How to plot the mean, and confidence interval bars using the plotmeans() function in the gplots package in R

Question

Problem:

I am attempting to produce a plot using the function plotmeans() in the gplots package. My goals are to display mean FID sightings (see the data frame's below called 'FID' and 'Mean_FID') with associated upper and lower confidence interval bars, and n labels

Dataframe Structure

I have a large data frame called 'FID' with 918 rows, and the main column headings are:-

FID = number of sightings
Year = 2015-2017
Month = January-December

I have modified the data frame called 'FID' to show the average sightings per month per year. The data frame is called "Mean_FID", with 36 rows and this was used to produce figure 4 (see below). The main column headings are:-

Year = number of sightings
Month = January-February
Frequency FID = Mean sightings per month per year

GOAL - Desire plot

I would like to incorporate all the features listed below into one desired plot using the function plotmeans()

ci.label = I would like to display the actual upper and lower interval values at the end of each confidence interval bar.

digits = I would like all confidence interval labels to have 3 significant digits (see figure 4).

n.label = I would like to show the number of observations in each group at the bottom of each interval bar on the x-axis in the plot space (see figure 1)

Dates = all months need to be displayed in chronological order between January-December on the x-axis

Adjust y-axis = the y-axis limits are in figures 1 + 2 (see below) are incorrect because the values state the number of rows in the data frame called 'FID', rather than the actual mean number of sightings e.g. April contains 111 sightings, but the y-axis in figures 1 and 2 states there were 390 sightings, which is incorrect. Figures 3 and 4 (see below) display the correct y-axis limits.

Issues

I have so far produced 4 plots, where each plot displays at least 1 or 2 of the desired features listed above. However, I cannot produce one plot containing all the desired features. I am feeling really confused as I have modified both my R-code and data frame in an attempt to produce the desired plot. I have tried many times and I really can't understand what I am doing wrong.

If anyone can help me solve this problem, I would like to express my deepest appreciation.

Thank you :)

Summarise Data Frame

#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 for figures 1, 2, 3, and 4 (see below):

##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

Problems with Figures 1, 2, 3, and 4 (see below):

Figure 1 (see below):

Incorrect y-axis limits - the plot shows the mean number of rows in the data frame rather than the mean number of FID sightings (e.g. there are 111 sightings in April, but the y-axis limits state the mean number of sightings is 390, which is not correct.
The months on the x-axis are not in chronological order - January-December
Good news because the n.labels are displayed on the x-axis.

Figure 2 (see below):

Incorrect y-axis limits - the plot shows the mean number of rows in the data frame rather than the mean number of FID sightings (e.g. there are 111 sightings in April, but the y-axis limits state the mean number of sightings is 390, which is not possible.
The months on the x-axis are not in chronological order - January-December
The adjoining line to connect each mean sighting per month is missing
The ci.labels are missing
The n.labels are missing

Figure 3 (see below):

The months on the x-axis are not in chronological order - January-December
The n.labels are displayed as n=1 for each grouping, which is incorrect
The confidence interval bars are missing
The ci.labels are missing
Good news because the y-axis limits are correct.

Sample 4 (see below):

The months on the x-axis are not in chronological order - January-December
The n.labels are displayed as n=3 for each grouping, which is incorrect
Good news, the ci.labels stating the upper and lower confidence intervals are displayed on the plot
One of the ci.labels overlaps the n.label for the month of November, so the values are ineligible

Figure 1

Figure 2

Figure 3

Figure 4

Dataframe called 'FID'

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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), 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))

Dataframe called '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))

score 1 · Accepted Answer · answered Aug 21 '20 at 14:48

1

I don't know gplots so I cannot help you with that, but here is some solution using ggplot2.

ggplot2 is considered by many to be the more versatile R package to make plots. It is not as straightforward as gplots seems to be, but you usually end up to exactly what you want.

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)

You can customize the labels using labs(), xlab or ylab, maybe add facet by year using facet_wrap, and so on. There are gazillions of tutorial to learn about ggplot2.

Also, there seems to be a bit of misunderstanding in your problem. n=113 means that there was 113 observation in January (over those 3 years). The mean of all these observation was 307 so your plot might have been correct.

I don't think I solved your problem but I hope that helped a tiny bit.

PS:

There might be an error, either in your example sample or in my understanding, as my data_plot has very different values than your data_plot.

answered Aug 21 '20 at 14:48

Dan Chaltiel

7,811
5
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Dear Dan. I am so sorry I did not notice you had answered my question. Thank you very much. You have helped a big deal. In regard to your comment above, there were 113 observations in January (over 3 years) but the mean cannot be 307 because I want to figure out the mean. sd, s.e, and ci for the row counts of observations (not the mean of the observations in FID). I tried using data.table() to figure out the correct mean – Alice Hobbs Sep 24 '20 at 04:20
FID_Table<-data.table(FID) – Alice Hobbs Sep 24 '20 at 04:22
FID_Counts<-FID_Table[, .N, by=.(Year, Month)] – Alice Hobbs Sep 24 '20 at 04:24
FID_Mean<-FID_Counts[, .(mean = mean(N)), by = .(Month)] – Alice Hobbs Sep 24 '20 at 04:25
Month mean 1: January 37.66667 2: February 31.33333 3: March 37.00000 4: April 37.00000 5: May 11.00000 6: August 29.66667 7: September 28.66667 8: October 27.33333 9: November 27.00000 10: December 33.66667 11: July 8.00000 – Alice Hobbs Sep 24 '20 at 04:26
I then tried to adapt your code (see above) to figure out the row counts of FID by Year and Month. I am nearly there, but the code produced negative lower confidence interval values, which is impossible because you cannot have a negative number of observations. – Alice Hobbs Sep 24 '20 at 04:48
If you are able to add some insight into what is going wrong here, I would be deeply appreciative. My biggest apologies for not replying sooner. I was so happy to find your post this morning. – Alice Hobbs Sep 24 '20 at 04:48
Is there anyway to produce a table showing: Month, Counts, Mean, Standard Deviation, Standard Error, Lower Confidence and Upper Confidence intervals in one table. Thank you if you can help. – Alice Hobbs Sep 24 '20 at 04:52
@AliceHobbs Unfortunately, I don't know `data.table` so I won't be able to help you with it. But you might want to add another variable in `group_by()`. Besides, your post is very long so I cannot find your edit. It's usual to add an "EDIT" section in the end of your post to add information after an answer has been submited. Also, it is totally possible to have a negative 95%CI when you calculate it using a normal approximation. This may be a hint that this approximation is not the best one (look for Poisson?), but this is a statistical issue that would belong best on stats.stackexchange.com. – Dan Chaltiel Sep 24 '20 at 12:26

How to plot the mean, and confidence interval bars using the plotmeans() function in the gplots package in R

1 Answers1

PS: