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Home Page: https://tpemartin.github.io/110-1-Economic-Data-Visualization/

License: MIT License

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110-1-economic-data-visualization's Introduction

110-1-Economic-Data-Visualization

110-1-economic-data-visualization's People

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gitter-badger jhchou97 wiwi11506 jeffhu520 ce9sa14hess jilufu yating2021 xie-hui-jun aliceliyijhen juanask8 rayecon1997 chengyijoe 711061109 y06112 kuochihyun ken9277 brigitahalim qbace

110-1-economic-data-visualization's Issues

axis_x_custom function building

dataSet1 <- 
  data.frame(
    x=1979:2018
  )
set.seed(2038)
dataSet1$y <- sample(10:40, length(dataSet1$x), T)
ggplot1 <- list()
ticks <- list()
ticks$major <- seq(1980, 2015, by=5)
ticks$minor <- c(1979, 2018)

majorLength = 3 #input$length
minor_majorRatio = 0.7 #input$ratio


ggplot()+
  geom_step(
    data=dataSet1,
    mapping=
      aes(
        x=x,
        y=y
      )
  ) +
  scale_x_continuous(
    breaks={
      breaks = 
        c(1979,seq(1985, 2015, by=5),2018)
      breaks
      },
    labels={
      labels = c(
  "1979", "85", "90", "95", "2000", "05", "10", "15", "18"
)
      labels}
  ) +
  theme(
    axis.ticks.length.x = unit(0,"mm")
  ) +
  geom_rug(
    mapping=aes(
      x=ticks$major
      ),
    outside=TRUE, # draw rug outside the plot panel
    size=0.5, #input$majorsize
    length=grid::unit(
      majorLength, 
      "mm"
    )
  ) +
  geom_rug(
    mapping=aes(
      x=ticks$minor
    ),
    outside = TRUE,
    size=0.5, #input$minorsize
    length=grid::unit(
      minor_majorRatio*majorLength,
      "mm"
    )
  )+
  coord_cartesian(clip="off")+
  theme(
    axis.text.x = element_text(
      margin = margin(
        12 #input$margin
      ),
      size=16 #input$textSize
    ))

# the same as
ggplot()+
  geom_step(
    data=dataSet1,
    mapping=
      aes(
        x=x,
        y=y
      )
  ) +
{
  list(
    scale_x_continuous(
      breaks = {
        breaks <-
          c(1979, seq(1985, 2015, by = 5), 2018)
        breaks
      },
      labels = {
        labels <- c(
          "1979", "85", "90", "95", "2000", "05", "10", "15", "18"
        )
        labels
      }
    ),
    theme(
      axis.ticks.length.x = unit(0, "mm")
    ),
    geom_rug(
      mapping = aes(
        x = ticks$major
      ),
      outside = TRUE, # draw rug outside the plot panel
      size = 0.5, # input$majorsize
      length = grid::unit(
        majorLength,
        "mm"
      )
    ),
    geom_rug(
      mapping = aes(
        x = ticks$minor
      ),
      outside = TRUE,
      size = 0.5, # input$minorsize
      length = grid::unit(
        minor_majorRatio * majorLength,
        "mm"
      )
    ),
    coord_cartesian(clip = "off"),
    theme(
      axis.text.x = element_text(
        margin = margin(
          12 # input$margin
        ),
        size = 16 # input$textSize
      )
    )
  )
}


ggplot1$plot1


ggplot1$plot1  -> # allow drawing outside the plot panel 
  ggplot1$plot2

ggplot1$plot2

ggplot1$plot2

Update econDV2 and .Rprofile

devtools::install_github("tpemartin/econDV2", force=T)

Then modify your .Rprofile file in your project. Locate gg <- list(...) change it to

gg <- list(
  dash = econDV2::ggdash,
  geom = econDV2::ggbrowse,
  aes = econDV2::ggaes,
  resize_image = econDV2::resize_image
)

Between the lines Data-vis tools we use

https://view.e.economist.com/?qs=215799c4a22ea81002143df06b3a4587d079a3cd013d463f486a35dd82731fc1ff4653d6593ebb4ee4eb36bae339d61cae6559f6a9ac86b0db1014790816b7a4be623d282933e5d048f34c7fe6ab0864

2.1 Example

ggplot()+
  geom_point(
    mapping = aes(
      x=c(53, 56, 58),
      y=c(49, 54.9, 58)
    ),
    shape=21,
    color="black",
    stroke=1, #input$stroke
    fill="blue", #input$fill
    size=5 #input$size
  )

互動式資料視覺化時間調查

目前時間：四 7~9

針對R語言中階使用者且有相當ggplot2程度使用者所設計的資料視覺化課程，本課程主要教授web app互動資訊儀表板的設計，課程目標有：

如何使用R進行前端web UI佈局，同時介紹兩個響應式前端框架(bootstrap, materialise）與R的揉合使用。
使用者端的互動式資料視覺化設計，從ggplot2的角度出發學習plotly.js互動圖形設計。
伺服器端的互動式資料視覺化設計，主要以Shiny為後端開發骨架，進一步帶入互動元素中常見的reactive programming概念。
Web app deployment基本概念。

有要修的同學在以下可以的時段：

可以按 thumb-up
不可以按thumb-down

不打算修的不用回應。

Final Project

Due: Feb. 6
Format: Rmd file with 3 graphs. It's knitted result is like Daily Chart in Economist.

Exercise: Taiwan Presidential Election

https://tpemartin.github.io/economic-data-visualization-2021/categorical-data.html#section-3

geom_rug as axis ticks.

breaks = c(
  1979,
  seq(1985, 2015, by=5),
  2018
)
labels = c(
  "1979", "85", "90", "95", "2000", "05", "10", "15", "18"
)

ggplot1 <- list()

ggplot()+
  geom_step(
    data=dataSet1,
    mapping=
      aes(
        x=x,
        y=y
      )
  ) -> ggplot1$plot0
ggplot1$plot0

x=interger
x=discrete
scale_x_discrete
scale_x_continuous

Fine tune x scale

ggplot1$plot0 +
  scale_x_continuous(
    breaks=breaks,
    labels=labels
  ) -> ggplot1$plot1

ggplot1$plot1

ggplot1$plot1 +
  theme(
    axis.ticks.length.x=unit(0, "mm")
  ) -> ggplot1$plot2

ggplot1$plot2

bigyears <- 
  seq(1980, 2015, by=5)
smallyears <-
  seq(1979, 2018)
ggplot1$plot2 +
  geom_rug(
    aes(
      x=bigyears
    ),
    outside = T,
    length=unit(
      2, #input$bigT
      "mm"
    )
  ) +
  geom_rug(
    aes(
      x=smallyears
    ),
    outside = T,
    length=unit(
      1, #input$smallT
      "mm"
    )
  )+
  coord_cartesian(clip="off")

4.7 Exercise 1

set.seed(2020)
type1 <- function(){
  dnorm(1990:2010, mean=1991, sd=3)*2500 -> x
    round(x, digits = 0)
}
type2 <- function(){
  dnorm(1994:2010, mean=1998, sd=2)*1000->x
  round(x, digits=0)
}
type3 <- function(){
  dnorm(2002:2010, mean=2005, sd=2.3)*800->x
  round(x, digits=0)
}

data_set4 <- data.frame(
  year=c(
    1990:2010,
    1994:2010,
    2002:2010),
  storage_type=
    c(
      rep("type1", 21),
      rep("type2", 17),
      rep("type3", 9)),
  sales_amount=c(
    type1(), 
    type2(),
    type3()
  )
)

Week 11 graph

The demo of post-image editing using Boxy SVG is what I am going to illustrate next week.

This semester I will drop one weekly graph. So if you are too busy preparing your final, you can skip one graph without affecting your grades. But I still encourage to submit your last weekly graph so that I can give you some comments.

Week 7 graph

https://github.com/tpemartin/110-1-Economic-Data-Visualization/blob/main/weekly_graph/week7.R