2 Data manipulation
library(knitr)
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
library(PKPDmisc)2.1 DATA IMPORT
Objectives:
- Import datasets and documents
- Perform basic data manipulation upon importing the data.
pk_data <- read_csv("../data/pk_data.csv")
#> Parsed with column specification:
#> cols(
#> ID = col_integer(),
#> TIME = col_double(),
#> DV = col_character(),
#> AMT = col_integer(),
#> DOSE = col_integer(),
#> FORM = col_character(),
#> SEX = col_character(),
#> WT = col_double(),
#> AGE = col_integer(),
#> RACE = col_character()
#> )head(pk_data)
#> # A tibble: 6 x 10
#> ID TIME DV AMT DOSE FORM SEX WT AGE RACE
#> <int> <dbl> <chr> <int> <int> <chr> <chr> <dbl> <int> <chr>
#> 1 1 0.00 <NA> 100 100 IV Female 56.8 28 Hispanic
#> 2 1 0.25 1273.5 NA 100 IV Female 56.8 28 Hispanic
#> 3 1 0.50 995.38 NA 100 IV Female 56.8 28 Hispanic
#> 4 1 1.00 1254.7 NA 100 IV Female 56.8 28 Hispanic
#> 5 1 2.00 1037.6 NA 100 IV Female 56.8 28 Hispanic
#> 6 1 3.00 1135.4 NA 100 IV Female 56.8 28 Hispanic2.2 DATA MANIPULATION
The goals of this section:
- Use data manipulation tools to prepare the dataset for analysis
- Rename “DV” column as “COBS”
pk_data_cobs <- pk_data %>% rename(COBS = DV)- Perform the following tasks:
- Ensure that the following columns are numeric and not text: TIME, COBS, WT, AGE, AMT and DOSEs
glimpse(pk_data_cobs)
#> Observations: 1,200
#> Variables: 10
#> $ ID <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, ...
#> $ TIME <dbl> 0.00, 0.25, 0.50, 1.00, 2.00, 3.00, 4.00, 6.00, 8.00, 12....
#> $ COBS <chr> NA, "1273.5", "995.38", "1254.7", "1037.6", "1135.4", "10...
#> $ AMT <int> 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA,...
#> $ DOSE <int> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 10...
#> $ FORM <chr> "IV", "IV", "IV", "IV", "IV", "IV", "IV", "IV", "IV", "IV...
#> $ SEX <chr> "Female", "Female", "Female", "Female", "Female", "Female...
#> $ WT <dbl> 56.8, 56.8, 56.8, 56.8, 56.8, 56.8, 56.8, 56.8, 56.8, 56....
#> $ AGE <int> 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 30, 30, 3...
#> $ RACE <chr> "Hispanic", "Hispanic", "Hispanic", "Hispanic", "Hispanic...unique_non_numerics(pk_data_cobs$COBS)
#> [1] "BQL"- Create a new column called BQLFLAG which takes a value of
0if there is a numerical value in CObs and1if there is “BQL” in COBS.
pk_data_cobs <- pk_data_cobs %>%
mutate(BQLFLAG = ifelse(is.na(COBS), 0,
ifelse(COBS == "BQL", 1, 0)),
NONNUMERICS = ifelse(COBS %in% unique_non_numerics(COBS), 1, 0),
COBS = as_numeric(COBS))
#> Warning in as_numeric(COBS): NAs introduced by coercionc. Create a new column called "GENDER" where:
i. Female = 0
ii. Male = 1
d. Create a new column called RACEN where:
i. Caucasian = 0
ii. Asian = 1
iii. Black = 2
iv. Hispanic = 3pk_data_cobs <- pk_data_cobs %>%
mutate(
GENDER = factor(SEX,
levels = c(0, 1),
labels = c("Female", "Male")),
RACEN = ifelse(RACE == "Caucasian", 0,
ifelse(RACE == "Asian", 1,
ifelse(RACE == "Black",2,
ifelse(RACE == "Hispanic", 3, -99))))) - Create a new column called “IDF” - unique subject ID as combination of formulation and ID
pk_data_cobs <- pk_data_cobs %>% mutate(IDF = paste(ID, FORM, sep = "-"))- Remove the following columns
- SEX
- RACE
pk_data_output <- pk_data_cobs %>%
select(-SEX, -RACE)head(pk_data_output) %>% kable()| ID | TIME | COBS | AMT | DOSE | FORM | WT | AGE | BQLFLAG | NONNUMERICS | GENDER | RACEN | IDF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00 | NA | 100 | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
| 1 | 0.25 | 1274 | NA | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
| 1 | 0.50 | 995 | NA | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
| 1 | 1.00 | 1255 | NA | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
| 1 | 2.00 | 1038 | NA | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
| 1 | 3.00 | 1135 | NA | 100 | IV | 56.8 | 28 | 0 | 0 | NA | 3 | 1-IV |
Save the above modifications as a new csv file
write_csv(pk_data_output, "../data/pk_data_output.csv")2.3 Descriptive Statistics
- show a summary for all demographic columns
# single row per id
sid_pk_data <- pk_data_cobs %>%
distinct(ID, .keep_all = TRUE)
sid_pk_data %>%
select(WT, AGE, RACE, SEX) %>%
mutate(RACE = as.factor(RACE),
SEX = as.factor(SEX)) %>%
summary
#> WT AGE RACE SEX
#> Min. :52.3 Min. :20.0 Asian : 8 Female:28
#> 1st Qu.:58.5 1st Qu.:31.0 Black :12 Male :22
#> Median :64.0 Median :39.5 Caucasian:17
#> Mean :64.1 Mean :38.5 Hispanic :13
#> 3rd Qu.:68.8 3rd Qu.:48.0
#> Max. :80.9 Max. :59.0- Count the number of subjects in each “Race” category
# fastest
sid_pk_data %>% count(RACE) %>% kable()| RACE | n |
|---|---|
| Asian | 8 |
| Black | 12 |
| Caucasian | 17 |
| Hispanic | 13 |
# more manual
sid_pk_data %>%
group_by(RACE) %>%
tally
# most manual but have more control over column names
sid_pk_data %>%
group_by(RACE) %>%
summarize(num_per_race = n()) %>%
kable()- calculate the min, mean, and max values for WT, AGE:
- by Sex
sid_pk_data %>%
group_by(SEX) %>%
summarize(
WT_min = min(WT),
WT_mean = mean(WT),
WT_max = max(WT),
AGE_min = min(AGE),
AGE_mean = mean(AGE),
AGE_max = max(AGE)
) %>%
kable()| SEX | WT_min | WT_mean | WT_max | AGE_min | AGE_mean | AGE_max |
|---|---|---|---|---|---|---|
| Female | 52.3 | 59.5 | 69.0 | 20 | 37.0 | 51 |
| Male | 64.3 | 70.0 | 80.9 | 28 | 40.5 | 59 |
there are also targeted verbs in the form <verb>_at that can specify what columns to act on, and which functions to run
sid_pk_data %>%
group_by(SEX) %>%
summarize_at(vars(WT, AGE), funs(min, mean, max)) %>%
kable()| SEX | WT_min | AGE_min | WT_mean | AGE_mean | WT_max | AGE_max |
|---|---|---|---|---|---|---|
| Female | 52.3 | 20 | 59.5 | 37.0 | 69.0 | 51 |
| Male | 64.3 | 28 | 70.0 | 40.5 | 80.9 | 59 |
- What is the Average numbers samples(observations) per individual in this dataset. Hint: make sure you are only counting samples, rows with AMT values are not considered observations.
# observations are those with NA AMT values
pk_data_cobs %>%
filter(is.na(AMT)) %>%
group_by(ID) %>%
summarize(num_obs = n()) %>%
# ungroup so no longer calculating by grouping variable ID
ungroup %>%
summarize(average_obs = mean(num_obs)) %>% kable()2.4 average_obs
22- Calculate the Mean, 5th, and 95th percentile concentration at each time point for each formulation and dose level. hint: you can use
?quantileto calculate various quantiles
pk_data_cobs %>%
mutate(COBS = as.numeric(COBS)) %>%
filter(!is.na(COBS)) %>%
group_by(TIME, FORM, DOSE) %>%
summarize(q05 = quantile(COBS, 0.05),
q50 = quantile(COBS, 0.5),
q95 = quantile(COBS, 0.95)) %>%
arrange(FORM, DOSE, TIME) %>%
head %>% kable()| TIME | FORM | DOSE | q05 | q50 | q95 |
|---|---|---|---|---|---|
| 0.25 | IV | 100 | 823 | 1716 | 2751 |
| 0.50 | IV | 100 | 1000 | 1537 | 2815 |
| 1.00 | IV | 100 | 1071 | 1423 | 2629 |
| 2.00 | IV | 100 | 750 | 1216 | 1980 |
| 3.00 | IV | 100 | 789 | 1059 | 1803 |
| 4.00 | IV | 100 | 561 | 909 | 1278 |
devtools::session_info()
#> Session info -------------------------------------------------------------
#> setting value
#> version R version 3.4.0 (2017-04-21)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> tz Europe/Prague
#> date 2017-06-05
#> Packages -----------------------------------------------------------------
#> package * version date source
#> assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
#> backports 1.1.0 2017-05-22 CRAN (R 3.4.0)
#> base * 3.4.0 2017-04-21 local
#> bindr 0.1 2016-11-13 CRAN (R 3.4.0)
#> bindrcpp * 0.1 2016-12-11 CRAN (R 3.4.0)
#> bookdown 0.4 2017-05-20 CRAN (R 3.4.0)
#> broom 0.4.2 2017-02-13 CRAN (R 3.4.0)
#> cellranger 1.1.0 2016-07-27 CRAN (R 3.4.0)
#> codetools 0.2-15 2016-10-05 CRAN (R 3.4.0)
#> colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
#> compiler 3.4.0 2017-04-21 local
#> datasets * 3.4.0 2017-04-21 local
#> devtools 1.13.1 2017-05-13 CRAN (R 3.4.0)
#> digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
#> dplyr * 0.6.0 2017-06-02 Github (tidyverse/dplyr@b064c4b)
#> evaluate 0.10 2016-10-11 CRAN (R 3.4.0)
#> forcats 0.2.0 2017-01-23 CRAN (R 3.4.0)
#> foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
#> ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.0)
#> glue 1.0.0 2017-04-17 CRAN (R 3.4.0)
#> graphics * 3.4.0 2017-04-21 local
#> grDevices * 3.4.0 2017-04-21 local
#> grid 3.4.0 2017-04-21 local
#> gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
#> haven 1.0.0 2016-09-23 CRAN (R 3.4.0)
#> highr 0.6 2016-05-09 CRAN (R 3.4.0)
#> hms 0.3 2016-11-22 CRAN (R 3.4.0)
#> htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
#> httr 1.2.1 2016-07-03 CRAN (R 3.4.0)
#> jsonlite 1.5 2017-06-01 CRAN (R 3.4.0)
#> knitr * 1.16 2017-05-18 CRAN (R 3.4.0)
#> lattice 0.20-35 2017-03-25 CRAN (R 3.4.0)
#> lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
#> lubridate 1.6.0 2016-09-13 CRAN (R 3.4.0)
#> magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
#> memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
#> methods 3.4.0 2017-04-21 local
#> mnormt 1.5-5 2016-10-15 CRAN (R 3.4.0)
#> modelr 0.1.0 2016-08-31 CRAN (R 3.4.0)
#> munsell 0.4.3 2016-02-13 CRAN (R 3.4.0)
#> nlme 3.1-131 2017-02-06 CRAN (R 3.4.0)
#> parallel 3.4.0 2017-04-21 local
#> PKPDmisc * 1.0.0 2017-06-02 Github (dpastoor/PKPDmisc@23e1f49)
#> plyr 1.8.4 2016-06-08 CRAN (R 3.4.0)
#> psych 1.7.5 2017-05-03 CRAN (R 3.4.0)
#> purrr * 0.2.2.2 2017-05-11 CRAN (R 3.4.0)
#> R6 2.2.1 2017-05-10 CRAN (R 3.4.0)
#> Rcpp 0.12.11 2017-05-22 CRAN (R 3.4.0)
#> readr * 1.1.1 2017-05-16 CRAN (R 3.4.0)
#> readxl 1.0.0 2017-04-18 CRAN (R 3.4.0)
#> reshape2 1.4.2 2016-10-22 CRAN (R 3.4.0)
#> rlang 0.1.1 2017-05-18 CRAN (R 3.4.0)
#> rmarkdown 1.5.9000 2017-06-03 Github (rstudio/rmarkdown@ea515ef)
#> rprojroot 1.2 2017-01-16 CRAN (R 3.4.0)
#> rvest 0.3.2 2016-06-17 CRAN (R 3.4.0)
#> scales 0.4.1 2016-11-09 CRAN (R 3.4.0)
#> stats * 3.4.0 2017-04-21 local
#> stringi 1.1.5 2017-04-07 CRAN (R 3.4.0)
#> stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
#> tibble * 1.3.3 2017-05-28 CRAN (R 3.4.0)
#> tidyr * 0.6.3 2017-05-15 CRAN (R 3.4.0)
#> tidyverse * 1.1.1 2017-01-27 CRAN (R 3.4.0)
#> tools 3.4.0 2017-04-21 local
#> utils * 3.4.0 2017-04-21 local
#> withr 1.0.2 2016-06-20 CRAN (R 3.4.0)
#> xml2 1.1.1 2017-01-24 CRAN (R 3.4.0)
#> yaml 2.1.14 2016-11-12 CRAN (R 3.4.0)