# dataprep: data preprocessing and plots

    library(dataprep)
    library(ggplot2)
    library(scales)

## Figure 1. Line plots for variables with names that are essentially numeric and logarithmic

    # Descriptive statistics
    descplot(data,5,65)
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Time used by descdata: 0.0439 secs
    #> Time used by descplot: 0.101 secs
    #> Warning: Removed 3 row(s) containing missing values (geom_path).

## Figure 2. Line plots for variables whose names are essentially numeric and logarithmic

    # Selected descriptive statistics, equal to descdata(data,5,65,c('na','min','max','IQR'))
    descplot(data,5,65,c(2,7:9))
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf
    #> Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf
    #> Time used by descdata: 0.0339 secs
    #> Time used by descplot: 0.0379 secs
    #> Warning: Removed 3 row(s) containing missing values (geom_path).

## Figure 3. Bar charts for the type of variable names that is character

    # Descriptive statistics
    descplot(data1,3,7)+
      ggplot2::theme(axis.text.x=ggplot2::element_text(angle=30,hjust=1,vjust=1.1))
    #> Time used by descdata: 0.00399 secs
    #> Time used by descplot: 0.00798 secs

## Figure 4. Bar charts for the type of variable names that is character

    # Selected descriptive statistics, equal to descplot(data1,3,7,c('min','max','IQR'))
    descplot(data1,3,7,7:9)+
      ggplot2::theme(axis.text.x=ggplot2::element_text(angle=30,hjust=1,vjust=1.1))
    #> Time used by descdata: 0.00299 secs
    #> Time used by descplot: 0.00598 secs

## Figure 5. Particle number size distributions in logarithmic scales

    # Top and bottom percentiles
    percplot(data,5,65,4)
    #> Time used by percdata: 0.0479 secs
    #> Time used by percplot: 0.0648 secs
    #> Warning: Removed 144 row(s) containing missing values (geom_path).

## Figure 6. Particle number size distributions in logarithmic scales with only one part

    # Top percentiles
    percplot(data,5,65,4,part=1)
    #> Time used by percdata: 0.0429 secs
    #> Time used by percplot: 0.0588 secs
    #> Warning: Removed 72 row(s) containing missing values (geom_path).

## Figure 7. Particle number size distributions in logarithmic scales with only one part

    # Bottom percentiles
    percplot(data,5,65,4,part=0)
    #> Time used by percdata: 0.0449 secs
    #> Time used by percplot: 0.0578 secs
    #> Warning: Removed 72 row(s) containing missing values (geom_path).

## Figure 8. Percentiles of modes in linear scales

    # Top and bottom percentiles
    percplot(data1,3,7,2)+
      ggplot2::theme(axis.text.x=ggplot2::element_text(angle=30,hjust=1,vjust=1.1))
    #> Time used by percdata: 0.00698 secs
    #> Time used by percplot: 0.017 secs

## Figure 9. Percentiles of modes in linear scales with only one part

    # Top percentiles
    percplot(data1,3,7,2,part=1)+
      ggplot2::theme(axis.text.x=ggplot2::element_text(angle=30,hjust=1,vjust=1.1))
    #> Time used by percdata: 0.00499 secs
    #> Time used by percplot: 0.014 secs

## Figure 10. Percentiles of modes in linear scales with only one part

    # Bottom percentiles
    percplot(data1,3,7,2,part=0)+
      ggplot2::theme(axis.text.x=ggplot2::element_text(angle=30,hjust=1,vjust=1.1))
    #> Time used by percdata: 0.00598 secs
    #> Time used by percplot: 0.014 secs