如何使用 R 中 gplots 包中的 plotmeans() 函数绘制均值和置信区间条
How to plot the mean, and confidence interval bars using the plotmeans() function in the gplots package in R
问题：
我正在尝试使用 gplots 包 中的函数 plotmeans() 来绘制图表。我的目标是显示 平均 FID 目击 （请参阅下面称为 'FID' 和 'Mean_FID' 的数据框）以及 相关的上下置信区间条，和 n 个标签
数据帧结构
- 我有一个名为 'FID' 的大型数据框，有 918 行，主要列标题是：-
- FID = 目击次数
- 年份 = 2015-2017
- 月份=January-December
- 我修改了名为 'FID' 的数据框以显示每年每月的平均目击事件。数据框称为“Mean_FID”，有 36 行，用于生成图 4（见下文）。主要的列标题是：-
- 年 = 目击次数
- 月份=January-February
- 频率 FID = 每年每月平均目击次数
目标 - 欲望情节
我想使用函数 plotmeans()
将下面列出的所有特征合并到一个所需的图中
ci.label =
我想在每个置信区间条的末尾显示实际的上限和下限区间值。
位数 =
我希望所有置信区间标签都具有 3 位有效数字（见图 4）。
n.label = 我想在图中 x-axis 的每个区间条的底部显示每个组中的观察数space（见图一）
Dates = x-axis
上的 January-December 之间的所有月份都需要按时间顺序显示
调整 y-axis = 图 1 + 2 中的 y-axis 限制（见下文）不正确，因为这些值说明了行数称为 'FID' 的数据框，而不是实际的平均目击次数，例如4 月包含 111 次目击事件，但图 1 和图 2 中的 y-axis 表示有 390 次目击事件，这是不正确的。图 3 和图 4（见下文）显示了正确的 y-axis 限制。
问题
到目前为止，我已经制作了 4 个图，每个图至少显示了上面列出的 1 或 2 个所需特征。但是，我无法生成包含所有所需功能的图。我感到非常困惑，因为我修改了我的 R-code 和数据框以试图生成所需的图。试了很多次，实在想不明白自己做错了什么。
如果有人能帮助我解决这个问题，我将表示最深切的谢意。
谢谢:)
汇总数据帧
#To begin with, I tried to find the correct values for
#the mean count of observations with associated standard
#deviation, standard error, and the upper and lower confidence
#intervals using dplyr() based on Dan Chaltiel's suggestions (below):
library(dplyr)
##Count the number of row observations and count by "Year" and "Month"
Summarised_FID_Count<-FID %>%
dplyr::mutate(Month=ordered(Month, levels=month_levels)) %>%
dplyr::count(Year, Month)
##Summarise the data frame "FID'
Summarise_FID_Data<-Summarised_FID_Count %>%
group_by(Month) %>%
dplyr::summarise(mean.month = mean(n, na.rm = TRUE),
sd.month = sd(n, na.rm = TRUE),
n.month = n()) %>%
dplyr::mutate(se.month = sd.month / sqrt(n.month),
lower.ci.month = mean.month - qt(1 - (0.05 / 2), n.month - 1) * se.month,
upper.ci.month = mean.month + qt(1 - (0.05 / 2), n.month - 1) * se.month)
##One problem, the output produces negative lower
##confidence interval values which I don't think is
##correct because you cannot have a negative number of
##observations.
# A tibble: 11 x 7
Month mean.month sd.month n.month se.month lower.ci.month upper.ci.month
<ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 January 37.7 5.69 3 3.28 23.5 51.8
2 February 31.3 4.93 3 2.85 19.1 43.6
3 March 37 5.29 3 3.06 23.9 50.1
4 April 37 12.3 3 7.09 6.47 67.5
5 May 11 7.94 3 4.58 -8.72 30.7
6 July 8 1.41 2 1 -4.71 20.7
7 August 29.7 9.29 3 5.36 6.59 52.7
8 September 28.7 16.4 3 9.49 -12.2 69.5
9 October 27.3 12.5 3 7.22 -3.73 58.4
10 November 27 17.7 3 10.2 -16.9 70.9
11 December 33.7 4.04 3 2.33 23.6 43.7
R-code 图 1、2、3 和 4（见下文）：
##Download package
library(gplots)
#Convert `month_vector` to a factor with ordered level
Month.label<- factor(FID, order = TRUE, levels =c('January',
'February',
'March',
'April',
'May',
'June',
'July',
'August',
'September',
'October',
'November',
'December'))
##Code for figure 1
dev.new()
plotmeans(FID~Month, data=FID,
ylab="Mean Blue Whale Sightings",
xlab="Months")
##Code for sample 2
dev.new
plotmeans(FID~Month, data=FID,
ci.label = TRUE,
xaxt = n,
digits = 3,
ylab="Mean Blue Whale Sightings",
xlab="Months")
axis(side = 1, at = seq(1, 12, by = 1), labels = FALSE)
text(seq(1, 12, by=1), par("usr")[3] - 0.2, labels=unique(month.label), srt = 75, pos = 1, xpd = TRUE, cex=0.3)
##Code for sample 3:
##Filter the data frame using the function count() in dplyr
New_FID<-FID %>% dplyr::select(Month, FID) %>%
dplyr::count(Month) %>% as.data.frame
##Examine the structure of the filtered data frame showing the month and total whale sightings
str(New_FID)
##Produce a new data frame
FID_Plotmeans<-as.data.frame(New_FID)
##Rename the columns
colnames(FID_Plotmeans)<-c("Month", "FID_Sightings")
##Plot the means
dev.new()
plotmeans(FID_Sightings,
data=New_Blue_Plotmeans,
ylab="Mean Blue Whale Sightings",
xlab="Months")
##Code for sample 4:
plotmeans(Frequency_FID~Month, data=Mean_FID,
text.n.label = Month.label,
ci.label = TRUE,
digits = 3,
ylab="Mean Blue Whale Sightings",
xlab="Months")
Warning messages:
1: In arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
zero-length arrow is of indeterminate angle and so skipped
2: In arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
zero-length arrow is of indeterminate angle and so skipped
图 1、2、3 和 4 的问题（见下文）：
图一（见下图）：
- 不正确的 y-axis 限制 - 该图显示数据框中的平均行数而不是 FID 目击事件的平均数（例如，4 月份有 111 次目击事件，但 y-axis 限制声明平均目击次数为 390，这是不正确的。
- x-axis 上的月份未按时间顺序排列 - January-December
- 好消息，因为 n.labels 显示在 x-axis.
图2（见下图）：
- 不正确的 y-axis 限制 - 该图显示数据框中的平均行数而不是 FID 目击事件的平均数（例如，4 月份有 111 次目击事件，但 y-axis 限制声明平均目击次数为 390，这是不可能的。
- x-axis 上的月份未按时间顺序排列 - January-December
- 缺少连接每个月平均目击事件的邻线
- 缺少 ci.label
- 缺少 n.label
图3（见下图）：
- x-axis 上的月份未按时间顺序排列 - January-December
- 每个分组的n.label显示为n=1，这是不正确的
- 缺少置信区间条
- 缺少 ci.label
- 好消息，因为 y-axis 限制是正确的。
示例 4（见下文）：
- x-axis 上的月份未按时间顺序排列 - January-December
- 每个分组的 n.label 显示为 n=3，这是不正确的
- 好消息，表示上下置信区间的 ci.label 显示在图上
- 其中一个 ci.label 与 11 月份的 n.label 重叠，因此这些值不符合条件
图1
[=49=
图2
图3
图4
名为 'FID'
的数据框
structure(list(FID = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
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2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L), Month = structure(c(5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
8L, 8L, 8L, 8L, 8L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("April", "August",
"December", "February", "January", "July", "March", "May", "November",
"October", "September"), class = "factor")), class = "data.frame", row.names = c(NA,
-917L))
数据帧调用 'Mean FID':
structure(list(Year = c(2015, 2016, 2017, 2015, 2016, 2017, 2015,
2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017,
2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016,
2017, 2015, 2016, 2017, 2015, 2016, 2017), Month = structure(c(5L,
5L, 5L, 4L, 4L, 4L, 8L, 8L, 8L, 1L, 1L, 1L, 9L, 9L, 9L, 7L, 7L,
7L, 6L, 6L, 6L, 2L, 2L, 2L, 12L, 12L, 12L, 11L, 11L, 11L, 10L,
10L, 10L, 3L, 3L, 3L), .Label = c("April", "August", "December",
"February", "January", "July", "June", "March", "May", "November",
"October", "September"), class = "factor"), Frequency_FID = c(28,
23, 31, 21, 25, 28, 26, 20, 30, 29, 19, 30, 4, 7, 21, 0, 0, 0,
0, 7, 7, 16, 30, 26, 9, 29, 27, 14, 31, 22, 8, 25, 28, 24, 24,
29)), class = "data.frame", row.names = c(NA, -36L))
我不知道 gplots 所以我无法帮助你，但这里有一些使用 ggplot2.
的解决方案
ggplot2 被许多人认为是制作绘图的更通用的 R 包。它并不像 gplots 看起来那么简单，但您通常最终会得到您想要的结果。
library(tidyverse) #loads dplyr and ggplot2
month_levels = c('January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November', 'December')
data_plot = FID %>%
mutate(Month=ordered(Month, levels=month_levels)) %>% #put months in the right order
group_by(Month) %>%
summarise(m=mean(FID), #calculate the summaries you want on the plot
n_FID=n(),
sem=sd(FID)/sqrt(n()),
ci_low=m-1.96*sem,
ci_hi=m+1.96*sem) %>%
ungroup()
p = ggplot(data_plot, aes(x=Month, y=m, ymin=ci_low, ymax=ci_hi)) +
geom_line(aes(group=1), size=1) +
geom_errorbar(width=0.2, color="blue") +
geom_point(size=2) +
geom_label(aes(y=240, label=paste0("n=", n_FID)))
p
ggsave("p.png", p)
您可以使用 labs()、xlab 或 ylab 自定义标签，也可以使用 facet_wrap 按年份添加分面，等等。关于 ggplot2。
有无数的教程可供学习
另外，你的问题好像有点误会。 n=113 表示 1 月份（这 3 年）有 113 次观察。所有这些观察的平均值是 307，所以你的情节可能是正确的。
我认为我没有解决您的问题，但我希望能有所帮助。
PS:
您的示例或我的理解可能存在错误，因为我的 data_plot 的值与您的 data_plot.
的值非常不同
问题：
我正在尝试使用 gplots 包 中的函数 plotmeans() 来绘制图表。我的目标是显示 平均 FID 目击 （请参阅下面称为 'FID' 和 'Mean_FID' 的数据框）以及 相关的上下置信区间条，和 n 个标签
数据帧结构
- 我有一个名为 'FID' 的大型数据框，有 918 行，主要列标题是：-
- FID = 目击次数
- 年份 = 2015-2017
- 月份=January-December
- 我修改了名为 'FID' 的数据框以显示每年每月的平均目击事件。数据框称为“Mean_FID”，有 36 行，用于生成图 4（见下文）。主要的列标题是：-
- 年 = 目击次数
- 月份=January-February
- 频率 FID = 每年每月平均目击次数
目标 - 欲望情节
我想使用函数 plotmeans()
将下面列出的所有特征合并到一个所需的图中ci.label = 我想在每个置信区间条的末尾显示实际的上限和下限区间值。
位数 = 我希望所有置信区间标签都具有 3 位有效数字（见图 4）。
n.label = 我想在图中 x-axis 的每个区间条的底部显示每个组中的观察数space（见图一）
Dates = x-axis
上的 January-December 之间的所有月份都需要按时间顺序显示调整 y-axis = 图 1 + 2 中的 y-axis 限制（见下文）不正确，因为这些值说明了行数称为 'FID' 的数据框，而不是实际的平均目击次数，例如4 月包含 111 次目击事件，但图 1 和图 2 中的 y-axis 表示有 390 次目击事件，这是不正确的。图 3 和图 4（见下文）显示了正确的 y-axis 限制。
问题
到目前为止，我已经制作了 4 个图，每个图至少显示了上面列出的 1 或 2 个所需特征。但是，我无法生成包含所有所需功能的图。我感到非常困惑，因为我修改了我的 R-code 和数据框以试图生成所需的图。试了很多次，实在想不明白自己做错了什么。
如果有人能帮助我解决这个问题，我将表示最深切的谢意。
谢谢:)
汇总数据帧
#To begin with, I tried to find the correct values for
#the mean count of observations with associated standard
#deviation, standard error, and the upper and lower confidence
#intervals using dplyr() based on Dan Chaltiel's suggestions (below):
library(dplyr)
##Count the number of row observations and count by "Year" and "Month"
Summarised_FID_Count<-FID %>%
dplyr::mutate(Month=ordered(Month, levels=month_levels)) %>%
dplyr::count(Year, Month)
##Summarise the data frame "FID'
Summarise_FID_Data<-Summarised_FID_Count %>%
group_by(Month) %>%
dplyr::summarise(mean.month = mean(n, na.rm = TRUE),
sd.month = sd(n, na.rm = TRUE),
n.month = n()) %>%
dplyr::mutate(se.month = sd.month / sqrt(n.month),
lower.ci.month = mean.month - qt(1 - (0.05 / 2), n.month - 1) * se.month,
upper.ci.month = mean.month + qt(1 - (0.05 / 2), n.month - 1) * se.month)
##One problem, the output produces negative lower
##confidence interval values which I don't think is
##correct because you cannot have a negative number of
##observations.
# A tibble: 11 x 7
Month mean.month sd.month n.month se.month lower.ci.month upper.ci.month
<ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 January 37.7 5.69 3 3.28 23.5 51.8
2 February 31.3 4.93 3 2.85 19.1 43.6
3 March 37 5.29 3 3.06 23.9 50.1
4 April 37 12.3 3 7.09 6.47 67.5
5 May 11 7.94 3 4.58 -8.72 30.7
6 July 8 1.41 2 1 -4.71 20.7
7 August 29.7 9.29 3 5.36 6.59 52.7
8 September 28.7 16.4 3 9.49 -12.2 69.5
9 October 27.3 12.5 3 7.22 -3.73 58.4
10 November 27 17.7 3 10.2 -16.9 70.9
11 December 33.7 4.04 3 2.33 23.6 43.7
R-code 图 1、2、3 和 4（见下文）：
##Download package
library(gplots)
#Convert `month_vector` to a factor with ordered level
Month.label<- factor(FID, order = TRUE, levels =c('January',
'February',
'March',
'April',
'May',
'June',
'July',
'August',
'September',
'October',
'November',
'December'))
##Code for figure 1
dev.new()
plotmeans(FID~Month, data=FID,
ylab="Mean Blue Whale Sightings",
xlab="Months")
##Code for sample 2
dev.new
plotmeans(FID~Month, data=FID,
ci.label = TRUE,
xaxt = n,
digits = 3,
ylab="Mean Blue Whale Sightings",
xlab="Months")
axis(side = 1, at = seq(1, 12, by = 1), labels = FALSE)
text(seq(1, 12, by=1), par("usr")[3] - 0.2, labels=unique(month.label), srt = 75, pos = 1, xpd = TRUE, cex=0.3)
##Code for sample 3:
##Filter the data frame using the function count() in dplyr
New_FID<-FID %>% dplyr::select(Month, FID) %>%
dplyr::count(Month) %>% as.data.frame
##Examine the structure of the filtered data frame showing the month and total whale sightings
str(New_FID)
##Produce a new data frame
FID_Plotmeans<-as.data.frame(New_FID)
##Rename the columns
colnames(FID_Plotmeans)<-c("Month", "FID_Sightings")
##Plot the means
dev.new()
plotmeans(FID_Sightings,
data=New_Blue_Plotmeans,
ylab="Mean Blue Whale Sightings",
xlab="Months")
##Code for sample 4:
plotmeans(Frequency_FID~Month, data=Mean_FID,
text.n.label = Month.label,
ci.label = TRUE,
digits = 3,
ylab="Mean Blue Whale Sightings",
xlab="Months")
Warning messages:
1: In arrows(x, li, x, pmax(y - gap, li), col = barcol, lwd = lwd, :
zero-length arrow is of indeterminate angle and so skipped
2: In arrows(x, ui, x, pmin(y + gap, ui), col = barcol, lwd = lwd, :
zero-length arrow is of indeterminate angle and so skipped
图 1、2、3 和 4 的问题（见下文）：
图一（见下图）：
- 不正确的 y-axis 限制 - 该图显示数据框中的平均行数而不是 FID 目击事件的平均数（例如，4 月份有 111 次目击事件，但 y-axis 限制声明平均目击次数为 390，这是不正确的。
- x-axis 上的月份未按时间顺序排列 - January-December
- 好消息，因为 n.labels 显示在 x-axis.
图2（见下图）：
- 不正确的 y-axis 限制 - 该图显示数据框中的平均行数而不是 FID 目击事件的平均数（例如，4 月份有 111 次目击事件，但 y-axis 限制声明平均目击次数为 390，这是不可能的。
- x-axis 上的月份未按时间顺序排列 - January-December
- 缺少连接每个月平均目击事件的邻线
- 缺少 ci.label
- 缺少 n.label
图3（见下图）：
- x-axis 上的月份未按时间顺序排列 - January-December
- 每个分组的n.label显示为n=1，这是不正确的
- 缺少置信区间条
- 缺少 ci.label
- 好消息，因为 y-axis 限制是正确的。
示例 4（见下文）：
- x-axis 上的月份未按时间顺序排列 - January-December
- 每个分组的 n.label 显示为 n=3，这是不正确的
- 好消息，表示上下置信区间的 ci.label 显示在图上
- 其中一个 ci.label 与 11 月份的 n.label 重叠，因此这些值不符合条件
图1
[=49=图2
图3
图4
名为 'FID'
的数据框structure(list(FID = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L,
76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L,
89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L,
123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L,
134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 144L, 145L,
146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L,
157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L,
168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L,
179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L,
190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L,
201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L,
212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L,
223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L,
234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L,
245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L,
256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L,
267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L,
278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L,
289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L,
300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L,
311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L,
322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L,
333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L,
366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L,
377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L,
388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L,
399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L,
410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L,
421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L,
432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L,
443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L,
454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L,
465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L,
476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L,
487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L,
498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L,
509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L,
520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L,
531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L,
542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 551L, 552L,
553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L,
564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L, 573L, 574L,
575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L, 584L, 585L,
586L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L, 596L,
597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L,
608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L, 618L,
619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 627L, 628L, 629L,
630L, 631L, 632L, 633L, 634L, 635L, 636L, 637L, 638L, 639L, 640L,
641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L, 650L, 651L,
652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L,
663L, 664L, 665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L, 673L,
674L, 675L, 676L, 677L, 678L, 679L, 680L, 681L, 682L, 683L, 684L,
685L, 686L, 687L, 688L, 689L, 690L, 691L, 692L, 693L, 694L, 695L,
696L, 697L, 698L, 699L, 700L, 701L, 702L, 703L, 704L, 705L, 706L,
707L, 708L, 709L, 710L, 711L, 712L, 713L, 714L, 715L, 716L, 717L,
718L, 719L, 720L, 721L, 722L, 723L, 724L, 725L, 726L, 727L, 728L,
729L, 730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L,
740L, 741L, 742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L, 750L,
751L, 752L, 753L, 754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L,
762L, 763L, 764L, 765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L,
773L, 774L, 775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 783L,
784L, 785L, 786L, 787L, 788L, 789L, 790L, 791L, 792L, 793L, 794L,
795L, 796L, 797L, 798L, 799L, 800L, 801L, 802L, 803L, 804L, 805L,
806L, 807L, 808L, 809L, 810L, 811L, 812L, 813L, 814L, 815L, 816L,
817L, 818L, 819L, 820L, 821L, 822L, 823L, 824L, 825L, 826L, 827L,
828L, 829L, 830L, 831L, 832L, 833L, 834L, 835L, 836L, 837L, 838L,
839L, 840L, 841L, 842L, 843L, 844L, 845L, 846L, 847L, 848L, 849L,
850L, 851L, 852L, 853L, 854L, 855L, 856L, 857L, 858L, 859L, 860L,
861L, 862L, 863L, 864L, 865L, 866L, 867L, 868L, 869L, 870L, 871L,
872L, 873L, 874L, 875L, 876L, 877L, 878L, 879L, 880L, 881L, 882L,
883L, 884L, 885L, 886L, 887L, 888L, 889L, 890L, 891L, 892L, 893L,
894L, 895L, 896L, 897L, 898L, 899L, 900L, 901L, 902L, 903L, 904L,
905L, 906L, 907L, 908L, 909L, 910L, 911L, 912L, 913L, 914L, 915L,
916L, 917L, 918L), Year = c(2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L), Month = structure(c(5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
8L, 8L, 8L, 8L, 8L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("April", "August",
"December", "February", "January", "July", "March", "May", "November",
"October", "September"), class = "factor")), class = "data.frame", row.names = c(NA,
-917L))
数据帧调用 'Mean FID':
structure(list(Year = c(2015, 2016, 2017, 2015, 2016, 2017, 2015,
2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017,
2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016, 2017, 2015, 2016,
2017, 2015, 2016, 2017, 2015, 2016, 2017), Month = structure(c(5L,
5L, 5L, 4L, 4L, 4L, 8L, 8L, 8L, 1L, 1L, 1L, 9L, 9L, 9L, 7L, 7L,
7L, 6L, 6L, 6L, 2L, 2L, 2L, 12L, 12L, 12L, 11L, 11L, 11L, 10L,
10L, 10L, 3L, 3L, 3L), .Label = c("April", "August", "December",
"February", "January", "July", "June", "March", "May", "November",
"October", "September"), class = "factor"), Frequency_FID = c(28,
23, 31, 21, 25, 28, 26, 20, 30, 29, 19, 30, 4, 7, 21, 0, 0, 0,
0, 7, 7, 16, 30, 26, 9, 29, 27, 14, 31, 22, 8, 25, 28, 24, 24,
29)), class = "data.frame", row.names = c(NA, -36L))
我不知道 gplots 所以我无法帮助你，但这里有一些使用 ggplot2.
ggplot2 被许多人认为是制作绘图的更通用的 R 包。它并不像 gplots 看起来那么简单，但您通常最终会得到您想要的结果。
library(tidyverse) #loads dplyr and ggplot2
month_levels = c('January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November', 'December')
data_plot = FID %>%
mutate(Month=ordered(Month, levels=month_levels)) %>% #put months in the right order
group_by(Month) %>%
summarise(m=mean(FID), #calculate the summaries you want on the plot
n_FID=n(),
sem=sd(FID)/sqrt(n()),
ci_low=m-1.96*sem,
ci_hi=m+1.96*sem) %>%
ungroup()
p = ggplot(data_plot, aes(x=Month, y=m, ymin=ci_low, ymax=ci_hi)) +
geom_line(aes(group=1), size=1) +
geom_errorbar(width=0.2, color="blue") +
geom_point(size=2) +
geom_label(aes(y=240, label=paste0("n=", n_FID)))
p
ggsave("p.png", p)
您可以使用 labs()、xlab 或 ylab 自定义标签，也可以使用 facet_wrap 按年份添加分面，等等。关于 ggplot2。
另外，你的问题好像有点误会。 n=113 表示 1 月份（这 3 年）有 113 次观察。所有这些观察的平均值是 307，所以你的情节可能是正确的。
我认为我没有解决您的问题，但我希望能有所帮助。
PS:
您的示例或我的理解可能存在错误，因为我的 data_plot 的值与您的 data_plot.