Reverse-score

df.trait_fa22 <- df.trait_fa22 %>% 
  mutate(LifeSatisfaction_7R = 8 - LifeSatisfaction_7R,
         TIPI_Criti_R = 8 - TIPI_Criti_R,
         TIPI_Anx_R = 8 - TIPI_Anx_R,
         TIPI_Reserv_R = 8 - TIPI_Reserv_R,
         TIPI_Disorg_R = 8 - TIPI_Disorg_R,
         TIPI_Conven_R = 8 - TIPI_Conven_R,
         CESD_4R = 5 - CESD_4R,
         CESD_6R = 5 - CESD_6R,
         loneliness_3R = 5 - loneliness_3R,
         loneliness_6R = 5 - loneliness_6R,
         PSS_2R = 6 - PSS_2R,
         PSS_3R = 6 - PSS_3R,
         selfComp_3R = 8 - selfComp_3R,
         selfComp_4R = 8 - selfComp_4R,
         selfComp_6R = 8 - selfComp_6R,
         )

Create mean scores

df.trait_fa22 <- df.trait_fa22 %>% 
  rowwise() %>% 
  mutate(extraversion = mean(c(TIPI_Extra,
                               TIPI_Reserv_R)),
         agreeableness = mean(c(TIPI_Criti_R,
                                TIPI_Symp)),
         conscientiousness = mean(c(TIPI_Depen,
                                    TIPI_Disorg_R)),
         emotionalStability = mean(c(TIPI_Anx_R,
                                     TIPI_EmoSta)),
         openToExperience = mean(c(TIPI_Open,
                                   TIPI_Conven_R)),
         stress = mean(c(Stress_1,
                         Stress_2)),
         TAI_5 = sum(c(TAI_7,
                     TAI_8,
                     TAI_15,
                     TAI_16,
                     TAI_18)),
         EROS = mean(c(EROS1_1,
                       Empathy_10,
                       EROS1_6,
                       EROS1_7,
                       Empathy_6,
                       EROS1_9)),
         Satwithlife = mean(c(LifeSatisfaction_3,
                              LifeSatisfaction_4,
                              LifeSatisfaction_1,
                              LifeSatisfaction_6,
                              LifeSatisfaction_9)),
         loneliness = mean(c(loneliness_1,
                             loneliness_2,
                             loneliness_3R,
                             loneliness_4,
                             loneliness_5,
                             loneliness_6R,
                             loneliness_7,
                             loneliness_8)),
         CESD = sum(c(CESD_1,
                      CESD_2,
                      NegativeWellBeing_1,
                      CESD_3,
                      CESD_4R,
                      NegativeWellBeing_10,
                      CESD_6R,
                      CESD_7,
                      CESD_8)),
         ideo = mean(c(PoliIdeology_general,
                       PoliIdeology_social,
                       PoliIdeology_economic)),
         GAD = sum(c(GAD_1,
                     GAD_2,
                     GAD_3,
                     GAD_4,
                     GAD_5,
                     GAD_6,
                     GAD_7)),
         socialphobia = sum(c(socialPhobia_1,
                     socialPhobia_2,
                     socialPhobia_3))) %>% 
  ungroup()

#Add empathy rescaled mean

#Create rescaled empathy items and average empathy score
# data <- df.trait_fa22 %>% 
#   mutate(Empathy_10 = as.numeric(Empathy_10),
#          Empathy_6 = as.numeric(Empathy_6),
#          Empathy_8 = as.numeric(Empathy_8),
#          Empathy_13 = as.numeric(Empathy_13),
#          Empathy_7 = as.numeric(Empathy_7),
#          Empathy_9 = as.numeric(Empathy_9),
#          Empathy_11 = as.numeric(Empathy_11)) %>% 
#   mutate(Empathy_10_scaled = (Empathy_10 - 1)/(5 - 1),
#          Empathy_10_scaled = (7 - 1)*Empathy_10_scaled + 1,
#          Empathy_6_scaled = (Empathy_6 - 1)/(5 - 1),
#          Empathy_6_scaled = (7 - 1)*Empathy_6_scaled + 1,
#          Empathy_8_scaled = (Empathy_8)/(5 - 1), #don't need to subtract 1 because scale begins at 0
#          Empathy_8_scaled = (7 - 1)*Empathy_8_scaled + 1,
#          Empathy_13_scaled = (Empathy_13)/(5 - 1),
#          Empathy_13_scaled = (7 - 1)*Empathy_13_scaled + 1,
#          Empathy_7_scaled = (Empathy_7)/(5 - 1),
#          Empathy_7_scaled = (7 - 1)*Empathy_7_scaled + 1,
#          Empathy_9_scaled = (Empathy_9)/(5 - 1),
#          Empathy_9_scaled = (7 - 1)*Empathy_9_scaled + 1,
#          Empathy_11_scaled = (Empathy_11)/(5 - 1),
#          Empathy_11_scaled = (7 - 1)*Empathy_11_scaled + 1
#          )
# 
# #Check 
# head(data$Empathy_10)
# head(data$Empathy_10_scaled)
# head(data$Empathy_6)
# head(data$Empathy_6_scaled)
# head(data$Empathy_8)
# head(data$Empathy_8_scaled)
# head(data$Empathy_13)
# head(data$Empathy_13_scaled)
# head(data$Empathy_7)
# head(data$Empathy_7_scaled)
# head(data$Empathy_9)
# head(data$Empathy_9_scaled)
# head(data$Empathy_11)
# head(data$Empathy_11_scaled)
# 
# #Average Empathy Score
# spring_data <- data %>% 
#   mutate(Empathy_10_scaled = as.numeric(Empathy_10_scaled),
#          Empathy_6_scaled  = as.numeric(Empathy_6_scaled),
#          Empathy_8_scaled = as.numeric(Empathy_8_scaled),
#          Empathy_13_scaled = as.numeric(Empathy_13_scaled),
#          Empathy_7_scaled = as.numeric(Empathy_7_scaled),
#          Empathy_9_scaled = as.numeric(Empathy_9_scaled),
#          Empathy_11_scaled = as.numeric(Empathy_11_scaled),
#          Empathy_12 = as.numeric(Empathy_12)) %>% 
#   rowwise() %>% 
#   mutate(Empathy_rescaled_mean = mean(c(Empathy_10_scaled,
#                                Empathy_6_scaled,
#                                Empathy_8_scaled,
#                                Empathy_13_scaled,
#                                Empathy_7_scaled,
#                                Empathy_9_scaled,
#                                Empathy_11_scaled,
#                                Empathy_12), na.rm = T)) %>% 
#   ungroup() %>% 
#   select(-c(Empathy_10_scaled, Empathy_6_scaled,Empathy_8_scaled,Empathy_13_scaled, Empathy_7_scaled, Empathy_9_scaled, Empathy_11_scaled)) #remove rescaled items. Can be added back later, if we want! Just don't want it to be confusing

write csv

#write.csv(df.trait_fa22,"~/Google Drive/Shared drives/Stanford Communities Project/2022-2023/MASTER/Network_survey/Fall 2022/df.trait_fa22.csv",row.names = F)