Combine two faceted plots on one plot - r

Question

Sorry if this is a duplicate question but I cannot seem to find the answer to my question anywhere. I have two plots and I would like to overlay plot two on plot one so that they form one plot. Is this possible? I will attach how both plots look separately. They are both facetted by the same variable which is by location and are on the same x and y-axis scale so theoretically should be possible.
Thank you.
## Plot one
Proxy<-read.csv("ALLRSL.csv",header=T)
p1<-ggplot()+
geom_ribbon(data=Proxy,aes(x=YEAR,ymin=LOWER,ymax=UPPER,fill=SITE),alpha=.5)+
geom_line(data=Proxy,aes(x=YEAR,y=RSL,col=SITE))+
facet_wrap(~ SITE,ncol= 1)+
scale_fill_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
scale_color_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
theme_classic()+
xlim(1900, 2020)+
theme(panel.grid.major.x = element_blank())+
theme(panel.grid.minor.x = element_blank())+
theme(panel.grid.minor.y = element_blank())+
theme(panel.grid.major.y = element_blank())+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)+
theme(legend.position="none")
p1
plot two
tgsm<-read.csv("tgsm.csv",header=T)
tgsm<-na.omit(tgsm)
tglonger<-pivot_longer(tgsm, cols=c(-Year),names_to="Site", values_to = "value")
p2<-ggplot()+
geom_point(data=tglonger,aes(x=Year,y=value,col=Site),alpha=.7,size=1)+
facet_wrap(~Site,ncol=1)+
theme_classic()+
xlim(1900,2020)+
scale_color_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
theme(panel.grid.major.x = element_blank())+
theme(panel.grid.minor.x = element_blank())+
theme(panel.grid.minor.y = element_blank())+
theme(panel.grid.major.y = element_blank())+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)+
theme(legend.position="none")
p2
Data
Proxy <- structure(list(RSL = c(-0.305251214, -0.306414006, -0.307194187,
-0.308202139, -0.309150572, -0.309679123), UPPER = c(-0.182716456,
-0.186724068, -0.189331305, -0.193118273, -0.197069799, -0.20118809
), LOWER = c(-0.416725663, -0.413606073, -0.411131729, -0.408930899,
-0.406531588, -0.404478981), YEAR = 1820:1825, SITE = structure(c(1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Little Swanport", "Lutregala",
"Tarra", "Wapengo"), class = "factor")), row.names = c(NA, 6L
), class = "data.frame")
tgsm <- structure(list(Year = 1993:1998, Lg2002 = c(-0.001164223, -0.002229453,
-0.002734792, -0.002977787, -0.002786098, -0.002026994), Wap2002 = c(-0.002531348,
-0.002051993, -0.001468704, -0.001182162, -0.001027132, -0.00020881
), Tar2002 = c(-0.029020612, -0.024330561, -0.019927593, -0.015682528,
-0.012907219, -0.009784772), LSP2002 = c(-0.034514531, -0.030171621,
-0.026095138, -0.021952898, -0.018480702, -0.014531318)), na.action = structure(c(`1` = 1L,
`2` = 2L, `3` = 3L, `4` = 4L, `5` = 5L, `6` = 6L, `7` = 7L, `8` = 8L,
`9` = 9L, `10` = 10L, `11` = 11L, `12` = 12L, `13` = 13L, `14` = 14L,
`15` = 15L, `16` = 16L, `17` = 17L, `18` = 18L, `19` = 19L, `20` = 20L,
`21` = 21L, `22` = 22L, `23` = 23L, `24` = 24L, `25` = 25L, `26` = 26L,
`27` = 27L, `28` = 28L, `29` = 29L, `30` = 30L, `31` = 31L, `32` = 32L,
`33` = 33L, `34` = 34L, `35` = 35L, `36` = 36L, `37` = 37L, `38` = 38L,
`39` = 39L, `40` = 40L, `41` = 41L, `42` = 42L, `43` = 43L, `44` = 44L,
`45` = 45L, `46` = 46L, `47` = 47L, `48` = 48L, `49` = 49L, `50` = 50L,
`51` = 51L, `52` = 52L, `53` = 53L, `54` = 54L, `55` = 55L, `56` = 56L,
`57` = 57L, `58` = 58L, `59` = 59L, `60` = 60L, `61` = 61L, `62` = 62L,
`63` = 63L, `64` = 64L, `65` = 65L, `66` = 66L, `67` = 67L, `68` = 68L,
`69` = 69L, `70` = 70L, `71` = 71L, `72` = 72L, `73` = 73L, `74` = 74L,
`75` = 75L, `76` = 76L, `77` = 77L, `78` = 78L, `79` = 79L, `80` = 80L,
`81` = 81L, `82` = 82L, `83` = 83L, `84` = 84L, `85` = 85L, `86` = 86L,
`87` = 87L, `88` = 88L, `89` = 89L, `90` = 90L, `91` = 91L, `92` = 92L,
`93` = 93L, `94` = 94L, `95` = 95L, `96` = 96L, `97` = 97L, `98` = 98L,
`99` = 99L, `100` = 100L, `101` = 101L, `102` = 102L, `103` = 103L,
`104` = 104L, `105` = 105L, `106` = 106L, `107` = 107L, `108` = 108L,
`109` = 109L, `110` = 110L, `111` = 111L, `112` = 112L, `113` = 113L,
`114` = 114L, `115` = 115L, `116` = 116L, `117` = 117L, `118` = 118L,
`119` = 119L, `120` = 120L, `121` = 121L, `122` = 122L, `123` = 123L,
`124` = 124L, `125` = 125L, `126` = 126L, `127` = 127L, `128` = 128L,
`129` = 129L, `130` = 130L, `131` = 131L, `132` = 132L, `133` = 133L,
`134` = 134L, `135` = 135L, `136` = 136L, `137` = 137L, `138` = 138L,
`139` = 139L, `140` = 140L, `141` = 141L, `142` = 142L, `143` = 143L,
`144` = 144L, `145` = 145L, `146` = 146L, `147` = 147L, `148` = 148L,
`149` = 149L, `150` = 150L, `151` = 151L, `152` = 152L, `153` = 153L,
`154` = 154L, `155` = 155L, `156` = 156L, `157` = 157L, `183` = 183L
), class = "omit"), row.names = 158:163, class = "data.frame")

Answer 1

See plot one how you can do that with patchwork.
However. Conceptually, I am guessing you want to add a sort of prediction to some historic values or so. I personally would put everything in one data frame and plot this. If there is a too large gap between the two time points, you can facet by timepoints (as in my suggestion).
The plots look a bit different than your plot because you only provided data for one Site in Proxy (so I filtered the other for what I thought is the equivalent, it will work nonetheless, because the faceting remains) - and I removed all those theme elements that are not relevant to the problem.
Plot one - combining plots.
library(tidyverse)
library(patchwork)
tgsm<-na.omit(tgsm)
tglonger <-
pivot_longer(tgsm, cols=c(-Year), names_to="SITE", values_to = "RSL") %>%
filter(SITE == "LSP2002") %>%
rename(YEAR = Year)
p1 <- ggplot() +
geom_ribbon(data = Proxy, aes(x = YEAR, ymin = LOWER, ymax = UPPER, fill = SITE), alpha = .5) +
geom_line(data = Proxy, aes(x = YEAR, y = RSL, col = SITE)) +
facet_wrap(~SITE) +
coord_cartesian(xlim = c(1800, 1830), ylim = c(-1, 0)) +
theme_classic() +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none"
)
p2 <- ggplot() +
geom_point(data = tglonger, aes(x = YEAR, y = RSL, col = SITE), alpha = .7, size = 1) +
facet_wrap(~SITE) +
coord_cartesian(xlim = c(1990, 2000), ylim = c(-1, 0)) +
theme_classic() +
## only one call to theme!!
theme(
## this is where the theme call is different to above
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line.y = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none",
)
p1 + p2
Suggestion for an alternative visualisation
df_new <-
bind_rows(time1 = Proxy, time2 = tglonger, .id = "timevar") %>%
mutate(SITE = "LSP2002")
ggplot(df_new)+
geom_point(aes(x=YEAR,y=RSL))+
facet_grid(SITE~timevar, scales = "free_x")+
theme(legend.position="none") +
theme(panel.spacing = unit(.5, "lines"))
You can also use this data frame in order to create a list of plots, and then stitch it together with patchwork. This approach doesn't allow to change individual plots though.
ls_p <-
df_new %>%
split(., .$timevar) %>%
map(~{ggplot(.x)+
geom_point(aes(x=YEAR,y=RSL))+
coord_cartesian(ylim = c(-0.4,0))+
facet_grid(~SITE, scales = "free_x")+
theme(legend.position="none") +
theme(panel.spacing = unit(.5, "lines"))})
library(patchwork)
wrap_plots(ls_p)