This package convert a raw data into descriptive statistics and separation of means using Tukey HSD for now
devtools::install_github("https://github.com/emperorDuke/fastevalR.git")
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R 4.1.0 >
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tidyverse
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lodaR :: installation link is below
devtools::install_github("https://github.com/emperorDuke/lodaR.git")
library(fastevalR)
data <- data.frame(
month = rep(month.abb[1:4], 4),
gender = rep(c('M', 'F'), each = 8),
location = rep(letters[1:4], 4),
age = c(rnorm(8, mean = 66.4), rnorm(8, mean = 60.4)),
height = c(rnorm(8, mean = 5.4), rnorm(8, mean = 6.4))
)
# print(data)
#
# month gender location age height
# 1 Jan M a 67.96101 7.347027
# 2 Feb M b 66.41855 4.457138
# 3 Mar M c 65.14273 5.749648
# 4 Apr M d 66.09282 6.555883
# 5 Jan M a 66.84869 4.698016
# 6 Feb M b 67.30455 6.060497
# 7 Mar M c 65.64137 4.235999
# 8 Apr M d 66.20104 3.847365
# 9 Jan F a 59.28777 6.721247
# 10 Feb F b 61.68628 6.961674
# 11 Mar F c 63.08159 6.541809
# 12 Apr F d 59.61394 5.800709
# 13 Jan F a 62.22129 6.591437
# 14 Feb F b 60.90156 4.613485
# 15 Mar F c 62.19581 5.459384
# 16 Apr F d 61.25008 5.806265
obj <- new(
'Separator',
data = data,
x = "month",
grouping_vars = "gender",
factor_vars = "location"
)
result <- obj$display_table()
# print(result)
#
# gender month age height
# 1 F Apr 60.65 ± 0.13a 6.04 ± 1.23a
# 2 F Feb 60.60 ± 0.13a 4.33 ± 0.28a
# 3 F Jan 61.28 ± 1.11a 5.98 ± 0.98a
# 4 F Mar 61.08 ± 0.05a 6.24 ± 1.13a
# 5 M Apr 66.67 ± 0.54a 5.19 ± 0.78a
# 6 M Feb 66.59 ± 0.85a 5.01 ± 0.59a
# 7 M Jan 67.80 ± 0.05a 5.14 ± 1.06a
# 8 M Mar 66.69 ± 0.13a 6.29 ± 0.73a
splitted_results <- obj$separate()
# print(splitted_results)
#
# $age
# # A tibble: 8 × 7
# gender month age letters mean s.e y.pt
# <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# 1 F Apr 60.65 ± 0.13 a 60.6 0.13 60.8
# 2 F Feb 60.60 ± 0.13 a 60.6 0.13 60.7
# 3 F Jan 61.28 ± 1.11 a 61.3 1.11 62.4
# 4 F Mar 61.08 ± 0.05 a 61.1 0.05 61.1
# 5 M Apr 66.67 ± 0.54 a 66.7 0.54 67.2
# 6 M Feb 66.59 ± 0.85 a 66.6 0.85 67.4
# 7 M Jan 67.80 ± 0.05 a 67.8 0.05 67.8
# 8 M Mar 66.69 ± 0.13 a 66.7 0.13 66.8
#
# $height
# # A tibble: 8 × 7
# gender month height letters mean s.e y.pt
# <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# 1 F Apr 6.04 ± 1.23 a 6.04 1.23 7.27
# 2 F Feb 4.33 ± 0.28 a 4.33 0.28 4.61
# 3 F Jan 5.98 ± 0.98 a 5.98 0.98 6.96
# 4 F Mar 6.24 ± 1.13 a 6.24 1.13 7.37
# 5 M Apr 5.19 ± 0.78 a 5.19 0.78 5.97
# 6 M Feb 5.01 ± 0.59 a 5.01 0.59 5.6
# 7 M Jan 5.14 ± 1.06 a 5.14 1.06 6.2
# 8 M Mar 6.29 ± 0.73 a 6.29 0.73 7.02
# print(obj$table_summary()) a list
#
# $F
# month age height
# 1 Apr 60.65 ± 0.13 a 6.04 ± 1.23 a
# 2 Feb 60.60 ± 0.13 a 4.33 ± 0.28 a
# 3 Jan 61.28 ± 1.11 a 5.98 ± 0.98 a
# 4 Mar 61.08 ± 0.05 a 6.24 ± 1.13 a
# 5 ... ... ...
# 6 p-value 0.79 0.55
#
# $M
# month age height
# 1 Apr 66.67 ± 0.54 a 5.19 ± 0.78 a
# 2 Feb 66.59 ± 0.85 a 5.01 ± 0.59 a
# 3 Jan 67.80 ± 0.05 a 5.14 ± 1.06 a
# 4 Mar 66.69 ± 0.13 a 6.29 ± 0.73 a
# 5 ... ... ...
# 6 p-value 0.40 0.68
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