Closed
Description
I'm playing around with some different viz at the moment and when I try to facet_wrap I get empty panels in places where I don't expect them. Also there are loads of extra occurrences of the color factors in the legend.
library(ggplot2)
library(plotly)
d2 <-structure(list(Year = c(2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015), Activity_Type = c("B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"), Group_Code = c("WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WGL", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMK", "WMK", "WMK", "WMK", "WMK", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WMB", "WMB", "WMB", "WMB", "WMB", "WMB", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMC", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WDL", "WGL", "WGL", "WGL", "WGL", "WGL", "WMC", "WMC", "WMC", "WMC", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMH", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMK", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT"), Code_Code = c("FC", "FS", "HHrt", "MGCID", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PMTID", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "NFrtP", "PMTID", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FS", "NFrtP", "PMTID", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "HHrt", "mSH033", "mSH075", "NFrtP", "PMTID", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "MGCID", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PMTID", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "PMTID", "RC", "SS", "FC", "FS", "HHrt", "mSH033", "mSH075", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "MGCID", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "HHrt", "NFrtP", "RTK", "SS", "VIGOR", "WTkg", "FC", "FS", "mSH001", "mSH033", "mSH075", "RC", "RTK", "SS", "STAND", "VIGOR", "FC", "FS", "HHrt", "MGCID", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "WTkg", "FC", "FS", "HHrt", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "MGCID", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FS", "PROJC", "RC", "SS", "STAND", "RTK", "SS", "VIGOR", "WTkg", "FC", "FS", "HHrt", "MGCID", "mSH001", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg", "FC", "FS", "HHrt", "mSH001", "mSH033", "mSH075", "mSH101", "NFrtP", "PROJC", "RC", "RTK", "SS", "STAND", "VIGOR", "W1", "W2", "W3", "WTkg"), count = c(2422L, 2900L, 1946L, 10915L, 6089L, 8846L, 7878L, 4215L, 1746L, 4915L, 12880L, 2903L, 2659L, 4577L, 4285L, 1413L, 2278L, 2275L, 2242L, 4498L, 477L, 478L, 224L, 1137L, 2123L, 1032L, 479L, 105L, 1469L, 2122L, 110L, 474L, 105L, 94L, 1246L, 27L, 27L, 216L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 566L, 308L, 2964L, 2964L, 543L, 8125L, 755L, 595L, 859L, 384L, 536L, 536L, 536L, 536L, 330L, 2521L, 2310L, 2514L, 1343L, 98L, 2367L, 2367L, 2509L, 2339L, 2618L, 2524L, 2520L, 2605L, 2340L, 2342L, 2194L, 2190L, 2187L, 2430L, 135L, 135L, 30L, 135L, 135L, 1423L, 1393L, 1333L, 3951L, 3951L, 1485L, 78L, 1401L, 919L, 1308L, 1207L, 1438L, 1386L, 1245L, 1075L, 828L, 6989L, 3088L, 2618L, 12876L, 3218L, 7660L, 7844L, 4442L, 1668L, 16340L, 3506L, 3143L, 3326L, 6537L, 2667L, 3061L, 3059L, 3051L, 3664L, 127L, 313L, 312L, 312L, 313L, 311L, 1L, 54L, 1959L, 3227L, 3227L, 54L, 54L, 54L, 54L, 54L, 2349L, 2360L, 2266L, 6183L, 762L, 1407L, 1794L, 3502L, 2004L, 5238L, 2379L, 2262L, 2337L, 2020L, 2231L, 1965L, 1965L, 1961L, 2177L, 661L, 402L, 204L, 1621L, 1616L, 1622L, 162L, 2000L, 402L, 261L, 352L, 255L, 229L, 10L, 3367L, 2967L, 2315L, 1413L, 15838L, 15798L, 1321L, 3581L, 16377L, 3011L, 1657L, 3135L, 3965L, 2551L, 3374L, 2934L, 2687L, 2341L, 568L, 771L, 546L, 8605L, 1811L, 4881L, 4522L, 4304L, 222L, 12932L, 746L, 547L, 721L, 7495L, 380L, 591L, 591L, 591L, 591L, 4L, 52L, 4L, 1L, 193L, 467L, 467L, 467L, 467L, 299L, 299L, 298L, 7963L, 2616L, 7586L, 5822L, 293L, 8433L, 299L, 299L, 299L, 155L, 299L, 299L, 299L, 299L, 299L, 639L, 494L, 60L, 2408L, 2274L, 1621L, 282L, 4527L, 523L, 440L, 420L, 500L, 232L, 138L, 138L, 137L, 152L, 2599L, 2438L, 1439L, 5850L, 23539L, 21080L, 2754L, 2404L, 24705L, 2543L, 1630L, 2232L, 3700L, 1131L, 2195L, 1821L, 1630L, 1618L)), class = "data.frame", row.names = c(NA, -268L), .Names = c("Year", "Activity_Type", "Group_Code", "Code_Code", "count"))
kd <- ggplot(d2, aes(x = as.factor(Year), y = count, colour = Group_Code)) +
geom_point() +
facet_wrap( ~ Code_Code , ncol = 10)
ggplotly(kd)Session info --------------------------------------------------------------------------------------
setting value
version R version 3.2.4 (2016-03-10)
system x86_64, darwin13.4.0
ui AQUA
language (EN)
collate en_US.UTF-8
tz America/Los_Angeles
date 2016-03-17
Packages ------------------------------------------------------------------------------------------
package * version date source
assertthat 0.1 2013-12-06 CRAN (R 3.2.0)
base64enc 0.1-3 2015-07-28 CRAN (R 3.2.0)
colorspace 1.2-6 2015-03-11 CRAN (R 3.2.0)
DBI 0.3.1 2014-09-24 CRAN (R 3.2.0)
devtools 1.10.0.9000 2016-02-26 Github (hadley/devtools@b6a23be)
digest 0.6.9 2016-01-08 CRAN (R 3.2.3)
dplyr 0.4.3 2015-09-01 CRAN (R 3.2.0)
ggplot2 * 2.1.0 2016-03-01 CRAN (R 3.2.4)
gridExtra 2.2.1 2016-02-29 CRAN (R 3.2.4)
gtable 0.2.0.9000 2016-02-26 Github (hadley/gtable@0ed36a4)
htmltools 0.3 2015-12-29 CRAN (R 3.2.3)
htmlwidgets 0.6.1 2016-03-07 Github (ramnathv/htmlwidgets@2e4ad57)
httr 1.1.0 2016-01-28 CRAN (R 3.2.3)
jsonlite 0.9.19 2015-11-28 CRAN (R 3.2.2)
labeling 0.3 2014-08-23 CRAN (R 3.2.0)
magrittr 1.5 2014-11-22 CRAN (R 3.2.0)
memoise 1.0.0 2016-01-29 CRAN (R 3.2.3)
munsell 0.4.3 2016-02-13 CRAN (R 3.2.3)
plotly * 3.4.6 2016-03-17 Github (ropensci/plotly@78e4612)
plyr 1.8.3.9000 2015-10-16 Github (hadley/plyr@9d8294e)
R6 2.1.2 2016-01-26 CRAN (R 3.2.3)
Rcpp 0.12.3 2016-01-10 CRAN (R 3.2.3)
scales 0.4.0.9000 2016-02-26 Github (hadley/scales@646b6a4)
tidyr 0.4.1.9000 2016-02-19 Github (hadley/tidyr@323f54e)
viridis 0.3.4 2016-03-15 Github (sjmgarnier/viridis@694d4b8)
withr 1.0.1 2016-02-04 CRAN (R 3.2.3)
yaml 2.1.13 2014-06-12 CRAN (R 3.2.0)