{"action":"created","comment":{"url":"https://api.github.com/repos/soc504-s2015-princeton/RepProj/pulls/comments/29554185","id":29554185,"diff_hunk":"@@ -0,0 +1,128 @@\n+#####################################################################################################################\n+#####   Assignment: Replication Project (Part 1)                                                                #####\n+#####   Due Date:   3/15/2015                                                                                   #####\n+#####   Authors:    Megan Blanchard and Kalyani Jayasankar                                                      #####\n+#####   Input:      Raw data file(1626360926english_merge_2010_americasbarometer_v14v3.dta)                     ##### \n+#####   Output:     Cleaned data files for analysis (clean2010data.dta, colorr_recode_subset.dta)               #####\n+#####   Notes:      This is the English merged 2010 data downloaded from http://www.americasbarometer.org       #####\n+#####               This file reads in the raw data and preps for analysis. In this file we:                    ##### \n+#####                      1-subset to 23/26 total countries                                                    #####\n+#####                      2-create 4 new variables (tone, region,colorr_recode & parent_occ) for anaylsis      #####\n+#####################################################################################################################\n+\n+#install.packages(\"foreign\", \"broom\")\n+\n+library(foreign)\n+library(dplyr)\n+library(ggplot2)\n+library(broom)\n+\n+#getwd()\n+setwd(\"~/Desktop/R working directory/RepProj\")\n+\n+#read in orginal data\n+merged.2010 <- read.dta(\"1626360926english_merge_2010_americasbarometer_v14v3.dta\", convert.factors = TRUE, missing.type = TRUE, convert.underscore = TRUE, warn.missing.labels = TRUE) \n+\n+#create cleaned data file\n+#subset to 23 countires for analysis- exclude Canada, US, and Haiti\n+clean.2010 <- merged.2010 %>%\n+    filter(!pais == \"Haiti\", !pais == \"Canada\", !pais == \"United States\") %>%  \n+    select(year, pais, idnum, weight1500, estratopri, upm, ur, q1, ed, colorr, ocup1anc, q2, weight1500) \n+      \n+#redefine factor\n+clean.2010$pais <- factor(clean.2010$pais)\n+\n+#create new var for analysis region\n+clean.2010 <- clean.2010 %>% \n+  mutate(region = ifelse(pais == \"Panama\" | pais == \"Costa Rica\"| pais == \"Honduras\"| pais == \"Mexico\"| pais ==\"Guatemala\"| pais == \"El Salvador\"| pais == \"Nicaragua\", \"Central America and Mexico\",\n+                  ifelse(pais == \"Bolivia\"| pais == \"Peru\"| pais == \"Venezuela\"| pais == \"Colombia\"| pais == \"Ecuador\", \"Andean\",\n+                  ifelse(pais == \"Argentina\" | pais == \"Chile\" | pais == \"Paraguay\" | pais == \"Uruguay\"| pais == \"Brazil\", \"Southern Cone and Brazil\", \"Caribbean\")))) \n+\n+# create variable for social orgin\n+unique(clean.2010$ocup1anc)\n+clean.2010 <- clean.2010 %>%\n+  mutate(parent_occ = ifelse(ocup1anc == \"Professional, intellectual and scientist\", 10, \n+                      ifelse(ocup1anc == \"Director (manager, head of the department, supervisor)\", 9,\n+                      ifelse(ocup1anc == \"Technician or mid-level professional\", 8, \n+                      ifelse(ocup1anc == \"Specialized worker\", 3, \n+                      ifelse(ocup1anc == \"Public official\", 7, \n+                      ifelse(ocup1anc == \"Office worker (secretary, offiice equipment operator, cashier, etc.)\", 7,\n+                      ifelse(ocup1anc == \"Merchant (street vendor, owner of commercial establishment or market stand, etc.)\", 4,\n+                      ifelse(ocup1anc == \"Warehous or market salesperson\", 6,\n+                      ifelse(ocup1anc == \"Employed, outside an office, in the service sector (hotel or restaurant worker, taxi drivier, etc.)\", 4,\n+                      ifelse(ocup1anc == \"Farm laborer, farmer, o agriculture and livestock producer agropecuario, and fisherman (owner of land)\", 1,\n+                      ifelse(ocup1anc == \"Agricultural worker (works on land for others)\", 1, \n+                      ifelse(ocup1anc == \"Artisan, craftsperson\", 5,\n+                      ifelse(ocup1anc == \"Domestic service\", 1,\n+                      ifelse(ocup1anc == \"Laborer\", 2, \n+                      ifelse(ocup1anc == \"Member of the armed forces or protection and security services (the police, fireman, watchman, etc.)\", 5, NA)))))))))))))))) \n+\n+clean.2010$parent_occ <- factor(clean.2010$parent_occ)\n+\n+#check for 39,238 respondents in 23 of the 26 countries, as stated in paper\n+write.dta(clean.2010, \"clean2010data.dta\")\n+\n+#output a dataset for graphs\n+#get counts for colorr by country in temporary dataset\n+test <- xtabs(formula = ~colorr + pais , data = clean.2010, exclude = \"97 Colud not be classified\")\n+test <- as.data.frame(test)\n+test$colorr <- as.numeric(test$colorr)\n+\n+#write a loop to organize recode\n+#the loop that crashes freqs, first for low end values of colorr, then for high end to flag categories to collapse\n+for(country in unique(test$pais)){\n+  minInd = min(which(test$pais == country)) - 1\n+  for (colorr in 1:6) { \n+    if (test[colorr +minInd, \"Freq\"] <= 30) {\n+      test[colorr +minInd + 1, \"Freq\"] <- test[colorr + minInd, \"Freq\"] + test[colorr + minInd + 1, \"Freq\"]\n+      test[colorr +minInd, \"Freq\"] <- 0 \n+    }\n+  }\n+}\n+\n+for(country in unique(test$pais)){\n+  minInd = min(which(test$pais == country)) - 1\n+  for (colorr in 11:6) { \n+    if (test[colorr + minInd, \"Freq\"] <= 30) {\n+        test[colorr + minInd - 1, \"Freq\"] <- test[colorr + minInd, \"Freq\"] + test[colorr + minInd - 1, \"Freq\"]\n+        test[colorr + minInd, \"Freq\"] <- 0 \n+    }\n+  }\n+}\n+\n+#Make a dataframe of what the minimum & maximum values are by country\n+min_max <- test %>%\n+  group_by(pais) %>%\n+  filter(Freq > 0) %>%\n+  select(colorr) %>%\n+  summarise(min = min(colorr), max = max(colorr))\n+\n+####collapse in single df with recoded values\n+cty.vec <- as.character(unique(test$pais))\n+\n+colorr_recode_subset <- clean.2010 %>%\n+  filter(!is.na(colorr)) %>%\n+  select(colorr, pais, region, ed, weight1500, parent_occ, q1, q2, ur, estratopri, upm) %>%\n+  mutate(colorr = as.numeric(colorr)) %>%\n+  arrange(pais, colorr) \n+\n+library(plyr)\n+colorr_recode_subset <- ldply(cty.vec, function(x){\n+  out <- colorr_recode_subset %>%\n+    filter(pais == x) %>%\n+    mutate(colorr_recode = ifelse(colorr <= filter(min_max, pais == x)$min, filter(min_max, pais == x)$min, \n+                           ifelse(colorr >= filter(min_max, pais == x)$max, filter(min_max, pais == x)$max, colorr))) %>%\n+    mutate(tone = ifelse(colorr_recode == 1 | colorr_recode == 2 | colorr_recode == 3, \"light\", 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