Acer BW under nutrients and disease
Ana M. Palacio
2020-09-08
General project set-up
# Libraries
library(reshape2)
library(tidyverse)
# Graphs
theme_set (theme_classic() + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.position="none",
axis.text.x = element_text(angle = 90, vjust = 0.5),
plot.title = element_text(size=12, face="bold"),
#panel.border = element_rect(colour = "black", fill=NA, size=1)
panel.border = element_blank()
))
# Data
# Fragment list
#CurrentFragments<-read.csv("8.Metadata/Frag_assignments.csv", header = T)
# Collection information
#Location<-read.csv("8.Metadata/Genotypes.csv", header = T)Meta data
- Treatments, Genotypes, Tags
# Treatment Info
Treatment<-read.csv("Data/Treatments.csv")
#duplicated(Treatment$Tag)
Treatment<-Treatment[!duplicated(Treatment$Tag), ]
#duplicated(Tags$Tag)
summary(Treatment)## Tag Genotype Nutrients Disease
## 201 : 1 Elkhorn : 28 Ambient :125 Ambient : 1
## 202 : 1 Kelsey-1: 26 Dead : 1 Dead : 1
## 204 : 1 U44 : 26 Experiment: 1 Extra : 6
## 205 : 1 FM19 : 25 Extra : 6 Pathogen:134
## 207 : 1 FM6 : 25 NH4 :127 Placebo :118
## 208 : 1 Acerv2 : 24
## (Other):254 (Other) :106
## Available
## : 0
## Dead : 4
## Experiment:249
## Extra? : 6
## Placebo : 1
##
## # Tags weight
Tags<-read.csv("Data/Tags_W.csv")
#duplicated(Tags$Tag)
Tags<-Tags[!duplicated(Tags$Tag), ]
#duplicated(Tags$Tag)
summary(Tags)## T_Type Tag T_AW T_BW Temperature
## B:200 201 : 1 Min. :10.01 Min. :1.393 Min. :23.33
## S: 72 202 : 1 1st Qu.:10.16 1st Qu.:1.448 1st Qu.:26.19
## 204 : 1 Median :16.26 Median :2.285 Median :26.36
## 205 : 1 Mean :14.65 Mean :2.061 Mean :26.16
## 207 : 1 3rd Qu.:16.32 3rd Qu.:2.298 3rd Qu.:26.82
## 208 : 1 Max. :16.48 Max. :2.333 Max. :27.88
## (Other):266
## Salinity X
## Min. :33.2 :271
## 1st Qu.:33.2 Guessed: 1
## Median :33.2
## Mean :33.2
## 3rd Qu.:33.2
## Max. :33.2
## Tags$SW_den<-(999.842594+0.06793952*(Tags$Temperature)-0.00909529*(Tags$Temp)^2+0.0001001685*
(Tags$Temperature)^3-0.000001120083*(Tags$Temperature)^4+0.000000006536332*(Tags$Temp)^5+(0.824493-0.0040899*
(Tags$Temperature)+0.000076438*(Tags$Temperature)^2-0.00000082467*(Tags$Temperature)^3+0.0000000053875*
(Tags$Temperature)^4)*(Tags$Salinity)+(-0.00572466+0.00010227*(Tags$Temperature)-0.0000016546*
(Tags$Temperature)^2)* (Tags$Salinity)^1.5+0.00048314*(Tags$Salinity)^2)*0.001
#Tags$Estimated_Density<-(-Tags$SW_den/((Tags$T_BW/Tags$T_AW)-1))
Tags %>%
group_by(T_Type) %>%
summarise_at(vars(T_AW, T_BW), funs(mean(., na.rm=TRUE)))BW data
# 1. BW data
BW_Tall<-read.csv("Data/BW_ Long_data.csv")
#BW_Tall$Estimated.W.density[BW_Tall$Estimated.W.density == "#VALUE!" ] <-NA
#BW_Tall$Estimated.W.density<-as.numeric(as.character(BW_Tall$Estimated.W.density))
#BW_Tall$Estimated_AW [BW_Tall$Estimated_AW == "#VALUE!" ] <-NA
#BW_Tall$Estimated_AW<-as.numeric(as.character(BW_Tall$Estimated_AW))
# 2. Data clean-up an types:
# Variable types
#BW_Tall$Time <- as.factor(BW_Tall$Time)
#BW_Tall$Time<-as.numeric(BW_Tall$Time)
BW_Tall$Date<-as.Date(BW_Tall$Date, "%Y-%m-%d")
BW_Tall$Day<-(as.numeric(BW_Tall$Date)-18486)
# Remove-unused data
#Extras <- BW_Tall[which (BW_Tall$Nutrients=="Extra"), ]
#BW_Tall <- droplevels(BW_Tall[!rownames(BW_Tall) %in%
# rownames(Extras), ])
# 3. Merge with treatments
BW_Tall<-plyr::join(BW_Tall, Treatment, by = "Tag",
type = "left", match = "all")
BW_Tall<-plyr::join(BW_Tall, Tags, by = "Tag",
type = "left", match = "all")
BW_Tall$Nutrients<-factor(BW_Tall$Nutrients,
levels= c("Ambient", "NH4"), ordered=TRUE)
BW_Tall$Disease<-factor(BW_Tall$Disease,
levels= c("Placebo", "Pathogen"), ordered=TRUE)
# 4. Replicates
BW_Tall$Tank<-factor(BW_Tall$Tank, ordered=FALSE)
BW_Tall$Genotype<-factor(BW_Tall$Genotype, ordered=FALSE)
summary(BW_Tall)## Tag Tank Date BW
## 201 : 3 3 : 94 Min. :2020-08-12 Min. : 2.686
## 202 : 3 1 : 93 1st Qu.:2020-08-12 1st Qu.: 3.897
## 204 : 3 2 : 93 Median :2020-08-21 Median : 4.604
## 205 : 3 4 : 93 Mean :2020-08-22 Mean : 5.152
## 207 : 3 6 : 93 3rd Qu.:2020-09-04 3rd Qu.: 6.145
## 208 : 3 7 : 93 Max. :2020-09-04 Max. :11.950
## (Other):742 (Other):201 NA's :1
## Temp Sal Notes Day
## Min. :27.0 Min. :32.10 :744 Min. : 0.00
## 1st Qu.:27.3 1st Qu.:32.50 New : 5 1st Qu.: 0.00
## Median :27.6 Median :34.30 Batch 2 : 1 Median : 9.00
## Mean :27.7 Mean :33.61 dead : 1 Mean :10.66
## 3rd Qu.:28.0 3rd Qu.:34.50 Dead : 1 3rd Qu.:23.00
## Max. :29.1 Max. :34.70 maybe broke at top: 1 Max. :23.00
## NA's :1 NA's :1 (Other) : 7
## Genotype Nutrients Disease Available T_Type
## Elkhorn : 80 Ambient:371 Placebo :353 : 0 B:548
## Kelsey-1: 74 NH4 :379 Pathogen:397 Dead : 10 S:212
## U44 : 74 NA's : 10 NA's : 10 Experiment:742
## FM6 : 73 Extra? : 8
## Acerv2 : 72 Placebo : 0
## FM14 : 72
## (Other) :315
## T_AW T_BW Temperature Salinity
## Min. :10.01 Min. :1.393 Min. :23.33 Min. :33.2
## 1st Qu.:10.14 1st Qu.:1.443 1st Qu.:26.15 1st Qu.:33.2
## Median :16.26 Median :2.285 Median :26.34 Median :33.2
## Mean :14.56 Mean :2.049 Mean :26.14 Mean :33.2
## 3rd Qu.:16.31 3rd Qu.:2.298 3rd Qu.:26.84 3rd Qu.:33.2
## Max. :16.48 Max. :2.333 Max. :27.88 Max. :33.2
##
## X SW_den
## :759 Min. :1.021
## Guessed: 1 1st Qu.:1.021
## Median :1.022
## Mean :1.022
## 3rd Qu.:1.022
## Max. :1.022
## BW
BW calculations
BW.data<-BW_Tall[order(BW_Tall$Tag), ]
# 3. Calculate days bw BW data points
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(Days = Day - lag(Day, default = Day[1]))
# 4. Calculate change in BW bw data points
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dBW = BW - lag(BW, default = BW[1]))
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dBW_r = dBW/(Days*lag(BW, default = BW[1])))
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dBW_r = dBW/(Days*lag(BW, default = BW[1])))
BW.data$dBW_r<-BW.data$dBW_r*1000
summary(BW.data)## Tag Tank Date BW
## 201 : 3 3 : 94 Min. :2020-08-12 Min. : 2.686
## 202 : 3 1 : 93 1st Qu.:2020-08-12 1st Qu.: 3.897
## 204 : 3 2 : 93 Median :2020-08-21 Median : 4.604
## 205 : 3 4 : 93 Mean :2020-08-22 Mean : 5.152
## 207 : 3 6 : 93 3rd Qu.:2020-09-04 3rd Qu.: 6.145
## 208 : 3 7 : 93 Max. :2020-09-04 Max. :11.950
## (Other):742 (Other):201 NA's :1
## Temp Sal Notes Day
## Min. :27.0 Min. :32.10 :744 Min. : 0.00
## 1st Qu.:27.3 1st Qu.:32.50 New : 5 1st Qu.: 0.00
## Median :27.6 Median :34.30 Batch 2 : 1 Median : 9.00
## Mean :27.7 Mean :33.61 dead : 1 Mean :10.66
## 3rd Qu.:28.0 3rd Qu.:34.50 Dead : 1 3rd Qu.:23.00
## Max. :29.1 Max. :34.70 maybe broke at top: 1 Max. :23.00
## NA's :1 NA's :1 (Other) : 7
## Genotype Nutrients Disease Available T_Type
## Elkhorn : 80 Ambient:371 Placebo :353 : 0 B:548
## Kelsey-1: 74 NH4 :379 Pathogen:397 Dead : 10 S:212
## U44 : 74 NA's : 10 NA's : 10 Experiment:742
## FM6 : 73 Extra? : 8
## Acerv2 : 72 Placebo : 0
## FM14 : 72
## (Other) :315
## T_AW T_BW Temperature Salinity
## Min. :10.01 Min. :1.393 Min. :23.33 Min. :33.2
## 1st Qu.:10.14 1st Qu.:1.443 1st Qu.:26.15 1st Qu.:33.2
## Median :16.26 Median :2.285 Median :26.34 Median :33.2
## Mean :14.56 Mean :2.049 Mean :26.14 Mean :33.2
## 3rd Qu.:16.31 3rd Qu.:2.298 3rd Qu.:26.84 3rd Qu.:33.2
## Max. :16.48 Max. :2.333 Max. :27.88 Max. :33.2
##
## X SW_den Days dBW
## :759 Min. :1.021 Min. : 0.000 Min. :-0.75820
## Guessed: 1 1st Qu.:1.021 1st Qu.: 0.000 1st Qu.: 0.00000
## Median :1.022 Median : 9.000 Median : 0.04440
## Mean :1.022 Mean : 7.603 Mean : 0.06337
## 3rd Qu.:1.022 3rd Qu.:14.000 3rd Qu.: 0.10390
## Max. :1.022 Max. :14.000 Max. : 0.92380
## NA's :1
## dBW_r
## Min. :-14.4104
## 1st Qu.: 0.9473
## Median : 1.5155
## Mean : 1.5129
## 3rd Qu.: 2.0000
## Max. : 10.8928
## NA's :259BW Exploration
SizeBiasCheck<- ggplot(BW.data, aes (BW, dBW_r, colour=factor(Genotype))) +
#geom_smooth(method = "lm")+
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 6,2),
expand = c(0, 0),
name=("AW mg / g*day"))
SizeBiasCheck
Genotype
BW_Genet<- ggplot(BW.data, aes (Genotype, dBW_r, colour=factor(Date))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 5,1),
expand = c(0, 0),
name=("dBW [mg / g*day]"))+
theme(legend.position = "bottom")
BW_Genet
BW_Genet+ facet_wrap(~Nutrients)
BW_Genet+ facet_wrap(~Disease)
#BW_Genet+ facet_wrap(Nutrients~Disease)Pre-treatment bias
BW_Bias<- ggplot(BW.data, aes (Genotype, dBW_r, colour=factor(Nutrients))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 5,1),
expand = c(0, 0),
name=("dBW [mg / g*day]"))
BW_Bias + facet_grid(~Disease)
AW
AW Calculations
# 1. Coral BW
BW.data$Coral_BW<-(BW.data$BW)-(BW.data$T_BW)
# 2. Water density
BW.data$SW_den<-(999.842594+0.06793952*(BW.data$Temp)-0.00909529*(BW.data$Temp)^2+0.0001001685*
(BW.data$Temp)^3-0.000001120083*(BW.data$Temp)^4+0.000000006536332*(BW.data$Temp)^5+(0.824493-0.0040899*
(BW.data$Temp)+0.000076438*(BW.data$Temp)^2-0.00000082467*(BW.data$Temp)^3+0.0000000053875*(BW.data$Temp)^4)*
(BW.data$Salinity)+(-0.00572466+0.00010227*(BW.data$Temp)-0.0000016546*(BW.data$Temp)^2)*
(BW.data$Salinity)^1.5+0.00048314*(BW.data$Salinity)^2)*0.001
# 3. Coral air weight
BW.data$Est_Coral_AW<-(BW.data$Coral_BW)*(1/(1-(BW.data$SW_den)/2.4))
# 4. Calculate change in AW bw data points
BW.data<-BW.data %>%
dplyr::group_by(Tag) %>%
dplyr::mutate(dAW = Est_Coral_AW - lag(Est_Coral_AW, default = Est_Coral_AW[1]))
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dAW_r = dAW/(Days*lag(Est_Coral_AW, default = Est_Coral_AW[1])))
BW.data$dAW_r<-BW.data$dAW_r*1000
summary(BW.data)## Tag Tank Date BW
## 201 : 3 3 : 94 Min. :2020-08-12 Min. : 2.686
## 202 : 3 1 : 93 1st Qu.:2020-08-12 1st Qu.: 3.897
## 204 : 3 2 : 93 Median :2020-08-21 Median : 4.604
## 205 : 3 4 : 93 Mean :2020-08-22 Mean : 5.152
## 207 : 3 6 : 93 3rd Qu.:2020-09-04 3rd Qu.: 6.145
## 208 : 3 7 : 93 Max. :2020-09-04 Max. :11.950
## (Other):742 (Other):201 NA's :1
## Temp Sal Notes Day
## Min. :27.0 Min. :32.10 :744 Min. : 0.00
## 1st Qu.:27.3 1st Qu.:32.50 New : 5 1st Qu.: 0.00
## Median :27.6 Median :34.30 Batch 2 : 1 Median : 9.00
## Mean :27.7 Mean :33.61 dead : 1 Mean :10.66
## 3rd Qu.:28.0 3rd Qu.:34.50 Dead : 1 3rd Qu.:23.00
## Max. :29.1 Max. :34.70 maybe broke at top: 1 Max. :23.00
## NA's :1 NA's :1 (Other) : 7
## Genotype Nutrients Disease Available T_Type
## Elkhorn : 80 Ambient:371 Placebo :353 : 0 B:548
## Kelsey-1: 74 NH4 :379 Pathogen:397 Dead : 10 S:212
## U44 : 74 NA's : 10 NA's : 10 Experiment:742
## FM6 : 73 Extra? : 8
## Acerv2 : 72 Placebo : 0
## FM14 : 72
## (Other) :315
## T_AW T_BW Temperature Salinity
## Min. :10.01 Min. :1.393 Min. :23.33 Min. :33.2
## 1st Qu.:10.14 1st Qu.:1.443 1st Qu.:26.15 1st Qu.:33.2
## Median :16.26 Median :2.285 Median :26.34 Median :33.2
## Mean :14.56 Mean :2.049 Mean :26.14 Mean :33.2
## 3rd Qu.:16.31 3rd Qu.:2.298 3rd Qu.:26.84 3rd Qu.:33.2
## Max. :16.48 Max. :2.333 Max. :27.88 Max. :33.2
##
## X SW_den Days dBW
## :759 Min. :1.021 Min. : 0.000 Min. :-0.75820
## Guessed: 1 1st Qu.:1.021 1st Qu.: 0.000 1st Qu.: 0.00000
## Median :1.021 Median : 9.000 Median : 0.04440
## Mean :1.021 Mean : 7.603 Mean : 0.06337
## 3rd Qu.:1.021 3rd Qu.:14.000 3rd Qu.: 0.10390
## Max. :1.021 Max. :14.000 Max. : 0.92380
## NA's :1 NA's :1
## dBW_r Coral_BW Est_Coral_AW dAW
## Min. :-14.4104 Min. : 0.433 Min. : 0.7537 Min. :-1.3199
## 1st Qu.: 0.9473 1st Qu.: 1.667 1st Qu.: 2.9013 1st Qu.: 0.0000
## Median : 1.5155 Median : 2.463 Median : 4.2878 Median : 0.0778
## Mean : 1.5129 Mean : 3.104 Mean : 5.4024 Mean : 0.1106
## 3rd Qu.: 2.0000 3rd Qu.: 4.036 3rd Qu.: 7.0246 3rd Qu.: 0.1811
## Max. : 10.8928 Max. :10.517 Max. :18.3077 Max. : 1.6120
## NA's :259 NA's :1 NA's :1 NA's :1
## dAW_r
## Min. :-37.212
## 1st Qu.: 1.905
## Median : 2.681
## Mean : 2.764
## 3rd Qu.: 3.568
## Max. : 33.234
## NA's :259AW Exploration
SizeBiasCheck<- ggplot(BW.data, aes (Est_Coral_AW, dAW_r, colour=factor(Genotype))) +
#stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
#stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("dAW [mg / g*day]"))
SizeBiasCheck
Genotype
AW_Genet<- ggplot(BW.data, aes (Genotype, dAW_r, colour=factor(Date))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
theme(legend.position = "none")+
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("AW mg / g*day"))+
theme(legend.position = "bottom")
AW_Genet
AW_Genet+ facet_wrap(~Nutrients)
AW_Genet+ facet_wrap(~Disease)
AW_Genet2<- ggplot(BW.data, aes (Date, dAW_r, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
theme(legend.position = "none")+
geom_jitter(shape=21, alpha=0.3)+
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("AW mg / g*day"))+
theme(legend.position = "bottom")
AW_Genet2
AW_Genet2 + facet_grid(Nutrients~Disease)
BW_Frag<- ggplot(BW.data, aes (Date, dAW_r, group=(Tag),
colour=factor(Genotype))) +
geom_line()+
geom_hline(yintercept = 0, linetype=3)+
#scale_x_continuous(name="Days",
# breaks = seq(0, 30, by=15)) +
scale_y_continuous(name="Growth (mg / g d)",
limits = c(-2, 10),
breaks = seq(-1, 10, by=1)) +
theme(legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))
BW_Frag+facet_wrap(~Genotype)
Tank
AW_Tank<- ggplot(BW.data, aes (Tank, dAW_r)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("Growth rate (mg / g day)"))+
geom_jitter(aes(colour=Genotype), shape=21, alpha=0.3)+
facet_wrap(~Date) +
theme(legend.position = "bottom")
AW_Tank
AW_Tank+ facet_wrap(Date~Nutrients)
AW_Tank+ facet_wrap(Date~Disease)
Treatments
AW_Disease<- ggplot(BW.data, aes (Disease, dAW_r)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-3, 10),
breaks = seq(-3, 10,2),
expand = c(0, 0),
name=("Growth rate (mg/g day)"))+
geom_jitter(aes(colour=Genotype), alpha=0.3)+
facet_wrap(~Date) +
theme(legend.position = "bottom")
AW_Disease
AW_Disease + facet_wrap(Date~Nutrients)
AW_Disease + facet_wrap(Date~Disease)
Pre-treatment bias
AW_Bias<- ggplot(BW.data, aes (Genotype, dAW_r, colour=factor(Date), shape=(Nutrients))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 5,1),
expand = c(0, 0),
name=("dBW [mg / g*day]"))
AW_Bias + facet_grid(~Disease)+
theme(legend.position = "bottom")
#Summary<-BW.data %>% group_by(Genotype, Nutrients, Disease) %>% count(Date)
#BW_Tall2<-BW.data %>% group_by(Disease, Genotype, Nutrients) %>% add_count(Date)
#write.csv(Summary, "numbers.csv")Packages used
# Creates bibliography
#knitr::write_bib(c(.packages()), "packages.bib")Henry, Lionel, and Hadley Wickham. 2019. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr.
Müller, Kirill, and Hadley Wickham. 2019. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2017a. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. https://CRAN.R-project.org/package=reshape2.
———. 2017b. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.
———. 2019a. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.
———. 2019b. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
Wickham, Hadley, and Lionel Henry. 2020. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, and Hiroaki Yutani. 2019. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.