Update imports

saketkc · Jan 12, 2024 · 5e762ac · 5e762ac
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,16 +1,16 @@
 Package: covmuller
 Type: Package
 Title: A tool to model COVID19 variant prevalence using data from GISAID
-Version: 0.1.2.0001
-Date: 2023-09-19
+Version: 0.1.2.0002
+Date: 2024-01-12
 Author: Saket Choudhary
 Maintainer: Saket Choudhary <[email protected]>
 Description: covmuller can be used to process data from GISAID and 
     perform modeling on variant prevalence with lots of supported visualisation.
-License: MIT
+License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.0
 URL: https://saketkc.github.io/covmuller
 BugReports: https://github.com/saketkc/covmuller/issues
 Depends:
@@ -30,6 +30,7 @@ Imports:
     magrittr,
     nnet,
     patchwork,
+    readr,
     reshape2,
     RJSONIO,
     scales,

diff --git a/NAMESPACE b/NAMESPACE
@@ -51,6 +51,7 @@ importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
 importFrom(dplyr,recode)
 importFrom(dplyr,rename)
+importFrom(dplyr,select)
 importFrom(dplyr,select_if)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarise_all)
@@ -77,7 +78,9 @@ importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,guide_axis)
 importFrom(ggplot2,guides)
 importFrom(ggplot2,labs)
+importFrom(ggplot2,position_dodge)
 importFrom(ggplot2,scale_color_identity)
+importFrom(ggplot2,scale_color_manual)
 importFrom(ggplot2,scale_fill_brewer)
 importFrom(ggplot2,scale_fill_gradient2)
 importFrom(ggplot2,scale_x_date)
@@ -101,7 +104,11 @@ importFrom(scales,cut_short_scale)
 importFrom(scales,label_number)
 importFrom(scales,label_percent)
 importFrom(splines,ns)
+importFrom(stats,as.formula)
+importFrom(stats,median)
+importFrom(stats,predict)
 importFrom(stringi,stri_split_fixed)
+importFrom(stringr,str_count)
 importFrom(stringr,str_split_fixed)
 importFrom(stringr,str_squish)
 importFrom(stringr,str_to_title)
@@ -114,4 +121,6 @@ importFrom(tsibble,scale_x_yearweek)
 importFrom(tsibble,yearmonth)
 importFrom(tsibble,yearweek)
 importFrom(utils,URLencode)
+importFrom(utils,read.csv)
+importFrom(utils,untar)
 importFrom(zoo,as.yearmon)
diff --git a/R/fetch_data.R b/R/fetch_data.R
@@ -118,7 +118,7 @@ GetIndiaHospitalization <- function(url = "") {
 #' @returns A data frame containing monthly cases for each state in long form
 
 #' @importFrom magrittr %>%
-#' @importFrom dplyr arrange group_by summarise_all rename
+#' @importFrom dplyr arrange group_by select summarise_all rename
 #' @importFrom readr read_csv
 #' @importFrom reshape2 melt
 #' @export

diff --git a/R/gisaid.R b/R/gisaid.R
@@ -8,6 +8,7 @@
 #' @importFrom stringr str_split_fixed str_trim
 #' @importFrom tools file_ext
 #' @importFrom data.table fread
+#' @importFrom utils untar
 #' @export
 ReadGISAIDMetada <- function(path, showProgress = FALSE, ...) {
   file.ext <- file_ext(path)
@@ -85,6 +86,7 @@ FormatGISAIDMetadata <- function(df, collection_col = "Collection date", submiss
 #' @returns A dataframe with all the instrument related metadata
 #' @importFrom stringi stri_split_fixed
 #' @importFrom dplyr bind_rows distinct
+#' @importFrom utils read.csv
 #' @export
 ReadAuspiceInstrument <- function(path) {
   metadata <- list()
@@ -94,7 +96,7 @@ ReadAuspiceInstrument <- function(path) {
   )) {
     date_path <- unlist(stri_split_fixed(str = file, pattern = "/"))
     date_path <- date_path[length(date_path) - 1]
-    metadata[[date_path]] <- read.csv(file, sep = "\t")
+    metadata[[date_path]] <- read.csv(file = file, sep = "\t")
   }
   seq_metadata <- bind_rows(metadata) %>% distinct()
 

diff --git a/R/model.R b/R/model.R
@@ -1,3 +1,4 @@
+#' @importFrom stats as.formula
 #' @importFrom magrittr %>%
 #' @importFrom tibble deframe
 #' @importFrom nnet multinom
@@ -14,6 +15,7 @@ FitMultinom <- function(data,
   return(fit)
 }
 
+#' @importFrom stats predict
 #' @importFrom magrittr %>%
 #' @importFrom tibble deframe
 #' @importFrom nnet multinom

diff --git a/R/utilities.R b/R/utilities.R
@@ -5,8 +5,15 @@
 #' @returns A vector with dates converted to MonthYear format (zoo::as.yearmon)
 #' @importFrom  dplyr pull
 #' @importFrom zoo as.yearmon
+#' @importFrom stringr str_count
 #' @export
 GetMonthYear <- function(datecol, datefmt = "%Y-%m-%d") {
+  number_dashes <- str_count(string = datecol, pattern = "-")
+  if (datefmt == "%Y-%m-%d"){
+      datecol[number_dashes == 1] <- paste0(datecol[number_dashes == 1], "-01")
+  } else if (datefmt == "%d-%m-%Y"){
+    datecol[number_dashes == 1] <- paste0("01-", datecol[number_dashes == 1])
+  }
   Date <- as.Date(datecol, format = datefmt)
   Month <- strftime(Date, "%m")
   Year <- strftime(Date, "%Y")

diff --git a/R/visualization.R b/R/visualization.R
@@ -31,6 +31,7 @@ CovmullerTheme <- function() {
 #' @importFrom hrbrthemes theme_ipsum
 #' @importFrom ggtext element_markdown
 #' @importFrom patchwork wrap_plots
+#' @importFrom stats median
 #' @export
 PlotTotalHeatmap <- function(df, color_legend = "Total cases") {
   df_india <- df %>% filter(State == "India")
@@ -111,7 +112,7 @@ PlotSequencedPropHeatmap <- function(df) {
 
 #' @importFrom magrittr %>%
 #' @importFrom tibble deframe
-#' @importFrom ggplot2 aes_string coord_cartesian ggplot geom_bar geom_text labs scale_y_continuous scale_x_discrete xlab ylab guide_axis
+#' @importFrom ggplot2 aes_string coord_cartesian ggplot geom_bar geom_text labs scale_y_continuous scale_x_discrete xlab ylab guide_axis position_dodge
 #' @importFrom scales comma label_percent label_number cut_short_scale
 #' @importFrom ggtext element_markdown
 #' @importFrom patchwork wrap_plots
@@ -189,7 +190,7 @@ BarPlot <- function(df, xaxis = "MonthYear",
   return(wrap_plots(p))
 }
 
-#' @importFrom ggplot2 ggplot geom_bar labs scale_fill_brewer scale_x_discrete xlab ylab guide_axis
+#' @importFrom ggplot2 ggplot geom_bar labs scale_fill_brewer scale_x_discrete xlab ylab guide_axis scale_color_manual
 #' @importFrom scales label_number cut_short_scale
 #' @importFrom ggtext element_markdown
 #' @importFrom patchwork wrap_plots
@@ -234,7 +235,7 @@ PlotMullerDailyPrevalence <- function(df, ncol = 4) {
   wrap_plots(p)
 }
 
-#' @importFrom ggplot2 ggplot geom_line geom_label scale_fill_brewer scale_y_continuous xlab ylab labs ggtitle guide_axis theme element_text
+#' @importFrom ggplot2 ggplot geom_line geom_label scale_fill_brewer scale_y_continuous xlab ylab labs ggtitle guide_axis theme element_text scale_color_manual
 #' @importFrom scales label_number cut_short_scale
 #' @importFrom gganimate transition_reveal view_follow animate gifski_renderer
 #' @importFrom tsibble scale_x_yearweek
@@ -254,7 +255,7 @@ PlotVariantPrevalenceAnimated <- function(df, title = NULL, caption = "**Source:
   ) +
     geom_line() +
     scale_x_yearweek(date_breaks = date_breaks, date_labels = "%d %b %Y", guide = guide_axis(angle = 90)) +
-    scale_y_continuous(label = label_number(accuracy = 1, scale_cut = cut_short_scale())) + # , trans = trans_y) +
+    scale_y_continuous(labels = label_number(accuracy = 1, scale_cut = cut_short_scale())) + # , trans = trans_y) +
     geom_label(hjust = 0, aes(label = variant), nudge_x = 10, show.legend = FALSE) +
     geom_point() +
     coord_cartesian(ylim = c(0, NA), clip = "off") +

diff --git a/docs/articles/USA_animated.gif b/docs/articles/USA_animated.gif
diff --git a/docs/articles/VariantAnimation-USA_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/VariantAnimation-USA_files/figure-html/unnamed-chunk-5-1.png
diff --git a/docs/articles/index.html b/docs/articles/index.html
diff --git a/vignettes/VariantAnimation-USA.Rmd b/vignettes/VariantAnimation-USA.Rmd
@@ -82,18 +82,49 @@ gisaid_usa <- CollapseLineageToVOCs(
 # Get weekly cases for USA
 
 ```{r, warning=FALSE, message=FALSE}
-confirmed <- covid19(country = "USA", level = 1) %>%
-  select(date, confirmed) %>%
-  filter(!is.na(confirmed))
-
-confirmed$daily_cases <- c(confirmed$confirmed[1], diff(confirmed$confirmed))
+GetCases <- function(){
+    data <- read.csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv")
+    confirmed <- data %>% select(date, United.States)
+colnames(confirmed)[2] <- c("cases")
+confirmed$MonthYear <- GetMonthYear(confirmed$date)
 confirmed$WeekYear <- tsibble::yearweek(confirmed$date)
+return (confirmed)
+}
 
-confirmed_subset_dateweekwise_long <- confirmed %>%
-  group_by(WeekYear) %>%
-  summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%
-  arrange(WeekYear) %>%
-  rename(WeekYearCollected = WeekYear)
+
+GetCasesLong <- function(){
+    data <- read.csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv")
+    confirmed <- data %>% select(date, United.States)
+colnames(confirmed)[2] <- c("cases")
+confirmed$MonthYear <- GetMonthYear(confirmed$date)
+confirmed$WeekYear <- tsibble::yearweek(confirmed$date)
+confirmed_subset_weekwise <- confirmed %>%
+      group_by(WeekYear) %>%
+      summarise(cases = mean(cases, na.rm=T)) %>%
+      arrange(WeekYear)
+confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)  
+confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%
+  rename(n = cases) %>%
+    rename(WeekYearCollected = WeekYear)
+}
+
+
+confirmed <- GetCases()
+confirmed_subset_dateweekwise_long <- GetCasesLong()
+
+# 
+# confirmed <- covid19(country = "USA", level = 1) %>%
+#   select(date, confirmed) %>%
+#   filter(!is.na(confirmed))
+# 
+# confirmed$daily_cases <- c(confirmed$confirmed[1], diff(confirmed$confirmed))
+# confirmed$WeekYear <- tsibble::yearweek(confirmed$date)
+# 
+# confirmed_subset_dateweekwise_long <- confirmed %>%
+#   group_by(WeekYear) %>%
+#   summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%
+#   arrange(WeekYear) %>%
+#   rename(WeekYearCollected = WeekYear)
 
 
 gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa)
@@ -133,7 +164,7 @@ voc_to_keep <- gisaid_usa_weekwise %>%
 gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)
 
 usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long)
-the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in the USA by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>", date_breaks = "28 days")
+the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in the USA by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>", date_breaks = "100 days")
 gganimate::anim_save(filename = here::here("docs/articles/USA_animated.gif"), animation = the_anim)
 ```
 
@@ -163,12 +194,12 @@ voc_to_keep <- gisaid_usa_weekwise %>%
 gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)
 
 usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long)
-the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in the USA by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>")
+the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in the USA by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>",  date_breaks = "100 days")
 gganimate::anim_save(filename = here::here("docs/articles/USA_animated_2021.gif"), animation = the_anim)
 ```
 
 ![](USA_animated_2021.gif)
-Look at cases in the past few weeks
+Look at cases from 2023
 
 ```{r}
 confirmed$MonthYear <- GetMonthYear(confirmed$date)
@@ -197,3 +228,34 @@ gganimate::anim_save(filename = here::here("docs/articles/USA_animated_2023.gif"
 ```
 
 ![](USA_animated_2023.gif)
+
+
+
+Look at cases in the past few weeks
+
+```{r}
+confirmed$MonthYear <- GetMonthYear(confirmed$date)
+
+confirmed_subset_dateweekwise_long <- confirmed %>%
+  filter(MonthYear > "April 2023") %>%
+  group_by(WeekYear) %>%
+  summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>%
+  arrange(WeekYear) %>%
+  rename(WeekYearCollected = WeekYear)
+
+gisaid_usa_subset <- gisaid_usa %>% filter(MonthYearCollected > "December 2022")
+gisaid_usa_weekwise <- SummarizeVariantsWeekwise(gisaid_usa_subset)
+
+voc_to_keep <- gisaid_usa_weekwise %>%
+  group_by(lineage_collapsed) %>%
+  summarise(n_sum = sum(n)) %>%
+  filter(n_sum > 50) %>%
+  pull(lineage_collapsed) %>%
+  unique()
+gisaid_usa_weekwise <- gisaid_usa_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)
+
+usa_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_usa_weekwise, confirmed_subset_dateweekwise_long)
+the_anim <- PlotVariantPrevalenceAnimated(usa_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in the USA by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>")
+gganimate::anim_save(filename = here::here("docs/articles/USA_animated_2024.gif"), animation = the_anim)
+```
+![](USA_animated_2024.gif)