ASSIGNMENT 4

Marks and Channels

A clever description that describes the stuff
Assignment
DataViz
Author

Yaotian Gao

Published

February 8, 2024

Code 
library(dplyr) 
Warning: package 'dplyr' was built under R version 4.2.3

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Code 
library(tidyr) 
Warning: package 'tidyr' was built under R version 4.2.3
Code 
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.2.3
Code 
# Read the dataset from a specified file path
data = read.csv("E:/01-文件/04-UI文件/2401-【课程】BCB/BCB/BCB520Practice/data.csv")

# Display the dataset in a nicely formatted table using knitr package
knitr::kable(data)
ENT ID YLD.2017.AB.Ran YLD.2017.Soda.Ran YLD.2017.Walla.Ran
52001 IDO854 91 45.0 71.0
52002 SRRN-6029 89 45.9 75.7
52003 SRRN-6028 84 40.9 75.3
52004 SRRN-6037 89 38.3 86.4
52005 LOUISE 90 52.6 75.9
52006 RIL29 89 43.7 59.0
52007 IDO629 81 45.8 72.2
52008 UC1110 95 45.5 88.0
52009 UC1642 90 49.3 80.9
52010 MT0921 85 38.6 78.1
52011 Choteau 89 51.4 84.0
52012 9223 88 56.8 84.9
52013 Attila-1RS 89 42.1 87.1
52014 Hahan-1RSMA 89 38.8 90.4
52015 10010/20 94 49.7 82.4
52016 MT0945 85 47.0 62.2
52017 Newana 82 45.9 88.3
52018 UC1682 88 42.7 59.0
52019 MT0813 91 51.9 65.4
52020 9229 83 45.5 87.9
52021 HW090006M 81 47.0 85.6
52022 H0800080 84 45.2 91.4
52023 H0800314 90 44.0 72.7
52024 MT1020 84 42.0 67.2
52025 9242 85 51.2 86.7
52026 Fortuna 96 51.6 71.8
52027 9260 87 46.7 59.2
52028 9232 93 51.1 41.5
52029 Tara2002 87 45.3 58.3
52030 UC1618 87 50.3 50.3
52031 9233 95 51.4 72.3
52032 WA8034 88 47.8 79.1
52033 IDO852 91 41.5 58.1
52034 IDO851 88 47.2 83.3
52035 WA8016 86 49.2 82.7
52036 IDO696 94 55.0 79.5
52037 28TH SAWSN-3045 92 43.9 68.4
52038 9247 93 41.7 85.8
52039 SRRN-6044 80 40.1 58.2
52040 9240 84 39.0 79.0
52041 Macon 102 44.6 74.9
52042 McNeal 88 41.5 61.6
52043 SRRN-6042 93 49.5 67.2
52044 RSI5 Yr5 Yr15 Gpc HMW 1 92 52.7 74.9
52045 Thatcher 91 48.6 80.8
52046 Jefferson 83 49.7 64.0
52047 IDO582 92 43.1 55.2
52048 IDO702 78 41.4 51.6
52049 IDO644 95 61.4 58.2
52050 WA8074 89 52.8 78.4
52051 IDO671 84 42.3 86.3
52052 SRRN-6099 85 52.8 63.1
52053 WA8123 82 47.5 63.6
52054 21TH HRWSN-2106 93 48.5 65.6
52055 H0800310 87 43.6 75.3
52056 H0900081 74 40.4 52.8
52057 Jerome 80 47.0 39.9
52058 Vida 87 42.7 60.9
52059 CENTENNIAL 94 43.2 51.8
52060 9252 93 41.9 60.6
52061 PENAWAWA 94 40.5 58.6
52062 UC1683 93 41.9 50.8
52063 Pomerelle 87 44.6 47.1
52064 MT1002 91 45.9 49.8
52065 Treasure 82 39.9 50.2
52066 Expresso 82 43.5 68.0
52067 HW080169 84 43.3 49.4
52068 Attila-1RSMA 84 45.9 57.2
52069 9259 95 48.6 55.8
52070 Cataldo 83 42.5 76.0
52071 11010-9, 10013-1 85 43.5 47.9
52072 9245 87 42.7 90.1
52073 SRRN-6049 88 42.8 49.6
52074 Kelse 93 45.1 62.0
52075 IDO687 94 48.0 85.1
52076 MT1053 87 41.7 92.7
52077 9256 84 47.2 54.9
52078 9253 99 43.7 50.0
52079 IDO560 87 42.8 38.6
52080 Alturas 93 40.5 77.5
52081 UC1551 91 53.6 85.4
52082 UC1554 76 45.9 78.0
52083 SRRN-6050 88 38.9 46.4
52084 IDO858 92 42.0 53.1
52085 UC1396 90 39.4 38.9
52086 PI610750 90 47.8 95.1
52087 Hollis 94 39.0 54.4
52088 LCS Atomo 82 39.9 37.5
52089 UC1395 84 40.4 48.5
52090 9241 91 42.0 47.9
52091 UC1603 83 39.9 49.4
52092 UI Winchester 85 45.1 36.2
52093 UC1601 78 44.1 43.8
52094 WA8099 80 40.5 74.8
52095 CAP151-3 82 48.7 44.6
52096 MT0415 83 46.5 61.2
52097 WHITEBIRD 86 47.5 80.9
52098 21TH HRWSN-2126 84 43.4 52.0
52099 LCS-Star 78 41.9 69.3
52100 Scarlet 80 37.9 55.2
52101 UC1552 83 39.5 74.6
52102 Jubilee 88 44.7 70.5
52103 SRRN-6032 86 44.4 54.0
52104 Otis 87 53.6 87.9
52105 IDO488 81 42.1 60.5
52106 SRRN-6019 83 51.9 57.6
52107 SRRN-6047 86 39.2 55.3
52108 MT1016 91 41.5 50.8
52109 Hahan-1RS 86 42.8 59.7
52110 MTHW1060 82 45.6 54.0
52111 SpCB-3004 84 41.3 63.2
52112 SY Capstone(IDO694?) 87 40.0 66.6
52113 UC1643 92 42.1 55.0
52114 9246 86 40.4 72.9
52115 chewink 80 48.0 78.1
52116 MTHW0771 87 40.5 54.5
52117 H0900009 84 45.3 52.9
52118 9225 83 46.1 56.6
52119 9254 87 43.0 45.7
52120 10014/7 93 40.1 77.2
52121 Lassik 80 40.4 62.1
52122 Summit 515 89 39.0 67.8
52123 MT0861 95 48.2 57.6
52124 9249 94 52.2 53.2
52125 Lolo 90 51.9 56.6
52126 RIL203 93 52.0 58.7
52127 MT1027 92 46.0 67.3
52128 UC1616 94 44.6 47.5
52129 UI Stone 95 40.5 69.3
52130 UI Pettit 92 42.0 55.0
52131 28TH SAWSN-3046 92 40.7 53.4
52132 WA8133 85 42.5 82.5
52133 SRRN-6109 88 49.0 43.0
52134 UC1602 87 46.1 75.1
52135 9228 92 44.6 64.2
52136 21TH HRWSN-2111 89 43.6 54.1
52137 Hi-Line 80 38.8 57.9
52138 AC BARRIE 86 42.9 49.6
52139 H0800103L 81 39.4 77.3
52140 IDO868 94 44.8 63.8
52141 UC1679 86 42.0 59.6
52142 9248 85 39.0 46.6
52143 WA8100 81 44.9 53.2
52144 MTHW0867 92 47.8 55.5
52145 9258 87 47.5 60.1
52146 IDO440 89 38.3 65.2
52147 Duclair 92 45.3 49.6
52148 UC896 5+10 Lr34/Yr18 Yr5 Gpc 89 38.8 48.2
52149 Blanca Grande 515 89 37.1 52.7
52150 9263 86 45.4 49.3
52151 SRRN-6098 93 48.8 54.6
52152 9261 80 37.6 76.2
52153 SRRN-6097 90 42.2 85.8
52154 9262 94 39.5 58.4
52155 SRRN-6030 96 38.0 76.2
52156 HR07024-5 95 37.8 54.1
52157 UI Lochsa 99 41.5 65.2
52158 CAP34-1 94 45.5 53.4
52159 HW090071M 93 46.1 65.1
52160 UC1599 80 38.8 63.2
52161 MTHW1069 87 45.9 67.4
52162 UI Platinum 96 50.0 62.1
52163 Blanca Fuerte 91 46.8 52.4
52164 Berkut 87 45.8 57.9
52165 SRRN-6027 88 43.2 51.0
52166 IDO686 91 47.1 61.3
52167 SRRN-6038 81 38.1 69.3
52168 HR07005-3 82 43.7 62.5
52169 IDO377s 85 47.2 59.3
52170 MT0802 92 39.9 64.3
Code 
# Transform data from wide to long format
long_data <- data %>% 
  pivot_longer(
    cols = starts_with("YLD"),  # Select columns that start with 'YLD'
    names_to = "Year_Rep",      # New column for year
    values_to = "Value"         # New column for values
  )

# Extract the year from 'Year_Rep' and create a new column 'Year'
long_data <- long_data %>%
  mutate(Year = sub("-.*", "", Year_Rep))  

ggplot(data=long_data, aes(x=Year, y=Value, fill=Year)) +
  geom_bar(stat="identity", fill="steelblue") +
  theme_minimal() +
  labs(title="Figure 1: Average Yield by Location",
       x="Location", y="Average Yield") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Code 
# 加载必要的包
library(ggplot2)

# 创建数据框
data <- data.frame(
  Region = c("YLD-2017-AB-Ran", "YLD-2017-Soda-Ran", "YLD-2017-Walla-Ran"),
  Average = c(87.82941176, 44.55117647, 64.11235294)
)

# 转换数据框以适用于ggplot
data_long <- reshape2::melt(data, id.vars = "Region")

# 创建热力图
ggplot(data_long, aes(x = variable, y = Region, fill = value)) +
  geom_tile() +
  scale_fill_gradient(low = "blue", high = "red") +
  labs(title="Figure 2: Misleading Visualization Using Color Intensity",x = "", y = "Location", fill = "Average Yield") +
  theme_minimal() +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())

Code 
ggplot(data=long_data, aes(x=Value, fill=Year)) +
  geom_density(alpha=0.5) +            
   scale_fill_manual(values=c("blue", "red", "green"))+
  theme_minimal() +                     # Use a minimal theme
  labs(title="Figure 3: Yield by Location",
       x="Location", y="Average Value", fill="Color Intensity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Code 
ggplot(data=long_data, aes(x=Value, fill=Year)) +
  geom_density(alpha=0.5) +            
   scale_fill_manual(values=c("lightgrey", "grey", "darkgrey"))+
  theme_minimal() +                     # Use a minimal theme
  labs(title="Figure 4: Misleading Visualization Using Color",
       x="Location", y="Average Value", fill="Color Intensity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Code 
ggplot(data=long_data, aes(x=Year, y=Value)) +  # 移除 fill=Year 来避免自动颜色填充
  geom_boxplot() +                              # 添加箱线图层
  theme_minimal() +                             # 使用简洁主题
  labs(title="Figure 5: Boxplot by Location", x="Location", y="BU/Ac") +  # 定制图表标题和轴标签
  theme(legend.position="none")                 # 移除图例

Code 
ggplot(data=long_data, aes(x=Year, y=Value, fill=Year)) +
  geom_boxplot() +                      # Add boxplot layer
  theme_minimal() +                     # Use a minimal theme
  labs(title="Figure 6: Misleading Visualization Using Color", x="Location", y="BU/Ac")  # Add labels

Code 
# 加载必要的库
library(ggplot2)
library(dplyr)
library(tidyr) # 用于pivot_longer函数

# 从给定路径读取CSV文件
file_path <- "E:/01-文件/04-UI文件/2401-【课程】BCB/BCB/BCB-Quarto Blog-/posts/MarksChannels/Average temperature.csv"
data <- read.csv(file_path, stringsAsFactors = FALSE)

# 转换数据结构以适应绘图需要
data_long <- pivot_longer(data, cols = Mar:Aug, names_to = "Month", values_to = "Temperature")

# 转换Month为有序因子，以确保它们按正确的顺序显示
data_long$Month <- factor(data_long$Month, levels = c('Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug'))

# 为不同的地点设置不同的形状
shapes <- c("Walla" = 16, "Soda" = 17, "Aberdeen" = 12) # 16=实心圆，17=实心三角形，18=实心菱形

# 为不同的地点设置颜色，并突出Aberdeen
colors <- c("Walla" = "grey", "Soda" = "grey", "Aberdeen" = "red") 

# 使用ggplot2创建折线图
p <- ggplot(data_long, aes(x = Month, y = Temperature, group = Location, color = Location, shape = Location)) +
  geom_line() + # 添加折线
  geom_point(size = 3) + # 添加点，并设置大小
  scale_shape_manual(values = shapes) + # 应用形状设置
  scale_color_manual(values = colors) + # 应用颜色设置
  theme_minimal() + # 使用简洁主题
  ggtitle("Figure 7: Average Monthly Temperature During Wheat Growing Season(Highlight)") + # 添加标题
  xlab("Month") + # X轴标签
  ylab("Temperature (°F)") # Y轴标签

# 打印绘图
print(p)

Code 
# 为不同的地点设置颜色，并突出Aberdeen
colors1 <- c("Walla" = "grey", "Soda" = "grey", "Aberdeen" = "grey") 

# 使用ggplot2创建折线图
p <- ggplot(data_long, aes(x = Month, y = Temperature, group = Location, color = Location, shape = Location)) +
  geom_line() + # 添加折线
  geom_point(size = 3) + # 添加点，并设置大小
  scale_shape_manual(values = shapes) + # 应用形状设置
  scale_color_manual(values = colors1) + # 应用颜色设置
  theme_minimal() + # 使用简洁主题
  ggtitle("Figure 8: Average Monthly Temperature During Wheat Growing Season(Common)") + # 添加标题
  xlab("Month") + # X轴标签
  ylab("Temperature (°F)") # Y轴标签

# 打印绘图
print(p)

Describe:

In Figure 1, I utilized lines as markers and position as the channel. Since position on a common scale is the highest-ranking channel for ordered attributes, it allows for easy differentiation of production levels across different locations.In Figure 2, I again used lines as markers but employed color as the channel. Because color is a lower-ranking channel for ordered attributes, it does not easily distinguish production levels across different locations.

In Figures 3 and 4, I used lines and areas as marks, differentiating them using color and position. The distinction is that, in Figure 3, I used contrasting colors like blue, red, and green to differentiate various locations, whereas in Figure 4, I used shades of gray, including light gray, gray, and dark gray, which made the chart more difficult to distinguish.

In Figures 5 and 6, both utilized lines and areas as marks, with position as the differentiator. The difference in Figure 6 is the addition of color as an extra channel. While redundancy can be desirable, area interferes with hue, with larger shapes having more visual salience.

In Figures 7 and 8, I demonstrated the use of lines to represent the average monthly temperatures during the wheat growing season across different locations. The key difference is that in Figure 7, I used a red popout to highlight Aberdeen’s temperature curve, making it easy to locate. In contrast, in Figure 8, I used the same color for all three locations, making it difficult to immediately identify the curve for Aberdeen.