--- title: "Data Analysis and Visualization <br> Community Data: Combined Report" author: Gagan Atreya date: today format: html: toc: true toc-location: left toc-depth: 4 theme: lumen fontsize: large code-fold: true code-tools: true code-summary: "Display code" code-overflow: wrap editor: markdown: wrap: 72 --- # **Section 1. Demographics** - Age- Gender- Socio-economic status- Nature of employment- Religious affiliation- Marital status- Ethnicity```{r, error = F, warning=F, message=F} options (digits = 2 )rm (list= ls ())## Install "pacman" package if not installed # (remove the # symbol from the line below): # install.packages("pacman") ## Load R packages: :: p_load (data.table, tidyverse, haven, labelled, vtable, ## Import datasets: ## These are newer datasets with new variables created in the individual analyses: <- fread ("/home/gagan/Desktop/oxford/data/cleanedds/dsgmb.csv" )$ Country <- "Gambia" $ id <- 1 : nrow (dsgmb)$ ID <- paste0 ("GMB" ,dsgmb$ id)<- fread ("/home/gagan/Desktop/oxford/data/cleanedds/dspak.csv" )$ Country <- "Pakistan" $ id <- 1 : nrow (dspak)$ ID <- paste0 ("PAK" ,dspak$ id)<- fread ("/home/gagan/Desktop/oxford/data/cleanedds/dstza.csv" )$ Country <- "Tanzania" $ id <- 1 : nrow (dstza)$ ID <- paste0 ("TZA" ,dstza$ id)<- fread ("/home/gagan/Desktop/oxford/data/cleanedds/dsuga.csv" )$ Country <- "Uganda" $ id <- 1 : nrow (dsuga)$ ID <- paste0 ("UGA" ,dsuga$ id)## Correct asterisk pattern for stargazer tables: <- "<em>*p<0.05; **p<0.01; ***p<0.001</em>" ## List of variables to retain from all datasets: <- c ("ID" , "Country" , "age" , "gender" , "ses" , "jobnature" , "religion" , "married" ,"IGF01" , "IGF02" , "IGF03" , "IGI01" , "IGI02" , "IGI03" , "OGF01" , "OGF02" , "OGF03" , "OGI01" , "OGI02" , "OGI03" ,"ENDBCL01" , "ENDBCL02" , "ENDBCL03" , "ENDBBL01" , "ENDBBL02" , "ENDBBL03" ,"EXPBCL01" , "EXPBCL02" , "EXPBCL03" , "EXPBBL01" , "EXPBBL02" , "EXPBBL03" , "empathic_concern_01" , "empathic_concern_02" , "empathic_concern_03" ,"perspective_taking_01" , "perspective_taking_02" , "perspective_taking_03" , "perspective_taking_04" , "history_discrimination" ,"og_hostility" , "og_cooperation" , "fight_outgroup" , "imagistic" ,"event_positive_affect" , "event_negative_affect" , "event_episodic_recall" ,"event_shared_perception" , "event_event_reflection" , "event_transformative_indiv" , "event_transformative_group" )## Subset datasets to only the columns in the above list: <- dsgmb[, ..list1]<- dspak[, ..list1]<- dstza[, ..list1]<- dsuga[, ..list1]## Merged dataset with needed columns only <- rbind (dsgmb1, dspak1, dstza1, dsuga1)## Rename the "Event_" columns with title case: <- ds[, 1 : 44 ]<- ds[, ! (2 : 44 )]colnames (ds02) <- stringr:: str_to_title (colnames (ds02))$ ID <- ds02$ Id<- ds02[, ! 1 ]<- merge (ds01, ds02, by = "ID" )rm (ds01, ds02)``` ## Variable: Sample size by Country ```{r, error = F, message = F, warning = F} <- table (ds$ Country)## Table of user language by country: ## Sample size by country: <- ds %>% # drop_na(Country) %>% lollipop_chart (x = Country,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Sample size by country" )+ theme_bw ()``` ## Variable: Age ```{r, error = F, message = F, warning = F} summary (ds$ age)%>% drop_na (age)%>% ggplot (aes (x = age))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 50 )+ geom_textvline (label = "Mean = 37.00" , xintercept = 37.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Age" , y = "Frequency" , title = "Age distribution (full sample)" )+ theme_bw ()%>% drop_na (age)%>% ggplot (aes (x = age))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 50 )+ # geom_textvline(label = "Mean = 37.00", # xintercept = 37.00, # vjust = 1.1, # lwd = 1.05, # linetype = 2)+ labs (x = "Age" , y = "Frequency" , title = "Age distribution by country" )+ facet_wrap (~ Country, nrow = 2 )+ theme_bw ()``` ## Variable: Gender ```{r, error = F, message = F, warning = F} $ gender <- ifelse (ds$ gender == "Male" , "Male" ,ifelse (ds$ gender == "Female" , "Female" , NA ))<- ds %>% drop_na (gender, age, Country) %>% lollipop_chart (x = gender,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Gender distribution (full sample)" )+ # facet_wrap(~Country, nrow = 2)+ theme_bw ()<- ds %>% drop_na (gender, age) %>% ggplot (aes (y = age, x = gender))+ geom_boxplot (fill = "grey" )+ labs (y = "Age" ,x = "" ,title = "Age and gender distribution by country" )+ facet_wrap (~ Country, nrow = 2 )+ coord_flip ()+ theme_bw ()``` ## Variable: Socio-economic status ```{r, error = F, message = F, warning = F} $ ses <- ifelse (ds$ ses == "" , NA , ds$ ses)table (ds$ ses)%>% drop_na (ses) %>% lollipop_chart (x = ses,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Socioeconomic status (full sample)" )+ theme_bw ()%>% drop_na (ses) %>% group_by (ses, Country) %>% summarise (count = n ()) %>% ggplot (aes (ses, count)) + geom_segment (aes (x= ses, xend= ses, y= 0 , yend= count))+ geom_point ()+ labs (x = "" , y = "Frequency" , title = "Socioeconomic status by country" )+ facet_wrap (vars (Country), nrow = 2 )+ coord_flip ()+ theme_bw ()``` ## Variable: Nature of employment ```{r, error = F, message = F, warning = F} $ jobnature <- ifelse (ds$ jobnature == "" , NA , ds$ jobnature)#sentence case: $ jobnature <- gsub ("( \\ D)( \\ D+)" , " \\ U \\ 1 \\ L \\ 2" , ds$ jobnature, perl = TRUE )$ jobnature <- ifelse (ds$ jobnature == "Non-government/self-employed" , "Non-government" , ds$ jobnature)table (ds$ jobnature)%>% drop_na (jobnature) %>% lollipop_chart (x = jobnature,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Nature of employment (full sample)" )+ theme_bw ()%>% drop_na (jobnature) %>% group_by (jobnature, Country) %>% summarise (count = n ()) %>% ggplot (aes (jobnature, count)) + geom_segment (aes (x= jobnature, xend= jobnature, y= 0 , yend= count))+ geom_point ()+ labs (x = "" , y = "Frequency" , title = "Nature of employment by country" )+ facet_wrap (vars (Country), nrow = 2 )+ coord_flip ()+ theme_bw ()``` ## Variable: Religious affiliation ```{r, error = F, message = F, warning = F} $ religion <- ifelse (ds$ religion == "" , NA , ds$ religion)$ religion <- ifelse (ds$ religion == "Christian (Catholic)" , "Christian: Catholic" , ifelse (ds$ religion == "Christian (Protestant)" , "Christian: Protestant" ,ifelse (ds$ religion == "Muslim (Shia)" , "Muslim: Shia" ,ifelse (ds$ religion == "Muslim (Sunni)" , "Muslim: Sunni" , ds$ religion))))table (ds$ religion)<- ds %>% drop_na (religion) %>% lollipop_chart (x = religion,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Religious distribution (full sample)" )+ theme_bw ()%>% drop_na (religion) %>% group_by (religion, Country) %>% summarise (count = n ()) %>% ggplot (aes (religion, count)) + geom_segment (aes (x= religion, xend= religion, y= 0 , yend= count))+ geom_point ()+ labs (x = "" , y = "Frequency" , title = "Religious distribution by country" )+ facet_wrap (vars (Country), nrow = 2 )+ coord_flip ()+ theme_bw ()``` ## Variable: Marital status ```{r, error = F, message = F, warning = F} $ married <- ifelse (ds$ married == "Not married" , "Unmarried" , ds$ married)$ married <- ifelse (ds$ married == "" , NA , ds$ married)table (ds$ married)## Marital status (full sample): %>% drop_na (married) %>% group_by (married) %>% summarise (count = n ()) %>% ggplot (aes (married, count)) + geom_segment (aes (x= married, xend= married, y= 0 , yend= count))+ geom_point ()+ labs (x = "" , y = "Frequency" , title = "Marital status (full sample)" )+ coord_flip ()+ theme_bw ()## Marital status by country: %>% drop_na (married) %>% group_by (married, Country) %>% summarise (count = n ()) %>% ggplot (aes (married, count)) + geom_segment (aes (x= married, xend= married, y= 0 , yend= count))+ geom_point ()+ labs (x = "" , y = "Frequency" , title = "Marital status by country" )+ facet_wrap (vars (Country), nrow = 2 )+ coord_flip ()+ theme_bw ()``` ## Variable: Ethnicity ```{r, error = F, message = F, warning = F} ## Gambia: <- as.data.frame (table (dsgmb$ Ethnicity))$ Var1 <- as.character (eth$ Var1)$ ethnicity <- ifelse (eth$ Freq < 2 , "Other" , eth$ Var1)<- as.list (eth$ ethnicity)<- dsgmb[, c ("Ethnicity" )]$ ethnicity <- ifelse (dsgmb2$ Ethnicity %in% l1, dsgmb2$ Ethnicity, "Other" )$ ethnicity <- ifelse (dsgmb2$ ethnicity == "Serere" , "Serer" ,ifelse (dsgmb2$ ethnicity == "" , NA , dsgmb2$ ethnicity))table (dsgmb2$ ethnicity)<- dsgmb2 %>% drop_na (ethnicity) %>% lollipop_chart (x = ethnicity,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Ethnic distribution: Gambia" )+ theme_bw ()## Pakistan: <- as.data.frame (table (dspak$ Ethnicity))$ Var1 <- as.character (eth$ Var1)$ ethnicity <- ifelse (eth$ Freq < 7 , "Other" , eth$ Var1)<- as.list (eth$ ethnicity)<- dspak[, c ("Ethnicity" )]$ ethnicity <- ifelse (dspak2$ Ethnicity %in% l1, dspak2$ Ethnicity, "Other" )$ ethnicity <- ifelse (dspak2$ ethnicity == "Serere" , "Serer" ,ifelse (dspak2$ ethnicity == "" , NA , dspak2$ ethnicity))table (dspak2$ ethnicity)<- dspak2 %>% drop_na (ethnicity) %>% lollipop_chart (x = ethnicity,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Ethnic distribution: Pakistan" )+ theme_bw ()## Tanzania: <- as.data.frame (table (dstza$ Ethnicity))$ Var1 <- as.character (eth$ Var1)$ ethnicity <- ifelse (eth$ Freq < 7 , "Other" , eth$ Var1)<- as.list (eth$ ethnicity)<- dstza[, c ("Ethnicity" )]$ ethnicity <- ifelse (dstza2$ Ethnicity %in% l1, dstza2$ Ethnicity, "Other" )$ ethnicity <- ifelse (dstza2$ ethnicity == "Serere" , "Serer" ,ifelse (dstza2$ ethnicity == "" , NA , dstza2$ ethnicity))table (dstza2$ ethnicity)<- dstza2 %>% drop_na (ethnicity) %>% lollipop_chart (x = ethnicity,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Ethnic distribution: Tanzania" )+ theme_bw ()## Uganda: <- as.data.frame (table (dsuga$ Ethnicity))$ Var1 <- as.character (eth$ Var1)$ ethnicity <- ifelse (eth$ Freq < 7 , "Other" , eth$ Var1)<- as.list (eth$ ethnicity)<- dsuga[, c ("Ethnicity" )]$ ethnicity <- ifelse (dsuga2$ Ethnicity %in% l1, dsuga2$ Ethnicity, "Other" )$ ethnicity <- ifelse (dsuga2$ ethnicity == "Serere" , "Serer" ,ifelse (dsuga2$ ethnicity == "" , NA , dsuga2$ ethnicity))table (dsuga2$ ethnicity)<- dsuga2 %>% drop_na (ethnicity) %>% lollipop_chart (x = ethnicity,line_color = "black" ,point_color = "black" )+ labs (y = "Frequency" ,x = "" ,title = "Ethnic distribution: Uganda" )+ theme_bw ()``` # **Section 2. Variables of interest** - Endorsement of Barrier Bound Leadership (BBL)- Endorsement of Barrier Crossing Leadership (BCL)- Ingroup fusion- Ingroup identification- Outgroup bonds (sum of outgroup fusion+identification)- Empathic concern- Perspective taking- Perceived history of discrimination- Imagistic items: - Event: Positive affect - Event: Negative affect - Event: Episodic recall - Event: Shared perception - Event: Reflection - Event: Transformative for individual - Event: Transformative for group - Imagistic event (sum of all above)## Variable: Endorsement of Barrier Bound Leadership (BBL) ```{r, error = F, message = F, warning=F} $ bbl <- (ds$ ENDBBL01+ ds$ ENDBBL02+ ds$ ENDBBL03)/ 3 $ bcl <- (ds$ ENDBCL01+ ds$ ENDBCL02+ ds$ ENDBCL03)/ 3 summary (ds$ bbl)%>% drop_na (bbl)%>% ggplot (aes (x = bbl))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ geom_textvline (label = "Mean = 4.00" , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Endorsement of BBL score" , y = "Frequency" , title = "Endorsement of BBL (full sample)" )+ theme_bw ()%>% drop_na (bbl, Country)%>% ggplot (aes (x = bbl))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Endorsement of BBL score" , y = "Frequency" , title = "Endorsement of BBL by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (bbl)%>% ggplot (aes (x = bbl,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Endorsement of BBL score" , y = "Frequency" , title = "Endorsement of BBL by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Endorsement of Barrier Crossing Leadership (BCL) ```{r, error = F, message = F, warning=F} summary (ds$ bcl)%>% drop_na (bcl)%>% ggplot (aes (x = bcl))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Endorsement of BCL score" , y = "Frequency" , title = "Endorsement of BCL (full sample)" )+ theme_bw ()%>% drop_na (bcl, Country)%>% ggplot (aes (x = bcl))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Endorsement of BCL score" , y = "Frequency" , title = "Endorsement of BCL by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (bcl)%>% ggplot (aes (x = bcl,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Endorsement of BCL score" , y = "Frequency" , title = "Endorsement of BCL by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Ingroup fusion ```{r, error = F, message = F, warning=F} $ igfusion <- (ds$ IGF01+ ds$ IGF02+ ds$ IGF03)/ 3 summary (ds$ igfusion)%>% drop_na (igfusion)%>% ggplot (aes (x = igfusion))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Ingroup fusion score" , y = "Frequency" , title = "Ingroup fusion (full sample)" )+ theme_bw ()%>% drop_na (igfusion, Country)%>% ggplot (aes (x = igfusion))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Ingroup fusion score" , y = "Frequency" , title = "Ingroup fusion by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (igfusion)%>% ggplot (aes (x = igfusion,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Ingroup fusion score" , y = "Frequency" , title = "Ingroup fusion by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Ingroup identification ```{r, error = F, message = F, warning=F} $ IGI01 <- ifelse (ds$ IGI01 %in% 1 : 7 , ds$ IGI01, NA )$ igidentification <- (ds$ IGI01+ ds$ IGI02+ ds$ IGI03)/ 3 summary (ds$ igidentification)%>% drop_na (igidentification)%>% ggplot (aes (x = igidentification))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Ingroup identification score" , y = "Frequency" , title = "Ingroup identification (full sample)" )+ theme_bw ()%>% drop_na (igidentification, Country)%>% ggplot (aes (x = igidentification))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Ingroup identification score" , y = "Frequency" , title = "Ingroup identification by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (igidentification)%>% ggplot (aes (x = igidentification,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Ingroup identification score" , y = "Frequency" , title = "Ingroup identification by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Outgroup bonds ```{r, error = F, message = F, warning=F} $ ogbonds <- (ds$ OGI01+ ds$ OGI02+ ds$ OGI03+ $ OGF01+ ds$ OGF02+ ds$ OGF03)/ 6 summary (ds$ ogbonds)%>% drop_na (ogbonds)%>% ggplot (aes (x = ogbonds))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 35 )+ geom_textvline (label = "Mean = 3.00" , xintercept = 3.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Outgroup bonds score" , y = "Frequency" , title = "Outgroup bonds (full sample)" )+ theme_bw ()%>% drop_na (ogbonds, Country)%>% ggplot (aes (x = ogbonds))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Outgroup bonds score" , y = "Frequency" , title = "Outgroup bonds by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (ogbonds)%>% ggplot (aes (x = ogbonds,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 3.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Outgroup bonds score" , y = "Frequency" , title = "Outgroup bonds by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Empathic concern ```{r, error = F, message = F, warning=F} $ empathic_concern <- (ds$ empathic_concern_01+ ds$ empathic_concern_02+ $ empathic_concern_03)/ 3 summary (ds$ empathic_concern)%>% drop_na (empathic_concern)%>% ggplot (aes (x = empathic_concern))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 35 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Empathic concern score" , y = "Frequency" , title = "Empathic concern (full sample)" )+ theme_bw ()%>% drop_na (empathic_concern, Country)%>% ggplot (aes (x = empathic_concern))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Empathic concern score" , y = "Frequency" , title = "Empathic concern by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (empathic_concern)%>% ggplot (aes (x = empathic_concern,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Empathic concern score" , y = "Frequency" , title = "Empathic concern by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Perspective taking ```{r, error = F, message = F, warning=F} $ perspective_taking <- (ds$ perspective_taking_01+ ds$ perspective_taking_02+ $ perspective_taking_03+ ds$ perspective_taking_04)/ 4 summary (ds$ perspective_taking)%>% drop_na (perspective_taking)%>% ggplot (aes (x = perspective_taking))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 35 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Perspective taking score" , y = "Frequency" , title = "Perspective taking (full sample)" )+ theme_bw ()%>% drop_na (perspective_taking, Country)%>% ggplot (aes (x = perspective_taking))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 25 )+ labs (x = "Perspective taking score" , y = "Frequency" , title = "Perspective taking by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (perspective_taking)%>% ggplot (aes (x = perspective_taking,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Perspective taking score" , y = "Frequency" , title = "Perspective taking by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Perceived history of discrimination ```{r, error = F, message = F, warning=F} summary (ds$ history_discrimination)%>% drop_na (history_discrimination)%>% ggplot (aes (x = history_discrimination))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 10 )+ geom_textvline (label = "Mean = 4.00" , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Perceived history of discrimination score" , y = "Frequency" , title = "Perceived history of discrimination (full sample)" )+ theme_bw ()%>% drop_na (history_discrimination, Country)%>% ggplot (aes (x = history_discrimination))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 10 )+ labs (x = "Perceived history of discrimination score" , y = "Frequency" , title = "Perceived history of discrimination by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (history_discrimination)%>% ggplot (aes (x = history_discrimination,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Perceived history of discrimination score" , y = "Frequency" , title = "Perceived history of discrimination by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Positive Affect ```{r, error = F, message = F, warning=F} summary (ds$ Event_positive_affect)%>% drop_na (Event_positive_affect)%>% ggplot (aes (x = Event_positive_affect))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 4.00" , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Positive affect score" , y = "Frequency" , title = "Event: Positive affect (full sample)" )+ theme_bw ()%>% drop_na (Event_positive_affect, Country)%>% ggplot (aes (x = Event_positive_affect))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Positive affect score" , y = "Frequency" , title = "Event: Positive affect by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_positive_affect)%>% ggplot (aes (x = Event_positive_affect,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 4.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Positive affect score" , y = "Frequency" , title = "Event: Positive affect by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Negative Affect ```{r, error = F, message = F, warning=F} summary (ds$ Event_negative_affect)%>% drop_na (Event_negative_affect)%>% ggplot (aes (x = Event_negative_affect))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 5.00" , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Negative affect score" , y = "Frequency" , title = "Event: Negative affect (full sample)" )+ theme_bw ()%>% drop_na (Event_negative_affect, Country)%>% ggplot (aes (x = Event_negative_affect))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Negative affect score" , y = "Frequency" , title = "Event: Negative affect by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_negative_affect)%>% ggplot (aes (x = Event_negative_affect,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Negative affect score" , y = "Frequency" , title = "Event: Negative affect by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Episodic recall ```{r, error = F, message = F, warning=F} summary (ds$ Event_episodic_recall)%>% drop_na (Event_episodic_recall)%>% ggplot (aes (x = Event_episodic_recall))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Episodic recall score" , y = "Frequency" , title = "Event: Episodic recall (full sample)" )+ theme_bw ()%>% drop_na (Event_episodic_recall, Country)%>% ggplot (aes (x = Event_episodic_recall))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Episodic recall score" , y = "Frequency" , title = "Event: Episodic recall by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_episodic_recall)%>% ggplot (aes (x = Event_episodic_recall,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Episodic recall score" , y = "Frequency" , title = "Event: Episodic recall by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Shared perception ```{r, error = F, message = F, warning=F} summary (ds$ Event_shared_perception)%>% drop_na (Event_shared_perception)%>% ggplot (aes (x = Event_shared_perception))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Shared perception score" , y = "Frequency" , title = "Event: Shared perception (full sample)" )+ theme_bw ()%>% drop_na (Event_shared_perception, Country)%>% ggplot (aes (x = Event_shared_perception))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Shared perception score" , y = "Frequency" , title = "Event: Shared perception by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_shared_perception)%>% ggplot (aes (x = Event_shared_perception,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Shared perception score" , y = "Frequency" , title = "Event: Shared perception by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Reflection ```{r, error = F, message = F, warning=F} $ Event_reflection <- ds$ Event_event_reflectionsummary (ds$ Event_reflection)%>% drop_na (Event_reflection)%>% ggplot (aes (x = Event_reflection))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 6.00" , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Reflection score" , y = "Frequency" , title = "Event: Reflection (full sample)" )+ theme_bw ()%>% drop_na (Event_reflection, Country)%>% ggplot (aes (x = Event_reflection))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Reflection score" , y = "Frequency" , title = "Event: Reflection by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_reflection)%>% ggplot (aes (x = Event_reflection,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 6.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Reflection score" , y = "Frequency" , title = "Event: Reflection by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Transformative for individual ```{r, error = F, message = F, warning=F} summary (ds$ Event_transformative_indiv)%>% drop_na (Event_transformative_indiv)%>% ggplot (aes (x = Event_transformative_indiv))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 5.00" , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Transformative for individual score" , y = "Frequency" , title = "Event: Transformative for individual (full sample)" )+ theme_bw ()%>% drop_na (Event_transformative_indiv, Country)%>% ggplot (aes (x = Event_transformative_indiv))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Transformative for individual score" , y = "Frequency" , title = "Event: Transformative for individual by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_transformative_indiv)%>% ggplot (aes (x = Event_transformative_indiv,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Transformative for individual score" , y = "Frequency" , title = "Event: Transformative for individual by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Event: Transformative for group ```{r, error = F, message = F, warning=F} summary (ds$ Event_transformative_group)%>% drop_na (Event_transformative_group)%>% ggplot (aes (x = Event_transformative_group))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 5.00" , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Transformative for group score" , y = "Frequency" , title = "Event: Transformative for group (full sample)" )+ theme_bw ()%>% drop_na (Event_transformative_group, Country)%>% ggplot (aes (x = Event_transformative_group))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Transformative for group score" , y = "Frequency" , title = "Event: Transformative for group by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_transformative_group)%>% ggplot (aes (x = Event_transformative_group,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 5.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Transformative for group score" , y = "Frequency" , title = "Event: Transformative for group by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` ## Variable: Imagistic event (sum of all subscales) ```{r, error = F, message = F, warning=F} $ Event_imagistic <- ds$ imagisticsummary (ds$ Event_imagistic)%>% drop_na (Event_imagistic)%>% ggplot (aes (x = Event_imagistic))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ geom_textvline (label = "Mean = 35.00" , xintercept = 35.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Imagistic score" , y = "Frequency" , title = "Event: Imagistic (full sample)" )+ theme_bw ()%>% drop_na (Event_imagistic, Country)%>% ggplot (aes (x = Event_imagistic))+ geom_histogram (color = "black" ,fill = "gray" ,bins = 15 )+ labs (x = "Event: Imagistic score" , y = "Frequency" , title = "Event: Imagistic by country" )+ facet_wrap (~ Country)+ theme_bw ()%>% drop_na (Event_imagistic)%>% ggplot (aes (x = Event_imagistic,y = Country))+ geom_boxplot (fill = "grey" )+ geom_textvline (label = " " , xintercept = 35.00 , vjust = 1.1 , lwd = 1.05 , linetype = 2 )+ labs (x = "Event: Imagistic score" , y = "Frequency" , title = "Event: Imagistic by country" )+ #facet_wrap(~Country, nrow = 2)+ theme_bw ()``` # **Section 3. Regression models: BBL/BCL vs group fusion/identification ** ```{r, error = F, message = F, warning = F} ## Create proper variables to use in regression models: $ BCL <- ds$ bcl$ BBL <- ds$ bbl$ Ingroup_fusion <- ds$ igfusion$ Ingroup_identification <- ds$ igidentification$ Outgroup_bonds <- ds$ ogbonds$ Age <- ds$ age$ ` Gender: ` <- factor (ds$ gender,levels = c ("Male" , "Female" ))$ ` SES: ` <- factor (ds$ ses,levels = c ("Lower middle" , "Middle" ,"Upper middle" , "Upper" ))$ Empathic_concern <- ds$ empathic_concern$ Perspective_taking <- ds$ perspective_taking$ Perceived_discrimination <- ds$ history_discrimination$ OG_hostility <- ds$ og_hostility$ OG_cooperation <- ds$ og_cooperation$ Fight_OG <- ds$ fight_outgroup``` ## Regression models: full sample ```{r, error = F, message = F, warning = F} ## Two regression models predicting endorsement of BBL vs BCL: <- lm (BBL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = ds)<- lm (BCL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = ds)``` ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm01, lm02, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Regression models: individual countries ```{r, error = F, message = F, warning = F} <- ds[ds$ Country== "Gambia" ,]<- ds[ds$ Country== "Pakistan" ,]<- ds[ds$ Country== "Tanzania" ,]<- ds[ds$ Country== "Uganda" ,]## Eight regression models predicting endorsement of BBL vs BCL for four countries <- lm (BBL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` ,data = dsgmb)<- lm (BCL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (BBL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` ,data = dspak)<- lm (BCL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (BBL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` ,data = dstza)<- lm (BCL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (BBL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` ,data = dsuga)<- lm (BCL~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsuga)``` ## Gambia: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_gmb_01, lm_gmb_02, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Pakistan: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_pak_01, lm_pak_02, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Tanzania: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_tza_01, lm_tza_02, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Uganda: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_uga_01, lm_uga_02, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` # **Section 4. Coefficient plots for section 3** ## Coefficient plots: full sample ```{r, error = F, message = F, warning = F, results = "asis"} <- modelplot (lm01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Endorsement of BBL" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Endorsement of BCL" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )``` ## Coefficient plots: individual countries ```{r, error = F, message = F, warning = F} <- modelplot (lm_gmb_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BBL: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_gmb_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BCL: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BBL: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BCL: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BBL: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BCL: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BBL: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "BCL: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- (mp01 / mp02)<- (mp03 / mp04)<- (mp05 / mp06)<- (mp07 / mp08)``` # **Section 5. Regression models: Outgroup hostility/cooperation vs group fusion/identification** ## Regression models: full sample ```{r, error = F, message = F, warning = F} ## Three regression models predicting OG related variables: <- lm (OG_hostility~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = ds)<- lm (OG_cooperation~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = ds)<- lm (Fight_OG~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = ds)``` ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm01, lm02, lm03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Regression models: individual countries ```{r, error = F, message = F, warning = F} ## Twelve regression models predicting endorsement of BBL vs BCL for four countries <- lm (OG_hostility~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (OG_cooperation~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (Fight_OG~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (OG_hostility~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (OG_cooperation~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (Fight_OG~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (OG_hostility~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (OG_cooperation~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (Fight_OG~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (OG_hostility~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsuga)<- lm (OG_cooperation~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsuga)<- lm (Fight_OG~ Ingroup_fusion+ Ingroup_identification+ Outgroup_bonds+ Empathic_concern+ + Perceived_discrimination+ Age+ ` Gender: ` + ` SES: ` , data = dsuga)``` ## Gambia: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_gmb_01, lm_gmb_02, lm_gmb_03, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Pakistan: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_pak_01, lm_pak_02, lm_pak_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Tanzania: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_tza_01, lm_tza_02, lm_tza_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Uganda: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_uga_01, lm_uga_02, lm_uga_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` # **Section 6. Coefficient plots for section 5** ## Coefficient plots: full sample ```{r, error = F, message = F, warning = F, results = "asis"} <- modelplot (lm01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup hostility" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup cooperation" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Fight outgroup" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )``` ## Coefficient plots: individual countries ```{r, error = F, message = F, warning = F} <- modelplot (lm_gmb_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup hostility: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_gmb_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup cooperation: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_gmb_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Fight outgroup: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup hostility: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup cooperation: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Fight outgroup: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup hostility: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup cooperation: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Fight outgroup: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup hostility: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup cooperation: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Fight outgroup: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )``` ## Gambia: ```{r, error = F, message = F, warning = F} ``` ## Pakistan: ```{r, error = F, message = F, warning = F} ``` ## Tanzania: ```{r, error = F, message = F, warning = F} ``` ## Uganda: ```{r, error = F, message = F, warning = F} ``` # **Section 7. Regression models: Group fusion/identification vs imagistic items** ## Regression models: full sample ```{r, error = F, message = F, warning = F} ## Three regression models predicting IG fusion/identification and OG bonds: <- lm (Ingroup_fusion~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = ds)<- lm (Ingroup_identification~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = ds)<- lm (Outgroup_bonds~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = ds)``` ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm01, lm02, lm03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Regression models: individual countries ```{r, error = F, message = F, warning = F} ## Twelve regression models predicting endorsement of BBL vs BCL for four countries <- lm (Ingroup_fusion~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (Ingroup_identification~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (Outgroup_bonds~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsgmb)<- lm (Ingroup_fusion~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (Ingroup_identification~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (Outgroup_bonds~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dspak)<- lm (Ingroup_fusion~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (Ingroup_identification~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (Outgroup_bonds~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dstza)<- lm (Ingroup_fusion~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsuga)<- lm (Ingroup_identification~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsuga)<- lm (Outgroup_bonds~ Event_positive_affect+ Event_negative_affect+ Event_episodic_recall+ + Event_reflection+ Event_transformative_indiv+ + Age+ ` Gender: ` + ` SES: ` , data = dsuga)``` ## Gambia: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_gmb_01, lm_gmb_02, lm_gmb_03, type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Pakistan: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_pak_01, lm_pak_02, lm_pak_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Tanzania: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_tza_01, lm_tza_02, lm_tza_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` ## Uganda: ```{r, error = F, message = F, warning = F, results = "asis"} stargazer (lm_uga_01, lm_uga_02, lm_uga_03,type = "html" , star.cutoffs = c (0.05 , 0.01 , 0.001 ),out = "table1.html" ,notes = starpattern, notes.append = F)``` # **Section 8. Coefficient plots for section 7** ## Coefficient plots: full sample ```{r, error = F, message = F, warning = F, results = "asis"} <- modelplot (lm01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup fusion" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup identification" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup bonds" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )``` ## Coefficient plots: individual countries ```{r, error = F, message = F, warning = F} <- modelplot (lm_gmb_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup fusion: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_gmb_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup identification: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_gmb_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup bonds: Gambia" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup fusion: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup identification: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_pak_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup bonds: Pakistan" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup fusion: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup identification: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_tza_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup bonds: Tanzania" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_01, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup fusion: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_02, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Ingroup identification: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )<- modelplot (lm_uga_03, coef_rename = TRUE ,coef_omit = 'Interc' ) + aes (color = ifelse (p.value < 0.05 , "Significant" , "Not significant" )) + scale_color_manual (values = c ("red" , "blue" ))+ labs (title = "Outgroup bonds: Uganda" , x = "OLS coefficients with 95% CI" )+ geom_vline (xintercept = 0 , linetype = 2 )``` ## Gambia: ```{r, error = F, message = F, warning = F} ``` ## Pakistan: ```{r, error = F, message = F, warning = F} ``` ## Tanzania: ```{r, error = F, message = F, warning = F} ``` ## Uganda: ```{r, error = F, message = F, warning = F} ``` 
