我是新手,使用R并学习如何使用the创建森林图 forestplot 功能。
我正在努力使用以下方法标记森林地块的y轴:
labeltext = matrix(c(data$BCT, data$present), nrows=40, ncol=2, byrow=TRUE, dimnames=NULL)
当我使用它时:
library(forestplot)
labeltext = matrix(c(data$BCT, data$present), nrow = 40, ncol = 2, byrow = TRUE, dimnames = NULL)
is_summary = FALSE
forestplot(labeltext,
graph.pos = 2,
mean = data$SMD,
lower = data$"lower_limit",
upper = data$"upper_limit",
is.summary = is_summary,
xlab = "<-------Comparator Better---------------Main Intervention Better-------->",
line.margin = unit (0.8, "cm"),
lineheight = unit (0.5, "cm"),
hrlz.lines =TRUE,
col = fpColors(box = "darkred", lines = "grey", summary = "black"),
graphwidth = unit (10, "cm"))
# Sample Input Data
data <- structure(list(
BCT = c("Adding objects to the environment (12.5) ", "Adding objects to the environment (12.5) ", "Action planning (1.4)", "Action planning (1.4)", "Body changes (12.6) ", "Body changes (12.6) ", "Behavioural practice/rehearsal (8.1)", "Behavioural practice/rehearsal (8.1)", "Credible source (9.2)", "Credible source (9.2)", "Demonstration of the behaviour (6.1) ", "Demonstration of the behaviour (6.1) ", "Feedback on behaviour (2.2) ", "Feedback on behaviour (2.2) ", "Goal setting (behaviour) (1.1)", "Goal setting (behaviour) (1.1)", "Graded tasks (8.7)", "Graded tasks (8.7)", "Generalisation of target behaviour (8.6) ", "Generalisation of target behaviour (8.6) ", "Information about health consequences (5.1)", "Information about health consequences (5.1)", "Instruction on how to perform the behaviour (4.1)", "Instruction on how to perform the behaviour (4.1)", "Monitoring of behaviour by others without feedback (2.1)", "Monitoring of behaviour by others without feedback (2.1)", "Monitoring of outcomes of behaviour without feedback (2.5)",
"Monitoring of outcomes of behaviour without feedback (2.5)", "Problem solving (1.2) ", "Problem solving (1.2) ", "Social Comparison (6.2)", "Social Comparison (6.2)", "Self-monitoring of behaviour (2.3) ", "Self-monitoring of behaviour (2.3) ", "Self-monitoring of outcome(s) of behaviour (2.4) ", "Self-monitoring of outcome(s) of behaviour (2.4) ", "Social support (emotional) (3.3)", "Social support (emotional) (3.3)", "Social support (unspecified) (3.1) ", "Social support (unspecified) (3.1) "),
present = c("present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present", "present ", "not present"),
SMD = c(1.69, 0.42, 0.78, 0.4, 0.36, 0.57, 0.71, 0.4, 2.23, 0.31, 0.32, 0.63, 0.45, 0.58, 0.79, 0.26, 0.55, 0.56, 1.39, 0.4, 0.6, 0.46, 0.7, 0.34, 2.53, 0.47, 0.36, 0.62, 0.2, 0.7, 0.03, 0.66, 0.68, 0.47, 1.14, 0.4, 1.13, 0.44, 0.67, 0.52),
lower_limit = c(-1.49, 0.22, -0.4, 0.06, 0.08, -0.33, -0.34, -0.04, 0.15, 0.2, 0.14, -0.4, -0.25, -0.26, -0.19, 0.11, -0.66, 0.23, -0.68, 0.19, -0.35, 0.11, -0.46, 0.15, 1.36, 0.05, -0.07, -0.2, -0.01, -0.18, -0.42, -0.14, -0.4, 0.06, -0.84, 0.16, -1.47, 0.24, -0.93, 0.27),
upper_limit = c(4.87, 0.62, 1.95, 0.74, 0.65, 1.48, 1.75, 0.84, 4.32, 0.42, 0.51, 1.67, 1.15, 1.43, 1.77, 0.41, 1.77, 0.89, 3.46, 0.6, 1.55, 0.81, 1.87, 0.53, 3.7, 0.89, 0.78, 1.43, 0.41, 1.58, 0.47, 1.46, 1.77, 0.88, 3.12, 0.64, 3.72, 0.65, 2.28, 0.78)),
.Names = c("BCT", "present", "SMD", "lower_limit", "upper_limit"),
row.names = c(NA, -40L), class = "data.frame")
我得到一个数字矩阵,R忽略我提供的scv文件中的文本。
当我为一列制作矩阵时似乎有效。 如何使用csv文件中的两列标记y轴?
我想实现这样的目标:
但不是参考&#39;,&#39; lact&#39;和&#39;饮食&#39;应该说&#39; BCT&#39;和&#39;现在&#39;。
我想要一个拥有BCT&#39; (第1栏)和&#39;现在&#39; (第2列)作为y轴标签。 SMD是效果度量,upper_limit和lower_limit是CI。见下面的样本数据
以下是代码:
DOMDocument
答案 0 :(得分:0)
您可能已将角色数据导入为因素。当您使用简化函数(如c()
和matrix()
)来重塑因子时,它们会转换为数值,因为矩阵不能包含因子。更谨慎的转变是
labeltext = as.matrix(data.frame(data$BCT, data$present))