Learn R Programming
micromapST (version 1.1.1)

micromapST: Linked Micromap Graphics Package

Description

Provides a easy and quick means of creating Linked Micromaps for any collection of geographically associated areas. The micromapST package uses the standard graphics and RColorBrewer packages to rapidly create highly readable linked micromap plots. This gives the user the ability to explore different views of their data quickly.

micromapST uses the border and name information contained in border group datasets to define the geographical areas used in creating the linked micromaps.

The micromapSEER function is included to help users of the specialized micromapST for NCI Seer areas called micromapSEER migrate to this package. The micromapSEER function, calls micromapST with the border group set to USSeerBG to generate linked micromaps for the 20 U. S. Seer Areas.

The micromapST contains border group datasets with the boundary and name information for the following::

  • USStatesBG - data from the original micromapST package for . the U. S. 50 states and the District of Columbia.
  • USSeerBG - data for the 21 U. S. Seer Areas.
  • KansasBG - data for the 105 counties in the state of Kansas.
  • NewYorkBG - data for the 62 counties in the state of New York.
  • MarylandBG - data for the 24 counties in the state of Maryland.
  • UtahBG = data for the 29 counties in the state of Utah
  • ChinaBG - 34 administrative areas in the country of China.
  • UKIrelandBG - 218 administrative areas in UK, Ireland and Isle of Man.
  • SeoulKoreaBG - 25 districts in Seoul S. Korea.
  • AfricaBG - 52 countries of the Africa continent.

Each plot row represents a single sub-area (state, county or province) within the border group area. Each column can be defined to present a different graphical representations of the user's data.

For linked micromaps, the primary columns are a MAP type, ID (sub-area name) and one or more glyphics. The statistical data is presented in the glyphics columns as one of the following graphics (glyphics):

  • arrows,
  • bars,
  • boxplots,
  • dots,
  • dots with confidence intervals,
  • dots with standard error,
  • dot with a significance marker,
  • time series line plots with or without confidence bands,
  • scatter plots, and
  • horizontal stacked (segmented, centered, and normalized) bars.

All border groups are distrbuted with the package as .rda datasets.

Usage

micromapST ( statsDFrame,  panelDesc,  rowNamesCol = NULL, rowNames   = NULL,  sortVar    = NULL,  ascend     = TRUE,  title      = c("", ""), plotNames  = NULL, axisScale  = NULL, staggerLab = NULL, bordGrp    = NULL, bordDir    = NULL, dataRegionsOnly = NULL, regionsB   = NULL, grpPattern = NULL, ignoreNoMatches = FALSE, colors     = NULL,  details    = NULL)

Arguments

statsDFrame
a data.frame containing data used with the following plots/glyphs:. arrow, bar, segbar, normbar, ctrbar, dot, dotse, dotconf, dotsignif, and scatdot plots. The data for the boxplot and time series plots (TS and TSConf) is more complex and multi-dimensional and is passed to the glyph generation routines via the panelData parameter (see below for more details.) The row.names of statsDFrame data.frame are used as the link identifier between the data row to the map boundary data. For U.S. state data, the link identifier must be the the state's 2 character abbreviations, full names, or 2-digit US FIPS codes as the id. For Seer Areas, the area identifier is the Seer Area abbreviates as defined in the USSeerBG document. Refer to the documentation on each border group for the exact names, abbrevviations and ids defined for each sub-area. The data columns in the statsDFrame are associated with each graphic using the col1, col2, and col3 vectors in the panelDesc data.frame by column name or number.
panelDesc
a data.frame that defines the description of each column: types, associated data columns in the stateFrame data.frame, column titles (top and bottom), reference values and text, and names of additional data.frames for complex glyphics (time series and boxplots). See section on panelDesc for more details.
rowNames
defines the type of value used as the row.names in the stateFrame. The options are:
  • ab - an abbreviation for the sub-area: 2 character state ID, postal code abbreviation, ISO abbreviation, or a generally accepted abbreviation for the sub-area;
  • id - sub-area ID, numerical integer identifiers. In the U.S. border groups, the state and county FIPs codes are used. An alias for id is FIPS.;
  • full - the full sub-area names;
  • alias - partial match aliases. Used only with the Seer Registry sub-area registry names as outputted by the SeerStat programs; or
  • alt_ab - an alternate sub-area abbreviation. In some areas, there area two accepted set of abbreviations. If the border group has an alternate abbreviation defined, this option allows the alternative abbreviation to be used as the rowNames in the data.

See the documentation on each border group for details on the full names, abbreviatations, optional alternate abbreviations and numeric identifiers defined for each area in the particular border group in the areaNamesAbbrsIDs R object. The default is ab. If a border group does not have one of these sets of name data, then the corresponding option will not be available. The linkage between data and boundaries is accomplished using the strings in the statsDFrame column identified by rowNamesCol or if no rowNamesCol is specified in the row.names information of the statsDFrame, The linkage values are validated to against the areaNamesAbbrsIDs data.frame for the specified border group for the micromapST call. If a sub-area in the border group is not referenced in the data, it is outlined on the map, but not colored. If the sub-area link cannot be matched, a warning message is generated and the execution of the package is stopped. Unless, the user has set the noMatchIgnore call argument to TRUE and the data will be ignored. The alias option is implemented to support with the USSeerBG border group and data generated by the Seer Stat program. The registry column identifying the Seer Area contains the Seer Area name and additional information. This option along with the alias field in the border group allows the package to use the registry column as the linkage to the boundary data using a wildcard or partial string allows the registry column generated by Seer Stat to be used as the linkage. This option is controlled the enableAlias variable in the border group's areaParms data.frame.

rowNamesCol
allows the user to specify the data.frame column that contains the sub-area string to be used to link the data row to the boundary data for the sub-area. The rowNames option above specifies which name information (full name, abbreviation, id, alternate abbreviations) the sub-area string is matched to in the border group name information. (see the border group documentation for more details.) The rowNamesCol value must be a column number or column name within the statsDFrame data.frame. The default value for rowNamesCol is to use row.names of the passed data.frame.
sortVar
defines the column name(s) or number(s) in the statsDFrame data.frame to be used to sort the statsDFrame data.frames before creating the state micromap. A vector of column names or numbers can be used sort on multiple columns and to break ties. For Example: sortVar=c(4,5) where columns 4 and 5 in the statsDFrame are used in the sort. If the user needs to sort the data based on information in the boxplot or time-series data, the best practice is to copy the data into the statsDFrame.
ascend
a logical value. If TRUE, sortVar will be sorted in ascending order. If FALSE, sortVar will be sorted in descending order. The default value is TRUE.
title
A character vector with one or two character strings to be used as the title of the overall micromap plot page. 
  
      For example:  
          \code{title = "micromapST Title"} or   
          \code{title = c("title line 1","title line 2")}
plotNames
defines the type of state names to be displayed when an id glyph column requested. The options are: ab or full. ab will display the sub-area abbreviations as defined in the border groups name information. full will display the full sub-area name as defined in the border group name information. (For the U.S. Stata name information, the full name for District of Columbia is shown as Dist. of Col.. because of space limitations). The default is full.
axisScale
defines the type of axis labels to be used and if scaling is applied. The acceptable values are o, w, s, and sn. o is the original method using the pretty function limiting the values to the range of the data. w is the default and uses the Wilkinson algorithm to generate the axis labels for a set of data, s uses the wilkinson algorithm, adjusts the range to cover the labels, and determines a scaling to apply to the labels. If 1000 is the scaling, "in the thousands" is added to the column titles. sn uses the wilkinson algorithm, adjusts the range to cover the labels, and checks each value to determine if it should be scaled. If it is scales, the scale identifier is added as a suffix. (e.g., 1240334 become 1.24M) "w" is the wilkinson algorithm without any scaling.
staggerLab
is a logical variable the controls if the axis labels are staggered and drawn on on two lines instead of one. If set to FALSE (the default), the labels are not staggered. If TRUE, two lines are used to draw the axis labels, alternating labels on each line.
bordDir
specifies the path to a border group dataset (.rda) that is external to the micromapST package. The name of the border group is specified in the bordGrp parameter.
bordGrp
specifies which preloaded border group to use for the area names, abbreviations, numeric identifier, and area boundary data. The supported border groups are USStatesBG and USSeerBG. The default bordGrp value is USSeerBG.
dataRegionsOnly
specifies to only map regions containing data when aP_Regions is set to TRUE. This indicates the name table contains the information to draw partial area maps of regions. When set to TRUE, only regions within the area are drawn that contain at least one sub-area with data provided by the caller. If FALSE, the entire area is drawn. The default is FALSE. Retional boundaries are not required, but suggested. See border group documentation for more details.
regionsB
when regional boundaries are provided, controls whether to overlay the boundaries on the map. If dataRegionsOnly is set to TRUE and only a subset of regions will be mapped and regional boundaries are provided, regionsB is et to TRUE. The default is FALSE. Independent of dataRegionsOnly, if set to TRUE, if present, regional boundaries are overlayed on the map, If set to FALSE, no regional boundaries are drawn. See border group documentation for more details.
grpPattern
The micromapST package generates the pattern of rows to panel groups automatically. The pattern is based on a maximum of 5 rows per group and number of rows can only decend from the edges toward the median row. The pattern generated by the package can be overriden by using the grpPattern to specify the pattern to use as a numeric vector. The provided vector must pass the following checks: a) must be a numeric vector, b) the sum of the values in the vector must equal the number of rows in the user supplied statsDFrame data frame c) the maximum number of rows per panel group is 5. d) the number of rows per panel must be integers and decending from the outside to the middle of the vector.
ignoreNoMatches
The micromapST package will automatically handle situations where there is no data for a sub-area in an area. However, it will stop processing if there is data for a non-existing sub-area. To instruct the package to ignore rows of data for sub-areas that do not have boundary information in the border group, set the call argument ignoreNoMatches to TRUE to get the package to detail the data rows from the processing.
colors
is a vector containing a vector of 12 or 24 color names or values ("#xxxxxx"). or the name of a color palette. The vector of 12 or 24 color names or "#xxxxxx" values are used to define the colors used for:
  • The 6 colors in each group for the states and symbols in the glyphs. One color per row (state).
  • 1 color for the median state and glyphics,
  • 1 foreground color for highlighted states in the map. This is used to highlight states already referenced previously or have meaning depend on the type of map requested. The usage is as follows: 
            "map"      - not used.
        "mapcum"   - highlight areas previously referenced
                     above.
        "maptail"  - highlight areas previously referenced 
                     above the median row and highlight 
                     remaining states below the median row.
        "mapmedian"- highlight all areas above the median 
                     in maps above the median row and 
                     highlight all areas below the median 
                     in maps below the median row.
  • 2 accent colors for "mapmedian" sub-area colors for above median and below median.
  • When an 12 additional colors are specified, they are used as the translucent colors in the tsconf confidence intervals bands. If only 12 colors are provided, the additional 12 translucent colors are generated using a 20% transparent version of the original color. 
              e.g., \code{adjustcolors(colors,0.2)}

The only color palette support is a gray palette to permit publication of the linked micromaps using a gray scale instead of color. By setting colors = "greys", "grays", or "bw", the entire plot will be generated using gray scale that has been balanced to maintain readability and reproduction without the use of color printing. Additional color palettes may be supported in future releases. If a colors vector is not provided, the package default colors will be used:

  • 6 state colors: "red", "orange", "green", "greenish blue", "lavender"
  • 1 median state color: "black"
  • 1 highlighed states: "light yellow" for "map", "mapcum", "maptail"
  • 2 highlighed states: "light red" and "light green" for "mapmedian" and
  • 12 translucent colors using the above colors at 20%.

It is strongly recommended to use the default.

details
defines the spacing, line widths and many other details of the plot layout, structure and content; see micromapGDefaults$details for more details. Generally details does not need to be specified, the default values will always be used and are strongly recommended. However, in a few cases, it may be desireable to turn off or disable a feature. In these cases, the user can specify just the specific variable and value in a list and pass it to micromapST via the details parameter. For example: 
     details=list(SNBar.Middle.Dot=FALSE,SBar.varht=FALSE)
The entire details variable list does not have to be passed. See the section on the micromapGDefaults$details for more details.

Value

Details

The micromapST function creates a linked micromap plot for data referencing a collection of geographic related areas, like the 50 US States and DC geographical areas or U.S. Seer Areas. The function provides links from a US state map to several forms of graphical charts: dot (dot), dot with confidence intervals (dotconf), dot standard error (dotse), dot with significance mark (dotsignif), arrow (arrow), bar chart (bar), time series (ts), time series with a confidence band (tsconf), horizontal stacked (segmented) bar (segbar), normalized bar (normbar), centered bar charts (ctrbar), scattered dot (scatdot), and box plots (boxplot). The data values for each column of graphs and each area are provided in the statsDFrame data.frame. The panelDesc data.frame specifies the type of chart, the column numbers in the statsDFrame with the statistics for the chart, column titles, reference values, etc. Additional data for boxplots and time series plots are provided through the panelData column in the panelDesc data.frame

References

Daniel B. Carr and Linda Williams Pickle, Visualizing Data Patterns with Micromaps, CRC Press, 2010 Linda Williams Pickle, James B. Pearson Jr., Daniel B. Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., http://www.jstatsoft.org/v63/i03/

See Also

micromapST, micromapSEER

Examples

Run this code


######
#   micromapST - Example # 01 - map with no cumulative shading,
#     2 columns of statistics: dot with 95% confidence interval, boxplot
#     sorted in descending order by state rates, using the default
#     border group of "USStatesBG", with default symbols.
####
# load sample data, compute boxplot

data(wflung00and95,wflung00and95US,wflung00cnty,envir=environment()) 

wfboxlist = boxplot(split(wflung00cnty$rate,wflung00cnty$stabr),plot=FALSE)  

# set up 4 column page layout

panelDesc01 <- data.frame(
  type=c("map","id","dotconf","boxplot"),    
  lab1=c("","","State Rate","County Rates"),  
  lab2=c("","","and 95% CI","(suppressed if 1-9 deaths)"), 
  lab3=c("","","Deaths per 100,000","Deaths per 100,000"), 
  col1=c(NA,NA,1,NA),col2=c(NA,NA,3,NA),col3=c(NA,NA,4,NA),     
  refVals=c(NA,NA,wflung00and95US[1,1],NA),   
  refTexts=c(NA,NA,"US Rate 2000-4",NA),       
  panelData= c("","","","wfboxlist")          
  ) 
# set up PDF output file, call package

pdf(file="Ex01-WFLung-2000-2004-State-Dot-County-Box.pdf",width=7.5,height=10)

micromapST(wflung00and95, panelDesc01, sortVar=1, ascend=FALSE,
               title=c("Ex01-White Female Lung Cancer Mortality, 2000-2004", 
                       "State Rates & County Boxplots")
           )  

dev.off()
##End Example 01

######
#   micromapST - Example # 02 - map with cumulative shading 
#                      from top down (mapcum), arrow and bar charts, 
#                      sorted in descending order by starting
#                      value of arrows (1950-69 rates) using default
#                      border group of "USStatesDF".  This 
#                      example also provides custom colors for the 
#                      linked micromaps, highlights, etc.
#   
####
# Load example data from package.

data(wmlung5070,wmlung5070US,envir=environment())  

panelDesc02 <- data.frame(
   type=c("mapcum","id","arrow","bar"),		
   lab1=c("","","Rates in","Percent Change"),       
   lab2=c("","","1950-69 and 1970-94","1950-69 To 1970-94"),  
   lab3=c("MAPCUM","","Deaths per 100,000","Percent"),
   col1=c(NA,NA,"RATEWM_50","PERCENT"), 		
   col2=c(NA,NA,"RATEWM_70",NA)		
 )
 
colorsRgb = matrix(c(                    # the basic 7 colors.
 213,  62,  79,   #region 1: red	    #D53E4F - Rust Red
 252, 141,  89,   #region 2: orange	    #FC8D59 - Brn/Org
 253, 225, 139,   #region 3: green	    #FEE08B - Pale Brn
 153, 213, 148,   #region 4: greenish blue  #99D594 - med Green
  50, 136, 189,   #region 5: lavendar 	    #3288BD - Blue
 255,   0, 255,   #region 6                 #FF00FF - Magenta    
 .00, .00, .00,   #region 7: black for median #000000 - Black
 230, 245, 152,   #non-highlighted foreground #E6F598 - YellowGreen
 255, 174, 185,   # alternate shape upper   #FFAEB9 - Mauve
 191, 239, 255,   # alternate shape lower   #BFEFFF - Cyan
 242, 242, 242,   # lightest grey for non-referenced sub-areas  #F2F2F2
 234, 234, 234),  # lighter grey for background - non-active sub-areas. #EAEAEA
 
  ncol=3,byrow=TRUE)

xcolors = c( grDevices::rgb(colorsRgb[,1],colorsRgb[,2],colorsRgb[,3],
                            maxColorValue=255),
              # set solid colors
            grDevices::rgb(colorsRgb[,1],colorsRgb[,2],colorsRgb[,3],64,
                            maxColorValue=255))   
              # set translucent colors for time series.

# set up reference names for color set
names(xcolors) =c("rustred","orange","lightbrown","mediumgreen", 
                  "blue","magenta", "black","yellowgreen",
                  "mauve","cyan","lightest grey","lighter grey",
                  "l_rustred","l_orange","vlightbrown","lightgreen", 
                  "lightblue","l_black","l_yelgreen","l_mauve",
                  "l_cyan","l_lightest grey","l_lighter grey")       

pdf(file="Ex02-WmLung50-70-Arrow-Bar.pdf",width=7.5,height=10)

micromapST(wmlung5070,panelDesc02,sortVar=1,ascend=FALSE,
        title=c("Ex02-Change in White Male Lung Cancer Mortality Rates",
                   "from 1950-69 to 1970-94-Diff colors"), colors=xcolors
            ) 

dev.off()
##End Example 02

######
#   micromapST - Example # 03 - Time Series Line Plots with Confidence Bands
#     maptail option highlights states from extremes to middle state
#     read in time series data set example using the default border group of "USStatesDF".
####

data(TSdata,envir=environment())  
temprates     <-  data.frame(TSdata[,,2])  

# TSdata structure is array of size c(51,15,4), 
# dimensions = 51 states, 15 years, (year label, point value, low limit, high limit)

panelDesc03   <- data.frame(                    
    type=c("maptail","id","tsconf","dot"),      
    lab1=c("","","Time Series","Female"),  
    lab2=c("","","Annual Rate per 100,000","Most Recent Rate (2010)"),  
    lab3=c("","","Years","Deaths per 100,000"), 
    lab4=c("","","Rate",""),		  
    col1=c(NA,NA,NA,15),        
    panelData =c(NA,NA,"TSdata",NA)
    )
ExTitle       <- c("Ex03-Time Series with Confidence bands",
                    "Annual Female Lung Cancer Mortality Rates, 1996-2010")

pdf(file="Ex03-Time-Series-with-Conf.pdf",width=7.5,height=10)

micromapST(temprates,panelDesc03,sortVar="P15",ascend=FALSE,title=ExTitle)  

dev.off()

# end of example 03


######
#   micromapST - Example 04 - dot followed by a scatter dot columns
#     use same data as Example 3 to compare 1996 & 2010 rates
#     mapmedian option shades states above or below the median (light yellow)
#     using the default border group of "USStatesBG"
#
#   USES data loaded for Example 03 (temprates).
#
####

panelDesc04 <- data.frame(                 
    type=c("mapmedian","id","dot","scatdot"),  
    lab1=c("","","Female Lung Cancer Mortality","Comparison of Rates"),   
    lab2=c("","","Rate in 1996 (Sort Variable)",
                      "in 1996 (x axis) and 2010 (y axis)"),   
    lab3=c("","","Deaths per 100,000","Deaths per 100,000 in 1996"), 
    lab4=c("","","","Rate in 2010"),	
    col1=c(NA,NA,1,1),                 
    col2=c(NA,NA,NA,15)		
    )
ExTitle <- c( "Ex04-Dot Plot for 1996, Scatter Plot Comparing 1996 to 2010",
                    "Female Lung Cancer Mortality Rates")

pdf(file="Ex04-Scatter-Dots.pdf",width=7.5,height=10)
micromapST(temprates,panelDesc04,sortVar=1,ascend=FALSE,title=ExTitle)  

dev.off()

##End Example 04

######
#   micromapST - Example 05 - horizontal stacked (segmented) bars
#     segbar plots the input data, normbar plots percent of total
#     package computes the percents from input data
#     input for the categories for each state must be in consecutive 
#     columns of the input data.frame using the default border group of "USStatesBG"
####

data(statePop2010,envir=environment())

panelDesc05 <- data.frame(                   
    type=c("map","id","segbar","normbar"), 
    lab1=c("","","Stacked Bar","Normalized Stacked Bar"), 
    lab2=c("","","Counts","Percent"),     
    col1=c(NA,NA,"Hisp","Hisp"),                     
    col2=c(NA,NA,"OtherWBH","OtherWBH")		  
    )

pdf(file="Ex05-Stkd-Bar-var-height.pdf",width=7.5,height=10)
micromapST(statePop2010, panelDesc05, sortVar="OtherWBH", ascend=FALSE,
     title=c("Ex05-Stacked Bars: 2010 Census Pop by Race, Sorted by Count Other Race",
             "Cat-L to R: Hispanic, non-Hisp White, Black, Other-sn-varbar"),
     details=list(SNBar.varht=TRUE), axisScale="sn" )  
dev.off()
## End Example 05

######
#
#   micromapST - Example 06 - horizontal stacked (segmented) bars
#     segbar plots the input data, normbar plots percent of total
#     package computes the percents from input data
#     input for the categories for each state must be in consecutive 
#     columns of the input data.frame using the default border group
#     of "USStatesBG".
#
#     Turning off the variable bar height and the midpoint dot features
#     in the horizontal stacked bars (segmented)
#
#  USES data loaded for Example 05 above - statePop2010.
#
####

panelDesc06= data.frame(                   
      type=c("map","id","segbar","normbar"), 
      lab1=c("","","Stacked Bar","Normalized Stacked Bar"), 
      lab2=c("","","Counts","Percent"),     
      col1=c(NA,NA,"Hisp","Hisp"),                     
      col2=c(NA,NA,"OtherWBH","OtherWBH")		  
    )

pdf(file="Ex06-Stkd-Bar-fixedheight-nodot.pdf",width=7.5,height=10)

micromapST(statePop2010,panelDesc06,sortVar=4,ascend=FALSE,
             title=c("Ex7-Stacked Bars: 2010 Census Pop by Race, Sorted by Other Race",
             "Cat-L to R: Hisp, non-Hisp White, Black, Other,ID-diamond"),
             details=list(SNBar.Middle.Dot=FALSE,SNBar.varht=FALSE,Id.Dot.pch=23)
           )  
dev.off()

## End Example 06


######
#   micromapST - Example 07 - centered (diverging) stacked bars
#
#     National 8th grade Math Proficiency NAEP Test Scores Data for 2011
#     source: National Center for Education Statistics, 
#     http://nces.ed.gov/nationsreportcard/naepdata/
#     bar segment values - % in each of 4 categories: 
#           % < Basic, % at Basic, % Proficient, % Advanced
#     using the default border group of "USStatesBG".
####

data(Educ8thData,envir=environment())  

# columns = State abbrev, State name, Avg Score, %s \<basic, 
#           basic, proficient, advanced

panelDesc07 <- data.frame(                 
    type=c("map","id","dot","ctrbar"),
    lab1=c("","","Avg. Scores","Math Proficiency"),         
    lab2=c("","","","<Basic, Basic, Proficient, Advanced"),  
    lab3=c("","","","% to Left of 0           |    % to Right"),  
    col1=c(NA,NA,"avgscore","PctBelowBasic"),col2=c(NA,NA,NA,"PctAdvanced")   
  )
ExTitle <- c("Ex07-Stacked Bars: Educational Progress (NAEP) in Math, 2011, 8th Grade",
             "Centered at Not-Prof vs. Prof")

pdf(file="Ex07-Educ-Centered-Bar.pdf",width=7.5,height=10)

micromapST(Educ8thData,panelDesc07,
             sortVar=3, 
             title=ExTitle)  

dev.off()

### End of example 07

#
#  Example # 08 - Maps Seer Registries using the micromapST function 
#   with the bordGrp = "USSeerBG".  
#

data(Seer18Area,envir=environment())

# set up 4 column page layout

panelDesc08 = data.frame(
  type=c("mapcum","id","dotsignif","arrow")    
  ,lab1=c("","","Rate Trend APC", "Rate Change")  
  ,lab2=c("","","Dot-Signif","2002-06 to 2007-11") 
  ,lab3=c("","","","") 
  ,col1=c(NA,NA,"RateTrendAPC","Rate20022006")
  ,col2=c(NA,NA,"pValue", "Rate20072011")
  ) 

ExTitle       <- c("Ex08 - SeerStat Data - 2002-6 and 2007-11",
                     "Dot with Signif., Arrow and Bar")

pdf(file="Ex08-DotSignificants.pdf",width=7.5,height=10)

micromapST(Seer18Area,panelDesc08,
        sortVar="Rate20022006",ascend=FALSE,
        title=ExTitle,
        rowNames="alias",rowNamesCol='Registry', 
        bordGrp="USSeerBG",
        plotNames="ab")

dev.off()
#
#  Both calls are effectively identical.
#
#### End of examples 08


#####
#   micromapST - Example 09 - A linked micromap of the counties of the state
#      of Kansas using the border group "KansasDF". The KansPopInc data is shown
#      using two dot glypics - current population and average increase per county.
#####

data(KansPopInc,envir=environment())

# set up 4 column page layout

panelDesc09 = data.frame(
  type=c("mapcum","id","dot","dot")    
  ,lab1=c("","","Population", "Average Inc.")  
  ,lab2=c("","","in 2000","per year") 
  ,lab3=c("","","UNITS","") 
  ,col1=c(NA,NA,"Pop","AvgInc")
  ) 

ExTitle       <- c("Ex09 - Kansas Pop data 8.5x14",
                     "Current Pop and Average Inc-scaling=sn")

pdf(file="Ex09-Kansas Population and Inc-8.5x14-sn.pdf",width=7.5,height=13)  
               # legal size page (8.5 x 14) to handle 105 counties.
               
#  use scaling "sn" to clean up x-axis labels.

micromapST(KansPopInc,panelDesc09,
        sortVar=c("AvgInc","Pop"),ascend=FALSE,
        title=ExTitle, 
        axisScale="sn",
        rowNames="full",rowNamesCol='County',
        bordGrp="KansasBG",
        plotNames="ab")

dev.off()        

#
##### Example # 09

#####
#
#  Example # 10 - Counties in Kansas on an 11 x 17 page
#

# set up 4 column page layout

panelDesc10 = data.frame(
  type=c("map","id","dot",        "dot")    
  ,lab1=c("",     "",  "Population", "Average Inc.")  
  ,lab2=c("",     "",  "in 2000",    "per year") 
  ,lab3=c("",     "",  "People",     "") 
  ,col1=c(NA,     NA,  "Pop",        "AvgInc")
  ) 

ExTitle       <- c("Ex10 - Kansas Pop data 11x17",
                     "Current Pop and Average Inc - scaling=e")

pdf(file="Ex10-Kansas Population and Inc-11x17.pdf", width=10, height=16)  
          # tabloid size page (11x17) to handle 105 counties.

#  Use default scaling = "e" and no staggered labels, 
#  Use full county names for data to boundary matching, 
#  but presented abbreviated county names 
#  in "id" glyphic column with large page.

micromapST(KansPopInc, panelDesc10, 
        sortVar=c("AvgInc","Pop"), ascend=FALSE, 
        title=ExTitle,
        rowNames="full", rowNamesCol='County',
        bordGrp="KansasBG",
        plotNames="ab")

dev.off()        
#
#
#### end of # Ex 10


#####
#
#   micromapST - Example 11 - A linked micromap of the counties of the state
#      of New York state using the border group "NewYorkDF". 
#      The pop/inc data is shown using two dot glyphs, an arrow and bar glyph
#      (2010 Population, an arrow showing the change in population from 2000 to 2010, 
#      Population in 2000, and a bar showing the amount of the change.)
#      
#####

data(nyPopData,envir=environment())

nyPopData$Dif00_10 <- nyPopData$Pop_2010 - nyPopData$Pop_2000

# set up 6 column page layout with colSize

panelDesc11 <- data.frame(
  type=c("map","id","dot",           "arrow",        "dot",     "bar")    
  ,lab1=c("",  "",  "Population in", "Increase from","Pop 2005","Incre")  
  ,lab2=c("",  "",  "2010",          "2000",         "",        "2000to2010") 
  ,lab3=c("",  "",  "",              "",             "",        "") 
  ,col1=c(NA,  NA,  "Pop_2010",      "Pop_2000",     "Pop_2000","Dif00_10")
  ,col2=c(NA,  NA,  "",              "Pop_2010",     "",        "")
  ,colSize=c(NA,NA, 15,              20,             5,        20)
  ) 

ExTitle       <- c("Ex11 - New York Population data",
                     "2010 Pop and since 2000-colSize,sn,stag")

pdf(file="Ex11-New York Pop 2010 and Change-sn colSize.pdf", 
     width=7.5, height=10.5) 

micromapST(nyPopData, panelDesc11, 
        sortVar="Pop_2000", ascend=FALSE,
        title=ExTitle,
        rowNames="full",rowNamesCol="Area", 
        axisScale="sn", staggerLab=TRUE,
        bordGrp="NewYorkBG"
      )

dev.off()        
#
#
#### End of Examples 11


#####
#   micromapST - Example 12 - A linked micromap of the counties of 
#      the state of Maryland using the border group "MarylandBG". 
#      The MarylandPopInc data is shown using two dot glypics - current 
#       population and average increase per county.
#      A "maptail" state map is used to show the counties in relationship
#      to the median county as sorted by the 1970 population.
#####

data(mdPopData,envir=environment())

# set up 5 column page layout

panelDesc12 = data.frame(
  type=c("maptail","id","dot","dot","arrow")    
  ,lab1=c("","","Population", "Population","Change")  
  ,lab2=c("","","in 1970","in 2000", "from 1970 to 2000") 
  ,lab3=c("","","","","") 
  ,col1=c(NA,NA,"X1970","X2010","X1970")
  ,col2=c(NA,NA,"","","X2010")
 ) 

ExTitle       <- c("Ex12 - Maryland Population-map",
                      "1970 and 2010 Pop and Change,stag,sn")

pdf(file="Ex12-MD Pop 1970 and 2010 plus change-map.pdf", width=7.5, height=10.5) 

micromapST(mdPopData, panelDesc12, 
        sortVar=2, ascend=FALSE, 
        title=ExTitle,
        rowNames="full", rowNamesCol='County',
        bordGrp="MarylandBG", 
        axisScale="sn", staggerLab=TRUE,
        plotNames="ab")

dev.off()        
#
#
#### End of Example 12

#####
#   micromapST - Example 13 - A linked micromap of the provinces, municipalities,
#      autonomous regions and special administrative regions of China using the 
#      border group of "ChinaDF". The ChinaPopInc data is shown
#      using two dot glypics - current population and average increase per area.
#####

data(cnPopData,envir=environment())

# set up 4 column page layout

panelDesc13 = data.frame(
  type=c("map","id","dot","bar")    
  ,lab1=c("","","Population", "Population")  
  ,lab2=c("","","in 2013","in 2013") 
  ,lab3=c("","","","") 
  ,col1=c(NA,NA,"pop2013","pop2013")
  ) 

ExTitle       <- c("Ex13 - China Population",
                     "in 2013 by area")

pdf(file="Ex13-China 2013 Population.pdf", width=7.5, height=10.5) 

micromapST(cnPopData, panelDesc13, 
        sortVar="pop2013", ascend=FALSE, 
        title=ExTitle,
        rowNames="full", rowNamesCol='area',
        bordGrp="ChinaBG",
        plotNames="full")

dev.off()        
#
#
#### End of Example 13


#####
#   micromapST - Example 14 - A linked micromap of the districts 
#      of the city Seoul South Korea, using the border group of 
#      "SeoulSKoreaBG". The included SeoulPopData dataset provides 
#      population and district area statistics for 2012.  
#      The micromapST generates two glyphics, a sorted dot
#      glyphic based on the population and a bar graph based on the area.
#####

data(SeoulPopData,envir=environment())

# set up 4 column page layout

panelDesc14 = data.frame(
  type=c("map","id","dot","bar")    
  ,lab1=c("","","Population", "Area")  
  ,lab2=c("","","in 2012","in 2012") 
  ,lab3=c("","","","sqkm") 
  ,col1=c(NA,NA,"Pop.2012","Area")
  ) 

ExTitle       <- c("Ex14 - Seoul Population",
                     "in 2012 by district")

pdf(file="Ex14-Seoul 2012 Population.pdf", width=7.5, height=10.5) 

micromapST(SeoulPopData,panelDesc14,
        sortVar=3, ascend=FALSE,               # sort based on the population
        title=ExTitle,
        rowNames="full", rowNamesCol='District',
        bordGrp="SeoulSKoreaBG",
        plotNames="full"
      )

dev.off()        
#
#
#### End of Example 14

#####
#   micromapST - Example 15 - A linked micromap of the counties in the 
#      state of Utah.  The UtahPopData data is shown using two dot glypics
#      - current population and average increase per area.
#####

data(UtahPopData,envir=environment())

#
#  Get population differences from 2011 to 2001 and 1991.
#   Data contains ",".  The comma's must be removed and values are 
#     converted to numbers.
#   If data is factors, need to add "as.character()" function 
#     to the formula below.

UtahPopData2 <- as.data.frame(sapply(UtahPopData, 
                        function(x) gsub(",","",x)),stringsAsFactors=FALSE)

UtahPopData2$Del1101 <- as.numeric(UtahPopData2$X2011) - as.numeric(UtahPopData2$X2001)
UtahPopData2$Del0191 <- as.numeric(UtahPopData2$X2001) - as.numeric(UtahPopData2$X1991)


# set up 5 column page layout

panelDesc15 = data.frame(
  type=c("map","id","dot","arrow","arrow")    
  ,lab1=c("","","Population", "2001-2011","Chg 1991-2001")  
  ,lab2=c("","","in 2011","pop change","pop change") 
  ,lab3=c("","","","","") 
  ,col1=c(NA,NA,"X2011","X2011","X2001")
  ,col2=c(NA,NA,NA,"X2001","X1991")
  ) 

ExTitle       <- c("Ex15 - Utah county population 2011",
                     " and changes last two decades,sn")

pdf(file="Ex15-Utah Population.pdf",width=7.5,height=10.5) 

micromapST(UtahPopData, panelDesc15, 
        sortVar="X2011", ascend=FALSE,
        title=ExTitle,
        rowNames="full",rowNamesCol='County', 
        axisScale="sn",
        bordGrp="UtahBG",
        plotNames="ab"
      )

dev.off()        
#
#
#### End of Example 15

####
#
#  Example 16 - use of state.x77 data table as data source
#     Data does not contain a row for DC, a missing sub-area.
#     Example also uses a smaller then 7.5 x 10 graphic space.
#

data(state,envir=environment())

stateData <- as.data.frame(state.x77)

rownames(stateData) <- state.abb

panelDesc16 <- data.frame(type = c("maptail", "id", "dot"),
                        lab1 = c("", "", "Murder"),
                        lab3 = c("", "", "Murders per 100K Population"),
                        col1 = c(NA, NA, 5))

ExTitle <- c("Ex16 - LM Plot of Murders in the United States",
                      "No DC row entry.")

pdf(file = "Ex16_state.x77_no_DC.pdf", width = 5, height = 9) 

micromapST(stateData, panelDesc16, 
           sortVar = 5, ascend = FALSE,
           title = ExTitle)

dev.off()

#
#  Example 17 - US state map based on data from state.x77 table with
#     DC row added to complete data.frame, but with missing values (NAs).
#     The DC row will be sorted to the bottom of the list size 
#     it does not contain any data.
#
#   Used data and the panelDesc data.frames (stateData and panelDesc16) used
#   in example 16.
#

# add DC as 51st state with missing data "NA" to stateData.  

stateData <- rbind(stateData, DC = rep(NA, 8))   
                  # missing values for DC row.

ExTitle <- c("Ex17 - LM Plot of Murders in the United States",
                  "DC row added with NA, decending.")

pdf(file = "Ex17_state.x77_DCasNA_D.pdf", width = 5, height = 9) 

micromapST(stateData, panelDesc16, 
           sortVar = 5, ascend = FALSE,
           title = ExTitle)

dev.off()

#
#
##### end of example # 17

####
#
#  Example 18 - use of Africa population data as data source
#      Demonstrates support for vertical oriented geographical areas.
#

data(AfricaPopData,envir=environment())

panelDesc18 <- data.frame(type = c("map", "id", "dot",      "dot",          "dot"),
                           lab1 = c("",      "","Population","Percentage Of","Est x2 Time"),
                           lab3 = c("",      "","People",    "Total",        "Years"),
                           col1 = c(NA,      NA,"Projection","PercOf",       "Est2Time"))


pdf(file = "Ex18_Africa Micromap-11x17.pdf", width = 11, height = 17) 

ExTitle <- c("Ex18-Africa Population Data",
               "Sorted by Population on 11x17")

micromapST(AfricaPopData, panelDesc18, 
           sortVar = "Projection", ascend = TRUE,
           title = ExTitle, 
           rowNames = "ab", rowNamesCol = "Abbr",
           bordGrp = "AfricaBG" )

dev.off()

#
#
##### End of Example 18

Run the code above in your browser using DataLab