diff --git a/R/.Rhistory b/R/.Rhistory
index 9187d92..f484971 100644
--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,271 +1,3 @@
-qqnorm(octo_data$issue_mmt)
-qqnorm(log(octo_data$issue_mmt))
-qqnorm(residuals(octo_data$issue_mmt))
-qqnorm(octo_data$issue_mmt)
-qqnorm(log(octo_data$issue_mmt))
-qqnorm(octo_data$issue_mmt)
-hist(log(octo_data$issue_mmt))
-hist(sqrt(octo_data$issue_mmt))
-#below are the models for the octo data, there should be analysis for each one
-octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
-summary(octo_mmtmodel1)
-#below are the models for the octo data, there should be analysis for each one
-octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
-summary(octo_mmtmodel1)
-# below this is the analysis for the octo data
-octo_data$new.age <- as.numeric(cut(octo_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
-table(octo_data$new.age)
-octo_data$new.age.factor <- as.factor(octo_data$new.age)
-hist(octo_data$new.age)
-#below are the models for the octo data, there should be analysis for each one
-octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
-summary(octo_mmtmodel1)
-hist(sqrt(octo_data$issue_mmt))
-hist(sqrt(octo_data$issue_mmt))
-hist(octo_data$issue_mmt)
-#right skewed data, need to transform
-library(rcompanion)
-install.packages(rcompanion)
-hist(sqrt(octo_data$issue_mmt))
-qqnorm(1/octo_data$issue_mmt)
-hist(1/octo_data$issue_mmt)
-hist(log(octo_data$issue_mmt))
-hist(sqrt(octo_data$issue_mmt))
-hist(log(octo_data$issue_mmt))
-octo_data$sqrt_issue_mmt <- sqrt(octo_data$issue_mmt)
-sqrt_issue_mmtmodel1 <- lm(underproduction_mean ~ sqrt_issue_mmt + new.age.factor, data=octo_data)
-summary(sqrt_issue_mmtmodel1)
-summary(issue_mmtmodel1)
-octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$wiki_mmt)
-wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + new.age.factor, data=octo_data)
-summary(wiki_mmtmodel1)
-g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 5)
-g3
-g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 0.05)
-g3
-g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 0.05) + theme_bw()
-g3
-g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
-g3
-g2 <- ggplot(octo_data, aes(issue_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
-g2
-g1 <- ggplot(octo_data, aes(sqrt_issue_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
-g1
-g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
-g3
-g2 <- ggplot(octo_data, aes(issue_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
-g2
-texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-source('powerAnalysis.R') #my little "lib"
-texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-library(texreg) #my little "lib"
-texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki_contrib.'  ),
-custom.coef.names=c('(Intercept)', 'MMT', 'Issues', 'Age-2', 'Age-3', 'Age-4', 'Wiki'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-glimpse(readme_df)
-library(tidyverse)
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-glimpse(readme_df)
-head(readme_df)
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-glimpse(readme_df)
-col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
-readme_df <- readme_df[,col_order]
-glimpse(readme_df)
-#TODO: turn character type into vector of numbers
-str_split(test, ", ")
-test <- "[0, 0, 0, 0]"
-#TODO: turn character type into vector of numbers
-str_split(test, ", ")
-#TODO: turn character type into vector of numbers
-str_split(gsub("[][]","", test), ", ")
-readme_df %>% add_column(cnt_before_all = str_split(gsub("[][]","", before_all_count), ", "))
-readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", before_all_count), ", "))
-readme_df %>% mutate("cnt_before_all" = str_split(gsub("[][]","", "before_all_count"), ", "))
-head(readme_df$before_all_cnt)
-head(readme_df$cnt_before_all)
-readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_count"), ", "))
-head(readme_df$cnt_before_all)
-View(readme_df)
-View(readme_df)
-readme_df$cnt_before_all
-readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_count"), ", "))
-str_split(gsub("[][]","", readme_df$before_all_count), ", ")
-#str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_cnt"), ", "))
-readme_df$cnt_before_all
-#str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-readme_df %>% mutate("cnt_before_all" = str_split(gsub("[][]","", "before_all_cnt"), ", "))
-readme_df$cnt_before_all
-str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-readme_df$cnt_before_all
-readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
-readme_df$cnt_after_all
-readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
-readme_df$cnt_before_mrg
-readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
-readme_df$cnt_after_mrg
-#TODO: figure out if one needs to expand the data into a different dataframe, and if so how
-readme_df <- subset(readme_df, select = -c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt"))
-drop <- c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt")
-readme_df = readme_df[,!(names(readme_df) %in% drop)]
-View(readme_df)
-library(tidyverse)
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
-readme_df <- readme_df[,col_order]
-glimpse(readme_df)
-readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-readme_df$cnt_before_all <- as.numeric(readme_df$cnt_before_all)
-View(readme_df)
-readme_df$cnt_before_all
-type(readme_df$cnt_before_all)
-typeof(readme_df$cnt_before_all)
-typeof(readme_df$cnt_before_all[0])
-readme_df$cnt_before_all <- unlist(str_split(gsub("[][]","", readme_df$before_all_cnt), ", "))
-readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-typeof(readme_df$cnt_before_all)
-typeof(readme_df$cnt_before_all[[0]])
-typeof(readme_df$cnt_before_all[0])
-sapply(readme_df, class)
-readme_df[,lapply(readme_df, unlist)]
-readme_df[,lapply(readme_df$cnt, unlist)]
-readme_df[,lapply(readme_df$cnt_before_all, unlist)]
-typeof(readme_df$cnt_before_all[0])
-View(readme_df)
-View(readme_df)
-readme_df$cnt_before_all <- as.numeric(str_split(gsub("[][]","", readme_df$before_all_cnt), ", "))
-readme_df$cnt_before_all <- as.numeric(str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")[[1]])
-readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-typeof(readme_df$cnt_before_all[0])
-typeof(readme_df$cnt_before_all[0][0])
-readme_df$cnt_before_all[0]
-unlist(readme_df$cnt_before_all[0])
-readme_df$cnt_before_all[0]
-readme_df$cnt_before_all
-test <- readme_df$cnt_before_all
-test
-as.numeric(test)
-test[0]
-test[1]
-as.numeric(test[1])
-unlist(test[1])
-as.numeric(unlist(test[1]))
-test2 <- as.numeric(unlist(test))
-test2
-print(entry)
-for (entry in test) {
-print(entry)
-}
-print(as.numeric(unlist(entry)))
-for (entry in test) {
-print(as.numeric(unlist(entry)))
-}
-test_two <- append(test_two, as.numeric(unlist(entry)))
-print(as.numeric(unlist(entry)))
-for (entry in test) {
-test_two <- append(test_two, as.numeric(unlist(entry)))
-print(as.numeric(unlist(entry)))
-}
-test_two <- c()
-for (entry in test) {
-test_two <- append(test_two, as.numeric(unlist(entry)))
-print(as.numeric(unlist(entry)))
-}
-readme_df$cnt_before_all <- as.numeric(readme_df$cnt_before_all)
-test_two
-readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
-iterator <- 0
-for (entry in test) {
-readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
-print(as.numeric(unlist(entry)))
-iterator <- iterator + 1
-}
-View(readme_df)
-library(tidyverse)
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
-readme_df <- readme_df[,col_order]
-glimpse(readme_df)
-head(readme_df)
-#this has to happen on the analysis side of things for a given row, it cannot happen on the storage side
-#this is a conversation of whether or not the data should be saved in terms of
-readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
-# test <- readme_df$cnt_before_all
-# as.numeric(unlist(test[1]))
-# test_two <- c()
-# iterator <- 0
-# for (entry in test) {
-#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
-#   print(as.numeric(unlist(entry)))
-#   iterator <- iterator + 1
-# }
-# test_two
-readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
-readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
-readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
-drop <- c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt")
-readme_df = readme_df[,!(names(readme_df) %in% drop)]
-#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
-new_test <- head(readme_df, 1)
-View(new_test)
-write.csv(readme_df, "r_readme_did.csv", row.names=FALSE)
-# as.numeric(unlist(test[1]))
-# test_two <- c()
-# iterator <- 0
-# for (entry in test) {
-#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
-#   print(as.numeric(unlist(entry)))
-#   iterator <- iterator + 1
-# }
-# test_two
-#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
-new_test <- head(readme_df, 1)
-View(new_test)
-longer <- new_test |>
-pivot_longer(cols = starts_with("cnt"),
-names_to = "window",
-values_to = "count"))
-longer <- new_test |>
-pivot_longer(cols = starts_with("cnt"),
-names_to = "window",
-values_to = "count")
-longer
-View(longer)
-longer |> unnest(count)
-new_longer <- longer |> unnest(count)
-View(new_longer)
-longer
-View(new_longer)
-longer <- new_test |>
-pivot_longer(cols = starts_with("cnt"),
-names_to = "window",
-values_to = "count") |>
-unnest(as.numeric(unlist(count)))
-longer
-longer <- new_test |>
 pivot_longer(cols = starts_with("cnt"),
 names_to = "window",
 values_to = "count") |>
@@ -510,3 +242,271 @@ longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, le
 View(longer)
 head(longer)
 sapply(longer, class)
+library(plyr)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
+readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
+readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
+readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+View(longer)
+#testing out analysis below
+longer[which(longer$observation_type == all)] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+#testing out analysis below
+longer[which(longer$observation_type == "all")] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+View(readme_df)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[5,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+View(readme_df)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[76,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[77,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[143,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[185,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- readme_df[231,]
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+longer[which(longer$observation_type == "all"),] |>
+mutate(D = ifelse(week >= 26, 1, 0)) |>
+lm(count ~ D + I(week - 26)) |>
+summary()
+longer[which(longer$observation_type == "all"),] |>
+mutate(D = ifelse(week >= 26, 1, 0)) |>
+lm(count ~ D * I(week - 26)) |>
+summary()
+longer[which(longer$observation_type == "all"),] |>
+mutate(D = ifelse(week >= 26, 1, 0)) |>
+lm(formula = count ~ D * I(week - 26)) |>
+summary()
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = ifelse(week >= 26, 1, 0)) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth(method = "lm")
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth(method = "lm")
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth()
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth()
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth(aes(x = week, y = count, color = D))
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth()
+sapply(longer, class)
+longer$count <- as.numeric(longer$count)
+sapply(longer, class)
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |>
+ggplot(aes(x = week, y = count)) +
+geom_point() +
+geom_vline(xintercept = 26)
+longer[which(longer$observation_type == "all"),] |>
+mutate(D = ifelse(week >= 26, 1, 0)) |>
+lm(formula = count ~ D * I(week - 26)) |>
+summary()
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth()
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth(se = False)
+longer[which(longer$observation_type == "all"),] |>
+select(count, week) |>
+mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+ggplot(aes(x = week, y = count, color = D)) +
+geom_point() +
+geom_smooth(se = FALSE)
diff --git a/R/didAnalysis.R b/R/didAnalysis.R
new file mode 100644
index 0000000..e69de29
diff --git a/R/didCleaning.R b/R/didCleaning.R
index 2722467..2c15fc2 100644
--- a/R/didCleaning.R
+++ b/R/didCleaning.R
@@ -32,7 +32,7 @@ readme_df = readme_df[,!(names(readme_df) %in% drop)]
 # }
 # test_two
 #Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
-new_test <- head(readme_df, 1)
+new_test <- readme_df[231,]
 longer <- new_test |> 
   pivot_longer(cols = starts_with("ct"),
                names_to = "window",
@@ -40,3 +40,24 @@ longer <- new_test |>
   unnest(count) 
 longer$observation_type <- gsub("^.*_", "", longer$window)
 longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+longer$count <- as.numeric(longer$count)
+
+#sapply(longer, class)
+
+#testing out analysis below
+longer[which(longer$observation_type == "all"),] |> 
+  ggplot(aes(x = week, y = count)) + 
+  geom_point() +
+  geom_vline(xintercept = 26) 
+
+longer[which(longer$observation_type == "all"),] |>
+  mutate(D = ifelse(week >= 26, 1, 0)) |>
+  lm(formula = count ~ D * I(week - 26)) |> 
+  summary()
+
+longer[which(longer$observation_type == "all"),] |>
+  select(count, week) |>
+  mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
+  ggplot(aes(x = week, y = count, color = D)) +
+  geom_point() + 
+  geom_smooth(se = FALSE)