added data cleaning

jhelvy · Oct 19, 2021 · 75688ff · 75688ff
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diff --git a/code/data_cleaning.R b/code/data_cleaning.R
@@ -0,0 +1,140 @@
+
+# Load libraries
+library(fastDummies)
+library(here)
+library(lubridate)
+library(tidyverse)
+
+# Change dplyr settings so I can view all columns 
+options(dplyr.width = Inf)
+
+# Import raw data
+p1 <- read_csv(here("data", "appleConjoint_p1.csv"))
+p2 <- read_csv(here("data", "appleConjoint_p2.csv"))
+p3 <- read_csv(here("data", "appleConjoint_p3.csv"))
+
+
+
+# Format and join the three surveys -------
+
+# Variables common to each survey: 
+# session  = a unique ID for the Run - should be the same across all surveys
+# created  = time stamp when survey was started
+# modified = time stamp when survey was last modified by respondent
+# ended    = time stamp when survey ended
+# expired  = time stamp when survey expired (if respondent didn't reach end)
+
+# Compute time values for each part
+p1 <- p1 %>% 
+  mutate(
+    created = ymd_hms(created, tz = "EST"),
+    ended =  ymd_hms(ended, tz = "EST"),
+    time_sec_p1 = as.numeric(ended - created, units = "secs")) %>%
+  # Select important columns
+  select(session, time_sec_p1, likeFruit, favFruit, screenout)
+
+p2 <- p2 %>% 
+  mutate(
+    created = ymd_hms(created),
+    ended =  ymd_hms(ended),
+    time_sec_p2 = as.numeric(ended - created, units = "secs")) %>%
+  # Select important columns
+  select(session, time_sec_p2, respondentID, starts_with("cbc"), cbcAllSame)
+
+p3 <- p3 %>% 
+  mutate(
+    created = ymd_hms(created),
+    ended =  ymd_hms(ended),
+    time_sec_p3 = as.numeric(ended - created, units = "secs")) %>%
+  # Select important columns
+  select(session, time_sec_p3, yearOfBirth:feedback)
+
+# Join all parts together using the session variable
+data <- p1 %>% 
+    left_join(p2, by = "session") %>% 
+    left_join(p3, by = "session") %>% 
+    # No longer need session variable
+    select(-session)
+head(data)
+
+
+
+
+# Filter out bad responses ---------
+
+nrow(data)
+
+# Drop people who got screened out
+data <- data %>% 
+    filter(!is.na(screenout), screenout == 2)
+nrow(data)
+
+# Drop anyone who didn't complete all choice questions
+data <- data %>% 
+  filter(!is.na(cbc1)) %>% 
+  filter(!is.na(cbc2)) %>% 
+  filter(!is.na(cbc3)) %>% 
+  filter(!is.na(cbc4)) %>% 
+  filter(!is.na(cbc5)) %>% 
+  filter(!is.na(cbc6)) %>% 
+  filter(!is.na(cbc7)) %>% 
+  filter(!is.na(cbc8))
+nrow(data)
+
+# Drop respondents who went too fast
+data <- data %>% 
+    mutate(
+        # First replace NA values with 0 seconds
+        time_sec_p1 = ifelse(is.na(time_sec_p1), 0, time_sec_p1),
+        time_sec_p2 = ifelse(is.na(time_sec_p2), 0, time_sec_p2),
+        time_sec_p3 = ifelse(is.na(time_sec_p3), 0, time_sec_p3),
+        # Now compute the total time
+        time_min_total = (time_sec_p1 + time_sec_p2 + time_sec_p3) / 60
+    )
+# Look at summary of completion times
+summary(data$time_min_total)
+# Drop anyone who finished in under the 10th percentile of completion times
+time_10 <- quantile(data$time_min_total, 0.1)
+nrow(data)
+data <- data %>% 
+    filter(time_min_total >= time_10)
+nrow(data)
+
+# Drop respondents that got the attention check question wrong
+data <- data %>% 
+  filter(cbcPractice == 2)
+nrow(data)
+
+
+
+
+
+# Create choice data ---------
+
+# First gather the data
+choiceData <- data %>% 
+    pivot_longer(
+        cols = cbc1:cbc8,
+        names_to = "qID",
+        values_to = "choice") %>% 
+    # Convert the qID variable to a number
+    mutate(qID = parse_number(qID))
+
+head(choiceData)
+
+# Read in choice questions and join it to the choiceData
+survey <- read_csv("https://raw.githubusercontent.com/jhelvy/formr4conjoint/master/survey/choice_questions.csv")
+choiceData <- choiceData %>% 
+    rename(respID = respondentID) %>% 
+    left_join(survey, by = c("respID", "qID"))
+
+# Convert choice column to 1 or 0 based on if the alternative was chosen 
+choiceData <- choiceData %>% 
+    mutate(choice = ifelse(choice == altID, 1, 0)) %>% 
+    # Drop unused variables
+    select(-image, -cbcPractice, -cbcAllSame)
+
+head(choiceData)
+
+# Save cleaned data for modeling
+write_csv(choiceData, here("data", "choiceData.csv"))
diff --git a/code/modeling.R b/code/modeling.R
@@ -0,0 +1,43 @@
+# Estimate multinomial logit (MNL) models
+
+# Load libraries
+library(logitr)
+library(tidyverse)
+library(fastDummies)
+library(janitor)
+library(here)
+options(dplyr.width = Inf) # So you can see all of the columns
+
+# -----------------------------------------------------------------------------
+# Load the data set:
+data <- read_csv(here("data", "choiceData.csv"))
+head(data)
+
+# Estimate MNL model
+
+# First create some dummy coded variables for categorical variables
+data <- dummy_cols(data, c('type', 'freshness'))
+
+# Clean up names of created variables
+data <- clean_names(data)
+
+# Estimate the model
+model <- logitr(
+    data   = data,
+    choice = "choice",
+    obsID  = "obs_id",
+    pars   = c(
+        "price", 
+        "type_fuji", "type_gala", "type_honeycrisp", "type_pink_lady",
+        "freshness_average", "freshness_excellent")
+)
+
+# View summary of results
+summary(model)
+
+# Check the 1st order condition: Is the gradient at the solution zero?
+model$gradient
+
+# 2nd order condition: Is the hessian negative definite?
+# (If all the eigenvalues are negative, the hessian is negative definite)
+eigen(model$hessian)$values
diff --git a/data/appleConjoint_p1.csv b/data/appleConjoint_p1.csv
@@ -0,0 +1,98 @@
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