Exercises

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
theoph <- read.csv("theoph.csv")
theoph$Subject = factor(theoph$Subject)
  1. Using select() make a new data frame containing only the subject ID, the Time and conc variables
theoph %>% select(Subject, Time, conc) %>%
  head()
##   Subject Time  conc
## 1       1 0.00  0.74
## 2       1 0.25  2.84
## 3       1 0.57  6.57
## 4       1 1.12 10.50
## 5       1 2.02  9.66
## 6       1 3.82  8.58
  1. Using a combination of select() and distinct() make a new data frame containing a single row for each subject with the subject ID, their weight and the dose
theoph %>% select(Subject, Wt, Dose) %>%
  distinct(Subject, Wt, Dose)
##    Subject   Wt Dose
## 1        1 79.6 4.02
## 2        2 72.4 4.40
## 3        3 70.5 4.53
## 4        4 72.7 4.40
## 5        5 54.6 5.86
## 6        6 80.0 4.00
## 7        7 64.6 4.95
## 8        8 70.5 4.53
## 9        9 86.4 3.10
## 10      10 58.2 5.50
## 11      11 65.0 4.92
## 12      12 60.5 5.30
  1. Using filter() make a new data frame containing only the first subject
theoph %>% filter(Subject == 1)
##    Subject   Wt Dose  Time  conc
## 1        1 79.6 4.02  0.00  0.74
## 2        1 79.6 4.02  0.25  2.84
## 3        1 79.6 4.02  0.57  6.57
## 4        1 79.6 4.02  1.12 10.50
## 5        1 79.6 4.02  2.02  9.66
## 6        1 79.6 4.02  3.82  8.58
## 7        1 79.6 4.02  5.10  8.36
## 8        1 79.6 4.02  7.03  7.47
## 9        1 79.6 4.02  9.05  6.89
## 10       1 79.6 4.02 12.12  5.94
## 11       1 79.6 4.02 24.37  3.28
  1. Using filter() make a new data frame containing only the first four subjects
theoph %>% filter(Subject == 1:4)
##    Subject   Wt Dose  Time conc
## 1        1 79.6 4.02  0.00 0.74
## 2        1 79.6 4.02  2.02 9.66
## 3        1 79.6 4.02  9.05 6.89
## 4        2 72.4 4.40  0.52 7.91
## 5        2 72.4 4.40  5.02 6.08
## 6        2 72.4 4.40 24.30 0.90
## 7        3 70.5 4.53  0.00 0.00
## 8        3 70.5 4.53  2.02 7.80
## 9        3 70.5 4.53  9.00 4.90
## 10       4 72.7 4.40  0.60 4.60
## 11       4 72.7 4.40  5.02 6.88
## 12       4 72.7 4.40 24.65 1.15
  1. Using group_by(), calculate the average concentration per subject (across all times)
theoph %>% group_by(Subject) %>%
  summarise(aveConc = mean(conc))
## # A tibble: 12 × 2
##    Subject aveConc
##    <fct>     <dbl>
##  1 1          6.44
##  2 2          4.82
##  3 3          5.09
##  4 4          4.94
##  5 5          5.78
##  6 6          3.53
##  7 7          3.91
##  8 8          4.27
##  9 9          4.89
## 10 10         5.93
## 11 11         4.51
## 12 12         5.41
  1. Using filter() make a new data frame containing only the first subject and pipe this to ggplot() to make a line plot with Time on the x-axis and conc on the y-axis
theoph %>% filter(Subject == 1) %>%
  ggplot(aes(x=Time, y=conc)) + geom_line() + ggtitle("Subject 1") +
  xlab("Time (hours)") + ylab("Concentration") + theme_bw()

  1. Using select() to extract just the Subject, Time and conc variables and pipe this to ggplot() to make a line plot with Time on the x-axis, conc on the y-axis and the lines colored by subject
theoph %>% select(Subject, Time, conc) %>%
  ggplot(aes(x=Time, y=conc, col=Subject)) + geom_line() +
  ggtitle("Serum Concentration for all Subjects") + 
  xlab("Time (hours)") + ylab("Concentration") + theme_bw()

8.Using select() to extract just the Subject, Time and conc variables and pipe this to ggplot to make a line plot with Time on the x-axis, conc on the y-axis and faceted by the subject ID

theoph %>% select(Subject, Time, conc) %>%
  ggplot(aes(x=Time, y=conc, col=Subject)) + geom_line() +
  ggtitle("Serum Concentration for all Subjects") + 
  xlab("Time (hours)") + ylab("Concentration") + 
  theme_bw() + facet_wrap(~ Subject)