DATA710 Memo3
Nazija Akter
3/1/2021
rm(list=ls())
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 411679 22 855480 45.7 NA 641491 34.3
## Vcells 785456 6 8388608 64.0 16384 1768739 13.5setwd("/Users/Nazija/Desktop/DATA 710/Lec 3")library(foreign) # this pack allows you to import Stata data
GSS <- read.dta("/Users/Nazija/Desktop/DATA 710/GSS1972_2014v12.dta")
library(descr)
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## extractm3<-GSS%>%
select(year, helppoor, helpblk, racmar, racdif1, racdif2)%>%
mutate(racmar2 = ifelse(racmar == "yes", 1, 0), #0 means they support interracial marriage, 1 means support laws against it (1 is racist)
racdif12 = ifelse(racdif1 == "yes", 1, 0),# 1 means that worse economic situation than whites mainly due to discrimination, 0 means doesn't believe it is mainly because of discrimination (0 is racist)
racdif22 =(ifelse(racdif2 == "yes", 1, 0)), #1 means racist, believe blacks are less able to learn, 0 means don't think differences mainly due to inborn ability of blacks
helppoor2 = 6-helppoor, #1 means individual responsibility, 5 means gov't responsibility
helpblk2 = 6 - helpblk)#%>% 1 means indiividual responsibility and shouldn't give them special treatment, 5 means gov't responsibility
#select(racmar2, racdif12, racdif22, helppoor2, helpblk2)
glimpse(m3)## Rows: 59,599
## Columns: 11
## $ year <int> 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,…
## $ helppoor <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ helpblk <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ racmar <fct> no, yes, yes, yes, no, no, yes, no, NA, NA, NA, NA, NA, NA,…
## $ racdif1 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ racdif2 <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ racmar2 <dbl> 0, 1, 1, 1, 0, 0, 1, 0, NA, NA, NA, NA, NA, NA, NA, NA, 1, …
## $ racdif12 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ racdif22 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ helppoor2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ helpblk2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…unique(m3$helppoor2)## [1] NA 5 3 4 1 2unique(m3$helpblk2)## [1] NA 3 1 5 4 2unique(m3$racmar2)## [1] 0 1 NAunique(m3$racdif12)## [1] NA 0 1unique(m3$racdif22)## [1] NA 1 0table(m3$helpblk, m3$helpblk2)##
## 1 2 3 4 5
## 1 0 0 0 0 2838
## 2 0 0 0 2751 0
## 3 0 0 9045 0 0
## 4 0 5687 0 0 0
## 5 9185 0 0 0 0table(m3$helppoor, m3$helppoor2)##
## 1 2 3 4 5
## 1 0 0 0 0 5079
## 2 0 0 0 3701 0
## 3 0 0 12968 0 0
## 4 0 4154 0 0 0
## 5 3306 0 0 0 0table(m3$racmar, m3$racmar2)##
## 0 1
## iap 0 0
## yes 0 6629
## no 22155 0
## dk 0 0
## na 0 0table(m3$racdif1, m3$racdif12)##
## 0 1
## iap 0 0
## yes 0 10197
## no 15505 0
## dk 0 0
## na 0 0table(m3$racdif2, m3$racdif22)##
## 0 1
## iap 0 0
## yes 0 3576
## no 22483 0
## dk 0 0
## na 0 0data<- m3%>%select(year, racmar2, racdif12, racdif22, helppoor2, helpblk2)Have attitudes towards race, racial inequality, and state policies changed over time? Variables chosen: racdif1 vs racdif2, helppoor vs helpblk
difsdata<- data%>% select(year, racdif12, racdif22)
difsdata = na.omit(difsdata)
difs1prop_table<-sjmisc::flat_table(difsdata, year, racdif12, margin = "row")
difs1prop_table## racdif12 0 1
## year
## 1977 59.00 41.00
## 1985 54.53 45.47
## 1986 55.35 44.65
## 1988 55.47 44.53
## 1989 57.77 42.23
## 1990 58.83 41.17
## 1991 58.05 41.95
## 1993 56.53 43.47
## 1994 57.06 42.94
## 1996 60.20 39.80
## 1998 62.22 37.78
## 2000 60.79 39.21
## 2002 65.62 34.38
## 2004 64.91 35.09
## 2006 64.01 35.99
## 2008 63.72 36.28
## 2010 61.82 38.18
## 2012 64.50 35.50
## 2014 64.97 35.03difs2prop_table<-sjmisc::flat_table(difsdata, year, racdif22, margin = "row")
difs2prop_table## racdif22 0 1
## year
## 1977 73.79 26.21
## 1985 78.39 21.61
## 1986 79.45 20.55
## 1988 80.55 19.45
## 1989 80.74 19.26
## 1990 81.47 18.53
## 1991 85.38 14.62
## 1993 86.75 13.25
## 1994 86.22 13.78
## 1996 89.66 10.34
## 1998 89.84 10.16
## 2000 87.40 12.60
## 2002 87.73 12.27
## 2004 91.11 8.89
## 2006 90.91 9.09
## 2008 89.02 10.98
## 2010 89.40 10.60
## 2012 90.01 9.99
## 2014 91.23 8.77plotdifs1data <- data.frame(difs1prop_table)%>%
filter(racdif12 == 1)
plotdifs1data## year racdif12 Freq
## 1 1977 1 41.00
## 2 1985 1 45.47
## 3 1986 1 44.65
## 4 1988 1 44.53
## 5 1989 1 42.23
## 6 1990 1 41.17
## 7 1991 1 41.95
## 8 1993 1 43.47
## 9 1994 1 42.94
## 10 1996 1 39.80
## 11 1998 1 37.78
## 12 2000 1 39.21
## 13 2002 1 34.38
## 14 2004 1 35.09
## 15 2006 1 35.99
## 16 2008 1 36.28
## 17 2010 1 38.18
## 18 2012 1 35.50
## 19 2014 1 35.03plotdifs1data1 <- data.frame(year = plotdifs1data$year, prop = plotdifs1data$Freq)%>%
#mutate_at(vars(year), funs(as.double))
mutate_at(vars(year), funs(as.character.factor))%>%
mutate_at(vars(year), funs(as.double))## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.#as.numeric(levels(f))[f]
plotdifs1data1## year prop
## 1 1977 41.00
## 2 1985 45.47
## 3 1986 44.65
## 4 1988 44.53
## 5 1989 42.23
## 6 1990 41.17
## 7 1991 41.95
## 8 1993 43.47
## 9 1994 42.94
## 10 1996 39.80
## 11 1998 37.78
## 12 2000 39.21
## 13 2002 34.38
## 14 2004 35.09
## 15 2006 35.99
## 16 2008 36.28
## 17 2010 38.18
## 18 2012 35.50
## 19 2014 35.03class(plotdifs1data1$year)## [1] "numeric"ggplot()+
geom_line(data = plotdifs1data1, aes(x = year, y = prop/100), color = "green")+
labs(title = "Belief that Discrimination Is Main Factor Behind Racial Inequality, 1977-2014", x = "Decades", y = "Proportion in Support")
plotdifs2data <- data.frame(difs2prop_table)%>%
filter(racdif22 == 1)
plotdifs2data## year racdif22 Freq
## 1 1977 1 26.21
## 2 1985 1 21.61
## 3 1986 1 20.55
## 4 1988 1 19.45
## 5 1989 1 19.26
## 6 1990 1 18.53
## 7 1991 1 14.62
## 8 1993 1 13.25
## 9 1994 1 13.78
## 10 1996 1 10.34
## 11 1998 1 10.16
## 12 2000 1 12.60
## 13 2002 1 12.27
## 14 2004 1 8.89
## 15 2006 1 9.09
## 16 2008 1 10.98
## 17 2010 1 10.60
## 18 2012 1 9.99
## 19 2014 1 8.77plotdifs2data1 <- data.frame(year = plotdifs2data$year, prop = plotdifs2data$Freq)%>%
#mutate_at(vars(year), funs(as.double))
mutate_at(vars(year), funs(as.character.factor))%>%
mutate_at(vars(year), funs(as.double))
#as.numeric(levels(f))[f]
plotdifs2data1## year prop
## 1 1977 26.21
## 2 1985 21.61
## 3 1986 20.55
## 4 1988 19.45
## 5 1989 19.26
## 6 1990 18.53
## 7 1991 14.62
## 8 1993 13.25
## 9 1994 13.78
## 10 1996 10.34
## 11 1998 10.16
## 12 2000 12.60
## 13 2002 12.27
## 14 2004 8.89
## 15 2006 9.09
## 16 2008 10.98
## 17 2010 10.60
## 18 2012 9.99
## 19 2014 8.77class(plotdifs2data1$year)## [1] "numeric"ggplot()+
geom_line(data = plotdifs2data1, aes(x = year, y = prop/100), color = "orange")+
labs(title = "Belief that Inborn Disability is Main Factor Behind Racial Inequality, 1977-2014", x = "Decades", y = "Proportion in Support")
Belief that discrimination is the main factor behind racial inequality has overall lowered between 1977 to 2014, but not as steadily as the belief that inborn-ability to learn is the main factor behind racial inequality. Rather, the belief that discrimination is the main cause experienced a spike every decade. Still, this is surprising since I believed that the two graphs would look like reverses of each other as the belief that discrimination is the main factor overtook the belief that inborn-ability is the main factor.
However, taking Bonillo-Silva’s work into account, this can be an indicator of how while overt or explicit racism, such as the belief that Blacks have less inborn-ability to learn may have died down, there is now a deracializing of race-related issues. The fact that fewer Americans understand discrimination to be a factor behind racial inequality shows this, as they may believe that we are in a “post-racial America”
ggplot()+
geom_line(data = plotdifs1data1, aes(x = year, y = prop/100), color = "darkred")+
geom_line(data = plotdifs2data1, aes(x = year, y = prop/100), color = "steelblue")+
labs(title = "Trends in What Main Factor Behind Racial Inequality Is Believed to Be", x = "Decades", y = "Proportion in Support")
combdifsplot <- data.frame(year = plotdifs1data1$year, Discrimination = plotdifs1data1$prop, Inborn_Ability = plotdifs2data1$prop)ggplot(data = combdifsplot)+
geom_line(aes(x = year, y = Discrimination))+
geom_line(aes(x = year, y = Inborn_Ability))
Have thye become less racist in the last decades, and more or less supportive of policies that might address racial inequality?
Have Americans become more or less racist
Variables chosen: racmar, racdif2
More or less supportive of policies aimed at addressing racial inequality?
helpdata<- data%>% select(year, helpblk2, helppoor2)
helpblkdata<-helpdata%>%select(year, helpblk2)
helpblkdata = na.omit(helpblkdata)
helpblkprop_table<-(sjmisc::flat_table(helpblkdata, year, helpblk2, margin = "row"))
#helpblkprop_table%>%
# filter(helpblk2 > 3)
#difs2prop_table<-sjmisc::flat_table(difsdata, year, racdif22, margin = "row")
#difs2prop_table
helpblkprop_table## helpblk2 1 2 3 4 5
## year
## 1975 40.88 12.36 21.48 8.70 16.57
## 1983 34.53 21.10 26.61 9.90 7.86
## 1984 31.38 17.73 31.38 9.79 9.72
## 1986 34.13 18.22 29.43 9.81 8.41
## 1987 27.48 15.81 29.41 11.56 15.75
## 1988 34.58 17.86 29.60 10.28 7.68
## 1989 34.30 19.06 27.88 9.73 9.03
## 1990 28.27 16.63 34.16 10.43 10.51
## 1991 26.91 19.27 31.70 11.52 10.60
## 1993 28.43 21.27 32.65 10.29 7.35
## 1994 29.95 23.97 30.10 7.53 8.45
## 1996 29.10 23.92 29.81 8.80 8.37
## 1998 28.68 21.76 32.22 10.58 6.76
## 2000 27.33 20.06 33.22 9.61 9.78
## 2002 31.80 19.89 31.69 8.09 8.54
## 2004 34.34 18.39 32.36 7.22 7.68
## 2006 29.00 18.74 33.98 7.55 10.73
## 2008 29.28 19.34 33.64 8.18 9.56
## 2010 31.16 20.12 30.64 8.70 9.38
## 2012 34.87 18.02 30.73 9.13 7.25
## 2014 32.61 18.06 31.13 8.94 9.25(plothelpblkdata<-data.frame(helpblkprop_table))## year helpblk2 Freq
## 1 1975 1 40.88
## 2 1983 1 34.53
## 3 1984 1 31.38
## 4 1986 1 34.13
## 5 1987 1 27.48
## 6 1988 1 34.58
## 7 1989 1 34.30
## 8 1990 1 28.27
## 9 1991 1 26.91
## 10 1993 1 28.43
## 11 1994 1 29.95
## 12 1996 1 29.10
## 13 1998 1 28.68
## 14 2000 1 27.33
## 15 2002 1 31.80
## 16 2004 1 34.34
## 17 2006 1 29.00
## 18 2008 1 29.28
## 19 2010 1 31.16
## 20 2012 1 34.87
## 21 2014 1 32.61
## 22 1975 2 12.36
## 23 1983 2 21.10
## 24 1984 2 17.73
## 25 1986 2 18.22
## 26 1987 2 15.81
## 27 1988 2 17.86
## 28 1989 2 19.06
## 29 1990 2 16.63
## 30 1991 2 19.27
## 31 1993 2 21.27
## 32 1994 2 23.97
## 33 1996 2 23.92
## 34 1998 2 21.76
## 35 2000 2 20.06
## 36 2002 2 19.89
## 37 2004 2 18.39
## 38 2006 2 18.74
## 39 2008 2 19.34
## 40 2010 2 20.12
## 41 2012 2 18.02
## 42 2014 2 18.06
## 43 1975 3 21.48
## 44 1983 3 26.61
## 45 1984 3 31.38
## 46 1986 3 29.43
## 47 1987 3 29.41
## 48 1988 3 29.60
## 49 1989 3 27.88
## 50 1990 3 34.16
## 51 1991 3 31.70
## 52 1993 3 32.65
## 53 1994 3 30.10
## 54 1996 3 29.81
## 55 1998 3 32.22
## 56 2000 3 33.22
## 57 2002 3 31.69
## 58 2004 3 32.36
## 59 2006 3 33.98
## 60 2008 3 33.64
## 61 2010 3 30.64
## 62 2012 3 30.73
## 63 2014 3 31.13
## 64 1975 4 8.70
## 65 1983 4 9.90
## 66 1984 4 9.79
## 67 1986 4 9.81
## 68 1987 4 11.56
## 69 1988 4 10.28
## 70 1989 4 9.73
## 71 1990 4 10.43
## 72 1991 4 11.52
## 73 1993 4 10.29
## 74 1994 4 7.53
## 75 1996 4 8.80
## 76 1998 4 10.58
## 77 2000 4 9.61
## 78 2002 4 8.09
## 79 2004 4 7.22
## 80 2006 4 7.55
## 81 2008 4 8.18
## 82 2010 4 8.70
## 83 2012 4 9.13
## 84 2014 4 8.94
## 85 1975 5 16.57
## 86 1983 5 7.86
## 87 1984 5 9.72
## 88 1986 5 8.41
## 89 1987 5 15.75
## 90 1988 5 7.68
## 91 1989 5 9.03
## 92 1990 5 10.51
## 93 1991 5 10.60
## 94 1993 5 7.35
## 95 1994 5 8.45
## 96 1996 5 8.37
## 97 1998 5 6.76
## 98 2000 5 9.78
## 99 2002 5 8.54
## 100 2004 5 7.68
## 101 2006 5 10.73
## 102 2008 5 9.56
## 103 2010 5 9.38
## 104 2012 5 7.25
## 105 2014 5 9.25plothelpblk <- plothelpblkdata%>%
mutate(helpblk2 = as.integer(helpblk2))%>%
mutate_at(vars(year), funs(as.character.factor))%>%
filter(helpblk2 > 3)%>%
#mutate(support = (helpblk2 > 3))%>%
group_by(year)%>%
summarize(proportion = sum(Freq))
plothelpblk## # A tibble: 21 x 2
## year proportion
## * <chr> <dbl>
## 1 1975 25.3
## 2 1983 17.8
## 3 1984 19.5
## 4 1986 18.2
## 5 1987 27.3
## 6 1988 18.0
## 7 1989 18.8
## 8 1990 20.9
## 9 1991 22.1
## 10 1993 17.6
## # … with 11 more rowsplothelpblk %>%
ggplot(aes(x = as.integer(year), y= proportion))+
geom_line(color= "red")+
labs(title = "Trend in Supporting Policies to Help Blacks", x = "Year", y = "Proportion")
helppoordata<-helpdata%>%select(year, helppoor2)
helppoordata = na.omit(helppoordata)
helppoorprop_table<-(sjmisc::flat_table(helppoordata, year, helppoor2, margin = "row"))
helppoorprop_table## helppoor2 1 2 3 4 5
## year
## 1975 13.40 10.70 35.77 10.15 29.97
## 1983 11.76 14.05 41.24 15.42 17.52
## 1984 8.73 14.69 47.27 11.36 17.96
## 1986 10.98 11.75 45.94 12.66 18.67
## 1987 9.93 12.20 42.96 12.49 22.42
## 1988 11.74 12.15 45.42 13.08 17.61
## 1989 8.91 14.11 44.54 15.42 17.02
## 1990 8.57 12.69 44.23 15.31 19.20
## 1991 8.33 13.41 44.21 16.67 17.38
## 1993 10.60 14.11 48.74 13.81 12.74
## 1994 11.96 16.39 44.85 13.81 12.99
## 1996 11.02 16.05 47.06 12.48 13.40
## 1998 12.48 17.15 44.38 13.02 12.97
## 2000 12.46 16.96 43.14 13.01 14.43
## 2002 11.71 12.95 47.30 10.70 17.34
## 2004 11.10 14.34 47.40 10.06 17.11
## 2006 10.67 13.33 46.73 10.42 18.85
## 2008 10.73 14.03 42.61 12.60 20.03
## 2010 12.54 14.78 44.55 11.27 16.87
## 2012 13.60 13.99 44.82 11.21 16.38
## 2014 13.37 14.71 42.43 12.82 16.67plothelppoordata<-data.frame(helppoorprop_table)
plothelppoor <- plothelppoordata%>%
mutate(helppoor2 = as.integer(helppoor2))%>%
mutate_at(vars(year), funs(as.character.factor))%>%
mutate(decade = ifelse(year >= 1970 & year < 1980, 1970,
ifelse(year >= 1980 & year < 1990, 1980,
ifelse(year >= 1990 & year < 2000, 1990,
ifelse(year >= 2000 & year < 2010, 2000, year)))))%>%
filter(helppoor2 != 3)%>%
mutate(support = (helppoor2 > 3))%>%
group_by(decade,support)%>%
summarize(proportion = sum(Freq))## `summarise()` has grouped output by 'decade'. You can override using the `.groups` argument.plothelppoor## # A tibble: 14 x 3
## # Groups: decade [7]
## decade support proportion
## <chr> <lgl> <dbl>
## 1 1970 FALSE 24.1
## 2 1970 TRUE 40.1
## 3 1980 FALSE 141
## 4 1980 TRUE 192.
## 5 1990 FALSE 153.
## 6 1990 TRUE 174.
## 7 2000 FALSE 128.
## 8 2000 TRUE 145.
## 9 2010 FALSE 27.3
## 10 2010 TRUE 28.1
## 11 2012 FALSE 27.6
## 12 2012 TRUE 27.6
## 13 2014 FALSE 28.1
## 14 2014 TRUE 29.5plothelppoor %>%
ggplot(aes(x = as.integer(decade), y= proportion/100, group = support, color = support))+
geom_line()+
labs(title = "Trend in Supporting Policies to Help Poor", x = "Year", y = "Proportion of Population")
plothelppoordata<-data.frame(helppoorprop_table)
plothelppoor <- plothelppoordata%>%
mutate(helppoor2 = as.integer(helppoor2))%>%
mutate_at(vars(year), funs(as.character.factor))%>%
filter(helppoor2 != 3)%>%
mutate(support = (helppoor2 > 3))%>%
group_by(year,support)%>%
summarize(proportion = sum(Freq))## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.plothelppoor## # A tibble: 42 x 3
## # Groups: year [21]
## year support proportion
## <chr> <lgl> <dbl>
## 1 1975 FALSE 24.1
## 2 1975 TRUE 40.1
## 3 1983 FALSE 25.8
## 4 1983 TRUE 32.9
## 5 1984 FALSE 23.4
## 6 1984 TRUE 29.3
## 7 1986 FALSE 22.7
## 8 1986 TRUE 31.3
## 9 1987 FALSE 22.1
## 10 1987 TRUE 34.9
## # … with 32 more rowsplothelppoor %>%
ggplot(aes(x = as.integer(year), y= proportion/100, group = support, color = support))+
geom_line()+
labs(title = "Trend in Supporting Policies to Help Poor", x = "Year", y = "Proportion of Population")
plothelppoordata<-data.frame(helppoorprop_table)
plothelppoor <- plothelppoordata%>%
mutate(helppoor2 = as.integer(helppoor2))%>%
mutate_at(vars(year), funs(as.character.factor))%>%
filter(helppoor2 > 3)%>%
group_by(year)%>%
summarize(proportion = sum(Freq))
plothelppoor## # A tibble: 21 x 2
## year proportion
## * <chr> <dbl>
## 1 1975 40.1
## 2 1983 32.9
## 3 1984 29.3
## 4 1986 31.3
## 5 1987 34.9
## 6 1988 30.7
## 7 1989 32.4
## 8 1990 34.5
## 9 1991 34.0
## 10 1993 26.6
## # … with 11 more rowsplothelppoor %>%
ggplot(aes(x = as.integer(year), y= proportion/100))+
geom_line(color = "steelblue")+
labs(title = "Trends in Supporting Policies to Help Poor", x = "Year", y = "Proportion of Population")
ggplot(color = data)+
geom_line(data = plothelppoor, aes(x = as.integer(year), y = proportion/100), color = "steelblue")+
geom_line(data = plothelpblk, aes(x = as.integer(year), y = proportion/100), color = "darkred")+
labs(title = "Trends in Supporting Policies to Help Poor vs. Blacks, 1975 - 2014", x = "Decades", y = "Proportion in Support")
The divide has remained the same.
ggplot(color = data)+
geom_line(data = plotdifs1data1, aes(x = year, y = prop/100), color = "green")+
geom_line(data = plothelpblk, aes(x = as.integer(year), y = proportion/100), color = "darkred")+
labs(title = "Belief in Discrimination vs Support for Policies to Help Blacks", x = "Decades", y = "Proportion in Support")
%>% mutate(decade = ifelse(year >= 1970 & year < 1980, 1970, ifelse(year >= 1980 & year < 1990, 1980, ifelse(year >= 1990 & year < 2000, 1990, ifelse(year >= 2000 & year < 2010, 2000, year)))))
variables: helppoor, helpblk, Traditional anti-black sentiment changed over time?
Attitudes towards structural barriers as the root bases of racial inequality changed over time?
racdif1 vs racdif2, helpblks
helppoor vs helpblk over time