风玫瑰与ggplot（R）？ [英] Wind rose with ggplot (R)?

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本文介绍了风玫瑰与ggplot（R）？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在寻找使用ggplot2创建
所以：我们已经通过方向和风速正确归档了数据，并对代码进行了编码按预期提高我们的超出范围的数据。看起来不错！



使用这个函数



现在我们加载真实数据。我们可以从URL加载： 

  data.in<  -  read.csv（file =http：// midcdmz ？.nrel.gov /应用/ plot.pl网站= NWTC和放大器;启动= 20010824&放大器;伊迪= 26安培;情绪= 3及EYR = 2062&放大器;年= 2013&放大器;当月= 1和天= 1&安培; endyear = 2013&放大器; endmonth = 12安培; endday指定= 31&安培;时间= 0&安培;研究所= 21&安培;研究所= 39&安培;类型=数据&安培; wrlevel = 2及预设= 0&安培;第一= 3及数学= 0&安培;第二= -1&安培;值= 0.0&安培;用户= 0&安培; axis = 1，
 col.names = c（date，hr，ws.80，wd.80））

或来自文件： 

  data.in<  - read.csv（file =A：/blah/20130101.csv，
 col.names = c（date，hr，ws.80，wd.80））



快捷方式



简单的使用方法这与M2数据是只传递给 spd 和 dir （速度和方向）的单独向量： p> 

 ＃尝试默认设置
 p1 < -  plot.windrose（spd = data.in $ w s.80，
 dir = data.in $ wd.80）

这个情节：





如果我们想要自定义垃圾箱，我们可以添加这些参数： 

  p2 < -  plot.windrose（spd = data.in $ ws.80， 
 dir = data.in $ wd.80，
 spdseq = c（0,3,6,12,20））



使用数据框和列名



为使绘图与 ggplot（）更加兼容，您还可以传入一个数据框和名称的速度和方向变量： 

  p.wr2 < -  plot.windrose（data = data.in，
 spd =ws.80，
 dir =wd.80）



用另一个变量faceting



我们也可以使用ggplot的faceting功能按月或年绘制数据。让我们先从 data.in 中的日期和时间信息获取时间戳，然后转换为月和年： 

 ＃首先从日期和时间列创建一个真正的POSIXCT时间戳
 data.in $ timestamp<  -  as.POSIXct（paste0（data.in $ date， ，data.in $ hr），
 tz =GMT，
格式=％m /％d /％Y％H：％M）
 
＃将时间戳转换为年和月
 data.in $ Year<  -  as.numeric（format（data.in $ timestamp，％Y））
 data.in $ month<  - 因子（格式（data.in $ timestamp，％B），
 levels = month.name）

然后，您可以应用faceting来显示风上涨的变化情况：月份：

 ＃recreate p.wr2，以便包括新数据
 p.wr2<  -  plot.windrose（data = data.in，
 spd =ws.80，
 dir =wd.80） 
＃现在生成分面
 pw r3 < -  p.wr2 + facet_wrap（〜月，
 ncol = 3）
＃并删除标签以便清晰
 p.wr3<  -  p.wr3 + theme（axis.text .x = element_blank（），
 axis.title.x = element_blank（））



评论



如何使用：


  • 输入是：


    • 速度向量（ spd ）和方向（ dir ）或数据的名称框架和包含速度和方向数据的列的名称。
    • 风速仓尺寸的可选值（ spdres ）和方向（ dirres ）。

    • palette 是 colorbrewer的名称顺序调色板，
    • countmax 设置风玫瑰的范围。

    • debug 是一个开关（0,1,2），用于启用不同级别的调试。我希望能够设置最高速度（ spdmax ）和计数（ countmax ），这样我就可以比较来自不同数据集的风速。如果风速超过（ spdmax ），那些被添加为灰色区域（见图）。我应该编码一些类似于 spdmin 的地方，以及颜色代码，风速小于这些地区。

    • 要求，我实施了使用自定义风速箱的方法。可以使用 spdseq = c（1,3,5,12）参数添加它们。

    • 使用通常的ggplot命令清除x轴的标签： p.wr3 + theme（axis.text.x = element_blank（），axis.title.x = element_blank（））

    • 最近ggplot2改变了bin的排序，所以这些图不起作用。我认为这是2.2版本。但是，如果您的情节看起来有点奇怪，请更改代码，以便测试2.2可能是2.1或2.0。

    I am looking for good R code (or package) that uses ggplot2 to create wind roses that show the frequency, magnitude and direction of winds.

    I'm particularly interested in ggplot2 as building the plot that way gives me the chance to leverage the rest of the functionality in there.

    Test data

    Download a year of weather data from the 80-m level on the National Wind Technology's "M2" tower. This link will create a .csv file that is automatically downloaded. You need to find that file (it's called "20130101.csv"), and read it in.

    # read in a data file
data.in <- read.csv(file = "A:/drive/somehwere/20130101.csv",
                    col.names = c("date","hr","ws.80","wd.80"),
                    stringsAsFactors = FALSE))

    This would work with any .csv file and will overwrite the column names.

    Sample data

    If you don't want to download that data, here are 10 data points that we will use to demo the process:

    data.in <- structure(list(date = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,

    1L, 1L), .Label = "1/1/2013", class = "factor"), hr = 1:9, ws.80 = c(5, 7, 7, 51.9, 11, 12, 9, 11, 17), wd.80 = c(30, 30, 30, 180, 180, 180, 269, 270, 271)), .Names = c("date", "hr", "ws.80", "wd.80" ), row.names = c(NA, -9L), class = "data.frame")

    解决方案

    For sake of argument we'll assume that we are using the data.in data frame, which has two data columns and some kind of date / time information. We'll ignore the date and time information initially.

    The ggplot function

    I've coded the function below. I'm interested in other people's experience or suggestions on how to improve this.

    # WindRose.R
require(ggplot2)
require(RColorBrewer)

plot.windrose <- function(data,
                      spd,
                      dir,
                      spdres = 2,
                      dirres = 30,
                      spdmin = 2,
                      spdmax = 20,
                      spdseq = NULL,
                      palette = "YlGnBu",
                      countmax = NA,
                      debug = 0){


# Look to see what data was passed in to the function
  if (is.numeric(spd) & is.numeric(dir)){
    # assume that we've been given vectors of the speed and direction vectors
    data <- data.frame(spd = spd,
                       dir = dir)
    spd = "spd"
    dir = "dir"
  } else if (exists("data")){
    # Assume that we've been given a data frame, and the name of the speed 
    # and direction columns. This is the format we want for later use.    
  }  

  # Tidy up input data ----
  n.in <- NROW(data)
  dnu <- (is.na(data[[spd]]) | is.na(data[[dir]]))
  data[[spd]][dnu] <- NA
  data[[dir]][dnu] <- NA

  # figure out the wind speed bins ----
  if (missing(spdseq)){
    spdseq <- seq(spdmin,spdmax,spdres)
  } else {
    if (debug >0){
      cat("Using custom speed bins \n")
    }
  }
  # get some information about the number of bins, etc.
  n.spd.seq <- length(spdseq)
  n.colors.in.range <- n.spd.seq - 1

  # create the color map
  spd.colors <- colorRampPalette(brewer.pal(min(max(3,
                                                    n.colors.in.range),
                                                min(9,
                                                    n.colors.in.range)),                                               
                                            palette))(n.colors.in.range)

  if (max(data[[spd]],na.rm = TRUE) > spdmax){    
    spd.breaks <- c(spdseq,
                    max(data[[spd]],na.rm = TRUE))
    spd.labels <- c(paste(c(spdseq[1:n.spd.seq-1]),
                          '-',
                          c(spdseq[2:n.spd.seq])),
                    paste(spdmax,
                          "-",
                          max(data[[spd]],na.rm = TRUE)))
    spd.colors <- c(spd.colors, "grey50")
  } else{
    spd.breaks <- spdseq
    spd.labels <- paste(c(spdseq[1:n.spd.seq-1]),
                        '-',
                        c(spdseq[2:n.spd.seq]))    
  }
  data$spd.binned <- cut(x = data[[spd]],
                         breaks = spd.breaks,
                         labels = spd.labels,
                         ordered_result = TRUE)
  # clean up the data
  data. <- na.omit(data)

  # figure out the wind direction bins
  dir.breaks <- c(-dirres/2,
                  seq(dirres/2, 360-dirres/2, by = dirres),
                  360+dirres/2)  
  dir.labels <- c(paste(360-dirres/2,"-",dirres/2),
                  paste(seq(dirres/2, 360-3*dirres/2, by = dirres),
                        "-",
                        seq(3*dirres/2, 360-dirres/2, by = dirres)),
                  paste(360-dirres/2,"-",dirres/2))
  # assign each wind direction to a bin
  dir.binned <- cut(data[[dir]],
                    breaks = dir.breaks,
                    ordered_result = TRUE)
  levels(dir.binned) <- dir.labels
  data$dir.binned <- dir.binned

  # Run debug if required ----
  if (debug>0){    
    cat(dir.breaks,"\n")
    cat(dir.labels,"\n")
    cat(levels(dir.binned),"\n")       
  }  

  # deal with change in ordering introduced somewhere around version 2.2
  if(packageVersion("ggplot2") > "2.2"){    
    cat("Hadley broke my code\n")
    data$spd.binned = with(data, factor(spd.binned, levels = rev(levels(spd.binned))))
    spd.colors = rev(spd.colors)
  }

  # create the plot ----
  p.windrose <- ggplot(data = data,
                       aes(x = dir.binned,
                           fill = spd.binned)) +
    geom_bar() + 
    scale_x_discrete(drop = FALSE,
                     labels = waiver()) +
    coord_polar(start = -((dirres/2)/360) * 2*pi) +
    scale_fill_manual(name = "Wind Speed (m/s)", 
                      values = spd.colors,
                      drop = FALSE) +
    theme(axis.title.x = element_blank())

  # adjust axes if required
  if (!is.na(countmax)){
    p.windrose <- p.windrose +
      ylim(c(0,countmax))
  }

  # print the plot
  print(p.windrose)  

  # return the handle to the wind rose
  return(p.windrose)
}

    Proof of Concept and Logic

    We'll now check that the code does what we expect. For this, we'll use the simple set of demo data.

    # try the default settings
p0 <- plot.windrose(spd = data.in$ws.80,
                   dir = data.in$wd.80)

    This gives us this plot: So: we've correctly binned the data by direction and wind speed, and have coded up our out-of-range data as expected. Looks good!

    Using this function

    Now we load the real data. We can load this from the URL:

    data.in <- read.csv(file = "http://midcdmz.nrel.gov/apps/plot.pl?site=NWTC&start=20010824&edy=26&emo=3&eyr=2062&year=2013&month=1&day=1&endyear=2013&endmonth=12&endday=31&time=0&inst=21&inst=39&type=data&wrlevel=2&preset=0&first=3&math=0&second=-1&value=0.0&user=0&axis=1",
                    col.names = c("date","hr","ws.80","wd.80"))

    or from file:

    data.in <- read.csv(file = "A:/blah/20130101.csv",
                    col.names = c("date","hr","ws.80","wd.80"))

    The quick way

    The simple way to use this with the M2 data is to just pass in separate vectors for spd and dir (speed and direction):

    # try the default settings
p1 <- plot.windrose(spd = data.in$ws.80,
                   dir = data.in$wd.80)

    Which gives us this plot:

    And if we want custom bins, we can add those as arguments:

    p2 <- plot.windrose(spd = data.in$ws.80,
                   dir = data.in$wd.80,
                   spdseq = c(0,3,6,12,20))

    Using a data frame and the names of columns

    To make the plots more compatible with ggplot(), you can also pass in a data frame and the name of the speed and direction variables:

    p.wr2 <- plot.windrose(data = data.in,
              spd = "ws.80",
              dir = "wd.80")

    Faceting by another variable

    We can also plot the data by month or year using ggplot's faceting capability. Let's start by getting the time stamp from the date and hour information in data.in, and converting to month and year:

    # first create a true POSIXCT timestamp from the date and hour columns
data.in$timestamp <- as.POSIXct(paste0(data.in$date, " ", data.in$hr),
                                tz = "GMT",
                                format = "%m/%d/%Y %H:%M")

# Convert the time stamp to years and months 
data.in$Year <- as.numeric(format(data.in$timestamp, "%Y"))
data.in$month <- factor(format(data.in$timestamp, "%B"),
                        levels = month.name)

    Then you can apply faceting to show how the wind rose varies by month:

    # recreate p.wr2, so that includes the new data
p.wr2 <- plot.windrose(data = data.in,
              spd = "ws.80",
              dir = "wd.80")
# now generate the faceting
p.wr3 <- p.wr2 + facet_wrap(~month,
                            ncol = 3)
# and remove labels for clarity
p.wr3 <- p.wr3 + theme(axis.text.x = element_blank(),
          axis.title.x = element_blank())

    Comments

    Some things to note about the function and how it can be used:

    • The inputs are:
      • vectors of speed (spd) and direction (dir) or the name of the data frame and the names of the columns that contain the speed and direction data.
      • optional values of the bin size for wind speed (spdres) and direction (dirres).
      • palette is the name of a colorbrewer sequential palette,
      • countmax sets the range of the wind rose.
      • debug is a switch (0,1,2) to enable different levels of debugging.
    • I wanted to be able to set the maximum speed (spdmax) and the count (countmax) for the plots so that I can compare windroses from different data sets
    • If there are wind speeds that exceed (spdmax), those are added as a grey region (see the figure). I should probably code something like spdmin as well, and color-code regions where the wind speeds are less than that.
    • Following a request, I implemented a method to use custom wind speed bins. They can be added using the spdseq = c(1,3,5,12) argument.
    • You can remove the degree bin labels using the usual ggplot commands to clear the x axis: p.wr3 + theme(axis.text.x = element_blank(),axis.title.x = element_blank()).
    • At some point recently ggplot2 changed the ordering of bins, so that the plots didn't work. I think this was version 2.2. But, if your plots look a bit weird, change the code so that test for "2.2" is maybe "2.1", or "2.0".

    这篇关于风玫瑰与ggplot（R）？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！

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