4-NP Analysis of %OC
Thurber
Import Data and Use Base R to Subset then Merge
We use the readxl package to import data from the “Data_Complete_%OC” sheet in “4NP Standard Curve and Data.xlsx”.
### Import the entire sheet
percoc <- read_excel("4NP Standard Curve and Data.xlsx",
sheet = "Data_Complete_%OC",
skip = 2)## New names:
## * `Mass of dust per trial (g)` -> `Mass of dust per trial (g)...5`
## * `4NP in trial (mg)` -> `4NP in trial (mg)...6`
## * `Mass released (mg)` -> `Mass released (mg)...7`
## * `%released` -> `%released...8`
## * `Mass of dust per trial (g)` -> `Mass of dust per trial (g)...9`
## * ... and 11 more problems names(percoc)## [1] "Days" "Mass dosed"
## [3] "Mass dust dosed(g)" "Conc on dust (mg/g)"
## [5] "Mass of dust per trial (g)...5" "4NP in trial (mg)...6"
## [7] "Mass released (mg)...7" "%released...8"
## [9] "Mass of dust per trial (g)...9" "4NP in trial (mg)...10"
## [11] "Mass released (mg)...11" "%released...12"
## [13] "Mass of dust per trial (g)...13" "4NP in trial (mg)...14"
## [15] "Mass released (mg)...15" "%released...16"
## [17] "Mass of dust per trial (g)...17" "4NP in trial (mg)...18"
## [19] "Mass released (mg)...19" "%released...20" ### Subset columns 1:4 and 5:8 to get the 4% OC data
percoc.4perc <- percoc[, c(1:4,5:8)]
names(percoc.4perc) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.4perc$Perc_OC <- 4
### Subset columns 1:4 and 9:12 to get the 10% OC data
percoc.10perc <- percoc[, c(1:4,9:12)]
names(percoc.10perc) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.10perc$Perc_OC <- 10
### Subset columns 1:4 and 13:16 to get the 50% OC data
percoc.50perc <- percoc[, c(1:4,13:16)]
names(percoc.50perc) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.50perc$Perc_OC <- 50
### Subset columns 1:4 and 17:20 to get the 70% OC data
percoc.70perc <- percoc[, c(1:4,17:20)]
names(percoc.70perc) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.70perc$Perc_OC <- 70
### Stack the 4, 10, 50, and 70 percent data to create a single data.frame
percoc <- rbind(percoc.4perc, percoc.10perc, percoc.50perc, percoc.70perc)
### "Study day" is coded as "Days.Trial"
percoc$Sample <- factor(round((percoc$Days - floor(percoc$Days))*10))
percoc$Days <- floor(percoc$Days)
### For percentage and proportion to numeric
percoc$Perc_Released <- as.numeric(percoc$Perc_Released)
percoc$Prop_Released <- percoc$Perc_Released / 100
### Remove/delete the temporary individual percentage data.frames
rm(percoc.4perc, percoc.10perc, percoc.50perc, percoc.70perc)
### Write the complete, reformatted data.frame to CSV
write.csv(percoc, "Four_NP_PercOC.csv", row.names = FALSE)
### Check to make sure everything is balanced and plot it just for fun
head(percoc)## # A tibble: 6 x 11
## Days Mass_Dosed Dust_Massed_Dos~ Conc_On_Dust Mass_Of_Dust_Pe~
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0 NA
## 2 5 18.0 5.5 3.27 0.88
## 3 5 15.6 5.6 2.79 1.01
## 4 5 22.3 7 3.19 1.4
## 5 10 18.0 5.5 3.27 1.33
## 6 10 15.6 5.6 2.79 1.21
## # ... with 6 more variables: FourNP_In_Trial <dbl>, Mass_Released <dbl>,
## # Perc_Released <dbl>, Perc_OC <dbl>, Sample <fct>, Prop_Released <dbl> table(percoc$Days, percoc$Perc_OC)##
## 4 10 50 70
## 0 1 1 1 1
## 5 3 3 3 3
## 10 3 3 3 3
## 15 3 3 3 3
## 20 3 3 3 3
## 25 3 3 3 3
## 30 3 3 3 3
## 35 3 3 3 3
## 40 3 3 3 3 ggplot(percoc, aes(x=Days, y=Perc_Released, group=factor(Perc_OC),
colour=factor(Perc_OC), shape=factor(Perc_OC),
linetype=factor(Perc_OC))) +
geom_point() +
stat_summary(fun.y="mean", geom="line") +
#stat_summary(fun.y="mean", geom="point", shape = "-", size=10) +
theme_bw() +
labs(shape = "OC%", linetype = "OC%", colour = "OC%") +
ylab("Percent 4-NP Released")
Import Data using Ranges and then Merge
We use the readxl package to import data from select ranges of the “Data_Complete_%OC” sheet in “4NP Standard Curve and Data.xlsx”.
### Import the 4% OC data
percoc.4 <- read_excel("4NP Standard Curve and Data.xlsx",
sheet = "Data_Complete_%OC",
col_types=c(rep("numeric",4), rep("numeric",4), rep("skip",12)),
skip=2)
names(percoc.4)## [1] "Days" "Mass dosed"
## [3] "Mass dust dosed(g)" "Conc on dust (mg/g)"
## [5] "Mass of dust per trial (g)" "4NP in trial (mg)"
## [7] "Mass released (mg)" "%released" names(percoc.4) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.4$Perc_OC <- 4
### Import the 10% OC data
percoc.10 <- read_excel("4NP Standard Curve and Data.xlsx",
sheet = "Data_Complete_%OC",
col_types=c(rep("numeric",4), rep("skip",4), rep("numeric",4), rep("skip",8)),
skip=2)
names(percoc.10)## [1] "Days" "Mass dosed"
## [3] "Mass dust dosed(g)" "Conc on dust (mg/g)"
## [5] "Mass of dust per trial (g)" "4NP in trial (mg)"
## [7] "Mass released (mg)" "%released" names(percoc.10) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.10$Perc_OC <- 10
### A more efficient way of selecting columns
selectcols <- rep("skip", 20)
selectcols[c(1:4, 13:16)] <- "numeric"
### Import the 50% OC data
percoc.50 <- read_excel("4NP Standard Curve and Data.xlsx",
sheet = "Data_Complete_%OC",
col_types = selectcols,
skip=2)
names(percoc.50)## [1] "Days" "Mass dosed"
## [3] "Mass dust dosed(g)" "Conc on dust (mg/g)"
## [5] "Mass of dust per trial (g)" "4NP in trial (mg)"
## [7] "Mass released (mg)" "%released" names(percoc.50) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.50$Perc_OC <- 50
### Import the 70% OC data
selectcols <- rep("skip", 20)
selectcols[c(1:4, 17:20)] <- "numeric"
percoc.70 <- read_excel("4NP Standard Curve and Data.xlsx",
sheet = "Data_Complete_%OC",
col_types = selectcols,
skip=2)
names(percoc.70)## [1] "Days" "Mass dosed"
## [3] "Mass dust dosed(g)" "Conc on dust (mg/g)"
## [5] "Mass of dust per trial (g)" "4NP in trial (mg)"
## [7] "Mass released (mg)" "%released" names(percoc.70) <- c("Days", "Mass_Dosed", "Dust_Massed_Dosed", "Conc_On_Dust", "Mass_Of_Dust_Per_Trial", "FourNP_In_Trial", "Mass_Released", "Perc_Released")
percoc.70$Perc_OC <- 70
### Stack the 4, 10, 50, and 70 percent data to create a single data.frame
percoc2 <- rbind(percoc.4, percoc.10, percoc.50, percoc.70)
percoc2$Sample <- factor(round((percoc2$Days - floor(percoc2$Days))*10))
percoc2$Days <- floor(percoc2$Days)
percoc2$Perc_Released <- as.numeric(percoc2$Perc_Released)
percoc2$Prop_Released <- percoc2$Perc_Released / 100
### Remove/delete the temporary individual percentage data.frames
rm(percoc.4, percoc.10, percoc.50, percoc.70)
### Write the complete, reformatted data.frame to CSV
write.csv(percoc2, "Four_NP_PercOC2.csv", row.names = FALSE)
### Check to make sure everything is balanced and plot it just for fun
head(percoc2)## # A tibble: 6 x 11
## Days Mass_Dosed Dust_Massed_Dos~ Conc_On_Dust Mass_Of_Dust_Pe~
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 0 0 NA
## 2 5 18.0 5.5 3.27 0.88
## 3 5 15.6 5.6 2.79 1.01
## 4 5 22.3 7 3.19 1.4
## 5 10 18.0 5.5 3.27 1.33
## 6 10 15.6 5.6 2.79 1.21
## # ... with 6 more variables: FourNP_In_Trial <dbl>, Mass_Released <dbl>,
## # Perc_Released <dbl>, Perc_OC <dbl>, Sample <fct>, Prop_Released <dbl> table(percoc2$Days, percoc2$Perc_OC)##
## 4 10 50 70
## 0 1 1 1 1
## 5 3 3 3 3
## 10 3 3 3 3
## 15 3 3 3 3
## 20 3 3 3 3
## 25 3 3 3 3
## 30 3 3 3 3
## 35 3 3 3 3
## 40 3 3 3 3 ggplot(percoc2, aes(x=Days, y=Perc_Released, group=factor(Perc_OC),
colour=factor(Perc_OC), shape=factor(Perc_OC),
linetype=factor(Perc_OC))) +
geom_point() +
stat_summary(fun.y="mean", geom="line") +
#stat_summary(fun.y="mean", geom="point", shape = "-", size=10) +
theme_bw() +
labs(shape = "OC%", linetype = "OC%", colour = "OC%") +
ylab("Percent 4-NP Released")