12-14-2020_PK_His-HSA vs Millipore-HSA

Import data

library(readr)
df <- read_csv("12-14-2020_PK_His-HSA vs Millipore-HSA.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   Time = col_double(),
##   Conc = col_double(),
##   ID = col_double(),
##   amt = col_double(),
##   weight = col_double()
## )

df

## # A tibble: 44 x 5
##      Time  Conc    ID   amt weight
##     <dbl> <dbl> <dbl> <dbl>  <dbl>
##  1  0     NA        1  139.   27.9
##  2  0.167 39.7      1   NA    NA  
##  3  0.5   45.6      1   NA    NA  
##  4  1     38.0      1   NA    NA  
##  5  2     38.5      1   NA    NA  
##  6  4     29.7      1   NA    NA  
##  7  6     28.6      1   NA    NA  
##  8 24     19.3      1   NA    NA  
##  9 48      7.01     1   NA    NA  
## 10 72      3.05     1   NA    NA  
## # ... with 34 more rows

Include two phases

Distribution: < 6 hrs Elimination >= 6 hrs

df$Phase = ""
df[which(df$Time < 6), 6] = "Distribution"
df[which(df$Time >= 6), 6] = "Elimination"
df$ID = factor(df$ID)
df

## # A tibble: 44 x 6
##      Time  Conc ID      amt weight Phase       
##     <dbl> <dbl> <fct> <dbl>  <dbl> <chr>       
##  1  0     NA    1      139.   27.9 Distribution
##  2  0.167 39.7  1       NA    NA   Distribution
##  3  0.5   45.6  1       NA    NA   Distribution
##  4  1     38.0  1       NA    NA   Distribution
##  5  2     38.5  1       NA    NA   Distribution
##  6  4     29.7  1       NA    NA   Distribution
##  7  6     28.6  1       NA    NA   Elimination 
##  8 24     19.3  1       NA    NA   Elimination 
##  9 48      7.01 1       NA    NA   Elimination 
## 10 72      3.05 1       NA    NA   Elimination 
## # ... with 34 more rows

Plot results figure

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:readr':
## 
##     col_factor

library(ggplot2)
p <- ggplot(df, aes(x=Time, y=Conc, color=ID, group=Phase)) + 
        geom_point() +
        geom_smooth(method = "lm", se = FALSE, size = 2) +
        scale_y_continuous(expand = expansion(mult = c(0,0.05)),
                           trans='log10', breaks=trans_breaks('log10', function(x) 10^x),
                           labels=trans_format('log10', math_format(10^.x))) +
        xlab("Time (h)") +
        ylab(c("Serum Concetration (log)\n(ng/ml)")) +
        scale_x_continuous(breaks=seq(0, 150, 25)) +
        theme_classic() +
        theme(text = element_text(size=20), 
              legend.justification=c(0,0), 
              legend.position=c(0.05,0),
              # legend.title = element_blank(),
              legend.text  = element_text( size = 12))
        
p

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 4 rows containing non-finite values (stat_smooth).

## Warning: Removed 4 rows containing missing values (geom_point).