How to Make Data Continuous R
Continuous Data
2020-12-09
Introduction
This document introduces you to a basic set of functions that describe data continuous data. The other two vignettes introduce you to functions that describe categorical data and visualization options.
Data
We have modified the mtcars data to create a new data set mtcarz. The only difference between the two data sets is related to the variable types.
str(mtcarz) #> 'data.frame': 32 obs. of 11 variables: #> $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... #> $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ... #> $ disp: num 160 160 108 258 360 ... #> $ hp : num 110 110 93 110 175 105 245 62 95 123 ... #> $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... #> $ wt : num 2.62 2.88 2.32 3.21 3.44 ... #> $ qsec: num 16.5 17 18.6 19.4 17 ... #> $ vs : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ... #> $ am : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... #> $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ... #> $ carb: Factor w/ 6 levels "1","2","3","4",..: 4 4 1 1 2 1 4 2 2 4 ... Data Screening
The ds_screener() function will screen a data set and return the following: - Column/Variable Names - Data Type - Levels (in case of categorical data) - Number of missing observations - % of missing observations
ds_screener(mtcarz) #> ----------------------------------------------------------------------- #> | Column Name | Data Type | Levels | Missing | Missing (%) | #> ----------------------------------------------------------------------- #> | mpg | numeric | NA | 0 | 0 | #> | cyl | factor | 4 6 8 | 0 | 0 | #> | disp | numeric | NA | 0 | 0 | #> | hp | numeric | NA | 0 | 0 | #> | drat | numeric | NA | 0 | 0 | #> | wt | numeric | NA | 0 | 0 | #> | qsec | numeric | NA | 0 | 0 | #> | vs | factor | 0 1 | 0 | 0 | #> | am | factor | 0 1 | 0 | 0 | #> | gear | factor | 3 4 5 | 0 | 0 | #> | carb | factor |1 2 3 4 6 8| 0 | 0 | #> ----------------------------------------------------------------------- #> #> Overall Missing Values 0 #> Percentage of Missing Values 0 % #> Rows with Missing Values 0 #> Columns With Missing Values 0 Summary Statistics
The ds_summary_stats function returns a comprehensive set of statistics including measures of location, variation, symmetry and extreme observations.
ds_summary_stats(mtcarz, mpg) #> -------------------------------- Variable: mpg -------------------------------- #> #> Univariate Analysis #> #> N 32.00 Variance 36.32 #> Missing 0.00 Std Deviation 6.03 #> Mean 20.09 Range 23.50 #> Median 19.20 Interquartile Range 7.38 #> Mode 10.40 Uncorrected SS 14042.31 #> Trimmed Mean 19.95 Corrected SS 1126.05 #> Skewness 0.67 Coeff Variation 30.00 #> Kurtosis -0.02 Std Error Mean 1.07 #> #> Quantiles #> #> Quantile Value #> #> Max 33.90 #> 99% 33.44 #> 95% 31.30 #> 90% 30.09 #> Q3 22.80 #> Median 19.20 #> Q1 15.43 #> 10% 14.34 #> 5% 12.00 #> 1% 10.40 #> Min 10.40 #> #> Extreme Values #> #> Low High #> #> Obs Value Obs Value #> 15 10.4 20 33.9 #> 16 10.4 18 32.4 #> 24 13.3 19 30.4 #> 7 14.3 28 30.4 #> 17 14.7 26 27.3 You can pass multiple variables as shown below:
ds_summary_stats(mtcarz, mpg, disp) #> -------------------------------- Variable: mpg -------------------------------- #> #> Univariate Analysis #> #> N 32.00 Variance 36.32 #> Missing 0.00 Std Deviation 6.03 #> Mean 20.09 Range 23.50 #> Median 19.20 Interquartile Range 7.38 #> Mode 10.40 Uncorrected SS 14042.31 #> Trimmed Mean 19.95 Corrected SS 1126.05 #> Skewness 0.67 Coeff Variation 30.00 #> Kurtosis -0.02 Std Error Mean 1.07 #> #> Quantiles #> #> Quantile Value #> #> Max 33.90 #> 99% 33.44 #> 95% 31.30 #> 90% 30.09 #> Q3 22.80 #> Median 19.20 #> Q1 15.43 #> 10% 14.34 #> 5% 12.00 #> 1% 10.40 #> Min 10.40 #> #> Extreme Values #> #> Low High #> #> Obs Value Obs Value #> 15 10.4 20 33.9 #> 16 10.4 18 32.4 #> 24 13.3 19 30.4 #> 7 14.3 28 30.4 #> 17 14.7 26 27.3 #> #> #> #> -------------------------------- Variable: disp -------------------------------- #> #> Univariate Analysis #> #> N 32.00 Variance 15360.80 #> Missing 0.00 Std Deviation 123.94 #> Mean 230.72 Range 400.90 #> Median 196.30 Interquartile Range 205.18 #> Mode 275.80 Uncorrected SS 2179627.47 #> Trimmed Mean 228.00 Corrected SS 476184.79 #> Skewness 0.42 Coeff Variation 53.72 #> Kurtosis -1.07 Std Error Mean 21.91 #> #> Quantiles #> #> Quantile Value #> #> Max 472.00 #> 99% 468.28 #> 95% 449.00 #> 90% 396.00 #> Q3 326.00 #> Median 196.30 #> Q1 120.83 #> 10% 80.61 #> 5% 77.35 #> 1% 72.53 #> Min 71.10 #> #> Extreme Values #> #> Low High #> #> Obs Value Obs Value #> 20 71.1 15 472 #> 19 75.7 16 460 #> 18 78.7 17 440 #> 26 79 25 400 #> 28 95.1 5 360 If you do not specify any variables, it will detect all the continuous variables in the data set and return summary statistics for each of them.
Frequency Distribution
The ds_freq_table function creates frequency tables for continuous variables. The default number of intervals is 5.
ds_freq_table(mtcarz, mpg, 4) #> Variable: mpg #> |---------------------------------------------------------------------------| #> | Bins | Frequency | Cum Frequency | Percent | Cum Percent | #> |---------------------------------------------------------------------------| #> | 10.4 - 16.3 | 10 | 10 | 31.25 | 31.25 | #> |---------------------------------------------------------------------------| #> | 16.3 - 22.1 | 13 | 23 | 40.62 | 71.88 | #> |---------------------------------------------------------------------------| #> | 22.1 - 28 | 5 | 28 | 15.62 | 87.5 | #> |---------------------------------------------------------------------------| #> | 28 - 33.9 | 4 | 32 | 12.5 | 100 | #> |---------------------------------------------------------------------------| #> | Total | 32 | - | 100.00 | - | #> |---------------------------------------------------------------------------| Histogram
A plot() method has been defined which will generate a histogram.
k <- ds_freq_table(mtcarz, mpg, 4) plot(k) 
Auto Summary
If you want to view summary statistics and frequency tables of all or subset of variables in a data set, use ds_auto_summary().
ds_auto_summary_stats(mtcarz, disp, mpg) #> -------------------------------- Variable: disp -------------------------------- #> #> ------------------------------ Summary Statistics ------------------------------ #> #> -------------------------------- Variable: disp -------------------------------- #> #> Univariate Analysis #> #> N 32.00 Variance 15360.80 #> Missing 0.00 Std Deviation 123.94 #> Mean 230.72 Range 400.90 #> Median 196.30 Interquartile Range 205.18 #> Mode 275.80 Uncorrected SS 2179627.47 #> Trimmed Mean 228.00 Corrected SS 476184.79 #> Skewness 0.42 Coeff Variation 53.72 #> Kurtosis -1.07 Std Error Mean 21.91 #> #> Quantiles #> #> Quantile Value #> #> Max 472.00 #> 99% 468.28 #> 95% 449.00 #> 90% 396.00 #> Q3 326.00 #> Median 196.30 #> Q1 120.83 #> 10% 80.61 #> 5% 77.35 #> 1% 72.53 #> Min 71.10 #> #> Extreme Values #> #> Low High #> #> Obs Value Obs Value #> 20 71.1 15 472 #> 19 75.7 16 460 #> 18 78.7 17 440 #> 26 79 25 400 #> 28 95.1 5 360 #> #> #> #> NULL #> #> #> ---------------------------- Frequency Distribution ---------------------------- #> #> Variable: disp #> |---------------------------------------------------------------------------| #> | Bins | Frequency | Cum Frequency | Percent | Cum Percent | #> |---------------------------------------------------------------------------| #> | 71.1 - 151.3 | 12 | 12 | 37.5 | 37.5 | #> |---------------------------------------------------------------------------| #> | 151.3 - 231.5 | 5 | 17 | 15.62 | 53.12 | #> |---------------------------------------------------------------------------| #> | 231.5 - 311.6 | 6 | 23 | 18.75 | 71.88 | #> |---------------------------------------------------------------------------| #> | 311.6 - 391.8 | 5 | 28 | 15.62 | 87.5 | #> |---------------------------------------------------------------------------| #> | 391.8 - 472 | 4 | 32 | 12.5 | 100 | #> |---------------------------------------------------------------------------| #> | Total | 32 | - | 100.00 | - | #> |---------------------------------------------------------------------------| #> #> #> -------------------------------- Variable: mpg -------------------------------- #> #> ------------------------------ Summary Statistics ------------------------------ #> #> -------------------------------- Variable: mpg -------------------------------- #> #> Univariate Analysis #> #> N 32.00 Variance 36.32 #> Missing 0.00 Std Deviation 6.03 #> Mean 20.09 Range 23.50 #> Median 19.20 Interquartile Range 7.38 #> Mode 10.40 Uncorrected SS 14042.31 #> Trimmed Mean 19.95 Corrected SS 1126.05 #> Skewness 0.67 Coeff Variation 30.00 #> Kurtosis -0.02 Std Error Mean 1.07 #> #> Quantiles #> #> Quantile Value #> #> Max 33.90 #> 99% 33.44 #> 95% 31.30 #> 90% 30.09 #> Q3 22.80 #> Median 19.20 #> Q1 15.43 #> 10% 14.34 #> 5% 12.00 #> 1% 10.40 #> Min 10.40 #> #> Extreme Values #> #> Low High #> #> Obs Value Obs Value #> 15 10.4 20 33.9 #> 16 10.4 18 32.4 #> 24 13.3 19 30.4 #> 7 14.3 28 30.4 #> 17 14.7 26 27.3 #> #> #> #> NULL #> #> #> ---------------------------- Frequency Distribution ---------------------------- #> #> Variable: mpg #> |-----------------------------------------------------------------------| #> | Bins | Frequency | Cum Frequency | Percent | Cum Percent | #> |-----------------------------------------------------------------------| #> | 10.4 - 15.1 | 6 | 6 | 18.75 | 18.75 | #> |-----------------------------------------------------------------------| #> | 15.1 - 19.8 | 12 | 18 | 37.5 | 56.25 | #> |-----------------------------------------------------------------------| #> | 19.8 - 24.5 | 8 | 26 | 25 | 81.25 | #> |-----------------------------------------------------------------------| #> | 24.5 - 29.2 | 2 | 28 | 6.25 | 87.5 | #> |-----------------------------------------------------------------------| #> | 29.2 - 33.9 | 4 | 32 | 12.5 | 100 | #> |-----------------------------------------------------------------------| #> | Total | 32 | - | 100.00 | - | #> |-----------------------------------------------------------------------| Group Summary
The ds_group_summary() function returns descriptive statistics of a continuous variable for the different levels of a categorical variable.
k <- ds_group_summary(mtcarz, cyl, mpg) k #> mpg by cyl #> ----------------------------------------------------------------------------------------- #> | Statistic/Levels| 4| 6| 8| #> ----------------------------------------------------------------------------------------- #> | Obs| 11| 7| 14| #> | Minimum| 21.4| 17.8| 10.4| #> | Maximum| 33.9| 21.4| 19.2| #> | Mean| 26.66| 19.74| 15.1| #> | Median| 26| 19.7| 15.2| #> | Mode| 22.8| 21| 10.4| #> | Std. Deviation| 4.51| 1.45| 2.56| #> | Variance| 20.34| 2.11| 6.55| #> | Skewness| 0.35| -0.26| -0.46| #> | Kurtosis| -1.43| -1.83| 0.33| #> | Uncorrected SS| 8023.83| 2741.14| 3277.34| #> | Corrected SS| 203.39| 12.68| 85.2| #> | Coeff Variation| 16.91| 7.36| 16.95| #> | Std. Error Mean| 1.36| 0.55| 0.68| #> | Range| 12.5| 3.6| 8.8| #> | Interquartile Range| 7.6| 2.35| 1.85| #> ----------------------------------------------------------------------------------------- ds_group_summary() returns a tibble which can be used for further analysis.
k$tidy_stats #> # A tibble: 3 x 15 #> cyl length min max mean median mode sd variance skewness kurtosis #> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 4 11 21.4 33.9 26.7 26 22.8 4.51 20.3 0.348 -1.43 #> 2 6 7 17.8 21.4 19.7 19.7 21 1.45 2.11 -0.259 -1.83 #> 3 8 14 10.4 19.2 15.1 15.2 10.4 2.56 6.55 -0.456 0.330 #> # ... with 4 more variables: coeff_var <dbl>, std_error <dbl>, range <dbl>, #> # iqr <dbl> Box Plot
A plot() method has been defined for comparing distributions.
k <- ds_group_summary(mtcarz, cyl, mpg) plot(k) 
Multiple Variables
If you want grouped summary statistics for multiple variables in a data set, use ds_auto_group_summary().
ds_auto_group_summary(mtcarz, cyl, gear, mpg) #> mpg by cyl #> ----------------------------------------------------------------------------------------- #> | Statistic/Levels| 4| 6| 8| #> ----------------------------------------------------------------------------------------- #> | Obs| 11| 7| 14| #> | Minimum| 21.4| 17.8| 10.4| #> | Maximum| 33.9| 21.4| 19.2| #> | Mean| 26.66| 19.74| 15.1| #> | Median| 26| 19.7| 15.2| #> | Mode| 22.8| 21| 10.4| #> | Std. Deviation| 4.51| 1.45| 2.56| #> | Variance| 20.34| 2.11| 6.55| #> | Skewness| 0.35| -0.26| -0.46| #> | Kurtosis| -1.43| -1.83| 0.33| #> | Uncorrected SS| 8023.83| 2741.14| 3277.34| #> | Corrected SS| 203.39| 12.68| 85.2| #> | Coeff Variation| 16.91| 7.36| 16.95| #> | Std. Error Mean| 1.36| 0.55| 0.68| #> | Range| 12.5| 3.6| 8.8| #> | Interquartile Range| 7.6| 2.35| 1.85| #> ----------------------------------------------------------------------------------------- #> #> #> #> mpg by gear #> ----------------------------------------------------------------------------------------- #> | Statistic/Levels| 3| 4| 5| #> ----------------------------------------------------------------------------------------- #> | Obs| 15| 12| 5| #> | Minimum| 10.4| 17.8| 15| #> | Maximum| 21.5| 33.9| 30.4| #> | Mean| 16.11| 24.53| 21.38| #> | Median| 15.5| 22.8| 19.7| #> | Mode| 10.4| 21| 15| #> | Std. Deviation| 3.37| 5.28| 6.66| #> | Variance| 11.37| 27.84| 44.34| #> | Skewness| -0.09| 0.7| 0.56| #> | Kurtosis| -0.38| -0.77| -1.83| #> | Uncorrected SS| 4050.52| 7528.9| 2462.89| #> | Corrected SS| 159.15| 306.29| 177.37| #> | Coeff Variation| 20.93| 21.51| 31.15| #> | Std. Error Mean| 0.87| 1.52| 2.98| #> | Range| 11.1| 16.1| 15.4| #> | Interquartile Range| 3.9| 7.08| 10.2| #> ----------------------------------------------------------------------------------------- Combination of Categories
To look at the descriptive statistics of a continuous variable for different combinations of levels of two or more categorical variables, use ds_group_summary_interact().
ds_group_summary_interact(mtcarz, mpg, cyl, gear) #> # A tibble: 8 x 17 #> cyl gear min max mean t_mean median mode range variance stdev #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 4 3 21.5 21.5 21.5 21.5 21.5 21.5 0 NA NA #> 2 6 3 18.1 21.4 19.8 19.8 19.8 18.1 3.30 5.44 2.33 #> 3 8 3 10.4 19.2 15.0 15.0 15.2 10.4 8.80 7.70 2.77 #> 4 4 4 21.4 33.9 26.9 26.9 25.8 22.8 12.5 23.1 4.81 #> 5 6 4 17.8 21 19.8 19.8 20.1 21 3.20 2.41 1.55 #> 6 4 5 26 30.4 28.2 28.2 28.2 26 4.40 9.68 3.11 #> 7 6 5 19.7 19.7 19.7 19.7 19.7 19.7 0 NA NA #> 8 8 5 15 15.8 15.4 15.4 15.4 15 0.8 0.32 0.566 #> # ... with 6 more variables: skew <dbl>, kurtosis <dbl>, coeff_var <dbl>, #> # q1 <dbl>, q3 <dbl>, iqrange <dbl> Multiple Variable Statistics
The ds_tidy_stats() function returns summary/descriptive statistics for variables in a data frame/tibble.
ds_tidy_stats(mtcarz, mpg, disp, hp) #> # A tibble: 3 x 16 #> vars min max mean t_mean median mode range variance stdev skew #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 disp 71.1 472 231. 228 196. 276. 401. 15361. 124. 0.420 #> 2 hp 52 335 147. 144. 123 110 283 4701. 68.6 0.799 #> 3 mpg 10.4 33.9 20.1 20.0 19.2 10.4 23.5 36.3 6.03 0.672 #> # ... with 5 more variables: kurtosis <dbl>, coeff_var <dbl>, q1 <dbl>, #> # q3 <dbl>, iqrange <dbl> Measures
If you want to view the measure of location, variation, symmetry, percentiles and extreme observations as tibbles, use the below functions. All of them, except for ds_extreme_obs() will work with single or multiple variables. If you do not specify the variables, they will return the results for all the continuous variables in the data set.
Measures of Location
ds_measures_location(mtcarz) #> # A tibble: 6 x 5 #> var mean trim_mean median mode #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 disp 231. 228 196. 276. #> 2 drat 3.60 3.58 3.70 3.07 #> 3 hp 147. 144. 123 110 #> 4 mpg 20.1 20.0 19.2 10.4 #> 5 qsec 17.8 17.8 17.7 17.0 #> 6 wt 3.22 3.20 3.32 3.44 Measures of Variation
ds_measures_variation(mtcarz) #> # A tibble: 6 x 7 #> var range iqr variance sd coeff_var std_error #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 disp 401. 205. 15361. 124. 53.7 21.9 #> 2 drat 2.17 0.840 0.286 0.535 14.9 0.0945 #> 3 hp 283 83.5 4701. 68.6 46.7 12.1 #> 4 mpg 23.5 7.38 36.3 6.03 30.0 1.07 #> 5 qsec 8.40 2.01 3.19 1.79 10.0 0.316 #> 6 wt 3.91 1.03 0.957 0.978 30.4 0.173 Measures of Symmetry
ds_measures_symmetry(mtcarz) #> # A tibble: 6 x 3 #> var skewness kurtosis #> <chr> <dbl> <dbl> #> 1 disp 0.420 -1.07 #> 2 drat 0.293 -0.450 #> 3 hp 0.799 0.275 #> 4 mpg 0.672 -0.0220 #> 5 qsec 0.406 0.865 #> 6 wt 0.466 0.417 Percentiles
ds_percentiles(mtcarz) #> # A tibble: 6 x 12 #> var min per1 per5 per10 q1 median q3 per95 per90 per99 max #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 disp 71.1 72.5 77.4 80.6 121. 196. 326 449 396. 468. 472 #> 2 drat 2.76 2.76 2.85 3.01 3.08 3.70 3.92 4.31 4.21 4.78 4.93 #> 3 hp 52 55.1 63.6 66 96.5 123 180 254. 244. 313. 335 #> 4 mpg 10.4 10.4 12.0 14.3 15.4 19.2 22.8 31.3 30.1 33.4 33.9 #> 5 qsec 14.5 14.5 15.0 15.5 16.9 17.7 18.9 20.1 20.0 22.1 22.9 #> 6 wt 1.51 1.54 1.74 1.96 2.58 3.32 3.61 5.29 4.05 5.40 5.42 Extreme Observations
ds_extreme_obs(mtcarz, mpg) #> # A tibble: 10 x 3 #> type value index #> <chr> <dbl> <int> #> 1 high 33.9 20 #> 2 high 32.4 18 #> 3 high 30.4 19 #> 4 high 30.4 28 #> 5 high 27.3 26 #> 6 low 10.4 15 #> 7 low 10.4 16 #> 8 low 13.3 24 #> 9 low 14.3 7 #> 10 low 14.7 17 Source: https://cran.r-project.org/web/packages/descriptr/vignettes/continuous-data.html
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