# Descriptive statistics in rigr

#### 2022-09-06

A key feature of many exploratory analyses is obtaining descriptive statistics for multiple variables. In the rigr package, we provide a function descrip() with improved output for descriptive statistics for an arbitrary number of variables. Key features include the ability to easily compute summary measures on strata or subsets of the variables specified. We go through examples making use of these key features below.

# Descriptive statistics with descrip

Throughout our examples, we’ll use the fev dataset. This dataset is included in the rigr package; see its documentation by running ?fev.

## Preparing our R session
library(rigr)
## rigr version 1.0.4: Regression, Inference, and General Data Analysis Tools in R
data(fev)

First, we can obtain default descriptive statistics for the dataset simply by running descrip().

descrip(fev)
##           N     Msng  Mean      Std Dev    Min       25%       Mdn
## seqnbr:     654     0   327.5     188.9     1.000     164.2     327.5
## subjid:     654     0   37170     23691     201.0     15811     36071
##    age:     654     0   9.931     2.954     3.000     8.000     10.00
##    fev:     654     0   2.637     0.8671    0.7910    1.981     2.547
## height:     654     0   61.14     5.704     46.00     57.00     61.50
##    sex:     654     0   1.514     0.5002    1.000     1.000     2.000
##  smoke:     654     0   1.099     0.2994    1.000     1.000     1.000
##            75%       Max
## seqnbr:     490.8     654.0
## subjid:     53638     90001
##    age:     12.00     19.00
##    fev:     3.118     5.793
## height:     65.50     74.00
##    sex:     2.000     2.000
##  smoke:     1.000     2.000

Since we input a dataframe, we can see that all variables have the same number of elements given in the N column. None of our variables have any missing values, as seen in the Msng column.

Rather than specifying the whole dataframe, if we are interested in only the variables fev and height, we can input only those two vectors into the descrip() function, as below.

descrip(fev$fev, fev$height)
##               N     Msng  Mean      Std Dev    Min       25%       Mdn
##    fev$fev: 654 0 2.637 0.8671 0.7910 1.981 2.547 ## fev$height:     654     0   61.14     5.704     46.00     57.00     61.50
##                75%       Max
##    fev$fev: 3.118 5.793 ## fev$height:     65.50     74.00

# Descriptive statistics for strata

Suppose we wish to obtain descriptive statistics of the fev and height variables, stratified by smoking status. To do this, we can use the strata parameter in descrip:

descrip(fev$fev, fev$height, strata = fev$smoke) ## N Msng Mean Std Dev Min 25% ## fev$fev:  All          654     0   2.637     0.8671    0.7910    1.981
##    fev$fev: Str no 589 0 2.566 0.8505 0.7910 1.920 ## fev$fev:    Str  yes    65     0   3.277     0.7500    1.694     2.795
## fev$height: All 654 0 61.14 5.704 46.00 57.00 ## fev$height:    Str  no    589     0   60.61     5.672     46.00     57.00
## fev$height: Str yes 65 0 65.95 3.193 58.00 63.50 ## Mdn 75% Max ## fev$fev:  All          2.547     3.118     5.793
##    fev$fev: Str no 2.465 3.048 5.793 ## fev$fev:    Str  yes   3.169     3.751     4.872
## fev$height: All 61.50 65.50 74.00 ## fev$height:    Str  no    61.00     64.50     74.00
## fev$height: Str yes 66.00 68.00 72.00 In the output, we can see that overall descriptive statistics, as well as descriptive statistics for each stratum (smoke = 1, smoke = 2) are returned in the table. # Descriptive statistics for subsets Now suppose we only want descriptive statistics for height and FEV for individuals over the age of 10. We first create an indicator variable for age > 10 outside of the descrip() function, and then give this variable to the subset parameter. greater_10 <- ifelse(fev$age > 10, 1, 0)
descrip(fev$fev, fev$height, subset = greater_10)
##               N     Msng  Mean      Std Dev    Min       25%       Mdn
##    fev$fev: 264 0 1.708 0.0000 1.708 1.708 1.708 ## fev$height:     264     0   57.00     0.0000    57.00     57.00     57.00
##                75%       Max
##    fev$fev: 1.708 1.708 ## fev$height:     57.00     57.00

# Above/Below

Suppose we want to know the proportion of individuals with FEV greater than 2, stratified by smoking status. We can use the strata argument as before, in addition to the above parameter to obtain this set of descriptive statistics:

descrip(fev$fev, strata = fev$smoke, above = 2)
##                      N     Msng  Mean      Std Dev    Min       25%
## fev$fev: All 654 0 2.637 0.8671 0.7910 1.981 ## fev$fev:    Str  no    589     0   2.566     0.8505    0.7910    1.920
## fev$fev: Str yes 65 0 3.277 0.7500 1.694 2.795 ## Mdn 75% Max Pr>2 ## fev$fev:  All          2.547     3.118     5.793     0.7446
## fev$fev: Str no 2.465 3.048 5.793 0.7199 ## fev$fev:    Str  yes   3.169     3.751     4.872     0.9692

From the output, we can see that 96.92% of the individuals in this dataset who smoke (smoking status 1) had an FEV greater than 2 L/sec, and 71.99% of the individuals in this dataset who were nonsmokers had an FEV greater than 2 L/sec.