rggplot2geom-pointscale-color-manual

ggplot2: scale_color and scale_shape of different lengths

I'm trying to re-create this plot in R, but can't seem to get things to match. As I understand it, if I only want 1 legend (shapes and colors in one legend), then scale_shape_manual and scale_color_manual need to have the same values (or the same number of values?). However, although I need geom_points to be colored by group (Season), I don't want these values in the legend (like the image below). Encoding scale_color and scale_shape with values of different lengths (3 shapes, 2 colors; 1 Seasons) gives me 2 legends - so I'm stuck. I'm also interested in theme_classic() not over-riding my theme() settings (I can't get the theme_classic() and my current settings in theme() to work together - theme_classic() wipes them out).

enter image description here

library(ggplot2)

ggplot(cdata2, aes(x = CYR, y = n_mean)) +
    annotate(geom = "rect", xmin = 2010, xmax = 2010.25, ymin = -Inf, ymax = Inf,
             fill = "lightblue", colour = NA, alpha = 0.4) +
    annotate(geom = "rect", xmin = 2013.5, xmax = 2013.75, ymin = -Inf, ymax = Inf,
             fill = "lightgreen", colour = NA, alpha = 0.4) +
    annotate(geom = "rect", xmin = 2017.5, xmax = 2017.75, ymin = -Inf, ymax = Inf,
             fill = "#E0E0E0", colour = NA, alpha = 0.4) +
    annotate(geom = "rect", xmin = 2011.5, xmax = 2011.75, ymin = -Inf, ymax = Inf,
             fill = "pink", colour = NA, alpha = 0.4) +
    annotate(geom = "rect", xmin = 2015.5, xmax = 2015.75, ymin = -Inf, ymax = Inf,
             fill = "pink", colour = NA, alpha = 0.4) +
    annotate(geom = "rect", xmin = 2018.5, xmax = 2018.75, ymin = -Inf, ymax = Inf,
             fill = "orange", colour = NA, alpha = 0.2) + 
    annotate(geom = "rect", xmin = 2022.5, xmax = 2022.75, ymin = -Inf, ymax = Inf,
           fill = "orange", colour = NA, alpha = 0.2) +
    
  
    # Color lines by SAV values (put in legend)
    geom_line(aes(y = n_mean, color = SAV), size = 1) + 
    
    # Grand mean (straight line); color lines by SAV levels (Not in legend)
    geom_line(data = cdata2, aes(y=grand_mean, color = SAV), linewidth = 0.75) +
    
    # Color points by season (not in legend)
    geom_point(aes(y=n_mean, shape=SAV, color = factor(Season)), size = 3) + 
  
  
    # Labels matching make 1 legend, instead of 2
    # Breaks matching make the shapes one the correct level
    scale_shape_manual(labels=c('Thalassia', 'Halodule', 'Syringodium', 'WET', 'DRY'),
                       values = c(16,17,15), 
                       breaks=c("ave_tt", "ave_hw", "ave_sf", 'WET', 'DRY')
                       ) + 
  
  scale_color_manual(labels=c('Thalassia', 'Halodule', 'Syringodium', 'WET', 'DRY'),
                     values=c("blue", "dark green", "red", "dark blue", "dark red"),
                     breaks=c("ave_tt", "ave_hw", "ave_sf", 'WET', 'DRY')
                     ) + 
  
    scale_x_continuous(breaks = c(2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,
                                  2017,2018,2019,2020,2021,2022,2023),
                       # Tick marks
                       
                       limits = c(2007,2023),
                       # Start and end from 2007 to 2023 (better than xlim())
                       
                       labels = c(2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,
                                  2017,2018,2019,2020,2021,2022,2023), 
                       expand=c(0, 0)) +   
                       # Start origin @ first break,
                       # c(-1,0) starts 2022 at origin and goes backwards!
                       # c(1<-0,0) squishes the plot long-way inward
  
  scale_y_continuous(
    expand = c(0, 0), # x-axis starts @ y=0
    limits = c(0, 30), # 0->1 y-axis
    breaks = c(0,5,10,15,20,25,30)) +  # y-axis step
  
    theme(panel.border = element_rect(fill = NA, color = "black"),
          panel.background = element_blank(), 
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(), 
          plot.title = element_text(hjust = 0.5), 
          axis.text.y = element_text(size = 11, face = "bold"), 
          axis.text.x = element_text(size = 10, vjust = 1.0, hjust=1.0, angle = 45, face = "bold"), 
          axis.title = element_text(size = 14, face = "bold"),
          legend.title=element_blank()) + 
    labs(x= NULL, y = "SAV (ave. % cover)") + 

    geom_text(data = cdata3, aes(label = paste0(round(n_mean[1],2)), x = 2022, y=17), color = "dark green")+
    geom_text(data = cdata3, aes(label = paste0(round(n_mean[3],2)), x = 2022, y=14.5), color = "blue")+
    geom_text(data = cdata3, aes(label = paste0(round(n_mean[2],2)), x = 2022, y=2), color = "red")

enter image description here

Data (2 dataframes):

> dput(cdata3)
structure(list(SAV = c("ave_hw", "ave_sf", "ave_tt"), N = c(286935L, 
286935L, 286935L), n_mean = c(15.3737370343434, 0.114431692544803, 
9.95752343992832)), class = "data.frame", row.names = c(NA, -3L
))

> dput(cdata2)
structure(list(CYR = c(2007, 2007, 2007, 2007.5, 2007.5, 2007.5, 
2008, 2008, 2008, 2008.5, 2008.5, 2008.5, 2009, 2009, 2009, 2009.5, 
2009.5, 2009.5, 2010, 2010, 2010, 2010.5, 2010.5, 2010.5, 2011, 
2011, 2011, 2011.5, 2011.5, 2011.5, 2012, 2012, 2012, 2012.5, 
2012.5, 2012.5, 2013, 2013, 2013, 2013.5, 2013.5, 2013.5, 2014, 
2014, 2014, 2014.5, 2014.5, 2014.5, 2015, 2015, 2015, 2015.5, 
2015.5, 2015.5, 2016, 2016, 2016, 2016.5, 2016.5, 2016.5, 2017, 
2017, 2017, 2017.5, 2017.5, 2017.5, 2018, 2018, 2018, 2018.5, 
2018.5, 2018.5, 2019, 2019, 2019, 2019.5, 2019.5, 2019.5, 2020, 
2020, 2020, 2020.5, 2020.5, 2020.5, 2021, 2021, 2021, 2021.5, 
2021.5, 2021.5, 2022, 2022, 2022, 2022.5, 2022.5, 2022.5), Season = c("DRY", 
"DRY", "DRY", "WET", "WET", "WET", "DRY", "DRY", "DRY", "WET", 
"WET", "WET", "DRY", "DRY", "DRY", "WET", "WET", "WET", "DRY", 
"DRY", "DRY", "WET", "WET", "WET", "DRY", "DRY", "DRY", "WET", 
"WET", "WET", "DRY", "DRY", "DRY", "WET", "WET", "WET", "DRY", 
"DRY", "DRY", "WET", "WET", "WET", "DRY", "DRY", "DRY", "WET", 
"WET", "WET", "DRY", "DRY", "DRY", "WET", "WET", "WET", "DRY", 
"DRY", "DRY", "WET", "WET", "WET", "DRY", "DRY", "DRY", "WET", 
"WET", "WET", "DRY", "DRY", "DRY", "WET", "WET", "WET", "DRY", 
"DRY", "DRY", "WET", "WET", "WET", "DRY", "DRY", "DRY", "WET", 
"WET", "WET", "DRY", "DRY", "DRY", "WET", "WET", "WET", "DRY", 
"DRY", "DRY", "WET", "WET", "WET"), SAV = c("ave_hw", "ave_sf", 
"ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", 
"ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", 
"ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", 
"ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", 
"ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", 
"ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", 
"ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", 
"ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", 
"ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", 
"ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", 
"ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", 
"ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", 
"ave_tt", "ave_hw", "ave_sf", "ave_tt", "ave_hw", "ave_sf", "ave_tt", 
"ave_hw", "ave_sf", "ave_tt"), N = c(8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L, 17390L, 17390L, 17390L, 8695L, 8695L, 8695L, 8695L, 
8695L, 8695L), n_mean = c(NaN, NaN, NaN, NaN, NaN, NaN, 8.98527191489362, 
0, 6.98, 21.8943971553191, 2.3153427893617, 7.64548463829787, 
9.77706959380851, 0.0319148936170213, 5.21412959574468, 26.0077821638298, 
0, 8.45628615744681, 11.1126796764706, 0.00588235294117647, 4.133759, 
11.6129787234043, 0, 13.866170212766, 11.8146809347826, 0, 5.52577639782609, 
20.0829787234043, 0, 13.0765957446809, 22.6851063829787, 0, 9.02340425531915, 
19.6428446808511, 1.03215106382979, 6.23103829787234, 23.0308695652174, 
0, 3.02434782608696, 19.7425531914894, 0, 12.098085106383, 21.1297872340426, 
0, 6.91914893617021, 15.8510638297872, 0, 8.59148936170213, 14.9243498297872, 
0, 13.0065012765957, 20.4765957446808, 0, 13.8042553191489, 12.8574468085106, 
0, 8.36808510638298, 16.768085106383, 0, 15.6893617021277, 18.0154255319149, 
0, 7.97234042553191, 16.6425531914894, 0, 9.99574468085106, 14.4212765957447, 
0, 10.9170212765957, 9.05957446808511, 0, 12.7340425531915, 10.3893617021277, 
0, 12.7276595744681, 13.4364065957447, 0, 12.4276595744681, 8.78936170212766, 
0.00212765957446809, 10.3127659574468, 17.968085106383, 0, 14.7765957446809, 
12.2234042553191, 0, 9.12765957446809, 12.0808510638298, 0, 10.6574468085106, 
7.06808510638298, 0.0106382978723404, 10.3914893617021, 11.6297872340426, 
0, 13.1787234042553), n_median = c(NA, NA, NA, NA, NA, NA, 3, 
0, 2.32, 20.7, 0, 0.2, 6.6, 0, 1.51, 26, 0, 2.71, 10.4, 0, 1.4772725, 
6.6, 0, 5.5, 7.9785715, 0, 0.55, 18, 0, 1.1, 13.5, 0, 2.1, 19.5, 
0, 1.8, 22.75, 0, 0.05, 19.1, 0, 0.6, 19, 0, 1.5, 10.7, 0, 1, 
12, 0, 8, 13, 0, 7.5, 11, 0, 3.5, 16.2, 0, 8.6, 16.5, 0, 0.6, 
14.1, 0, 0.5, 12, 0, 6.5, 8, 0, 5, 8.2, 0, 4.3, 11.6, 0, 8.8, 
6.8, 0, 6.5, 19, 0, 11.5, 10.1, 0, 5.7, 8.7, 0, 4.7, 5, 0, 4.1, 
9.5, 0, 3), sd = c(NA, NA, NA, NA, NA, NA, 11.7085445738399, 
0, 11.7037840986077, 20.9798396749426, 10.4019772483536, 10.7510377253969, 
9.45639825464561, 0.21646978819132, 8.50015402914711, 23.4389588168003, 
0, 11.951876499548, 9.07443174861995, 0.0235312823702472, 5.14547979546789, 
12.4331709472457, 0, 15.7887893178601, 13.3600974353576, 0, 8.86461330488266, 
18.2140229059275, 0, 18.0923244678731, 24.2988009061075, 0, 12.5755580996472, 
15.6950429641397, 6.36880461080622, 7.76768756867771, 14.8923349009443, 
0, 5.78304099792998, 14.3358394751566, 0, 16.6545356701125, 16.5113577583701, 
0, 10.4140034508656, 16.0767802318981, 0, 12.6812861503859, 14.8715784407585, 
0, 14.8710278913095, 20.0770341464803, 0, 15.5527562398657, 11.3350420672201, 
0, 11.3192875331138, 15.1709732834689, 0, 18.2528957714625, 16.4433980698121, 
0, 12.017818981421, 15.7909769267971, 0, 15.8837685790693, 14.142045197017, 
0, 15.6274080485381, 8.84612691530042, 0, 17.0140681969259, 10.1987764434213, 
0, 15.6842268952042, 11.6353578097032, 0, 14.0963614866631, 7.50745634994085, 
0.0144313192127546, 12.1654309051214, 13.4313362297506, 0, 16.249629491747, 
10.4357129517743, 0, 10.7546123117319, 10.6263854112514, 0, 13.699083133139, 
7.87975827536278, 0.0721565960637732, 13.3007061558202, 10.4435339370082, 
0, 19.2295746932576), se = c(NA, NA, NA, NA, NA, NA, 0.125564861065502, 
0, 0.125513808741503, 0.224992153153388, 0.111552962006418, 0.115296358978348, 
0.101412376707997, 0.00232146691740417, 0.0911574152512433, 0.251364256999733, 
0, 0.128174403118456, 0.097316088654574, 0.000252354353940351, 
0.0551811928082687, 0.133335904624953, 0, 0.169322252188291, 
0.143276456583713, 0, 0.0950659521349834, 0.19533096032589, 0, 
0.194025841028627, 0.260585381948376, 0, 0.134862893986958, 0.168316896842368, 
0.0683003819190261, 0.083302292971616, 0.159708489042777, 0, 
0.0620185314119729, 0.153740516646043, 0, 0.178606695677658, 
0.17707122604843, 0, 0.111681933497115, 0.172410726497054, 0, 
0.13599674353743, 0.159485892457608, 0, 0.159479988251101, 0.215310279370048, 
0, 0.166790984492437, 0.121559342697649, 0, 0.121390387805456, 
0.162696664863314, 0, 0.195747841000456, 0.176342412249412, 0, 
0.128881583974495, 0.169345712560374, 0, 0.170340829489904, 0.151662226602697, 
0, 0.16759156597591, 0.0948677002579155, 0, 0.182462397083852, 
0.109373794418269, 0, 0.1682009030752, 0.124779991023567, 0, 
0.151172305015315, 0.0805115193942914, 0.000154764461160278, 
0.130464604867841, 0.144040436193989, 0, 0.174264397818995, 0.111914750688714, 
0, 0.115334693583787, 0.0805815788598744, 0, 0.103882336756974, 
0.084504162480305, 0.000773822305801391, 0.142639532180634, 0.111998624556905, 
0, 0.206221948379678), grand_mean = c(NA, NA, NA, NA, NA, NA, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832, 15.3737370343434, 0.114431692544803, 
9.95752343992832, 15.3737370343434, 0.114431692544803, 9.95752343992832, 
15.3737370343434, 0.114431692544803, 9.95752343992832, 15.3737370343434, 
0.114431692544803, 9.95752343992832)), row.names = c(NA, -96L
), class = "data.frame")
Solution

  • While I'm sure that this could be achieved via just color and shape the easiest approach IMHO would be to add the fill to color the points by Season which requires to switch to shapes which allow for a fill aes. And for the legend you could set fill color via the override.aes argument of guide_legend.

    Additionally I simplified your code a bit by putting the specs for your rects in a data frame which allows to use just one annotate.

    library(ggplot2)

rects <- tibble::tribble(
  ~xmin, ~xmax, ~fill, ~alpha,
  2010, 2010.25, "lightblue", .4,
  2013.5, 2013.75, "lightgreen", .4,
  2017.5, 2017.75, "#E0E0E0", .4,
  2011.5, 2011.75, "pink", .4,
  2015.5, 2015.75, "pink", .4,
  2018.5, 2018.75, "orange", .2,
  2022.5, 2022.75, "orange", .2
)

breaks <- c("ave_tt", "ave_hw", "ave_sf")
labels <- c("Thalassia", "Halodule", "Syringodium")
names(labels) <- breaks

pal_color <- c("blue", "dark green", "red")
names(pal_color) <- breaks

pal_shape <- c(21, 24, 22)
names(pal_shape) <- breaks

pal_fill <- c("dark blue", "dark red")
names(pal_fill) <- c("WET", "DRY")

ggplot(cdata2, aes(x = CYR, y = n_mean)) +
  annotate(
    geom = "rect", xmin = rects$xmin, xmax = rects$xmax, ymin = -Inf, ymax = Inf,
    fill = rects$fill, colour = NA, alpha = rects$alpha
  ) +
  geom_line(aes(y = n_mean, color = SAV), linewidth = 1) +
  geom_line(aes(y = grand_mean, color = SAV), linewidth = 0.75) +
  geom_point(aes(y = n_mean, shape = SAV, fill = Season), size = 3) +
  geom_text(data = cdata3, aes(
    label = round(n_mean, 2),
    color = SAV,
    y = floor(n_mean) + 2
  ), x = 2023, hjust = 0, show.legend = FALSE)
scale_shape_manual(
  labels = labels,
  values = pal_shape,
  breaks = breaks
) +
  scale_color_manual(
    labels = labels,
    values = pal_color,
    breaks = breaks
  ) +
  scale_fill_manual(
    values = pal_fill,
    guide = "none"
  ) +
  scale_x_continuous(
    breaks = seq(2007, 2023),
    limits = c(2007, 2024),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    expand = c(0, 0),
    limits = c(0, 30),
    breaks = seq(0, 30, 5)
  ) +
  guides(shape = guide_legend(override.aes = list(fill = "blue"))) +
  theme(
    panel.border = element_rect(fill = NA, color = "black"),
    panel.background = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(hjust = 0.5),
    axis.text.y = element_text(size = 11, face = "bold"),
    axis.text.x = element_text(
      size = 10, vjust = 1.0, hjust = 1.0,
      angle = 45, face = "bold"
    ),
    axis.title = element_text(size = 14, face = "bold"),
    legend.title = element_blank(),
    legend.position = "top",
    legend.justification = c(0, 1),
    legend.direction = "vertical"
  ) +
  labs(x = NULL, y = "SAV (ave. % cover)")

    enter image description here