2022-06-09
This is an example of fungicide sensitivity test in vitro for Sclerotinia sclerotiorum by discriminatory concentrations approach WMNCSRP_fungicide_sensitivity_invitro:
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Serial dilution method with 6 concentrations for each fungicide from 22 baseline isolates (never exposed to fungicides) + 22 from betwenn farmer fields and fungicide field trials
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EC50 estimated from dose-response curve by ezec-package R (Kamvar, 2016)
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Identify a single concentration that can be a discriminatory concentration
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Use DC to assess sensitivity of large collection
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Data analyses using reproducible methods
The 23 own created functions were compelled in a package FastconcR in https://github.com/EdgarNietoFungi/FastconcR
Knowledge of baseline sensitivity of a pathogen population to fungicides is important for detection of resistance and for achieving effective disease management strategies (Brent and Holloman 2007). QoI, DMI, MBC and SDHI fungicides are used to control S. sclerotiorum in an IPM approach in soybean and dry bean in the U.S. DMI fungicides are considered to medium risk of resistance emergence, whereas QoI, SDHI and MBC are high risk (Fungicide Resistance Action Committee 2016). MBC and dicarboximide fungicides are used for white mold in Brazil, wherein resistance to MBC fungicide, Thiophanate methyl, has been reported (Lehner et al. 2015). Fungicide resistance has also been reported in China to the MBC, Carbendazim and dicarboximide, Dimethachlon (Ma et al. 2009).
| Fungicide | Efficacy of Control | Launched | |
|---|---|---|---|
| Thiophanate methyl (MBC) | fair control | 1970 | |
| Tetraconazole (DMI) | fair control | 1988 | |
| Picoxystrobin (QoI) | good-very good control | 2000 | |
| Boscalid (SDHI) | very good control | 2003 |
Examples of these include: Topsin, Domark, Aproach & Endura. Several other products are mixtures
MBC: Methyl Benzimidazole Carbamates DMI: DeMethylation Inhibitors QoI: Quinone outside Inhibitors SDHI: Succinate dehydrogenase inhibitors
Field applications of fungicides in the United States on soybean show control of S. sclerotiorum is low to fair for QoI, DMI and MBC fungicides, and good for SDHI fungicides (The North Central Regional Committee on Soybean Diseases 2015). However, it is unknown whether efficacy is related to reduce sensitivity within populations under study.
- Isolates S. sclerotiorum collected from producer fields and fungicide research trials, which already have been in contact with MBC (FRAC group 1), DMI (FRAC group 3), SdhI (FRAC group 7), and QoI (FRAC group 11) fungicides will have less sensitivity (except FRAC group 3) compared to the baseline, because these fungicides have a high risk of resistance and resistance to mostly these groups have been reported previously in S. sclerotiorum;
- Isolates S. sclerotiorum collected from dry bean will have lower sensitivity compared to the isolates from soybean for the three group fungicides (except FRAC group 3), which have fewer fungicide applications;
- Isolates S. sclerotiorum from Brazil and Mexico will have less sensitivity compared to the USA isolates for the three group fungicides (except FRAC group 3) because of different populations and management practices used in tropical and sub-tropical environment.
baseline.isolates <- c(1, 8, 12, 20, 21, 74, 87, 118, 123, 129, 449, 461, 467, 475, 558, 564, 568, 581, 645, 667, 800)
survey.isolates <- c( 2385, 2386, 2098, 2099, 2100, 2139, 2140, 2143, 2220, 2222, 2223, 2320, 2362, 2388, 2390 )
Farmer.fields <- c(136:442, 455, 456, 466, 468, 470, 471, 478:554, 602:610, 612:635, 671, 672, 682:690, 695:797, 810:823, 834, 835, 848:1024, 1255: 1326, 1491: 1500, 1661:1670, 1831: 2246, 2303: 2342,2362: 2381, 2393: 2573)
baseline.isolates.2 <- c(1: 136, 444: 454, 457: 465, 467, 469, 472:477, 555:601, 611, 636:670, 673: 680, 691:694 , 798: 809, 824: 833, 836: 847)
drybean <- c(1, 5, 12,13:118, 123:128, 133:135, 145, 146, 152, 155:160, 182: 185, 194:200, 205, 220, 223, 248, 253:255, 274, 279, 280, 290, 294, 304: 309, 323, 358, 393, 395, 396, 397, 400:402, 405, 408, 409, 434, 443:465, 467, 469: 493, 495:505, 555:613, 615: 760, 762: 819, 824:833, 835:855, 858:921, 966: 971, 980, 981, 985: 996, 998: 1009, 1220:1225 , 1857: 1940, 2442: 2569)
soybean <- c(143, 147, 181, 187, 188, 189,202, 257: 259, 264:268, 276, 281, 289, 293, 295, 310 , 399, 412:417, 419, 425:427, 439, 440, 494, 506:554, 834, 972:979, 982, 997, 1010: 1022, 1025:1219, 1229: 1856, 1941:2441 )
SI.Production.Field <- c( 698:741, 743:744, 746:760, 762: 778,
786: 797, 810:819, 848: 855, 858: 914)
SI.Screening.Nursery.Field <- c(444:454, 457:465, 467, 469, 472:477, 555: 582, 584: 601, 611, 636:670, 673:681,691:694, 798:809, 824: 833 , 836:847)# also taking into account WM Monitor
mexican.isolates <- c(242, 248, 615:632, 779, 1857: 1940)
brazilian.isolates <- c(400:402, 972:979, 1010:1022, 1227:1254, 2417:2569)
whitemold.inventory <- read_csv("data/whitemold.inventory.csv") %>% mutate(Field_Year= paste (Field, Year,sep = "_year_"))## New names:
## Rows: 2326 Columns: 40
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (30): Host, State, County, Country, Field, Form.ID, Serial_agar_dilution... dbl
## (8): ...1, ID, Year, lat, long, Applications, Rate, Number.of.Years.1 lgl (2):
## company_current_season, company_previous_seasons
## â„ą Use `spec()` to retrieve the full column specification for this data. â„ą
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
for (variable in unique(whitemold.inventory$State)) {
assign(variable, whitemold.inventory %>% filter (State == variable) %>% pull(ID), envir = .GlobalEnv)
}
for (variable in unique(whitemold.inventory$Field_Year)) {
assign(variable, whitemold.inventory %>% filter (Field_Year == variable)%>% pull(ID), envir = .GlobalEnv)
}
IDA <- ID
#MAKE THE SAME BY COUNTY
for (variable in unique(whitemold.inventory$County)) {
assign(variable, whitemold.inventory %>% filter (County == variable)%>% pull(ID), envir = .GlobalEnv)
}#Get range function
get_range <- function(mynumber) {
bb <- boxplot.stats(mynumber)
cc <- bb$stats
dd <- max(cc)
ee <- min(cc)
return(data.frame(upper = dd, lower = ee))
}
##Function.Reading data for serial.dilution
reading_data_serial_dilution <- function(filename){
data <-
read.csv(filename)
# data <- subset(dareading_data("data/Benomyl.Colletotrichum.csv")ta, select = -X)
data$repeats <- rep_len(1:4, length.out = nrow(data))
# renaming ID for the current names
data$ID[ data$ID =="12B"] <- "12"
#Although 74SS1 belong to # 74, the one what I used was specifically this:74SS1
data$ID[ data$ID =="74SS1"] <- "74"
data$ID[ data$ID =="62-02"] <- "2098"
data$ID[ data$ID =="62-03"] <- "2099"
data$ID[ data$ID =="62-04"] <- "2100"
data$ID[ data$ID =="78-01"] <- "2139"
data$ID[ data$ID =="78-02"] <- "2140"
data$ID[ data$ID =="78-05"] <- "2143"
data$ID[ data$ID =="S-01"] <- "2320"
data$ID[ data$ID =="H-01"] <- "2220"
data$ID[ data$ID =="H-03"] <- "2222"
data$ID[ data$ID =="H-04"] <- "2223"
data$ID[ data$ID =="I-20"] <- "2362"
data$ID[ data$ID =="419"] <- "2385"
data$ID[ data$ID =="413"] <- "2386"
data$ID[ data$ID =="W212"] <- "2388"
data$ID[ data$ID =="318"] <- "2390"
data$ID[data$ID == "54C"] <- "2453"
data$ID[data$ID == "65B"] <- "2528"
data$ID[data$ID == "51C"] <- "2449"
data$ID[data$ID == "71B"] <- "2505"
data$ID[data$ID == "64D"] <- "2518"
data$ID[data$ID == "53B"] <- "2536"
data$ID[data$ID == "60A"] <- "2559"
data$ID[data$ID == "H-01"] <- "2220"
data$ID[data$ID == "H-03"] <- "2222"
data$ID[data$ID == "419"] <- "2385"
data$ID[data$ID == "78-02"] <- "2140"
data$ID <- as.numeric(data$ID)
data <- data %>%
mutate(polar= replace(polar, polar == 0, 0.6)) %>% #replacing 0 cm growth for the size of plug that is 0.6
mutate(ecuatorial= replace(ecuatorial, ecuatorial == 0, 0.6)) %>% #replacing 0 cm growth for the size of plug that is 0.6
# group_by(ID, experimental_replicate, dose, ecuatorial, polar, repeats) %>%
mutate(growth = ((ecuatorial + polar) / 2)) %>%
group_by(ID, dose) %>%
mutate(growth_range = list(get_range(growth))) %>%
unnest(cols = c(growth_range)) %>%
filter(growth <= upper & growth >= lower) %>%
dplyr::rename(response = growth) %>%
ungroup() %>%
select(c(ID, experimental_replicate, repeats, dose, response))
}
##Function.Reading data for Discriminatory concentration
reading_data_discriminatory_concentration <- function(filename) {
data <- filename
# data <- subset(data, select = -X)
#data$repeats <- rep_len(1:3, length.out = nrow(data))
# renaming ID for the current names
data$ID[data$ID == "12B"] <- "12"
#Although 74SS1 belong to # 74, the one what I used was specifically this:74SS1
data$ID[data$ID == "74SS1"] <- "74"
data$ID[data$ID == "62-02"] <- "2098"
data$ID[data$ID == "62-03"] <- "2099"
data$ID[data$ID == "62-04"] <- "2100"
data$ID[data$ID == "78-01"] <- "2139"
data$ID[data$ID == "78-02"] <- "2140"
data$ID[data$ID == "78-05"] <- "2143"
data$ID[data$ID == "S-01"] <- "2320"
data$ID[data$ID == "H-01"] <- "2220"
data$ID[data$ID == "H-03"] <- "2222"
data$ID[data$ID == "H-04"] <- "2223"
data$ID[data$ID == "I-20"] <- "2362"
data$ID[data$ID == "419"] <- "2385"
data$ID[data$ID == "413"] <- "2386"
data$ID[data$ID == "W212"] <- "2388"
data$ID[data$ID == "318"] <- "2390"
data$ID[data$ID == "54C"] <- "2453"
data$ID[data$ID == "65B"] <- "2528"
data$ID[data$ID == "51C"] <- "2449"
data$ID[data$ID == "71B"] <- "2505"
data$ID[data$ID == "64D"] <- "2518"
data$ID[data$ID == "53B"] <- "2536"
data$ID[data$ID == "60A"] <- "2559"
data$ID[data$ID == "H-01"] <- "2220"
data$ID[data$ID == "H-03"] <- "2222"
data$ID[data$ID == "419"] <- "2385"
data$ID[data$ID == "78-02"] <- "2140"
data$ID <- as.numeric(data$ID)
data <- data %>%
mutate(response = replace(response, response == 0, 0.6)) %>%
#replacing 0 cm growth for the size of plug that is 0.6
# group_by(ID, experimental_replicate, concentration, growth, repeats) %>%
group_by(ID) %>%
mutate(response_range = list(get_range(response))) %>%
unnest() %>%
mutate(control_range = list(get_range(control))) %>%
unnest() %>%
filter(response <= upper &
response >= lower,
control <= upper1 & control >= lower1) %>%
ungroup()
}
#getting EC50
getting_EC50 <- function(filename){
getting.EC50 <- EC_table(filename, form = response ~ dose)
getting.EC50 <- getting.EC50 %>%
dplyr::rename(ID = sample ) %>% # renaming
# mutate(ID = as.factor(ID)) %>%
dplyr::rename(EC50 = Estimate.50) %>%
group_by(ID, EC50) %>%
mutate(source = ifelse(
ID %in% baseline.isolates.2,# if they are in this group
"baseline",
ifelse(
ID %in% survey.isolates, # if they are in this group, that is named farmer fiels also ahead
"survey_isolates",
"treatmentyear2016_isolates" # if they are in this group, that is named fungicide field trials also ahead
)), Host = ifelse(
ID %in% drybean,
"Drybean", ifelse(
ID %in% soybean,
"Soybean","different_host"))) %>% ungroup() %>%
mutate(
source = as.factor(source),
Host = as.factor(Host)) %>%
#taking out "different host" becuse does not belong to drybean nor soybean
filter(!ID == 8, !ID == 129) %>%
mutate(source = recode(source, survey_isolates = "Farmer fields", treatmentyear2016_isolates = "Fungicide field trials", baseline = "Baseline")) %>% mutate(source = as.factor(source)) %>%
ungroup()
}
##
getting_DC <- function(filename){
data <-filename
data1<- data %>% group_by(ID, dose) %>% summarise(mean_response = mean(response, na.rm = TRUE)) %>%
ungroup() %>%
spread(dose, mean_response) %>%
dplyr::rename(control = `0`) %>% #taking out "different host" becuse does not belong to drybean nor soybean
filter(!ID == 8, !ID == 129)
data2 <- as_data_frame( lapply(data1,function(x) {x[1] ; x}))
colnames(data2)[-c(1:2)] <- paste("RG_", colnames(data2[,-c(1:2)]), sep = "")
data3 <- data2 %>% mutate_each(funs((./control)*100), starts_with("RG_")) %>%
select(-c(control))
}
##
getting_DC_2 <- function(filename){
data <-filename
data1 <-data %>% #joining
# normalizing data
mutate(logEC50 = (log(EC50))) #%>%
}
lineal_model <- function (vardep, varindep, data) {
lm(paste(vardep, "~", varindep), data = data)
}
wrangling_DC<- function(filename){
data <-filename
data1 <-data %>%
mutate(source = ifelse(
ID %in% baseline.isolates.2,
"Baseline",
ifelse(
ID %in% Farmer.fields,
"Producer Fields","Fungicide Field Trials"
)), Host = ifelse(
ID %in% drybean,
"Drybean", ifelse(
ID %in% soybean,
"Soybean","different_host"))) %>%
mutate(
source = as.factor(source),
Host = as.factor(Host)) %>%
#taking out "different host" becuse does not belong to drybean nor soybean
filter(!ID == 8, !ID == 129) %>%
mutate(
country = ifelse(
ID %in% mexican.isolates,
"Mexico",
ifelse(ID %in% brazilian.isolates,
"Brazil", "USA")))%>%
mutate(Field_Year= case_when(
ID %in% NA_year_1973~"NA_year_1973",
ID %in% NA_year_1974~"NA_year_1974",
ID %in% NA_year_1963~"NA_year_1963",
ID %in% NA_year_1975~"NA_year_1975",
ID %in% NA_year_1977~"NA_year_1977",
ID %in% NA_year_1976~"NA_year_1976",
ID %in% NA_year_1978~"NA_year_1978",
ID %in% NA_year_1979~"NA_year_1979",
ID %in% NA_year_1980~"NA_year_1980",
ID %in% NA_year_2003~"NA_year_2003",
ID %in% NA_year_1982~"NA_year_1982",
ID %in% NA_year_1983~"NA_year_1983",
ID %in% NA_year_1984~"NA_year_1984",
ID %in% NA_year_1985~"NA_year_1985",
ID %in% NA_year_1987~"NA_year_1987",
ID %in% NA_year_1990~"NA_year_1990",
ID %in% NA_year_1965~"NA_year_1965",
ID %in% NA_year_1989~"NA_year_1989",
ID %in% NA_year_1991~"NA_year_1991",
ID %in% NA_year_1992~"NA_year_1992",
ID %in% NA_year_1969~"NA_year_1969",
ID %in% NA_year_1981~"NA_year_1981",
ID %in% NA_year_1993~"NA_year_1993",
ID %in% NA_year_1994~"NA_year_1994",
ID %in% NA_year_1995~"NA_year_1995",
ID %in% NA_year_1996~"NA_year_1996",
ID %in% NA_year_1997~"NA_year_1997",
ID %in% WM_production_31_year_1997~"WM_production_31_year_1997",
ID %in% WM_production_32_year_1997~"WM_production_32_year_1997",
ID %in% NA_year_1988~"NA_year_1988",
ID %in% WM_production_33_year_1997~"WM_production_33_year_1997",
ID %in% NA_year_NA~"NA_year_NA",
ID %in% NA_year_1998~"NA_year_1998",
ID %in% NA_year_1999~"NA_year_1999",
ID %in% NA_year_2000~"NA_year_2000",
ID %in% NA_year_2002~"NA_year_2002",
ID %in% WM_nursery_1_year_2003~"WM_nursery_1_year_2003",
ID %in% WM_nursery_2_year_2003~"WM_nursery_2_year_2003",
ID %in% WM_nursery_3_year_2003~"WM_nursery_3_year_2003",
ID %in% WM_nursery_4_year_2003~"WM_nursery_4_year_2003",
ID %in% WM_nursery_5_year_2003~"WM_nursery_5_year_2003",
ID %in% WM_production_35_year_2003~"WM_production_35_year_2003",
ID %in% NA_year_2004~"NA_year_2004",
ID %in% NA_year_2005~"NA_year_2005",
ID %in% NA_year_2006~"NA_year_2006",
ID %in% WM_production_36_year_2004~"WM_production_36_year_2004",
ID %in% WM_production_37_year_2004~"WM_production_37_year_2004",
ID %in% WM_production_38_year_2004~"WM_production_38_year_2004",
ID %in% WM_production_39_year_2004~"WM_production_39_year_2004",
ID %in% WM_production_40_year_2004~"WM_production_40_year_2004",
ID %in% WM_production_41_year_2004~"WM_production_41_year_2004",
ID %in% WM_production_42_year_2004~"WM_production_42_year_2004",
ID %in% WM_production_43_year_2004~"WM_production_43_year_2004",
ID %in% WM_production_44_year_2004~"WM_production_44_year_2004",
ID %in% WM_nursery_6_year_2004~"WM_nursery_6_year_2004",
ID %in% WM_nursery_7_year_2004~"WM_nursery_7_year_2004",
ID %in% WM_nursery_8_year_2004~"WM_nursery_8_year_2004",
ID %in% WM_nursery_9_year_2004~"WM_nursery_9_year_2004",
ID %in% WM_nursery_10_year_2004~"WM_nursery_10_year_2004",
ID %in% WM_nursery_11_year_2004~"WM_nursery_11_year_2004",
ID %in% WM_nursery_12_year_2004~"WM_nursery_12_year_2004",
ID %in% WM_nursery_24_year_2005~"WM_nursery_24_year_2005",
ID %in% WM_nursery_13_year_2005~"WM_nursery_13_year_2005",
ID %in% WM_nursery_14_year_2005~"WM_nursery_14_year_2005",
ID %in% WM_nursery_15_year_2005~"WM_nursery_15_year_2005",
ID %in% WM_nursery_16_year_2005~"WM_nursery_16_year_2005",
ID %in% WM_nursery_17_year_2005~"WM_nursery_17_year_2005",
ID %in% WM_nursery_18_year_2005~"WM_nursery_18_year_2005",
ID %in% WM_nursery_19_year_2006~"WM_nursery_19_year_2006",
ID %in% NA_year_2007~"NA_year_2007",
ID %in% WM_production_1_year_2007~"WM_production_1_year_2007",
ID %in% WM_production_2_year_2007~"WM_production_2_year_2007",
ID %in% WM_production_3_year_2007~"WM_production_3_year_2007",
ID %in% WM_production_4_year_2007~"WM_production_4_year_2007",
ID %in% WM_production_5_year_2007~"WM_production_5_year_2007",
ID %in% WM_production_6_year_2007~"WM_production_6_year_2007",
ID %in% WM_production_7_year_2007~"WM_production_7_year_2007",
ID %in% WM_production_8_year_2007~"WM_production_8_year_2007",
ID %in% WM_production_9_year_2007~"WM_production_9_year_2007",
ID %in% NA_year_2008~"NA_year_2008",
ID %in% WM_production_10_year_2008~"WM_production_10_year_2008",
ID %in% WM_nursery_20_year_2008~"WM_nursery_20_year_2008",
ID %in% WM_production_11_year_2008~"WM_production_11_year_2008",
ID %in% WM_production_12_year_2008~"WM_production_12_year_2008",
ID %in% WM_production_13_year_2008~"WM_production_13_year_2008",
ID %in% NA_year_2009~"NA_year_2009",
ID %in% WM_nursery_21_year_2008~"WM_nursery_21_year_2008",
ID %in% WM_production_38_year_2009~"WM_production_38_year_2009",
ID %in% WM_nursery_22_year_2009~"WM_nursery_22_year_2009",
ID %in% WM_nursery_23_year_2009~"WM_nursery_23_year_2009",
ID %in% WM_production_14_year_2009~"WM_production_14_year_2009",
ID %in% WM_production_15_year_2009~"WM_production_15_year_2009",
ID %in% WM_production_16_year_2009~"WM_production_16_year_2009",
ID %in% WM_production_17_year_2009~"WM_production_17_year_2009",
ID %in% WM_production_18_year_2009~"WM_production_18_year_2009",
ID %in% WM_production_19_year_2009~"WM_production_19_year_2009",
ID %in% WM_production_20_year_2009~"WM_production_20_year_2009",
ID %in% WM_production_21_year_2010~"WM_production_21_year_2010",
ID %in% WM_production_22_year_2010~"WM_production_22_year_2010",
ID %in% WM_production_23_year_2010~"WM_production_23_year_2010",
ID %in% WM_production_24_year_2010~"WM_production_24_year_2010",
ID %in% WM_production_25_year_2010~"WM_production_25_year_2010",
ID %in% WM_production_26_year_2010~"WM_production_26_year_2010",
ID %in% WM_production_27_year_2010~"WM_production_27_year_2010",
ID %in% WM_production_28_year_2010~"WM_production_28_year_2010",
ID %in% WM_production_29_year_2010~"WM_production_29_year_2010",
ID %in% WM_production_30_year_2010~"WM_production_30_year_2010",
ID %in% NA_year_2011~"NA_year_2011",
ID %in% NA_year_2012~"NA_year_2012",
ID %in% NA_year_2013~"NA_year_2013",
ID %in% NA_year_2014~"NA_year_2014",
ID %in% NA_year_2016~"NA_year_2014",
ID %in% po_year_2016~"NA_year_2016",
ID %in% flo_year_2016~"flo_year_2016",
ID %in% lan_year_2016~"lan_year_2016",
ID %in% ho_year_2017~"ho_year_2017",
ID %in% an_year_2017~"an_year_2017",
ID %in% dod_year_2017~"dod_year_2017",
ID %in% va_year_2017~"va_year_2017",
ID %in% na_year_2017~"na_year_2017",
ID %in% sa_year_2017~"sa_year_2017",
ID %in% han_year_2017~"han_year_2017",
ID %in% cu_year_2017~"cu_year_2017",
ID %in% mon_year_2017~"mon_year_2017",
ID %in% ant_year_2017~"ant_year_2017",
ID %in% holt_year_2017~"holt_year_2017",
ID %in% arr_year_2017~"arr_year_2017",
ID %in% ini_year_2017~"ini_year_2017",
ID %in% ley_year_2017~"ley_year_2017",
ID %in% pre_year_2017~"pre_year_2017",
ID %in% ru_year_2017~"ru_year_2017",
ID %in% ca_year_2017~"ca_year_2017",
ID %in% st_year_2017~"st_year_2017",
ID %in% Al_year_2015~"Al_year_2015",
ID %in% Hi_year_2015~"Hi_year_2015",
ID %in% In_year_2015~"In_year_2015",
ID %in% mont_year_2015~"mont_year_2015",
ID %in% Sand_year_2015~"Sand_year_2015",
ID %in% Sani_year_2015~"Sani_year_2015",
ID %in% St_year_2015~"St_year_2015",
ID %in% wau_year_2015~"wau_year_2015",
ID %in% la_year_2015~"la_year_2015",
ID %in% co_year_2015~"co_year_2015",
ID %in% cern_year_2015~"cern_year_2015",
ID %in% cer_year_2015~"cer_year_2015",
ID %in% NA_year_2010~"NA_year_2010",
ID %in% NA_year_2017~"NA_year_2017",
TRUE ~ "no specific field"),
State= case_when(
ID %in% NE~"NE",
ID %in% CO~"CO",
ID %in% IDA~"ID",
ID %in% CA~"CA",
ID %in% NY~"NY",
ID %in% NA~"NA",
ID %in% WA~"WA",
ID %in% NC~"NC",
ID %in% AZ~"AZ",
ID %in% MT~"MT",
ID %in% WI~"WI",
ID %in% MI~"MI",
ID %in% ON~"ON",
ID %in% AB~"AB",
ID %in% KS~"KS",
ID %in% ND~"ND",
ID %in% MD~"MD",
ID %in% Arusha~"Arusha",
ID %in% SK~"SK",
ID %in% OK~"OK",
ID %in% DF~"DF",
ID %in% OH~"OH",
ID %in% SIN~"SIN",
ID %in% IA~"IA",
ID %in% PA~"PA",
ID %in% Herts~"Herts",
ID %in% Unknown~"Unknown",
ID %in% FL~"FL",
ID %in% GA~"GA",
ID %in% MS~"MS",
ID %in% LA~"LA",
ID %in% KY~"KY",
ID %in% OR~"OR",
ID %in% MG~"MG",
ID %in% GO~"GO",
ID %in% Herverlee~"Herverlee",
# ID %in% Mooi Rive~"Mooi Rive",
ID %in% Marathon~"Marathon",
ID %in% SC~"SC",
ID %in% MN~"MN",
ID %in% Blenheim~"Blenheim",
ID %in% Barranquita~"Barranquita",
ID %in% Victoria~"Victoria",
ID %in% Tasmania~"Tasmania",
ID %in% DE~"DE",
ID %in% Guerbigny~"Guerbigny",
ID %in% QUE~"QUE",
ID %in% Kigali~"Kigali",
ID %in% birdseed~"birdseed",
ID %in% Kessenich~"Kessenich",
ID %in% Elen~"Elen",
ID %in% Moshi~"Moshi",
ID %in% Peronne~"Peronne",
ID %in% Loudéac~"Loudéac",
# ID %in% Bordeaux-Hourtin~"Bordeaux-Hourtin",
ID %in% BA~"BA",
ID %in% RS~"RS",
ID %in% MO~"MO",
ID %in% PR~"PR",
TRUE ~ "no specific state"
),
County= case_when(
ID %in% Lambari~"Lambari",
ID %in% `Porto Firme`~"Porto Firme",
ID %in% `UnaĂ`~"Unai",
TRUE ~ "no specific county"
)) %>%
mutate(
Field_Year = ifelse(
County == "Lambari",
"Lambari",
ifelse(
County == "Porto Firme",
"Porto Firme",
ifelse(County == "Unai", "Unai", Field_Year
)))) %>% mutate(
Field_Year = recode(
Field_Year,
WM_production_21_year_2010 = "NE.10.fld21.DB",
WM_production_1_year_2007 = "ND.07.fld01.DB",
WM_production_9_year_2007 = "WA.07.fld09.DB",
lan_year_2016 = "MI.16.East.Lansing.SB",
WM_production_10_year_2008 = "WA.08.fld10.DB",
WM_production_25_year_2010 = "ND.10.fld25.DB",
mont_year_2015= "MI.15.Montcalm.SB",
na_year_2017 = "IA.17.Nashua.SB",
mon_year_2017 = "MI.17.Montcalm.SB",
Baseline = "Baseline",
han_year_2017 = "WI.17.Hancock.SB",
NA_year_2010 = "BR.2010.fld01.DB",
pre_year_2017 = "MX.2017.pre.DB",
ca_year_2017 = "MX.2017.ca.DB",
ley_year_2017 = "MX.2017.ley.DB",
cer_year_2015 = "WI.2015.cer.SB",
`Porto Firme` = "BR.2010.por.DB",
`Unai` = "BR.2010.unai.DB",
`Lambari` = "BR.2010.lam.DB"
))
}
#NEED TO MODIFI HERE WHEN BRAZIL, MAKE BT COUNTY NOR EVEN STATE
##wrangling survey to get the EC50DC
wrangling_survey <- function(filename) {
data <- filename
data1 <- data %>%
group_by(ID) %>%
summarise(
mean_response = mean(response, na.rm = TRUE),
mean_control = mean(control, na.rm = TRUE)
) %>% ungroup() %>%
mutate(RG = (mean_response / mean_control) * 100)
}
wrangling_DC_2<- function(filename){
data <-filename
data1 <-data %>%
group_by(ID) %>% #keeping the highest value of each ID value
top_n(1, EC50DC) %>%
ungroup()
}
wrangling_DC_3<- function(filename){
data <-filename
data1 <-data %>%
mutate(Field_Year = ifelse(source == "Baseline", "Baseline", Field_Year),
Field_Year = as.factor(Field_Year)) %>%
group_by(Field_Year) %>% filter(n()>=9) %>%
ungroup() %>%
filter(!Field_Year =="no specific field" ) %>%
mutate(Field_Year = fct_reorder(Field_Year, EC50DC, mean))
}
#test source
test_kruskal_source <- function(filename, vardep, varindep) {
data <- filename
vardep <- data$EC50DC
varindep <- data$source
data1 <- data %>%
do(tidy(shapiro.test(vardep)))
if (data1$p.value > 0.05) {
print ("normal")# I have to add the ANOVA
} else {
kruskal.test(as.numeric(vardep) ~ as.factor(varindep), data = data1)
}}
test_Dunn_source <- function(filename, vardep, varindep) {
data <- filename
vardep <- data$EC50DC
varindep <- data$source
data1 <- data %>%
do(tidy(shapiro.test(vardep)))
if (data1$p.value > 0.05) {
print ("normal")# I have to add the ANOVA
} else {
kruskal.test(as.numeric(vardep) ~ as.factor(varindep), data = data1)
if (data1$p.value > 0.05) {
print ("No stats difference")
} else{
DunnTest(as.numeric(vardep) ~ as.factor(varindep), data = data1, method="bonferroni")}}}
#test host
test_kruskal_host <- function(filename, vardep, varindep) {
data <- filename
vardep <- data$EC50DC
varindep <- data$Host
data1 <- data %>%
do(tidy(shapiro.test(vardep)))
if (data1$p.value > 0.05) {
print ("normal")# I have to add the ANOVA
} else {
kruskal.test(as.numeric(vardep) ~ as.factor(varindep), data = data1)
}}
test_Dunn_host <- function(filename, vardep, varindep) {
data <- filename
vardep <- data$EC50DC
varindep <- data$Host
data1 <- data %>%
do(tidy(shapiro.test(vardep)))
if (data1$p.value > 0.05) {
print ("normal")# I have to add the ANOVA
} else {
kruskal.test(as.numeric(vardep) ~ as.factor(varindep), data = data1)
if (data1$p.value > 0.05) {
print ("No stats difference")
} else{
DunnTest(as.numeric(vardep) ~ as.factor(varindep), data = data1, method="bonferroni")}}}
test_field <- function(filename, vardep, varindep) {
data <- filename
vardep <- data$EC50DC
varindep <- data$Field_Year
data1 <- data %>%
do(tidy(shapiro.test(vardep)))
if (data1$p.value > 0.05) {
print ("normal")# I have to add the ANOVA
} else {
kruskal.test(as.numeric(vardep) ~ as.factor(varindep), data = data1)
if (data1$p.value > 0.05) {
print ("No stats difference")
}
else{
DunnTest(as.numeric(vardep) ~ as.factor(varindep), data = data1, method="bonferroni")}
}}
###
myplot_model_1_boscalid <- function(filename, RG, logEC50, lm) {
data <- filename
RG <- data$RG
logEC50 <- data$logEC50
Host <- data$Host
source <- data$source
ggplot(data = filename, aes(x = RG, y = logEC50)) +
geom_point(aes(
shape = Host,
fill= source
),
size = 2, stroke= 1, colour= "black") + scale_shape_manual(values = c(21, 24))+ scale_fill_grey(start = 0, end = 1) +
geom_smooth(
method = "lm",
se = FALSE,
colour = "black",
size = 0.3
) +
theme(
plot.title = element_text(
size = 18,
face = "bold",
hjust = 0.5,
family = "Arial"
),
axis.title = element_text(
size = 18,
face = "bold",
hjust = 0.5
),
axis.text = element_text(
face = "bold",
size = 18,
family = "Arial"
),
panel.background = element_rect(fill = "white", colour = "grey50")
) + labs(x =
"Relative growth (%)", y = expression(bold(Log (EC[bold("50")])))) + geom_abline(
intercept = 0,
slope = 1,
color = "black",
lty = "dashed",
size = 0.3
) + expand_limits(x = c(15, 65)) + scale_x_continuous(name = waiver(), breaks = c(15, 25, 35, 45, 55, 65)) + geom_label(aes(x = 53,
y = -2.75)
,
label = c(paste (
" Y =",
paste0 (round(summary(lm)[[4]][2], 4), "x"),
"-",
round(summary(lm)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(lm)[[9]][1], 4
), "\n p < 0.001"))
)),size = 3.5,
fontface = "bold")
}
### tetraconazole
myplot_model_1_tetraconazole <- function(filename, RG, logEC50, lm) {
data <- filename
RG <- data$RG
logEC50 <- data$logEC50
Host <- data$Host
source <- data$source
ggplot(data = filename, aes(x = RG, y = logEC50)) +
geom_point(aes(
shape = Host,
fill= source
),
size = 2, stroke= 1, colour= "black") + scale_shape_manual(values = c(21, 24))+ scale_fill_grey(start = 0, end = 1) +
geom_smooth(
method = "lm",
se = FALSE,
colour = "black",
size = 0.3
) +
theme(
plot.title = element_text(
size = 18,
face = "bold",
hjust = 0.5,
family = "Arial"
),
axis.title = element_text(
size = 18,
face = "bold",
hjust = 0.5
),
axis.text = element_text(
face = "bold",
size = 18,
family = "Arial"
),
panel.background = element_rect(fill = "white", colour = "grey50")
) + labs(x =
"Relative growth (%)", y = expression(bold(Log (EC[bold("50")])))) + geom_abline(
intercept = 0,
slope = 1,
color = "black",
lty = "dashed",
size = 0.3
) + expand_limits(x = c(15, 65)) + scale_x_continuous(name = waiver(), breaks = c(15, 25, 35, 45, 55, 65)) + geom_label(aes(x = 53,
y = -0.75)
,
label = c(paste (
" Y =",
paste0 (round(summary(lm)[[4]][2], 4), "x"),
"-",
round(summary(lm)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(lm)[[9]][1], 4
), "\n p < 0.001"))
)),size = 3.5,
fontface = "bold")
}
#### picoxystrobin
myplot_model_1_picoxystrobin <- function(filename, RG, logEC50, lm) {
data <- filename
RG <- data$RG
logEC50 <- data$logEC50
Host <- data$Host
source <- data$source
ggplot(data = filename, aes(x = RG, y = logEC50)) +
geom_point(aes(
shape = Host,
fill= source
),
size = 2, stroke= 1, colour= "black") + scale_shape_manual(values = c(21, 24))+ scale_fill_grey(start = 0, end = 1) +
geom_smooth(
method = "lm",
se = FALSE,
colour = "black",
size = 0.3
) +
theme(
plot.title = element_text(
size = 18,
face = "bold",
hjust = 0.5,
family = "Arial"
),
axis.title = element_text(
size = 18,
face = "bold",
hjust = 0.5
),
axis.text = element_text(
face = "bold",
size = 18,
family = "Arial"
),
axis.text.y = element_text(
angle = 20,
hjust = 1),
panel.background = element_rect(fill = "white", colour = "grey50")
) + labs(x =
"Relative growth (%)", y = expression(bold(Log (EC[bold("50")])))) + geom_abline(
intercept = 0,
slope = 1,
color = "black",
lty = "dashed",
size = 0.3
) + expand_limits(x = c(15, 65)) + scale_x_continuous(name = waiver(), breaks = c(15, 25, 35, 45, 55, 65)) + geom_label(aes(x = 28,
y = -4.30)
,
label = c(paste (
" Y =",
paste0 (round(summary(lm)[[4]][2], 4), "x"),
"-",
round(summary(lm)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(lm)[[9]][1], 4
), "\n p < 0.001"))
)),size = 3.5,
fontface = "bold")
}
####
myplot_model_2 <- function(filename, EC50, Estimate.50DC, Host, source) {
data <- filename
EC50 <- data$EC50
Estimate.50DC <- data$Estimate.50DC
Host <- data$Host
source <- data$source
ggplot(data = filename, aes(x = EC50, y = Estimate.50DC)) +
geom_point(aes(
shape = Host,
fill= source
),
size = 2, stroke= 1, colour= "black") + scale_shape_manual(values = c(21, 24))+ scale_fill_grey(start = 0, end = 1) +
geom_smooth(method = "lm",
se = FALSE,
colour = "black",
size = 0.3) +
theme(
plot.title = element_text(
size = 18,
face = "bold",
hjust = 0.5,
family = "Arial"
),
axis.title = element_text(size = 18, face = "bold", hjust = 0.5),
axis.text = element_text(
face = "bold",
size = 12,
family = "Arial"
),
panel.background = element_rect(fill = "white", colour = "grey50")
) +
labs(x =
expression(bold(EC[bold("50")]) ~ (bold(mg/L ~ bold(
"a.i."
)))), y = expression(bold(EC[bold("50") ~ (bold("D"))]) ~ (bold(mg/L ~ bold(
"a.i."
))))) + geom_abline(
intercept = 0,
slope = 1,
color = "black",
lty = "dashed",
size = 0.3)
}
#label
generate_label <- function(HSD){
# Extract labels and factor levels from Tukey post-hoc
Dunn.levels <- HSD[[1]][,2]
Dunn.labels <- multcompLetters(Dunn.levels)['Letters']
plot.labels <- names(Dunn.labels[['Letters']])
plot.levels <- data.frame(plot.labels, Dunn.levels, labels = Dunn.labels[['Letters']],
stringsAsFactors = FALSE)
return(plot.levels)
}
###
generate_label_2 <- function(HSD, flev, surveyy, highlabel){
# Extract labels and factor levels from Tukey post-hoc
Dunn.levels <- HSD[[1]][,2]
Dunn.labels <- multcompLetters(Dunn.levels)['Letters']
plot.labels <- names(Dunn.labels[['Letters']])
# Get highest quantile for Dunn's 5 number summary and add a bit of space to buffer between
# upper quantile and label placement
boxplot.df <- plyr:: ddply(surveyy, flev, function (x) max(fivenum(x$EC50DC)) + highlabel)
# Create a data frame out of the factor levels and Dunn's homogenous group letters
plot.levels <- data.frame(plot.labels, labels = Dunn.labels[['Letters']],
stringsAsFactors = FALSE)
# Merge it with the labels
labels.df <- merge(plot.levels, boxplot.df, by.x = 'plot.labels', by.y = flev, sort = FALSE)
return(labels.df)
}
###
myplot_comparison <- function(filename, Field_Year,EC50DC, highlabel) {
data <- filename
Field_Year <- data$Field_Year
EC50DC<- data$EC50DC
ggplot(data = filename, aes(x = Field_Year, y = EC50DC)) + geom_boxplot() +
geom_text(data = generate_label_2(test_field(filename), 'Field_Year', filename,highlabel),
aes(x = plot.labels, y = V1, label = labels), size= 3) + labs(x = "Field", y = expression(bold(EC[bold("50") ~ (bold("D"))]) ~
(bold(mg/L ~ bold(
"a.i."
))))) + theme(
panel.border = element_rect(
colour = "black",
fill = NA,
size = 0.5
),
axis.title = element_text(size = 11, face = "bold", hjust = 0.5),
axis.text.x = element_text(
face = "bold",
size = 8,
family = "Arial",
angle = 30,
hjust = 1
),
axis.text.y = element_text(
face = "bold",
size = 8,
family = "Arial"
),
panel.background = element_rect(fill = "white", colour = "grey50")
)
}
myplot_baseline <- function(filename, EC50DC, source) {
data <- filename
EC50DC <- data$EC50DC
source <- data$source
data %>% filter(source == "Baseline") %>% mutate(
Baseline = ifelse(
ID %in% SI.Screening.Nursery.Field,
# if they are in this group
"Contemporary isolates*",
"Historic isolates**"
)
) %>% ggplot(aes(x = Baseline, y = EC50DC, )) + geom_violin() + geom_jitter(aes(shape = Baseline), size = 6) + labs(x = "Baseline", y = expression(bold(EC[bold("50") ~ (bold("D"))]) ~
(bold(mg/L ~ bold(
"a.i."
))))) + theme(
panel.border = element_rect(
colour = "black",
fill = NA,
size = 1
),
axis.title = element_text(
size = 18,
face = "bold",
hjust = 0.5
),
axis.text.x = element_text(
face = "bold",
size = 10,
family = "Arial",
hjust = 0.5
),
axis.text.y = element_text(
face = "bold",
size = 10,
family = "Arial"
),
panel.background = element_rect(fill = "white", colour = "grey50")
)
}1. Thiophanate methyl
2. Tetraconazole
3. Boscalid
4. Picoxystrobin
#TM
hey <-
reading_data_serial_dilution("data/TM_serialdi_BL-WMN_survey-WMNCSRP(validationdata).csv") %>%
filter(!experimental_replicate == 1 |
!dose == 1.5 | !repeats == 3) #something weird happened with the repeats 3 so Iam revoming those
#Check here because there are more repeats for control, in that case create a new variable or column
## First try with postdoc Thomas
survey.TM.thomas <- read.csv("data/DD_survey-WMNCSRP.csv") %>%
select(
ID,
repeats,
experimental_replicate,
TM.ecuatorial,
TM.polar,
control.ecuatorial,
control.polar
) %>%
mutate(TM.growth = ((TM.ecuatorial + TM.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = TM.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Second try with Edgar
survey.TM.USA <-
read.csv("data/DD_survey-WMNCSRP_TM.csv") %>%
mutate(TM.growth = ((TM.ecuatorial + TM.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = TM.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Third try with Cristian
survey.TM.more.USA <-
read.csv("data/TM.Cristian.csv") %>%
mutate(TM.growth = ((TM.ecuatorial + TM.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = TM.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
#Tetraconazole
hey2 <- reading_data_serial_dilution("data/tetraconazole_serialdi_BL-WMN_survey-WMNCSRP(validationdata).csv")
## First try with postdoc Thomas
survey.tetraconazole.thomas <- read.csv("data/DD_survey-WMNCSRP.csv") %>%
select(
ID,
repeats,
experimental_replicate,
tetraconazole.ecuatorial,
tetraconazole.polar,
control.ecuatorial,
control.polar
) %>%
mutate(tetraconazole.growth = ((tetraconazole.ecuatorial + tetraconazole.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = tetraconazole.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Second try with Edgar
survey.tetraconazole.USA <-
read.csv("data/DD_survey-WMNCSRP_tetraconazole.csv") %>%
mutate(tetraconazole.growth = ((tetraconazole.ecuatorial + tetraconazole.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = tetraconazole.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Third try with Cristian
survey.tetraconazole.more.USA <-
read.csv("data/tetraconazole.Cristian.csv") %>%
mutate(tetraconazole.growth = ((tetraconazole.ecuatorial + tetraconazole.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = tetraconazole.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Fourth try with Edgar
survey.tetraconazole.MX.BZ <-
read.csv("data/DD_survey-WMNCSRP_tetraconazole.Mexican.Brazilian.csv") %>% mutate(tetraconazole.growth = ((tetraconazole.ecuatorial + tetraconazole.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = tetraconazole.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
#Boscalid
hey3 <- reading_data_serial_dilution("data/boscalid_serialdilution_baseline(validationdata).csv") %>% filter(!dose == 0.4 & !ID == 800)
## First try with postdoc Thomas
survey.boscalid.thomas <- read.csv("data/DD_survey-WMNCSRP.csv") %>%
select(
ID,
repeats,
experimental_replicate,
boscalid.ecuatorial,
boscalid.polar,
control.ecuatorial,
control.polar
) %>%
mutate(boscalid.growth = ((boscalid.ecuatorial + boscalid.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = boscalid.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Second try with Edgar
survey.boscalid.USA <-
read.csv("data/DD_survey-WMNCSRP_boscalid.csv") %>%
mutate(boscalid.growth = ((boscalid.ecuatorial + boscalid.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = boscalid.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Third try with Cristian
survey.boscalid.more.USA <-
read.csv("data/Boscalid.Cristian.csv") %>%
mutate(boscalid.growth = ((boscalid.ecuatorial + boscalid.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = boscalid.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Fourth try with Edgar
survey.boscalid.MX.BZ <-
read.csv("data/DD_survey-WMNCSRP_boscalid.Brazilian.Mexican.csv") %>% mutate(boscalid.growth = ((boscalid.ecuatorial + boscalid.polar) / 2),
control.growth = ((control.ecuatorial + control.polar) / 2)) %>%
dplyr::rename(response = boscalid.growth, control = control.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
# #Picoxystrobin
hey4 <- reading_data_serial_dilution("data/picoxystrobin_serialdi_BL-WMN_survey-WMNCSRP(validationdata).csv")
## First try with postdoc Thomas
survey.picoxystrobin.thomas <- read.csv("data/DD_survey-WMNCSRP.csv") %>%
select(
ID,
repeats,
experimental_replicate,
picoxystrobin.ecuatorial,
picoxystrobin.polar,
sham.ecuatorial,
sham.polar
) %>%
mutate(picoxystrobin.growth = ((picoxystrobin.ecuatorial + picoxystrobin.polar) / 2),
sham.growth = ((sham.ecuatorial + sham.polar) / 2)) %>%
dplyr::rename(response = picoxystrobin.growth, control = sham.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Second try with Edgar
survey.picoxystrobin.USA <-
read.csv("data/DD_survey-WMNCSRP_picoxystrobin.csv") %>%
mutate(picoxystrobin.growth = ((picoxystrobin.ecuatorial + picoxystrobin.polar) / 2),
sham.growth = ((sham.ecuatorial + sham.polar) / 2)) %>%
dplyr::rename(response = picoxystrobin.growth, control = sham.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Third try with Cristian
survey.picoxystrobin.more.USA <-
read.csv("data/picoxystrobin.Cristian.csv") %>%
mutate(picoxystrobin.growth = ((picoxystrobin.ecuatorial + picoxystrobin.polar) / 2),
sham.growth = ((sham.ecuatorial + sham.polar) / 2)) %>%
dplyr::rename(response = picoxystrobin.growth, control = sham.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()
## Fourth try with Edgar
survey.picoxystrobin.MX.BZ <-
read.csv("data/DD_survey-WMNCSRP_picoxystrobin.Mexican.Brazilian.csv") %>% mutate(picoxystrobin.growth = ((picoxystrobin.ecuatorial + picoxystrobin.polar) / 2),
sham.growth = ((sham.ecuatorial + sham.polar) / 2)) %>%
dplyr::rename(response = picoxystrobin.growth, control = sham.growth) %>%
select(c(ID, experimental_replicate, repeats, response, control)) %>%
reading_data_discriminatory_concentration()Fungicide sensitivity was determined from 22 baseline isolates plus 21 additional isolates from FF and FFT randomly selected. Serial dilution by fungicide eas carried out from 5-6 concentrations with 4 repetitions per isolate and 2 experimental replications. Plates were incubated in darkness at 23 ± 2 °C for 42 h and diameter measured with digital calipers. A dose-response curve was fit to estimate the EC50 for each fungicide.
# TM
TM.EC50 <- getting_EC50(hey)
# Tetraconazole
tetraconazole.EC50 <- getting_EC50(hey2)
# Boscalid
boscalid.EC50 <- getting_EC50(hey3)
#Picoxystrobin
picoxystrobin.EC50 <- getting_EC50(hey4)
We identified the concentration with the best prediction of EC50 for each fungicide, known as discriminatory concentration (DC), by linear regression of % mycelial growth vs. log(EC50). The concentration yielding the highest coefficient of determination (r2) was selected as the DC for each fungicide. For the remaining isolates among the 207, growth on the DC was used to estimate EC50, which is termed EC50(D).
#TM
#getting_EC50 releases ID with factor
TM <- getting_DC(hey) ## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
TM.joined <- TM %>% left_join(TM.EC50)## Joining, by = "ID"
TM.joined.2 <- getting_DC_2(TM.joined)
#Boscalid
boscalid <- getting_DC(hey3) ## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
boscalid.joined <- boscalid%>% left_join(boscalid.EC50)## Joining, by = "ID"
boscalid.joined.2 <- getting_DC_2(boscalid.joined)
###Dose chosen as DC. Although there is one dose with higher r2, there is no difference between and this was selected
# Linear model of log EC50 and relative growth at 0.2 ppm, check normality and homogeneity of variances
finalRG_0.2 <- lm(logEC50 ~ RG_0.2, boscalid.joined.2)
summary(finalRG_0.2)##
## Call:
## lm(formula = logEC50 ~ RG_0.2, data = boscalid.joined.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27096 -0.02645 0.01943 0.05137 0.25614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.338863 0.128376 -33.80 <2e-16 ***
## RG_0.2 0.054589 0.003376 16.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1056 on 37 degrees of freedom
## Multiple R-squared: 0.8761, Adjusted R-squared: 0.8727
## F-statistic: 261.5 on 1 and 37 DF, p-value: < 2.2e-16
#plot(finalRG0.2)
##taking out outliers from Q-Q PLOT $ LEVERAGE TEST, ISOLATES # 1032 from treatment, 2222 & 2223 from Farmer fields##
boscalid.joined.2.clean <-
boscalid.joined.2 %>% filter(!ID == "1032", !ID == "2222",!ID == "2223") %>%
select(ID, logEC50, RG_0.2, EC50) %>%
dplyr::rename(RG = RG_0.2) %>%
wrangling_DC()
summary(boscalid.lm <- lineal_model("logEC50","RG",data = boscalid.joined.2.clean))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22927 -0.03254 0.01107 0.03859 0.16920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.412096 0.105765 -41.72 <2e-16 ***
## RG 0.056687 0.002757 20.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08019 on 34 degrees of freedom
## Multiple R-squared: 0.9256, Adjusted R-squared: 0.9234
## F-statistic: 422.8 on 1 and 34 DF, p-value: < 2.2e-16
# Getting the EC50DC according to the model
boscalid.EC50DC <- boscalid.joined.2.clean %>% mutate(Estimate.50DC =
#using the model the intercept and the coefficient of the model
exp(boscalid.lm[[1]][[1]] + boscalid.lm[[1]][[2]] * RG)) %>%# here I need to do this dynamic
select(ID, Estimate.50DC, EC50) %>%
wrangling_DC()
# Linear Regression graph of log EC50 and relative growth at 0.2 ppm
###
myplot_model_1_boscalid(boscalid.joined.2.clean, lm = boscalid.lm) ## `geom_smooth()` using formula 'y ~ x'
######### Linear model of EC50 and EC50DC just to corroborate is a GOOD RELATIONSHIP, check normality and homogeneity of variances
summary(boscalid.lm.2 <- lineal_model("Estimate.50DC","EC50",data = boscalid.EC50DC))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0189959 -0.0045486 -0.0005029 0.0039102 0.0234536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.014242 0.005271 2.702 0.0107 *
## EC50 0.865738 0.046429 18.646 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008695 on 34 degrees of freedom
## Multiple R-squared: 0.9109, Adjusted R-squared: 0.9083
## F-statistic: 347.7 on 1 and 34 DF, p-value: < 2.2e-16
round(boscalid.lm.2[[1]][[2]],4)## [1] 0.8657
round(boscalid.lm.2[[1]][[1]],3) ## [1] 0.014
myplot_model_2(boscalid.EC50DC)+ expand_limits(x = c(0.04, 0.25), y = c(0.04, 0.25)) + scale_x_continuous(name = waiver(),
breaks = c(0.05, 0.1, 0.15, 0.20, 0.25)) + scale_y_continuous(breaks = c(0.05, 0.1, 0.15, 0.20, 0.25)) + geom_label(aes(x = 0.20,
y = 0.10)
,
label = c(paste (
" Y =",
paste0 (round(summary(boscalid.lm.2)[[4]][2], 4), "x"),
round(summary(boscalid.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(boscalid.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),size = 5,
fontface = "bold")## `geom_smooth()` using formula 'y ~ x'
round(boscalid.lm[[1]][[2]],4)## [1] 0.0567
round(boscalid.lm[[1]][[1]],3) ## [1] -4.412
#Tetraconazole
tetraconazole <- getting_DC(hey2) ## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
tetraconazole.joined <- tetraconazole%>% left_join(tetraconazole.EC50)## Joining, by = "ID"
tetraconazole.joined.2 <- getting_DC_2(tetraconazole.joined)
###Dose chosen as DC. Although there is one dose with higher r2, there is no difference between and this was selected
# Linear model of log EC50 and relative growth at 2 ppm, check normality and homogeneity of variances
finalRG_2 <- lm(logEC50 ~ RG_2, tetraconazole.joined.2)
summary(finalRG_2)##
## Call:
## lm(formula = logEC50 ~ RG_2, data = tetraconazole.joined.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15348 -0.04122 0.01195 0.04652 0.19668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.429298 0.052190 -27.39 <2e-16 ***
## RG_2 0.039056 0.001389 28.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08135 on 38 degrees of freedom
## Multiple R-squared: 0.9541, Adjusted R-squared: 0.9529
## F-statistic: 790.1 on 1 and 38 DF, p-value: < 2.2e-16
#plot(finalRG0.2)
##taking out outliers from Q-Q PLOT $ LEVERAGE TEST, ISOLATES # 1032 from treatment, 2222 & 2223 from Farmer fields##
tetraconazole.joined.2.clean <-
tetraconazole.joined.2 %>% filter(!ID == "21", !ID == "558",!ID == "667") %>%
select(ID, logEC50, RG_2, EC50) %>%
dplyr::rename(RG = RG_2) %>%
wrangling_DC()
summary(tetraconazole.lm <- lineal_model("logEC50","RG",data = tetraconazole.joined.2.clean))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14413 -0.04247 0.02069 0.03689 0.11121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.669458 0.076294 -21.88 <2e-16 ***
## RG 0.045156 0.001958 23.06 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06204 on 35 degrees of freedom
## Multiple R-squared: 0.9382, Adjusted R-squared: 0.9365
## F-statistic: 531.8 on 1 and 35 DF, p-value: < 2.2e-16
# Getting the EC50DC according to the model
tetraconazole.EC50DC <- tetraconazole.joined.2.clean %>% mutate(Estimate.50DC =
#using the model the intercept and the coefficient of the model
exp(tetraconazole.lm[[1]][[1]] + tetraconazole.lm[[1]][[2]] * RG)) %>%# here I need to do this dynamic
select(ID, Estimate.50DC, EC50) %>% wrangling_DC()
# Linear Regression graph of log EC50 and relative growth at 0.2 ppm
###
myplot_model_1_tetraconazole(tetraconazole.joined.2.clean, lm = tetraconazole.lm) ## `geom_smooth()` using formula 'y ~ x'
######### Linear model of EC50 and EC50DC just to corroborate is a GOOD RELATIONSHIP, check normality and homogeneity of variances
summary(tetraconazole.lm.2 <- lineal_model("Estimate.50DC","EC50",data = tetraconazole.EC50DC))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10710 -0.03721 -0.01551 0.05445 0.15171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14852 0.05618 2.644 0.0122 *
## EC50 0.86314 0.05000 17.264 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06418 on 35 degrees of freedom
## Multiple R-squared: 0.8949, Adjusted R-squared: 0.8919
## F-statistic: 298 on 1 and 35 DF, p-value: < 2.2e-16
round(tetraconazole.lm.2[[1]][[2]],4)## [1] 0.8631
round(tetraconazole.lm.2[[1]][[1]],3)## [1] 0.149
myplot_model_2(tetraconazole.EC50DC)+ expand_limits(x = c(0.27, 1.8), y = c(0.27, 1.8)) + scale_x_continuous(name = waiver(),
breaks = c(0.3, 0.6, 0.9, 1.2, 1.5,1.8)) + scale_y_continuous(breaks = c(0.3, 0.6, 0.9, 1.2, 1.5,1.8)) + geom_label(aes(x = 1.5,
y = 0.6)
,
label = c(paste (
" Y =",
paste0 (round(summary(tetraconazole.lm.2)[[4]][2], 4), "x"),
round(summary(tetraconazole.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(tetraconazole.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),size = 5,
fontface = "bold")## `geom_smooth()` using formula 'y ~ x'
## Picoxystrobin
picoxystrobin<- getting_DC(hey4) ## `summarise()` has grouped output by 'ID'. You can override using the `.groups`
## argument.
picoxystrobin.joined <- picoxystrobin%>% left_join(picoxystrobin.EC50)## Joining, by = "ID"
picoxystrobin.joined.2 <- getting_DC_2(picoxystrobin.joined)
###Dose chosen as DC. Although there is one dose with higher r2, there is no difference between and this was selected
# Linear model of log EC50 and relative growth at 0.01 ppm, check normality and homogeneity of variances
finalRG_0.01 <- lm(logEC50 ~ RG_0.01, picoxystrobin.joined.2)
summary(finalRG_0.01)##
## Call:
## lm(formula = logEC50 ~ RG_0.01, data = picoxystrobin.joined.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.249660 -0.053292 0.004241 0.055571 0.188057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.685437 0.158498 -42.18 <2e-16 ***
## RG_0.01 0.040251 0.002815 14.30 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09447 on 38 degrees of freedom
## Multiple R-squared: 0.8433, Adjusted R-squared: 0.8392
## F-statistic: 204.5 on 1 and 38 DF, p-value: < 2.2e-16
##taking out outliers from Q-Q PLOT $ LEVERAGE TEST, ISOLATES # 1032 from treatment, 2222 & 2223 from Farmer fields##
picoxystrobin.joined.2.clean <-
picoxystrobin.joined.2 %>% filter(!ID == "1032", !ID == "558",!ID == "581") %>%
select(ID, logEC50, RG_0.01, EC50) %>%
dplyr::rename(RG = RG_0.01) %>%
wrangling_DC()
summary(picoxystrobin.lm <- lineal_model("logEC50","RG",data = picoxystrobin.joined.2.clean))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.180702 -0.055184 -0.000729 0.049534 0.147093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.571213 0.139542 -47.09 <2e-16 ***
## RG 0.038131 0.002507 15.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07633 on 35 degrees of freedom
## Multiple R-squared: 0.8686, Adjusted R-squared: 0.8648
## F-statistic: 231.3 on 1 and 35 DF, p-value: < 2.2e-16
# Getting the EC50DC according to the model
picoxystrobin.EC50DC <- picoxystrobin.joined.2.clean %>% mutate(Estimate.50DC =
#using the model the intercept and the coefficient of the model
exp(picoxystrobin.lm[[1]][[1]] + picoxystrobin.lm[[1]][[2]] * RG)) %>%# here I need to do this dynamic
select(ID, Estimate.50DC, EC50) %>% wrangling_DC()
# Linear Regression graph of log EC50 and relative growth at 0.2 ppm
###
summary(picoxystrobin.lm.2 <- lineal_model("Estimate.50DC","EC50",data = picoxystrobin.EC50DC))##
## Call:
## lm(formula = paste(vardep, "~", varindep), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0015409 -0.0006240 -0.0002075 0.0007106 0.0026937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001884 0.000798 2.361 0.0239 *
## EC50 0.838025 0.066339 12.632 1.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008963 on 35 degrees of freedom
## Multiple R-squared: 0.8201, Adjusted R-squared: 0.815
## F-statistic: 159.6 on 1 and 35 DF, p-value: 1.347e-14
round(picoxystrobin.lm.2[[1]][[2]],4)## [1] 0.838
round(picoxystrobin.lm.2[[1]][[1]],3)## [1] 0.002
myplot_model_1_picoxystrobin(picoxystrobin.joined.2.clean, lm = picoxystrobin.lm) ## `geom_smooth()` using formula 'y ~ x'
######### Linear model of EC50 and EC50DC just to corroborate is a GOOD RELATIONSHIP, check normality and homogeneity of variances
myplot_model_2(picoxystrobin.EC50DC)+ expand_limits(x = c(0.005, 0.018), y = c(0.005, 0.018)) + scale_x_continuous(name = waiver(),
breaks = c(0.0075, 0.01, 0.0125, 0.0150, 0.018)) + scale_y_continuous(breaks = c(0.0075, 0.01, 0.0125, 0.0150, 0.018)) + geom_label(aes(x = 0.015,
y = 0.009)
,
label = c(paste (
" Y =",
paste0 (round(summary(picoxystrobin.lm.2)[[4]][2], 4), "x"),
round(summary(picoxystrobin.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(picoxystrobin.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),size = 5,
fontface = "bold")## `geom_smooth()` using formula 'y ~ x'
#Function to clean
picoxystrobin.joined.2 %>% filter(!EC50 %in% Outlier(EC50))## # A tibble: 36 Ă— 15
## ID RG_0.01 RG_0.02 RG_0.04 RG_0.06 RG_0.1 Estimate.10 SE.10 EC50
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 55.8 37.4 19.6 15.4 10.0 0.00169 0.000264 0.0123
## 2 12 55.8 39.7 25.3 19.5 12.2 0.00124 0.000198 0.0128
## 3 20 56.7 37.5 21.4 15.0 8.74 0.00181 0.000291 0.0127
## 4 21 56.9 23.3 14.0 8.90 8.90 0.00268 0.000564 0.0111
## 5 74 49.9 24.1 14.5 12.0 9.84 0.00135 0.000497 0.00906
## 6 87 49.8 25.0 15.0 11.7 9.10 0.00136 0.000557 0.00916
## 7 118 55.0 36.1 24.0 18.9 9.89 0.00126 0.000226 0.0120
## 8 123 54.2 38.4 20.7 11.6 8.17 0.00179 0.000337 0.0121
## 9 449 56.0 30.8 19.0 18.0 11.5 0.00135 0.000494 0.0111
## 10 461 53.9 34.8 20.7 18.3 11.8 0.00115 0.000458 0.0111
## # … with 26 more rows, and 6 more variables: SE.50 <dbl>, Estimate.90 <dbl>,
## # SE.90 <dbl>, source <fct>, Host <fct>, logEC50 <dbl>
#based on object table1 which has all the complete isolates used in this study,
# I have to keep Field adn Year separate tahts why reading again the main table from data
whitemold.inventory.2 <- read_csv("data/whitemold.inventory.csv")## New names:
## • `` -> `...1`
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 2326 Columns: 40
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (30): Host, State, County, Country, Field, Form.ID, Serial_agar_dilution...
## dbl (8): ...1, ID, Year, lat, long, Applications, Rate, Number.of.Years.1
## lgl (2): company_current_season, company_previous_seasons
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.
Colletotrichum <-
whitemold.inventory.2 %>%
# filter(ID %in% lasiodiplodia) %>%
mutate(country = ifelse(
ID %in% mexican.isolates,
"Mexico",
ifelse(ID %in% brazilian.isolates,
"Brazil", "USA")),
source = ifelse(
ID %in% baseline.isolates.2,
"Baseline",
ifelse(
ID %in% Farmer.fields,
"Producer Fields","Fungicide Field Trials"
)), Host = ifelse(
ID %in% drybean,
"Drybean", ifelse(
ID %in% soybean,
"Soybean","different_host")),
source = as.factor(source),
Host = as.factor(Host),
country = as.factor(country),
Field = as.factor(Field),
Year = as.factor(Year),
State = as.factor(State),
County = as.factor(County),
Fungicide_current_season = as.factor(Fungicide_current_season),
molecule.s._current_season = as.factor(molecule.s._current_season),
Group_MOA_current_season = as.factor(Group_MOA_current_season),
Applications = as.factor(Applications),
Rate = as.factor(Rate)
) %>% select(
ID,
source,#we will change later to the real variable "source"
Host,
country,
Field,
Year,
State,
County,
Fungicide_current_season,
molecule.s._current_season,
Group_MOA_current_season,
Applications,
Rate
)
controls.in.the.plots <- Colletotrichum %>% filter( Fungicide_current_season == "control") %>% select(ID) %>% pull()
controls.in.the.plots.2 <- c(1175, 1195, 1327, 1443, 1446, 1461, 1541, 1710)We randomly selected 154 isolates: 86 from dry beans (from 9 states with 1-17 isolates/state, and 2 states from Brazil) and 66 from soybeans (6 NE, 18 IA, 13 WI & 18 MI and 4 from Mexico) from the collection (baseline, FF, and FF) plus 5 same isolates used in the serial dilution approach
TM.joined.2 <- TM.joined.2 %>% select(ID, EC50)
survey.TM <- bind_rows(survey.TM.thomas,
survey.TM.USA,
survey.TM.more.USA,
#survey.TM.MX.BZ, there are the same the other fungicides
TM.joined.2)
#Have to analyze the controls
#wranglind_DC_2
survey.TM.2 <- survey.TM%>%
wrangling_DC() %>%
mutate(Field_Year = ifelse(source == "Baseline", "Baseline", Field_Year),
Field_Year = as.factor(Field_Year)) %>%
dplyr::rename(EC50DC = EC50) %>% #change here to be able to join to other datframes to get the total used
select(ID, EC50DC) %>%
# is here where now I have to remove the isolates from controls from fields and potato host isolates provided by Dr.Chilvers at the end
dplyr::filter(! ID %in% controls.in.the.plots, ! ID %in% 2570:2574)
survey.boscalid.thomas.2 <- wrangling_survey(survey.boscalid.thomas) %>%
mutate(EC50DC = exp (boscalid.lm [[1]][[1]] + boscalid.lm[[1]][[2]] * RG))# exponential is the opposite of log
#using the model the intercept and the coefficient of the model
survey.boscalid.USA.2 <- wrangling_survey(survey.boscalid.USA) %>%
mutate(EC50DC = exp (boscalid.lm [[1]][[1]] + boscalid.lm[[1]][[2]] * RG))#
##
survey.boscalid.more.USA <- survey.boscalid.more.USA %>% mutate(response = (response/10), control = (control/10))
survey.boscalid.more.USA.2 <- wrangling_survey(survey.boscalid.more.USA) %>%
mutate(EC50DC = exp (boscalid.lm [[1]][[1]] + boscalid.lm[[1]][[2]] * RG))#
##
survey.boscalid.MX.BZ.2 <- wrangling_survey(survey.boscalid.MX.BZ) %>%
mutate(EC50DC = exp (boscalid.lm [[1]][[1]] + boscalid.lm[[1]][[2]] * RG))#
#Gathering
boscalid.EC50.2 <- boscalid.EC50 %>% select(ID, EC50) %>%
dplyr::rename(EC50DC = EC50)#just renaming in purpose to join the following dataframe
survey.boscalid <- bind_rows(survey.boscalid.thomas.2,
survey.boscalid.USA.2,
survey.boscalid.more.USA.2,
survey.boscalid.MX.BZ.2,
boscalid.EC50.2) %>% select(ID,EC50DC)%>%
# is here where now I have to remove the isolates from controls from fields and potato host isolates provided by Dr. Chilvers at the end
dplyr::filter(! ID %in% controls.in.the.plots, ! ID %in% 2570:2574 )
#Have to analyze the controls
#fix the function here
#wranglind_DC_2
#Check this
IDA <- ID
survey.boscalid.complete <- survey.boscalid%>%
wrangling_DC() %>%
wrangling_DC_2()
survey.boscalid.fields <- survey.boscalid%>%
wrangling_DC() %>%
wrangling_DC_2() %>%
wrangling_DC_3()
###For comparisons between hosts I had to take out the ones coming from baseline otherwise would not be fair
survey.boscalid.complete.host <- survey.boscalid.complete %>%
filter(!source=="Baseline")
# Tetraconazole
#any particular case check so far by this way
survey.tetraconazole.thomas.2 <- wrangling_survey(survey.tetraconazole.thomas) %>%
mutate(EC50DC = exp (tetraconazole.lm [[1]][[1]] + tetraconazole.lm[[1]][[2]] * RG))# exponential is the opposite of log
#using the model the intercept and the coefficient of the model
##
survey.tetraconazole.USA.2 <- wrangling_survey(survey.tetraconazole.USA) %>%
mutate(EC50DC = exp (tetraconazole.lm [[1]][[1]] + tetraconazole.lm[[1]][[2]] * RG))#
##
survey.tetraconazole.more.USA <- survey.tetraconazole.more.USA %>% mutate(response = (response/10), control = (control/10))
survey.tetraconazole.more.USA.2 <- wrangling_survey(survey.tetraconazole.more.USA) %>%
mutate(EC50DC = exp (tetraconazole.lm [[1]][[1]] + tetraconazole.lm[[1]][[2]] * RG))#
survey.tetraconazole.MX.BZ.2 <- wrangling_survey(survey.tetraconazole.MX.BZ) %>%
mutate(EC50DC = exp (tetraconazole.lm [[1]][[1]] + tetraconazole.lm[[1]][[2]] * RG))#
#Gathering
tetraconazole.EC50.2 <- tetraconazole.EC50 %>% select(ID, EC50) %>%
dplyr::rename(EC50DC = EC50)#just rebaming in purpose to join the following dataframe
survey.tetraconazole <- bind_rows(survey.tetraconazole.thomas.2,
survey.tetraconazole.USA.2,
survey.tetraconazole.more.USA.2,
survey.tetraconazole.MX.BZ.2,
tetraconazole.EC50.2) %>% select(ID, EC50DC) %>%
# is here where now I have to remove the isolates from controls from fields and potato host isolates provided by Dr. Chilvers at the end
dplyr::filter(! ID %in% controls.in.the.plots, ! ID %in% 2570:2574 )
#Have to analyze the controls
#wranglind_DC_2
survey.tetraconazole.complete <- survey.tetraconazole%>%
wrangling_DC() %>%
wrangling_DC_2()
survey.tetraconazole.fields <- survey.tetraconazole%>%
wrangling_DC() %>%
wrangling_DC_2() %>%
wrangling_DC_3()
# count(Field_Year) %>%
# filter(n >= 9)
###For comparisons between hosts I had to take out the ones coming from baseline otherwise would not be fair
survey.tetraconazole.complete.host <- survey.tetraconazole.complete %>%
filter(!source=="Baseline")
# picoxystrobin
#any particular case check so far by this way
survey.picoxystrobin.thomas.2 <- wrangling_survey(survey.picoxystrobin.thomas) %>%
mutate(EC50DC = exp (picoxystrobin.lm [[1]][[1]] + picoxystrobin.lm[[1]][[2]] * RG))# exponential is the opposite of log
#using the model the intercept and the coefficient of the model
##
#
survey.picoxystrobin.USA.2 <- wrangling_survey(survey.picoxystrobin.USA) %>%
mutate(EC50DC = exp (picoxystrobin.lm [[1]][[1]] + picoxystrobin.lm[[1]][[2]] * RG))#
##
survey.picoxystrobin.more.USA <- survey.picoxystrobin.more.USA %>% mutate(response = (response/10), control = (control/10))
survey.picoxystrobin.more.USA.2 <- wrangling_survey(survey.picoxystrobin.more.USA) %>%
mutate(EC50DC = exp (picoxystrobin.lm [[1]][[1]] + picoxystrobin.lm[[1]][[2]] * RG))#
##
survey.picoxystrobin.MX.BZ.2 <- wrangling_survey(survey.picoxystrobin.MX.BZ) %>%
mutate(EC50DC = exp (picoxystrobin.lm [[1]][[1]] + picoxystrobin.lm[[1]][[2]] * RG))#
#Gathering
picoxystrobin.EC50.2 <- picoxystrobin.EC50 %>% select(ID, EC50) %>%
dplyr::rename(EC50DC = EC50)#just rebaming in purpose to join the following dataframe
survey.picoxystrobin <- bind_rows(survey.picoxystrobin.thomas.2,
survey.picoxystrobin.USA.2,
survey.picoxystrobin.more.USA.2,
survey.picoxystrobin.MX.BZ.2,
picoxystrobin.EC50.2) %>% select(ID, EC50DC) %>%
# is here where now I have to remove the isolates from controls from fields and potato host isolates provided by Dr. Chilvers at the end
dplyr::filter(! ID %in% controls.in.the.plots, ! ID %in% 2570:2574 )
#Have to analyze the controls
survey.picoxystrobin.complete <- survey.picoxystrobin%>%
wrangling_DC() %>%
wrangling_DC_2()
survey.picoxystrobin.fields <- survey.picoxystrobin%>%
wrangling_DC() %>%
wrangling_DC_2() %>%
wrangling_DC_3()
###For comparisons between hosts I had to take out the ones coming from baseline otherwise would not be fair
survey.picoxystrobin.complete.host <- survey.picoxystrobin.complete %>%
filter(!source=="Baseline")test_kruskal_source(survey.boscalid.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 18.701, df = 2, p-value = 8.694e-05
test_Dunn_source(survey.boscalid.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Fungicide Field Trials-Baseline 4.187608 1.00000
## Producer Fields-Baseline -51.975684 0.11835
## Producer Fields-Fungicide Field Trials -56.163291 0.00017 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
generate_label( test_Dunn_source(survey.boscalid.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() ))## plot.labels Dunn.levels
## Fungicide Field Trials-Baseline Fungicide Field Trials 1.0000000000
## Producer Fields-Baseline Producer Fields 0.1183489091
## Producer Fields-Fungicide Field Trials Baseline 0.0001666306
## labels
## Fungicide Field Trials-Baseline a
## Producer Fields-Baseline b
## Producer Fields-Fungicide Field Trials ab
survey.boscalid.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() %>%
group_by(source) %>% summarize(round(range(EC50DC),3), average= round(mean(EC50DC), 2), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'source'. You can override using the
## `.groups` argument.
## # A tibble: 6 Ă— 6
## # Groups: source [3]
## source `round(range(EC50DC), 3)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Baseline 0.066 0.11 21 0.0353 0.008
## 2 Baseline 0.222 0.11 21 0.0353 0.008
## 3 Fungicide Field Trials 0.062 0.11 83 0.0219 0.002
## 4 Fungicide Field Trials 0.163 0.11 83 0.0219 0.002
## 5 Producer Fields 0.042 0.1 282 0.0290 0.002
## 6 Producer Fields 0.18 0.1 282 0.0290 0.002
#generate_label(
test_kruskal_host(survey.boscalid.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 1.6187, df = 1, p-value = 0.2033
#generate_label
test_Dunn_host(survey.boscalid.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Soybean-Drybean 14.53249 0.2033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
survey.boscalid.complete.host %>%
group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() %>%
group_by(Host) %>% summarize(round(range(EC50DC),3), average= round(mean(EC50DC),2), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'Host'. You can override using the
## `.groups` argument.
## # A tibble: 4 Ă— 6
## # Groups: Host [2]
## Host `round(range(EC50DC), 3)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Drybean 0.049 0.1 229 0.0298 0.002
## 2 Drybean 0.18 0.1 229 0.0298 0.002
## 3 Soybean 0.042 0.1 136 0.0246 0.002
## 4 Soybean 0.163 0.1 136 0.0246 0.002
generate_label( test_field (survey.boscalid.fields))## plot.labels Dunn.levels
## ND.07.fld01.DB-NE.10.fld21.DB ND.07.fld01.DB 1.000000e+00
## WA.07.fld09.DB-NE.10.fld21.DB WA.07.fld09.DB 1.000000e+00
## MI.16.East.Lansing.SB-NE.10.fld21.DB MI.16.East.Lansing.SB 1.000000e+00
## WA.08.fld10.DB-NE.10.fld21.DB WA.08.fld10.DB 1.000000e+00
## ND.10.fld25.DB-NE.10.fld21.DB ND.10.fld25.DB 2.477204e-01
## MI.15.Montcalm.SB-NE.10.fld21.DB MI.15.Montcalm.SB 4.244188e-02
## IA.17.Nashua.SB-NE.10.fld21.DB IA.17.Nashua.SB 2.054563e-02
## Baseline-NE.10.fld21.DB Baseline 2.087706e-02
## WI.17.Hancock.SB-NE.10.fld21.DB WI.17.Hancock.SB 2.713616e-02
## MI.17.Montcalm.SB-NE.10.fld21.DB MI.17.Montcalm.SB 3.951429e-03
## BR.2010.por.DB-NE.10.fld21.DB BR.2010.por.DB 9.775535e-04
## BR.2010.unai.DB-NE.10.fld21.DB BR.2010.unai.DB 2.807856e-04
## MX.2017.pre.DB-NE.10.fld21.DB MX.2017.pre.DB 2.305319e-04
## MX.2017.ca.DB-NE.10.fld21.DB MX.2017.ca.DB 9.975110e-05
## BR.2010.lam.DB-NE.10.fld21.DB BR.2010.lam.DB 9.373366e-06
## MX.2017.ley.DB-NE.10.fld21.DB MX.2017.ley.DB 4.057426e-06
## WA.07.fld09.DB-ND.07.fld01.DB NE.10.fld21.DB 1.000000e+00
## MI.16.East.Lansing.SB-ND.07.fld01.DB ND.07.fld01.DB 1.000000e+00
## WA.08.fld10.DB-ND.07.fld01.DB WA.07.fld09.DB 1.000000e+00
## ND.10.fld25.DB-ND.07.fld01.DB MI.16.East.Lansing.SB 1.000000e+00
## MI.15.Montcalm.SB-ND.07.fld01.DB WA.08.fld10.DB 3.562009e-01
## IA.17.Nashua.SB-ND.07.fld01.DB ND.10.fld25.DB 2.152783e-01
## Baseline-ND.07.fld01.DB MI.15.Montcalm.SB 2.312318e-01
## WI.17.Hancock.SB-ND.07.fld01.DB IA.17.Nashua.SB 2.586017e-01
## MI.17.Montcalm.SB-ND.07.fld01.DB Baseline 4.618881e-02
## BR.2010.por.DB-ND.07.fld01.DB WI.17.Hancock.SB 1.080346e-02
## BR.2010.unai.DB-ND.07.fld01.DB MI.17.Montcalm.SB 3.178176e-03
## MX.2017.pre.DB-ND.07.fld01.DB BR.2010.por.DB 2.699022e-03
## MX.2017.ca.DB-ND.07.fld01.DB BR.2010.unai.DB 1.199445e-03
## BR.2010.lam.DB-ND.07.fld01.DB MX.2017.pre.DB 1.167211e-04
## MX.2017.ley.DB-ND.07.fld01.DB MX.2017.ca.DB 5.256504e-05
## MI.16.East.Lansing.SB-WA.07.fld09.DB BR.2010.lam.DB 1.000000e+00
## WA.08.fld10.DB-WA.07.fld09.DB MX.2017.ley.DB 1.000000e+00
## ND.10.fld25.DB-WA.07.fld09.DB NE.10.fld21.DB 1.000000e+00
## MI.15.Montcalm.SB-WA.07.fld09.DB ND.07.fld01.DB 1.000000e+00
## IA.17.Nashua.SB-WA.07.fld09.DB WA.07.fld09.DB 1.000000e+00
## Baseline-WA.07.fld09.DB MI.16.East.Lansing.SB 1.000000e+00
## WI.17.Hancock.SB-WA.07.fld09.DB WA.08.fld10.DB 1.000000e+00
## MI.17.Montcalm.SB-WA.07.fld09.DB ND.10.fld25.DB 6.327472e-01
## BR.2010.por.DB-WA.07.fld09.DB MI.15.Montcalm.SB 1.384048e-01
## BR.2010.unai.DB-WA.07.fld09.DB IA.17.Nashua.SB 4.323582e-02
## MX.2017.pre.DB-WA.07.fld09.DB Baseline 3.913987e-02
## MX.2017.ca.DB-WA.07.fld09.DB WI.17.Hancock.SB 1.854095e-02
## BR.2010.lam.DB-WA.07.fld09.DB MI.17.Montcalm.SB 2.007667e-03
## MX.2017.ley.DB-WA.07.fld09.DB BR.2010.por.DB 1.030144e-03
## WA.08.fld10.DB-MI.16.East.Lansing.SB BR.2010.unai.DB 1.000000e+00
## ND.10.fld25.DB-MI.16.East.Lansing.SB MX.2017.pre.DB 1.000000e+00
## MI.15.Montcalm.SB-MI.16.East.Lansing.SB MX.2017.ca.DB 1.000000e+00
## IA.17.Nashua.SB-MI.16.East.Lansing.SB BR.2010.lam.DB 1.000000e+00
## Baseline-MI.16.East.Lansing.SB MX.2017.ley.DB 1.000000e+00
## WI.17.Hancock.SB-MI.16.East.Lansing.SB NE.10.fld21.DB 1.000000e+00
## MI.17.Montcalm.SB-MI.16.East.Lansing.SB ND.07.fld01.DB 1.000000e+00
## BR.2010.por.DB-MI.16.East.Lansing.SB WA.07.fld09.DB 1.000000e+00
## BR.2010.unai.DB-MI.16.East.Lansing.SB MI.16.East.Lansing.SB 6.220960e-01
## MX.2017.pre.DB-MI.16.East.Lansing.SB WA.08.fld10.DB 6.198408e-01
## MX.2017.ca.DB-MI.16.East.Lansing.SB ND.10.fld25.DB 3.352475e-01
## BR.2010.lam.DB-MI.16.East.Lansing.SB MI.15.Montcalm.SB 4.755599e-02
## MX.2017.ley.DB-MI.16.East.Lansing.SB IA.17.Nashua.SB 3.227069e-02
## ND.10.fld25.DB-WA.08.fld10.DB Baseline 1.000000e+00
## MI.15.Montcalm.SB-WA.08.fld10.DB WI.17.Hancock.SB 1.000000e+00
## IA.17.Nashua.SB-WA.08.fld10.DB MI.17.Montcalm.SB 1.000000e+00
## Baseline-WA.08.fld10.DB BR.2010.por.DB 1.000000e+00
## WI.17.Hancock.SB-WA.08.fld10.DB BR.2010.unai.DB 1.000000e+00
## MI.17.Montcalm.SB-WA.08.fld10.DB MX.2017.pre.DB 1.000000e+00
## BR.2010.por.DB-WA.08.fld10.DB MX.2017.ca.DB 1.000000e+00
## BR.2010.unai.DB-WA.08.fld10.DB BR.2010.lam.DB 1.000000e+00
## MX.2017.pre.DB-WA.08.fld10.DB MX.2017.ley.DB 1.000000e+00
## MX.2017.ca.DB-WA.08.fld10.DB NE.10.fld21.DB 1.000000e+00
## BR.2010.lam.DB-WA.08.fld10.DB ND.07.fld01.DB 1.952766e-01
## MX.2017.ley.DB-WA.08.fld10.DB WA.07.fld09.DB 1.606252e-01
## MI.15.Montcalm.SB-ND.10.fld25.DB MI.16.East.Lansing.SB 1.000000e+00
## IA.17.Nashua.SB-ND.10.fld25.DB WA.08.fld10.DB 1.000000e+00
## Baseline-ND.10.fld25.DB ND.10.fld25.DB 1.000000e+00
## WI.17.Hancock.SB-ND.10.fld25.DB MI.15.Montcalm.SB 1.000000e+00
## MI.17.Montcalm.SB-ND.10.fld25.DB IA.17.Nashua.SB 1.000000e+00
## BR.2010.por.DB-ND.10.fld25.DB Baseline 1.000000e+00
## BR.2010.unai.DB-ND.10.fld25.DB WI.17.Hancock.SB 1.000000e+00
## MX.2017.pre.DB-ND.10.fld25.DB MI.17.Montcalm.SB 1.000000e+00
## MX.2017.ca.DB-ND.10.fld25.DB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-ND.10.fld25.DB BR.2010.unai.DB 1.000000e+00
## MX.2017.ley.DB-ND.10.fld25.DB MX.2017.pre.DB 1.000000e+00
## IA.17.Nashua.SB-MI.15.Montcalm.SB MX.2017.ca.DB 1.000000e+00
## Baseline-MI.15.Montcalm.SB BR.2010.lam.DB 1.000000e+00
## WI.17.Hancock.SB-MI.15.Montcalm.SB MX.2017.ley.DB 1.000000e+00
## MI.17.Montcalm.SB-MI.15.Montcalm.SB NE.10.fld21.DB 1.000000e+00
## BR.2010.por.DB-MI.15.Montcalm.SB ND.07.fld01.DB 1.000000e+00
## BR.2010.unai.DB-MI.15.Montcalm.SB WA.07.fld09.DB 1.000000e+00
## MX.2017.pre.DB-MI.15.Montcalm.SB MI.16.East.Lansing.SB 1.000000e+00
## MX.2017.ca.DB-MI.15.Montcalm.SB WA.08.fld10.DB 1.000000e+00
## BR.2010.lam.DB-MI.15.Montcalm.SB ND.10.fld25.DB 1.000000e+00
## MX.2017.ley.DB-MI.15.Montcalm.SB MI.15.Montcalm.SB 1.000000e+00
## Baseline-IA.17.Nashua.SB IA.17.Nashua.SB 1.000000e+00
## WI.17.Hancock.SB-IA.17.Nashua.SB Baseline 1.000000e+00
## MI.17.Montcalm.SB-IA.17.Nashua.SB WI.17.Hancock.SB 1.000000e+00
## BR.2010.por.DB-IA.17.Nashua.SB MI.17.Montcalm.SB 1.000000e+00
## BR.2010.unai.DB-IA.17.Nashua.SB BR.2010.por.DB 1.000000e+00
## MX.2017.pre.DB-IA.17.Nashua.SB BR.2010.unai.DB 1.000000e+00
## MX.2017.ca.DB-IA.17.Nashua.SB MX.2017.pre.DB 1.000000e+00
## BR.2010.lam.DB-IA.17.Nashua.SB MX.2017.ca.DB 1.000000e+00
## MX.2017.ley.DB-IA.17.Nashua.SB BR.2010.lam.DB 1.000000e+00
## WI.17.Hancock.SB-Baseline MX.2017.ley.DB 1.000000e+00
## MI.17.Montcalm.SB-Baseline NE.10.fld21.DB 1.000000e+00
## BR.2010.por.DB-Baseline ND.07.fld01.DB 1.000000e+00
## BR.2010.unai.DB-Baseline WA.07.fld09.DB 1.000000e+00
## MX.2017.pre.DB-Baseline MI.16.East.Lansing.SB 1.000000e+00
## MX.2017.ca.DB-Baseline WA.08.fld10.DB 1.000000e+00
## BR.2010.lam.DB-Baseline ND.10.fld25.DB 1.000000e+00
## MX.2017.ley.DB-Baseline MI.15.Montcalm.SB 1.000000e+00
## MI.17.Montcalm.SB-WI.17.Hancock.SB IA.17.Nashua.SB 1.000000e+00
## BR.2010.por.DB-WI.17.Hancock.SB Baseline 1.000000e+00
## BR.2010.unai.DB-WI.17.Hancock.SB WI.17.Hancock.SB 1.000000e+00
## MX.2017.pre.DB-WI.17.Hancock.SB MI.17.Montcalm.SB 1.000000e+00
## MX.2017.ca.DB-WI.17.Hancock.SB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-WI.17.Hancock.SB BR.2010.unai.DB 1.000000e+00
## MX.2017.ley.DB-WI.17.Hancock.SB MX.2017.pre.DB 1.000000e+00
## BR.2010.por.DB-MI.17.Montcalm.SB MX.2017.ca.DB 1.000000e+00
## BR.2010.unai.DB-MI.17.Montcalm.SB BR.2010.lam.DB 1.000000e+00
## MX.2017.pre.DB-MI.17.Montcalm.SB MX.2017.ley.DB 1.000000e+00
## MX.2017.ca.DB-MI.17.Montcalm.SB NE.10.fld21.DB 1.000000e+00
## BR.2010.lam.DB-MI.17.Montcalm.SB ND.07.fld01.DB 1.000000e+00
## MX.2017.ley.DB-MI.17.Montcalm.SB WA.07.fld09.DB 1.000000e+00
## BR.2010.unai.DB-BR.2010.por.DB MI.16.East.Lansing.SB 1.000000e+00
## MX.2017.pre.DB-BR.2010.por.DB WA.08.fld10.DB 1.000000e+00
## MX.2017.ca.DB-BR.2010.por.DB ND.10.fld25.DB 1.000000e+00
## BR.2010.lam.DB-BR.2010.por.DB MI.15.Montcalm.SB 1.000000e+00
## MX.2017.ley.DB-BR.2010.por.DB IA.17.Nashua.SB 1.000000e+00
## MX.2017.pre.DB-BR.2010.unai.DB Baseline 1.000000e+00
## MX.2017.ca.DB-BR.2010.unai.DB WI.17.Hancock.SB 1.000000e+00
## BR.2010.lam.DB-BR.2010.unai.DB MI.17.Montcalm.SB 1.000000e+00
## MX.2017.ley.DB-BR.2010.unai.DB BR.2010.por.DB 1.000000e+00
## MX.2017.ca.DB-MX.2017.pre.DB BR.2010.unai.DB 1.000000e+00
## BR.2010.lam.DB-MX.2017.pre.DB MX.2017.pre.DB 1.000000e+00
## MX.2017.ley.DB-MX.2017.pre.DB MX.2017.ca.DB 1.000000e+00
## BR.2010.lam.DB-MX.2017.ca.DB BR.2010.lam.DB 1.000000e+00
## MX.2017.ley.DB-MX.2017.ca.DB MX.2017.ley.DB 1.000000e+00
## MX.2017.ley.DB-BR.2010.lam.DB NE.10.fld21.DB 1.000000e+00
## labels
## ND.07.fld01.DB-NE.10.fld21.DB ab
## WA.07.fld09.DB-NE.10.fld21.DB abc
## MI.16.East.Lansing.SB-NE.10.fld21.DB abcd
## WA.08.fld10.DB-NE.10.fld21.DB abcde
## ND.10.fld25.DB-NE.10.fld21.DB abcde
## MI.15.Montcalm.SB-NE.10.fld21.DB acde
## IA.17.Nashua.SB-NE.10.fld21.DB acde
## Baseline-NE.10.fld21.DB acde
## WI.17.Hancock.SB-NE.10.fld21.DB acde
## MI.17.Montcalm.SB-NE.10.fld21.DB cde
## BR.2010.por.DB-NE.10.fld21.DB cde
## BR.2010.unai.DB-NE.10.fld21.DB de
## MX.2017.pre.DB-NE.10.fld21.DB de
## MX.2017.ca.DB-NE.10.fld21.DB de
## BR.2010.lam.DB-NE.10.fld21.DB e
## MX.2017.ley.DB-NE.10.fld21.DB e
## WA.07.fld09.DB-ND.07.fld01.DB b
## MI.16.East.Lansing.SB-ND.07.fld01.DB ab
## WA.08.fld10.DB-ND.07.fld01.DB abc
## ND.10.fld25.DB-ND.07.fld01.DB abcd
## MI.15.Montcalm.SB-ND.07.fld01.DB abcde
## IA.17.Nashua.SB-ND.07.fld01.DB abcde
## Baseline-ND.07.fld01.DB acde
## WI.17.Hancock.SB-ND.07.fld01.DB acde
## MI.17.Montcalm.SB-ND.07.fld01.DB acde
## BR.2010.por.DB-ND.07.fld01.DB acde
## BR.2010.unai.DB-ND.07.fld01.DB cde
## MX.2017.pre.DB-ND.07.fld01.DB cde
## MX.2017.ca.DB-ND.07.fld01.DB de
## BR.2010.lam.DB-ND.07.fld01.DB de
## MX.2017.ley.DB-ND.07.fld01.DB de
## MI.16.East.Lansing.SB-WA.07.fld09.DB e
## WA.08.fld10.DB-WA.07.fld09.DB e
## ND.10.fld25.DB-WA.07.fld09.DB b
## MI.15.Montcalm.SB-WA.07.fld09.DB ab
## IA.17.Nashua.SB-WA.07.fld09.DB abc
## Baseline-WA.07.fld09.DB abcd
## WI.17.Hancock.SB-WA.07.fld09.DB abcde
## MI.17.Montcalm.SB-WA.07.fld09.DB abcde
## BR.2010.por.DB-WA.07.fld09.DB acde
## BR.2010.unai.DB-WA.07.fld09.DB acde
## MX.2017.pre.DB-WA.07.fld09.DB acde
## MX.2017.ca.DB-WA.07.fld09.DB acde
## BR.2010.lam.DB-WA.07.fld09.DB cde
## MX.2017.ley.DB-WA.07.fld09.DB cde
## WA.08.fld10.DB-MI.16.East.Lansing.SB de
## ND.10.fld25.DB-MI.16.East.Lansing.SB de
## MI.15.Montcalm.SB-MI.16.East.Lansing.SB de
## IA.17.Nashua.SB-MI.16.East.Lansing.SB e
## Baseline-MI.16.East.Lansing.SB e
## WI.17.Hancock.SB-MI.16.East.Lansing.SB b
## MI.17.Montcalm.SB-MI.16.East.Lansing.SB ab
## BR.2010.por.DB-MI.16.East.Lansing.SB abc
## BR.2010.unai.DB-MI.16.East.Lansing.SB abcd
## MX.2017.pre.DB-MI.16.East.Lansing.SB abcde
## MX.2017.ca.DB-MI.16.East.Lansing.SB abcde
## BR.2010.lam.DB-MI.16.East.Lansing.SB acde
## MX.2017.ley.DB-MI.16.East.Lansing.SB acde
## ND.10.fld25.DB-WA.08.fld10.DB acde
## MI.15.Montcalm.SB-WA.08.fld10.DB acde
## IA.17.Nashua.SB-WA.08.fld10.DB cde
## Baseline-WA.08.fld10.DB cde
## WI.17.Hancock.SB-WA.08.fld10.DB de
## MI.17.Montcalm.SB-WA.08.fld10.DB de
## BR.2010.por.DB-WA.08.fld10.DB de
## BR.2010.unai.DB-WA.08.fld10.DB e
## MX.2017.pre.DB-WA.08.fld10.DB e
## MX.2017.ca.DB-WA.08.fld10.DB b
## BR.2010.lam.DB-WA.08.fld10.DB ab
## MX.2017.ley.DB-WA.08.fld10.DB abc
## MI.15.Montcalm.SB-ND.10.fld25.DB abcd
## IA.17.Nashua.SB-ND.10.fld25.DB abcde
## Baseline-ND.10.fld25.DB abcde
## WI.17.Hancock.SB-ND.10.fld25.DB acde
## MI.17.Montcalm.SB-ND.10.fld25.DB acde
## BR.2010.por.DB-ND.10.fld25.DB acde
## BR.2010.unai.DB-ND.10.fld25.DB acde
## MX.2017.pre.DB-ND.10.fld25.DB cde
## MX.2017.ca.DB-ND.10.fld25.DB cde
## BR.2010.lam.DB-ND.10.fld25.DB de
## MX.2017.ley.DB-ND.10.fld25.DB de
## IA.17.Nashua.SB-MI.15.Montcalm.SB de
## Baseline-MI.15.Montcalm.SB e
## WI.17.Hancock.SB-MI.15.Montcalm.SB e
## MI.17.Montcalm.SB-MI.15.Montcalm.SB b
## BR.2010.por.DB-MI.15.Montcalm.SB ab
## BR.2010.unai.DB-MI.15.Montcalm.SB abc
## MX.2017.pre.DB-MI.15.Montcalm.SB abcd
## MX.2017.ca.DB-MI.15.Montcalm.SB abcde
## BR.2010.lam.DB-MI.15.Montcalm.SB abcde
## MX.2017.ley.DB-MI.15.Montcalm.SB acde
## Baseline-IA.17.Nashua.SB acde
## WI.17.Hancock.SB-IA.17.Nashua.SB acde
## MI.17.Montcalm.SB-IA.17.Nashua.SB acde
## BR.2010.por.DB-IA.17.Nashua.SB cde
## BR.2010.unai.DB-IA.17.Nashua.SB cde
## MX.2017.pre.DB-IA.17.Nashua.SB de
## MX.2017.ca.DB-IA.17.Nashua.SB de
## BR.2010.lam.DB-IA.17.Nashua.SB de
## MX.2017.ley.DB-IA.17.Nashua.SB e
## WI.17.Hancock.SB-Baseline e
## MI.17.Montcalm.SB-Baseline b
## BR.2010.por.DB-Baseline ab
## BR.2010.unai.DB-Baseline abc
## MX.2017.pre.DB-Baseline abcd
## MX.2017.ca.DB-Baseline abcde
## BR.2010.lam.DB-Baseline abcde
## MX.2017.ley.DB-Baseline acde
## MI.17.Montcalm.SB-WI.17.Hancock.SB acde
## BR.2010.por.DB-WI.17.Hancock.SB acde
## BR.2010.unai.DB-WI.17.Hancock.SB acde
## MX.2017.pre.DB-WI.17.Hancock.SB cde
## MX.2017.ca.DB-WI.17.Hancock.SB cde
## BR.2010.lam.DB-WI.17.Hancock.SB de
## MX.2017.ley.DB-WI.17.Hancock.SB de
## BR.2010.por.DB-MI.17.Montcalm.SB de
## BR.2010.unai.DB-MI.17.Montcalm.SB e
## MX.2017.pre.DB-MI.17.Montcalm.SB e
## MX.2017.ca.DB-MI.17.Montcalm.SB b
## BR.2010.lam.DB-MI.17.Montcalm.SB ab
## MX.2017.ley.DB-MI.17.Montcalm.SB abc
## BR.2010.unai.DB-BR.2010.por.DB abcd
## MX.2017.pre.DB-BR.2010.por.DB abcde
## MX.2017.ca.DB-BR.2010.por.DB abcde
## BR.2010.lam.DB-BR.2010.por.DB acde
## MX.2017.ley.DB-BR.2010.por.DB acde
## MX.2017.pre.DB-BR.2010.unai.DB acde
## MX.2017.ca.DB-BR.2010.unai.DB acde
## BR.2010.lam.DB-BR.2010.unai.DB cde
## MX.2017.ley.DB-BR.2010.unai.DB cde
## MX.2017.ca.DB-MX.2017.pre.DB de
## BR.2010.lam.DB-MX.2017.pre.DB de
## MX.2017.ley.DB-MX.2017.pre.DB de
## BR.2010.lam.DB-MX.2017.ca.DB e
## MX.2017.ley.DB-MX.2017.ca.DB e
## MX.2017.ley.DB-BR.2010.lam.DB b
survey.boscalid.fields.2 <- survey.boscalid.fields %>%
mutate(Field_difference = ifelse(
Field_Year == "Baseline"| Field_Year == "NE.10.fld21.DB"|
Field_Year == "ND.07.fld01.DB"|Field_Year == "WA.07.fld09.DB",
"Yes","No")) %>%
mutate(Country_difference = ifelse(
country == "USA",
"Yes","No"))
####
myplot_comparison(survey.boscalid.fields.2,highlabel = 0.01) + expand_limits(y = c(0.06, 0.18)) +
scale_y_continuous(breaks = c(0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18)) + geom_hline(yintercept =
0.1606226, linetype = "dashed")# make dynamic the value of intercept, create a new fucntion
test_kruskal_source(survey.tetraconazole.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 2.6865, df = 2, p-value = 0.261
test_Dunn_source(survey.tetraconazole.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() )##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Fungicide Field Trials-Baseline 37.89652 0.5794
## Producer Fields-Baseline 10.90838 1.0000
## Producer Fields-Fungicide Field Trials -26.98814 0.3759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
generate_label(test_Dunn_source(survey.tetraconazole.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() ))## plot.labels Dunn.levels
## Fungicide Field Trials-Baseline Fungicide Field Trials 0.5793826
## Producer Fields-Baseline Producer Fields 1.0000000
## Producer Fields-Fungicide Field Trials Baseline 0.3758844
## labels
## Fungicide Field Trials-Baseline a
## Producer Fields-Baseline a
## Producer Fields-Fungicide Field Trials a
survey.tetraconazole.complete %>%
group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() %>%
group_by(source) %>% summarize(range(EC50DC), average= mean(EC50DC), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'source'. You can override using the
## `.groups` argument.
## # A tibble: 6 Ă— 6
## # Groups: source [3]
## source `range(EC50DC)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Baseline 0.372 1.03 22 0.368 0.079
## 2 Baseline 1.58 1.03 22 0.368 0.079
## 3 Fungicide Field Trials 0.690 1.19 47 0.183 0.027
## 4 Fungicide Field Trials 1.83 1.19 47 0.183 0.027
## 5 Producer Fields 0.197 1.04 321 0.416 0.023
## 6 Producer Fields 2.27 1.04 321 0.416 0.023
#generate_label
test_kruskal_host(survey.tetraconazole.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 2.93, df = 1, p-value = 0.08694
#generate_label
test_Dunn_host(survey.tetraconazole.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Soybean-Drybean 19.57865 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#)
survey.tetraconazole.complete.host %>%
group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() %>%
group_by(Host) %>% summarize(range(EC50DC), average= mean(EC50DC), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'Host'. You can override using the
## `.groups` argument.
## # A tibble: 4 Ă— 6
## # Groups: Host [2]
## Host `range(EC50DC)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Drybean 0.197 1.03 229 0.442 0.029
## 2 Drybean 2.27 1.03 229 0.442 0.029
## 3 Soybean 0.361 1.12 139 0.302 0.026
## 4 Soybean 1.83 1.12 139 0.302 0.026
generate_label( test_field (survey.tetraconazole.fields))## plot.labels Dunn.levels
## ND.07.fld01.DB-NE.10.fld21.DB ND.07.fld01.DB 1.000000e+00
## MX.2017.pre.DB-NE.10.fld21.DB MX.2017.pre.DB 1.000000e+00
## MX.2017.ca.DB-NE.10.fld21.DB MX.2017.ca.DB 1.000000e+00
## Baseline-NE.10.fld21.DB Baseline 7.288913e-02
## MX.2017.ley.DB-NE.10.fld21.DB MX.2017.ley.DB 1.450813e-01
## BR.2010.por.DB-NE.10.fld21.DB BR.2010.por.DB 6.266112e-01
## MI.17.Montcalm.SB-NE.10.fld21.DB MI.17.Montcalm.SB 5.673756e-02
## WA.07.fld09.DB-NE.10.fld21.DB WA.07.fld09.DB 2.160034e-02
## WA.08.fld10.DB-NE.10.fld21.DB WA.08.fld10.DB 6.496539e-03
## IA.17.Nashua.SB-NE.10.fld21.DB IA.17.Nashua.SB 1.536112e-03
## MI.16.East.Lansing.SB-NE.10.fld21.DB MI.16.East.Lansing.SB 1.357638e-02
## BR.2010.lam.DB-NE.10.fld21.DB BR.2010.lam.DB 2.558938e-04
## ND.10.fld25.DB-NE.10.fld21.DB ND.10.fld25.DB 1.804037e-05
## BR.2010.unai.DB-NE.10.fld21.DB BR.2010.unai.DB 9.497434e-07
## MX.2017.pre.DB-ND.07.fld01.DB NE.10.fld21.DB 1.000000e+00
## MX.2017.ca.DB-ND.07.fld01.DB ND.07.fld01.DB 1.000000e+00
## Baseline-ND.07.fld01.DB MX.2017.pre.DB 1.000000e+00
## MX.2017.ley.DB-ND.07.fld01.DB MX.2017.ca.DB 1.000000e+00
## BR.2010.por.DB-ND.07.fld01.DB Baseline 1.000000e+00
## MI.17.Montcalm.SB-ND.07.fld01.DB MX.2017.ley.DB 1.000000e+00
## WA.07.fld09.DB-ND.07.fld01.DB BR.2010.por.DB 1.000000e+00
## WA.08.fld10.DB-ND.07.fld01.DB MI.17.Montcalm.SB 1.000000e+00
## IA.17.Nashua.SB-ND.07.fld01.DB WA.07.fld09.DB 3.232033e-01
## MI.16.East.Lansing.SB-ND.07.fld01.DB WA.08.fld10.DB 1.000000e+00
## BR.2010.lam.DB-ND.07.fld01.DB IA.17.Nashua.SB 6.508085e-02
## ND.10.fld25.DB-ND.07.fld01.DB MI.16.East.Lansing.SB 7.468676e-03
## BR.2010.unai.DB-ND.07.fld01.DB BR.2010.lam.DB 7.015396e-04
## MX.2017.ca.DB-MX.2017.pre.DB ND.10.fld25.DB 1.000000e+00
## Baseline-MX.2017.pre.DB BR.2010.unai.DB 1.000000e+00
## MX.2017.ley.DB-MX.2017.pre.DB NE.10.fld21.DB 1.000000e+00
## BR.2010.por.DB-MX.2017.pre.DB ND.07.fld01.DB 1.000000e+00
## MI.17.Montcalm.SB-MX.2017.pre.DB MX.2017.pre.DB 1.000000e+00
## WA.07.fld09.DB-MX.2017.pre.DB MX.2017.ca.DB 1.000000e+00
## WA.08.fld10.DB-MX.2017.pre.DB Baseline 9.410956e-01
## IA.17.Nashua.SB-MX.2017.pre.DB MX.2017.ley.DB 3.054874e-01
## MI.16.East.Lansing.SB-MX.2017.pre.DB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-MX.2017.pre.DB MI.17.Montcalm.SB 6.406962e-02
## ND.10.fld25.DB-MX.2017.pre.DB WA.07.fld09.DB 8.050138e-03
## BR.2010.unai.DB-MX.2017.pre.DB WA.08.fld10.DB 8.690408e-04
## Baseline-MX.2017.ca.DB IA.17.Nashua.SB 1.000000e+00
## MX.2017.ley.DB-MX.2017.ca.DB MI.16.East.Lansing.SB 1.000000e+00
## BR.2010.por.DB-MX.2017.ca.DB BR.2010.lam.DB 1.000000e+00
## MI.17.Montcalm.SB-MX.2017.ca.DB ND.10.fld25.DB 1.000000e+00
## WA.07.fld09.DB-MX.2017.ca.DB BR.2010.unai.DB 1.000000e+00
## WA.08.fld10.DB-MX.2017.ca.DB NE.10.fld21.DB 1.000000e+00
## IA.17.Nashua.SB-MX.2017.ca.DB ND.07.fld01.DB 8.595254e-01
## MI.16.East.Lansing.SB-MX.2017.ca.DB MX.2017.pre.DB 1.000000e+00
## BR.2010.lam.DB-MX.2017.ca.DB MX.2017.ca.DB 1.960945e-01
## ND.10.fld25.DB-MX.2017.ca.DB Baseline 2.884335e-02
## BR.2010.unai.DB-MX.2017.ca.DB MX.2017.ley.DB 3.764441e-03
## MX.2017.ley.DB-Baseline BR.2010.por.DB 1.000000e+00
## BR.2010.por.DB-Baseline MI.17.Montcalm.SB 1.000000e+00
## MI.17.Montcalm.SB-Baseline WA.07.fld09.DB 1.000000e+00
## WA.07.fld09.DB-Baseline WA.08.fld10.DB 1.000000e+00
## WA.08.fld10.DB-Baseline IA.17.Nashua.SB 1.000000e+00
## IA.17.Nashua.SB-Baseline MI.16.East.Lansing.SB 1.000000e+00
## MI.16.East.Lansing.SB-Baseline BR.2010.lam.DB 1.000000e+00
## BR.2010.lam.DB-Baseline ND.10.fld25.DB 1.000000e+00
## ND.10.fld25.DB-Baseline BR.2010.unai.DB 4.796523e-01
## BR.2010.unai.DB-Baseline NE.10.fld21.DB 6.935555e-02
## BR.2010.por.DB-MX.2017.ley.DB ND.07.fld01.DB 1.000000e+00
## MI.17.Montcalm.SB-MX.2017.ley.DB MX.2017.pre.DB 1.000000e+00
## WA.07.fld09.DB-MX.2017.ley.DB MX.2017.ca.DB 1.000000e+00
## WA.08.fld10.DB-MX.2017.ley.DB Baseline 1.000000e+00
## IA.17.Nashua.SB-MX.2017.ley.DB MX.2017.ley.DB 1.000000e+00
## MI.16.East.Lansing.SB-MX.2017.ley.DB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-MX.2017.ley.DB MI.17.Montcalm.SB 1.000000e+00
## ND.10.fld25.DB-MX.2017.ley.DB WA.07.fld09.DB 1.000000e+00
## BR.2010.unai.DB-MX.2017.ley.DB WA.08.fld10.DB 6.181705e-01
## MI.17.Montcalm.SB-BR.2010.por.DB IA.17.Nashua.SB 1.000000e+00
## WA.07.fld09.DB-BR.2010.por.DB MI.16.East.Lansing.SB 1.000000e+00
## WA.08.fld10.DB-BR.2010.por.DB BR.2010.lam.DB 1.000000e+00
## IA.17.Nashua.SB-BR.2010.por.DB ND.10.fld25.DB 1.000000e+00
## MI.16.East.Lansing.SB-BR.2010.por.DB BR.2010.unai.DB 1.000000e+00
## BR.2010.lam.DB-BR.2010.por.DB NE.10.fld21.DB 1.000000e+00
## ND.10.fld25.DB-BR.2010.por.DB ND.07.fld01.DB 9.403029e-01
## BR.2010.unai.DB-BR.2010.por.DB MX.2017.pre.DB 2.171075e-01
## WA.07.fld09.DB-MI.17.Montcalm.SB MX.2017.ca.DB 1.000000e+00
## WA.08.fld10.DB-MI.17.Montcalm.SB Baseline 1.000000e+00
## IA.17.Nashua.SB-MI.17.Montcalm.SB MX.2017.ley.DB 1.000000e+00
## MI.16.East.Lansing.SB-MI.17.Montcalm.SB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-MI.17.Montcalm.SB MI.17.Montcalm.SB 1.000000e+00
## ND.10.fld25.DB-MI.17.Montcalm.SB WA.07.fld09.DB 1.000000e+00
## BR.2010.unai.DB-MI.17.Montcalm.SB WA.08.fld10.DB 1.000000e+00
## WA.08.fld10.DB-WA.07.fld09.DB IA.17.Nashua.SB 1.000000e+00
## IA.17.Nashua.SB-WA.07.fld09.DB MI.16.East.Lansing.SB 1.000000e+00
## MI.16.East.Lansing.SB-WA.07.fld09.DB BR.2010.lam.DB 1.000000e+00
## BR.2010.lam.DB-WA.07.fld09.DB ND.10.fld25.DB 1.000000e+00
## ND.10.fld25.DB-WA.07.fld09.DB BR.2010.unai.DB 1.000000e+00
## BR.2010.unai.DB-WA.07.fld09.DB NE.10.fld21.DB 6.968058e-01
## IA.17.Nashua.SB-WA.08.fld10.DB ND.07.fld01.DB 1.000000e+00
## MI.16.East.Lansing.SB-WA.08.fld10.DB MX.2017.pre.DB 1.000000e+00
## BR.2010.lam.DB-WA.08.fld10.DB MX.2017.ca.DB 1.000000e+00
## ND.10.fld25.DB-WA.08.fld10.DB Baseline 1.000000e+00
## BR.2010.unai.DB-WA.08.fld10.DB MX.2017.ley.DB 1.000000e+00
## MI.16.East.Lansing.SB-IA.17.Nashua.SB BR.2010.por.DB 1.000000e+00
## BR.2010.lam.DB-IA.17.Nashua.SB MI.17.Montcalm.SB 1.000000e+00
## ND.10.fld25.DB-IA.17.Nashua.SB WA.07.fld09.DB 1.000000e+00
## BR.2010.unai.DB-IA.17.Nashua.SB WA.08.fld10.DB 1.000000e+00
## BR.2010.lam.DB-MI.16.East.Lansing.SB IA.17.Nashua.SB 1.000000e+00
## ND.10.fld25.DB-MI.16.East.Lansing.SB MI.16.East.Lansing.SB 1.000000e+00
## BR.2010.unai.DB-MI.16.East.Lansing.SB BR.2010.lam.DB 1.000000e+00
## ND.10.fld25.DB-BR.2010.lam.DB ND.10.fld25.DB 1.000000e+00
## BR.2010.unai.DB-BR.2010.lam.DB BR.2010.unai.DB 1.000000e+00
## BR.2010.unai.DB-ND.10.fld25.DB NE.10.fld21.DB 1.000000e+00
## labels
## ND.07.fld01.DB-NE.10.fld21.DB ab
## MX.2017.pre.DB-NE.10.fld21.DB ab
## MX.2017.ca.DB-NE.10.fld21.DB ab
## Baseline-NE.10.fld21.DB abc
## MX.2017.ley.DB-NE.10.fld21.DB abc
## BR.2010.por.DB-NE.10.fld21.DB abc
## MI.17.Montcalm.SB-NE.10.fld21.DB abc
## WA.07.fld09.DB-NE.10.fld21.DB ac
## WA.08.fld10.DB-NE.10.fld21.DB ac
## IA.17.Nashua.SB-NE.10.fld21.DB ac
## MI.16.East.Lansing.SB-NE.10.fld21.DB ac
## BR.2010.lam.DB-NE.10.fld21.DB ac
## ND.10.fld25.DB-NE.10.fld21.DB c
## BR.2010.unai.DB-NE.10.fld21.DB c
## MX.2017.pre.DB-ND.07.fld01.DB b
## MX.2017.ca.DB-ND.07.fld01.DB ab
## Baseline-ND.07.fld01.DB ab
## MX.2017.ley.DB-ND.07.fld01.DB ab
## BR.2010.por.DB-ND.07.fld01.DB abc
## MI.17.Montcalm.SB-ND.07.fld01.DB abc
## WA.07.fld09.DB-ND.07.fld01.DB abc
## WA.08.fld10.DB-ND.07.fld01.DB abc
## IA.17.Nashua.SB-ND.07.fld01.DB ac
## MI.16.East.Lansing.SB-ND.07.fld01.DB ac
## BR.2010.lam.DB-ND.07.fld01.DB ac
## ND.10.fld25.DB-ND.07.fld01.DB ac
## BR.2010.unai.DB-ND.07.fld01.DB ac
## MX.2017.ca.DB-MX.2017.pre.DB c
## Baseline-MX.2017.pre.DB c
## MX.2017.ley.DB-MX.2017.pre.DB b
## BR.2010.por.DB-MX.2017.pre.DB ab
## MI.17.Montcalm.SB-MX.2017.pre.DB ab
## WA.07.fld09.DB-MX.2017.pre.DB ab
## WA.08.fld10.DB-MX.2017.pre.DB abc
## IA.17.Nashua.SB-MX.2017.pre.DB abc
## MI.16.East.Lansing.SB-MX.2017.pre.DB abc
## BR.2010.lam.DB-MX.2017.pre.DB abc
## ND.10.fld25.DB-MX.2017.pre.DB ac
## BR.2010.unai.DB-MX.2017.pre.DB ac
## Baseline-MX.2017.ca.DB ac
## MX.2017.ley.DB-MX.2017.ca.DB ac
## BR.2010.por.DB-MX.2017.ca.DB ac
## MI.17.Montcalm.SB-MX.2017.ca.DB c
## WA.07.fld09.DB-MX.2017.ca.DB c
## WA.08.fld10.DB-MX.2017.ca.DB b
## IA.17.Nashua.SB-MX.2017.ca.DB ab
## MI.16.East.Lansing.SB-MX.2017.ca.DB ab
## BR.2010.lam.DB-MX.2017.ca.DB ab
## ND.10.fld25.DB-MX.2017.ca.DB abc
## BR.2010.unai.DB-MX.2017.ca.DB abc
## MX.2017.ley.DB-Baseline abc
## BR.2010.por.DB-Baseline abc
## MI.17.Montcalm.SB-Baseline ac
## WA.07.fld09.DB-Baseline ac
## WA.08.fld10.DB-Baseline ac
## IA.17.Nashua.SB-Baseline ac
## MI.16.East.Lansing.SB-Baseline ac
## BR.2010.lam.DB-Baseline c
## ND.10.fld25.DB-Baseline c
## BR.2010.unai.DB-Baseline b
## BR.2010.por.DB-MX.2017.ley.DB ab
## MI.17.Montcalm.SB-MX.2017.ley.DB ab
## WA.07.fld09.DB-MX.2017.ley.DB ab
## WA.08.fld10.DB-MX.2017.ley.DB abc
## IA.17.Nashua.SB-MX.2017.ley.DB abc
## MI.16.East.Lansing.SB-MX.2017.ley.DB abc
## BR.2010.lam.DB-MX.2017.ley.DB abc
## ND.10.fld25.DB-MX.2017.ley.DB ac
## BR.2010.unai.DB-MX.2017.ley.DB ac
## MI.17.Montcalm.SB-BR.2010.por.DB ac
## WA.07.fld09.DB-BR.2010.por.DB ac
## WA.08.fld10.DB-BR.2010.por.DB ac
## IA.17.Nashua.SB-BR.2010.por.DB c
## MI.16.East.Lansing.SB-BR.2010.por.DB c
## BR.2010.lam.DB-BR.2010.por.DB b
## ND.10.fld25.DB-BR.2010.por.DB ab
## BR.2010.unai.DB-BR.2010.por.DB ab
## WA.07.fld09.DB-MI.17.Montcalm.SB ab
## WA.08.fld10.DB-MI.17.Montcalm.SB abc
## IA.17.Nashua.SB-MI.17.Montcalm.SB abc
## MI.16.East.Lansing.SB-MI.17.Montcalm.SB abc
## BR.2010.lam.DB-MI.17.Montcalm.SB abc
## ND.10.fld25.DB-MI.17.Montcalm.SB ac
## BR.2010.unai.DB-MI.17.Montcalm.SB ac
## WA.08.fld10.DB-WA.07.fld09.DB ac
## IA.17.Nashua.SB-WA.07.fld09.DB ac
## MI.16.East.Lansing.SB-WA.07.fld09.DB ac
## BR.2010.lam.DB-WA.07.fld09.DB c
## ND.10.fld25.DB-WA.07.fld09.DB c
## BR.2010.unai.DB-WA.07.fld09.DB b
## IA.17.Nashua.SB-WA.08.fld10.DB ab
## MI.16.East.Lansing.SB-WA.08.fld10.DB ab
## BR.2010.lam.DB-WA.08.fld10.DB ab
## ND.10.fld25.DB-WA.08.fld10.DB abc
## BR.2010.unai.DB-WA.08.fld10.DB abc
## MI.16.East.Lansing.SB-IA.17.Nashua.SB abc
## BR.2010.lam.DB-IA.17.Nashua.SB abc
## ND.10.fld25.DB-IA.17.Nashua.SB ac
## BR.2010.unai.DB-IA.17.Nashua.SB ac
## BR.2010.lam.DB-MI.16.East.Lansing.SB ac
## ND.10.fld25.DB-MI.16.East.Lansing.SB ac
## BR.2010.unai.DB-MI.16.East.Lansing.SB ac
## ND.10.fld25.DB-BR.2010.lam.DB c
## BR.2010.unai.DB-BR.2010.lam.DB c
## BR.2010.unai.DB-ND.10.fld25.DB b
survey.tetraconazole.fields.2 <- survey.tetraconazole.fields %>%
mutate(Field_difference = ifelse(
Field_Year == "Baseline"| Field_Year == "NE.10.fld21.DB"|
Field_Year == "BR.2010.unai.DB",
"Yes","No")) %>%
mutate(Country_difference = ifelse(
country == "USA",
"Yes","No"))
####
myplot_comparison(survey.tetraconazole.fields.2, highlabel = 0.1) + expand_limits(y = c(0.5, 1.6)) + scale_y_continuous(breaks = c(0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6)) 
test_kruskal_source(survey.picoxystrobin.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 13.434, df = 2, p-value = 0.00121
##
test_Dunn_source(survey.picoxystrobin.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Fungicide Field Trials-Baseline 50.17540 0.2006
## Producer Fields-Baseline 80.97263 0.0041 **
## Producer Fields-Fungicide Field Trials 30.79723 0.0876 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# generate the label
generate_label( test_Dunn_source(survey.picoxystrobin.complete %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup()))## plot.labels Dunn.levels
## Fungicide Field Trials-Baseline Fungicide Field Trials 0.200586495
## Producer Fields-Baseline Producer Fields 0.004142246
## Producer Fields-Fungicide Field Trials Baseline 0.087632317
## labels
## Fungicide Field Trials-Baseline ab
## Producer Fields-Baseline a
## Producer Fields-Fungicide Field Trials b
survey.picoxystrobin.complete %>%
group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup()%>%
group_by(source) %>% summarize(round(range(EC50DC),3), average= round(mean(EC50DC), 2), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'source'. You can override using the
## `.groups` argument.
## # A tibble: 6 Ă— 6
## # Groups: source [3]
## source `round(range(EC50DC), 3)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Baseline 0.011 0.01 22 0.00279 0.001
## 2 Baseline 0.021 0.01 22 0.00279 0.001
## 3 Fungicide Field Trials 0.008 0.01 85 0.00241 0
## 4 Fungicide Field Trials 0.021 0.01 85 0.00241 0
## 5 Producer Fields 0.006 0.02 289 0.00322 0
## 6 Producer Fields 0.027 0.02 289 0.00322 0
#generate_label
test_kruskal_host(survey.picoxystrobin.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Kruskal-Wallis rank sum test
##
## data: as.numeric(vardep) by as.factor(varindep)
## Kruskal-Wallis chi-squared = 38.07, df = 1, p-value = 6.827e-10
#generate_label
test_Dunn_host(survey.picoxystrobin.complete.host %>% group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup())##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Soybean-Drybean -70.70362 6.8e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
survey.picoxystrobin.complete.host %>%
group_by(ID) %>%
top_n(1, EC50DC) %>% #keeping the highest value of each ID value
ungroup() %>%
group_by(Host) %>% summarize(round(range(EC50DC),3), average= round(mean(EC50DC),2), N= n(), sd = sd(EC50DC), se = round(sd/sqrt(N),3))## `summarise()` has grouped output by 'Host'. You can override using the
## `.groups` argument.
## # A tibble: 4 Ă— 6
## # Groups: Host [2]
## Host `round(range(EC50DC), 3)` average N sd se
## <fct> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Drybean 0.009 0.02 228 0.00326 0
## 2 Drybean 0.027 0.02 228 0.00326 0
## 3 Soybean 0.006 0.01 146 0.00228 0
## 4 Soybean 0.021 0.01 146 0.00228 0
test_field (survey.picoxystrobin.fields)##
## Dunn's test of multiple comparisons using rank sums : bonferroni
##
## mean.rank.diff pval
## Baseline-WI.2015.cer.SB -0.36363636 1.00000
## IA.17.Nashua.SB-WI.2015.cer.SB 10.97058824 1.00000
## MI.17.Montcalm.SB-WI.2015.cer.SB 16.85294118 1.00000
## ND.10.fld25.DB-WI.2015.cer.SB 30.27777778 1.00000
## NE.10.fld21.DB-WI.2015.cer.SB 24.16666667 1.00000
## WA.08.fld10.DB-WI.2015.cer.SB 42.25000000 1.00000
## MI.16.East.Lansing.SB-WI.2015.cer.SB 36.93750000 1.00000
## BR.2010.lam.DB-WI.2015.cer.SB 48.83333333 1.00000
## WI.17.Hancock.SB-WI.2015.cer.SB 48.80769231 1.00000
## MI.15.Montcalm.SB-WI.2015.cer.SB 55.30000000 1.00000
## WA.07.fld09.DB-WI.2015.cer.SB 60.56250000 1.00000
## MX.2017.pre.DB-WI.2015.cer.SB 63.30000000 1.00000
## BR.2010.por.DB-WI.2015.cer.SB 73.00000000 1.00000
## ND.07.fld01.DB-WI.2015.cer.SB 100.75000000 0.04059 *
## BR.2010.unai.DB-WI.2015.cer.SB 84.00000000 0.55079
## MX.2017.ca.DB-WI.2015.cer.SB 113.70000000 0.01244 *
## MX.2017.ley.DB-WI.2015.cer.SB 126.77272727 0.00105 **
## IA.17.Nashua.SB-Baseline 11.33422460 1.00000
## MI.17.Montcalm.SB-Baseline 17.21657754 1.00000
## ND.10.fld25.DB-Baseline 30.64141414 1.00000
## NE.10.fld21.DB-Baseline 24.53030303 1.00000
## WA.08.fld10.DB-Baseline 42.61363636 1.00000
## MI.16.East.Lansing.SB-Baseline 37.30113636 1.00000
## BR.2010.lam.DB-Baseline 49.19696970 1.00000
## WI.17.Hancock.SB-Baseline 49.17132867 1.00000
## MI.15.Montcalm.SB-Baseline 55.66363636 1.00000
## WA.07.fld09.DB-Baseline 60.92613636 0.61999
## MX.2017.pre.DB-Baseline 63.66363636 1.00000
## BR.2010.por.DB-Baseline 73.36363636 0.43890
## ND.07.fld01.DB-Baseline 101.11363636 0.00193 **
## BR.2010.unai.DB-Baseline 84.36363636 0.09286 .
## MX.2017.ca.DB-Baseline 114.06363636 0.00054 ***
## MX.2017.ley.DB-Baseline 127.13636364 1.5e-05 ***
## MI.17.Montcalm.SB-IA.17.Nashua.SB 5.88235294 1.00000
## ND.10.fld25.DB-IA.17.Nashua.SB 19.30718954 1.00000
## NE.10.fld21.DB-IA.17.Nashua.SB 13.19607843 1.00000
## WA.08.fld10.DB-IA.17.Nashua.SB 31.27941176 1.00000
## MI.16.East.Lansing.SB-IA.17.Nashua.SB 25.96691176 1.00000
## BR.2010.lam.DB-IA.17.Nashua.SB 37.86274510 1.00000
## WI.17.Hancock.SB-IA.17.Nashua.SB 37.83710407 1.00000
## MI.15.Montcalm.SB-IA.17.Nashua.SB 44.32941176 1.00000
## WA.07.fld09.DB-IA.17.Nashua.SB 49.59191176 1.00000
## MX.2017.pre.DB-IA.17.Nashua.SB 52.32941176 1.00000
## BR.2010.por.DB-IA.17.Nashua.SB 62.02941176 1.00000
## ND.07.fld01.DB-IA.17.Nashua.SB 89.77941176 0.03422 *
## BR.2010.unai.DB-IA.17.Nashua.SB 73.02941176 0.68972
## MX.2017.ca.DB-IA.17.Nashua.SB 102.72941176 0.00988 **
## MX.2017.ley.DB-IA.17.Nashua.SB 115.80213904 0.00054 ***
## ND.10.fld25.DB-MI.17.Montcalm.SB 13.42483660 1.00000
## NE.10.fld21.DB-MI.17.Montcalm.SB 7.31372549 1.00000
## WA.08.fld10.DB-MI.17.Montcalm.SB 25.39705882 1.00000
## MI.16.East.Lansing.SB-MI.17.Montcalm.SB 20.08455882 1.00000
## BR.2010.lam.DB-MI.17.Montcalm.SB 31.98039216 1.00000
## WI.17.Hancock.SB-MI.17.Montcalm.SB 31.95475113 1.00000
## MI.15.Montcalm.SB-MI.17.Montcalm.SB 38.44705882 1.00000
## WA.07.fld09.DB-MI.17.Montcalm.SB 43.70955882 1.00000
## MX.2017.pre.DB-MI.17.Montcalm.SB 46.44705882 1.00000
## BR.2010.por.DB-MI.17.Montcalm.SB 56.14705882 1.00000
## ND.07.fld01.DB-MI.17.Montcalm.SB 83.89705882 0.08613 .
## BR.2010.unai.DB-MI.17.Montcalm.SB 67.14705882 1.00000
## MX.2017.ca.DB-MI.17.Montcalm.SB 96.84705882 0.02532 *
## MX.2017.ley.DB-MI.17.Montcalm.SB 109.91978610 0.00163 **
## NE.10.fld21.DB-ND.10.fld25.DB -6.11111111 1.00000
## WA.08.fld10.DB-ND.10.fld25.DB 11.97222222 1.00000
## MI.16.East.Lansing.SB-ND.10.fld25.DB 6.65972222 1.00000
## BR.2010.lam.DB-ND.10.fld25.DB 18.55555556 1.00000
## WI.17.Hancock.SB-ND.10.fld25.DB 18.52991453 1.00000
## MI.15.Montcalm.SB-ND.10.fld25.DB 25.02222222 1.00000
## WA.07.fld09.DB-ND.10.fld25.DB 30.28472222 1.00000
## MX.2017.pre.DB-ND.10.fld25.DB 33.02222222 1.00000
## BR.2010.por.DB-ND.10.fld25.DB 42.72222222 1.00000
## ND.07.fld01.DB-ND.10.fld25.DB 70.47222222 1.00000
## BR.2010.unai.DB-ND.10.fld25.DB 53.72222222 1.00000
## MX.2017.ca.DB-ND.10.fld25.DB 83.42222222 0.74837
## MX.2017.ley.DB-ND.10.fld25.DB 96.49494949 0.13406
## WA.08.fld10.DB-NE.10.fld21.DB 18.08333333 1.00000
## MI.16.East.Lansing.SB-NE.10.fld21.DB 12.77083333 1.00000
## BR.2010.lam.DB-NE.10.fld21.DB 24.66666667 1.00000
## WI.17.Hancock.SB-NE.10.fld21.DB 24.64102564 1.00000
## MI.15.Montcalm.SB-NE.10.fld21.DB 31.13333333 1.00000
## WA.07.fld09.DB-NE.10.fld21.DB 36.39583333 1.00000
## MX.2017.pre.DB-NE.10.fld21.DB 39.13333333 1.00000
## BR.2010.por.DB-NE.10.fld21.DB 48.83333333 1.00000
## ND.07.fld01.DB-NE.10.fld21.DB 76.58333333 1.00000
## BR.2010.unai.DB-NE.10.fld21.DB 59.83333333 1.00000
## MX.2017.ca.DB-NE.10.fld21.DB 89.53333333 0.38642
## MX.2017.ley.DB-NE.10.fld21.DB 102.60606061 0.06162 .
## MI.16.East.Lansing.SB-WA.08.fld10.DB -5.31250000 1.00000
## BR.2010.lam.DB-WA.08.fld10.DB 6.58333333 1.00000
## WI.17.Hancock.SB-WA.08.fld10.DB 6.55769231 1.00000
## MI.15.Montcalm.SB-WA.08.fld10.DB 13.05000000 1.00000
## WA.07.fld09.DB-WA.08.fld10.DB 18.31250000 1.00000
## MX.2017.pre.DB-WA.08.fld10.DB 21.05000000 1.00000
## BR.2010.por.DB-WA.08.fld10.DB 30.75000000 1.00000
## ND.07.fld01.DB-WA.08.fld10.DB 58.50000000 1.00000
## BR.2010.unai.DB-WA.08.fld10.DB 41.75000000 1.00000
## MX.2017.ca.DB-WA.08.fld10.DB 71.45000000 1.00000
## MX.2017.ley.DB-WA.08.fld10.DB 84.52272727 0.25989
## BR.2010.lam.DB-MI.16.East.Lansing.SB 11.89583333 1.00000
## WI.17.Hancock.SB-MI.16.East.Lansing.SB 11.87019231 1.00000
## MI.15.Montcalm.SB-MI.16.East.Lansing.SB 18.36250000 1.00000
## WA.07.fld09.DB-MI.16.East.Lansing.SB 23.62500000 1.00000
## MX.2017.pre.DB-MI.16.East.Lansing.SB 26.36250000 1.00000
## BR.2010.por.DB-MI.16.East.Lansing.SB 36.06250000 1.00000
## ND.07.fld01.DB-MI.16.East.Lansing.SB 63.81250000 1.00000
## BR.2010.unai.DB-MI.16.East.Lansing.SB 47.06250000 1.00000
## MX.2017.ca.DB-MI.16.East.Lansing.SB 76.76250000 0.48390
## MX.2017.ley.DB-MI.16.East.Lansing.SB 89.83522727 0.05784 .
## WI.17.Hancock.SB-BR.2010.lam.DB -0.02564103 1.00000
## MI.15.Montcalm.SB-BR.2010.lam.DB 6.46666667 1.00000
## WA.07.fld09.DB-BR.2010.lam.DB 11.72916667 1.00000
## MX.2017.pre.DB-BR.2010.lam.DB 14.46666667 1.00000
## BR.2010.por.DB-BR.2010.lam.DB 24.16666667 1.00000
## ND.07.fld01.DB-BR.2010.lam.DB 51.91666667 1.00000
## BR.2010.unai.DB-BR.2010.lam.DB 35.16666667 1.00000
## MX.2017.ca.DB-BR.2010.lam.DB 64.86666667 1.00000
## MX.2017.ley.DB-BR.2010.lam.DB 77.93939394 1.00000
## MI.15.Montcalm.SB-WI.17.Hancock.SB 6.49230769 1.00000
## WA.07.fld09.DB-WI.17.Hancock.SB 11.75480769 1.00000
## MX.2017.pre.DB-WI.17.Hancock.SB 14.49230769 1.00000
## BR.2010.por.DB-WI.17.Hancock.SB 24.19230769 1.00000
## ND.07.fld01.DB-WI.17.Hancock.SB 51.94230769 1.00000
## BR.2010.unai.DB-WI.17.Hancock.SB 35.19230769 1.00000
## MX.2017.ca.DB-WI.17.Hancock.SB 64.89230769 1.00000
## MX.2017.ley.DB-WI.17.Hancock.SB 77.96503497 0.48668
## WA.07.fld09.DB-MI.15.Montcalm.SB 5.26250000 1.00000
## MX.2017.pre.DB-MI.15.Montcalm.SB 8.00000000 1.00000
## BR.2010.por.DB-MI.15.Montcalm.SB 17.70000000 1.00000
## ND.07.fld01.DB-MI.15.Montcalm.SB 45.45000000 1.00000
## BR.2010.unai.DB-MI.15.Montcalm.SB 28.70000000 1.00000
## MX.2017.ca.DB-MI.15.Montcalm.SB 58.40000000 1.00000
## MX.2017.ley.DB-MI.15.Montcalm.SB 71.47272727 1.00000
## MX.2017.pre.DB-WA.07.fld09.DB 2.73750000 1.00000
## BR.2010.por.DB-WA.07.fld09.DB 12.43750000 1.00000
## ND.07.fld01.DB-WA.07.fld09.DB 40.18750000 1.00000
## BR.2010.unai.DB-WA.07.fld09.DB 23.43750000 1.00000
## MX.2017.ca.DB-WA.07.fld09.DB 53.13750000 1.00000
## MX.2017.ley.DB-WA.07.fld09.DB 66.21022727 1.00000
## BR.2010.por.DB-MX.2017.pre.DB 9.70000000 1.00000
## ND.07.fld01.DB-MX.2017.pre.DB 37.45000000 1.00000
## BR.2010.unai.DB-MX.2017.pre.DB 20.70000000 1.00000
## MX.2017.ca.DB-MX.2017.pre.DB 50.40000000 1.00000
## MX.2017.ley.DB-MX.2017.pre.DB 63.47272727 1.00000
## ND.07.fld01.DB-BR.2010.por.DB 27.75000000 1.00000
## BR.2010.unai.DB-BR.2010.por.DB 11.00000000 1.00000
## MX.2017.ca.DB-BR.2010.por.DB 40.70000000 1.00000
## MX.2017.ley.DB-BR.2010.por.DB 53.77272727 1.00000
## BR.2010.unai.DB-ND.07.fld01.DB -16.75000000 1.00000
## MX.2017.ca.DB-ND.07.fld01.DB 12.95000000 1.00000
## MX.2017.ley.DB-ND.07.fld01.DB 26.02272727 1.00000
## MX.2017.ca.DB-BR.2010.unai.DB 29.70000000 1.00000
## MX.2017.ley.DB-BR.2010.unai.DB 42.77272727 1.00000
## MX.2017.ley.DB-MX.2017.ca.DB 13.07272727 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
survey.picoxystrobin.fields.2 <- survey.picoxystrobin.fields %>%
mutate(Field_difference = ifelse(
Field_Year == "Baseline"| Field_Year == "ND.07.fld01.DB"|
Field_Year == "BR.2010.unai.DB"|Field_Year == "MX.2017.ca.DB"|Field_Year == "MX.2017.ley.DB" ,
"Yes","No"))%>%
mutate(Country_difference = ifelse(
country == "USA",
"Yes","No"))
####
myplot_comparison(survey.picoxystrobin.fields.2,highlabel = 0.005) + scale_y_continuous(breaks = c(0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.020, 0.022, 0.024)) + geom_hline(yintercept =
0.02148932, linetype = "dashed")
##
table1 <-
survey.boscalid.complete %>%
bind_rows(wrangling_DC(survey.TM.2)) %>%
bind_rows(survey.tetraconazole.complete) %>%
bind_rows(survey.picoxystrobin.complete) %>%
select(-c(EC50DC)) %>%
distinct(ID, .keep_all = TRUE) %>%
group_by(source, Host, country, Field_Year, State ) %>%
summarize(N= n()) %>%
ungroup() %>%
add_row(source = "Total", N = sum(.$N))## `summarise()` has grouped output by 'source', 'Host', 'country', 'Field_Year'.
## You can override using the `.groups` argument.
hola.2 <- survey.TM.2 %>% select(-c(EC50DC)) %>%
pull(ID) %>% unique()
## Corroborating
survey.boscalid.complete %>%
bind_rows(survey.TM.2) %>%
bind_rows(survey.tetraconazole.complete) %>%
bind_rows(survey.picoxystrobin.complete) %>%
select(-c(EC50DC)) %>%
pull(ID) %>%
unique()## [1] 26 274 307 456 478 479 482 504 505 698 699 710 711 724 725
## [16] 731 732 738 739 745 746 751 752 755 756 757 764 765 771 772
## [31] 786 787 811 812 813 814 817 818 824 838 851 852 853 854 855
## [46] 858 859 860 861 862 867 870 871 877 878 884 885 891 892 896
## [61] 897 901 902 905 906 908 909 911 912 914 1058 1081 1087 1109 1127
## [76] 1128 1134 1135 1139 1328 1329 1330 1331 1332 1340 1345 1365 1366 1392 1393
## [91] 1502 1582 1620 1622 1671 1672 1691 1692 1712 1713 1721 1722 1731 1791 1832
## [106] 1844 1849 1870 1872 1885 1941 1942 1962 1963 1965 1972 2001 2002 2012 2013
## [121] 2014 2015 2027 2029 2030 2031 2033 2097 2108 2110 2111 2119 2120 2121 2122
## [136] 2129 2130 2131 2132 2149 2150 2160 2169 2170 2189 2190 2199 2383 2384 2388
## [151] 2407 2408 2449 2453 2518 2528 2536 2559 1082 1084 1092 1096 1098 1099 1108
## [166] 1110 1400 1403 1427 1434 1436 1587 1590 1593 1595 1612 1614 1618 1628 1725
## [181] 1727 1732 1747 1767 1771 1793 2293 2294 2295 2296 2297 2298 2299 2301 2302
## [196] 700 701 702 703 704 705 706 707 708 709 713 714 715 716 717
## [211] 718 719 720 721 726 727 728 729 730 733 734 735 736 737 740
## [226] 741 743 744 747 748 749 750 753 754 758 759 760 762 763 766
## [241] 767 768 769 770 788 789 790 791 792 793 794 795 796 797 810
## [256] 815 816 819 863 864 865 866 868 869 872 873 874 875 876 879
## [271] 880 881 882 883 886 887 888 889 890 893 894 895 898 899 900
## [286] 903 904 907 910 1884 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896
## [301] 1898 1899 1901 1902 1903 1904 1905 1906 1907 1920 1921 1922 1923 1924 1926
## [316] 1928 1929 1930 1931 2457 2481 2483 2484 2485 2486 2487 2488 2489 2490 2504
## [331] 2505 2506 2507 2508 2509 2510 2511 2512 2513 2542 2545 2546 2548 2550 2551
## [346] 2552 2554 2557 1 12 20 21 74 87 118 123 449 461 467 475
## [361] 558 564 568 581 645 667 1025 1026 1027 1029 1032 1033 2098 2099 2100
## [376] 2139 2140 2143 2220 2222 2223 2320 2362 2385 2386 2390 1059 1064 1066 1070
## [391] 1071 1072 1113 1115 1122 1123 1124 1126 1382 1384 1385 1387 1388 1389 1390
## [406] 1391 1501 1503 1506 1520 1522 1530 1533 1537 1680 1739 1746 1751 1763 1769
## [421] 1775 1961 1967 1968 1969 2382 800 1842 1843 1845 1847 1848 1852 2035 2036
## [436] 2109 2112 2113 2114 2115 2117 2133 2134 2136 2151 2152 2153 2154 2155 2156
## [451] 2157 2158 2161 2162 2163 2164 2165 2167 2168 2191 2192 2193 2194 2195 2196
## [466] 2198 2547 1136 1140 1145 1146 1150 1163 1167 1349 1379 1401 1423 1451 1487
## [481] 1633 1634 1639 1643 1644 1652 1653 1656 1660 1720 1736 1738 1759 1807 2251
## [496] 2258 2261 2265 2266 2274 2277 2278 2281 2289 2409 2410 2411 2412 2413 2414
## [511] 2415 2416
boscalid.less.sensive <-survey.boscalid.fields.2 %>% arrange(desc(EC50DC))
boscalid.less.sensive[1:11,] %>% arrange(ID)## # A tibble: 11 Ă— 10
## ID EC50DC source Host country Field_Year State County Field_difference
## <dbl> <dbl> <fct> <fct> <chr> <fct> <chr> <chr> <chr>
## 1 449 0.175 Baseline Dryb… USA Baseline MI no sp… Yes
## 2 564 0.222 Baseline Dryb… USA Baseline WA no sp… Yes
## 3 790 0.172 Producer… Dryb… USA WA.08.fld… WA no sp… No
## 4 1436 0.163 Fungicid… Soyb… USA IA.17.Nas… IA no sp… No
## 5 1612 0.161 Fungicid… Soyb… USA WI.17.Han… WI no sp… No
## 6 1884 0.160 Producer… Dryb… Mexico MX.2017.l… SIN no sp… No
## 7 1889 0.164 Producer… Dryb… Mexico MX.2017.l… SIN no sp… No
## 8 1902 0.172 Producer… Dryb… Mexico MX.2017.p… SIN no sp… No
## 9 1922 0.180 Producer… Dryb… Mexico MX.2017.c… SIN no sp… No
## 10 2488 0.161 Producer… Dryb… Brazil BR.2010.l… MG Lamba… No
## 11 2557 0.161 Producer… Dryb… Brazil BR.2010.u… MG Unai No
## # … with 1 more variable: Country_difference <chr>
boscalid.less.sensive.2 <-boscalid.less.sensive %>% select(ID) %>% pull()
boscalid.less.sensive.3 <- sort(boscalid.less.sensive.2[1:10])
boscalid.less.sensive.3## [1] 449 564 790 1436 1612 1889 1902 1922 2488 2557
picoxystrobin.less.sensive <-survey.picoxystrobin.fields.2 %>% arrange(desc(EC50DC))
picoxystrobin.less.sensive[1:11,] %>% arrange(ID)## # A tibble: 11 Ă— 10
## ID EC50DC source Host country Field_Year State County Field_difference
## <dbl> <dbl> <fct> <fct> <chr> <fct> <chr> <chr> <chr>
## 1 706 0.0211 Producer… Dryb… USA ND.07.fld… ND no sp… Yes
## 2 1884 0.0272 Producer… Dryb… Mexico MX.2017.l… SIN no sp… Yes
## 3 1887 0.0251 Producer… Dryb… Mexico MX.2017.l… SIN no sp… Yes
## 4 1890 0.0240 Producer… Dryb… Mexico MX.2017.l… SIN no sp… Yes
## 5 1895 0.0230 Producer… Dryb… Mexico MX.2017.l… SIN no sp… Yes
## 6 1921 0.0231 Producer… Dryb… Mexico MX.2017.c… SIN no sp… Yes
## 7 1922 0.0235 Producer… Dryb… Mexico MX.2017.c… SIN no sp… Yes
## 8 1926 0.0215 Producer… Dryb… Mexico MX.2017.c… SIN no sp… Yes
## 9 1928 0.0238 Producer… Dryb… Mexico MX.2017.c… SIN no sp… Yes
## 10 2548 0.0244 Producer… Dryb… Brazil BR.2010.u… MG Unai Yes
## 11 2550 0.0253 Producer… Dryb… Brazil BR.2010.u… MG Unai Yes
## # … with 1 more variable: Country_difference <chr>
picoxystrobin.less.sensive.2 <-picoxystrobin.less.sensive %>% select(ID) %>% pull()
picoxystrobin.less.sensive.3 <- sort(picoxystrobin.less.sensive.2[1:10])
picoxystrobin.less.sensive.3## [1] 1884 1887 1890 1895 1921 1922 1926 1928 2548 2550
survey.picoxystrobin.complete %>% group_by( Host) %>% summarise(ave=mean(EC50DC))## # A tibble: 2 Ă— 2
## Host ave
## <fct> <dbl>
## 1 Drybean 0.0160
## 2 Soybean 0.0142
survey.boscalid.fields %>% select(-c(EC50DC)) %>%
distinct(ID, .keep_all = TRUE) %>%
group_by(Field_Year ) %>%
summarize(N= n()) ## # A tibble: 17 Ă— 2
## Field_Year N
## <fct> <int>
## 1 NE.10.fld21.DB 9
## 2 ND.07.fld01.DB 12
## 3 WA.07.fld09.DB 16
## 4 MI.16.East.Lansing.SB 17
## 5 WA.08.fld10.DB 12
## 6 ND.10.fld25.DB 9
## 7 MI.15.Montcalm.SB 9
## 8 IA.17.Nashua.SB 16
## 9 Baseline 21
## 10 WI.17.Hancock.SB 12
## 11 MI.17.Montcalm.SB 17
## 12 BR.2010.por.DB 10
## 13 BR.2010.unai.DB 9
## 14 MX.2017.pre.DB 10
## 15 MX.2017.ca.DB 10
## 16 BR.2010.lam.DB 9
## 17 MX.2017.ley.DB 11
#based on object table1 which is correct, just
lasiodiplodia <- survey.boscalid.complete %>%
bind_rows(wrangling_DC(survey.TM.2)) %>%
bind_rows(survey.tetraconazole.complete) %>%
bind_rows(survey.picoxystrobin.complete) %>%
select(-c(EC50DC)) %>%
distinct(ID, .keep_all = TRUE) %>%
select(ID) %>%
pull()
colnames(whitemold.inventory)## [1] "...1"
## [2] "ID"
## [3] "Year"
## [4] "Host"
## [5] "State"
## [6] "County"
## [7] "Country"
## [8] "Field"
## [9] "Form.ID"
## [10] "Serial_agar_dilution_for_creating_the_model_..N.20."
## [11] "Origin"
## [12] "hyphal_tip..ht."
## [13] "DNA.Extraction"
## [14] "lat"
## [15] "long"
## [16] "Size.of.field..acres."
## [17] "Percentage.Affected"
## [18] "History"
## [19] "Plot"
## [20] "Number.of.Years"
## [21] "Fungicide.This.Year"
## [22] "Chemigation"
## [23] "Applied.for.White.Mold"
## [24] "Fungicide_current_season"
## [25] "molecule.s._current_season"
## [26] "company_current_season"
## [27] "Timing"
## [28] "Applications"
## [29] "Rate"
## [30] "Pre.2015.Fungicides"
## [31] "Number.of.Years.1"
## [32] "Fungicide_previous_seasons"
## [33] "molecule.s._previous_seasons"
## [34] "company_previous_seasons"
## [35] "Timing..Soybean"
## [36] "Notes"
## [37] "Apparently.low.sensitiviy"
## [38] "Group_MOA_current_season"
## [39] "Group_MOA_previous_seasons"
## [40] "Field_Names"
## [41] "Field_Year"
# I have to keep Field and Year separate tahts why reading again the main table from data
tableS1 <- wrangling_DC(filename = whitemold.inventory) %>% mutate(source = as.factor(source),
Host = as.factor(Host),
country = as.factor(country),
Field = as.factor(Field),
Year = as.factor(Year),
State = as.factor(State),
County = as.factor(County),
Fungicide_current_season = as.factor(Fungicide_current_season),
molecule.s._current_season = as.factor(molecule.s._current_season),
Group_MOA_current_season = as.factor(Group_MOA_current_season),
Applications = as.factor(Applications),
Rate = as.factor(Rate))%>% select(
ID,
source,#we will change later to the real variable "source"
Host,
country,
Field,
Year,
State,
County,
Fungicide_current_season,
molecule.s._current_season,
Group_MOA_current_season,
Applications,
Rate, Field_Year) %>% filter(ID %in% lasiodiplodia )a.tet.baseline <-
myplot_baseline(survey.tetraconazole.complete) + expand_limits(y = c(0.2, 1.8)) + scale_y_continuous(breaks = c(0.2,0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6,1.8 )) + theme(
legend.position = "none"
) + labs(tag = "A") + theme(plot.tag = element_text(face = "bold"))
##
b.bos.baseline <- myplot_baseline(survey.boscalid.complete)+ expand_limits(y = c(0.04, 0.24)) + scale_y_continuous(breaks = c(0.04,0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24))+ theme(
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
) + labs(tag = "B") + theme(plot.tag = element_text(face = "bold"))
####
c.pico.baseline <- myplot_baseline(survey.picoxystrobin.complete) + expand_limits(y = c(0.008, 0.024)) + scale_y_continuous(breaks = c(0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.020, 0.022, 0.024 ))+ theme(
axis.title.y = element_blank(),
legend.position = "none"
) + labs(tag = "C") + theme(plot.tag = element_text(face = "bold"))
#Graph together
a.tet.baseline| (b.bos.baseline/c.pico.baseline)
A. Tetraconazole
B. Boscalid
C. Picoxystrobin
a.tet <-
myplot_model_1_tetraconazole (tetraconazole.joined.2.clean, lm = tetraconazole.lm)+ theme(
legend.position = "none"
) + labs(tag = "A") + theme( plot.tag = element_text(face = "bold"))
#
b.bos <-
myplot_model_1_boscalid (boscalid.joined.2.clean, lm = boscalid.lm) + theme(
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
) + labs(tag = "B") + theme( plot.tag = element_text(face = "bold"))
###
c.pico <-
myplot_model_1_picoxystrobin (picoxystrobin.joined.2.clean, lm = picoxystrobin.lm) + theme(axis.title.y = element_blank(), legend.position = "none")+ labs(tag = "C") + theme( plot.tag = element_text(face = "bold"))
#Graph together
a.tet | (b.bos / c.pico)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
###
aa.tet <-
myplot_model_2(tetraconazole.EC50DC) + expand_limits(x = c(0.27, 1.8), y = c(0.27, 1.8)) + scale_x_continuous(name = waiver(),
breaks = c(0.3, 0.6, 0.9, 1.2, 1.5, 1.8)) + scale_y_continuous(breaks = c(0.3, 0.6, 0.9, 1.2, 1.5, 1.8)) + theme(
legend.position = "none"
) + geom_label(
aes(x = 0.6,
y = 1.5)
,
label = c(paste (
" Y =",
paste0 (round(summary(tetraconazole.lm.2)[[4]][2], 4), "x"),
round(summary(tetraconazole.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(tetraconazole.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),
size = 3,
fontface = "bold"
) + labs(tag = "A") + theme( plot.tag = element_text(face = "bold"))
bb.bos <-
myplot_model_2(boscalid.EC50DC) + expand_limits(x = c(0.04, 0.25), y = c(0.04, 0.25)) + scale_x_continuous(name = waiver(),
breaks = c(0.05, 0.1, 0.15, 0.20, 0.25)) + scale_y_continuous(breaks = c(0.05, 0.1, 0.15, 0.20, 0.25)) + theme(axis.title.x = element_blank(), axis.title.y = element_blank(), legend.position = "none") + geom_label(
aes(x = 0.08,
y = 0.2)
,
label = c(paste (
" Y =",
paste0 (round(summary(boscalid.lm.2)[[4]][2], 4), "x"),
round(summary(boscalid.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(boscalid.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),
size = 3,
fontface = "bold"
) + labs(tag = "B") + theme( plot.tag = element_text(face = "bold"))
###
cc.pico <-
myplot_model_2(picoxystrobin.EC50DC) + expand_limits(x = c(0.005, 0.018), y = c(0.005, 0.018)) + scale_x_continuous(name = waiver(),
breaks = c(0.0075, 0.01, 0.0125, 0.0150, 0.018)) + scale_y_continuous(breaks = c(0.0075, 0.01, 0.0125, 0.0150, 0.018)) + theme(
axis.title.y = element_blank(),
legend.position = "none",
axis.text.y = element_text(angle = 20,
hjust = 1), axis.text.x = element_text(angle = 20,
hjust = 1)
) + geom_label(
aes(x = 0.0075,
y = 0.0148)
,
label = c(paste (
" Y =",
paste0 (round(summary(picoxystrobin.lm.2)[[4]][2], 4), "x"),
round(summary(picoxystrobin.lm.2)[[4]][1], 3) *
-1,
paste ("\n R =",
paste0 (round(
summary(picoxystrobin.lm.2)[[9]][1], 4
), "\n p < 0.001"))
)),
size = 3,
fontface = "bold"
) + labs(tag = "C") + theme(plot.tag = element_text(face = "bold"))
## together
aa.tet | (bb.bos / cc.pico)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
A. Tetraconazole
B. Boscalid
C. Picoxystrobin
aaa.tet <-
myplot_comparison(survey.tetraconazole.fields.2, highlabel = 0.1) + expand_limits(y = c(0.4, 2.5)) + scale_y_continuous(breaks = c(0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2, 2.2, 2.4)) + theme(plot.margin = unit(c(0.5, 0.5, 0.5, 0.25), "cm")) +
labs(tag = "A") + theme(plot.tag = element_text(face = "bold"))
bbb.bos <-
myplot_comparison(survey.boscalid.fields.2, highlabel = 0.017) + expand_limits(y = c(0.05, 0.24)) +
scale_y_continuous(breaks = c(0.06, 0.1, 0.14, 0.18, 0.22)) + geom_hline(yintercept = boscalid.less.sensive$EC50DC[9]
, linetype = "dashed") + theme(plot.margin = unit(c(0, 0, 0, 1), "cm")) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
) + #theme(plot.margin = unit(c(-1, 1, 1, 1), "cm"))
labs(tag = "B") + theme(plot.tag = element_text(face = "bold")) # make dynamic the value of intercept, create a new fucntion
ccc.pico <-
myplot_comparison(survey.picoxystrobin.fields.2, highlabel = 0.007) + expand_limits(y = c(0.008, 0.032)) +
scale_y_continuous(
breaks = c(
0.008,
0.012,
0.016,
0.020,
0.024,
0.028,
0.032
)
) + geom_hline(yintercept =
picoxystrobin.less.sensive$EC50DC[10] , linetype = "dashed") +# + theme(plot.margin = unit(c(1, 1, 1, 1), "cm"))
theme(axis.title.y = element_blank(), legend.position = "none") + theme(plot.margin = unit(c(0, 0, -1, 0), "cm")) + labs(tag = "C") + theme(plot.tag = element_text(face = "bold"))
#Together
aaa.tet| (bbb.bos/ccc.pico)
Shield:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
