R或matlab中的季节性情节图

时间：2017-12-02 23:30:00

标签： r matlab boxplot

enter image description here 

DATE        obs1        obs2    obs3    
1981-01-01  2032.409    3142.46 1741.143
1981-01-02  2023.687    3870.04 1735.256
1981-01-03  2014.274    4126.25 1728.556
1981-01-04  2005.795    2615.91 1722.985
1981-01-05  2000.674    2940.83 1722.317
1981-01-06  1998.477    3258.69 1723.937
1981-01-07  1997.014    3371.6  1724.104
1981-01-08  1995.576    3184.13 1722.624
1981-01-09  1993.706    3540.76 1719.592
1981-01-10  1991.286    3312.43 1715.156
1981-01-11  1988.633    3028.65 1710.141
1981-01-12  1986.147    3212.79 1705.183
1981-01-13  1984.229    3193.23 1700.789
1981-01-14  1982.756    3294.52 1697.785
1981-01-15  1981.548    3553.78 1696.068
1981-01-16  1980.561    3492.28 1694.544
1981-01-17  1979.792    2452.09 1692.36
1981-01-18  1979.224    1873.82 1689.525
1981-01-19  1978.845    3218.28 1686.452

如上图所示，我需要在R中绘制季节性（冬季，春季，夏季和秋季）框图，以获取每日数据。我有10年以上格式的数据用于不同的电台。情节应该在一个图中，每个季节都有多个箱形图。

2 个答案:

答案 0 :(得分：2)

使用tidyverse和lubridate的解决方案。 tidyverse包括dplyr和tidyr执行数据争论，ggplot2创建情节。 lubridate用于处理数据框中的日期。

由于您提供的数据集不是很有用，因为它仅包含1月份的一些记录，因此无法创建显示季节性差异的箱线图，因此我决定创建一个新的示例数据框。我的示例数据框的结构类似于您的数据集，它应该为您提供一些提示作为您现实世界问题的起点。

# Set the seed for reproducibility
set.seed(123)

library(tidyverse)
library(lubridate)

# Create example data frame
dt <- data_frame(DATE = seq(ymd("1980-01-01"), ymd("1989-12-31"), by = 1)) %>%
  mutate(obs1 = rnorm(nrow(.), mean = 0, sd = 1),
         obs2 = rnorm(nrow(.), mean = 1, sd = 2),
         obs3 = rnorm(nrow(.), mean = 2, sd = 3))

head(dt)
# # A tibble: 6 x 4
#         DATE        obs1       obs2      obs3
#       <date>       <dbl>      <dbl>     <dbl>
# 1 1980-01-01 -0.56047565  0.7874145 2.7827006
# 2 1980-01-02 -0.23017749  0.1517417 8.5720252
# 3 1980-01-03  1.55870831  0.7193725 1.3293478
# 4 1980-01-04  0.07050839  0.5454177 0.3253155
# 5 1980-01-05  0.12928774  1.4101239 6.1245771
# 6 1980-01-06  1.71506499 -0.6491910 4.5034395

tail(dt)
# # A tibble: 6 x 4
#         DATE       obs1       obs2       obs3
#       <date>      <dbl>      <dbl>      <dbl>
# 1 1989-12-26 -0.3629796  0.6750946  0.8586325
# 2 1989-12-27  0.1102218  2.8572337  9.8541328
# 3 1989-12-28 -0.2700741  1.7614026  1.9109596
# 4 1989-12-29  0.6920973  0.5275611 -0.4756240
# 5 1989-12-30  0.9282803  1.3811225  1.5222535
# 6 1989-12-31  0.5931301 -1.6638739  4.1157087

示例数据框包含10年的记录，包含3个观察组。每列的值均为正态分布，具有不同的均值和标准差。

第一步是通过将数据集从宽格式转换为长格式来处理数据框，并添加一个显示季节信息的列。

dt2 <- dt %>% 
  # Convert data frame from lwide format to long format
  gather(Observation, Value, -DATE) %>%
  # Remove "obs" in the Observation column
  mutate(Observation = str_replace(Observation, "obs", "")) %>%
  # Convert the DATE column to date class
  mutate(DATE = ymd(DATE)) %>%
  # Create Month column
  mutate(Month = month(DATE)) %>%
  # Create Season column
  mutate(Season = case_when(
    Month %in% c(12, 1, 2)      ~ "winter",
    Month %in% c(3, 4, 5)       ~ "spring",
    Month %in% c(6, 7, 8)       ~ "summer",
    Month %in% c(9, 10, 11)     ~ "fall",
    TRUE                        ~ NA_character_
  ))

之后，我们可以使用ggplot2创建箱线图。请注意，我使用stat_summary为每个组添加一条红线来表示平均值。

# Create a boxplot using ggplot2
# Specify the aesthetics
ggplot(dt2, aes(x = Season, y = Value, fill = Observation)) + 
  # Specify the geom to be boxplot
  geom_boxplot() +                
  # Add a red line to the mean
  stat_summary(aes(ymax = ..y.., ymin = ..y..),
               fun.y = "mean", 
               geom = "errorbar",              # Use geom_errorbar to add line as mean
               color = "red", 
               width = 0.7, 
               position = position_dodge(width = 0.75), # Add the line to each group
               show.legend = FALSE)

enter image description here

答案 1 :(得分：2)

好的......第一步是建立一个可以检测给定日期下降的季节的函数。幸运的是，我很久以前就已经开发出了可以处理南半球季节的方法（相反）。

该函数没有实现任何健全性检查，因为我在已经清理过的数据集中使用它，但是最终实现一些并不困难（除非您决定在使用之前清理数据集）。它以矢量化方式工作，以最大化Matlab中的计算性能。

就是这样：

function season = GetSeason(date,southern_hemisphere)

    if (nargin == 1)
        southern_hemisphere = false;
    end

    [~,month,day] = datevec(date);
    offset = month + (day / 100);

    winter = (offset < 3.21) | (offset >= 12.22);
    spring = ~winter & (offset < 6.21);
    summer = ~winter & ~spring & (offset < 9.23);
    autumn = ~winter & ~spring & ~summer;

    offset(spring) = 0;
    offset(summer) = 1;
    offset(autumn) = 2;
    offset(winter) = 3;

    if (southern_hemisphere)
        offset = offset + 2;
    end

    season = mod(offset,4) + 1;
end

现在，您的脚本中的第一步是从数据集文件中提取您的观察结果。为了为您创建一个完整的演示，我创建了一个Excel数据集。但您也可以使用CSV数据集，而Matlab处理的代码或其他文件格式几乎没有变化：

% detect the dataset columns format
opts = detectImportOptions('data.xlsx');

% impose a specific format for the dataset columns
opts = setvartype(opts,{'datetime' 'double' 'double' 'double'});

% extract data in a table variable
data = readtable('data.xlsx',opts);

% sanitize the table variable removing the rows with missing or invalid values
data = rmmissing(data);

% sort the table variable rows by date (default first rows, default ascending)
data = sortrows(data);

第二项测试包括获得观察日期的相应季节：

seasons = GetSeason(data.Date);

第三步，假设我们仅针对名为Obs1的第一列观察结果执行所有此过程：

spring_1 = data.Obs1(seasons == 1);
summer_1 = data.Obs1(seasons == 2);
autumn_1 = data.Obs1(seasons == 3);
winter_1 = data.Obs1(seasons == 4);

第四步也是最后一步是在一个图中绘制每个季节的一个箱图（分组变量groups必须作为参数传递到boxplot函数中，以便知道后者如何它必须绘制和使用哪些值的许多框）：

groups = [
    ones(size(spring_1));
    2 * ones(size(summer_1));
    3 * ones(size(autumn_1));
    4 * ones(size(winter_1));
];

figure();
boxplot([spring_1; summer_1; autumn_1; winter_1],groups);
set(gca,'XTickLabel',{'Spring' 'Summer' 'Autumn' 'Winter'});

结果如下：

Result

更新所有观察的完整工作代码 

opts = detectImportOptions('data.xlsx');
opts = setvartype(opts,{'datetime' 'double' 'double' 'double'});

data = readtable('data.xlsx',opts);
data = rmmissing(data);
data = sortrows(data);

seasons = GetSeason(data.Date);

spring_1 = data.Obs1(seasons == 1);
summer_1 = data.Obs1(seasons == 2);
autumn_1 = data.Obs1(seasons == 3);
winter_1 = data.Obs1(seasons == 4);
spring_2 = data.Obs2(seasons == 1);
summer_2 = data.Obs2(seasons == 2);
autumn_2 = data.Obs2(seasons == 3);
winter_2 = data.Obs2(seasons == 4);
spring_3 = data.Obs3(seasons == 1);
summer_3 = data.Obs3(seasons == 2);
autumn_3 = data.Obs3(seasons == 3);
winter_3 = data.Obs3(seasons == 4);

plot_data = [
    spring_1;
    summer_1;
    autumn_1;
    winter_1;
    spring_2; 
    summer_2; 
    autumn_2;
    winter_2;
    spring_3;
    summer_3;
    autumn_3;
    winter_3
];

plot_groups = [
    (1 * ones(size(spring_1))) (1  * ones(size(spring_1)));
    (1 * ones(size(summer_1))) (2  * ones(size(summer_1)));
    (1 * ones(size(autumn_1))) (3  * ones(size(autumn_1)));
    (1 * ones(size(winter_1))) (4  * ones(size(winter_1)));
    (2 * ones(size(spring_2))) (5  * ones(size(spring_2)));
    (2 * ones(size(summer_2))) (6  * ones(size(summer_2)));
    (2 * ones(size(autumn_2))) (7  * ones(size(autumn_2)));
    (2 * ones(size(winter_2))) (8  * ones(size(winter_2)));
    (3 * ones(size(spring_3))) (9  * ones(size(spring_3)));
    (3 * ones(size(summer_3))) (10 * ones(size(summer_3)));
    (3 * ones(size(autumn_3))) (11 * ones(size(autumn_3)));
    (3 * ones(size(winter_3))) (12 * ones(size(winter_3)))
];

labels_obs = {'' '' '' '' '' '' '' '' '' '' '' ''};
labels_season = repmat({'Spring' 'Summer' 'Autumn' 'Winter'},1,3);

figure('Units','normalized','Position',[0.05 0.1 0.9 0.8]);
boxplot(plot_data,plot_groups, ...
    'BoxStyle','outline', ...
    'FactorGap',[5 1], ...
    'Labels',{labels_obs; labels_season}, ...
    'Notch','on');

colors = repmat('wcyg',1,3);
h = findobj(gca,'Tag','Box');

for i = 1:numel(h)
    patch(get(h(i),'XData'),get(h(i),'YData'),colors(i),'FaceAlpha',0.5);
end

h = findall(allchild(findall(gca,'Type','hggroup')),'Type','text','String','');
positions = cell2mat(get(h,'pos'));
positions_new = num2cell([mean(reshape(positions(:,1),4,[]))' positions(1:4:end,2:end)],2);
set(h(1:4:end),{'Position'},positions_new,{'String'},{'Observations 3'; 'Observations 2'; 'Observations 1'})

h = findall(allchild(findall(gca,'Type','hggroup')),'Type','text','String','');
delete(h);

结果：

Result Update

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