Unit 5 - Descriptives
PSYC 640 - Fall 2024
Reminders
Journal Entries
Lab 1 to be posted soon (Due 10/1)
Last Class
- Used various functions to wrangle data
select()filter()mutate()
Today…
Entering into the world of describing our data with numerical values.
Let’s get caught up
Getting the code a little cleaner. And doing it all in one step
#import sleep data - Your path may be different
sleep_data <- import(here("lectures", "data", "Sleep_Data.csv")) %>%
#Select only the variables we are interested in
select(-c('StartDate', 'EndDate', 'Status', 'Progress',
'Duration__in_seconds_', 'Finished', 'RecordedDate',
'DistributionChannel', 'UserLanguage', 'SC0', 'SC1',
'SC2', 'SC3', 'SC4', 'SC5', 'Attention')) %>%
#rename the variable names so they make sense
setNames(c('age', 'gender', 'roommate', 'other_sleep', 'bed_read', 'bed_study', 'bed_hw', 'attention1', 'bed_internet',
'bed_tv', 'bed_eat', 'bed_friends', 'bed_videogame', 'bed_readp', 'BL_1', 'BL_2', 'BL_3', 'BL_4',
'BL_5', 'attention2', 'BL_6', 'BL_7', 'sleepsat1', 'sleepsat2', 'sleepsat3', 'sleepsat4', 'sleepsat5',
'sleepsat6', 'ESS1', 'ESS2', 'ESS3', 'ESS4', 'ESS5', 'ESS6', 'ESS7', 'ESS8', 'ESS00', 'ashs1', 'ashs2',
'ashs3', 'ashs4', 'ashs5', 'attention3', 'ashs6', 'ashs7', 'ashs8', 'ashs9', 'ashs10', 'ashs11',
'ashs12', 'ashs13', 'ashs14', 'ashs15', 'ashs16', 'ashs17', 'attention4', 'ashs18', 'ashs19',
'ashs20', 'ashs21', 'ashs22', 'ashs23', 'ashs24', 'ashs26', 'ashs27', 'ashs28', 'attention5',
'ashs29', 'ashs30', 'ashs31', 'ashs32', 'ashs33', 'id')) %>%
# Only include college aged participants
filter(age >= 17, age <= 25) %>%
# Rescale the ESS
mutate(ESS1m1 = ESS1 - 1,
ESS2m1 = ESS2 - 1,
ESS3m1 = ESS3 - 1,
ESS4m1 = ESS4 - 1,
ESS5m1 = ESS5 - 1,
ESS6m1 = ESS6 - 1,
ESS7m1 = ESS7 - 1,
ESS8m1 = ESS8 - 1) %>%
# Create a total score for the ESS
mutate(ess_total = ESS1m1 + ESS2m1 + ESS3m1 +
ESS4m1 + ESS5m1 + ESS6m1 + ESS7m1 + ESS8m1) %>%
# Recode the attention check variables
mutate(attention1r = recode(attention1,
`1`=0,
`2`=0,
`3`=0,
`4`=0,
`5`=1),
attention2r = recode(attention2,
`1`=1,
`2`=0,
`3`=0,
`4`=0,
`5`=0),
attention3r = recode(attention3,
`1`=0,
`2`=0,
`3`=0,
`4`=1,
`5`=0),
attention4r = recode(attention4,
`1`=1,
`2`=0,
`3`=0,
`4`=0,
`5`=0,
`6`=0),
attention5r = recode(attention5,
`1`=1,
`2`=0,
`3`=0,
`4`=0,
`5`=0,
`6`=0)) %>%
# Compute a total Attention Score
mutate(att_sum = (attention1r + attention2r + attention3r +
attention4r + attention5r))Address the Inattention
Create two new datasets labeled (1) “data_attend” and (2) “data_distract”. In each dataset have those who were paying attention in the “data_attend” and those who were not in the “data_distract”. Paying attention is operationalized as having a score of 5 on the aggregated variable.
Get the sample size of each of these datasets (use Google to search for things like number of rows)
Save the cleaned up file
This is now a file that we will use in the future to import. That way we don’t have to make sure that the code above is in all of our scripts/recipes moving forward!
Descriptive Statistics
Some Terminology
| Population | Sample |
|---|---|
| \(\mu\) (mu) = Population Mean | \(\bar{X}\) (x bar) = Sample Mean |
| \(\sigma\) (sigma) = Population Standard Deviation | \(s\) = \(\hat{\sigma}\) = Sample Standard Deviation |
| \(\sigma^2\) (sigma squared) = Population Variance | \(s^2\) = \(\hat{\sigma^2}\) = Sample Variance |
Measures of Central Tendency
For a given set of observations, measures of central tendency allow us to get the “gist” of the data.
They tell us about where the “average” or the “mid-point” of the data lies or how much deviation there is from a central point.
Let’s take a look at the data that we have already loaded in, and complete some of these tasks.
Mean/Average
\[ \bar{X} = \frac{X_1+X_2+...+X\_{N-1}X_N}{N} \]
OR
\[ \bar{X} = \frac{1}{N}\sum_{i=1}^{N} X_i \]
Mean in R
A quick way to find the mean is to use the aptly named mean() function from base R. Use this function to get the average age and ess total in our dataset.
Oh no! A mean of NA makes no sense…
We forgot to account for the missing variables in our variable! We got NA! The reason for this is that the mean is calculated by using every value for a given variable, so if you don’t remove (or impute) the missing values before getting the mean, it won’t work.
Let’s try that again, but using the additional argument to eliminate (or remove) the NA’s from the variable prior to computing the mean.
Median
The median is the middle value of a set of observations: 50% of the data points fall below the median, and 50% fall above.
To find the median, we can use the median() function. Use it on the age variable.
Measures of Variability
The overall spread of the data; How far from the middle?
Range
The range gives us the distance between the smallest and largest value in a dataset.
You can find the range using the range() function, which will output the minimum and maximum values.
Find the range of the ess_total variable.
Variance and Standard Deviation
68-95-99.7 Rule
For nearly normally distributed data:
about 68% falls within 1 SD of the mean,
about 95% falls within 2 SD of the mean,
about 99.7% falls within 3 SD of the mean.
It is possible for observations to fall 4, 5, or more standard deviations away from the mean, but these occurrences are very rare if the data are nearly normal.
Variance
The sum of squared deviations
\[\sigma^2 = \frac{1}{N}\sum_{i=1}^N(X-\bar{X})^2\]
\[\hat{\sigma}^2 = s^2 = \frac{1}{N-1}\sum_{i=1}^N(X-\bar{X})^2\]
| \(i\) (observation) | \(X_i\) (value) | \(\bar{X}\) (sample mean) | \(X_i - \bar{X}\) (deviation from mean) | \((X_i - \bar{X})^2\) (squared deviation) |
|---|---|---|---|---|
| 1 | 56 | 36.6 | 19.4 | 376.36 |
| 2 | 31 | 36.6 | -5.6 | 31.36 |
| 3 | 56 | 36.6 | 19.4 | 376.36 |
| 4 | 8 | 36.6 | -28.6 | 817.96 |
| 5 | 32 | 36.6 | -4.6 | 21.16 |
Why do we use the squared deviation in the calculation of variance?
To get rid of negative values so that observations equally distant from the mean are weighted equally
To weigh larger deviations from the mean
In-Class Activity: Variance
Open up Instagram (that is still a thing right?)
Identify a celebrity and look at their most recent instagram posts.
Let’s calculate the variance of their likes.
Variance in R
To find the variance and standard deviation, we use var() and sd(), respectively. Find the variance and standard deviation of the age variable.
Summarizing Data
So far we have been calculating various descriptive statistics (somewhat painstakingly) using an assortment of different functions. So what if we have a dataset with a bunch of variables we want descriptive statistics for? Surely we don’t want to calculate descriptives for each variable by hand…
Fortunately for us, there is a function called describe() from the {psych} package, which we can use to quickly summarize a whole set of variables in a dataset.
Be sure to first install the package prior to putting it into your library code chunk. Reminder: anytime you add a library, be sure you actually run the code line library(psych). Otherwise, you will have a hard time trying to use the next functions.
Let’s use it with our sleep dataset!
describe()
This function automatically calculates all of the descriptives we reviewed above (and more!). Use the describe() function from the psych package on the entire sleep_data dataset.
Notes: If you load a library at the beginning, you can directly call any function from it. Instead, you can call a function by library_name::function_name without loading the entire library.
vars n mean sd median trimmed mad min max
age 1 1452 19.56 1.59 19.0 19.39 1.48 17 25
gender 2 1452 1.43 0.56 1.0 1.37 0.00 1 3
roommate 3 1452 1.55 0.50 2.0 1.56 0.00 1 2
other_sleep 4 1452 1.65 0.95 1.0 1.45 0.00 1 5
bed_read 5 1452 1.67 0.96 1.0 1.47 0.00 1 5
bed_study 6 1452 1.90 1.05 2.0 1.72 1.48 1 5
bed_hw 7 1452 2.11 1.19 2.0 1.95 1.48 1 5
attention1 8 1452 4.64 0.88 5.0 4.89 0.00 1 5
bed_internet 9 1452 3.62 1.05 4.0 3.67 1.48 1 5
bed_tv 10 1452 3.30 1.22 4.0 3.35 1.48 1 5
bed_eat 11 1452 1.96 1.10 2.0 1.77 1.48 1 5
bed_friends 12 1452 1.76 0.96 2.0 1.58 1.48 1 5
bed_videogame 13 1452 1.96 1.15 2.0 1.77 1.48 1 5
bed_readp 14 1452 2.10 1.16 2.0 1.93 1.48 1 5
BL_1 15 1448 4.49 1.11 5.0 4.80 0.00 1 5
BL_2 16 1448 4.23 1.24 5.0 4.48 0.00 1 5
BL_3 17 1448 4.08 1.22 5.0 4.28 0.00 1 5
BL_4 18 1448 4.26 1.34 5.0 4.57 0.00 1 5
BL_5 19 1448 2.57 1.57 2.0 2.46 1.48 1 5
attention2 20 1448 1.07 0.48 1.0 1.00 0.00 1 5
BL_6 21 1448 2.09 1.14 2.0 1.90 1.48 1 5
BL_7 22 1448 4.25 1.15 5.0 4.49 0.00 1 5
sleepsat1 23 1447 3.09 1.16 3.0 3.11 1.48 1 5
sleepsat2 24 1447 3.14 1.18 3.0 3.16 1.48 1 5
sleepsat3 25 1447 3.11 1.23 3.0 3.13 1.48 1 5
sleepsat4 26 1447 3.03 1.34 3.0 3.04 1.48 1 5
sleepsat5 27 1447 3.78 1.17 4.0 3.91 1.48 1 5
sleepsat6 28 1447 2.75 1.13 3.0 2.74 1.48 1 5
ESS1 29 1447 2.29 0.98 2.0 2.24 1.48 1 4
ESS2 30 1447 2.29 0.87 2.0 2.25 1.48 1 4
ESS3 31 1447 1.65 0.82 1.0 1.52 0.00 1 4
ESS4 32 1447 2.58 1.01 3.0 2.60 1.48 1 4
ESS5 33 1447 3.02 0.92 3.0 3.11 1.48 1 4
ESS6 34 1447 1.11 0.38 1.0 1.00 0.00 1 4
ESS7 35 1447 1.64 0.79 1.0 1.52 0.00 1 4
ESS8 36 1447 1.21 0.55 1.0 1.06 0.00 1 4
ESS00 37 1447 2.21 0.88 2.0 2.14 1.48 1 4
ashs1 38 1438 2.58 1.28 2.0 2.45 1.48 1 6
ashs2 39 1438 3.26 1.49 3.0 3.21 1.48 1 6
ashs3 40 1438 2.31 1.39 2.0 2.10 1.48 1 6
ashs4 41 1438 1.98 1.12 2.0 1.78 1.48 1 6
ashs5 42 1437 2.72 1.42 2.0 2.59 1.48 1 6
attention3 43 1438 4.00 0.00 4.0 4.00 0.00 4 4
ashs6 44 1438 1.55 1.34 1.0 1.15 0.00 1 6
ashs7 45 1438 1.76 1.02 1.0 1.58 0.00 1 6
ashs8 46 1438 3.17 1.45 3.0 3.13 1.48 1 6
ashs9 47 1437 2.73 1.24 3.0 2.63 1.48 1 6
ashs10 48 1437 1.73 1.14 1.0 1.48 0.00 1 6
ashs11 49 1438 4.03 1.37 4.0 4.09 1.48 1 6
ashs12 50 1438 2.44 1.53 2.0 2.23 1.48 1 6
ashs13 51 1437 4.28 1.47 5.0 4.41 1.48 1 6
ashs14 52 1437 4.38 1.26 5.0 4.47 1.48 1 6
ashs15 53 1437 2.69 1.26 2.0 2.57 1.48 1 6
ashs16 54 1436 3.27 1.43 3.0 3.23 1.48 1 6
ashs17 55 1437 3.87 1.37 4.0 3.87 1.48 1 6
attention4 56 1437 1.20 0.87 1.0 1.00 0.00 1 6
ashs18 57 1437 1.75 0.96 2.0 1.58 1.48 1 6
ashs19 58 1437 2.41 1.12 2.0 2.30 1.48 1 6
ashs20 59 1437 1.57 1.02 1.0 1.33 0.00 1 6
ashs21 60 1437 2.59 1.56 2.0 2.42 1.48 1 6
ashs22 61 1437 1.66 0.95 1.0 1.47 0.00 1 6
ashs23 62 1437 1.33 0.84 1.0 1.11 0.00 1 6
ashs24 63 1436 2.44 1.28 2.0 2.29 1.48 1 6
ashs26 64 1437 3.23 1.26 3.0 3.16 1.48 1 6
ashs27 65 1437 3.25 1.65 3.0 3.19 1.48 1 6
ashs28 66 1436 3.75 1.63 4.0 3.80 1.48 1 6
attention5 67 1437 1.10 0.63 1.0 1.00 0.00 1 6
ashs29 68 1437 2.28 1.28 2.0 2.09 1.48 1 6
ashs30 69 1437 3.94 1.38 4.0 3.96 1.48 1 6
ashs31 70 1437 2.61 1.38 2.0 2.47 1.48 1 6
ashs32 71 1437 4.87 1.19 5.0 5.04 1.48 1 6
ashs33 72 1437 4.73 1.32 5.0 4.91 1.48 1 6
id 73 1452 21734.02 7471.43 21089.5 20852.52 2684.99 11918 76858
ESS1m1 74 1447 1.29 0.98 1.0 1.24 1.48 0 3
ESS2m1 75 1447 1.29 0.87 1.0 1.25 1.48 0 3
ESS3m1 76 1447 0.65 0.82 0.0 0.52 0.00 0 3
ESS4m1 77 1447 1.58 1.01 2.0 1.60 1.48 0 3
ESS5m1 78 1447 2.02 0.92 2.0 2.11 1.48 0 3
ESS6m1 79 1447 0.11 0.38 0.0 0.00 0.00 0 3
ESS7m1 80 1447 0.64 0.79 0.0 0.52 0.00 0 3
ESS8m1 81 1447 0.21 0.55 0.0 0.06 0.00 0 3
ess_total 82 1447 7.79 3.58 8.0 7.72 2.97 0 24
attention1r 83 1452 0.82 0.38 1.0 0.91 0.00 0 1
attention2r 84 1448 0.97 0.17 1.0 1.00 0.00 0 1
attention3r 85 1438 1.00 0.00 1.0 1.00 0.00 1 1
attention4r 86 1437 0.93 0.25 1.0 1.00 0.00 0 1
attention5r 87 1437 0.97 0.17 1.0 1.00 0.00 0 1
att_sum 88 1437 4.70 0.63 5.0 4.82 0.00 1 5
range skew kurtosis se
age 8 0.87 0.54 0.04
gender 2 0.83 -0.36 0.01
roommate 1 -0.20 -1.96 0.01
other_sleep 4 1.75 2.87 0.02
bed_read 4 1.61 2.18 0.03
bed_study 4 1.15 0.64 0.03
bed_hw 4 0.92 -0.15 0.03
attention1 4 -2.50 5.33 0.02
bed_internet 4 -0.41 -0.69 0.03
bed_tv 4 -0.29 -1.02 0.03
bed_eat 4 1.22 0.81 0.03
bed_friends 4 1.42 1.72 0.03
bed_videogame 4 1.14 0.40 0.03
bed_readp 4 0.97 0.04 0.03
BL_1 4 -2.15 3.31 0.03
BL_2 4 -1.44 0.76 0.03
BL_3 4 -1.06 -0.17 0.03
BL_4 4 -1.59 0.97 0.04
BL_5 4 0.48 -1.34 0.04
attention2 4 7.11 51.85 0.01
BL_6 4 1.12 0.62 0.03
BL_7 4 -1.47 1.09 0.03
sleepsat1 4 -0.18 -1.09 0.03
sleepsat2 4 -0.20 -1.04 0.03
sleepsat3 4 -0.11 -1.12 0.03
sleepsat4 4 -0.05 -1.24 0.04
sleepsat5 4 -0.78 -0.33 0.03
sleepsat6 4 0.16 -0.94 0.03
ESS1 3 0.27 -0.94 0.03
ESS2 3 0.27 -0.59 0.02
ESS3 3 1.10 0.45 0.02
ESS4 3 0.00 -1.11 0.03
ESS5 3 -0.54 -0.70 0.02
ESS6 3 4.02 19.11 0.01
ESS7 3 1.07 0.54 0.02
ESS8 3 3.04 9.55 0.01
ESS00 3 0.42 -0.49 0.02
ashs1 5 0.81 -0.06 0.03
ashs2 5 0.28 -0.94 0.04
ashs3 5 0.99 0.07 0.04
ashs4 5 1.30 1.41 0.03
ashs5 5 0.64 -0.45 0.04
attention3 0 NaN NaN 0.00
ashs6 5 2.41 4.45 0.04
ashs7 5 1.49 2.08 0.03
ashs8 5 0.30 -0.87 0.04
ashs9 5 0.61 -0.16 0.03
ashs10 5 1.84 3.11 0.03
ashs11 5 -0.33 -0.73 0.04
ashs12 5 0.86 -0.35 0.04
ashs13 5 -0.58 -0.65 0.04
ashs14 5 -0.47 -0.54 0.03
ashs15 5 0.85 0.21 0.03
ashs16 5 0.29 -0.83 0.04
ashs17 5 -0.07 -0.93 0.04
attention4 5 4.77 22.14 0.02
ashs18 5 1.72 3.67 0.03
ashs19 5 0.92 0.80 0.03
ashs20 5 2.09 4.23 0.03
ashs21 5 0.71 -0.64 0.04
ashs22 5 1.83 3.83 0.03
ashs23 5 3.23 11.47 0.02
ashs24 5 0.92 0.38 0.03
ashs26 5 0.44 -0.45 0.03
ashs27 5 0.15 -1.21 0.04
ashs28 5 -0.08 -1.25 0.04
attention5 5 6.63 44.55 0.02
ashs29 5 1.13 0.73 0.03
ashs30 5 -0.13 -0.96 0.04
ashs31 5 0.72 -0.29 0.04
ashs32 5 -1.00 0.44 0.03
ashs33 5 -0.86 -0.17 0.03
id 64940 6.54 44.82 196.07
ESS1m1 3 0.27 -0.94 0.03
ESS2m1 3 0.27 -0.59 0.02
ESS3m1 3 1.10 0.45 0.02
ESS4m1 3 0.00 -1.11 0.03
ESS5m1 3 -0.54 -0.70 0.02
ESS6m1 3 4.02 19.11 0.01
ESS7m1 3 1.07 0.54 0.02
ESS8m1 3 3.04 9.55 0.01
ess_total 24 0.30 0.23 0.09
attention1r 1 -1.70 0.90 0.01
attention2r 1 -5.54 28.66 0.00
attention3r 0 NaN NaN 0.00
attention4r 1 -3.49 10.18 0.01
attention5r 1 -5.38 26.92 0.00
att_sum 4 -2.79 10.04 0.02 vars n mean sd median trimmed mad min max
age 1 1452 19.56 1.59 19.0 19.39 1.48 17 25
gender 2 1452 1.43 0.56 1.0 1.37 0.00 1 3
roommate 3 1452 1.55 0.50 2.0 1.56 0.00 1 2
other_sleep 4 1452 1.65 0.95 1.0 1.45 0.00 1 5
bed_read 5 1452 1.67 0.96 1.0 1.47 0.00 1 5
bed_study 6 1452 1.90 1.05 2.0 1.72 1.48 1 5
bed_hw 7 1452 2.11 1.19 2.0 1.95 1.48 1 5
attention1 8 1452 4.64 0.88 5.0 4.89 0.00 1 5
bed_internet 9 1452 3.62 1.05 4.0 3.67 1.48 1 5
bed_tv 10 1452 3.30 1.22 4.0 3.35 1.48 1 5
bed_eat 11 1452 1.96 1.10 2.0 1.77 1.48 1 5
bed_friends 12 1452 1.76 0.96 2.0 1.58 1.48 1 5
bed_videogame 13 1452 1.96 1.15 2.0 1.77 1.48 1 5
bed_readp 14 1452 2.10 1.16 2.0 1.93 1.48 1 5
BL_1 15 1448 4.49 1.11 5.0 4.80 0.00 1 5
BL_2 16 1448 4.23 1.24 5.0 4.48 0.00 1 5
BL_3 17 1448 4.08 1.22 5.0 4.28 0.00 1 5
BL_4 18 1448 4.26 1.34 5.0 4.57 0.00 1 5
BL_5 19 1448 2.57 1.57 2.0 2.46 1.48 1 5
attention2 20 1448 1.07 0.48 1.0 1.00 0.00 1 5
BL_6 21 1448 2.09 1.14 2.0 1.90 1.48 1 5
BL_7 22 1448 4.25 1.15 5.0 4.49 0.00 1 5
sleepsat1 23 1447 3.09 1.16 3.0 3.11 1.48 1 5
sleepsat2 24 1447 3.14 1.18 3.0 3.16 1.48 1 5
sleepsat3 25 1447 3.11 1.23 3.0 3.13 1.48 1 5
sleepsat4 26 1447 3.03 1.34 3.0 3.04 1.48 1 5
sleepsat5 27 1447 3.78 1.17 4.0 3.91 1.48 1 5
sleepsat6 28 1447 2.75 1.13 3.0 2.74 1.48 1 5
ESS1 29 1447 2.29 0.98 2.0 2.24 1.48 1 4
ESS2 30 1447 2.29 0.87 2.0 2.25 1.48 1 4
ESS3 31 1447 1.65 0.82 1.0 1.52 0.00 1 4
ESS4 32 1447 2.58 1.01 3.0 2.60 1.48 1 4
ESS5 33 1447 3.02 0.92 3.0 3.11 1.48 1 4
ESS6 34 1447 1.11 0.38 1.0 1.00 0.00 1 4
ESS7 35 1447 1.64 0.79 1.0 1.52 0.00 1 4
ESS8 36 1447 1.21 0.55 1.0 1.06 0.00 1 4
ESS00 37 1447 2.21 0.88 2.0 2.14 1.48 1 4
ashs1 38 1438 2.58 1.28 2.0 2.45 1.48 1 6
ashs2 39 1438 3.26 1.49 3.0 3.21 1.48 1 6
ashs3 40 1438 2.31 1.39 2.0 2.10 1.48 1 6
ashs4 41 1438 1.98 1.12 2.0 1.78 1.48 1 6
ashs5 42 1437 2.72 1.42 2.0 2.59 1.48 1 6
attention3 43 1438 4.00 0.00 4.0 4.00 0.00 4 4
ashs6 44 1438 1.55 1.34 1.0 1.15 0.00 1 6
ashs7 45 1438 1.76 1.02 1.0 1.58 0.00 1 6
ashs8 46 1438 3.17 1.45 3.0 3.13 1.48 1 6
ashs9 47 1437 2.73 1.24 3.0 2.63 1.48 1 6
ashs10 48 1437 1.73 1.14 1.0 1.48 0.00 1 6
ashs11 49 1438 4.03 1.37 4.0 4.09 1.48 1 6
ashs12 50 1438 2.44 1.53 2.0 2.23 1.48 1 6
ashs13 51 1437 4.28 1.47 5.0 4.41 1.48 1 6
ashs14 52 1437 4.38 1.26 5.0 4.47 1.48 1 6
ashs15 53 1437 2.69 1.26 2.0 2.57 1.48 1 6
ashs16 54 1436 3.27 1.43 3.0 3.23 1.48 1 6
ashs17 55 1437 3.87 1.37 4.0 3.87 1.48 1 6
attention4 56 1437 1.20 0.87 1.0 1.00 0.00 1 6
ashs18 57 1437 1.75 0.96 2.0 1.58 1.48 1 6
ashs19 58 1437 2.41 1.12 2.0 2.30 1.48 1 6
ashs20 59 1437 1.57 1.02 1.0 1.33 0.00 1 6
ashs21 60 1437 2.59 1.56 2.0 2.42 1.48 1 6
ashs22 61 1437 1.66 0.95 1.0 1.47 0.00 1 6
ashs23 62 1437 1.33 0.84 1.0 1.11 0.00 1 6
ashs24 63 1436 2.44 1.28 2.0 2.29 1.48 1 6
ashs26 64 1437 3.23 1.26 3.0 3.16 1.48 1 6
ashs27 65 1437 3.25 1.65 3.0 3.19 1.48 1 6
ashs28 66 1436 3.75 1.63 4.0 3.80 1.48 1 6
attention5 67 1437 1.10 0.63 1.0 1.00 0.00 1 6
ashs29 68 1437 2.28 1.28 2.0 2.09 1.48 1 6
ashs30 69 1437 3.94 1.38 4.0 3.96 1.48 1 6
ashs31 70 1437 2.61 1.38 2.0 2.47 1.48 1 6
ashs32 71 1437 4.87 1.19 5.0 5.04 1.48 1 6
ashs33 72 1437 4.73 1.32 5.0 4.91 1.48 1 6
id 73 1452 21734.02 7471.43 21089.5 20852.52 2684.99 11918 76858
ESS1m1 74 1447 1.29 0.98 1.0 1.24 1.48 0 3
ESS2m1 75 1447 1.29 0.87 1.0 1.25 1.48 0 3
ESS3m1 76 1447 0.65 0.82 0.0 0.52 0.00 0 3
ESS4m1 77 1447 1.58 1.01 2.0 1.60 1.48 0 3
ESS5m1 78 1447 2.02 0.92 2.0 2.11 1.48 0 3
ESS6m1 79 1447 0.11 0.38 0.0 0.00 0.00 0 3
ESS7m1 80 1447 0.64 0.79 0.0 0.52 0.00 0 3
ESS8m1 81 1447 0.21 0.55 0.0 0.06 0.00 0 3
ess_total 82 1447 7.79 3.58 8.0 7.72 2.97 0 24
attention1r 83 1452 0.82 0.38 1.0 0.91 0.00 0 1
attention2r 84 1448 0.97 0.17 1.0 1.00 0.00 0 1
attention3r 85 1438 1.00 0.00 1.0 1.00 0.00 1 1
attention4r 86 1437 0.93 0.25 1.0 1.00 0.00 0 1
attention5r 87 1437 0.97 0.17 1.0 1.00 0.00 0 1
att_sum 88 1437 4.70 0.63 5.0 4.82 0.00 1 5
range skew kurtosis se
age 8 0.87 0.54 0.04
gender 2 0.83 -0.36 0.01
roommate 1 -0.20 -1.96 0.01
other_sleep 4 1.75 2.87 0.02
bed_read 4 1.61 2.18 0.03
bed_study 4 1.15 0.64 0.03
bed_hw 4 0.92 -0.15 0.03
attention1 4 -2.50 5.33 0.02
bed_internet 4 -0.41 -0.69 0.03
bed_tv 4 -0.29 -1.02 0.03
bed_eat 4 1.22 0.81 0.03
bed_friends 4 1.42 1.72 0.03
bed_videogame 4 1.14 0.40 0.03
bed_readp 4 0.97 0.04 0.03
BL_1 4 -2.15 3.31 0.03
BL_2 4 -1.44 0.76 0.03
BL_3 4 -1.06 -0.17 0.03
BL_4 4 -1.59 0.97 0.04
BL_5 4 0.48 -1.34 0.04
attention2 4 7.11 51.85 0.01
BL_6 4 1.12 0.62 0.03
BL_7 4 -1.47 1.09 0.03
sleepsat1 4 -0.18 -1.09 0.03
sleepsat2 4 -0.20 -1.04 0.03
sleepsat3 4 -0.11 -1.12 0.03
sleepsat4 4 -0.05 -1.24 0.04
sleepsat5 4 -0.78 -0.33 0.03
sleepsat6 4 0.16 -0.94 0.03
ESS1 3 0.27 -0.94 0.03
ESS2 3 0.27 -0.59 0.02
ESS3 3 1.10 0.45 0.02
ESS4 3 0.00 -1.11 0.03
ESS5 3 -0.54 -0.70 0.02
ESS6 3 4.02 19.11 0.01
ESS7 3 1.07 0.54 0.02
ESS8 3 3.04 9.55 0.01
ESS00 3 0.42 -0.49 0.02
ashs1 5 0.81 -0.06 0.03
ashs2 5 0.28 -0.94 0.04
ashs3 5 0.99 0.07 0.04
ashs4 5 1.30 1.41 0.03
ashs5 5 0.64 -0.45 0.04
attention3 0 NaN NaN 0.00
ashs6 5 2.41 4.45 0.04
ashs7 5 1.49 2.08 0.03
ashs8 5 0.30 -0.87 0.04
ashs9 5 0.61 -0.16 0.03
ashs10 5 1.84 3.11 0.03
ashs11 5 -0.33 -0.73 0.04
ashs12 5 0.86 -0.35 0.04
ashs13 5 -0.58 -0.65 0.04
ashs14 5 -0.47 -0.54 0.03
ashs15 5 0.85 0.21 0.03
ashs16 5 0.29 -0.83 0.04
ashs17 5 -0.07 -0.93 0.04
attention4 5 4.77 22.14 0.02
ashs18 5 1.72 3.67 0.03
ashs19 5 0.92 0.80 0.03
ashs20 5 2.09 4.23 0.03
ashs21 5 0.71 -0.64 0.04
ashs22 5 1.83 3.83 0.03
ashs23 5 3.23 11.47 0.02
ashs24 5 0.92 0.38 0.03
ashs26 5 0.44 -0.45 0.03
ashs27 5 0.15 -1.21 0.04
ashs28 5 -0.08 -1.25 0.04
attention5 5 6.63 44.55 0.02
ashs29 5 1.13 0.73 0.03
ashs30 5 -0.13 -0.96 0.04
ashs31 5 0.72 -0.29 0.04
ashs32 5 -1.00 0.44 0.03
ashs33 5 -0.86 -0.17 0.03
id 64940 6.54 44.82 196.07
ESS1m1 3 0.27 -0.94 0.03
ESS2m1 3 0.27 -0.59 0.02
ESS3m1 3 1.10 0.45 0.02
ESS4m1 3 0.00 -1.11 0.03
ESS5m1 3 -0.54 -0.70 0.02
ESS6m1 3 4.02 19.11 0.01
ESS7m1 3 1.07 0.54 0.02
ESS8m1 3 3.04 9.55 0.01
ess_total 24 0.30 0.23 0.09
attention1r 1 -1.70 0.90 0.01
attention2r 1 -5.54 28.66 0.00
attention3r 0 NaN NaN 0.00
attention4r 1 -3.49 10.18 0.01
attention5r 1 -5.38 26.92 0.00
att_sum 4 -2.79 10.04 0.02NOTE: Some variables are not numeric and are categorical variables of type character. By default, the describe() function forces non-numeric variables to be numeric and attempts to calculate descriptives for them. These variables are marked with an asterisk (*). In this case, it doesn’t make sense to calculate descriptive statistics for these variables, so we get a warning message and a bunch of NaN’s and NA’s for these variables.
A better approach would be to remove non-numeric variables before you attempt to run numerical calculations on your dataset.