Lessons learned from open-water dreissenid mussel control projects in North America
Angelique Dahlberg
Did the number of treatments per year increase over time? (Figure 1a)
# Load data into R dataframes
TreatsOverTime <- sqlQuery(cnxn,
"SELECT tbl_treatment.Year_treated, Count(tbl_treatment.Year_treated) AS CountOfYear_treated FROM tbl_lakes
INNER JOIN (lu_treatment_goals INNER JOIN tbl_treatment ON lu_treatment_goals.Treatment_goal_id = tbl_treatment.Treatment_goal_id) ON tbl_lakes.LakeID = tbl_treatment.lakeid
GROUP BY tbl_treatment.Year_treated,
IIf([lu_treatment_goals].[Treatment_goal_id] In (1,3,4),'include','exclude')
HAVING (((IIf([lu_treatment_goals].[Treatment_goal_id] In (1,3,4),'include','exclude'))='include'));",
stringsAsFactors = FALSE) #excludes research projects
# Modify table
colnames(TreatsOverTime) =c("year", "treatmentNumber")
str(TreatsOverTime)## 'data.frame': 14 obs. of 2 variables:
## $ year : num 2004 2005 2006 2008 2009 ...
## $ treatmentNumber: int 1 1 1 1 1 2 6 3 2 3 ...# View data
TreatsOverTime %>%
kable() %>%
kable_classic(full_width=F, html_font = "Times New Roman", c("striped", "hover"))| year | treatmentNumber |
|---|---|
| 2004 | 1 |
| 2005 | 1 |
| 2006 | 1 |
| 2008 | 1 |
| 2009 | 1 |
| 2011 | 2 |
| 2014 | 6 |
| 2015 | 3 |
| 2016 | 2 |
| 2017 | 3 |
| 2018 | 1 |
| 2019 | 2 |
| 2020 | 1 |
| 2021 | 2 |
# Check for normality
ggqqplot(TreatsOverTime$"treatmentNumber", ylab="treatmentNumber")
ggqqplot(TreatsOverTime$"year", ylab="year")
# Plot
Plot_TreatsPerYear <- ggplot(TreatsOverTime, aes(x=year, y=treatmentNumber, color='Projects')) +
geom_smooth(method='lm', color='black') +
geom_point(aes(x=year, y=treatmentNumber, color='Projects'), color='black') +
theme_classic() +
xlim(2004,2021) +
ylim(-1,6.5) +
labs(color='Legend', x="Year", y="Treatments (#)") #+
#geom_text(x=2012, y=-1, size=3.5, aes(label="R = 0.28, p = 0.21"), color='black')
Plot_TreatsPerYear## `geom_smooth()` using formula 'y ~ x'
# Kendall rank correlation test
r1 <- cor.test(TreatsOverTime$"treatmentNumber", TreatsOverTime$"year", method = "kendall", exact=FALSE)
r1##
## Kendall's rank correlation tau
##
## data: TreatsOverTime$treatmentNumber and TreatsOverTime$year
## z = 1.2557, p-value = 0.2092
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2773501Do the number of pre-treatment survey types increase over time? (Figure 1c)
# Load data into R dataframes
preTreat_trend <- sqlQuery(cnxn,
"SELECT tbl_treatment.Year_treated, tbl_lakes.Lake_name, Count(tbl_pretreatment_obs.observation_detail_id) AS CountOfobservation_detail_id, tbl_lakes.Eradication_WithinLT, tbl_treatment.Treatment_goal_id
FROM (tbl_lakes INNER JOIN (lu_treatment_goals INNER JOIN tbl_treatment ON lu_treatment_goals.Treatment_goal_id = tbl_treatment.Treatment_goal_id) ON tbl_lakes.LakeID = tbl_treatment.lakeid) INNER JOIN tbl_pretreatment_obs ON tbl_treatment.treatment_ID = tbl_pretreatment_obs.treatment_ID
GROUP BY tbl_treatment.Year_treated, tbl_lakes.Lake_name, tbl_pretreatment_obs.treatment_ID, tbl_lakes.Eradication_WithinLT, tbl_treatment.Treatment_goal_id
HAVING (((tbl_treatment.Treatment_goal_id)<>2));",
stringsAsFactors = FALSE) #excludes research projects
# Modify table
colnames(preTreat_trend) =c("yearTreated", "lakeName", "preTreatSurveys", "dreissenidPresence", "treatmentGoal")
preTreat_trend$treatmentGoal <- gsub("1", "EPE", preTreat_trend$treatmentGoal)
preTreat_trend$treatmentGoal <- gsub("3", "S", preTreat_trend$treatmentGoal)
preTreat_trend$treatmentGoal <- gsub("4", "RRE", preTreat_trend$treatmentGoal)
preTreat_trend$treatmentGoal <- as.factor(preTreat_trend$treatmentGoal) #Make categorical
preTreat_trend$dreissenidPresence <- as.factor(preTreat_trend$dreissenidPresence) #Make categorical
str(preTreat_trend)## 'data.frame': 22 obs. of 5 variables:
## $ yearTreated : num 2004 2006 2008 2011 2011 ...
## $ lakeName : chr "Lake Ossawinamakee" "Millbrook Quarry" "Base Lake" "Lake Irene" ...
## $ preTreatSurveys : int 4 3 2 3 4 4 2 3 1 1 ...
## $ dreissenidPresence: Factor w/ 2 levels "Absent","Present": 2 1 2 2 2 2 2 2 2 2 ...
## $ treatmentGoal : Factor w/ 3 levels "EPE","RRE","S": 3 1 1 2 2 2 2 2 2 2 ...# View data
preTreat_trend %>%
kable() %>%
kable_classic(full_width=F, html_font = "Times New Roman", c("striped", "hover"))| yearTreated | lakeName | preTreatSurveys | dreissenidPresence | treatmentGoal |
|---|---|---|---|---|
| 2004 | Lake Ossawinamakee | 4 | Present | S |
| 2006 | Millbrook Quarry | 3 | Absent | EPE |
| 2008 | Base Lake | 2 | Present | EPE |
| 2011 | Lake Irene | 3 | Present | RRE |
| 2011 | Rose Lake | 4 | Present | RRE |
| 2014 | Christmas Lake | 4 | Present | RRE |
| 2014 | Christmas Lake | 2 | Present | RRE |
| 2014 | Christmas Lake | 3 | Present | RRE |
| 2014 | Lake Independence | 1 | Present | RRE |
| 2014 | Lake Winnipeg | 1 | Present | RRE |
| 2015 | Lake Independence | 3 | Present | RRE |
| 2015 | Ruth Lake | 3 | Present | RRE |
| 2016 | Lake Minnewashta | 3 | Present | RRE |
| 2017 | Billmeyer Quarry | 2 | Absent | EPE |
| 2017 | Lake Marion | 4 | Present | RRE |
| 2017 | Lake Minnewashta | 5 | Present | RRE |
| 2018 | Richland Chambers Reservoir | 1 | Absent | RRE |
| 2019 | Bone Lake | 4 | Present | RRE |
| 2019 | Lake Minnetonka (SAB) | 3 | Present | S |
| 2020 | Highland Lake | 2 | Present | EPE |
| 2021 | Highland Lake | 2 | Present | EPE |
| 2021 | Valley Lo Lake | 2 | Absent | EPE |
# Plot
preTreatGoalEPE <- subset(preTreat_trend, treatmentGoal %in% c('EPE'))
preTreatGoalS <- subset(preTreat_trend, treatmentGoal %in% c('S'))
preTreatGoalRRE <- subset(preTreat_trend, treatmentGoal %in% c('RRE'))
Plot_preTreat_trend <- ggplot(preTreatGoalRRE, aes(x=yearTreated, y=preTreatSurveys)) +
geom_smooth(data=preTreatGoalRRE, method='lm', color="blue") +
geom_smooth(data=preTreatGoalEPE, method='lm', color="red") +
geom_point(data=preTreatGoalRRE, color="blue", size=2, shape=17) +
geom_point(data=preTreatGoalEPE, color="red", size=2, shape=18) +
theme_classic() +
xlim(2004,2021) +
ylim(0,5) +
labs(x="Year", y="# pre-treatment \nsurvey methods") #+
#geom_text(x=2008, y=0, size=3.5, aes(label="R = -0.60, p = 0.14"), color = "red") +
#geom_text(x=2017, y=0, size=3.5, aes(label="R = 0.10, p = 0.73"), color = "blue")
Plot_preTreat_trend## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
# Check for normality
ggqqplot(preTreatGoalEPE$"preTreatSurveys", ylab="preTreatSurveys")
ggqqplot(preTreatGoalEPE$"yearTreated", ylab="yearTreated") # not normal
ggqqplot(preTreatGoalRRE$"preTreatSurveys", ylab="preTreatSurveys")
ggqqplot(preTreatGoalRRE$"yearTreated", ylab="yearTreated")
ggqqplot(preTreatGoalS$"preTreatSurveys", ylab="preTreatSurveys")
ggqqplot(preTreatGoalS$"yearTreated", ylab="yearTreated")
# Pearson correlation test
r2EPE <- cor.test(preTreatGoalEPE$"preTreatSurveys", preTreatGoalEPE$"yearTreated", method = "kendall")## Warning in cor.test.default(preTreatGoalEPE$preTreatSurveys,
## preTreatGoalEPE$yearTreated, : Cannot compute exact p-value with tiesr2EPE##
## Kendall's rank correlation tau
##
## data: preTreatGoalEPE$preTreatSurveys and preTreatGoalEPE$yearTreated
## z = -1.4852, p-value = 0.1375
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.5976143r2RRE <- cor.test(preTreatGoalRRE$"preTreatSurveys", preTreatGoalRRE$"yearTreated", method = "pearson")
r2RRE##
## Pearson's product-moment correlation
##
## data: preTreatGoalRRE$preTreatSurveys and preTreatGoalRRE$yearTreated
## t = 0.35507, df = 12, p-value = 0.7287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4531284 0.6000803
## sample estimates:
## cor
## 0.1019657Has the size of the treated area relative to the entire lake changed over time? (Figure 1b)
# Load data into R dataframes
treatSize_lakeSize <- sqlQuery(cnxn,
"SELECT tbl_lakes.Lake_name, tbl_treatment.Year_treated, tbl_lakes.Area, tbl_treatment.Treat_Area, Round([tbl_treatment].[Treat_area]/[tbl_lakes].[area]*100,4) AS PercentTreatArea, tbl_lakes.Eradication_WithinLT, tbl_treatment.Treatment_goal_id
FROM tbl_lakes INNER JOIN (lu_treatment_goals INNER JOIN tbl_treatment ON lu_treatment_goals.Treatment_goal_id = tbl_treatment.Treatment_goal_id) ON tbl_lakes.LakeID = tbl_treatment.lakeid
GROUP BY tbl_lakes.Lake_name, tbl_treatment.treatment_ID, tbl_treatment.Year_treated, tbl_lakes.Area, tbl_treatment.Treat_Area, Round([tbl_treatment].[Treat_area]/[tbl_lakes].[area]*100,4), tbl_lakes.Eradication_WithinLT, tbl_treatment.Treatment_goal_id
HAVING (((tbl_treatment.Treatment_goal_id)=1 Or (tbl_treatment.Treatment_goal_id)=4));",
stringsAsFactors = FALSE) #excludes research and suppression projects
# Modify table
colnames(treatSize_lakeSize) =c("lakeName", "yearTreated", "lakeArea", "treatmentArea", "percentTreated", "dreissenidPresence", "treatmentGoal")
treatSize_lakeSize$treatmentGoal <- gsub("1", "EPE", treatSize_lakeSize$treatmentGoal)
treatSize_lakeSize$treatmentGoal <- gsub("4", "RRE", treatSize_lakeSize$treatmentGoal)
treatSize_lakeSize$treatmentGoal <- as.factor(treatSize_lakeSize$treatmentGoal) #Make categorical
treatSize_lakeSize$dreissenidPresence <- as.factor(treatSize_lakeSize$dreissenidPresence) #Make categorical
str(treatSize_lakeSize)## 'data.frame': 24 obs. of 7 variables:
## $ lakeName : chr "Base Lake" "Base Lake" "Billmeyer Quarry" "Bone Lake" ...
## $ yearTreated : num 2008 2009 2017 2019 2014 ...
## $ lakeArea : num 115 115 30 221 267 ...
## $ treatmentArea : num 115 115 14.8 0.57 0.07 0.75 0.75 0.75 11 1.5 ...
## $ percentTreated : num 100 100 49.3333 0.2574 0.0262 ...
## $ dreissenidPresence: Factor w/ 2 levels "Absent","Present": 2 2 1 2 2 2 2 2 2 1 ...
## $ treatmentGoal : Factor w/ 2 levels "EPE","RRE": 1 1 1 2 2 2 2 2 2 1 ...# View data
treatSize_lakeSize %>%
kable() %>%
kable_classic(full_width=F, html_font = "Times New Roman", c("striped", "hover"))| lakeName | yearTreated | lakeArea | treatmentArea | percentTreated | dreissenidPresence | treatmentGoal |
|---|---|---|---|---|---|---|
| Base Lake | 2008 | 115.00 | 115.00 | 100.0000 | Present | EPE |
| Base Lake | 2009 | 115.00 | 115.00 | 100.0000 | Present | EPE |
| Billmeyer Quarry | 2017 | 30.00 | 14.80 | 49.3333 | Absent | EPE |
| Bone Lake | 2019 | 221.45 | 0.57 | 0.2574 | Present | RRE |
| Christmas Lake | 2014 | 267.17 | 0.07 | 0.0262 | Present | RRE |
| Christmas Lake | 2014 | 267.17 | 0.75 | 0.2807 | Present | RRE |
| Christmas Lake | 2014 | 267.17 | 0.75 | 0.2807 | Present | RRE |
| Christmas Lake | 2014 | 267.17 | 0.75 | 0.2807 | Present | RRE |
| Christmas Lake | 2015 | 267.17 | 11.00 | 4.1172 | Present | RRE |
| Crosley Lake | 2016 | 3.00 | 1.50 | 50.0000 | Absent | EPE |
| Highland Lake | 2020 | 102.80 | 39.00 | 37.9377 | Present | EPE |
| Highland Lake | 2021 | 102.80 | 39.00 | 37.9377 | Present | EPE |
| Lake Independence | 2014 | 832.02 | 0.50 | 0.0601 | Present | RRE |
| Lake Independence | 2015 | 832.02 | 0.75 | 0.0901 | Present | RRE |
| Lake Irene | 2011 | 639.26 | 10.00 | 1.5643 | Present | RRE |
| Lake Marion | 2017 | 530.30 | 6.00 | 1.1314 | Present | RRE |
| Lake Minnewashta | 2016 | 679.70 | 29.00 | 4.2666 | Present | RRE |
| Lake Minnewashta | 2017 | 679.70 | 1.00 | 0.1471 | Present | RRE |
| Lake Winnipeg | 2014 | NA | NA | NA | Present | RRE |
| Millbrook Quarry | 2006 | 12.00 | 12.00 | 100.0000 | Absent | EPE |
| Richland Chambers Reservoir | 2018 | 41356.00 | 5.00 | 0.0121 | Absent | RRE |
| Rose Lake | 2011 | 1200.47 | 10.00 | 0.8330 | Present | RRE |
| Ruth Lake | 2015 | 599.20 | 2.89 | 0.4823 | Present | RRE |
| Valley Lo Lake | 2021 | 28.00 | 14.00 | 50.0000 | Absent | EPE |
# Plot
treatLakeEPE <- subset(treatSize_lakeSize, treatmentGoal %in% c('EPE'))
treatLakeRRE <- subset(treatSize_lakeSize, treatmentGoal %in% c('RRE'))
Plot_treatSize_lakeSize <- ggplot(treatLakeRRE, aes(x=yearTreated, y=percentTreated)) +
geom_smooth(data=treatLakeRRE, method='lm', color="blue") +
geom_smooth(data=treatLakeEPE, method='lm', color="red") +
geom_point(data=treatLakeRRE, color="blue", size=2, shape=17) +
geom_point(data=treatLakeEPE, color="red", size=2, shape=18) +
theme_classic() +
xlim(2004,2021) +
ylim(-25,120) +
labs(x="Year", y="Lake treated (%)") #+
#geom_text(x=2008, y=-24, size=3.5, aes(label="R = -0.96, p = 0.0001"), color = "red") +
#geom_text(x=2017, y=-24, size=3.5, aes(label="R = -0.10, p = 0.61"), color = "blue")
Plot_treatSize_lakeSize## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing missing values (geom_point).
# Check for normality
ggqqplot(treatLakeEPE$"yearTreated", ylab="yearTreated")
ggqqplot(treatLakeEPE$"percentTreated", ylab="percentTreated")
ggqqplot(treatLakeRRE$"yearTreated", ylab="yearTreated")
ggqqplot(treatLakeRRE$"percentTreated", ylab="percentTreated")## Warning: Removed 1 rows containing non-finite values (stat_qq).## Warning: Removed 1 rows containing non-finite values (stat_qq_line).
## Warning: Removed 1 rows containing non-finite values (stat_qq_line).
# Kendall rank correlation test
r3EPE <- cor.test(treatLakeEPE$"yearTreated", treatLakeEPE$"percentTreated", method = "pearson", exact=FALSE)
r3EPE##
## Pearson's product-moment correlation
##
## data: treatLakeEPE$yearTreated and treatLakeEPE$percentTreated
## t = -8.7455, df = 6, p-value = 0.0001237
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.9934801 -0.8034764
## sample estimates:
## cor
## -0.9629427r3RRE <- cor.test(treatLakeRRE$"yearTreated", treatLakeRRE$"percentTreated", method = "kendall", exact=FALSE)
r3RRE##
## Kendall's rank correlation tau
##
## data: treatLakeRRE$yearTreated and treatLakeRRE$percentTreated
## z = -0.51112, p-value = 0.6093
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1043707Does treatment size change with the number of pre-treatment surveys conducted? (included in text but not figure)
# Load data into R dataframes
PreTreatSurveyOverTime <- sqlQuery(cnxn,
"SELECT Count(tbl_pretreatment_obs.observation_detail_id) AS CountOfobservation_detail_id, tbl_treatment.Treat_Area
FROM (tbl_lakes INNER JOIN (lu_treatment_goals INNER JOIN tbl_treatment ON lu_treatment_goals.Treatment_goal_id = tbl_treatment.Treatment_goal_id) ON tbl_lakes.LakeID = tbl_treatment.lakeid) INNER JOIN tbl_pretreatment_obs ON tbl_treatment.treatment_ID = tbl_pretreatment_obs.treatment_ID
GROUP BY tbl_treatment.Treat_Area, tbl_lakes.Lake_name, tbl_treatment.Treatment_goal_id, tbl_treatment.treatment_ID
HAVING (((tbl_treatment.Treatment_goal_id)<>2));",
stringsAsFactors = FALSE) #excludes research projects
# Modify table
colnames(PreTreatSurveyOverTime) =c("Number of pre-treatment surveys", "Treatment area (acres)")
# View data
PreTreatSurveyOverTime %>%
kable() %>%
kable_classic(full_width=F, html_font = "Times New Roman", c("striped", "hover"))| Number of pre-treatment surveys | Treatment area (acres) |
|---|---|
| 1 | NA |
| 4 | 0.07 |
| 1 | 0.50 |
| 4 | 0.57 |
| 2 | 0.75 |
| 3 | 0.75 |
| 3 | 0.75 |
| 5 | 1.00 |
| 3 | 2.89 |
| 1 | 5.00 |
| 4 | 6.00 |
| 3 | 10.00 |
| 4 | 10.00 |
| 3 | 12.00 |
| 2 | 14.00 |
| 2 | 14.80 |
| 4 | 26.00 |
| 3 | 29.00 |
| 2 | 39.00 |
| 2 | 39.00 |
| 2 | 115.00 |
| 3 | 163.83 |
# Check for normality
ggqqplot(PreTreatSurveyOverTime$"Number of pre-treatment surveys", ylab="Number of pre-treatment surveys")
ggqqplot(PreTreatSurveyOverTime$"Treatment area (acres)", ylab="Treatment area (acres)")## Warning: Removed 1 rows containing non-finite values (stat_qq).## Warning: Removed 1 rows containing non-finite values (stat_qq_line).
## Warning: Removed 1 rows containing non-finite values (stat_qq_line).
# Kendall rank correlation test
r4 <- cor.test(PreTreatSurveyOverTime$"Number of pre-treatment surveys", PreTreatSurveyOverTime$"Treatment area (acres)", method = "kendall", exact=FALSE)
r4##
## Kendall's rank correlation tau
##
## data: PreTreatSurveyOverTime$"Number of pre-treatment surveys" and PreTreatSurveyOverTime$"Treatment area (acres)"
## z = -1.0742, p-value = 0.2827
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.185998Figure for paper
plot <- ggarrange(Plot_TreatsPerYear, Plot_treatSize_lakeSize, Plot_preTreat_trend,
labels = c("a)", "b)", "c)"),
font.label = list(size = 11, color = "black", face = "bold", family = NULL),
align = c("h"),
ncol = 2, nrow = 2)## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'plot
Close and remove channel
close(cnxn)
rm(cnxn)#♥ Session info
sessionInfo()## R version 3.6.2 (2019-12-12)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RODBC_1.3-16 ggpubr_0.4.0 kableExtra_1.3.4 data.table_1.14.0
## [5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
## [9] readr_1.4.0 tidyr_1.1.3 tibble_3.1.1 ggplot2_3.3.5
## [13] tidyverse_1.3.1 knitr_1.36
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 jsonlite_1.7.2 viridisLite_0.4.0 splines_3.6.2
## [5] carData_3.0-4 modelr_0.1.8 assertthat_0.2.1 highr_0.9
## [9] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.4 backports_1.2.1
## [13] lattice_0.20-44 glue_1.4.2 digest_0.6.27 ggsignif_0.6.3
## [17] rvest_1.0.2 colorspace_2.0-1 cowplot_1.1.1 htmltools_0.5.1.1
## [21] Matrix_1.3-3 pkgconfig_2.0.3 broom_0.7.10 haven_2.4.1
## [25] scales_1.1.1 webshot_0.5.2 svglite_2.0.0 mgcv_1.8-35
## [29] generics_0.1.1 farver_2.1.0 car_3.0-12 ellipsis_0.3.2
## [33] withr_2.4.3 cli_2.5.0 magrittr_2.0.1 crayon_1.4.2
## [37] readxl_1.3.1 evaluate_0.14 fs_1.5.0 fansi_0.4.2
## [41] nlme_3.1-152 rstatix_0.7.0 xml2_1.3.2 tools_3.6.2
## [45] hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1
## [49] compiler_3.6.2 jquerylib_0.1.4 systemfonts_1.0.2 rlang_0.4.11
## [53] grid_3.6.2 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.11
## [57] gtable_0.3.0 abind_1.4-5 DBI_1.1.2 R6_2.5.1
## [61] lubridate_1.7.10 utf8_1.2.1 stringi_1.6.1 Rcpp_1.0.6
## [65] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1 xfun_0.22