森林图，排序和汇总多个变量
Forest Plot, ordering and summarizing multiple variables
我有以下数据：
DF<-structure(list(ref = structure(c(15L, 15L, 16L, 19L, 2L, 12L,
11L, 23L, 6L, 21L, 5L, 13L, 8L, 22L, 26L, 27L, 20L, 17L, 9L,
7L, 24L, 25L, 18L, 1L, 3L, 14L, 16L, 12L, 23L, 6L, 21L, 8L, 22L,
26L, 27L, 20L, 17L, 9L, 7L, 24L, 25L, 18L, 4L, 1L, 14L, 16L,
19L, 2L, 11L, 23L, 21L, 8L, 26L, 27L, 17L, 9L, 7L, 24L, 1L, 10L,
14L), .Label = c("Bob 2012", "Bob 2016", "Arnez 2004",
"Smithy 2013", "Smithy 2014", "Smithy 2016", "Carole 2011", "Craig 2014",
"Fansa 2008", "Johnson 2010", "Joel 2017", "Joelo 2016",
"Bob2 2017", "Bob2 2020", "Hunter 2015", "Hush 2016",
"Lock 2012", "Mcdoo 2012", "Nick 2018", "Park 2015", "Joe 2012",
"Sour 2017", "Shoe 2008", "Vega 2009", "West 2004",
"West2016", "Smith 2016"), class = "factor"), yi = c(1,
0.909090909090909, 1, 1, 0.98780487804878, 0.933333333333333,
0.882352941176471, 0.980519480519481, 0.977272727272727, 1, 1,
0.98019801980198, 0.959183673469388, 1, 1, 0.982758620689655,
0.96969696969697, 0.6875, 1, 1, 1, 1, 1, 1, 0.75, 0.969811320754717,
0, 0.0333333333333333, 0.064935064935065, 0.0227272727272727,
0, 0.0204081632653061, 0.142857142857143, 0.0384615384615384,
0.120689655172414, 0.0303030303030303, 0.0625, 0, 0.0625000000000001,
0.148148148148148, 0.333333333333333, 0.0322580645161291, 0.0625,
0, 0.0150943396226415, 0, 0.027027027027027, 0.0182926829268293,
0.0588235294117647, 0.0324675324675325, 0.0416666666666667, 0.0408163265306122,
0.192307692307692, 0.103448275862069, 0.0625, 0.03125, 0, 0,
0.037037037037037, 0.0526315789473685, 0.0264150943396226), ci.lb = c(0.968401784273333,
0.745137584391619, 0.957452087056599, 0.954039295289784, 0.963597464688465,
0.809439442909756, 0.67719312002544, 0.951199930155904, 0.905001120558666,
0.929555376052338, 0.880663089089027, 0.941246506281999, 0.880901216198665,
0.880663089089027, 0.934891169467222, 0.927453022366531, 0.874486623056924,
0.435962472420225, 0.946947080517241, 0.946947080517241, 0.937267052265125,
0.861434988827223, 0.945257646596841, 0.937267052265125, 0.384687131024181,
0.945252480837292, 0, 0, 0.0306637200529119, 0, 0, 0, 0.00329369106613314,
0, 0.0474215778277017, 0, 0, 0, 0.00112833931883988, 0.0347070885129207,
0.0895601878163022, 0, 0, 0, 0.00321663954072449, 0, 0, 0.00226571557474109,
0, 0.00919930409127839, 0, 0.000687698884629828, 0.0597984369364536,
0.0359775093204114, 0, 0, 0, 0, 0, 0, 0.00995385402759386), ci.ub = c(1,
0.998207039140277, 1, 1, 0.999812850010077, 0.998780552481617,
0.997483360196549, 0.997584224395838, 1, 1, 1, 0.999688395336243,
0.99931230111537, 1, 1, 1, 1, 0.895437964404381, 1, 1, 1, 1,
1, 1, 0.992197756884658, 0.987557818958737, 0.0425479129434015,
0.137561603224075, 0.11002605111172, 0.0949988794413338, 0.070444623947662,
0.0855442043005818, 0.384071178226987, 0.157747957353967, 0.21886405934029,
0.125513376943076, 0.249041832299857, 0.0530529194827593, 0.179245839830917,
0.311107006224451, 0.628630049521593, 0.133296666133586, 0.249041832299857,
0.062732947734875, 0.0340807242282984, 0.0425479129434015, 0.112387275591248,
0.0458586358986253, 0.235428911558493, 0.0674429497369029, 0.170235847270992,
0.119098783801335, 0.36946549674197, 0.197004260641943, 0.249041832299857,
0.129288064181111, 0.0530529194827593, 0.062732947734875, 0.152166113984736,
0.212219059832308, 0.0497506289906541), TypeTwo = structure(c(2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L), .Label = c("BAR",
"FOO"), class = "factor"), Variable = c("Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Gas", "Gas", "Gas",
"Gas", "Gas", "Gas", "Gas", "Gas", "Gas",
"Gas", "Gas", "Gas", "Gas", "Gas", "Gas",
"Gas")), row.names = c(NA, -61L), yi.names = "yi", ci.lb.names = "ci.lb", ci.ub.names = "ci.ub", digits = c(est = 4,
se = 4, test = 4, pval = 4, ci = 4, var = 4, sevar = 4, fit = 4,
het = 4), class = c("escalc", "data.frame"))
我用这段代码创建了一个森林图：
DF%>%ggplot(aes(x=yi,y=ref,xmin=ci.lb,xmax=ci.ub,color=TypeTwo, group=TypeTwo))+geom_point()+geom_errorbarh(height=.1, size=.5)+geom_vline(xintercept = 0,color="black", linetype="dashed", alpha=.5)+facet_grid(Variable~.,scales="free",space="free")+labs(title="Forest Plot Combined",x="Effect Size",y="Study")
看起来像这样：
我想对图表重新排序，使“Foo”和“Bar”彼此靠近（如下图所示），并且我在不同的数据集中有单独的“摘要”数据，我'如果有意义的话，我想在每个组的每个方面下添加一条线。如何为此添加新行但将其保留在侧面？
我完全不知道该怎么做，请帮忙！
有很多方法可以解决这个问题，但这里有一个。请注意，由于您在多个方面进行了相同的研究，并且 TypeTwo 不一致，我们必须做一些技巧才能在每个方面进行排序。
我还按效果大小排序，因为这很令人愉快而且很常见。
你的 Hunter 2015 数据有误，它有两个死亡效果大小，所以这就是为什么有一个红色条和绿色条的原因。
使用一些随机数据获得平均效果：
library(tidyverse)
avg <- data.frame(
Variable = c('Death', 'Gas', 'Vein Problems'),
yi = c(0.9, 0.1, 0.1),
ci.lb = c(0.5, 0, 0),
ci.ub = c(1, 0.5, 0.5),
TypeTwo = 'mean effect',
ref = ''
)
DF2 <- bind_rows(DF, avg) %>%
arrange(desc(TypeTwo), yi) %>%
mutate(ref2 = fct_inorder(paste(ref, Variable)))
ggplot(DF2, aes(x=yi,y=ref2,xmin=ci.lb,xmax=ci.ub,color=TypeTwo, group=TypeTwo))+
geom_point()+
geom_errorbarh(height=.1, size=.5)+
geom_vline(xintercept = 0,color="black", linetype="dashed", alpha=.5)+
facet_grid(Variable~.,scales="free",space="free")+
scale_y_discrete(breaks = DF2$ref2, labels = DF2$ref) +
labs(title="Forest Plot Combined",x="Effect Size",y="Study")
我有以下数据：
DF<-structure(list(ref = structure(c(15L, 15L, 16L, 19L, 2L, 12L,
11L, 23L, 6L, 21L, 5L, 13L, 8L, 22L, 26L, 27L, 20L, 17L, 9L,
7L, 24L, 25L, 18L, 1L, 3L, 14L, 16L, 12L, 23L, 6L, 21L, 8L, 22L,
26L, 27L, 20L, 17L, 9L, 7L, 24L, 25L, 18L, 4L, 1L, 14L, 16L,
19L, 2L, 11L, 23L, 21L, 8L, 26L, 27L, 17L, 9L, 7L, 24L, 1L, 10L,
14L), .Label = c("Bob 2012", "Bob 2016", "Arnez 2004",
"Smithy 2013", "Smithy 2014", "Smithy 2016", "Carole 2011", "Craig 2014",
"Fansa 2008", "Johnson 2010", "Joel 2017", "Joelo 2016",
"Bob2 2017", "Bob2 2020", "Hunter 2015", "Hush 2016",
"Lock 2012", "Mcdoo 2012", "Nick 2018", "Park 2015", "Joe 2012",
"Sour 2017", "Shoe 2008", "Vega 2009", "West 2004",
"West2016", "Smith 2016"), class = "factor"), yi = c(1,
0.909090909090909, 1, 1, 0.98780487804878, 0.933333333333333,
0.882352941176471, 0.980519480519481, 0.977272727272727, 1, 1,
0.98019801980198, 0.959183673469388, 1, 1, 0.982758620689655,
0.96969696969697, 0.6875, 1, 1, 1, 1, 1, 1, 0.75, 0.969811320754717,
0, 0.0333333333333333, 0.064935064935065, 0.0227272727272727,
0, 0.0204081632653061, 0.142857142857143, 0.0384615384615384,
0.120689655172414, 0.0303030303030303, 0.0625, 0, 0.0625000000000001,
0.148148148148148, 0.333333333333333, 0.0322580645161291, 0.0625,
0, 0.0150943396226415, 0, 0.027027027027027, 0.0182926829268293,
0.0588235294117647, 0.0324675324675325, 0.0416666666666667, 0.0408163265306122,
0.192307692307692, 0.103448275862069, 0.0625, 0.03125, 0, 0,
0.037037037037037, 0.0526315789473685, 0.0264150943396226), ci.lb = c(0.968401784273333,
0.745137584391619, 0.957452087056599, 0.954039295289784, 0.963597464688465,
0.809439442909756, 0.67719312002544, 0.951199930155904, 0.905001120558666,
0.929555376052338, 0.880663089089027, 0.941246506281999, 0.880901216198665,
0.880663089089027, 0.934891169467222, 0.927453022366531, 0.874486623056924,
0.435962472420225, 0.946947080517241, 0.946947080517241, 0.937267052265125,
0.861434988827223, 0.945257646596841, 0.937267052265125, 0.384687131024181,
0.945252480837292, 0, 0, 0.0306637200529119, 0, 0, 0, 0.00329369106613314,
0, 0.0474215778277017, 0, 0, 0, 0.00112833931883988, 0.0347070885129207,
0.0895601878163022, 0, 0, 0, 0.00321663954072449, 0, 0, 0.00226571557474109,
0, 0.00919930409127839, 0, 0.000687698884629828, 0.0597984369364536,
0.0359775093204114, 0, 0, 0, 0, 0, 0, 0.00995385402759386), ci.ub = c(1,
0.998207039140277, 1, 1, 0.999812850010077, 0.998780552481617,
0.997483360196549, 0.997584224395838, 1, 1, 1, 0.999688395336243,
0.99931230111537, 1, 1, 1, 1, 0.895437964404381, 1, 1, 1, 1,
1, 1, 0.992197756884658, 0.987557818958737, 0.0425479129434015,
0.137561603224075, 0.11002605111172, 0.0949988794413338, 0.070444623947662,
0.0855442043005818, 0.384071178226987, 0.157747957353967, 0.21886405934029,
0.125513376943076, 0.249041832299857, 0.0530529194827593, 0.179245839830917,
0.311107006224451, 0.628630049521593, 0.133296666133586, 0.249041832299857,
0.062732947734875, 0.0340807242282984, 0.0425479129434015, 0.112387275591248,
0.0458586358986253, 0.235428911558493, 0.0674429497369029, 0.170235847270992,
0.119098783801335, 0.36946549674197, 0.197004260641943, 0.249041832299857,
0.129288064181111, 0.0530529194827593, 0.062732947734875, 0.152166113984736,
0.212219059832308, 0.0497506289906541), TypeTwo = structure(c(2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L), .Label = c("BAR",
"FOO"), class = "factor"), Variable = c("Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Death", "Death", "Death", "Death", "Death", "Death",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Vein Problems", "Vein Problems",
"Vein Problems", "Gas", "Gas", "Gas",
"Gas", "Gas", "Gas", "Gas", "Gas", "Gas",
"Gas", "Gas", "Gas", "Gas", "Gas", "Gas",
"Gas")), row.names = c(NA, -61L), yi.names = "yi", ci.lb.names = "ci.lb", ci.ub.names = "ci.ub", digits = c(est = 4,
se = 4, test = 4, pval = 4, ci = 4, var = 4, sevar = 4, fit = 4,
het = 4), class = c("escalc", "data.frame"))
我用这段代码创建了一个森林图：
DF%>%ggplot(aes(x=yi,y=ref,xmin=ci.lb,xmax=ci.ub,color=TypeTwo, group=TypeTwo))+geom_point()+geom_errorbarh(height=.1, size=.5)+geom_vline(xintercept = 0,color="black", linetype="dashed", alpha=.5)+facet_grid(Variable~.,scales="free",space="free")+labs(title="Forest Plot Combined",x="Effect Size",y="Study")
看起来像这样：
我想对图表重新排序，使“Foo”和“Bar”彼此靠近（如下图所示），并且我在不同的数据集中有单独的“摘要”数据，我'如果有意义的话，我想在每个组的每个方面下添加一条线。如何为此添加新行但将其保留在侧面？
我完全不知道该怎么做，请帮忙！
有很多方法可以解决这个问题，但这里有一个。请注意，由于您在多个方面进行了相同的研究，并且 TypeTwo 不一致，我们必须做一些技巧才能在每个方面进行排序。
我还按效果大小排序，因为这很令人愉快而且很常见。
你的 Hunter 2015 数据有误，它有两个死亡效果大小，所以这就是为什么有一个红色条和绿色条的原因。
使用一些随机数据获得平均效果：
library(tidyverse)
avg <- data.frame(
Variable = c('Death', 'Gas', 'Vein Problems'),
yi = c(0.9, 0.1, 0.1),
ci.lb = c(0.5, 0, 0),
ci.ub = c(1, 0.5, 0.5),
TypeTwo = 'mean effect',
ref = ''
)
DF2 <- bind_rows(DF, avg) %>%
arrange(desc(TypeTwo), yi) %>%
mutate(ref2 = fct_inorder(paste(ref, Variable)))
ggplot(DF2, aes(x=yi,y=ref2,xmin=ci.lb,xmax=ci.ub,color=TypeTwo, group=TypeTwo))+
geom_point()+
geom_errorbarh(height=.1, size=.5)+
geom_vline(xintercept = 0,color="black", linetype="dashed", alpha=.5)+
facet_grid(Variable~.,scales="free",space="free")+
scale_y_discrete(breaks = DF2$ref2, labels = DF2$ref) +
labs(title="Forest Plot Combined",x="Effect Size",y="Study")