2 Intro: Cancer Experiemnt
library(tidyverse) # super helpful everything!
library(haven) # inporting SPSS, SAS, & Stata data files2.1 Source of Data
Mid-Michigan Medical Center, Midland, Michigan, 1999: A study of oral condition of cancer patients.
2.2 Description of the Study
The data set contains part of the data for a study of oral condition of cancer patients conducted at the Mid-Michigan Medical Center. The oral conditions of the patients were measured and recorded at the initial stage, at the end of the second week, at the end of the fourth week, and at the end of the sixth week. The variables age, initial weight and initial cancer stage of the patients were recorded. Patients were divided into two groups at random: One group received a placebo and the other group received aloe juice treatment.
Sample size n = 25 patients with neck cancer.
The treatment is Aloe Juice.
2.3 Variables
IDpatient identification numbertrttreatment group0placebo1aloe juice
agepatient’s age, in yearsweightinpatient’s weight (pounds) at the initial stagestageinitial cancer stage- coded
1through4
- coded
totalcinoral condition at the initial stagetotalcw2oral condition at the end of week 2totalcw4oral condition at the end of week 4totalcw6oral condition at the end of week 6
2.4 Import Data
The Cancer dataset is saved in SPSS format, which is evident from the .sav ending on the file name.
The haven package is downloaded as part of the tidyverse set of packages, but is not automatically loaded. It must have its own library() function call (see above). The haven::read_spss() function works very simarly to the readxl::read_excel() function we used last chapter (Wickham and Miller 2019).
- Make sure the dataset is saved in the same folder as this file
- Make sure the that folder is the working directory
Observations: 25
Variables: 9
$ ID <dbl> 1, 5, 6, 9, 11, 15, 21, 26, 31, 35, 39, 41, 45, 2, 12, 14,...
$ TRT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1...
$ AGE <dbl> 52, 77, 60, 61, 59, 69, 67, 56, 61, 51, 46, 65, 67, 46, 56...
$ WEIGHIN <dbl> 124.0, 160.0, 136.5, 179.6, 175.8, 167.6, 186.0, 158.0, 21...
$ STAGE <dbl> 2, 1, 4, 1, 2, 1, 1, 3, 1, 1, 4, 1, 1, 2, 4, 1, 2, 1, 4, 2...
$ TOTALCIN <dbl> 6, 9, 7, 6, 6, 6, 6, 6, 6, 6, 7, 6, 8, 7, 6, 4, 6, 6, 12, ...
$ TOTALCW2 <dbl> 6, 6, 9, 7, 7, 6, 11, 11, 9, 4, 8, 6, 8, 16, 10, 6, 11, 7,...
$ TOTALCW4 <dbl> 6, 10, 17, 9, 16, 6, 11, 15, 6, 8, 11, 9, 9, 9, 11, 8, 11,...
$ TOTALCW6 <dbl> 7, 9, 19, 3, 13, 11, 10, 15, 8, 7, 11, 6, 10, 10, 9, 7, 14...2.5 Wrangle Data
cancer_clean <- cancer_raw %>%
dplyr::rename_all(tolower) %>%
dplyr::mutate(id = factor(id)) %>%
dplyr::mutate(trt = factor(trt,
labels = c("Placebo",
"Aloe Juice"))) %>%
dplyr::mutate(stage = factor(stage))Observations: 25
Variables: 9
$ id <fct> 1, 5, 6, 9, 11, 15, 21, 26, 31, 35, 39, 41, 45, 2, 12, 14,...
$ trt <fct> Placebo, Placebo, Placebo, Placebo, Placebo, Placebo, Plac...
$ age <dbl> 52, 77, 60, 61, 59, 69, 67, 56, 61, 51, 46, 65, 67, 46, 56...
$ weighin <dbl> 124.0, 160.0, 136.5, 179.6, 175.8, 167.6, 186.0, 158.0, 21...
$ stage <fct> 2, 1, 4, 1, 2, 1, 1, 3, 1, 1, 4, 1, 1, 2, 4, 1, 2, 1, 4, 2...
$ totalcin <dbl> 6, 9, 7, 6, 6, 6, 6, 6, 6, 6, 7, 6, 8, 7, 6, 4, 6, 6, 12, ...
$ totalcw2 <dbl> 6, 6, 9, 7, 7, 6, 11, 11, 9, 4, 8, 6, 8, 16, 10, 6, 11, 7,...
$ totalcw4 <dbl> 6, 10, 17, 9, 16, 6, 11, 15, 6, 8, 11, 9, 9, 9, 11, 8, 11,...
$ totalcw6 <dbl> 7, 9, 19, 3, 13, 11, 10, 15, 8, 7, 11, 6, 10, 10, 9, 7, 14...# A tibble: 25 x 9
id trt age weighin stage totalcin totalcw2 totalcw4 totalcw6
<fct> <fct> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 1 Placebo 52 124 2 6 6 6 7
2 5 Placebo 77 160 1 9 6 10 9
3 6 Placebo 60 136. 4 7 9 17 19
4 9 Placebo 61 180. 1 6 7 9 3
5 11 Placebo 59 176. 2 6 7 16 13
6 15 Placebo 69 168. 1 6 6 6 11
7 21 Placebo 67 186 1 6 11 11 10
8 26 Placebo 56 158 3 6 11 15 15
9 31 Placebo 61 213. 1 6 9 6 8
10 35 Placebo 51 189 1 6 4 8 7
# ... with 15 more rows