自动为组中的每个级别制作时间序列图
Automatically make time series plots for each level in a group
我原来的问题可以在这里看到 ()，但我想我会问不同的问题，让它开放给许多不同的方式来回答这个问题，而不是一个“如何”的问题修正我糟糕的尝试。
我想创建时间序列图的过程如下图 quicker/automatic（即，不需要用户一次输入一个物种名称）。也许有一个“if”循环。告诉 R 循环遍历数据中所有唯一的通用名称并使用下面的代码打印（或保存到 png）绘图（每个物种的“common_name”作为各自绘图的标题） .如果没有足够的数据用于绘图，R 应该打印一条消息：“没有足够的数据用于绘图”，或者类似的东西。
这是我的数据样本（如您所见，有 100 多种物种可供绘制）。此数据样本仅显示 3 个物种，47 个站点中的 5 个站点，以及 16 年数据中的 3 年。
data <- structure(list(year = c(2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L), season = structure(c(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, 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, 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), .Label = c("dry", "wet"), class = "factor"),
site = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L), common_name = structure(c(68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L,
105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L,
105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L), .Label = c("Atlantic Mud Crab",
"Atlantic Needlefish", "Banded Blenny", "Banded Brittle Star",
"Banded Killifish", "Bandtail Puffer", "Barracuda spp", "Bigclaw Snapping Shrimp",
"Bigeye Mojarra", "Blenny spp", "Blue Crab", "Blue Crab spp",
"Blue Striped Grunt", "Bluethroat Pikeblenny", "Bonefish",
"Brittle Star spp", "Broadback Mud Crab", "Brown Shrimp",
"Bryozoan Shrimp", "Chain Pipefish", "Checkered Puffer",
"Chub spp", "Clown Goby", "Code Goby", "Combtooth Blenny spp",
"Crested Blenny", "Crested Goby", "Crossbanded Grass Shrimp",
"Cushion Sea Star", "Daggerblade Grass Shrimp", "Darter Goby",
"Dusky Pipefish", "Dwarf Seahorse", "Estuarine Snapping Shrimp",
"False Zostera Shrimp", "Fiddler Crab spp", "Flagfin Mojarra",
"Flatback Mud Crab", "Florida Blenny", "Florida Grass Shrimp",
"Florida Grassflat Crab", "Frillfin Goby", "Fringed Pipefish",
"Furrowed Mud Crab", "Giant Decorator crab", "Giant Tiger Prawn",
"Glass Shrimp", "Goby spp", "Goby spp (Ctenogobius spp)",
"Goldspotted Killifish", "Grass Shrimp (H obliquimanus)",
"Grass Shrimp (Leander spp)", "Grass Shrimp (Nikoides schmitti)",
"Grass Shrimp (P mundusnovus)", "Grass Shrimp (Palaemon spp)",
"Grass Shrimp (Palaemonidae spp)", "Grass Shrimp (Periclimenes spp)",
"Grass Shrimp (Thor spp)", "Grass Shrimp Spp", "Gray Snapper",
"Great Barracuda", "Grunt spp", "Gulf Flounder", "Gulf Killifish",
"Gulf Pipefish", "Gulf Toadfish", "Halfbeak spp", "Hardhead Silverside",
"Harlequin Brittle Star", "Harris Mud Crab", "Highfin Blenny",
"Hogchoker", "Horseshoe Crab", "Iridescent Shrimp", "Jack spp",
"Jewel Cichlid", "Killifish spp", "Least Puffer", "Lesser Blue Crab",
"Lined Seahorse", "Lined Sole", "Lobate Mud Crab", "Longnose Spider Crab",
"Longsnout Seahorse", "Longtail Grass Shrimp", "Mangrove Gambusia",
"Mangrove Rivulus", "Manning Grass Shrimp", "Marsh Killifish",
"Marsh Shrimp", "Mayan Cichlid", "Mojarra spp", "Mud Crab spp",
"Mullet spp", "Needlefish spp", "Oyster Mud Crab", "Pearl Blenny",
"Pinfish", "Pink Shrimp", "Pink Shrimp spp", "Pipefish spp",
"Porgy spp", "Puffer spp", "Pugnose Pipefish", "Rainwater Killifish",
"Red-Algae Shrimp", "Redear Sardine", "Redfin Needlefish",
"Roughneck Shrimp", "Sailfin Molly", "Sailor's Choice", "Saltmarsh Mud Crab",
"Sargassum Fish", "Sargassum Pipefish", "Sargassum Shrimp",
"Sargassum Swimming Crab", "Say Mud Crab", "Schoolmaster Snapper",
"Sea Star spp", "Seabream", "Seahorse spp", "Sheepshead",
"Sheepshead Minnow", "Silver Jenny", "Silverside spp", "Slender Mojarra",
"Slender Sargassum Shrimp", "Small Spine Sea Star", "Smooth Mud Crab",
"Snapper spp", "Snapping Shrimp (A viridari)", "Snapping Shrimp (A. angulosus)",
"Snapping Shrimp spp", "Southern Pink Shrimp", "Southern Puffer",
"Southern Sennet", "Spaghetti Eel", "Speckled Worm Eel",
"Spider Crab spp", "Sponge Spider Crab", "Spotted Pink Shrimp",
"Spotted Whiff", "Squat Grass Shrimp", "Stone Crab", "Striped Mullet",
"Swimming Crab spp", "Timicu", "Tomtate", "Tripletail", "White Grunt",
"White Mullet", "Whitespotted Filefish", "Yellowfin Mojarra",
"Zostera Shrimp"), class = "factor"), num = c(0L, 1L, 0L,
4L, 2L, 0L, 0L, 0L, 4L, 0L, 5L, 24L, 0L, 0L, 0L, 0L, 1L,
5L, 0L, 2L, 3L, 0L, 0L, 38L, 25L, 0L, 14L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 9L, 0L, 5L, 20L, 10L, 0L, 17L, 0L, 0L, 0L,
66L, 2L, 64L, 0L, 5L, 4L, 0L, 12L, 49L, 0L, 0L, 2L, 0L, 2L,
0L, 0L, 0L, 0L, 0L, 1L, 4L, 0L, 1L, 4L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 16L, 12L, 12L, 0L, 0L, 26L, 2L,
0L, 0L)), class = "data.frame", row.names = c(NA, -90L))
这就是我开始一次为一个物种绘制一个图的方式：
# Select species
rain <- subset(data,common_name == "Rainwater Killifish",
select = c(year,
season,
site,
common_name,
num))
cdata2 <- ddply(rain, c("year", "season"), summarise,
N = length(num),
n_mean = mean(num),
n_median = median(num),
sd = sd(num),
se = sd / sqrt(N))
cdata2$year_season <- paste(cdata2$year, "_", cdata2$season, sep = "")
cdata2 <-cdata2 %>% mutate(year=ifelse(season=="wet",year+0.75,year+0.25))
ggplot(cdata2, aes(x = year, y = n_mean, color = season)) +
annotate(geom = "rect", xmin = 2010, xmax = 2010.5, ymin = -Inf, ymax = Inf,
fill = "lightblue", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2013.5, xmax = 2014, ymin = -Inf, ymax = Inf,
fill = "lightgreen", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2017.5, xmax = 2018, ymin = -Inf, ymax = Inf,
fill = "#E0E0E0", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2011.5, xmax = 2012, ymin = -Inf, ymax = Inf,
fill = "pink", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2015.5, xmax = 2016, ymin = -Inf, ymax = Inf,
fill = "pink", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2018.5, xmax = 2019, ymin = -Inf, ymax = Inf,
fill = "orange", colour = NA, alpha = 0.4) +
geom_errorbar(aes(ymin=n_mean-se, ymax=n_mean+se),
width=.2,
color = "black") +
geom_point(color = "black",
shape = 21,
size = 3,
aes(fill = season)) +
scale_fill_manual(values=c("white", "#C0C0C0")) +
scale_x_continuous(breaks=c(2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2018,2019,2020)) +
theme(panel.border = element_rect(fill = NA, color = "black"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="Year", y = "Mean count") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(axis.text.y = element_text(size = 10, face = "bold")) +
theme(axis.text.x = element_text(size = 10, face = "bold")) +
theme(axis.title = element_text(size = 14, face = "bold"))
您可以将数据框嵌套到物种组，然后使用 mutate 和 map 组合为每个物种组创建一个图。然后你可以使用 deframe 将名称和值列转换为命名列表：
library(tidyverse)
plots <-
data %>%
nest(-common_name) %>%
mutate(
plt = data %>% map(possibly(~ {
.x %>%
ggplot(aes(year, num, color = site)) +
geom_point()
}, NA))
) %>%
select(common_name, plt) %>%
deframe()
plots[["Mojarra spp"]]
plots[["Rainwater Killifish"]]
如果绘图失败，possibly 函数将 return NA。
这是使用较新的 tidyverse 包，如 https://r4ds.had.co.nz/many-models.html#nested-data
中所述
是的，这就是 for 循环的用途！您已经完成了所有艰苦的工作。现在只需粘贴您在循环中编写的代码并将 common_name == "Rainwater Killifish" 替换为 common_name == species
allspecies <- unique(data$common_name)
for(species in allspecies){
## insert your code here
ggsave(paste0(species, ".png"))
}
我原来的问题可以在这里看到 (
我想创建时间序列图的过程如下图 quicker/automatic（即，不需要用户一次输入一个物种名称）。也许有一个“if”循环。告诉 R 循环遍历数据中所有唯一的通用名称并使用下面的代码打印（或保存到 png）绘图（每个物种的“common_name”作为各自绘图的标题） .如果没有足够的数据用于绘图，R 应该打印一条消息：“没有足够的数据用于绘图”，或者类似的东西。
这是我的数据样本（如您所见，有 100 多种物种可供绘制）。此数据样本仅显示 3 个物种，47 个站点中的 5 个站点，以及 16 年数据中的 3 年。
data <- structure(list(year = c(2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L), season = structure(c(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, 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, 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), .Label = c("dry", "wet"), class = "factor"),
site = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L,
5L, 5L, 5L), common_name = structure(c(68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L,
105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L,
105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L,
92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L,
68L, 92L, 105L, 68L, 92L, 105L, 68L, 92L, 105L), .Label = c("Atlantic Mud Crab",
"Atlantic Needlefish", "Banded Blenny", "Banded Brittle Star",
"Banded Killifish", "Bandtail Puffer", "Barracuda spp", "Bigclaw Snapping Shrimp",
"Bigeye Mojarra", "Blenny spp", "Blue Crab", "Blue Crab spp",
"Blue Striped Grunt", "Bluethroat Pikeblenny", "Bonefish",
"Brittle Star spp", "Broadback Mud Crab", "Brown Shrimp",
"Bryozoan Shrimp", "Chain Pipefish", "Checkered Puffer",
"Chub spp", "Clown Goby", "Code Goby", "Combtooth Blenny spp",
"Crested Blenny", "Crested Goby", "Crossbanded Grass Shrimp",
"Cushion Sea Star", "Daggerblade Grass Shrimp", "Darter Goby",
"Dusky Pipefish", "Dwarf Seahorse", "Estuarine Snapping Shrimp",
"False Zostera Shrimp", "Fiddler Crab spp", "Flagfin Mojarra",
"Flatback Mud Crab", "Florida Blenny", "Florida Grass Shrimp",
"Florida Grassflat Crab", "Frillfin Goby", "Fringed Pipefish",
"Furrowed Mud Crab", "Giant Decorator crab", "Giant Tiger Prawn",
"Glass Shrimp", "Goby spp", "Goby spp (Ctenogobius spp)",
"Goldspotted Killifish", "Grass Shrimp (H obliquimanus)",
"Grass Shrimp (Leander spp)", "Grass Shrimp (Nikoides schmitti)",
"Grass Shrimp (P mundusnovus)", "Grass Shrimp (Palaemon spp)",
"Grass Shrimp (Palaemonidae spp)", "Grass Shrimp (Periclimenes spp)",
"Grass Shrimp (Thor spp)", "Grass Shrimp Spp", "Gray Snapper",
"Great Barracuda", "Grunt spp", "Gulf Flounder", "Gulf Killifish",
"Gulf Pipefish", "Gulf Toadfish", "Halfbeak spp", "Hardhead Silverside",
"Harlequin Brittle Star", "Harris Mud Crab", "Highfin Blenny",
"Hogchoker", "Horseshoe Crab", "Iridescent Shrimp", "Jack spp",
"Jewel Cichlid", "Killifish spp", "Least Puffer", "Lesser Blue Crab",
"Lined Seahorse", "Lined Sole", "Lobate Mud Crab", "Longnose Spider Crab",
"Longsnout Seahorse", "Longtail Grass Shrimp", "Mangrove Gambusia",
"Mangrove Rivulus", "Manning Grass Shrimp", "Marsh Killifish",
"Marsh Shrimp", "Mayan Cichlid", "Mojarra spp", "Mud Crab spp",
"Mullet spp", "Needlefish spp", "Oyster Mud Crab", "Pearl Blenny",
"Pinfish", "Pink Shrimp", "Pink Shrimp spp", "Pipefish spp",
"Porgy spp", "Puffer spp", "Pugnose Pipefish", "Rainwater Killifish",
"Red-Algae Shrimp", "Redear Sardine", "Redfin Needlefish",
"Roughneck Shrimp", "Sailfin Molly", "Sailor's Choice", "Saltmarsh Mud Crab",
"Sargassum Fish", "Sargassum Pipefish", "Sargassum Shrimp",
"Sargassum Swimming Crab", "Say Mud Crab", "Schoolmaster Snapper",
"Sea Star spp", "Seabream", "Seahorse spp", "Sheepshead",
"Sheepshead Minnow", "Silver Jenny", "Silverside spp", "Slender Mojarra",
"Slender Sargassum Shrimp", "Small Spine Sea Star", "Smooth Mud Crab",
"Snapper spp", "Snapping Shrimp (A viridari)", "Snapping Shrimp (A. angulosus)",
"Snapping Shrimp spp", "Southern Pink Shrimp", "Southern Puffer",
"Southern Sennet", "Spaghetti Eel", "Speckled Worm Eel",
"Spider Crab spp", "Sponge Spider Crab", "Spotted Pink Shrimp",
"Spotted Whiff", "Squat Grass Shrimp", "Stone Crab", "Striped Mullet",
"Swimming Crab spp", "Timicu", "Tomtate", "Tripletail", "White Grunt",
"White Mullet", "Whitespotted Filefish", "Yellowfin Mojarra",
"Zostera Shrimp"), class = "factor"), num = c(0L, 1L, 0L,
4L, 2L, 0L, 0L, 0L, 4L, 0L, 5L, 24L, 0L, 0L, 0L, 0L, 1L,
5L, 0L, 2L, 3L, 0L, 0L, 38L, 25L, 0L, 14L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 9L, 0L, 5L, 20L, 10L, 0L, 17L, 0L, 0L, 0L,
66L, 2L, 64L, 0L, 5L, 4L, 0L, 12L, 49L, 0L, 0L, 2L, 0L, 2L,
0L, 0L, 0L, 0L, 0L, 1L, 4L, 0L, 1L, 4L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 16L, 12L, 12L, 0L, 0L, 26L, 2L,
0L, 0L)), class = "data.frame", row.names = c(NA, -90L))
这就是我开始一次为一个物种绘制一个图的方式：
# Select species
rain <- subset(data,common_name == "Rainwater Killifish",
select = c(year,
season,
site,
common_name,
num))
cdata2 <- ddply(rain, c("year", "season"), summarise,
N = length(num),
n_mean = mean(num),
n_median = median(num),
sd = sd(num),
se = sd / sqrt(N))
cdata2$year_season <- paste(cdata2$year, "_", cdata2$season, sep = "")
cdata2 <-cdata2 %>% mutate(year=ifelse(season=="wet",year+0.75,year+0.25))
ggplot(cdata2, aes(x = year, y = n_mean, color = season)) +
annotate(geom = "rect", xmin = 2010, xmax = 2010.5, ymin = -Inf, ymax = Inf,
fill = "lightblue", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2013.5, xmax = 2014, ymin = -Inf, ymax = Inf,
fill = "lightgreen", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2017.5, xmax = 2018, ymin = -Inf, ymax = Inf,
fill = "#E0E0E0", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2011.5, xmax = 2012, ymin = -Inf, ymax = Inf,
fill = "pink", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2015.5, xmax = 2016, ymin = -Inf, ymax = Inf,
fill = "pink", colour = NA, alpha = 0.4) +
annotate(geom = "rect", xmin = 2018.5, xmax = 2019, ymin = -Inf, ymax = Inf,
fill = "orange", colour = NA, alpha = 0.4) +
geom_errorbar(aes(ymin=n_mean-se, ymax=n_mean+se),
width=.2,
color = "black") +
geom_point(color = "black",
shape = 21,
size = 3,
aes(fill = season)) +
scale_fill_manual(values=c("white", "#C0C0C0")) +
scale_x_continuous(breaks=c(2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2018,2019,2020)) +
theme(panel.border = element_rect(fill = NA, color = "black"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
labs(x="Year", y = "Mean count") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(axis.text.y = element_text(size = 10, face = "bold")) +
theme(axis.text.x = element_text(size = 10, face = "bold")) +
theme(axis.title = element_text(size = 14, face = "bold"))
您可以将数据框嵌套到物种组，然后使用 mutate 和 map 组合为每个物种组创建一个图。然后你可以使用 deframe 将名称和值列转换为命名列表：
library(tidyverse)
plots <-
data %>%
nest(-common_name) %>%
mutate(
plt = data %>% map(possibly(~ {
.x %>%
ggplot(aes(year, num, color = site)) +
geom_point()
}, NA))
) %>%
select(common_name, plt) %>%
deframe()
plots[["Mojarra spp"]]
plots[["Rainwater Killifish"]]
如果绘图失败，possibly 函数将 return NA。
这是使用较新的 tidyverse 包，如 https://r4ds.had.co.nz/many-models.html#nested-data
是的，这就是 for 循环的用途！您已经完成了所有艰苦的工作。现在只需粘贴您在循环中编写的代码并将 common_name == "Rainwater Killifish" 替换为 common_name == species
allspecies <- unique(data$common_name)
for(species in allspecies){
## insert your code here
ggsave(paste0(species, ".png"))
}