Enhancement of the Command-Line Environment for use in the Introductory Statistics Course and Beyond

Figures & data

Fig. 1 The pivot() function call to generate a data frame of aggregated data.

Table

1	ACCT	W	3	0	4.667	3.0
2	ADMN	W	4	0	7.500	5.0
3	FINC	W	1	0	7.000	7.0
4	MKTG	W	5	0	8.200	8.0
5	SALE	W	5	0	6.600	8.0
6	ACCT	M	2	0	7.000	7.0
7	ADMN	M	2	0	15.500	15.5
8	FINC	M	3	0	11.333	10.0
9	MKTG	M	1	0	18.000	18.0
10	SALE	M	9	1	12.333	13.0

Table

Years_n	Years_na	Years_mean	Years_sd	Years_sk	Years_IQR	p0	p25	p50	p75	p100
36	1	9.389	5.724	0.67	8	1	5	9	13	24

Table

Gender	Dept_n	Dept_na	ACCT	ADMN	FINC	MKTG	SALE
1	W	18	1	3	4	1	5	5
2	M	18	0	2	2	3	1	10

Table

Gender Dept	W	M
–––-	––––-	––––-
ACCT	63237.16	59626.20
ADMN	81434.00	80963.34
FINC	57139.90	72967.60
MKTG	64496.02	99062.66
SALE	64188.25	86150.97

Fig. 2 Default (left) and 100% stacked (right) bar charts.

Fig. 3 Default histogram (left) and with a superimposed density plot (right).

Fig. 4 Scatterplots of two continuous variables, with the least-squares line and 95% confidence intervals (left) and a more enhanced version (right).

Fig. 5 Trellis boxplots (left) and superimposed Trellis violin, box, and scatterplots (right).

Fig. 6 Data visualization and text output for t-test of the mean difference.

Fig. 7 Data visualizations for t-test of paired differences, dependent groups.

Fig. 8 A scatterplot of factor and response variable from the one-way ANOVA (left) and interaction plot of two factors from the two-way ANOVA (right).

Fig. 9 Default scatterplot matrix (left) and density plots of the distribution of residuals (right) from a regression analysis.

Fig. 10 Fit the same data with residuals about the least-squares line (left) and the null-model line (right) in the respective scatterplots.

Fig. 11 Scatterplots of two continuous variables at two levels of a third, categorical variable, on the same panel (left) and a Trellis plot (right).

Fig. 12 Interactive Shiny histogram with Histogram() parameter values.

Table 1 lessR simulation functions to facilitate teaching statistical concepts.

Function Call	Output
simFlips()	Simulate coin flips sampled from a binomial distribution and plot the running mean across trials
simMeans()	Simulate sample means over repeated sampling from a normal distribution and plot the sorted means
simCLT()	Simulate the central limit theorem with repeated samples from normal, lognormal, uniform, and inverted normal distributions
simCImean()	Simulate repeated sampling from the same normal distribution and plot the confidence intervals

Fig. 13 Fifty confidence intervals of the mean across repeated simulated samples.

Table 2 lessR probability functions that generate a customized, corresponding visualization in lieu of standard probability tables.

Function Call	Output
prob_norm()	Plot normal curve probability over a specified interval
prob_tcut()	Plot a t-distribution curve against the corresponding
	normal curve with specified tail cut-offs
prob_znorm()	Plot a normal curve with shaded intervals by
	standard deviation

Fig. 14 Normal curve probability for the standardized normal (left) and for a normal distribution with a mean of 50 and standard deviation of 10 (right).

Fig. 15 For the” orange” color theme with a” black” sub_theme, the default bar chart (left) and a time series Trellis visualization (right).

Fig. 16 lessR vignettes.