Author: Grant Nguyen
Date: 2021-01-26

# assertable Template

#### Grant Nguyen

#### 2021-01-26

## Data

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.

    for(i in 1:3) {
      data <- CO2
      data$id_var <- i
      write.csv(data,file=paste0("../data/file_",i,".csv"),row.names=F)
    }

## File Check and Import

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"

    check_files(filenames)

    ## [1] "All results are present"

    master_data <- import_files(filenames,FUN=fread)

    ## [1] "All results are present"

    head(master_data)

    ##    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      1

## Checking Dimensions

This dataset should have 84 * 3 rows, and six columns: Plant, Type, Treatment, conc, uptake, and id_var.

    assert_nrows(master_data,(84*3))

    ## [1] "All rows present"

    assert_colnames(master_data,c("plant","type","treatment","conc","uptake","id_var"))

    ## 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 Treatment

Oops, forgot to capitalize the column names. Trying again.

    assert_nrows(master_data,(84*3))

    ## [1] "All rows present"

    assert_colnames(master_data,c("Plant","Type","Treatment","conc","uptake","id_var"))

    ## [1] "All column names present"

## Checking IDs

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.

    assert_values(master_data, colnames = "Type", test="in", test_val = c("Quebec","Mississippi"))

    ## [1] "Variable Type passed in test"

    assert_values(master_data, colnames = c("uptake","conc"), test="gt", test_val = 0)

    ## [1] "Variable uptake passed gt test"
    ## [1] "Variable conc passed gt test"

    assert_values(master_data, colnames = c("uptake","conc"), test="lt", test_val = 1500)

    ## [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

    assert_values(master_data, colnames = "conc", test="gt", test_val = master_data$uptake * 6)

    ##     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 above

Bummer. Let’s finally do some subsetting of our data.

    new_data <- master_data[master_data$Type == "Quebec" & master_data$Plant %in% c("Qn2","Qn3") & uptake > 20,]

Now, let’s see if our values of concs can uniquely identify our observations.

    assert_ids(new_data, list(Plant=c("Qn2","Qn3"), conc=concs))

    ##    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 dataset

Rough, 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)

    print(head(vetting_data))

    ##    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            3

Hmm, 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!