Between(PanelS$C)
PanelS$D_bar <- Between(PanelS$D)
mod <- plm(A ~ B + C + D + A_bar + B_bar + C_bar + D_bar, model = "pooling", effect="individual", data = PanelS)
summary(mod)
# Pooling Model
#
# Call:
# plm(formula = A ~ B + C + D + A_bar + B_bar + C_bar + D_bar,
# data = PanelS, effect = "individual", model = "pooling")
#
# Balanced Panel: n = 10, T = 20, N = 200
#
# Residuals:
# Min. 1st Qu. Median 3rd Qu. Max.
# -0.06143690 -0.01311792 0.00070253 0.01186605 0.05107105
#
# Coefficients:
# Estimate Std. Error t-value Pr(>|t|)
# (Intercept) -0.00000000000001042 0.03313743211380626 0.0000 1.000000
# B -0.00076930351859426 0.00020566635571130 -3.7405 0.000242 ***
# C 0.10827039012266901 0.00949296134830719 11.4053 < 0.00000000000000022 ***
# D -0.04222788490989914 0.01136058813979121 -3.7171 0.000264 ***
# A_bar 0.99999999999911215 0.09632471140222754 10.3816 < 0.00000000000000022 ***
# C_bar -0.10827039012256123 0.01033406661607372 -10.4770 < 0.00000000000000022 ***
# D_bar 0.04222788490990802 0.03874710199411169 1.0898 0.277145
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Total Sum of Squares: 0.17549
# Residual Sum of Squares: 0.07128
# R-Squared: 0.59382
# Adj. R-Squared: 0.58119
# F-statistic: 47.0268 on 6 and 193 DF, p-value: < 0.000000000000000222
Note that it seems like you want to estimate a fixed effects model but your estimation has model = "fd" to estimate a first-differenced model. Also note that the cross sectional averages will drop out of the estimation of a fixed effects model.

R: stacked geom_area plot displays blank polygons

I need some help regarding transforming a geom_bar into a geom_area plot. This is my df:
dput(df)
df <- structure(list(new_day = c(-25L, 3L, 7L, -7L, 3L, 7L, -7L, 0L,
-25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L, -7L,
0L, -25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L, -7L, 0L, 3L, 7L, -7L,
0L, -25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L,
-25L, 3L, 7L, -7L, 0L, 3L, -7L, 0L, -25L, 7L, 3L, 7L, -7L, 0L,
-25L, 3L, 7L, -7L, 0L, -25L, 3L, 7L, 3L, 7L, -7L, 0L, -25L, 3L,
7L, -7L, 0L, 7L, -25L, 3L, 7L, -7L, 0L, 3L, 7L, -25L, -25L, -25L,
-25L, -25L, -25L, -25L), order = structure(c(8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 11L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 13L,
13L, 10L, 10L, 10L, 10L, 10L, 7L, 7L, 7L, 7L, 7L, 2L, 2L, 2L,
2L, 2L, 7L, 7L, 7L, 7L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L,
9L, 9L, 9L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 13L, 13L, 14L, 14L,
14L, 14L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 13L, 13L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 1L, 7L, 5L, 2L,
12L, 2L, 2L), .Label = c("Alteromonadales", "Betaproteobacteriales",
"Caulobacterales", "Chitinophagales", "Flavobacteriales", "Parvibaculales",
"Pseudomonadales", "Rhizobiales", "Rhodobacterales", "Rhodospirillales",
"Sneathiellales", "Sphingobacteriales", "Sphingomonadales", "Thalassobaculales"
), class = "factor"), family = structure(c(13L, 13L, 13L, 13L,
12L, 12L, 12L, 12L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L,
16L, 7L, 7L, 7L, 7L, 7L, 11L, 11L, 11L, 11L, 11L, 1L, 1L, 1L,
1L, 1L, 11L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L, 4L, 4L,
4L, 4L, 4L, 14L, 14L, 14L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 16L,
16L, 17L, 17L, 17L, 17L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 16L,
16L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L, 8L, 8L,
8L, 10L, 10L, 6L, 11L, 3L, 1L, 9L, 1L, 1L), .Label = c("Burkholderiaceae",
"Chitinophagaceae", "Flavobacteriaceae", "Gallaecimonadaceae",
"Hyphomonadaceae", "Idiomarinaceae", "Magnetospiraceae", "Methylophilaceae",
"NS11-12_marine_group", "Parvibaculaceae", "Pseudomonadaceae",
"Rhizobiaceae", "Rhizobiales_unclassified", "Rhodobacteraceae",
"Sneathiellaceae", "Sphingomonadaceae", "Thalassobaculaceae"), class = "factor"),
genus = structure(c(16L, 16L, 16L, 16L, 7L, 7L, 7L, 7L, 3L,
3L, 3L, 3L, 3L, 19L, 19L, 19L, 19L, 19L, 24L, 24L, 24L, 24L,
24L, 14L, 14L, 14L, 14L, 14L, 17L, 17L, 17L, 17L, 17L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L,
10L, 10L, 10L, 2L, 2L, 2L, 2L, 2L, 22L, 22L, 22L, 20L, 20L,
23L, 23L, 23L, 23L, 11L, 11L, 11L, 11L, 11L, 8L, 8L, 8L,
21L, 21L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 11L, 11L,
11L, 11L, 11L, 13L, 13L, 9L, 14L, 4L, 6L, 12L, 1L, 18L), .Label = c("Burkholderiaceae_unclassified",
"Cupriavidus", "Ferrovibrio", "Flavobacteriaceae_unclassified",
"Gallaecimonas", "GKS98_freshwater_group", "Hoeflea", "Hyphomonas",
"Idiomarina", "Marivivens", "Methylotenera", "NS11-12_marine_group_ge",
"Parvibaculum", "Pseudomonas", "Pseudorhodobacter", "Rhizobiales_unclassified",
"Rhodoferax", "RS62_marine_group", "Sphingomonadaceae_unclassified",
"Sphingopyxis", "Sphingorhabdus", "Terrimonas", "Thalassobaculum",
"uncultured"), class = "factor"), Abundance = c(0.758296593899054,
0.728046713738242, 0.421798852637834, 0.185971692147469,
7.36584152568739, 11.0004160226707, 1.93134577450352, 19.7144376530921,
46.2350237547082, 25.8715062086956, 22.1549641486618, 34.4112477828867,
20.4937613394223, 3.73518219692229, 15.9295990367068, 13.8490383262387,
13.3481723220855, 20.3866145291388, 0.165618346100574, 8.86991024549668,
8.5330814375361, 6.86819004205197, 5.72129192186814, 1.04512973253723,
3.77880217461655, 6.47871112880127, 1.12084852451492, 0.903754246093232,
19.0854333497858, 15.7152146349298, 12.3768753373503, 15.8790763239117,
10.2875187327705, 2.82159106304821, 4.22393981370602, 8.82452898193968,
4.8507226701533, 6.19619716749583, 8.28477594908417, 8.05201189383953,
9.7404731686272, 9.84535225459449, 1.7940554465653, 2.62276259756813,
2.74008811315788, 0.543937440677315, 0.55325167765205, 0.910457573040239,
0.451385497886567, 0.655661306732001, 6.59400178917785, 1.92570846362683,
2.62192443054515, 2.10049053655497, 2.13139299576524, 0.20799245164738,
0.324291631088576, 0.369492771993701, 1.52162438803598, 0.151864202275619,
0.420953084533189, 0.391517677365401, 0.29116200940885, 0.232440441774702,
4.21428798609281, 0.859779996836882, 1.33107018783728, 1.013155122065,
0.447286602320585, 0.165001492967355, 0.285983094976304,
0.377758692391269, 0.21556919104275, 0.314057858254493, 0.354649793637887,
0.338799824269294, 0.218027624939685, 0.914324162324944,
1.22932824654674, 0.731649603629864, 0.566393265064962, 0.247942012186621,
1.73171328618728, 0.636597714441988, 0.505393049999761, 0.491318560043637,
0.990988961717433, 0.195417142399681, 0.210412739808352,
0.476107780140271, 0.936663899397428, 0.251540964619117,
0.963667386912928, 0.504905545701818, 0.296220086916766,
0.240809811677774)), class = "data.frame", row.names = c(52L,
68L, 72L, 93L, 165L, 169L, 190L, 194L, 246L, 262L, 266L, 287L,
291L, 343L, 359L, 363L, 384L, 388L, 440L, 456L, 460L, 481L, 485L,
634L, 650L, 654L, 675L, 679L, 731L, 747L, 751L, 772L, 776L, 844L,
848L, 869L, 873L, 925L, 941L, 945L, 966L, 970L, 1022L, 1038L,
1042L, 1063L, 1067L, 1216L, 1232L, 1236L, 1313L, 1329L, 1333L,
1354L, 1358L, 1426L, 1451L, 1455L, 1507L, 1527L, 1717L, 1721L,
1742L, 1746L, 2186L, 2202L, 2206L, 2227L, 2231L, 2380L, 2396L,
2400L, 3075L, 3079L, 3294L, 3298L, 3350L, 3366L, 3370L, 3391L,
3395L, 3467L, 4223L, 4239L, 4243L, 4264L, 4268L, 4433L, 4437L,
4708L, 4805L, 4902L, 5193L, 5969L, 7909L, 8006L))
and this is the structure:
> str(df)
'data.frame': 96 obs. of 5 variables:
$ new_day : int -25 3 7 -7 3 7 -7 0 -25 3 ...
$ order : Factor w/ 14 levels "Alteromonadales",..: 8 8 8 8 8 8 8 8 11 11 ...
$ family : Factor w/ 17 levels "Burkholderiaceae",..: 13 13 13 13 12 12 12 12 15 15 ...
$ genus : Factor w/ 24 levels "Burkholderiaceae_unclassified",..: 16 16 16 16 7 7 7 7 3 3 ...
$ Abundance: num 0.758 0.728 0.422 0.186 7.366 ...
my data is about relative abundances of species over time, I removed rare species so it doesn't add up to 100 % anymore,
but that is fine, it is about 98 % per date. However, I get these weird free polygons and triangles which I recognize from incorrect grouping etc., but the group parameter did not change anything here. I also tried several position and stat arguments, which did not help. Maybe it is about the order of factors or something?
What I'm looking for is a stacked plot of the abundances of cumulated orders without empty spaces in between etc. Create proportional geom_area plot directly in ggplot2
# area plot combining species on order level
ggplot(df, aes(x = new_day, y = Abundance, fill = order)) +
geom_area(stat = "identity") +
geom_vline(aes(xintercept = 0), linetype = "dashed", size = 1.2)
I get fewer weird shapes when going to a more detailed hierarchical level (genus instead of order)
# area plot on genus level
ggplot(df, aes(x = new_day, y = Abundance, fill = genus)) +
geom_area(stat = "identity", position = "stack") +
geom_vline(aes(xintercept = 0), linetype = "dashed", size = 1.2)
but these are still more blank areas than there should be by the sum of abundances for a given time
# total abundance per day
sum(subset(df, new_day == -25)$Abundance)
[1] 98.03997
Any suggestions on how to fix this?

The problem is that you sometimes have several abundance values for one new_day, even with more detailed hierarchical levels.
This is what creates discontinuities in the area plot. You need to have only one unique value for each new_day. In my example below, I just take the first abundance value after grouping by new_day and order, but it is probably not relevant for what you want to show. (You may want to take the mean or attributes these values to other new_day points in between, whatever you need).
The remaining little gaps are caused by the missing abundance values, since as you said, it does not add up to 100%. This is not a big deal, but you can probably fix it by replacing the missing values by 0.
EDIT : Now doing the sum of abundance values as you mentioned, and removing the small remaining gaps by replacing missing values by 0.
library(tidyverse)
df %>%
# Sum abundance values, to only keep one per point
group_by(new_day, order) %>%
summarise(abundance=sum(Abundance)) %>%
ungroup() %>%
# Replace missing values by 0
spread(key=order, value=abundance) %>%
gather(key=order, value=abundance, -new_day) %>%
replace_na(list(abundance=0)) -> data
ggplot(data, aes(x = new_day, y = abundance, fill=order)) +
geom_area(stat = "identity") +
geom_vline(aes(xintercept = 0), linetype = "dashed", size = 1.2)

Put a title of plot inside a for loop

I have a code with a nested for loop that runs perfect and gives me 4X4 plots in a page. I need to insert the title in each plot. Below is my code.
What I wanted to do is create a vector and assign my titles inside it, as shown in code and then read it inside loop. For that, I need to convert the index i into number and use the position of second vector.
This is my approach which may not be that good so either you can use mine or give your own idea. You can play with any random datasets and use simple plot/histogram. The vectors represent the day of week and time of day respectively.
#set dimension
par(mfcol=c(4,4))
#vector definition
days<-c(1,2,3,4)
hours<-c(8,14,18,22)
#Title vector
D1<-c("Monday (7-8 am)","Monday (1-2 pm)","Monday (5-6 pm)",
"Monday (9-10 pm)")
D2<-c("Wednesday (7-8 am)","Wednesday (1-2 pm)","Wednesday (5-6 pm)",
"Wednesday (9-10 pm)")
D3<-c("Friday (7-8 am)","Friday (1-2 pm)","Friday (5-6 pm)",
"Friday (9-10 pm)")
D4<-c("Saturday (7-8 am)","Saturday (1-2 pm)","Saturday (5-6 pm)",
"Saturday (9-10 pm)")
#Loop
for (i in days)
{
for (j in hours)
{
# set positioning of the histogram
par("plt" = c(0.2,0.95,0.35,0.84))
# plot the histogram
hist(path$TT[path$days==i & path$hours==j], breaks = seq(0,60,by=3), xlab="Travel Time",
ylab="Number of paths",col="blue", **main=D??**, mgp=c(2.5,1,0))
}
}
here is data sample-> dput(path)
structure(list(days = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L), hours = c(7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L), TT = c(34.82720833,
34.13870083, 30.59218805, 35.1616205, 34.87982204, 30.74262596,
35.19981237, 35.14235172, 31.6716496, 29.84148401, 31.32268062,
30.58250275, 35.26514263, 33.55230269, 34.97001136, 31.09735713,
29.90509108, 33.78335499, 33.08419061, 33.9702478, 32.68267307,
32.88848951, 30.16693345, 32.85994732, 30.83277565, 34.62568305,
34.13923292, 33.50498645, 31.31095608, 34.31001321, 33.99902318,
33.7909643, 34.33340843, 32.30046602, 34.74999297, 29.87097318,
32.91255436, 30.37869556, 35.22453148, 33.91415576, 30.87027627,
34.32036758, 34.14405484, 32.52770687, 30.63412371, 30.69590367,
34.10350198, 33.51383263, 31.19792969, 35.26664132, 33.79975778,
30.9254123, 33.58382797, 32.47180323, 35.07275967, 30.97518331,
34.09754282, 31.30283331, 35.03617718, 35.0447385, 34.48088429,
34.93546837, 30.97837093, 31.14469741, 30.92743268, 34.10879646,
30.4886625, 35.00307314, 31.41065689, 31.82113768, 30.38511722,
30.39628127, 31.89778508, 31.5036342, 30.78847263, 30.63294595,
34.40494811, 32.57036077, 31.96399169, 33.90064885, 31.64029012,
34.1366935, 35.24047602, 30.50038163, 35.26178882, 30.67850437,
31.28041078, 31.13586861, 34.03564851, 30.45301463, 31.46075363,
32.79463877, 34.37256141, 31.14590299, 32.98806056, 34.61871373,
34.50000295, 33.64822723, 31.79305995, 32.95337037, 31.97535842,
33.01756184, 30.27499142, 31.52636985, 33.88390737, 29.86033691,
33.10717421, 31.13912362, 34.03308637, 29.82060846, 30.29160216,
30.68720702, 32.21043532, 32.38637581, 29.87286573, 31.91229798,
33.07799897, 30.41662694, 32.24261367, 35.3258724, 29.81198078,
29.87369792, 29.5469277, 31.07479327, 29.93749303, 31.32897414,
32.11042476, 31.74139691, 29.35309499, 31.91510643, 28.43111183,
30.64316778, 28.82045246, 31.2966231, 32.88217249, 28.85142648,
32.61772627, 28.89998879, 29.09439029, 31.17275104, 30.14374991,
32.54361297, 30.50674627, 32.01595442, 30.50549694, 30.92120556,
28.56600115, 32.6272292, 32.01189691, 32.48467475, 32.63696512,
30.92335971, 31.05045202, 30.7754939, 31.40027579, 29.12356583,
31.77973836, 28.78119827, 31.44082345, 30.73383322, 32.04126499,
30.09865077, 32.23577216, 29.08265343, 30.49423226, 31.46262176,
29.84828538, 30.18785884, 29.51834908, 29.37202672, 31.50806652,
32.40830835, 30.48030326, 31.25898945, 28.36670284, 31.28059981,
29.34232677, 30.09806882, 32.11127774, 29.59171523, 30.61713837,
29.76958526, 31.85824615, 32.16215903, 29.84655136, 31.07721122,
28.65494456, 30.9843114, 32.54863022, 31.46634971, 31.89779842,
32.82481805, 32.14782935, 32.08964421, 31.60785849, 32.91857557,
31.71183437, 31.81246841, 32.98599723, 28.95747656, 28.84662181,
31.71611474, 31.62086303, 32.53920721, 30.42499004, 28.99300588,
29.61203445, 32.4920689, 29.36255767, 32.6194317, 31.04202451,
28.75123245, 30.13704325, 30.92045914, 32.57753631, 30.83279548,
28.8546849, 30.74245368, 29.03716971, 28.37275181, 30.86814322,
30.61960665, 30.42719574, 30.27684903, 32.91275304, 29.80632759,
29.50108563, 32.6131215, 30.03530353, 30.24898855, 29.97890411,
29.91508311, 30.66431902, 29.44062756, 30.78040092, 30.42641885,
32.52252736, 32.02849124, 28.44168133, 28.77193919, 32.3661733,
32.50081923, 30.78754405, 29.31429942, 29.25319403, 29.41670938,
34.79250707, 28.45292865, 33.30658009, 36.95793072, 31.1241599,
29.47446652, 37.93368226, 29.99169743, 34.53286071, 33.30080173,
32.07298455, 34.59538339, 33.19895485, 32.39419483, 31.37985584,
33.10293436, 29.39098815, 29.6792889, 35.03296983, 37.90584009,
30.95003357, 33.20300797, 37.19244019, 35.17202829, 33.36301054,
35.45811104, 32.30603702, 35.90719466, 32.53788221, 32.98462237,
34.40384647, 34.60599035, 36.12782575, 34.22463048, 29.98624712,
35.806683, 36.85504472, 35.98104837, 35.97362738, 35.43026929,
29.52289309, 29.0544412, 28.38438112, 29.31043103, 34.55714132,
31.35110246, 35.45463173, 32.52063466, 29.64833452, 31.74827447,
31.19599864, 35.86874035, 31.36035725, 30.90048731, 36.67327499,
30.0504123, 37.41148645, 33.68205359, 29.2592527, 28.82514246,
30.62364715, 37.55578321, 32.25899523, 34.31735337, 37.1286007,
30.09667053, 37.77301539, 37.28325032, 33.82381014, 33.64911154,
32.23733708, 35.36476734, 31.19880018, 29.1404291, 30.72636631,
34.77003685, 37.31098961, 31.55246022, 28.51524079, 35.97250119,
35.08409392, 36.5458489, 37.35540297, 30.23406879, 29.17387163,
33.74088357, 29.40765925, 29.98726349, 29.58959745, 31.96605073,
31.94788415, 33.60347166, 28.43148601, 29.65454367, 36.06816061,
29.96597865, 31.90935292, 28.59771444, 32.44428733, 31.50734498,
30.23029062, 32.7213003, 33.17963215, 30.84546259, 35.61594726,
31.1375163, 33.58903731, 36.3755896, 30.15521544, 32.64832733,
29.75419547, 32.87727257, 32.86349263, 30.87051665, 34.99052692,
29.32459293, 29.75063939, 29.31336196, 30.26155711, 37.78471798,
29.29637466, 33.63983534, 29.0707227, 37.23740461, 30.46483145,
32.5191104, 32.38759822, 35.67256593, 31.96392716, 33.3250217,
35.46341363, 28.75439972, 33.2611733, 30.02014914, 35.78496489,
32.96781502, 31.43534921, 35.07596123, 34.52762462, 30.26655854,
35.32014083, 37.55183466, 34.14971103, 36.29105196, 32.40044715,
36.0587327, 31.83769864, 33.92873059, 34.70263617, 30.80816039,
30.68630199, 31.01802064, 30.80777532, 35.05333618, 27.06058834,
27.79241831, 27.33752079, 27.77903509, 26.947812, 27.8862964,
27.39365377, 27.9236377, 26.78983708, 27.98767273, 27.93024624,
27.84690108, 27.32830243, 26.81574528, 27.11055277, 27.39296015,
28.00610613, 27.71688355, 27.62271524, 27.69926561, 26.77071774,
26.75407601, 27.54772857, 26.85613667, 27.43762662, 27.45478206,
27.70204762, 27.66985159, 27.46593956, 28.00153523, 27.85391116,
26.78324156, 27.51476443, 27.54375831, 27.45536832, 27.25299275,
27.42563343, 27.35861323, 27.89703515, 27.94359525, 27.02701474,
28.01213784, 27.05632904, 27.219231, 28.00160216, 27.06621867,
26.83356071, 26.85138171, 26.9857268, 26.84488214, 27.04212578,
27.90226659, 26.88270484, 27.36445874, 27.98903653, 26.74879158,
27.91409337, 27.04442553, 27.76393403, 26.97261286, 26.82558533,
27.40286709, 26.90959192, 27.61358064, 27.67649126, 27.98923329,
27.27538051, 27.93429854, 27.24070111, 27.79609001, 27.51659686,
27.60029289, 26.85518925, 27.31821322, 27.1642527, 27.27570585,
27.67152235, 26.96014272, 27.89962397, 27.84824436)), .Names = c("days",
"hours", "TT"), class = "data.frame", row.names = c(NA, -480L
))

It seems like you have 4*4=16 plots, with 16 titles in 4 vectors.
Try this argument in your function,
main=get(paste0("D",i))[which(hours==j)]
get()function can get the object with the given object name.
I use some simulated data, just to check the titles. Looks good,
Sample codes:
x<-rnorm(50)#my simulated data
for (i in days)
{
for (j in hours)
{
hist(x,xlab="Travel Time",
ylab="Number of paths",col="blue",
main=get(paste0("D",i))[which(hours==j)], mgp=c(2.5,1,0))
}
}

This is revised to match the data that was provided.
The main idea here is to make a matrix of the titles and simply access the matrix each time you print. The code in the question looped through hours and days. Because I want to know the index, I have change this to looping through the indices, 1:4. That means that where the original code used the loop variable (hours or days) I use the index to select elements from hours or days.
I am assuming that we already have the data.frame and the OP's lists D1, D2, D3 and D4.
LabelMat = matrix(c(D1, D2, D3, D4), nrow=4)
for (i in 1:4) {
for (j in 1:4) {
# set positioning of the histogram
par("plt" = c(0.2,0.95,0.35,0.84))
# plot the histogram
hist(path$TT[path$days==days[i] & path$hours==hours[j]], breaks = seq(0,60,by=3),
xlab="Travel Time", ylab="Number of paths",
col="blue", main = LabelMat[i,j], mgp=c(2.5,1,0))
}
}

R ggplot geom_bar facet dodge

I'm having some trouble producing a faceted bar_plot in ggplot2. Perhaps it is something very obvious, but I can't figure it out:( I've the following dataset:
structure(list(COUNTRY = structure(c(1L, 4L, 7L, 10L, 13L, 16L,
19L, 2L, 5L, 8L, 11L, 14L, 17L, 20L, 3L, 6L, 9L, 12L, 15L, 18L,
2L, 5L, 8L, 11L, 14L, 17L, 20L, 3L, 6L, 9L, 12L, 15L, 18L, 1L,
4L, 7L, 10L, 13L, 16L, 19L, 3L, 6L, 9L, 12L, 15L, 18L, 1L, 4L,
7L, 10L, 13L, 16L, 19L, 2L, 5L, 8L, 11L, 14L, 17L, 20L), .Label = c("Angola",
"Botswana", "Burundi", "Comoros", "Eritrea", "Ethiopia", "Kenya",
"Lesotho", "Madagascar", "Malawi", "Mozambique", "Namibia", "Rwanda",
"Somalia", "South Africa", "Swaziland", "Tanzania", "Uganda",
"Zambia", "Zimbabwe"), class = "factor"), Year = structure(c(2L,
2L, 14L, 16L, 16L, 11L, 12L, 2L, 4L, 15L, 5L, 10L, 16L, 16L,
2L, 17L, 14L, 11L, 12L, 10L, 2L, 4L, 15L, 5L, 10L, 16L, 16L,
2L, 17L, 14L, 11L, 12L, 10L, 2L, 2L, 14L, 16L, 16L, 11L, 12L,
2L, 17L, 14L, 11L, 12L, 10L, 2L, 2L, 14L, 16L, 16L, 11L, 12L,
2L, 4L, 15L, 5L, 10L, 16L, 16L), .Label = c("1998", "2000", "2001/2",
"2002", "2003", "2003/4", "2004", "2005", "2005/6", "2006", "2006/7",
"2007", "2007/8", "2008/9", "2009", "2010", "2011"), class = "factor"),
sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L), .Label = c("m", "f", "b"), class = "factor"),
location = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Urban", "Rural", "Total",
"Capital.City", "Other.Cities.towns", "Urban.Non.slum", "Urban.Slum"
), class = "factor"), percent = c(60.4, 42.3, 85.4919452426806,
96.3, 90.2847535659154, 87.7347421555771, 87.7323067592087,
80.4, 80.6, 93.8186266493188, 75.0109418832216, 36.8, 87.1059275774722,
90.1216932603937, 66.8, 83.6279398931798, 89.690685909038,
88.8207941092749, 94.6139558774441, 88.0251085200726, 70.4,
54.7, 86.1919805548309, 56.9792710715853, 13.1, 75.6355555697382,
86.8196674671991, 42.5, 61.9452522893308, 77.597285694676,
88.3453320625631, 94.5192341778471, 80.6271302923487, 44.1,
29, 77.8542469357068, 90, 86.7073851186482, 83.8921034867784,
76.4094871587916, 49.3, 63.952805392032, 77.004884485532,
88.6723566877386, 93.9560433940531, 82.3095948307742, 56.1,
31.1, 80.0235653889704, 91.5, 88.3809682134183, 85.5656196766576,
80.0539027063387, 77, 61.2, 89.2538966046165, 59.6756344409838,
23, 79.6749544074645, 86.9507859695728)), .Names = c("COUNTRY",
"Year", "sex", "location", "percent"), row.names = c(1L, 4L,
7L, 10L, 13L, 16L, 19L, 22L, 25L, 28L, 31L, 34L, 37L, 40L, 43L,
46L, 49L, 52L, 55L, 58L, 62L, 65L, 68L, 71L, 74L, 77L, 80L, 83L,
86L, 89L, 92L, 95L, 98L, 101L, 104L, 107L, 110L, 113L, 116L,
119L, 123L, 126L, 129L, 132L, 135L, 138L, 141L, 144L, 147L, 150L,
153L, 156L, 159L, 162L, 165L, 168L, 171L, 174L, 177L, 180L), class = "data.frame")
I am trying to make a bar_plot which shows the percentage of people living in rural, urban areas (and the average) for a number of countries, and wish to show this split by gender. I can plot one of these categories on a simple bar plot by using a subset call within the ggplot function as follows:
ggplot(edu_melt[c(edu_melt$sex!="b" & edu_melt$location==c("Urban")), ], aes(x=COUNTRY, y=percent, fill=sex)) + geom_bar(position="dodge", width=0.5) + facet_grid(~location) + labs(x="Country") + theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1))
I would however like to compare the data across the location (e.g. urban, rural, and both). I thought this would be a simple case of introducing a facet_wrap call, however I get some odd behaviour where the data is plotted across the three facets - I would expect 20 pairs of bars on each facet, however this code produces 20 pairs of bars spread over the three facets?!
ggplot(edu_melt_over[c(edu_melt_over$sex!="b"),], aes(x=COUNTRY, y=percent, fill=sex)) + geom_bar(position="dodge", width=0.5, space=1) + facet_wrap(~location, nrow=3) + labs(x="Country", title="Proportion Net Primary School Enrolement in ESA") + theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1))
I'm not sure why this is happening, but have searched for hints and tips and tried a number of approaches, but get the same result. Anybody have any idea how I could produce this plot?
Thanks
Marty

Your data looks odd as you don't seem to have any combinations of male and female in the same strata (e.g. Angola has a male urban percent but no female). This is the data not the plotting.
ggplot(edu_melt[edu_melt$sex!="b", ], aes(x=COUNTRY, y=percent, fill=sex)) +
geom_bar(position="dodge", width=0.25) + facet_grid(location~.) + labs(x="Country") +
theme(axis.text.x = element_text(angle=30))

Ggplot2 geom_line error

I have a daaset which consists of data points over a time series for the proportion of people living in urban/rural areas for a number of countries. Sadly, not all countries have data for the same years. I have been trying to produce a simple line plot to show the different proportions of people living in different locations by year, but as each country has a different number of data points I am running into trouble.
I think this is because some of the countries only have data for a single year and using geom_line from ggplot2 throws the following error:
geom_path: Each group consist of only one observation. Do you need to
adjust the group aesthetic?
I was hoping that there would be some way to override this, or perhaps just plot a single point where a COUNTRY only has data for a single year. Does anyone know if this is possible, or indeed, if this is actually what this error means?!!?
Any help greatly appreciated!!!
Thanks
Here is my data:
structure(list(COUNTRY = structure(c(1L, 2L, 2L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L,
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L,
14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L,
2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L,
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L), class = "factor", .Label = c("Comoros",
"Eritrea", "Ethiopia", "Kenya", "Lesotho", "Madagascar", "Malawi",
"Namibia", "Rwanda", "South Africa", "Swaziland", "Tanzania",
"Zambia", "Zimbabwe")), Year = structure(c(5L, 12L, 4L, 25L,
16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L,
9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L,
24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L,
8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L,
22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L,
21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L,
20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L,
22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L,
13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L,
8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L,
5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L,
6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L,
9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L,
5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L,
7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L,
19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L,
1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L,
4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L,
24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L,
7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L,
24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L,
23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L,
1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L,
10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L), class = "factor", .Label = c("1992",
"1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000",
"2000/1", "2001/2", "2002", "2003", "2003/4", "2004", "2005",
"2005/6", "2006", "2006/7", "2007", "2007/8", "2008/9", "2009",
"2010", "2011")), location = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("Urban",
"Rural", "Total", "Capital.City", "Other.Cities.towns", "Urban.Non.slum",
"Urban.Slum"), class = "factor"), percent = c(63.0434782608696,
93.8, 87, 79.5642604795185, 65.4240807416892, 63.0791092522326,
90.448386469558, 85.9419999774024, 92.7603614781794, 84.0437368780105,
89.9792286718626, 91.0916571421351, 87.1132950026762, 73.8624315865239,
60.8311005575454, 66.7, 96, 86.8, 90.6243926153181, 90.6911141749493,
90.7602286016099, 93.0377175475414, 86.073106379954, 84.253722056373,
77.8178199148702, 97.3, 91.8332260789258, 89.612164524266, 89.9070989918367,
94.9, 85.1351949905457, 94.8358752154967, 92.9, 89.656599879838,
90.2634019334124, 94.4, 91.6241263241579, 76.7337303943862, 68.4233513070184,
74.15601627144, 88.4802888646634, 85.4643913454376, 89.7457528950664,
81.3025210084024, 83.0579155525397, 71.5857386620092, 86.2324062094295,
87.687478493975, 63.5379061371841, 78.5, 40.7, 51.7763728811622,
32.2441768813334, 22.3138981723172, 83.3699691175754, 69.6742912391579,
76.0526239692028, 83.7290062290807, 77.4758329101792, 83.8081963934296,
67.5805226154664, 55.8951299980461, 41.9921451192584, 52.2, 92.5,
77.6, 82.0322170392223, 85.2850090044269, 70.8031150919282, 47.108593681531,
82.2215412952297, 78.3643348536815, 74.4253468485616, 94.8, 90.1711142192198,
85.0338348718722, 86.3134329333052, 90.4, 79.2813256726705, 90.7077549957666,
82.5, 77.7236217339155, 75.3278238729086, 77.7, 78.4592126267142,
67.1145693585691, 55.3459024734839, 57.8463881286199, 83.5604620304044,
83.9259722574938, 84.4589780509803, 73.3992444632325, 77.544833952707,
63.0503715222555, 75.6808008503601, 85.6943513045284, 63.4, 84.2,
51, 55.7151220012609, 34.9, 26.6, 85, 72.5, 79.2, 83.8, 80.3,
84.9, 69.6, 59, 46, 54, 93, 78.7, 83.2, 85.9, 76.7, 57.5, 83.8,
80.4, 75.6, 95, 90.4, 85.6, 86.9, 90.6, 82.2, 91.5, 84.5, 79.9,
78.1, 80.9, 81.2, 68.1, 56.8, 59.6, 84.9, 84.4, 86.5, 77, 79.1337842548663,
65.6, 79.1, 86.3, 68.421052631579, 96.1, 93.3, 93.461209969107,
82.2712525836501, 88.2708936990495, 87.6298001816506, 87.6386027991385,
93.1818181818183, 86.6666666666668, 88.1030398041979, 90.4761904761904,
83.4297434324662, 86.3744073211853, 83.6107223166148, 78.3, NA,
72.8, 80.952380952381, 87.5, 96.9073193030442, 99.1348508752745,
85.5297651573129, 86.4793919321843, 79.4520547945208, 98.2, 92.4613307718678,
85.4590408924955, 83.9378238341966, 92.1, 81.1594202898552, 96.0232554251852,
NA, 88.0377726639494, 83.690767555447, 93.4, 90.0349966633017,
71.2508707571865, 72, 79.4082828804656, 91.8032786885246, 84.5238095238095,
87.8787878787881, 75.6097560975609, 81.0643061692494, 68.4708412135189,
84.9056603773584, 89.5522388059702, 61.6438356164384, 91.7, 79.5,
77.0004220956012, 61.061381883032, 58.756042602018, 91.2594694272412,
85.20149612163, 92.4956062313464, 82.622382662868, 91.4036416540165,
91.6169313256523, 89.2957214499669, 67.6757501795213, 48.1479760952102,
NA, NA, 94.2, 94.3553068539161, 91.8799748693178, 89.3739230258784,
92.1418739343887, 86.4757947454868, 81.0102236379536, 77.0100025126874,
NA, 91.3720851411616, 92.2, 92.5003150086683, 97.8260869565219,
87.1461797069698, 93.5168077834096, NA, 90.1780793791367, 92.9758067301415,
94.9, 91.8829499602467, 81.749280834314, 65.1853441661798, 69.0503609949116,
87.2562445664681, 85.8298270239758, 90.6673511683335, 83.2861189801694,
84.9006282245266, 73.65452177457, 87.3075692692965, 85.5310215524833,
83.3333333333333, NA, NA, 98.5990187756088, 84.4640706359058,
NA, 93.9158337759274, 91.5744358611439, 100, NA, NA, NA, 88.7824144772468,
85.1972665683085, 89.54493171236, NA, NA, 89.8, NA, 100, 97.6261376125643,
96.3196943955923, 92.0952338262334, 87.9266080431752, 80.9429968520701,
NA, NA, 92.8, 95.2886158200472, 100, 86.4199793410402, NA, NA,
89.9001648604344, NA, NA, 91.5033109800214, 83.8918470610424,
73.9339911532972, 88.6921281548131, 94.309068022859, 85.3299585067346,
93.7362934447331, 86.5384615384618, 83.7424288707868, NA, 86.3836615391687,
88.1866796344726, 58.1081081081081, NA, NA, 75.7976468146464,
62.1289432084197, NA, 88.1488735873722, 84.2108238885019, 89.8335978405451,
NA, NA, NA, 86.9222656846515, 70.3584041024493, 70.9023609260137,
NA, NA, 85.9, NA, 89.8689917369566, 90.3864925686512, 92.628169473785,
80.9468895007753, 78.7885741638367, 75.4005791241575, NA, NA,
88.4, 87.7139456942162, 92.3809523809525, 83.7645232075473, NA,
NA, 89.567507133125, NA, NA, 91.6433898994358, 73.6225283043976,
65.9223049858496, 72.3148320483822, 86.2596215693035, 85.6224026570651,
87.4940330171337, 78.7499999999997, 81.9949404453665, NA, 84.5563115043796,
87.0190820047277)), .Names = c("COUNTRY", "Year", "location",
"percent"), row.names = c(NA, -336L), class = "data.frame")
I want to produce a simple plot with ggplot2 that is facetted by COUNTRY. I can do this fine using geom_point:
ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_point() + facet_wrap(~COUNTRY)
However, if I try and produce a line plot with geom_line (ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line() + facet_wrap(~COUNTRY))
I get the following error:
geom_path: Each group consist of only one observation. Do you need to
adjust the group aesthetic?
I had thought that this could be because a couple of the countries have only one year's worth of data so I subsetted the date to remove these three countries like so:
ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line(data=meas_melt[!meas_melt$COUNTRY %in% c('Comoros','South Africa','Swaziland'),]) + facet_wrap(~COUNTRY)
However, I get the same error!

#Sven's answer is correct but fixes only part of the problem. Note how there's no plot for Comoros, South Africe, or Swaziland. This is because in your data, sometimes year is, e.g., 2006 or 2007, and sometimes it is "2006/7".
data[meas_melt$COUNTRY=="Swaziland",]
COUNTRY Year location percent
32 Swaziland 2006/7 Urban 94.83588
80 Swaziland 2006/7 Rural 90.70775
128 Swaziland 2006/7 Total 91.50000
176 Swaziland 2006/7 Capital.City 96.02326
224 Swaziland 2006/7 Other.Cities.towns 93.51681
272 Swaziland 2006/7 Urban.Non.slum NA
320 Swaziland 2006/7 Urban.Slum NA
Those countries really have only one "year" (hence, no line). More importantly, these odd year designations distort your x-axis. You can see that using the scales="free" argument to facet_wrap(...):
ggplot(meas_melt, aes(x=Year,y=percent, color=location)) +
geom_line(aes(group=location)) +facet_wrap(~COUNTRY, scales="free") +
theme(axis.text.x=element_text(angle=90, vjust=0.5, size=8),
legend.position="bottom")
Which produces this:

You have to specify aes(group = location) inside geom_line:
library(ggplot2)
ggplot(meas_melt, aes(Year, percent, colour=location)) +
geom_line(aes(group = location)) +
facet_wrap(~COUNTRY)

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