矩阵R从数字转换为csv中提供的字符

我对使用R和学习如何使用 forestplot功能

我正在努力使用以下方法标记林地的y轴：

labeltext = matrix()

当我使用此选项时：

labeltext = matrix(c(data$BCT, data$present), nrows=40, ncol=2, byrow=TRUE, dimnames=NULL)

我得到一个数字矩阵，R忽略我提供的scv文件中的文本

当我为一列创建矩阵时，它似乎起作用。 如何使用csv文件中的两列标记y轴

我想实现这样的目标：

但与其说是“参考”、“乳酸”和“饮食”，不如说是“BCT”和“现在”

我想要一个具有“BCT”（第1列）和“present”（第2列）作为y轴标签的森林图。SMD是效果度量，上限和下限是CI。见下面的示例数据

代码如下：

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")

库（forestplot）
labeltext=矩阵（c（数据$BCT，数据$present），nrow=40，ncol=2，byrow=TRUE，dimnames=NULL）
is_summary=FALSE
forestplot（labeltext，
图1.pos=2，
平均值=数据$SMD，
下限=数据$“下限”，
上限=数据$“上限”，
is.summary=is_summary，
xlab=“”，
线裕度=单位（0.8，“厘米”），
线宽=单位（0.5，“厘米”），
hrlz.lines=TRUE，
col=fpColors（box=“darkred”，line=“grey”，summary=“black”），
图宽=单位（10，“厘米”）
#样本输入数据
数据您很可能将角色数据作为因素导入。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。更仔细的转换将是

labeltext = as.matrix(data.frame(data$BCT, data$present))

如果您提供一个示例输入，那么帮助您会更容易。也许可以提供一个链接，显示所需的输出。@MrFlick这里是指向我的csv文件的链接：。我想使用文件的前两列在森林图上标记y轴。@mrFlick我现在编辑了这个问题。谢谢。@flick先生，谢谢你，成功了！：）在这里，我如何投票支持你的答案？@flick先生，我没有足够的“声望”投票支持你的答案。