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b:head_first_statistics:visualization [2025/09/02 22:55] – [Histogram skewedness] hkimscilb:head_first_statistics:visualization [2026/09/01 23:14] (current) – [Scatter plot] hkimscil
Line 91: Line 91:
 </code> </code>
 {{:b:head_first_statistics:pasted:20240904-082258.png}} {{:b:head_first_statistics:pasted:20240904-082258.png}}
 +
 +<tabbox histogram.r>
 +<code>
 +dat.iq <- rnorm(1000, 100, 15)
 +head(dat.iq)
 +tail(dat.iq)
 +head(dat.iq, n=12)
 +tail(dat.iq, n=12)
 +
 +mean(dat.iq)
 +sd(dat.iq)
 +
 +hist(dat.iq)
 +hist(dat.iq, breaks=30, col='lightblue')
 +
 +set.seed(101)
 +dat.iq <- rnorm(1000, 100, 15)
 +head(dat.iq)
 +tail(dat.iq)
 +head(dat.iq, n=12)
 +tail(dat.iq, n=12)
 +
 +mean(dat.iq)
 +sd(dat.iq)
 +
 +hist(dat.iq)
 +hist(dat.iq, breaks=30, col='lightblue')
 +</code>
 +<tabbox histogram.out>
 +<code>
 +> dat.iq <- rnorm(1000, 100, 15)
 +> head(dat.iq)
 +[1]  95.50853 103.02007  96.27211  93.36366  89.79188  64.85473
 +> tail(dat.iq)
 +[1] 105.04459 123.00613 106.41126  83.46953  90.00495 111.90697
 +> head(dat.iq, n=12)
 + [1]  95.50853 103.02007  96.27211  93.36366  89.79188  64.85473  90.68743 103.88809 100.67983  80.20597  65.00360
 +[12] 101.32801
 +> tail(dat.iq, n=12)
 + [1]  93.27641  81.50208 119.88851  88.58754  74.30448  67.81593 105.04459 123.00613 106.41126  83.46953  90.00495
 +[12] 111.90697
 +
 +> mean(dat.iq)
 +[1] 100.433
 +> sd(dat.iq)
 +[1] 14.82424
 +
 +> hist(dat.iq)
 +> hist(dat.iq, breaks=30, col='lightblue')
 +
 +> set.seed(101)
 +> dat.iq <- rnorm(1000, 100, 15)
 +> head(dat.iq)
 +[1]  95.10945 108.28693  89.87584 103.21539 104.66154 117.60949
 +> tail(dat.iq)
 +[1] 103.68009  96.19170 119.76069  83.43941 100.44177  87.48531
 +> head(dat.iq, n=12)
 + [1]  95.10945 108.28693  89.87584 103.21539 104.66154 117.60949 109.28185  98.30899 113.75542  96.65111 107.89672
 +[12]  88.07733
 +> tail(dat.iq, n=12)
 + [1] 100.97563  88.75387 115.34405 105.59120 111.13368  98.84508 103.68009  96.19170 119.76069  83.43941 100.44177
 +[12]  87.48531
 +
 +> mean(dat.iq)
 +[1] 99.47707
 +> sd(dat.iq)
 +[1] 14.38667
 +
 +> hist(dat.iq)
 +> hist(dat.iq, breaks=30, col='lightblue')
 +</code>
 +{{.:pasted:20260901-230508.png}}
 +</tabbox>
 +
 ====== Scatter plot ====== ====== Scatter plot ======
 <code> <code>
Line 132: Line 206:
  
  
- <code># Simple Scatterplot +<code># Simple Scatterplot 
-attach(mtcars+plot(mtcars$wt, mtcars$mpg, main="Scatterplot Example",
-plot(wt, mpg, main="Scatterplot Example",+
    xlab="Car Weight ", ylab="Miles Per Gallon ",     xlab="Car Weight ", ylab="Miles Per Gallon ", 
    pch=19)</code>    pch=19)</code>
- +{{.:pasted:20260901-230946.png?600}}
-{{:b:head_first_statistics:pasted:20240904-083016.png}}+
  
 explanatory (설명) variable at x axis explanatory (설명) variable at x axis
Line 148: Line 220:
  
 <code># Add fit lines <code># Add fit lines
-abline(lm(mpg~wt), col="red") # regression line (y~x)+abline(lm(mtcars$mpg ~ mtcars$wt), col="red", lwd=2) # regression line (y~x)
 </code> </code>
-{{:b:head_first_statistics:pasted:20240904-083157.png}} +{{.:pasted:20260901-231239.png?600}} 
 +{{{.:pasted:20260901-231245.png}}
 Outlier에 대한 주의 Outlier에 대한 주의
 [{{:pearson-6.png? |}}] [{{:pearson-6.png? |}}]
Line 164: Line 236:
 <WRAP clear/> <WRAP clear/>
 ====== Histogram skewedness ====== ====== Histogram skewedness ======
-[{{:c:ps1-1:2019:pasted:20190909-111001.png|modality}}] +<WRAP column half>
-<WRAP clear/>.+
 <code> <code>
 #### ####
Line 189: Line 260:
       add = TRUE, col = "red", lwd = 2)       add = TRUE, col = "red", lwd = 2)
 </code> </code>
 +</WRAP>
  
 +<WRAP column half>
 +{{:b:head_first_statistics:pasted:20250903-074821.png}}
 +</WRAP>
 +<WRAP clear/>
 +<WRAP column half>
 <code> <code>
 set.seed(1) set.seed(1)
Line 210: Line 287:
       add = TRUE, col = "red", lwd = 2)       add = TRUE, col = "red", lwd = 2)
 </code> </code>
 +</WRAP>
  
 +<WRAP column half>
 +{{:b:head_first_statistics:pasted:20250903-074830.png}}
 +</WRAP>
 +
 +<WRAP clear/>
 +<WRAP column half>
 <code> <code>
 ## ##
Line 233: Line 317:
       add = TRUE, col = "red", lwd = 2)       add = TRUE, col = "red", lwd = 2)
 </code> </code>
-{{:b:head_first_statistics:pasted:20250903-074821.png}} +</WRAP> 
-{{:b:head_first_statistics:pasted:20250903-074830.png}} +<WRAP column half> 
-{{:b:head_first_statistics:pasted:20250903-074836.png}}+{{:b:head_first_statistics:pasted:20250903-082513.png}} 
 +</WRAP> 
 +<WRAP clear/>
  
-====== box plot ======+====== Histogram Modality====== 
 +<WRAP column half> 
 +Unimodal  
 +<code> 
 +### unimodal data  
 +set.seed(1) 
 +d.1 <- rnorm(500, 10, 2) 
 +hist(d.1, breaks = 30, probability = T, 
 +     main = "Hist with Unimodal distrib", 
 +     xlab = "Value", ylab = "Density",  
 +     col = "lightblue", border = "black"
 +lines(density(d.1),  
 +      col = "darkred", lwd = 2) 
 +</code> 
 +</WRAP> 
 + 
 +<WRAP column half> 
 +{{:b:head_first_statistics:pasted:20250903-083409.png}} 
 +</WRAP> 
 + 
 +<WRAP clear/>
  
 +Bimodal distribution
 +<WRAP column half>
 +<code>
 +### bimodal data 
 +set.seed(1)
 +d.1 <- rnorm(500, 10, 2)
 +d.2 <- rnorm(500, 20, 2)
 +d.all <- c(d.1, d.2)
 +hist(d.all, breaks = 30, probability = T,
 +     main = "Hist with bimodal distrib",
 +     xlab = "Value", ylab = "Density", 
 +     col = "lightblue", border = "black")
 +lines(density(d.all), 
 +      col = "darkred", lwd = 2)
 +</code>
 +</WRAP>
 +
 +<WRAP column half>
 +{{:b:head_first_statistics:pasted:20250903-083524.png}}
 +</WRAP>
 +<WRAP clear/>
 +
 +<WRAP column half>
 +<code>
 +### multi-modal data 
 +# Parameters for the first normal distribution (Mode 1)
 +m.1 <- 50
 +sd.1 <- 5
 +
 +# Parameters for the second normal distribution (Mode 2)
 +m.2 <- 100
 +sd.2 <- 15
 +
 +m.3 <- 160
 +sd.3 <- 6
 +
 +# Mixing proportion for Mode 1
 +prop.1 <- 0.3
 +# Mixing proportion for Mode 2
 +prop.2 <- 0.6 # This is 1 - prop1
 +# Mixing proportion for Mode 2
 +prop.3 <- 1.0 # This is 1 - prop1
 +
 +# Number of samples to generate
 +n.sam <- 1000
 +
 +# Create an empty vector to store the combined samples
 +
 +mm.dist <- numeric(n.sam)
 +set.seed(1)
 +for (i in 1:n.sam) {
 +  # Randomly choose which distribution to sample from
 +  tmp <- runif(1)
 +  if (tmp < prop.1) {
 +    mm.dist[i] <- rnorm(1, mean = m.1, sd = sd.1)
 +  } else if (tmp < prop.2) {
 +    mm.dist[i] <- rnorm(1, mean = m.2, sd = sd.2)
 +  } else {
 +    mm.dist[i] <- rnorm(1, mean = m.3, sd = sd.3)
 +  }
 +
 +}
 +
 +hist(mm.dist, breaks = 30, 
 +     main = "Multimodal Distribution", 
 +     xlab = "Value", ylab = "Density", 
 +     freq = FALSE, probability = T,
 +     col = "lightblue", border = "black")
 +lines(density(mm.dist), 
 +      col = "darkred", lwd = 2)
 +
 +</code>
 +</WRAP>
 +<WRAP column half>
 +{{:b:head_first_statistics:pasted:20250908-082219.png}}
 +</WRAP>
 +<WRAP clear/>
 +
 +
 +====== box plot ======
 +<WRAP column half>
 <code> <code>
 # Boxplot of MPG by Car Cylinders # Boxplot of MPG by Car Cylinders
Line 246: Line 433:
     ylab="Miles Per Gallon")     ylab="Miles Per Gallon")
 </code> </code>
-{{:c:ps1-1:2019:pasted:20190909-111438.png}}+</WRAP>
  
 +<WRAP column half>
 +{{:c:ps1-1:2019:pasted:20190909-111438.png}}
 +</WRAP>
 +<WRAP clear/>
 +====== see also ======
 +https://r-graph-gallery.com/
  
b/head_first_statistics/visualization.1756853707.txt.gz · Last modified: by hkimscil

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