We will use the CO2 dataset, which has 64 rows and 5 columns of data from an experiment related to the cold tolerances of plants. First, we take in the CO2 dataset and save the whole dataset three times into three separate csv files as data/file_#.csv, with a unique id_var.
First, use check_files to make sure the files exist. We can use the system.file command to locate them within the assertable package. Then, run import_files to bring them in. We’ll call the combined data object master_data.
filenames <- paste0("file_",c(1:3),".csv")
filenames <- system.file("extdata", filenames, package = "assertable")
filenames## [1] "C:/Users/grantng/AppData/Local/Temp/RtmpGeH3Km/Rinst6ea45a575202/assertable/extdata/file_1.csv"
## [2] "C:/Users/grantng/AppData/Local/Temp/RtmpGeH3Km/Rinst6ea45a575202/assertable/extdata/file_2.csv"
## [3] "C:/Users/grantng/AppData/Local/Temp/RtmpGeH3Km/Rinst6ea45a575202/assertable/extdata/file_3.csv"## [1] "All results are present"## [1] "All results are present"## Plant Type Treatment conc uptake id_var
## 1: Qn1 Quebec nonchilled 95 16.0 1
## 2: Qn1 Quebec nonchilled 175 30.4 1
## 3: Qn1 Quebec nonchilled 250 34.8 1
## 4: Qn1 Quebec nonchilled 350 37.2 1
## 5: Qn1 Quebec nonchilled 500 35.3 1
## 6: Qn1 Quebec nonchilled 675 39.2 1This dataset should have 84 * 3 rows, and six columns: Plant, Type, Treatment, conc, uptake, and id_var.
## [1] "All rows present"## Error in assert_colnames(master_data, c("plant", "type", "treatment", : These columns exist in colnames but not in your dataframe: plant type treatment and these exist in your dataframe but not in colnames: Plant Type TreatmentOops, forgot to capitalize the column names. Trying again.
## [1] "All rows present"## [1] "All column names present"We believe the dataset should be unique by Plant, conc, and id_var (where id_var just represents the replication number of the dataset). Let’s check this.
plants <- unique(master_data$Plant)
concs <- unique(master_data$conc)
id_vars <- unique(master_data$id_var)
id_list <- list(Plant=plants, conc=concs, id_var=id_vars)
assert_ids(master_data,id_list)## [1] "Data is identified by id_vars: Plant conc id_var"Now, let’s make sure that there are only two values in Type: Quebec and Mississippi. Let’s also make sure that uptake and conc are more than 0 and less than 1500.
## [1] "Variable Type passed in test"## [1] "Variable uptake passed gt test"
## [1] "Variable conc passed gt test"## [1] "Variable uptake passed lt test"
## [1] "Variable conc passed lt test"Finally, let’s assert that all values of conc must be at least 6 times the value of uptake
## Plant Type Treatment conc uptake id_var
## 1: Qn1 Quebec nonchilled 95 16.0 1
## 2: Qn1 Quebec nonchilled 175 30.4 1
## 3: Qn3 Quebec nonchilled 95 16.2 1
## 4: Qn3 Quebec nonchilled 175 32.4 1
## 5: Qn1 Quebec nonchilled 95 16.0 2
## 6: Qn1 Quebec nonchilled 175 30.4 2
## 7: Qn3 Quebec nonchilled 95 16.2 2
## 8: Qn3 Quebec nonchilled 175 32.4 2
## 9: Qn1 Quebec nonchilled 95 16.0 3
## 10: Qn1 Quebec nonchilled 175 30.4 3
## 11: Qn3 Quebec nonchilled 95 16.2 3
## 12: Qn3 Quebec nonchilled 175 32.4 3## Error in assert_values(master_data, colnames = "conc", test = "gt", test_val = master_data$uptake * : 12 Rows for variable conc not more than the test value(s) in the dataset aboveBummer. Let’s finally do some subsetting of our data.
Now, let’s see if our values of concs can uniquely identify our observations.
## Plant conc
## 1: Qn2 95
## 2: Qn3 95## Error in assert_ids(new_data, list(Plant = c("Qn2", "Qn3"), conc = concs)): The above combinations of id variables do not exist in your datasetRough, let’s take 95 out of our concs level and try it again.
new_concs <- c(175,250,350,500,675,1000)
assert_ids(new_data, list(Plant=c("Qn2","Qn3"),conc=new_concs))## Plant conc n_duplicates
## 1: Qn2 175 3
## 2: Qn2 250 3
## 3: Qn2 350 3
## 4: Qn2 500 3
## 5: Qn2 675 3
## 6: Qn2 1000 3
## 7: Qn3 175 3
## 8: Qn3 250 3
## 9: Qn3 350 3
## 10: Qn3 500 3
## 11: Qn3 675 3
## 12: Qn3 1000 3## Error in assert_ids(new_data, list(Plant = c("Qn2", "Qn3"), conc = new_concs)): These combinations of id variables have n_duplicates duplicate observations per combination (36 total duplicates)Let’s first get the actual rows and look at them.
new_concs <- c(175,250,350,500,675,1000)
vetting_data <- assert_ids(new_data, list(Plant=c("Qn2","Qn3"),conc=new_concs),
ids_only=F, warn_only=T)## Warning in assert_ids(new_data, list(Plant = c("Qn2", "Qn3"), conc
## = new_concs), : These rows of data are all of the observations with
## duplicated id_vars, and have n_duplicates duplicate observations per
## combination of id_varnames (36 total duplicates)## Plant conc Type Treatment uptake id_var n_duplicates duplicate_id
## 1: Qn2 175 Quebec nonchilled 27.3 1 3 1
## 2: Qn2 175 Quebec nonchilled 27.3 2 3 2
## 3: Qn2 175 Quebec nonchilled 27.3 3 3 3
## 4: Qn2 250 Quebec nonchilled 37.1 1 3 1
## 5: Qn2 250 Quebec nonchilled 37.1 2 3 2
## 6: Qn2 250 Quebec nonchilled 37.1 3 3 3Hmm, we forgot to include the values of id_var in the actual id_vars argument. Now, let’s try it the last time with the id_var character vector included.
new_concs <- c(175,250,350,500,675,1000)
assert_ids(new_data, list(Plant=c("Qn2","Qn3"), conc=new_concs, id_var=id_vars))## [1] "Data is identified by id_vars: Plant conc id_var"Awesome! Now you’re a data wizard!