如何使用 R 提取数据帧的最小和最大行并在布局中绘制多个图形

Question

I want to extract data row associated with min and max from 4 data set.

I already coded to check result of min and max using data set.

#Question

1. I want to show result to extract min and max of X1.frac, X1.frac1 and X1.frac2 rows per each time(945, 955, 965, 975)

   ##expected result##

   1. example - fracdata_test_min.csv(Min)

   time	frac	time	Value
   945	0.904265	945	Min
   955	0.919962	955	Min
   965	0.920854	965	Min
   975	0.925369	975	Min

   2. example - fracdata_test_max.csv(Max)

   time	frac	time	Value
   945	0.965208	945	Min
   955	0.995463	955	Min
   965	0.982396	965	Min
   975	0.973242	975	Min

2. I want show result for df_cv_min, df_cv_mean, df_cv_max using one graph.

    ### my code library(tidyverse) library(ggplot2) library(reshape2) library(cowplot) df=read.csv("D:/fracdata_test.csv",header = T) ##---------------------------time interval (10) int=df %>% select(X1.time.1) %>% slice(2) %>% as.numeric()-df %>% select(X1.time) %>% slice(1) %>% as.numeric() ##---------------------------max time value (10935) time_max=df %>% select(X1.time.1) %>% as.matrix() %>% max() ##---------------------------min time value (945) time_min=df %>% select(X1.time.1) %>% as.matrix() %>% min() ##---------------------------number_time_point (1000) ntp=(time_max-time_min)/int+1 ##---------------------------number_measurement n_meas=df %>% nrow()/ntp ##---------------------------Measurement_Encoding Meas=gl(n_meas,ntp) ##--------------------------Add Measurement_Encoding into the main dataset df=cbind(df,Meas) %>% mutate(Meas=factor(Meas)) ##---------------------------Plotting y_min=df %>% select(X1.frac.1) %>% as.matrix() %>% min() y_max=df %>% select(X1.frac.1) %>% as.matrix() %>% max() df_cv=df %>% dcast(Meas~time, value.var="X1.frac.1") %>% select(-Meas) ##------------time time=seq(time_min, time_max, int) ##------------Mean data df_cv_mean=df_cv %>% apply(.,2,function(x)mean(x)) %>% data.frame() %>% set_names("frac") %>% mutate(time=time, Value="Mean") ##------------Min data df_cv_min=df_cv %>% apply(.,2,function(x)min(x)) %>% data.frame() %>% set_names("frac") %>% mutate(time=time, Value="Min") ##------------Max data df_cv_max=df_cv %>% apply(.,2,function(x)max(x)) %>% data.frame() %>% set_names("frac") %>% mutate(time=time, Value="Max") ##-----------Merging three data df_3data=rbind(df_cv_mean, df_cv_min, df_cv_max) %>% mutate(Value=factor(Value)) ##-----------Plotting df_3data Plot_B=df_3data %>% ggplot(.,aes(x=time, y=frac))+geom_line(aes(group=Value, color=Value))+ geom_smooth(method="auto",aes(group=Value,color=Value))+ scale_colour_manual(values = c("red","blue","orange"))+ scale_x_continuous(breaks=0:8000*1000,limits=c(time_min,time_max)) + scale_y_continuous(breaks=0:8000*0.1,limits=c(y_min,y_max)) + theme_classic()+labs(subtitle = "B. Original data (mean, min, max values)")

#Data set(test.csv)

X1.time	X1.frac	X1.time.1	X1.frac.1	X1.time.2	X1.frac.2
945	0.937752593	945	0.965208348	945	0.904265228
955	0.959463167	955	0.954415107	955	0.919962471
965	0.982386049	965	0.959723958	965	0.920854173
975	0.973241841	975	0.925369792	975	0.928773106

Answer 1

Get the data in long format for 'frac' columns and get min and max value for every X1.time value.

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(cols = contains('frac')) %>%
  group_by(X1.time) %>%
  summarise(min_value = min(value), 
            max_value = max(value))

#  X1.time min_value max_value
#    <dbl>     <dbl>     <dbl>
#1     945     0.904     0.965
#2     955     0.920     0.959
#3     965     0.921     0.982
#4     975     0.925     0.973

Another option is to use rowwise :

df %>%
  rowwise() %>%
  transmute(X1.time, 
            min_value = min(c_across(contains('frac'))), 
            max_value = max(c_across(contains('frac')))) %>%
  ungroup

data

It is helpful if you share data in a reproducble format which is easier to copy.

df <- structure(list(X1.time = c(945, 955, 965, 975), X1.frac = c(0.937752593, 
0.959463167, 0.982386049, 0.973241841), X1.time.1 = c(945, 955, 
965, 975), X1.frac.1 = c(0.965208348, 0.954415107, 0.959723958, 
0.925369792), X1.time.2 = c(945, 955, 965, 975), X1.frac.2 = c(0.904265228, 
0.919962471, 0.920854173, 0.928773106)), row.names = c(NA, -4L), 
class = "data.frame")