Decennial Census, 1940

Import data

library(readr)
pop_1940 <- read.csv("~/madness/1940data.csv")
 

#print data
pop_1940
##   Variable Pop_Total Pop_Mental
## 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   9674759      54736
## 8     Male   4730717      29574
## 9   Female   4944042      25162

Clean data

# delete rows: 1,2,3,4,7
pop_1940_cleaned <- pop_1940[-c(1,2,3,4,7),]

#print data
pop_1940_cleaned
##   Variable Pop_Total Pop_Mental
## 5     Male  45823031     288238
## 6   Female  45605134     248391
## 8     Male   4730717      29574
## 9   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
##          Variable Pop_Total Pop_Mental
## 5      White_Male  45823031     288238
## 6    White_Female  45605134     248391
## 8   Nonwhite_Male   4730717      29574
## 9 Nonwhite_Female   4944042      25162
#load packages 
library(dplyr) #for %>%
library(tidyr) #for separate() function

#separate Variable to two columns: Race and Gender
pop_1940_processed <- 
  pop_1940_cleaned %>% 
  separate(col = Variable, into = c("Race", "Gender"), sep = "_")

#print data
pop_1940_processed
##       Race Gender Pop_Total Pop_Mental
## 5    White   Male  45823031     288238
## 6    White Female  45605134     248391
## 8 Nonwhite   Male   4730717      29574
## 9 Nonwhite Female   4944042      25162

Decennial Census, 1970

Import Data

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         29227

join 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
##       Race Gender Pop_Mental_40 Pop_Mental_70
## 1    White   Male        288238        194405
## 2    White Female        248391        157578
## 3 Nonwhite   Male         29574         45567
## 4 Nonwhite Female         25162         29227
pop_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 70
# Interested in the change between 1940 and 1970, Overall

library(scales)
library(ggplot2)

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 1940 and 1970, Race

pop_long %>%
  group_by(Year, Race) %>%
  summarise(Pop = sum(Pop_Mental)) %>%
  ggplot(aes(x = Race, y = Pop, fill = Race)) +
  geom_col(position = "dodge")+
  scale_y_continuous(labels = comma_format())

# Interested in the change between 1940 and 1970, 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())

Unit 2 Reaction

Mental Institutions played a significant role in the lives of those who were affected by them. Mental Institutions are intended to provide treatment and shelter from those suffering from mental illnesses, such as schizophrenia, bipolarism, etc. Overall treatment of patients has improved since their inception. Their prevalence in modern society has shrunk however, with less people being sent and kept at these facilities.

The use of more medication and new living arrangements in mental wards was documented by Emil Kraepelin, the director of the psychiatric facility in Heidelberg, Germany. Kraepelin explains the concept of a surveillance ward, where many patients can be monitored simultaneously, while receiving their medication.1 Instead of being locked up, these patients were able to be treated and assisted by nurses, along with Kraepelin noting, due to being monitored, they were able to rest and sleep, allowing them to be “satisfactorily influenced.”2

At the time of the 1940 census, there were nearly 600,000 individuals residing in mental institutions across the US. The population of the country was 131 million, and the US had almost recovered from the Great Depression.3 Something that could be further investigated/researched could be if the 1930 census had a higher percentage of Americans in mental institutions, as this year was at the beginning of the Great Depression. Stress from losing your job or home could have contributed to more people be admitted to these facilities, and as the economy recovered, it would be interesting to see if this number went down by the time of the 1940 census as conditions improved.

Three decades later, the number of institutionalized individuals has dropped to around 400,000. The population of the US has also grown to just over 200 million, indicating a smaller percentage of institutionalized Americans.4 The graph will also help to illustrate this difference, with the institutionalized population dropping by nearly 200,000, when compared to the 1940 census.

The drop in individuals can be credited to several factors. Less illnesses and disorders are classified as “severe,” resulting in less hospitalizations. For example, Kraepelin states that “epileptics require” special and on-going examinations.5 In the modern era, people suffering from epilepsy can live amongst the general population without any major issues, due to medication being available that is able to treat their epilepsy.6 Modern medication has made it easier for individuals suffering from mental illnesses to go about their lives without long term hospitalization, reducing the affects of their illnesses that at one time would have them institutionalized.

Bibliography

“About the 1940 Census,” n.d. https://1940census.archives.gov/about.asp. “Deinstitutionalization - Special Reports | The New Asylums | FRONTLINE.” PBS. Public Broadcasting Service, n.d.

https://www.pbs.org/wgbh/pages/frontline/shows/asylums/special/excerpt.html.

Eghigian, Greg. 2010. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. New Brunswick, N.J.: Rutgers University Press

US Population by Year, n.d. https://www.multpl.com/united-states-population/table/by-year.

  1. Eghigian, Greg. 2010. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. New Brunswick, N.J.: Rutgers University Press. 201↩︎

  2. Eghigian, Greg. 2010. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. New Brunswick, N.J.: Rutgers University Press. 204↩︎

  3. “About the 1940 Census,” n.d. https://1940census.archives.gov/about.asp.↩︎

  4. US Population by Year, n.d. https://www.multpl.com/united-states-population/table/by-year.↩︎

  5. Eghigian, Greg. 2010. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. New Brunswick, N.J.: Rutgers University Press. 204↩︎

  6. https://www.pbs.org/wgbh/pages/frontline/shows/asylums/special/excerpt.html↩︎