---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------
# Besides what we have seen in (L) and (N1), there are many ways to get simply bar and
# circular charts:
 
    
  
 

da = c(237.5,137.5,125); co = c("red","yellow","blue")
StripNames = c("A","B","C"); StripC(da,co)           # (1)
BarNames = c("A","B","C"); BarC(da,co)               # (2)
noGrid=1; BarNames = c("A","B","C"); BarC(da,co)     # (3)
PIE2C( da, c("grey50", "grey75", "grey95") )         # (4)
PieC( da, c("grey50", "grey75", "grey95") )          # (5)
PIE_( da, 4 )                                        # (6)
Pie_C( da,co, 27)                                    # (7)
Pie2( da )                                           # (8)
PIE2( da )           # as (8) without grid [as PIE_(da,1)]
 
      
# While Pie, PIE, PIEh, Bar, BAR, Strip, PIE_, PIE2 automatically chooses the colors,
# with PieC, PIEC, PIEH, BarC, BARC, StripC you can add colors
 
# In addition to PIE2C (4), Pie2C automatically adds letters.
 
# For simplicity, names are automatically assigned to the circular charts. If I have to
# distinguish different charts I can give them different names with commands PIE_C,
# Pie_C, PIE_, Pie_, similar to commands with 2 instead of _, to which I add, after a
# comma, a number that indicates the name of the first sector: letters A, B, ..., Z are
# indicated by numbers 1,2, ..., 26, letters a, b, ..., z by numbers 27,28, ..., 52.
 
# If you type DimCa <<- n with n < 1 and (instead of BARC and BarC) you use BARCC and
# BarCC, you can write the BarNames with smaller dimension.
 
# You can also indicate a single color:
BarNames = c("A","B","C"); BarC(da,"yellow")
BarNames = c("A","B","C"); BARC(da,"yellow")
# data:  237.5 137.5 125 
#    %:  47.5 27.5 25 
         
# In USA from 1949 to 1966 several statistical studies (Framingham Study) on the
# cardiovascular disease of the people was conducted. Here are the bar charts (for
# different cities, in different age groups) of those affected by coronary thrombosis
# (A: between 40 and 60 years of age, B: between 30 and 70, C: between 45 and 65),
# divided into non-smokers (grey) and smokers (brown); the incidence on 1000 people is
# represented (from ABC Television).
                      
# To build different diagrams on the same reference system (↑) I can use the command:
# BARM(x, col, M, G)    In x I put the data (0 between one group and another), in col
# the colors (0 between one group and another), in M the maximum height of the graph,
# in G the heights of the grid lines.
D = c(15.4,45.8,0,47.5,79,0,28.6,61.4)
co=c("grey","brown",0,"grey","brown",0,"grey","brown")
BARM(D, co, 80, (1:8)*10)
# data:  15.4  45.8  0  47.5  79  0  28.6  61.4 
aboveX(permil",0)
underX("A",1); underX("B",4); underX("C",7)
# 0, 1, 2, ... are the abscissas of the beginning of the various columns
#
# Obviously with BARM I can also make single histograms:
        
chol = c(16,22,38,40,59,66)
BARM(colest, "yellow", 70, (1:7)*10)
aboveX("permil",0); underX(c("<200","<225","<250","<275","<300",">=300"),(1:6)-1/2)
# Incidence of coronary thrombosis by level (mg/dl) of cholesterol (people with high
# cholesterol levels have the risk of coronary damage 2-4 times greater than normal).
#
press = c(9.5,41,48.5,81.5)
BARM(press, "cyan", 81.5, (1:8)*10)
aboveX("permil",0); underX(c("<80","<90","<95",">=95"), (1:4)-1/2)
# Incidence of coronary diseases by level of diastolic pressure (men aged 40 to 49
# years).
 
Other examples of use