Untitled
Caitlin Feltus
3/10/2021
Decennial Census, 1940
Import data
library(readr)## Warning: package 'readr' was built under R version 4.0.3pop_1940 <- read_csv("~/madness/institutionalPop_1940.csv")
# print data
pop_1940## # A tibble: 9 x 3
## Variable Pop_Total Pop_Mental
## <chr> <dbl> <dbl>
## 1 Total 101102924 591355
## 2 Male 50553748 317812
## 3 Female 50549176 273553
## 4 White 91428165 536629
## 5 Male 45823031 288238
## 6 Female 45605134 248391
## 7 Nonwhite 9574759 54736
## 8 Male 4730717 29574
## 9 Female 4944042 25162Clean Data
# delete rows: 1,2,3,4,7
pop_1940_cleaned <- pop_1940[-c(1,2,3,4,7),]
# print data
pop_1940_cleaned## # A tibble: 4 x 3
## Variable Pop_Total Pop_Mental
## <chr> <dbl> <dbl>
## 1 Male 45823031 288238
## 2 Female 45605134 248391
## 3 Male 4730717 29574
## 4 Female 4944042 25162# rename values
pop_1940_cleaned[1,1] <- "White_Male"
pop_1940_cleaned[2,1] <- "White_Female"
pop_1940_cleaned[3,1] <- "Nonwhite_Male"
pop_1940_cleaned[4,1] <- "Nonwhite_Female"
# print data
pop_1940_cleaned## # A tibble: 4 x 3
## Variable Pop_Total Pop_Mental
## <chr> <dbl> <dbl>
## 1 White_Male 45823031 288238
## 2 White_Female 45605134 248391
## 3 Nonwhite_Male 4730717 29574
## 4 Nonwhite_Female 4944042 25162# separate Variable to two columns: Race and Gender
library(dplyr)## Warning: package 'dplyr' was built under R version 4.0.3library(tidyr)## Warning: package 'tidyr' was built under R version 4.0.3pop_1940_processed <-
pop_1940_cleaned %>%
separate(col = Variable, into = c("Race","Gender"), sep = "_")
# print data
pop_1940_processed## # A tibble: 4 x 4
## Race Gender Pop_Total Pop_Mental
## <chr> <chr> <dbl> <dbl>
## 1 White Male 45823031 288238
## 2 White Female 45605134 248391
## 3 Nonwhite Male 4730717 29574
## 4 Nonwhite Female 4944042 25162Decennial Census, 1970
Import data
library(readr)
pop70_cleaned <- read_csv("~/madness/pop70_cleaned.csv")
#print data
pop70_cleaned## # A tibble: 4 x 3
## Race Gender Pop_Mental_70
## <chr> <chr> <dbl>
## 1 White Male 194405
## 2 White Female 157578
## 3 Nonwhite Male 45567
## 4 Nonwhite Female 29227join data
pop_40N70 <- pop_1940_processed %>%
select(-Pop_Total) %>%
left_join(pop70_cleaned, by = c ("Race", "Gender")) %>%
rename(Pop_Mental_40 = Pop_Mental)
pop_40N70## # A tibble: 4 x 4
## Race Gender Pop_Mental_40 Pop_Mental_70
## <chr> <chr> <dbl> <dbl>
## 1 White Male 288238 194405
## 2 White Female 248391 157578
## 3 Nonwhite Male 29574 45567
## 4 Nonwhite Female 25162 29227pop_long <- pop_40N70 %>%
pivot_longer(3:4, names_to = "Census_Year", values_to = "Pop_Mental") %>%
mutate(Year = stringr::str_remove(Census_Year, "Pop_Mental_")) %>%
select(-Census_Year)
pop_long## # A tibble: 8 x 4
## Race Gender Pop_Mental Year
## <chr> <chr> <dbl> <chr>
## 1 White Male 288238 40
## 2 White Male 194405 70
## 3 White Female 248391 40
## 4 White Female 157578 70
## 5 Nonwhite Male 29574 40
## 6 Nonwhite Male 45567 70
## 7 Nonwhite Female 25162 40
## 8 Nonwhite Female 29227 70library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.4library(scales)## Warning: package 'scales' was built under R version 4.0.4# Interested in the change between 40 and 70, overall
pop_long %>%
group_by(Year) %>%
summarise(Pop = sum(Pop_Mental)) %>%
ggplot(aes (x = Year, y = Pop)) +
geom_col() +
scale_y_continuous(labels = comma_format())
# Interested in the change between 40 and 70, race
pop_long %>%
group_by(Year, Race) %>%
summarise(Pop = sum(Pop_Mental))%>%
ggplot(aes (x = Year, y = Pop, fill = Race)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = comma_format())
# Interested in the change between 40 and 70, gender
pop_long %>%
group_by(Year, Gender) %>%
summarise(Pop = sum(Pop_Mental))%>%
ggplot(aes (x = Gender,y = Pop, fill = Year)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = comma_format())
Mental institutions have evolved many times throughout history and have constantly been challenged in order to get to where they are today. The roles for these mental institutions have been affected by many different factors over the years. One factor is gender roles and societal norms or social classes. This is seen in the Elizabeth Packard excerpt and the Limerick District statistical report. Something to keep in mind is that there is always bias when reading these excerpts, and that is taken into consideration. The Limerick District reported things like demographics that indicated more women died in this institution than men, and the people institutionalized were mostly the uneducated working class. According to the asylum there were 3 meals a day for patients and places to play sports, dance or play cards. About only 10% of patents a year were being discharged, and each year they were taking in more people than they were discharging, this is a bad stat and indicates the institution was not very successful with fixing their patients. This reflects now on the future, the data we looked at in class from the 1940s to the 1970s shows improvements over the years and that less people were being institutionalized, this is very different than the Limerick Districts.
This is interesting when comparing the Limerick District reports to Elizabeth Packards excerpts about her experience and other women she knew experiences. Women were being forced into these institutions by their husbands or families that didn’t want to deal with them anymore it seemed like. Elizabeth brings out a huge factor that changed the demographics of institutions over the years and is a great example of how asylums were used as a place to just get rid of people and send people off to. It was a place where they abused medicine and their patients according to Elizabeth. These are huge factors that affect the role of institutions over the years.
In the 20th century institutions became a place where patients would actually heal and be fixed, instead of a place where they would be thrown into and forgotten about. This is reflected in the Emil Kraeplein excerpt, where Kraeplein insisted that these physiological disorders must be understood and not just pushed to the back burner. He would look for any statistical correlations in peoples symptoms, and his experimental treatments and ideas set motion to the positive change in psychiatric clinics to this day. His Heidlburg institution had observational rooms where doctors can learn about their patents and address the needs of these people. This reflects now to this day as mental institutions are places where people go to get help and find solutions.
In the late 19th century is when deinstitutionalization became, large numbers of psychiatric patients were removed from public asylums to a variety of community and institutional settings. This is seen in the Department of Health and Social Security in the Eghigian novel. This lowered the population of people in institutions from 558,000 to 72,000 in about 40 years (Eghigian, 357). This is because of a change of thinking how to understand and treat medical disorders. Another factor addressed during this time is that not every patient is the same and each patient needs their own unique help. New treatments and drugs are becoming available to more people, and treatments tailored to specific people are also incorporated. The open door policy was introduced resulting in less people being institutionalized too, people with minor problems were able to get one day treatment instead of kept in the asylum like they would have in the 1800s. There are pros and cons to this, people were actually encouraged to leave when they were cured instead of locked up like they used to be. But also people could leave early without receiving all of the proper treatment, resulting in potential homlessness if parents/ family couldn’t take them in. This is a lot of growth as a way to manage people affected by mental illness.
These are all factors that have changed the role of mental institutions throughout the years, and all reflect how mental hospitals now are in the 21st century.
Work Cited:
Eghigian, Greg, editor. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. Rutgers University Press, 2010. Accessed 24 Mar. 2021.