Dargantes Group
Dargantes, et.al.
2022-10-20
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
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intersect, setdiff, setequal, union── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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# A tibble: 45 × 19
Weeds Concentr…¹ T0 T1 T2 T3 T4 T5 T6
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Weed1 0.5% T 3.33e-5 1.77e-2 7.91e-2 1.61e-1 1.72e-1 1.81e-1 0.185
2 Weed1 0.3% T -3.33e-5 9.76e-2 2.21e-1 2.10e-1 1.96e-1 1.75e-1 0.157
3 Weed1 0.1% T 0 1.07e-3 6.3 e-3 7.00e-3 9.13e-3 1.06e-2 0.0112
4 Weed1 Enro 0 3.33e-5 4.33e-4 -5.67e-4 -6.00e-4 -8.33e-4 -0.00107
5 Weed1 5% DMSO 0 8.90e-3 2.44e-2 2.82e-2 6.39e-2 6.69e-2 0.113
6 Weed2 0.5% T 3.33e-5 2.00e-2 7.55e-2 1.58e-1 1.81e-1 1.91e-1 0.194
7 Weed2 0.3% T -3.33e-5 5.00e-4 2.30e-2 7.32e-2 1.67e-1 1.81e-1 0.189
8 Weed2 0.1% T -3.33e-5 5.14e-2 7.52e-2 7.32e-2 6.85e-2 4.98e-2 0.00703
9 Weed2 Enro 0 3.33e-5 4.33e-4 -5.67e-4 -6.00e-4 -8.33e-4 -0.00107
10 Weed2 5% DMSO 0 8.90e-3 2.44e-2 2.82e-2 6.39e-2 6.69e-2 0.113
# … with 35 more rows, 10 more variables: T7 <dbl>, T8 <dbl>, T9 <dbl>,
# T10 <dbl>, T11 <dbl>, T12 <dbl>, T13 <dbl>, T14 <dbl>, T15 <dbl>,
# T16 <dbl>, and abbreviated variable name ¹Concentrations
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable namestibble [45 × 19] (S3: tbl_df/tbl/data.frame)
$ Weeds : chr [1:45] "Weed1" "Weed1" "Weed1" "Weed1" ...
$ Concentrations: chr [1:45] "0.5% T" "0.3% T" "0.1% T" "Enro" ...
$ T0 : num [1:45] 3.33e-05 -3.33e-05 0.00 0.00 0.00 ...
$ T1 : num [1:45] 1.77e-02 9.76e-02 1.07e-03 3.33e-05 8.90e-03 ...
$ T2 : num [1:45] 0.079067 0.221 0.0063 0.000433 0.0244 ...
$ T3 : num [1:45] 0.161433 0.209933 0.007 -0.000567 0.02825 ...
$ T4 : num [1:45] 0.17247 0.1963 0.00913 -0.0006 0.06385 ...
$ T5 : num [1:45] 0.180567 0.175 0.010567 -0.000833 0.0669 ...
$ T6 : num [1:45] 0.18477 0.15697 0.01117 -0.00107 0.11285 ...
$ T7 : num [1:45] 0.1877 0.1468 0.019 -0.0012 0.153 ...
$ T8 : num [1:45] 0.1986 0.1363 0.0192 -0.0012 0.1774 ...
$ T9 : num [1:45] 0.201 0.12873 0.00843 -0.0014 0.1847 ...
$ T10 : num [1:45] 0.1836 0.11707 0.00783 -0.00153 0.1978 ...
$ T11 : num [1:45] 0.1525 0.1054 0.00697 -0.00163 0.21125 ...
$ T12 : num [1:45] 0.1199 0.1047 0.0065 -0.00173 0.21145 ...
$ T13 : num [1:45] 0.10057 0.09963 0.00617 -0.0017 0.2341 ...
$ T14 : num [1:45] 0.08703 0.09693 0.00587 -0.00183 0.2398 ...
$ T15 : num [1:45] 0.0765 0.0958 0.0056 -0.0019 0.2631 ...
$ T16 : num [1:45] 0.0561 0.0924 0.00383 -0.002 0.26145 ...Some Summary Statistics for the different types of Weeds
# A tibble: 9 × 5
Weeds variable n mean sd
<fct> <chr> <dbl> <dbl> <dbl>
1 Weed1 BacCon 85 0.081 0.085
2 Weed2 BacCon 85 0.079 0.107
3 Weed3 BacCon 85 0.088 0.091
4 Weed4 BacCon 85 0.076 0.084
5 Weed5 BacCon 85 0.054 0.102
6 Weed6 BacCon 85 0.102 0.099
7 Weed7 BacCon 85 0.103 0.091
8 Weed8 BacCon 85 0.096 0.1
9 Weed9 BacCon 85 0.06 0.073Some Summary Statistics for the different levels of Concentrations
# A tibble: 5 × 5
Concentrations variable n mean sd
<fct> <chr> <dbl> <dbl> <dbl>
1 0.1% T BacCon 153 0.016 0.073
2 0.3% T BacCon 153 0.111 0.072
3 0.5% T BacCon 153 0.142 0.077
4 5% DMSO BacCon 153 0.143 0.091
5 Enro BacCon 153 -0.001 0.001Some Summary Statistics for the different levels of Concentrations and types of Weeds
Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
Please use `tibble::as_tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.# A tibble: 45 × 6
Weeds Concentrations variable n mean sd
<fct> <fct> <chr> <dbl> <dbl> <dbl>
1 Weed1 0.1% T BacCon 17 0.008 0.005
2 Weed1 0.3% T BacCon 17 0.128 0.054
3 Weed1 0.5% T BacCon 17 0.127 0.065
4 Weed1 5% DMSO BacCon 17 0.143 0.094
5 Weed1 Enro BacCon 17 -0.001 0.001
6 Weed2 0.1% T BacCon 17 -0.038 0.076
7 Weed2 0.3% T BacCon 17 0.147 0.072
8 Weed2 0.5% T BacCon 17 0.145 0.067
9 Weed2 5% DMSO BacCon 17 0.143 0.094
10 Weed2 Enro BacCon 17 -0.001 0.001
11 Weed3 0.1% T BacCon 17 0.043 0.073
12 Weed3 0.3% T BacCon 17 0.088 0.063
13 Weed3 0.5% T BacCon 17 0.167 0.072
14 Weed3 5% DMSO BacCon 17 0.143 0.094
15 Weed3 Enro BacCon 17 -0.001 0.001
16 Weed4 0.1% T BacCon 17 0.025 0.039
17 Weed4 0.3% T BacCon 17 0.086 0.064
18 Weed4 0.5% T BacCon 17 0.124 0.08
19 Weed4 5% DMSO BacCon 17 0.143 0.094
20 Weed4 Enro BacCon 17 -0.001 0.001
21 Weed5 0.1% T BacCon 17 -0.043 0.106
22 Weed5 0.3% T BacCon 17 0.033 0.036
23 Weed5 0.5% T BacCon 17 0.139 0.061
24 Weed5 5% DMSO BacCon 17 0.143 0.094
25 Weed5 Enro BacCon 17 -0.001 0.001
26 Weed6 0.1% T BacCon 17 0.058 0.077
27 Weed6 0.3% T BacCon 17 0.126 0.082
28 Weed6 0.5% T BacCon 17 0.186 0.081
29 Weed6 5% DMSO BacCon 17 0.143 0.094
30 Weed6 Enro BacCon 17 -0.001 0.001
31 Weed7 0.1% T BacCon 17 0.065 0.087
32 Weed7 0.3% T BacCon 17 0.163 0.061
33 Weed7 0.5% T BacCon 17 0.145 0.056
34 Weed7 5% DMSO BacCon 17 0.143 0.094
35 Weed7 Enro BacCon 17 -0.001 0.001
36 Weed8 0.1% T BacCon 17 0.021 0.028
37 Weed8 0.3% T BacCon 17 0.149 0.064
38 Weed8 0.5% T BacCon 17 0.167 0.109
39 Weed8 5% DMSO BacCon 17 0.143 0.094
40 Weed8 Enro BacCon 17 -0.001 0.001
41 Weed9 0.1% T BacCon 17 0.005 0.003
42 Weed9 0.3% T BacCon 17 0.081 0.052
43 Weed9 0.5% T BacCon 17 0.073 0.03
44 Weed9 5% DMSO BacCon 17 0.143 0.094
45 Weed9 Enro BacCon 17 -0.001 0.001#Visualization
Weeds
Boxplot
Concentrations
Boxplot
#Analysis
ANOVA Table (type III tests)
Effect DFn DFd F p p<.05 ges
1 Weeds 1.92 30.64 13.794 6.52e-05 * 0.060
2 Concentrations 1.55 24.74 27.895 1.95e-06 * 0.484
3 Weeds:Concentrations 32.00 512.00 9.831 5.03e-36 * 0.107It shows that Weeds, Concentrations and the interaction between Weeds and Concentrations are statistically significant.
#Effect of Weeds at each concentration
# A tibble: 5 × 9
Concentrations Effect DFn DFd F p `p<.05` ges p.adj
<fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
1 0.1% T Weeds 2.02 32.4 11.3 0.000177 "*" 0.242 0.000885
2 0.3% T Weeds 1.45 23.2 11.4 0.000962 "*" 0.301 0.00481
3 0.5% T Weeds 2.44 39.0 13.6 0.000011 "*" 0.163 0.000055
4 5% DMSO Weeds 8 128 0 1 "" 0 1
5 Enro Weeds 8 128 0 1 "" 0 1 Considering the Bonferroni adjusted p-value (p.adj), it can be seen that the simple main effect of weeds was not significant at the concentrations 5%DMSO and Enro. It becomes significant at 0.1% T (p = 0.0.000177), 0.3% T (p=0.000962), and 0.5% (p = 0.0.000011).
Pairwise comparisons among types of weeds
# A tibble: 36 × 10
.y. group1 group2 n1 n2 statistic df p p.adj p.adj…¹
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
1 BacCon Weed1 Weed2 85 85 0.312 84 7.56e-1 1 e+0 ns
2 BacCon Weed1 Weed3 85 85 -1.12 84 2.66e-1 1 e+0 ns
3 BacCon Weed1 Weed4 85 85 1.57 84 1.2 e-1 1 e+0 ns
4 BacCon Weed1 Weed5 85 85 3.90 84 1.92e-4 7 e-3 **
5 BacCon Weed1 Weed6 85 85 -2.83 84 6 e-3 2.09e-1 ns
6 BacCon Weed1 Weed7 85 85 -3.78 84 2.88e-4 1 e-2 *
7 BacCon Weed1 Weed8 85 85 -3.57 84 6 e-4 2.2 e-2 *
8 BacCon Weed1 Weed9 85 85 5.40 84 6.07e-7 2.19e-5 ****
9 BacCon Weed2 Weed3 85 85 -1.04 84 3.01e-1 1 e+0 ns
10 BacCon Weed2 Weed4 85 85 0.516 84 6.07e-1 1 e+0 ns
11 BacCon Weed2 Weed5 85 85 3.71 84 3.72e-4 1.3 e-2 *
12 BacCon Weed2 Weed6 85 85 -3.64 84 4.66e-4 1.7 e-2 *
13 BacCon Weed2 Weed7 85 85 -4.36 84 3.61e-5 1 e-3 **
14 BacCon Weed2 Weed8 85 85 -3.17 84 2 e-3 7.7 e-2 ns
15 BacCon Weed2 Weed9 85 85 2.56 84 1.2 e-2 4.43e-1 ns
16 BacCon Weed3 Weed4 85 85 2.35 84 2.1 e-2 7.52e-1 ns
17 BacCon Weed3 Weed5 85 85 5.45 84 4.99e-7 1.8 e-5 ****
18 BacCon Weed3 Weed6 85 85 -1.57 84 1.19e-1 1 e+0 ns
19 BacCon Weed3 Weed7 85 85 -1.88 84 6.4 e-2 1 e+0 ns
20 BacCon Weed3 Weed8 85 85 -0.998 84 3.21e-1 1 e+0 ns
21 BacCon Weed3 Weed9 85 85 3.84 84 2.38e-4 9 e-3 **
22 BacCon Weed4 Weed5 85 85 3.84 84 2.41e-4 9 e-3 **
23 BacCon Weed4 Weed6 85 85 -3.46 84 8.51e-4 3.1 e-2 *
24 BacCon Weed4 Weed7 85 85 -4.70 84 9.93e-6 3.57e-4 ***
25 BacCon Weed4 Weed8 85 85 -4.23 84 5.87e-5 2 e-3 **
26 BacCon Weed4 Weed9 85 85 3.49 84 7.82e-4 2.8 e-2 *
27 BacCon Weed5 Weed6 85 85 -5.81 84 1.08e-7 3.89e-6 ****
28 BacCon Weed5 Weed7 85 85 -6.39 84 8.81e-9 3.17e-7 ****
29 BacCon Weed5 Weed8 85 85 -5.33 84 8.21e-7 2.96e-5 ****
30 BacCon Weed5 Weed9 85 85 -0.827 84 4.1 e-1 1 e+0 ns
31 BacCon Weed6 Weed7 85 85 -0.129 84 8.98e-1 1 e+0 ns
32 BacCon Weed6 Weed8 85 85 1.04 84 3.01e-1 1 e+0 ns
33 BacCon Weed6 Weed9 85 85 5.29 84 9.49e-7 3.42e-5 ****
34 BacCon Weed7 Weed8 85 85 1.27 84 2.07e-1 1 e+0 ns
35 BacCon Weed7 Weed9 85 85 6.50 84 5.46e-9 1.97e-7 ****
36 BacCon Weed8 Weed9 85 85 5.50 84 4.09e-7 1.47e-5 ****
# … with abbreviated variable name ¹p.adj.signifPairwise comparisons show that the mean bacterial concentration was significantly different between Weeds 1 and 5, Weeds 1 and 9, Weeds 2 and 5, Weeds 2 and 6, Weeds 2 and 7, Weeds 3 and 5, Weeds 3 and 9, Weeds 4 and 5, Weeds 4 and 6, Weeds 4 and 7, Weeds 4and 8, Weeds 4 and 9, Weeds 5 and 6, Weeds 5 and 7, Weeds 5 and 8, Weeds 6 and 9, Weeds 7 and 9, and Weeds 8 and 9.