CT
Jens Daniel Müller & Lara Burchardt
15 April, 2020
Last updated: 2020-04-15
Checks: 7 0
Knit directory: Baltic_Productivity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9cc640c | jens-daniel-mueller | 2020-04-15 | minor aesthetic changes before re-knitting |
| html | be8517f | jens-daniel-mueller | 2020-04-15 | Build site. |
| Rmd | 170667e | jens-daniel-mueller | 2020-04-15 | added plots for PPP |
| Rmd | 01a1f1b | LSBurchardt | 2020-04-14 | var_all unified to names(nc$var) |
| Rmd | 18ca5af | LSBurchardt | 2020-04-14 | #3 ppp ident updated, saving works, enumeration still ongoing |
| html | c4a1b00 | jens-daniel-mueller | 2020-04-14 | Build site. |
| Rmd | 19eeda2 | jens-daniel-mueller | 2020-04-14 | correct NGS limits, all csv files recreated, PPP identification started |
| Rmd | a165baa | LSBurchardt | 2020-04-14 | #3 ppp identificiation: problems when no ppp is found, then if function returns error; saving not finished yet, currrently df_ppp_temp is overwritten every loop –> exist function missing, will be added shortly |
| Rmd | ac9cad0 | LSBurchardt | 2020-04-09 | vroom to write_/read_csv; as.data.frame to as_tibble; tidyverse piping |
| Rmd | d00d732 | jens-daniel-mueller | 2020-04-08 | formatting header # corrected, not knitted yet |
| html | 6f29d15 | jens-daniel-mueller | 2020-04-08 | Build site. |
| Rmd | f33624a | jens-daniel-mueller | 2020-04-08 | knitted with deployment detection |
| Rmd | bf0b055 | LSBurchardt | 2020-04-07 | #3 vroom to write.csv/read_csv; theoretically ready to knit |
| Rmd | abc2d8d | LSBurchardt | 2020-04-07 | #3 deployment values implemented, plotted |
| Rmd | 6907a02 | LSBurchardt | 2020-04-07 | NGS limits changed to: 58.5-59.0 in all files; pivot-wider problem solved in CT.Rmd |
| html | a98bf60 | jens-daniel-mueller | 2020-04-06 | Build site. |
| Rmd | 5d352db | jens-daniel-mueller | 2020-04-06 | calculated CT in equilibrium with atmosphere and updated CT dygraph |
| Rmd | 662c74a | jens-daniel-mueller | 2020-04-03 | added explanatory comments |
| Rmd | c619811 | LSBurchardt | 2020-04-03 | #3 comments |
| html | a4ab61e | jens-daniel-mueller | 2020-03-31 | Build site. |
| Rmd | 1518256 | jens-daniel-mueller | 2020-03-31 | updated dygraphs |
| html | 67a6e2b | jens-daniel-mueller | 2020-03-31 | Build site. |
| Rmd | 5e275d2 | jens-daniel-mueller | 2020-03-31 | CT and flux calculation included |
| Rmd | ef65647 | LSBurchardt | 2020-03-31 | #3 flux calculations and graphs prepared, calculations are not running on my PC though |
| Rmd | 3f94314 | LSBurchardt | 2020-03-27 | #3 windspeed extraction working |
| Rmd | 2ca3f71 | LSBurchardt | 2020-03-23 | #3 extracting S,T, pco2 for all routes; windspeed from GETM with problem |
| html | abc5f7d | jens-daniel-mueller | 2020-03-18 | Build site. |
| Rmd | 10176c9 | jens-daniel-mueller | 2020-03-18 | knit after Lara updated CT |
| html | b80d3a6 | jens-daniel-mueller | 2020-03-18 | Build site. |
| Rmd | 8023757 | jens-daniel-mueller | 2020-03-18 | knit after Lara updated CT |
| Rmd | 03ea2de | LSBurchardt | 2020-03-18 | #1 ready to knit to html: CT with Plot, probably little more explanatory background needed for webside |
| html | 03646b4 | jens-daniel-mueller | 2020-03-16 | Build site. |
| html | 6394f58 | jens-daniel-mueller | 2020-03-16 | Build site. |
| Rmd | 9b0eb49 | LSBurchardt | 2020-03-15 | #1 ready to knit html: SSS working, CT, MLD_pCO2 and SST updated to new data |
| html | e1acb2d | jens-daniel-mueller | 2020-03-09 | Build site. |
| html | c15b71c | jens-daniel-mueller | 2020-03-09 | Build site. |
| Rmd | 9332cae | jens-daniel-mueller | 2020-03-09 | restructered content into chapters, rebuild site, except SSS |
| Rmd | fa8ef89 | Burchardt | 2020-02-28 | #1 new CT.rmd and sss.rmd |
| Rmd | 1663eee | Burchardt | 2020-02-13 | #1 CT new |
library(tidyverse)
library(ncdf4)
library(vroom)
library(lubridate)
library(geosphere)
library(dygraphs)
library(xts)
library(here)
library(seacarb)
library(zoo)
library(RColorBrewer)# route
select_route <- c("E", "F", "G", "W", "X")
# variable names in 2d and 3d GETM files
var <- "SSS_east"
# latitude limits
low_lat <- 58.5
high_lat <- 59.01 Regional mean salinity
The mean salinity was calculated across all measurments made between march - september in the NGS subregion.
nc <- nc_open(paste("data/Finnmaid/", "FM_all_2019_on_standard_tracks.nc", sep = ""))
# read required vectors from netcdf file
route <- ncvar_get(nc, "route")
route <- unlist(strsplit(route, ""))
date_time <- ncvar_get(nc, "time")
latitude_east <- ncvar_get(nc, "latitude_east")
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
#dim(array) # should have 2 dimensions: 544 coordinate, 2089 time steps
fillvalue <- ncatt_get(nc, var, "_FillValue")
array[array == fillvalue$value] <- NA
rm(fillvalue)
#i <- 5
for (i in seq(1,length(route),1)){
if(route[i] %in% select_route) {
slice <- array[i,]
value <- mean(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
sd <- sd(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
date <- ymd("2000-01-01") + date_time[i]
temp <- bind_cols(date = date, var=var, value = value, sd = sd)
if (exists("timeseries", inherits = FALSE)){
timeseries <- bind_rows(timeseries, temp)
} else{timeseries <- temp}
rm(temp, value, date, sd)
}
}
nc_close(nc)
fm_sss__ngs <- timeseries %>%
mutate(sss = value,
year = year(date),
month = month(date))
fm_sss_ngs_monthlymean <- fm_sss__ngs %>%
filter(month >=3 , month <=9) %>%
summarise(sss_mean = mean(sss, na.rm = TRUE))
rm(array,fm_sss__ngs,nc, timeseries, date_time,
i, latitude_east, route, slice, var)The mean salinity between March and September for the NGS subregion for all years is 6.56.
2 Finnmaid data extraction
pCO2 and SST observations in NGS were extracted for all crossings.
nc <- nc_open(paste("data/Finnmaid/", "FM_all_2019_on_standard_tracks.nc", sep = ""))
#names(nc$var) # uncomment to print variable names and select relevant index
index <- c(9,11) #index of wanted variables SST_east and pCO2 east
var_all <- c(names(nc$var[index]))
# read required vectors from netcdf file
route <- ncvar_get(nc, "route")
route <- unlist(strsplit(route, ""))
date_time <- ncvar_get(nc, "time")
latitude_east <- ncvar_get(nc, "latitude_east")
for (var in var_all) {
#print(var)
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
#dim(array) # should have 2 dimensions: 544 coordinate, 2089 time steps
fillvalue <- ncatt_get(nc, var, "_FillValue")
array[array == fillvalue$value] <- NA
rm(fillvalue)
for (i in seq(1,length(route),1)){
if(route[i] %in% select_route) {
slice <- array[i,]
value <- mean(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
sd <- sd(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
date <- ymd("2000-01-01") + date_time[i]
fm_ngs_all_routes_part <- bind_cols(date = date, var=var, value = value, sd = sd, route=route[i])
if (exists("fm_ngs_all_routes", inherits = FALSE)){
fm_ngs_all_routes <- bind_rows(fm_ngs_all_routes, fm_ngs_all_routes_part)
} else{fm_ngs_all_routes <- fm_ngs_all_routes_part}
rm(fm_ngs_all_routes_part, value, date, sd, slice)
}
}
rm(array, var,i)
}
nc_close(nc)
fm_ngs_all_routes %>%
write_csv(here::here("data/_summarized_data_files/", file = "fm_ngs_all_routes.csv"))
rm(nc, fm_ngs_all_routes, latitude_east, route,date_time)3 GETM windspeed
Reanalysis windspeed data as used in the GETM model run were used.
filesList_2d <- list.files(path= "data", pattern = "Finnmaid.E.2d.20", recursive = TRUE)
file <- filesList_2d[1]
nc <- nc_open(paste("data/", file, sep = ""))
names(nc$var)
index <- c(11,12) # index of wanted variables u10 and v10
var_all <- c(names(nc$var[index]))
lon <- ncvar_get(nc, "lonc")
lat <- ncvar_get(nc, "latc", verbose = F)
nc_close(nc)
rm(file, nc)
for (var in var_all){
for (n in 1:length(filesList_2d)) {
file <- filesList_2d[n]
nc <- nc_open(paste("data/", file, sep = ""))
time_units <- nc$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc, "time")
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
dim(array) # should be 2d with dimensions: 544 coordinate, 31d*(24h/d/3h)=248 time steps
array <- as.data.frame(t(array), xy=TRUE)
array <- as_tibble(array)
gt_windspeed_ngs_part <- array %>%
set_names(as.character(lat)) %>%
mutate(date_time = t) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat<high_lat) %>%
group_by(date_time) %>%
summarise_all("mean") %>%
ungroup() %>%
mutate(var = var)
if (exists("gt_windspeed_ngs")) {gt_windspeed_ngs <- bind_rows(gt_windspeed_ngs, gt_windspeed_ngs_part)
}else {gt_windspeed_ngs <- gt_windspeed_ngs_part}
nc_close(nc)
rm(array, nc, t, gt_windspeed_ngs_part)
print(n) # to see working progress
}
print(paste("gt_", var, "_ngs.csv", sep = "")) # to see working progress
rm(n, file, time_units)
}
rm(filesList_2d, var, var_all, lat, lon)
gt_windspeed_ngs <- gt_windspeed_ngs %>%
group_by(date_time, var) %>%
summarise(mean_value= mean(value)) %>%
pivot_wider(values_from = mean_value, names_from = var) %>%
mutate(U_10 = round(sqrt(u10^2 + v10^2), 3)) %>%
select(-c(u10, v10))
gt_windspeed_ngs %>%
write_csv(here::here("data/_summarized_data_files/", file = paste("gt_windspeed_ngs.csv")))
rm(gt_windspeed_ngs)4 CT calculation
CT was calculated from measured pCO2 based on a fixed mean alkalinity value of 1650 µmol kg-1.
df <- read_csv(here::here("data/_summarized_data_files/", file = "fm_ngs_all_routes.csv"))
df <- df %>%
select(date, var, value, route)
df <- df %>%
# drop_na() %>%
pivot_wider(values_from = value, names_from = var) %>%
drop_na()
#calculation of CT based on pCO2 (var1) and alkalinity (var2) as input parameters
#calculation of CT in theoretical equilibrium with atmosphere (CT_equi) based on pCO2_air (var1) and alkalinity (var2) as input parameters
df <- df %>%
rename(SST = SST_east,
pCO2 = pCO2_east) %>%
#drop_na() %>%
mutate(CT = carb(24,
var1=pCO2,
var2=1650*1e-6,
S=pull(fm_sss_ngs_monthlymean),
T=SST,
k1k2="m10", kf="dg", ks="d", gas="insitu")[,16]*1e6) %>%
mutate(year = year(date),
pCO2_air = 400 - 2*(2015-year),
CT_equi = carb(24,
var1=pCO2_air,
var2=1650*1e-6,
S=pull(fm_sss_ngs_monthlymean),
T=SST,
k1k2="m10", kf="dg", ks="d", gas="insitu")[,16]*1e6) %>%
select(-c(pCO2_air, year))
df %>%
write_csv(here::here("data/_summarized_data_files/", file = "fm_CT_ngs.csv"))
rm(df)5 Air-sea CO2 flux
The CO2 flux across the sea surface was calculated according to Wanninkhof (2014).
df_1 <- read_csv(here::here("data/_summarized_data_files/", file = "fm_CT_ngs.csv"))
df_2 <- read_csv(here::here("data/_summarized_data_files/", file = "gt_windspeed_ngs.csv"))
df_2 <- df_2 %>%
mutate(date = as.Date(date_time)) %>%
select(date, U_10) %>%
group_by(date) %>%
summarise_all("mean") %>%
ungroup()
df <- full_join(df_1, df_2, by = "date") %>%
arrange(date)
rm(df_1,df_2)
df <- df %>%
mutate(year = year(date),
pCO2_int = na.approx(pCO2, na.rm = FALSE), #na.approx: replacing NA with interpolated values
SST_int = na.approx(SST, na.rm = FALSE)) %>%
filter(!is.na(pCO2_int))
#Calculation of the Schmidt number as a funktion of temperature according to Wanninkhof (2014)
Sc_W14 <- function(tem) {
2116.8 - 136.25 * tem + 4.7353 * tem^2 - 0.092307 * tem^3 + 0.0007555 * tem^4
}
Sc_W14(20)
# calculate flux F [mol m–2 d–1]
df <- df %>%
mutate(pCO2_air = 400 - 2*(2015-year),
dpCO2 = pCO2_int - pCO2_air,
dCO2 = dpCO2 * K0(S=pull(fm_sss_ngs_monthlymean), T=SST_int),
k = 0.251 * U_10^2 * (Sc_W14(SST_int)/660)^(-0.5),
flux_daily = k*dCO2*1e-5*24)
df %>%
write_csv(here::here("data/_merged_data_files/", file = paste("gt_fm_flux_ngs.csv")))
rm(df, Sc_W14)6 Time series CT, MLD, SST, windspeed, and air-sea fluxes
# read CT and flux data
gt_fm_flux_ngs <- read_csv(here::here("data/_merged_data_files/", file = "gt_fm_flux_ngs.csv"))
gt_fm_flux_ngs <- gt_fm_flux_ngs %>%
mutate(date = as.Date(date))
ts_xts_CT <- xts(cbind(gt_fm_flux_ngs$CT, gt_fm_flux_ngs$CT_equi), order.by = gt_fm_flux_ngs$date)
names(ts_xts_CT) <- c("CT", "CT_equi")
ts_xts_SST <- xts(gt_fm_flux_ngs$SST, order.by = gt_fm_flux_ngs$date)
names(ts_xts_SST) <- "SST"
ts_xts_windspeed <- xts(gt_fm_flux_ngs$U_10, order.by = gt_fm_flux_ngs$date)
names(ts_xts_windspeed) <- "Windspeed"
ts_xts_flux <- xts(gt_fm_flux_ngs$flux_daily, order.by = gt_fm_flux_ngs$date)
names(ts_xts_flux) <- "Daily Flux"
# read MLD data
gt_mld_fm_pco2_ngs <-
read_csv(here::here("data/_merged_data_files/", file = "gt_mld_fm_pco2_ngs.csv"))
gt_mld_fm_pco2_ngs <- gt_mld_fm_pco2_ngs %>%
mutate(date = as.Date(date))
ts_xts_mld5 <- xts(gt_mld_fm_pco2_ngs$value_mld5, order.by = gt_mld_fm_pco2_ngs$date)
names(ts_xts_mld5) <- "mld_age_5"
ts_xts_CT %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("CT") %>%
dyAxis("y", label = "CT") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_SST %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("SST") %>%
dyAxis("y", label = "SST") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_mld5 %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("mld_age_5") %>%
dyAxis("y", label = "mld_age_5") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_windspeed %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("Windspeed") %>%
dyAxis("y", label = "Windspeed [m/s]") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_flux %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("Daily Flux") %>%
dyAxis("y", label = "Daily Flux") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5)rm(gt_fm_flux_ngs, gt_mld_fm_pco2_ngs,
ts_xts_CT, ts_xts_flux, ts_xts_mld5, ts_xts_SST, ts_xts_windspeed)7 Identify continous periods without data gaps
# time
time_low <- 03 # 03 as month March
time_high <- 09 # 09 as month September
deployment_gap <- 7df <-
read_csv(here::here("data/_summarized_data_files/", file = "fm_CT_ngs.csv"))
df <- df %>%
mutate (month = month(date),
year = year(date)) %>%
filter (month >= time_low & month <= time_high) %>%
group_by(year) %>%
mutate(deployment =
as.factor(cumsum(c(TRUE,diff(date)>= deployment_gap)))) %>% # deployment +1, when data gap > 7 days
ungroup()
df %>%
ggplot(aes( x = as.Date(yday(date)), y = year, color = deployment))+
geom_point()+
scale_y_reverse(breaks = seq(2000,2030,1))+
theme(axis.title.x = element_blank())
df %>%
write_csv(here::here("data/_summarized_data_files/", file="fm_CT_ngs_deployments.csv"))8 Identifying primary productio periods (PPP)
The following criteria are implemented in the following to find periods of primary production:
- CT is lower than at equilibrium with the atmosphere
- period must be within one deployment
- starts at a day followed by week in which CT decreased at least by 20
- ends at day where this is not true for the next week
PPPs are numbered.
#criteria
decrease_start <- 20
timespan_start <- 7 #in days
decrease_end <- 20
timespan_end <- 7 #in days
# databasis
df <-
read_csv(here::here("data/_summarized_data_files/", file = "fm_CT_ngs_deployments.csv"))
# identification
#ppp identified per year, per deployment, only when CT < CT_equi
df_ppp <- df %>%
filter(CT < CT_equi)
years <- (unique(df_ppp$year))
for (n in years){
deployment <- df_ppp %>%
filter(year == n)
deployment <- max(deployment$deployment)
for (d in 1:deployment){
df_ppp_temp <- df_ppp %>%
filter(year == n , deployment == d) %>%
mutate(ppp = NA)
for (x in 1:nrow(df_ppp_temp)){
##start criteria
lag_start <- df_ppp_temp %>%
filter(date >= date[x], date <= (date[x]+ duration(timespan_start, 'days')))
roll_index <- which.max(lag_start$date)
roll_value <- lag_start$CT[roll_index]
if (is_empty(roll_value)== FALSE){
roll_index_df <- grep(roll_value, df_ppp_temp)
if (df_ppp_temp$CT[x]-roll_value >= decrease_start){
df_ppp_temp$ppp[x:roll_index_df] = as.numeric(x)
} else{}
} else {}
##end criteria
lag_end <- df_ppp_temp %>%
filter(date <= date[x], date >= (date[x]- duration(timespan_end, 'days')))
roll_index <- which.max(lag_end$date)
roll_value <- lag_end$CT[roll_index]
if (is_empty(roll_value) == FALSE){
roll_index_df <- grep(roll_value, df_ppp_temp)
if (df_ppp_temp$CT[x]-roll_value >= decrease_end){
df_ppp_temp$ppp[x:roll_index_df] = as.numeric(x)
} else{}
}else {next}
if (exists("fm_ppp", inherits = FALSE)){
fm_ppp <- bind_rows(fm_ppp, df_ppp_temp)
} else{fm_ppp <- df_ppp_temp}
# print(paste("year:", n,
# " *** deployment:", d,
# " *** day:", x,
# " *** ppp:",df_ppp_temp$ppp[x]))
} #end loop through df_ppp_temp
}#end of loop through deployments
}#end of loop through years
fm_ppp_dup <- fm_ppp[!duplicated(fm_ppp),] %>%
arrange(date)
fm_ppp_dup_na <- fm_ppp_dup %>%
drop_na()
ggplot()+
geom_point(data = fm_ppp_dup, aes(as.Date(yday(date)), year, color = as.factor(ppp)))+
scale_y_reverse(breaks = seq(2000,2030,1))+
theme(axis.title.x = element_blank(),
legend.position = "bottom")
9 CT during PPP
ggplot()+
geom_point(data = fm_ppp_dup, aes(as.Date(yday(date)), CT, color = as.factor(ppp)))+
theme(axis.title.x = element_blank(),
legend.position = "bottom")+
facet_grid(year~.)
10 Tasks / open questions
- check drop_na() before CT calculation, because thise removes quite a lot data points where only SST is missing.
- drop_na() before pivot_wider: leaves 2016 observations
- drop_na() after pivot_wider or second drop_na() just before CT calculations: leaves 1982 observations, we loose timeperiods without SST value from: “2005-8-25” to “2005-9-24” and “2006-01-29” to “2006-03-04”
- still we need to keep drop_na() after pivot_wider for now, because CT calculation does not accept NA data in any column
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 seacarb_3.2.13 oce_1.2-0 gsw_1.0-5
[5] testthat_2.3.2 here_0.1 xts_0.12-0 zoo_1.8-7
[9] dygraphs_1.1.1.6 geosphere_1.5-10 lubridate_1.7.4 vroom_1.2.0
[13] ncdf4_1.17 forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[17] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_3.0.0
[21] ggplot2_3.3.0 tidyverse_1.3.0 workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 whisker_0.4 knitr_1.28 xml2_1.3.0
[5] magrittr_1.5 hms_0.5.3 rvest_0.3.5 tidyselect_1.0.0
[9] bit_1.1-15.2 colorspace_1.4-1 lattice_0.20-41 R6_2.4.1
[13] rlang_0.4.5 fansi_0.4.1 broom_0.5.5 xfun_0.12
[17] dbplyr_1.4.2 modelr_0.1.6 withr_2.1.2 git2r_0.26.1
[21] ellipsis_0.3.0 htmltools_0.4.0 assertthat_0.2.1 bit64_0.9-7
[25] rprojroot_1.3-2 digest_0.6.25 lifecycle_0.2.0 haven_2.2.0
[29] rmarkdown_2.1 sp_1.4-1 compiler_3.6.3 cellranger_1.1.0
[33] pillar_1.4.3 scales_1.1.0 backports_1.1.5 generics_0.0.2
[37] jsonlite_1.6.1 httpuv_1.5.2 pkgconfig_2.0.3 rstudioapi_0.11
[41] munsell_0.5.0 httr_1.4.1 tools_3.6.3 grid_3.6.3
[45] nlme_3.1-145 gtable_0.3.0 utf8_1.1.4 DBI_1.1.0
[49] cli_2.0.2 readxl_1.3.1 yaml_2.2.1 crayon_1.3.4
[53] farver_2.0.3 later_1.0.0 promises_1.1.0 htmlwidgets_1.5.1
[57] fs_1.4.0 vctrs_0.2.4 glue_1.3.2 evaluate_0.14
[61] labeling_0.3 reprex_0.3.0 stringi_1.4.6