【发布时间】：2021-11-03 15:31:12
【问题描述】：
我的数据由一列鱼类数量以及每次捕获的相应时间和地点组成。
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))
我想创建一个函数，首先汇总每个物种的数据，然后为每个唯一的物种名称保存一个 png 文件图（如果文件名 = NULL，则将其打印到屏幕上）。我已经做到了这一点，但不知道要解决什么......
GetMatrix=function(data, commonToSum) {
newdat<-filter(data,common_name %in% commonToSum)
cdata2 <- plyr::ddply(data, 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 <- within(cdata2, year[year == 2005 & season == 'wet'] <- 2005.75)
cdata2 <- within(cdata2, year[year == 2006 & season == 'wet'] <- 2006.75)
cdata2 <- within(cdata2, year[year == 2007 & season == 'wet'] <- 2007.75)
cdata2 <- within(cdata2, year[year == 2008 & season == 'wet'] <- 2008.75)
cdata2 <- within(cdata2, year[year == 2009 & season == 'wet'] <- 2009.75)
cdata2 <- within(cdata2, year[year == 2010 & season == 'wet'] <- 2010.75)
cdata2 <- within(cdata2, year[year == 2011 & season == 'wet'] <- 2011.75)
cdata2 <- within(cdata2, year[year == 2012 & season == 'wet'] <- 2012.75)
cdata2 <- within(cdata2, year[year == 2013 & season == 'wet'] <- 2013.75)
cdata2 <- within(cdata2, year[year == 2014 & season == 'wet'] <- 2014.75)
cdata2 <- within(cdata2, year[year == 2015 & season == 'wet'] <- 2015.75)
cdata2 <- within(cdata2, year[year == 2016 & season == 'wet'] <- 2016.75)
cdata2 <- within(cdata2, year[year == 2017 & season == 'wet'] <- 2017.75)
cdata2 <- within(cdata2, year[year == 2018 & season == 'wet'] <- 2018.75)
cdata2 <- within(cdata2, year[year == 2019 & season == 'wet'] <- 2019.75)
cdata2 <- within(cdata2, year[year == 2020 & season == 'wet'] <- 2020.75)
cdata2 <- within(cdata2, year[year == 2005 & season == 'dry'] <- 2005.25)
cdata2 <- within(cdata2, year[year == 2006 & season == 'dry'] <- 2006.25)
cdata2 <- within(cdata2, year[year == 2007 & season == 'dry'] <- 2007.25)
cdata2 <- within(cdata2, year[year == 2008 & season == 'dry'] <- 2008.25)
cdata2 <- within(cdata2, year[year == 2009 & season == 'dry'] <- 2009.25)
cdata2 <- within(cdata2, year[year == 2010 & season == 'dry'] <- 2010.25)
cdata2 <- within(cdata2, year[year == 2011 & season == 'dry'] <- 2011.25)
cdata2 <- within(cdata2, year[year == 2012 & season == 'dry'] <- 2012.25)
cdata2 <- within(cdata2, year[year == 2013 & season == 'dry'] <- 2013.25)
cdata2 <- within(cdata2, year[year == 2014 & season == 'dry'] <- 2014.25)
cdata2 <- within(cdata2, year[year == 2015 & season == 'dry'] <- 2015.25)
cdata2 <- within(cdata2, year[year == 2016 & season == 'dry'] <- 2016.25)
cdata2 <- within(cdata2, year[year == 2017 & season == 'dry'] <- 2017.25)
cdata2 <- within(cdata2, year[year == 2018 & season == 'dry'] <- 2018.25)
cdata2 <- within(cdata2, year[year == 2019 & season == 'dry'] <- 2019.25)
cdata2 <- within(cdata2, year[year == 2020 & season == 'dry'] <- 2020.25)
}
Plot<-function(data,common_name,fileName=NULL) {
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"))
}
spSummary <- data %>%
group_by(common_name) %>%
dplyr::summarize(total.numbers=sum(num)) %>%
arrange(-total.numbers)
spSummary
splist<-spSummary$common_name
dataList<-list()
filenameVal<-paste0(1:length(splist),splist,"- IBBEAM_trend_plot.png")
setwd('C:/Users/...Trend plots')
for(run in 1:length(splist)) {
dataList[[run]]<-GetMatrix(data,splist[run])
Plot(data=dataList[[run]],splist[run],fileName=filenameVal[run])
print(paste(run,splist[run]))
}
【问题讨论】：
-
您好，您能提供一个小的示例数据集，以便我们重现您的代码吗？只需添加几行“counts_data.csv”，包括两年、两个季节（以及代码中需要的所有列）。您可以在“数据框”标题下找到一个示例，例如 here。
-
我使用了“dput”方法并将其添加到我的原始帖子中。感谢您的帮助！
标签： r function if-statement ggplot2 summarize