Hi R-listers,

I am new to R and programming.

I have a large dataframe composed of two grouping variables (species,
population, with populations nested in species) and tens of continuously
numeric variables. For each numeric variable, I want to make a boxplot with
population as the X axis and the boxes filled according to which species it
is belonging to. But, that is a definitely tedious work. I followed the
tutorials in the ggplot2 website. But, I have not found any way to ease this
work.

I am really thirsting for an automatic approach to plot the numeric
variables one by one in a PDF file, assigning the variable name as the y
label. Can anyone share any brilliant scripts with me. I think it must be
helpful to others who have little programming experience like me.

the code I used to make boxplot on (dataframe: All, group variable: species
and popluation, and numeric variable: conlen) is as followed:

pcon<-ggplot(All, aes(population,conlen))
pcon+geom_boxplot(aes(fill=species))

Thank you in advance.

Regards
Mao J-F

a part of my dataframe: All

species    population    conlen    tscale    fscale    tseen    w100s
nfsee
Py    YXPy01    8.6    153    69    111    1.680851064    94
Py    YXPy01    8.1    173    74    139    1.848484849    133
Py    YLPy01    6.5    138    58    99    1.520833333    48
Py    YLPy01    5.9    153    67    118    1.355140187    107
Py    KMPy01    6.1    113    48    75    1.470588235    51
Py    KMPy01    5.1    129    54    100    1.176470588    68
Py    KMPy01    3.9    109    37    30    1.5    22
Py    KMPy01    5    128    55    71    1.46875    64
Py    KMPy01    4.7    132    54    32    1.5    28
Py    KMPy01    5.8    113    52    65    1.136363636    45
Py    KMPy01    4.7    114    42    71    1.131147541    61
Py    KMPy01    5    120    77    131    1.403361345    119
Py    GSPy02    6.2    152    59    102    1.348837209    43
Py    GSPy02    6.2    111    41    64    2.805555556    36
Py    GSPy02    6.7    130    56    67    1.757575758    33
Py    GSPy02    6.6    115    47    78    1.603174603    63
Py    GSPy02    8.9    137    61    102    1.767676768    99
Py    GSPy02    6.2    157    68    115    1.459016393    61
Py    BCPy01    5.3    91    39    24    1.263157895    19
Py    BCPy01    6.1    100    46    53    1.117647059    17
Py    BCPy01    4.5    81    32    46    1.32    25
Py    LJPy01    6.6    170    65    72    2.035714286    56
Py    LJPy01    6.9    104    46    58    1.8    55
Py    LJPy01    8.6    161    66    38    1.794117647    34
Py    LJPy01    5.4    123    40    22    2.428571429    21
Py    LJPy01    6.8    123    54    57    2.044444444    46
Py    LJPy01    8.6    166    77    77    1.847457627    59
Py    LJPy01    6    132    51    91    1.119047619    84
Py    LJPy01    6.8    108    45    27    1.814814815    27
Py    LJPy01    6.2    115    48    70    1.765957447    47
Py    LJPy01    8    168    80    132    2.036363636    111
Pd    CYPd01    6.7    138    57    23    1.555555556    9
Pd    CYPd01    6.8    121    46    53    1.973684211    38
Pd    CYPd01    5.9    114    52    60    1.25    12
Pd    CYPd01    5.2    119    53    53    1.432432432    37
Pd    CYPd01    7.6    118    46    63    2    23
Pd    CYPd01    6.1    144    61    24    1.428571429    14
Pd    CYPd01    5.5    130    46    62    1.32    54
Pd    CYPd01    6.6    153    57    83    1.558441558    77
Pd    CYPd02    5.9    111    32    51    1.3    10
Pd    CYPd02    7.1    121    51    80    1.451612903    31
Pd    CYPd02    7.3    150    68    127    1.681415929    113
Pd    CYPd02    5.6    121    38    64    1.228571429    36
Pd    CYPd02    7.2    140    62    88    1.585365854    41
Pd    CYPd02    6.1    113    54    91    1.256756757    74
Pd    CYPd03    4.6    109    45    57    1.09375    32
Pd    CYPd03    4.9    115    44    45    1.235294118    17
Pd    CYPd03    6.4    134    44    64    1.209302326    45
Pd    CYPd03    4.6    96    42    41    1.15    21
Pd    CYPd03    5.6    131    43    45    1.771428571    35
Pd    CYPd03    6.1    124    48    59    1.578947368    38
Pd    CYPd03    5.2    110    57    71    1.340425532    47
Pd    CYPd03    5.5    118    57    83    1.625    48
Pd    CYPd03    6.1    106    61    95    1.559322034    60
Pd    CYPd03    6.2    121    64    100    1.707692308    65
Pd    CYPd03    5.1    99    38    28    1.43    20
Pd    CYPd03    5.1    132    45    47    1.791666667    24
Pd    YLPd01    6.15    120    43    46    1.446    21
Pt    BXPd01    4.6    64    18    23    2.166666667    18
Pt    BXPd01    5.1    87    26    38    2.25    32
Pt    BXPd01    4.8    89    27    50    2.130434783    46
Pt    BXPd01    6    97    29    31    2.684210526    19
Pt    BXPd01    5.2    98    32    54    2.292682927    41
Pt    GYPt01    4.3    98    27    8    4    5
Pt    GYPt01    4    82    27    51    2.78125    32
Pt    GYPt01    5    106    35    8    4.333333333    6
Pt    GYPt01    5.1    86    24    25    3.375    16
Pt    GYPt01    4.6    79    25    21    2.631578947    19
Pt    GYPt01    5    80    30    23    2.823529412    17
Pt    NSPt01    5.3    107    27    37    2.85    33
Pt    NSPt01    5.4    85    26    38    2.27    32
Pt    NSPt01    5.4    102    31    50    5.32    40
Pt    NSPt01    5.1    84    23    29    5.32    23
Pt    NSPt01    NA    NA    NA    NA    NA    NA
Pt    NSPt01    4.1    57    17    24    2.7    18

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