Three dataset Comparison
Bill Chen
3/15/2021
Data source: https://docs.google.com/spreadsheets/d/1VO7GpObj5UEIBdmTKQJqq03oxMrTmHuE6ZTsBkBfffc/edit#gid=0
Following suggestion to find the average. this is only for the dose of 309 and 314.
Using selected data to get the results
library(readr)
df <- read_csv("C:/Users/billc/Documents/Juvena/PK/Comparison_of_three_1.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## Time = col_double(),
## Conc = col_double(),
## ID = col_double(),
## amt = col_double(),
## Type = col_character()
## )df## # A tibble: 46 x 5
## Time Conc ID amt Type
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 0 NA 1 312. Albumin-Elisa-300ug
## 2 0.05 55.5 1 NA Albumin-Elisa-300ug
## 3 0.167 49.9 1 NA Albumin-Elisa-300ug
## 4 0.333 51.8 1 NA Albumin-Elisa-300ug
## 5 0.5 48.7 1 NA Albumin-Elisa-300ug
## 6 1 37.9 1 NA Albumin-Elisa-300ug
## 7 2 27.8 1 NA Albumin-Elisa-300ug
## 8 4 20.0 1 NA Albumin-Elisa-300ug
## 9 6 16.3 1 NA Albumin-Elisa-300ug
## 10 10 8.71 1 NA Albumin-Elisa-300ug
## # ... with 36 more rowsdf$Label = ""
df[which(df$Time<=6), 6] = "Distribution"
df[which(df$Time >6), 6] = "Elimination"
df$comb_Label = interaction(df$Type, df$Label, drop=TRUE, sep="_", lex.order=TRUE)
df## # A tibble: 46 x 7
## Time Conc ID amt Type Label comb_Label
## <dbl> <dbl> <dbl> <dbl> <chr> <chr> <fct>
## 1 0 NA 1 312. Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 2 0.05 55.5 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 3 0.167 49.9 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 4 0.333 51.8 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 5 0.5 48.7 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 6 1 37.9 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 7 2 27.8 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 8 4 20.0 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 9 6 16.3 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-300ug_Dist~
## 10 10 8.71 1 NA Albumin-Elisa-~ Eliminati~ Albumin-Elisa-300ug_Elim~
## # ... with 36 more rowslibrary(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:readr':
##
## col_factorlibrary(ggplot2)
p <- ggplot(df, aes(x=Time, y=Conc, color=Type, group=comb_Label)) +
geom_point(aes(shape=Type), size=3) +
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).
Following suggestion to find the average. this is only for the dose of 101 and 84.
Using selected data to get the results
library(readr)
df <- read_csv("C:/Users/billc/Documents/Juvena/PK/Comparison_of_three_2.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## Time = col_double(),
## Conc = col_double(),
## ID = col_double(),
## amt = col_double(),
## Type = col_character()
## )df## # A tibble: 46 x 5
## Time Conc ID amt Type
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 0 NA 1 92.7 Albumin-Elisa-100ug
## 2 0.05 10.9 1 NA Albumin-Elisa-100ug
## 3 0.167 9.35 1 NA Albumin-Elisa-100ug
## 4 0.333 8.03 1 NA Albumin-Elisa-100ug
## 5 0.5 7.65 1 NA Albumin-Elisa-100ug
## 6 1 7.28 1 NA Albumin-Elisa-100ug
## 7 2 5.76 1 NA Albumin-Elisa-100ug
## 8 4 5.03 1 NA Albumin-Elisa-100ug
## 9 6 3.72 1 NA Albumin-Elisa-100ug
## 10 10 2.51 1 NA Albumin-Elisa-100ug
## # ... with 36 more rowsdf$Label = ""
df[which(df$Time<=6), 6] = "Distribution"
df[which(df$Time >6), 6] = "Elimination"
df$comb_Label = interaction(df$Type, df$Label, drop=TRUE, sep="_", lex.order=TRUE)
df## # A tibble: 46 x 7
## Time Conc ID amt Type Label comb_Label
## <dbl> <dbl> <dbl> <dbl> <chr> <chr> <fct>
## 1 0 NA 1 92.7 Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 2 0.05 10.9 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 3 0.167 9.35 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 4 0.333 8.03 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 5 0.5 7.65 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 6 1 7.28 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 7 2 5.76 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 8 4 5.03 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 9 6 3.72 1 NA Albumin-Elisa-~ Distribut~ Albumin-Elisa-100ug_Dist~
## 10 10 2.51 1 NA Albumin-Elisa-~ Eliminati~ Albumin-Elisa-100ug_Elim~
## # ... with 36 more rowslibrary(scales)
library(ggplot2)
p <- ggplot(df, aes(x=Time, y=Conc, color=Type, group=comb_Label)) +
geom_point(aes(shape=Type), size=3) +
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).