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 × 20
id Weeds Concentrati…¹ T0 T1 T2 T3 T4 T5
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Weed1 0.5% T 0 3.73e-2 6.4 e-2 8.92e-2 9.14e-2 9.79e-2
2 2 Weed1 0.3% T -3.33e-5 9.40e-3 3.17e-2 5.33e-2 7.62e-2 1.22e-1
3 3 Weed1 0.1% T 0 5.80e-3 9.73e-3 2.04e-2 3.53e-2 8.02e-2
4 4 Weed1 Enro 0 3.33e-5 4.33e-4 -5.67e-4 -6.00e-4 -8.33e-4
5 5 Weed1 5% DMSO 0 8.90e-3 2.44e-2 2.82e-2 6.39e-2 6.69e-2
6 1 Weed2 0.5% T 3.33e-5 5.2 e-2 9.38e-2 1.25e-1 1.22e-1 1.20e-1
7 2 Weed2 0.3% T 3.33e-5 3.50e-3 4.93e-3 5.83e-3 1.14e-2 1.43e-2
8 3 Weed2 0.1% T 0 2.24e-2 4.82e-2 7.45e-2 1.05e-1 1.90e-1
9 4 Weed2 Enro 0 3.33e-5 4.33e-4 -5.67e-4 -6.00e-4 -8.33e-4
10 5 Weed2 5% DMSO 0 8.90e-3 2.44e-2 2.82e-2 6.39e-2 6.69e-2
# … with 35 more rows, 11 more variables: T6 <dbl>, 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 × 20] (S3: tbl_df/tbl/data.frame)
$ id : num [1:45] 1 2 3 4 5 1 2 3 4 5 ...
$ 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] 0.00 -3.33e-05 0.00 0.00 0.00 ...
$ T1 : num [1:45] 3.73e-02 9.40e-03 5.80e-03 3.33e-05 8.90e-03 ...
$ T2 : num [1:45] 0.064 0.0317 0.009733 0.000433 0.0244 ...
$ T3 : num [1:45] 0.0892 0.053267 0.020433 -0.000567 0.02825 ...
$ T4 : num [1:45] 0.0914 0.0762 0.0353 -0.0006 0.0639 ...
$ T5 : num [1:45] 0.097867 0.122467 0.080233 -0.000833 0.0669 ...
$ T6 : num [1:45] 0.09787 0.17257 0.17773 -0.00107 0.11285 ...
$ T7 : num [1:45] 0.0813 0.2139 0.2711 -0.0012 0.153 ...
$ T8 : num [1:45] 0.0716 0.2448 0.3258 -0.0012 0.1774 ...
$ T9 : num [1:45] 0.0633 0.268 0.3507 -0.0014 0.1847 ...
$ T10 : num [1:45] 0.0575 0.28137 0.37357 -0.00153 0.1978 ...
$ T11 : num [1:45] 0.0488 0.29613 0.3986 -0.00163 0.21125 ...
$ T12 : num [1:45] 0.0247 0.29947 0.42617 -0.00173 0.21145 ...
$ T13 : num [1:45] 0.00577 0.35597 0.4481 -0.0017 0.2341 ...
$ T14 : num [1:45] -0.00303 0.37493 0.45113 -0.00183 0.2398 ...
$ T15 : num [1:45] -0.0115 0.368 0.4104 -0.0019 0.2631 ...
$ T16 : num [1:45] -0.0141 0.3678 0.3841 -0.002 0.2614 ...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 0.129 0.143
2 Weed2 BacCon 85 0.102 0.113
3 Weed3 BacCon 85 0.079 0.186
4 Weed4 BacCon 85 0.103 0.188
5 Weed5 BacCon 85 0.106 0.129
6 Weed6 BacCon 85 0.087 0.103
7 Weed7 BacCon 85 0.117 0.096
8 Weed8 BacCon 85 0.108 0.218
9 Weed9 BacCon 85 0.145 0.139Some 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 0.256 0.166
2 0.3% T BacCon 153 0.1 0.164
3 0.5% T BacCon 153 0.044 0.118
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 0.245 0.18
2 Weed1 0.3% T BacCon 17 0.208 0.135
3 Weed1 0.5% T BacCon 17 0.047 0.04
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 0.235 0.133
7 Weed2 0.3% T BacCon 17 0.073 0.055
8 Weed2 0.5% T BacCon 17 0.062 0.052
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 0.311 0.197
12 Weed3 0.3% T BacCon 17 -0.133 0.125
13 Weed3 0.5% T BacCon 17 0.072 0.044
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 0.296 0.202
17 Weed4 0.3% T BacCon 17 0.187 0.113
18 Weed4 0.5% T BacCon 17 -0.109 0.123
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 0.252 0.182
22 Weed5 0.3% T BacCon 17 0.028 0.026
23 Weed5 0.5% T BacCon 17 0.108 0.045
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 0.195 0.077
27 Weed6 0.3% T BacCon 17 -0.014 0.024
28 Weed6 0.5% T BacCon 17 0.113 0.067
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 0.201 0.084
32 Weed7 0.3% T BacCon 17 0.104 0.069
33 Weed7 0.5% T BacCon 17 0.137 0.066
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 0.32 0.22
37 Weed8 0.3% T BacCon 17 0.221 0.198
38 Weed8 0.5% T BacCon 17 -0.142 0.107
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 0.244 0.139
42 Weed9 0.3% T BacCon 17 0.231 0.169
43 Weed9 0.5% T BacCon 17 0.108 0.05
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 1.38 22.08 15.320 2.63e-04 * 0.034
2 Concentrations 1.11 17.71 34.702 9.87e-06 * 0.432
3 Weeds:Concentrations 32.00 512.00 23.915 1.78e-81 * 0.307It 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 1.42 22.7 7.57 0.006 * 0.066 0.018
2 0.3% T Weeds 1.2 19.2 23.6 0.0000533 * 0.517 0.000160
3 0.5% T Weeds 1.17 18.6 36.2 0.0000047 * 0.649 0.0000141
4 5% DMSO Weeds 8 128 NaN NaN <NA> 0 NaN
5 Enro Weeds 8 128 NaN NaN <NA> 0 NaN 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 = 0.018), 0.3% T (p=0.0001599), and 0.5% (p = 0.0000141).
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 3.28 84 0.001 0.054 ns
2 BacCon Weed1 Weed3 85 85 2.46 84 0.016 0.576 ns
3 BacCon Weed1 Weed4 85 85 2.65 84 0.01 0.345 ns
4 BacCon Weed1 Weed5 85 85 1.89 84 0.062 1 ns
5 BacCon Weed1 Weed6 85 85 2.71 84 0.008 0.297 ns
6 BacCon Weed1 Weed7 85 85 1.06 84 0.293 1 ns
7 BacCon Weed1 Weed8 85 85 1.69 84 0.094 1 ns
8 BacCon Weed1 Weed9 85 85 -3.62 84 0.000504 0.018 *
9 BacCon Weed2 Weed3 85 85 1.73 84 0.088 1 ns
10 BacCon Weed2 Weed4 85 85 -0.0584 84 0.954 1 ns
11 BacCon Weed2 Weed5 85 85 -0.504 84 0.615 1 ns
12 BacCon Weed2 Weed6 85 85 1.72 84 0.09 1 ns
13 BacCon Weed2 Weed7 85 85 -2.07 84 0.042 1 ns
14 BacCon Weed2 Weed8 85 85 -0.363 84 0.718 1 ns
15 BacCon Weed2 Weed9 85 85 -4.72 84 0.00000936 0.000337 ***
16 BacCon Weed3 Weed4 85 85 -1.14 84 0.258 1 ns
17 BacCon Weed3 Weed5 85 85 -2.64 84 0.01 0.356 ns
18 BacCon Weed3 Weed6 85 85 -0.701 84 0.485 1 ns
19 BacCon Weed3 Weed7 85 85 -2.38 84 0.02 0.709 ns
20 BacCon Weed3 Weed8 85 85 -1.16 84 0.249 1 ns
21 BacCon Weed3 Weed9 85 85 -3.03 84 0.003 0.116 ns
22 BacCon Weed4 Weed5 85 85 -0.174 84 0.862 1 ns
23 BacCon Weed4 Weed6 85 85 0.783 84 0.436 1 ns
24 BacCon Weed4 Weed7 85 85 -0.782 84 0.436 1 ns
25 BacCon Weed4 Weed8 85 85 -0.769 84 0.444 1 ns
26 BacCon Weed4 Weed9 85 85 -3.26 84 0.002 0.059 ns
27 BacCon Weed5 Weed6 85 85 2.47 84 0.016 0.565 ns
28 BacCon Weed5 Weed7 85 85 -1.33 84 0.186 1 ns
29 BacCon Weed5 Weed8 85 85 -0.115 84 0.909 1 ns
30 BacCon Weed5 Weed9 85 85 -3.02 84 0.003 0.122 ns
31 BacCon Weed6 Weed7 85 85 -4.65 84 0.000012 0.000432 ***
32 BacCon Weed6 Weed8 85 85 -0.887 84 0.378 1 ns
33 BacCon Weed6 Weed9 85 85 -3.87 84 0.000218 0.008 **
34 BacCon Weed7 Weed8 85 85 0.409 84 0.684 1 ns
35 BacCon Weed7 Weed9 85 85 -2.81 84 0.006 0.222 ns
36 BacCon Weed8 Weed9 85 85 -2.57 84 0.012 0.428 ns
# … with abbreviated variable name ¹p.adj.signifPairwise comparisons show that the mean bacterial concentration was significantly different between Weeds 1 and 9, Weeds 2 and 9, Weeds 6 and 7, and Weeds 6 and 9.