您好R / ggplot专家!
R和ggplot学习器在这里。
我正在研究场景,并在思考如何以最佳方式显示数据。 我需要你们的建议和指导。
R可再现的ggplot :
High
1 4.1 - 4.8
2 >= 6.2
3 2.3 - 4.4
到目前为止很好!
以最好的方式表示当前场景中的数据。这是它的外观。
我很难理解以下几点:
如何显示网格线之间月份的类别(“ Jan”,“ Feb”,“ Mar”)。部门也是如此。这样我就可以为每个组合制作一个类似于网格的区域。
现在,所有气泡相互重叠。我想以不重叠的方式放置气泡。为此,我正在考虑在数据框中再添加一列,并随机分配一个值,以便将其绘制在网格区域内。但是我发现很难理解,当我的x / y已经是library(ggrepel)
# Create the data frame.
sales_data <- data.frame(
emp_name <- c("Sam", "Dave", "John", "Harry", "Clark", "Kent", "Kenneth", "Richard", "Clement", "Toby"),
month <- as.factor(c("Jan", "Feb", "Mar", "Jan", "Feb", "Mar", "Jan", "Feb", "Mar", "Jan")),
dept_name <- as.factor(c("Production", "Services", "Support", "Support", "Services", "Production", "Production", "Support", "Support", "Support")),
revenue <- c(100, 200, 300, 400, 500, 600, 500, 400, 300, 200)
)
sales_data$month <- factor(sales_data$month, levels = c("Jan", "Feb", "Mar"))
categorical_bubble_chart <- ggplot(sales_data, aes(x= month, y = dept_name, size = revenue, fill = revenue, label = revenue)) +
geom_point(shape = 21, show.legend = FALSE)
categorical_bubble_chart
和month
时,我可以提供什么随机值来使每个气泡彼此不同?
自从过去5到6个小时以来,我一直在思考解决方案,但是找不到解决方案。 任何方向或建议都将受到高度赞赏,并为以后的读者学习。
答案 0 :(得分:2)
您是否正在寻找类似的东西?我无法在您的数据中找到每个方面的泡泡,所以我赚了一笔。
require(ggplot2)
# Create the data frame.
sales_data <- data.frame(
emp_name = c("Sam", "Dave", "John", "Harry", "Clark", "Kent", "Kenneth", "Richard", "Clement", "Toby"),
month = as.factor(c("Jan", "Feb", "Mar", "Jan", "Feb", "Mar", "Jan", "Feb", "Mar", "Jan")),
dept_name = as.factor(c("Production", "Services", "Support", "Support", "Services", "Production", "Production", "Support", "Support", "Support")),
revenue = c(100, 200, 300, 400, 500, 600, 500, 400, 300, 200)
)
sales_data$month <- factor(sales_data$month, levels = c("Jan", "Feb", "Mar"))
categorical_bubble_chart <- ggplot(sales_data, aes(x= revenue, y = revenue, size = revenue, fill = revenue, label = revenue)) +
geom_point(shape = 21, show.legend = FALSE) +
facet_grid(dept_name~month)
categorical_bubble_chart
给予:
答案 1 :(得分:1)
作为@ Wietze314方法的替代方法,构建了“快速又脏”的单图:
model = models.Sequential()
model.add(layers.Embedding(FEATURES_NUMBER, 30))
model.add(layers.LSTM(32, return_sequences=True))
model.compile(optimizer="adam",
loss='categorical_crossentropy', metrics=['acc'])
history = model.fit(train_data,
train_labels,
epochs=10,
batch_size=128,
validation_data=(validation_data,validation_labels)
)