lpath = paste("C://Users/", Sys.info( )["login"], "/R.lib", sep = "")
.libPaths(lpath)
library(tidyverse)
## -- Attaching packages ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(knitr)
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(here)
## here() starts at C:/Users/LayC/Projects/cira3-mortality
##
## Attaching package: 'here'
## The following object is masked from 'package:lubridate':
##
## here
library(readxl)
library(mvmeta)
## This is mvmeta 1.0.3. For an overview type: help('mvmeta-package').
library(dlnm)
## This is dlnm 2.3.9. For details: help(dlnm) and vignette('dlnmOverview').
if(!dir.exists(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"))){ dir.create(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), recursive = TRUE)}
load(here("output/y4-final-manuscript-output/an_ci_dtC2_yr_season.Rdata")) # MCMC based estimates, with CI, for seasons, years, and temperature ranges; dtC2 and dtC 1 are still running
full_pop2010 <- read_xls(here("data/City-County-Cluster-2014-06-24_POP2010.xls"), "City-County-Cluster-2014-06-24_") # 2010 populations used for normalization
raw_boston_base <- read_xlsx(here("output/y4-final-manuscript-output/2019-02-05_projected_mortality_2003-2013-dr.xlsx"), "baseline") # baseline central estimate
raw_boston_dtc6 <- read_xlsx(here("output/y4-final-manuscript-output/2019-02-05_projected_mortality_2003-2013-dr.xlsx"), "dtC_6") # dt_C central estimate
summary_out_mean <- read_csv(here("output/y4-final-manuscript-output/2020-02-04_city_dT_AN_y4_dif_proj-hind_summary.csv")) # summary central estimate file
## Parsed with column specification:
## cols(
## cluster = col_double(),
## city = col_character(),
## CITYNAME = col_character(),
## FIPS_txt = col_character(),
## dT_c = col_double(),
## baseline_proj_mort = col_double(),
## sum_an_total = col_double(),
## sum_an_total_bnd = col_double()
## )
full_pop2010 %>% mutate(CITYNAME = casefold(CITYNAME) , FIPStext = FIPStext) %>%
select(CITYNAME, City) %>% unique () %>%
anti_join(city_ci_sums [, c("CITYNAME", "FIPS_txt" )])
## Joining, by = "CITYNAME"
full_pop2010 <- full_pop2010 %>% mutate(CITYNAME = casefold(CITYNAME) , FIPS_txt = FIPStext)
city_ci_sums [, c("CITYNAME", "FIPS_txt" )] %>% anti_join(full_pop2010) %>% unique()
## Joining, by = c("CITYNAME", "FIPS_txt")
## all good
city_ci_sums %>% filter(!is.na(CITYNAME)) %>% pull(CITYNAME) %>% unique() %>% length() # 208 = yes
## [1] 208
Check the MCMC output for match against the values used in mapping. First normalize by 2010 population.
city_ci_sums_pop <- full_pop2010 %>%
mutate(pop_over65 = USA_Counties.AGE_65_74 + USA_Counties.AGE_75_84 + USA_Counties.AGE_85_UP) %>%
group_by(CITYNAME, Cluster ) %>%
summarise(n_counties = n() ,
pop2010 = sum(USA_Counties.POP2010),
pop_over65 = sum(pop_over65)) %>%
right_join(city_ci_sums) %>%
filter(!is.na(CITYNAME)) %>%
pivot_longer(cols = mean_val:last_col()) %>%
mutate(value = as.numeric(value)) %>%
mutate (an_pop2010_100k = case_when(grepl("an_", tm_var) ~ (value/pop2010)*100000,
TRUE ~ value) ) %>%
ungroup()
## Joining, by = "CITYNAME"
nrow(city_ci_sums)
## [1] 222193
city_ci_sums_pop
Check calculations for consistency with both MCMC and proj-hind used in mapping
boston_base_yr_mean <- raw_boston_base %>%
select(city, CITYNAME, cluster, year, gcm, dT_c , FIPS_txt, doy, tmean0, tmean15, rr_tmean0:rr_total,
tmean0_an_2010_100k:tot_an_2010_100k, an_tmean0:an_total ) %>%
pivot_longer(-(city:doy)) %>%
mutate( type = case_when (grepl( "an_", name ) ~ "mean_year_sum" , TRUE ~ "mean_year_mean" )) %>%
group_by(city, CITYNAME, cluster, year, gcm, dT_c , FIPS_txt, type, name) %>%
summarise(yr_sum = sum(value) , yr_mean =mean(value) ) %>%
ungroup()%>%
mutate(value = case_when (grepl( "an_", name ) ~ yr_sum
, TRUE ~ yr_mean))%>%
group_by(city, CITYNAME, cluster, FIPS_txt, name, type) %>%
summarise(value = mean(value))%>% ungroup()
boston_dtc6_yr_mean <- raw_boston_dtc6 %>%
select(city, CITYNAME, cluster, year, gcm, dT_c , FIPS_txt, doy, tmean0, tmean15, rr_tmean0:rr_total,tmean0_an_2010_100k:tot_an_2010_100k, an_tmean0:an_total )%>%
pivot_longer(-(city:doy)) %>%
mutate( type = case_when (grepl( "an_", name ) ~ "mean_year_sum" , TRUE ~ "mean_year_mean" )) %>%
group_by(city, CITYNAME, cluster, year, gcm, dT_c , FIPS_txt, type, name) %>%
summarise(yr_sum = sum(value) , yr_mean =mean(value) ) %>%
ungroup()%>%
mutate(value = case_when (grepl( "an_", name ) ~ yr_sum
, TRUE ~ yr_mean))%>%
group_by(city, CITYNAME, cluster, FIPS_txt, name, type) %>%
summarise(value = mean(value))%>% ungroup()
boston_base_yr_mean %>%
left_join(boston_dtc6_yr_mean, by = c("city", "CITYNAME", "cluster", "FIPS_txt", "name", "type" ), suffix = c("_base", "_dt6") ) %>%
mutate(value_dif = value_dt6 - value_base)%>% ### the raw sumamry matches that in MATCHES 2020-02-04-city_dT_AN_y4_dif_proj-hind_summary.csv (whole number match; rounding off)
filter(name %in% c("an_total" ))
summary_out_mean %>% filter(CITYNAME =="boston")
Create data for plotting and tables. Divide the MCMC output into the aggregates for year, season, and temperature range.
city_ci_sums_year <- city_ci_sums_pop %>%
filter(!is.na(dT_c) & season ==12) %>%
mutate( Cluster= factor(cluster, levels = 1:9, labels = c("1: NE Coast", "2: NE", "3: Midwest", "4: SE Central", "5: W Coast",
"6: N Gulf", "7: SE Reach", "8: SW", "9: West" )),
`Historical period` = factor(time_period, levels = c(1,2,3,4),
labels = c("1973-1983", "1983-1993", "1993-2003", "2003-2013") ) )
city_ci_sums_season <- city_ci_sums_pop %>% filter(!is.na(dT_c) & season %in% 1:4) %>%
mutate( Cluster= factor(cluster, levels = 1:9, labels = c("1: NE Coast", "2: NE", "3: Midwest", "4: SE Central", "5: W Coast",
"6: N Gulf", "7: SE Reach", "8: SW", "9: West" )),
`Historical period` = factor(time_period, levels = c(1,2,3,4),
labels = c("1973 -1983", "1983 -1993", "1993 -2003", "2003 -2013") ),
Season = factor(season, levels = c(1:4),
labels = c("Winter\n(Dec-Feb)", "Spring\n(Mar-June)", "Summer\n(July-Sep)", "Fall\n(Oct-Nov)")))
city_ci_sums_tmprng <- city_ci_sums_pop %>% filter(!is.na(dT_c) & season == 0) %>%
mutate( Cluster= factor(cluster, levels = 1:9, labels = c("1: NE Coast", "2: NE", "3: Midwest", "4: SE Central", "5: W Coast",
"6: N Gulf", "7: SE Reach", "8: SW", "9: West" )),
`Historical period` = factor(time_period, levels = c(1,2,3,4),
labels = c("1973-1983", "1983-1993", "1993-2003", "2003-2013") ),
`Range C` = factor(tmp_rng, levels = c(-1, -0.5,0 , 0.5, 1),
labels = c("Coldest", "Cold", "Mid",
"Hot","Hottest" )))
This should be very similar, within rounding error. If it is not, it indicates an error somewhere in the aggregation of the mean value for AN.
difs <- city_ci_sums_year %>% select(-Cluster, -FIPS_txt) %>%
filter( name == "mean_val" & tm_var == "an_total" & time_period == 4) %>%
left_join(summary_out_mean) %>%
select(cluster, CITYNAME ,city, dT_c, pop2010, tm_var, value,sum_an_total, n_samp) %>%
mutate( mcmc_mean_an_2010_norm =( value/pop2010)*100000,
central_est_an_2010_norm = (sum_an_total/pop2010)*100000 , # normalize by 2010 population
dif_2010_norm = central_est_an_2010_norm-mcmc_mean_an_2010_norm ) # get difference
## Joining, by = c("CITYNAME", "dT_c", "cluster")
difs
difs%>% # slight differences. Nothing that greatly affects output.
ggplot(aes(dif_2010_norm)) +
geom_histogram() +
xlab("Difference\nMCMC mean AN - Central estimate AN (per 100k 2010 pop)")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
difs%>%
ggplot(aes(x = cluster, y = dif_2010_norm, color = dT_c )) + geom_point() +
ylab("Difference\nMCMC mean AN - Central estimate AN (per 100k 2010 pop)")
Create table for manuscript, per Marcus Sarofim request for a table of national totals with lower and upper range. Note that this is normalized to 2010 population.
tp_adapt_sum <- city_ci_sums_year %>% filter(tm_var == "an_total" ) %>%
select(Cluster, CITYNAME, FIPS_txt, `Historical period`, `Degrees C change` = dT_c, tm_var, n_samp, name, an_pop2010_100k) %>%
pivot_wider(names_from = "name", values_from = "an_pop2010_100k") %>%
group_by(`Degrees C change`, `Historical period` ) %>%
summarise(`National change in deaths due to temperature ` = round(sum(mean_val),0),
`Lower Range` = round(sum(lci_66),0),
`Upper Range` = round(sum(uci_66),0))
tp_adapt_sum %>%
write.csv(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/summary_AN_tp-adapt_dif.csv"))
tp_adapt_sum %>% knitr::kable( padding = 2)
Degrees C change | Historical period | National change in deaths due to temperature | Lower Range | Upper Range |
---|---|---|---|---|
2 | 1973-1983 | 1352 | 553 | 2097 |
2 | 1983-1993 | 456 | -42 | 919 |
2 | 1993-2003 | 29 | -355 | 389 |
2 | 2003-2013 | -129 | -474 | 217 |
3 | 1973-1983 | 2669 | 1433 | 3833 |
3 | 1983-1993 | 990 | 197 | 1725 |
3 | 1993-2003 | 267 | -320 | 822 |
3 | 2003-2013 | -71 | -645 | 492 |
4 | 1973-1983 | 4282 | 2492 | 6056 |
4 | 1983-1993 | 1719 | 642 | 2787 |
4 | 1993-2003 | 690 | -147 | 1499 |
4 | 2003-2013 | 114 | -723 | 941 |
5 | 1973-1983 | 6389 | 3766 | 8920 |
5 | 1983-1993 | 2744 | 1152 | 4337 |
5 | 1993-2003 | 1359 | 78 | 2594 |
5 | 2003-2013 | 451 | -755 | 1660 |
6 | 1973-1983 | 10402 | 7860 | 12989 |
6 | 1983-1993 | 4825 | 2953 | 6665 |
6 | 1993-2003 | 2883 | 1428 | 4331 |
6 | 2003-2013 | 1280 | -371 | 2944 |
tp_y4_sum <- city_ci_sums_year %>% filter(tm_var == "an_total"& time_period == 4 ) %>%
select(Cluster, CITYNAME, FIPS_txt, `Degrees C change` = dT_c, tm_var, n_samp, name, an_pop2010_100k) %>%
pivot_wider(names_from = "name", values_from = "an_pop2010_100k") %>%
group_by(`Degrees C change` ) %>%
summarise(`National change in deaths due to temperature ` = round(sum(mean_val), 0),
`Lower Range` = round(sum(lci_66), 0),
`Upper Range` = round(sum(uci_66), 0))
tp_y4_sum%>%
write.csv(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/summary_AN_y4_dif.csv"))
tp_y4_sum %>% knitr::kable( padding = 2)
Degrees C change | National change in deaths due to temperature | Lower Range | Upper Range |
---|---|---|---|
2 | -129 | -474 | 217 |
3 | -71 | -645 | 492 |
4 | 114 | -723 | 941 |
5 | 451 | -755 | 1660 |
6 | 1280 | -371 | 2944 |
First, plot attributatble risk for projection - hindcast relative to season and the time period of the model fit. This shows adaptation over time, and how it differs by cluster.
for (i in 2:6) {# i == 2
ci_season_rr_2010100k_tp_facet <- city_ci_sums_season %>%
filter(season %in% c(1:4) & grepl("rr_total", tm_var) & dT_c == i) %>% # select out just the seasonal values and rr
select( -an_pop2010_100k ) %>%
pivot_wider() %>%
ggplot(aes(x = Season,
y = mean_val,
color = Cluster,
group = Cluster)) +
geom_point( position = position_dodge(width = 0.8), size = 0.9 ) +
geom_errorbar(aes(x=Season, ymin = lci_66, ymax = uci_66), width = 0 ,
position = position_dodge(width = 0.8) )+
scale_color_viridis_d(option = "plasma" ) +
ylab(bquote('Seasonal AR'[Delta*degree*C==.(i)]))+ # paste0("Total Attributable Risk\nRelative to Hindcast at ",i ," Δ°C" ))
theme_classic() +
facet_wrap( vars(`Historical period`))
print(ci_season_rr_2010100k_tp_facet)
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_rr_season_ci_2010100k_tp_facet.pdf" ), plot = ci_season_rr_2010100k_tp_facet )
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_rr_season_ci_2010100k_tp_facet.png"), plot = ci_season_rr_2010100k_tp_facet )}
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Next plot attributable risk relative to hindcast against year; this is to have an option for comparison.
for (i in 2:6) {# i = 2
rr_season_ci_2010100k_seas_facet <- city_ci_sums_season %>%
filter(season %in% c(1:4) & grepl("rr_total", tm_var)& dT_c == i) %>% # select out just the seasonal values and rr
select( -an_pop2010_100k ) %>%
pivot_wider() %>%
#mutate(`Historical period` = str_wrap(`Historical period`, width = 4)) %>%
ggplot(aes(x = `Historical period`,
y = mean_val,
color = Cluster,
group = Cluster)) +
geom_point( position = position_dodge(width = 0.8) , size = 0.9) +
geom_errorbar(aes(x=`Historical period`,
ymin = lci_66, ymax = uci_66), width = 0 , position = position_dodge(width = 0.8) )+
ylab(bquote('Seasonal AR '[Delta*degree*C==.(i)])) +# paste0("Total Attributable Risk\nRelative to Hindcast at ",i ," Δ°C")) +
scale_x_discrete(labels = function(`Historical period`) str_wrap(`Historical period` , width = 4) ) +
scale_color_viridis_d(option = "plasma" ) +
theme_classic() +
facet_wrap( vars(Season) )
print(rr_season_ci_2010100k_seas_facet)
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_rr_season_ci_2010100k_seas_facet.pdf" ), plot = rr_season_ci_2010100k_seas_facet )
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_rr_season_ci_2010100k_seas_facet.png"), plot = rr_season_ci_2010100k_seas_facet )}
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Also plot the attributable mortality, which accounts for seasonal differences in mortality not related to temperature.
for (i in 2:6) {
ci_season_rr_2010100k_tp_facet <- city_ci_sums_season %>%
filter(season %in% c(1:4) & grepl("an_total", tm_var) & dT_c == i) %>% # select out just the seasonal values and rr
mutate(value = an_pop2010_100k) %>% select( -an_pop2010_100k ) %>%
pivot_wider() %>%
ggplot(aes(x = Season,
y = mean_val,
color = Cluster,
group = Cluster)) +
geom_point( position = position_dodge(width = 0.8), size = 0.9 ) +
geom_errorbar(aes(x=Season, ymin = lci_66, ymax = uci_66), width = 0 ,
position = position_dodge(width = 0.8), size = 0.9 )+
scale_color_viridis_d(option = "plasma" ) +
ylab(bquote('Seasonal AN'[Delta*degree*C==.(i)](per~100~k~2010~pop)))+ #paste0("Total Attributable Deaths\nRelative to Hindcast at ",i ," Δ°C" )) +
theme_classic() +
facet_wrap( vars(`Historical period`))
print(ci_season_rr_2010100k_tp_facet)
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_an_season_ci_2010100k_tp_facet.pdf" ), plot = ci_season_rr_2010100k_tp_facet )
ggsave( paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i, "_an_season_ci_2010100k_tp_facet.png"), plot = ci_season_rr_2010100k_tp_facet ) }
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Plot just the estimates from the last historical period to show changes by season
for (i in 2:6) {
an_season_ci_2010100k_tp4 <- city_ci_sums_season %>%
filter(season %in% c(1:4) & grepl("an_total", tm_var)& time_period == 4 & dT_c == i) %>% # select out just the seasonal values and rr
select( -value ) %>% rename(value = an_pop2010_100k) %>%
pivot_wider() %>% # select out just the seasonal values and rr %>%
ggplot(aes(x = Season, y = mean_val, color = Cluster, group = Cluster)) +
geom_point( position = position_dodge(width = 0.8) ) +
geom_errorbar(aes(x=Season, ymin = lci_66, ymax = uci_66),
width = 0 , position = position_dodge(width = 0.8) )+
scale_color_viridis_d(option = "plasma" ) +
ylab( bquote('Seasonal AN'[Delta*degree*C==.(i)](per~100~k~2010~pop)))+ #paste0("Total Attributable Deaths Relative to Hindcast\nRelative to Hindcast at ",i ," Δ°C" )) +
theme_classic()
print(an_season_ci_2010100k_tp4)
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_season_ci_2010100k_tp4.pdf" ), plot = an_season_ci_2010100k_tp4 )
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_season_ci_2010100k_tp4.png"), plot = an_season_ci_2010100k_tp4 ) }
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Plot the differences by historical period across clusters.
for (i in 2:6) {
an_yr_ci_2010100k <- city_ci_sums_year %>%
filter( grepl("an_total", tm_var) & dT_c == i) %>% # select out just the seasonal values and an
select( -value ) %>% rename(value = an_pop2010_100k) %>%
pivot_wider() %>%
ggplot(aes(group = `Historical period` ,
x = Cluster, y = mean_val, color = `Historical period`)) +
geom_point(position = position_dodge(width = 0.8) ) +
geom_errorbar(aes(x=Cluster, ymin = lci_66, ymax = uci_66), width = 0 ,
position = position_dodge(width = 0.8) )+
scale_color_viridis_d( ) +
ylab( bquote('Yearly AN '[Delta*degree*C==.(i)](per~100~k~2010~pop))) +# paste0("Yearly Temperature Attributable Deaths\nRelative to Hindcast at ",i ," Δ°C" ))
theme_classic()
print(an_yr_ci_2010100k )
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_yr_ci_2010100k.pdf" ), plot = an_yr_ci_2010100k )
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_yr_ci_2010100k.png"), plot = an_yr_ci_2010100k ) }
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Plot the death attributable to mortality against the temperature ranges. The coldest are below the loweset exterior knot, the hottest above the highest exterior knot. The Cold are between the lowest exterior and the lowest interior. The Mid are between the lowest interior and the highest interior. The Hot are between the highest interiror and the highest exterior.
for (i in 2:6) {# i = 2
an_temp_ci_2010100k_facet_tp <- city_ci_sums_tmprng %>%
filter( grepl("an_total", tm_var) & dT_c == i) %>% # select out just the seasonal values and an
select( -value ) %>% rename(value = an_pop2010_100k) %>%
pivot_wider() %>%
ggplot(aes(x = `Range C`, y = mean_val, group = Cluster, color = Cluster)) +
geom_point( position = position_dodge(width = 0.8) ) +
geom_errorbar(aes(x=`Range C`, ymin = lci_66, ymax = uci_66), width = 0 , position = position_dodge(width = 0.8) )+
ylab(bquote('AN'[Delta*degree*C==.(i)](per~100~k~2010~pop)) ) + #paste0("Total Attributable Death Relative to Hindcast\nRelative to Hindcast at ",i ," Δ°C" )
theme_classic() +
scale_color_viridis_d(option = "plasma" ) +
facet_wrap( vars(`Historical period`))
expression("Temperature " ( degree~C))
print(an_temp_ci_2010100k_facet_tp )
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_temp_ci_2010100k_facet_tp.pdf" ), plot = an_temp_ci_2010100k_facet_tp )
ggsave(paste0(here("output/y4-final-manuscript-output/2020-02-24-QA-Manu-fig-tab-PROJ/"), "/dT_C", i,
"_an_temp_ci_2010100k_facet_tp.png"), plot = an_temp_ci_2010100k_facet_tp ) }
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devtools::session_info()
## - Session info ---------------------------------------------------------------
## setting value
## version R version 3.6.2 (2019-12-12)
## os Windows 10 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Denver
## date 2020-02-26
##
## - Packages -------------------------------------------------------------------
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
## backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.1)
## broom 0.5.4 2020-01-27 [1] CRAN (R 3.6.2)
## callr 3.4.2 2020-02-12 [1] CRAN (R 3.6.2)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 3.6.0)
## cli 2.0.1 2020-01-08 [1] CRAN (R 3.6.2)
## colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
## DBI 1.1.0 2019-12-15 [1] CRAN (R 3.6.2)
## dbplyr 1.4.2 2019-06-17 [1] CRAN (R 3.6.2)
## desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.1)
## devtools 2.2.2 2020-02-17 [1] CRAN (R 3.6.2)
## digest 0.6.24 2020-02-12 [1] CRAN (R 3.6.2)
## dlnm * 2.3.9 2019-03-11 [1] CRAN (R 3.6.0)
## dplyr * 0.8.4 2020-01-31 [1] CRAN (R 3.6.2)
## ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.2)
## evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.2)
## fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.2)
## farver 2.0.3 2020-01-16 [1] CRAN (R 3.6.2)
## forcats * 0.4.0 2019-02-17 [1] CRAN (R 3.6.0)
## fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
## generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.0)
## ggplot2 * 3.2.1 2019-08-10 [1] CRAN (R 3.6.2)
## glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
## haven 2.2.0 2019-11-08 [1] CRAN (R 3.6.2)
## here * 0.1 2017-05-28 [1] CRAN (R 3.6.0)
## highr 0.8 2019-03-20 [1] CRAN (R 3.6.0)
## hms 0.5.3 2020-01-08 [1] CRAN (R 3.6.2)
## htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.2)
## httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.2)
## jsonlite 1.6.1 2020-02-02 [1] CRAN (R 3.6.2)
## kableExtra * 1.1.0 2019-03-16 [1] CRAN (R 3.6.2)
## knitr * 1.28 2020-02-06 [1] CRAN (R 3.6.2)
## labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
## lattice 0.20-38 2018-11-04 [2] CRAN (R 3.6.2)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.0)
## lifecycle 0.1.0 2019-08-01 [1] CRAN (R 3.6.2)
## lubridate * 1.7.4 2018-04-11 [1] CRAN (R 3.6.0)
## magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.0)
## Matrix 1.2-18 2019-11-27 [2] CRAN (R 3.6.2)
## memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.1)
## mgcv 1.8-31 2019-11-09 [1] CRAN (R 3.6.2)
## mixmeta 1.0.7 2019-12-09 [1] CRAN (R 3.6.2)
## modelr 0.1.5 2019-08-08 [1] CRAN (R 3.6.2)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.0)
## mvmeta * 1.0.3 2019-12-10 [1] CRAN (R 3.6.2)
## nlme 3.1-144 2020-02-06 [1] CRAN (R 3.6.2)
## pillar 1.4.3 2019-12-20 [1] CRAN (R 3.6.2)
## pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.2)
## pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.1)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 3.6.2)
## processx 3.4.2 2020-02-09 [1] CRAN (R 3.6.2)
## ps 1.3.2 2020-02-13 [1] CRAN (R 3.6.2)
## purrr * 0.3.3 2019-10-18 [1] CRAN (R 3.6.2)
## R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.2)
## Rcpp 1.0.3 2019-11-08 [1] CRAN (R 3.6.2)
## readr * 1.3.1 2018-12-21 [1] CRAN (R 3.6.0)
## readxl * 1.3.1 2019-03-13 [1] CRAN (R 3.6.0)
## remotes 2.1.1 2020-02-15 [1] CRAN (R 3.6.2)
## reprex 0.3.0 2019-05-16 [1] CRAN (R 3.6.0)
## rlang 0.4.4 2020-01-28 [1] CRAN (R 3.6.2)
## rmarkdown 2.1 2020-01-20 [1] CRAN (R 3.6.2)
## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
## rstudioapi 0.11 2020-02-07 [1] CRAN (R 3.6.2)
## rvest 0.3.5 2019-11-08 [1] CRAN (R 3.6.2)
## scales 1.1.0 2019-11-18 [1] CRAN (R 3.6.2)
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.1)
## stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
## testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.2)
## tibble * 2.1.3 2019-06-06 [1] CRAN (R 3.6.2)
## tidyr * 1.0.2 2020-01-24 [1] CRAN (R 3.6.2)
## tidyselect 1.0.0 2020-01-27 [1] CRAN (R 3.6.2)
## tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 3.6.2)
## tsModel 0.6 2013-06-24 [1] CRAN (R 3.6.0)
## usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.1)
## vctrs 0.2.3 2020-02-20 [1] CRAN (R 3.6.2)
## viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
## webshot 0.5.2 2019-11-22 [1] CRAN (R 3.6.2)
## withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
## xfun 0.12 2020-01-13 [1] CRAN (R 3.6.2)
## xml2 1.2.2 2019-08-09 [1] CRAN (R 3.6.2)
## yaml 2.2.1 2020-02-01 [1] CRAN (R 3.6.2)
##
## [1] C:/Users/LayC/R.Lib
## [2] C:/Program Files/R/R-3.6.2/library