stat.test <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Isobutylene ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting… Isob… Scent… No Sc… 8 13 27 0.0731 0.219 ns
## 2 Office Isob… Scent… No Sc… 6 10 4.5 0.00519 0.0156 *
## 3 Washroom Isob… Scent… No Sc… 8 11 22.5 0.0777 0.233 ns
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Isobutylene ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 8 1.300000 0.9739463 0.4 0.500
## 2 Office Scent-free policy 16 6 0.750000 0.2738613 0.5 0.500
## 3 Washroom Scent-free policy 16 8 2.125000 2.4164614 0.5 0.875
## 4 Meeting Room No Scent-free policy 16 13 5.538462 11.6537933 0.5 1.000
## 5 Office No Scent-free policy 16 10 6.050000 11.1491156 1.0 1.250
## 6 Washroom No Scent-free policy 16 11 5.954546 11.4115173 1.0 1.500
## median Q3 max
## 1 1.00 1.750 3.0
## 2 0.75 1.000 1.0
## 3 1.00 2.125 6.5
## 4 2.00 4.000 44.0
## 5 2.50 3.875 37.5
## 6 2.00 3.750 40.0
p <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Isobutylene",
fill = "Policy", palette = "grey",
# fill = "Policy", palette = "jco",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test, label = "p", tip.length = 0.01, y.position = 6.5), ylim = c(0, 7), title = "a) 1-Propene, 2-methyl- (Isobutylene) Cas 115-11-7", ylab = "Mean Isobutylene", xlab = "", legend.title = element_blank())
p
## Ignoring unknown labels:
## • colour : <object>
## • linetype : <object>
## • shape : <object>
## Warning: `label` cannot be a <ggplot2::element_blank> object.
# 2. Acetone
stat.test1 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Acetone ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test1
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meetin… Acet… Scent… No Sc… 16 16 76.5 0.0545 0.164 ns
## 2 Office Acet… Scent… No Sc… 15 16 46.5 0.00388 0.0116 *
## 3 Washro… Acet… Scent… No Sc… 16 16 41 0.00111 0.00333 **
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Acetone ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 16 19.06250 15.83969 1.5 11.875
## 2 Office Scent-free policy 16 15 18.93333 13.81623 5.0 10.250
## 3 Washroom Scent-free policy 16 16 16.65625 11.03588 5.0 8.750
## 4 Meeting Room No Scent-free policy 16 16 69.31250 90.49767 4.5 14.000
## 5 Office No Scent-free policy 16 16 79.31250 88.22204 11.0 18.750
## 6 Washroom No Scent-free policy 16 16 64.40625 68.27011 11.0 20.625
## median Q3 max
## 1 15.25 21.125 71.5
## 2 11.50 24.500 52.0
## 3 14.75 21.625 45.5
## 4 29.25 82.500 300.0
## 5 41.00 88.000 290.0
## 6 36.75 83.875 275.0
# 2. Create the grouped bar plot
p1 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Acetone",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
# 3. Add p-values
stat_pvalue_manual(stat.test1, label = "p", tip.length = 0.01, y.position = 82), ylim = c(0, 90), title = "b) Acetone Cas 67-64-1", ylab = "Mean Acetone", xlab = "", legend.title = element_blank())
p1
## Ignoring unknown labels:
## • colour : <object>
## • linetype : <object>
## • shape : <object>
## Warning: `label` cannot be a <ggplot2::element_blank> object.
# 3. Butane
stat.test2 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Butane ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test2
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meetin… Buta… Scent… No Sc… 15 16 39 1.38e-3 0.00414 **
## 2 Office Buta… Scent… No Sc… 13 16 32 1.64e-3 0.00492 **
## 3 Washro… Buta… Scent… No Sc… 16 16 38.5 7.58e-4 0.00227 **
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Butane ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 15 2.766667 3.755314 0.5 0.500
## 2 Office Scent-free policy 16 13 2.653846 3.799038 0.5 1.000
## 3 Washroom Scent-free policy 16 16 2.718750 3.628447 0.5 0.500
## 4 Meeting Room No Scent-free policy 16 16 41.937500 101.348717 1.5 3.000
## 5 Office No Scent-free policy 16 16 33.656250 79.996608 1.0 3.875
## 6 Washroom No Scent-free policy 16 16 36.625000 86.850350 2.0 3.875
## median Q3 max
## 1 1.00 2.750 14.0
## 2 1.50 2.000 14.5
## 3 1.00 2.750 14.0
## 4 5.00 8.375 370.0
## 5 5.75 8.875 305.0
## 6 5.75 13.750 345.0
p2 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Butane",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test2, label = "p.adj", tip.length = 0.01, y.position = 43), ylim = c(0, 45), title = "c) Butane Cas 106-97-8", ylab = "Mean Butane", xlab = "") + theme(legend.position = "none")
p2
# 4. Butane2Meth
stat.test3 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Butane2Meth ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test3
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting Ro… Buta… Scent… No Sc… 15 15 73 0.103 0.309 ns
## 2 Office Buta… Scent… No Sc… 15 14 71 0.142 0.426 ns
## 3 Washroom Buta… Scent… No Sc… 16 15 61 0.02 0.06 ns
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Butane2Meth ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 15 3.566667 3.250641 0.5 1.250
## 2 Office Scent-free policy 16 15 3.666667 2.919556 1.0 1.250
## 3 Washroom Scent-free policy 16 16 3.437500 3.506541 1.0 1.375
## 4 Meeting Room No Scent-free policy 16 15 38.266667 98.268740 1.0 2.000
## 5 Office No Scent-free policy 16 14 37.750000 99.123806 1.0 2.625
## 6 Washroom No Scent-free policy 16 15 35.833333 98.217628 1.5 2.500
## median Q3 max
## 1 2.5 5.000 12
## 2 2.5 5.500 11
## 3 2.0 4.125 14
## 4 3.5 12.250 380
## 5 4.0 7.625 375
## 6 4.0 8.750 385
p3 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Butane2Meth",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test3, label = "p", tip.length = 0.01, y.position = 40), ylim = c(0, 45), title = "d) Butane,2-methyl Cas 78-78-4", ylab = "Mean Butane,2-methyl", xlab = "") + theme(legend.position = "none")
p3
# 5. Hexane2Methyl
stat.test4 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Hexane2Methyl ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test4
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting … Hexa… Scent… No Sc… 6 9 13.5 0.12 0.36 ns
## 2 Office Hexa… Scent… No Sc… 6 9 14 0.136 0.408 ns
## 3 Washroom Hexa… Scent… No Sc… 8 9 14 0.0333 0.0999 ns
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Hexane2Methyl ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 6 1.583333 1.319722 0.5 0.625
## 2 Office Scent-free policy 16 6 2.000000 2.509980 0.5 0.625
## 3 Washroom Scent-free policy 16 8 12.687500 33.867323 0.5 0.500
## 4 Meeting Room No Scent-free policy 16 9 14.722222 33.967918 1.0 1.000
## 5 Office No Scent-free policy 16 9 12.111111 27.237127 1.0 1.500
## 6 Washroom No Scent-free policy 16 9 13.944444 33.273905 0.5 1.500
## median Q3 max
## 1 1.25 1.875 4.0
## 2 1.00 1.750 7.0
## 3 0.50 1.125 96.5
## 4 2.00 7.500 105.0
## 5 2.00 5.500 84.5
## 6 2.00 5.000 102.5
p4 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Hexane2Methyl",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test4, label = "p", tip.length = 0.01, y.position = 15), ylim = c(0, 17), title = "e) Hexane,2-methyl Cas 591-76-4", ylab = "Mean Hexane,2-methyl", xlab = "") + theme(legend.position = "none")
p4
# 6. Pentane
stat.test5 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Pentane ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test5
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting… Pent… Scent… No Sc… 13 16 66 0.0992 0.298 ns
## 2 Office Pent… Scent… No Sc… 11 15 45.5 0.0579 0.174 ns
## 3 Washroom Pent… Scent… No Sc… 11 15 32 0.00935 0.0280 *
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Pentane ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 13 5.115385 4.416606 1.0 1.500
## 2 Office Scent-free policy 16 11 5.772727 5.031718 1.0 1.500
## 3 Washroom Scent-free policy 16 11 4.681818 4.423080 1.0 1.500
## 4 Meeting Room No Scent-free policy 16 16 37.937500 84.005134 1.0 3.375
## 5 Office No Scent-free policy 16 15 37.933333 79.440873 1.5 5.750
## 6 Washroom No Scent-free policy 16 15 39.933333 88.046024 2.0 3.750
## median Q3 max
## 1 4.50 7.500 15.5
## 2 3.50 8.750 16.0
## 3 2.50 7.000 14.5
## 4 6.25 31.625 340.0
## 5 8.50 22.750 305.0
## 6 8.00 20.250 340.0
p5 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Pentane",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test5, label = "p", tip.length = 0.01, y.position = 41), ylim = c(0, 42), title = "f) Pentane Cas 109-66-0", ylab = "Mean Pentane", xlab = "") + theme(legend.position = "none")
p5
# 7. Trichlorofluromethane
stat.test6 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Trichlorofluromethane ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test6
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting R… Tric… Scent… No Sc… 10 11 42.5 0.364 1 ns
## 2 Office Tric… Scent… No Sc… 9 11 36 0.275 0.825 ns
## 3 Washroom Tric… Scent… No Sc… 10 10 24.5 0.0375 0.112 ns
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Trichlorofluromethane ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 10 1.0500000 0.4377975 0.5 0.625
## 2 Office Scent-free policy 16 9 0.8888889 0.3333333 0.5 0.500
## 3 Washroom Scent-free policy 16 10 1.0500000 0.8959787 0.5 0.500
## 4 Meeting Room No Scent-free policy 16 11 1.7727273 1.6787441 1.0 1.000
## 5 Office No Scent-free policy 16 11 1.7727273 1.7939292 0.5 1.000
## 6 Washroom No Scent-free policy 16 10 1.6500000 0.9442810 1.0 1.000
## median Q3 max
## 1 1 1.5 1.5
## 2 1 1.0 1.5
## 3 1 1.0 3.5
## 4 1 1.5 6.5
## 5 1 1.5 6.0
## 6 1 2.0 3.5
p6 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Trichlorofluromethane",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test6, label = "p", tip.length = 0.01, y.position = 1.8), ylim = c(0, 2), title = "g) Trichlorofluoro-methane (CFC-11) Cas 75-69-4", ylab = "Mean Trichlorofluoro-methane", xlab = "") + theme(legend.position = "none")
p6
# 8. Ethylbenzene
stat.test7 <- ASEQ_data %>%
group_by(Site) %>%
wilcox_test(Ethylbenzene ~ Policy) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance() %>%
add_xy_position(x = "Site", dodge = 0.8) # Ensure dodge matches the bar plot
stat.test7
## # A tibble: 3 × 15
## Site .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 Meeting … Ethy… Scent… No Sc… 4 3 4 0.593 1 ns
## 2 Office Ethy… Scent… No Sc… 5 3 0 0.0325 0.0975 ns
## 3 Washroom Ethy… Scent… No Sc… 4 2 3 0.806 1 ns
## # ℹ 5 more variables: y.position <dbl>, groups <named list>, x <dbl>,
## # xmin <dbl>, xmax <dbl>
Summarize(Ethylbenzene ~ Site + Policy, ASEQ_data)
## Site Policy n nvalid mean sd min Q1
## 1 Meeting Room Scent-free policy 16 4 1.37500 0.4787136 1.0 1.00
## 2 Office Scent-free policy 16 5 1.00000 0.3535534 0.5 1.00
## 3 Washroom Scent-free policy 16 4 1.75000 0.9574271 1.0 1.00
## 4 Meeting Room No Scent-free policy 16 3 19.66667 31.0577419 0.5 1.75
## 5 Office No Scent-free policy 16 3 17.50000 25.9855729 2.0 2.50
## 6 Washroom No Scent-free policy 16 2 25.50000 34.6482323 1.0 13.25
## median Q3 max
## 1 1.25 1.625 2.0
## 2 1.00 1.000 1.5
## 3 1.50 2.250 3.0
## 4 3.00 29.250 55.5
## 5 3.00 25.250 47.5
## 6 25.50 37.750 50.0
p7 <- ggpar(ggbarplot(
ASEQ_data, x = "Site", y = "Ethylbenzene",
fill = "Policy", palette = "grey",
# add = "median",
add = "mean",
position = position_dodge(0.8)
) +
stat_pvalue_manual(stat.test7, label = "p", tip.length = 0.01, y.position = 26), ylim = c(0, 30), title = "h) Ethylbenzene*ab Cas 100-41-4", ylab = "Mean Ethylbenzene", xlab = "") + theme(legend.position = "none")
p7
#ggarrange(p, p1, p2, p3, p4, p5, p6, p7,
# ncol = 2, nrow = 4)
#ggsave("VOCs.jpg", width = 35, height = 40, units = c("cm"), dpi = 500)
