By Jake Spaulding

Decennial Census, 1940

Import Data

library(readr)
pop_1940 <- read.csv("~/OneDrive - Plymouth State University/madness/institutionalPop_1940.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     535529
## 5     Male  45823031     288238
## 6   Female  45605134     248391
## 7 Nonwhite   9574759      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("~/OneDrive - Plymouth State University/madness/popdata1970.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
library(ggplot2)
library(scales)
# Interested in the change between 40 and 70, overall
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())

Based off the graph above there was a clear change in the amount of people in the mental institutions. There was a rise in the nonwhite population which is probably the reason for the rise of nonwhites in mental institutions. Also, in 1970 Hispanic population was counted in the survey which would make the numbers increase. I think the decrease in white population is due to the bad reputations mental institutions were getting. During the previous years some of the mental institutions got exposed to the media by different journalists. They spoke of the awful experiments they did and what not. This would cause deinstitutionalization; this is when different laws were passed to change the institutions from less restrictive setting to give the patients more freedom than locking them up. This also made it so patients who were required to be there legally to be able to come and go. This would give a lot of mental patient’s opportunities to leave and use different options to help them. Another factor could have been World War 2. Everyone had to sign up to be drafted and if some of these people’s illnesses weren’t bad, they easily could have been sent in to help fight. A lot of elderly people were sent to retirement homes which would have a large decrease in numbers. Before the elderly could be checked into these places to be taken care of because they didn’t have many nursing homes back then. Finally, there could have been new medical break throughs which would end up making some of these people diseases treatable. If that were the case, then the numbers would drop a lot.

The change in mental institutions conditions were very great during these times. Before they treated the patients as if it was a jail. The staff was very mean and strict often beating the patients. The government pushed for assessments before someone was admitted to figure out if someone needed the institution or if medicine could help them live on their own which was a lot different from institutions long ago where people could just be dropped off there. There was a big change in public opinion of the mentally ill as well. People use to be scared of them so they would drop them off in the institutions but they became more excepted by the public. This helped them be able to have more freedom and better treatment as well. Overall there were many things that attributed to the changes in mental instutions, but deinstitutionalization affected the population of the institutions the most. If people didn’t speak up to get more freedom for the people in there nothing would’ve been done and the number probably would’ve been more than less.