https://github.com/yunzhu0304/tinystatr
The tinystatr is an R package for statistical analysis. It automatically assesses the normality and homogeneity of variance of input data, selects appropriate statistical methods based on the characteristics of the data, and returns the statistical analysis results.
Our statistical analysis process is based on the following two workflows: two groups, and more than two groups.
🍒For two groups:
- Normality test and homogeneity of variance test.
- Both datasets are normally distributed and variance equal, will apply the t-test.
- Both datasets are normally distributed but the variance is unequal, will apply the Welch’s t-test.
- One of the datasets is non-normally distributed, and the sample size in a single group is more than 30, the choice of statistical method will depend on their variances. Equal to the Welch’s t-test, unequal to the Mann-Whitney test(Wilcoxon test).
- One of the datasets is non-normally distributed but the sample size in a single group is less than 30 or both datasets are non-normally distributed, will apply the Wilcoxon test.
🍇For more than two groups:
- Normality test and homogeneity of variance test.
- One of the datasets is non-normally distributed, will apply the Kruskal-Wallis test and Dunn’s test (Bonferroni test) as the post-hoc test.
- All of the datasets are normally distributed, the choice of statistical method will depend on their variances. Equal to the ANOVA test (post-hoc: Tukey HSD), unequal to the Kruskal-Wallis test (post-hoc: Dunn’s test).
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("yunzhu0304/tinystatr")library(tinystatr)data("ToothGrowth")
data("HairEyeColor")df <- ToothGrowth%>%
filter(dose %in% c("0.5","1"))
> df
len supp dose
1 4.2 VC 0.5
2 11.5 VC 0.5
3 7.3 VC 0.5
4 5.8 VC 0.5
5 6.4 VC 0.5
6 10.0 VC 0.5
After executing the function(stat2()), the results will include assessments of normality, homogeneity of variance, and the selected statistical method.
# Dataframe with multiple columns, need to filter variable
> result <- stat2(data = df,variable = "supp",id="VC",group = "dose",value = "len",
formula = len ~ dose)
Normally distributed
Variance equal
t-test
p = 6.81e-07We also obtain a list named stat2result, which contains two data frames. One is stat, used to store the statistical result. The other is normal, used to store the results of normality, mean and sd value.
We also obtain an S4 object of class statresult, which includes:
- stat: A data frame containing the statistical test results.
- normal: A data frame with normality test results for each group.
- p_position: A data frame indicating the position of p-values for visualization purposes.
> result@stat
group1 group2 n1 n2 p variable method
1 0.5 1 10 10 6.81e-07 VC t test
> result@normal
group variable normal meanvalue sd
1 0.5 VC TRUE 7.98 2.746634
2 1.0 VC TRUE 16.77 2.515309
> result@p_position
group1 group2 y.position
1 0.5 1 30.576If the datasets only contain information on groups and values, we will ignore the variable and id.
# Dataframe with only two columns (group,value)
data("HairEyeColor")
df <- as.data.frame(HairEyeColor)[,c(3,4)]
> result <- stat2(data = df,group = "Sex",value = "Freq", formula = Freq ~ Sex) # Ignoring variable and id
Non-normally distributed
wilcoxon test
p = 0.88
> result@stat
group1 group2 n1 n2 p variable method
1 Male Female 16 16 0.88 id Wilcoxon test
> result@normal
group variable normal meanvalue sd
1 Male id FALSE 17.4375 16.00820
2 Female id FALSE 19.5625 20.71382
> result@p_position
group1 group2 y.position
1 Male Female 73.92
# Dataframe with multiple columns, need to filter variable
data("ToothGrowth")
df <- ToothGrowth
> df
len supp dose
1 4.2 VC 0.5
2 11.5 VC 0.5
3 7.3 VC 0.5
...
11 16.5 VC 1.0
12 16.5 VC 1.0
13 15.2 VC 1.0
...
21 23.6 VC 2.0
22 18.5 VC 2.0
23 33.9 VC 2.0
...
31 15.2 OJ 0.5
32 21.5 OJ 0.5
33 17.6 OJ 0.5
...After executing the function(stat3()), we will obtain the statistical result and an S4 object.
> result <- stat3(data = df, group = "dose", value = "len", variable = "supp", id = "OJ", formula = len ~ dose)
All groups have 3 or more samples.
Normally distributed
Variance equal
Anova
> result@stat
group2 group1 p.adj posthoc variable p1 P1method p.adj.signif
1 1 0.5 1.584138e-05 hsd OJ 8.887164e-08 ANOVA ****
2 2 0.5 9.386773e-08 hsd OJ 8.887164e-08 ANOVA ****
3 2 1 1.309258e-01 hsd OJ 8.887164e-08 ANOVA ns
> result@normal
group variable normal meanvalue sd
1 0.5 OJ TRUE 13.23 4.459709
2 1.0 OJ TRUE 22.70 3.910953
3 2.0 OJ TRUE 26.06 2.655058
> result@normal
group variable normal meanvalue sd
1 0.5 OJ TRUE 13.23 4.459709
2 1.0 OJ TRUE 22.70 3.910953
3 2.0 OJ TRUE 26.06 2.655058If the datasets only contain information on groups and values, we will ignore the variable and id.
# Dataframe with only two columns (group,value)
data("HairEyeColor")
df <- as.data.frame(HairEyeColor)[,c(2,4)]
> result <- stat3(data = df,group = "Eye",value = "Freq", formula = Freq ~ Eye) # Ignoring variable and id
All groups have 3 or more samples.
Non-normally distributed
Variance unequal
Kruskal-Wallis
> result@stat
group1 group2 p.adj posthoc variable p1 P1method p.adj.signif
1 Brown Blue 1.0000000 bonferroni id 0.0637 K_W ns
2 Brown Hazel 0.6709306 bonferroni id 0.0637 K_W ns
3 Brown Green 0.3683564 bonferroni id 0.0637 K_W ns
4 Blue Hazel 0.3466669 bonferroni id 0.0637 K_W ns
5 Blue Green 0.1764459 bonferroni id 0.0637 K_W ns
6 Hazel Green 1.0000000 bonferroni id 0.0637 K_W ns
> result@normal
group variable normal meanvalue sd
1 Brown id TRUE 27.500 23.348295
2 Blue id TRUE 26.875 21.463840
3 Hazel id FALSE 11.625 9.694439
4 Green id TRUE 8.000 4.598136
> result@p_position
group1 group2 y.position
1 Brown Blue 73.92000
2 Brown Hazel 79.83360
3 Brown Green 86.22029
4 Blue Hazel 93.11791
5 Blue Green 100.56734
6 Hazel Green 108.61273- Comparing Means in R
- Learning Statistics with R
- [HOW CAN I DO POST-HOC PAIRWISE COMPARISONS IN R? | R FAQ]