{"action":"created","comment":{"url":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/pulls/comments/57576160","id":57576160,"diff_hunk":"@@ -106,16 +171,150 @@ Lec09 to the project directory `HW-2`.\n ```{r, echo=FALSE, cache=TRUE}\n # DO NOT EDIT THIS SECTION!\n profiles <- read.csv(\"profiles.csv\", header=TRUE) %>% tbl_df()\n+profiles <- profiles %>% sample_frac(0.1)\n+\n+profiles <- mutate(profiles, female = ifelse(sex==\"f\", 1, 0))\n+p_female = mean(profiles$female)\n+\n+attach(profiles)\n ```\n \n \n ```{r, echo=FALSE, fig.width=12, fig.height=6}\n # Feel free to make multiple code blocks, but set echo, fig.width, fig.height as \n # above\n+\n+hist(profiles$age)\n+\"seems like a good place to start\"\n+\n+mc <- select(profiles, age, diet, drinks, drugs, education, sex, orientation, pets, religion, sign, smokes, speaks, status)\n+#(multiple choince questions)\n+\n+ggplot(mc, aes(x=age)) + geom_histogram(bins = 11) + facet_grid(.~sex)\n+\"looks pretty much the same between the two, boring\"\n+\n ```\n \n \n ```{r, echo=FALSE, fig.width=12, fig.height=6}\n # Feel free to make multiple code blocks, but set echo, fig.width, fig.height as \n # above\n+\n+\n+\"Let's just group random things by sex\"\n+group_by(mc, drugs, sex) %>% summarize(n())\n+group_by(mc, diet, sex) %>% summarize(n())\n+group_by(mc, pets, sex) %>% summarize(n())\n+\n+\"this isn't terribly productive, so I'm not going to show all that I tried. Diet seemed like a good option though.\"\n+\n+veg <- filter(mc, diet == \"anything\" | diet == \"mostly vegetarian\" | diet == \"strictly vegetarian\")\n+veg_df <- veg %>% group_by(diet, sex) %>% summarize(n())\n+veg_2 <- veg %>% group_by(diet) %>%  summarize(n())\n+veg_df <- inner_join(veg_2, veg_df, by=c(\"diet\" = \"diet\")) ## why did it have to be like this to work??\n+veg_df = rename(veg_df, group_total =  `n().x`, sex_total = `n().y`)\n+veg_df <- mutate(veg_df, expect = group_total * (ifelse(sex==\"f\", p_female, 1-p_female)) %>%  round(digits = 1))\n+veg_df <- mutate(veg_df, difference = sex_total - expect %>%  round(digits = 1))\n+veg_df <- mutate(veg_df, percent_difference = (difference / sex_total * 100) %>% round(digits = 1))\n+\n+## ^ all that can be made a function to make this repeatable\n+datatable(veg_df, options = list(searching = FALSE, pageLength = 20))\n+\n+\"Narrowing in on vegetarians, we can compare the expected amounts based on the percent female to the actual amounts that are vegetarian.\"\n+```\n+\n+```{r, echo=FALSE, fig.width=6, fig.height=6}\n+veg_df <- mutate(veg_df, d_s = paste(diet, sex, sep = \"_\"))\n+\n+ggplot(veg_df, aes(x = d_s, y = percent_difference, fill = sex)) + geom_bar(stat=\"identity\") +\n+  theme(axis.text.x = element_text(angle = 25, hjust = 1))\n+\n+\"It seems like vegetarians are more likely to be female (and less likely to be male) than expected by random chance.\"\n+\n+```\n+\n+```{r, echo=FALSE, fig.width=12, fig.height=6}\n+find.query <- function(char.vector, query){\n+  which.has.query <- grep(query, char.vector, ignore.case = TRUE)\n+  length(which.has.query) != 0\n+}\n+profile.has.query <- function(data.frame, query){\n+  query <- tolower(query)\n+  has.query <- apply(data.frame, 1, find.query, query=query)\n+  return(has.query)\n+}\n+\n+sex.by.query <- function(df, query){\n+  profiles <- mutate(profiles, has_query = profile.has.query(df, query))\n+  return(group_by(profiles, has_query) %>% summarize(prop_female = mean(female)))\n+}\n+\n+count.by.query <- function(df, query){\n+  profiles <- mutate(profiles, has_query = profile.has.query(df, query))\n+  return(group_by(profiles, has_query) %>% summarize(count = n()))\n+}\n+## This only works for this assignment, but it lets me quickly check a lot of things. Also, it's slow and they're redundent. Good enough.\n+\n+essays <- select(profiles, contains(\"essay\"), `female`)\n+\n+##We can quickly check proportions by the two functions above\"\n+\n+\"\\n\\n\n+Looking at the essays might be more productive. Let's check random words\"\n+\n+\n+\"\\\"Swag\\\"\"\n+count.by.query(essays, \"swag\")\n+sex.by.query(essays, \"swag\")\n+\n+\"\\\"beer\\\"\"\n+count.by.query(essays, \"beer\")\n+sex.by.query(essays, \"beer\")\n+\n+\"\\\"Drake\\\"\"\n+count.by.query(essays, \"Drake\")\n+sex.by.query(essays, \"Drake\")\n+\n+\"I did this for a bunch of others, but it's slow\"\n+\n+```\n+\n+```{r, echo=FALSE, fig.width=12, fig.height=6}\n+\n+essays <- mutate(essays, female = ifelse(female==1, \"FEMALE$$\", \"MALE$$\"))\n+#tag them with a string we can use to differentiate wihtout context\n+mutate(essays, end_of_line = \"@@@\\n\") %>% write.csv(\"essays.csv\")\n+#tag the end of the line, write to csv\n+\n+\"Fortunately, python exists. I counted the number of respondents that used a given word, for total, male and female.\"\n+\n+w_counts <- read.csv(\"word_count.csv\", header=TRUE)\n+w_counts <- select(w_counts, -`X`)\n+\n+\n+w_counts <- mutate(w_counts, expected_female = (in_total*p_female) %>% round(digits = 1))\n+\n+w_counts <- mutate(w_counts, percent_diff_female = ((in_females - expected_female)/expected_female) %>% round(digits = 3) * 100)\n+\n+w_counts <- arrange(w_counts, -percent_diff_female)\n+\n+\n+datatable(w_counts, options = list(searching = TRUE, pageLength = 20))\n+\n ```\n+> Now we have a data table of all the most common words, and how often they were used in males and females, and the percent difference between observed and expected\n+\n+```{r, echo=FALSE, fig.width=12, fig.height=6}\n+\n+top_20_female_words = top_n(w_counts, 20, percent_diff_female)\n+\n+\n+p <- ggplot(top_20_female_words, aes(x = reorder(word, -percent_diff_female), y = percent_diff_female)) + geom_bar(stat=\"identity\")\n+p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))\n+p\n+\n+```\n+> The top 20 words found disporportionately in female responses. Percentage is percent more than the expected value, based off of the overall percent female. E.g. \"female\" occurs more than 100% times more than it would if it were randomly distributed. \"Heels\" shows the greatest difference, ignoreing \"female\"\" and \"mefemale,\" which is a weird artifact of the data scraping.","path":"HW-2.Rmd","position":296,"original_position":296,"commit_id":"cd81943b383c13011c293bc71b5f4f3bca670c19","original_commit_id":"cd81943b383c13011c293bc71b5f4f3bca670c19","user":{"login":"rudeboybert","id":4574203,"avatar_url":"https://avatars.githubusercontent.com/u/4574203?v=3","gravatar_id":"","url":"https://api.github.com/users/rudeboybert","html_url":"https://github.com/rudeboybert","followers_url":"https://api.github.com/users/rudeboybert/followers","following_url":"https://api.github.com/users/rudeboybert/following{/other_user}","gists_url":"https://api.github.com/users/rudeboybert/gists{/gist_id}","starred_url":"https://api.github.com/users/rudeboybert/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rudeboybert/subscriptions","organizations_url":"https://api.github.com/users/rudeboybert/orgs","repos_url":"https://api.github.com/users/rudeboybert/repos","events_url":"https://api.github.com/users/rudeboybert/events{/privacy}","received_events_url":"https://api.github.com/users/rudeboybert/received_events","type":"User","site_admin":false},"body":"I believe what you are trying to capture is also known as the term frequency–inverse document frequency [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf). \r\n\r\n> The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.\r\n\r\nSo while \"and\" appears a lot in women's essays, it would have a low TF-IDF b/c its a fairly common word, but \"heels\" would have a high TF-IDF because men tend to use it much less.","created_at":"2016-03-28T14:22:28Z","updated_at":"2016-03-28T14:22:28Z","html_url":"https://github.com/Middlebury-Data-Science/HW-2/pull/12#discussion_r57576160","pull_request_url":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/pulls/12","_links":{"self":{"href":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/pulls/comments/57576160"},"html":{"href":"https://github.com/Middlebury-Data-Science/HW-2/pull/12#discussion_r57576160"},"pull_request":{"href":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/pulls/12"}}},"pull_request":{"url":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/pulls/12","id":63897005,"html_url":"https://github.com/Middlebury-Data-Science/HW-2/pull/12","diff_url":"https://github.com/Middlebury-Data-Science/HW-2/pull/12.diff","patch_url":"https://github.com/Middlebury-Data-Science/HW-2/pull/12.patch","issue_url":"https://api.github.com/repos/Middlebury-Data-Science/HW-2/issues/12","number":12,"state":"open","locked":false,"title":"Carter 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