library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'lubridate' was built under R version 4.5.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# 1. Load the data
Mental_Health_Survey <- read.csv("survey.csv")
# 2. Filter out rows with NA values (excluding comments and state)
# We use if_all() because filter() requires a logical vector, not a data frame
Mental_Health_Survey <- Mental_Health_Survey %>%
filter(if_all(-c(comments, state), ~ !is.na(.)))
# 3. Clean the Gender column
# Standardizing common entries to 'Male', 'Female', and 'Other'
Mental_Health_Survey <- Mental_Health_Survey %>%
mutate(Gender = case_when(
tolower(Gender) %in% c("m", "male", "male-ish", "maile", "mal", "male (cis)", "make", "male ", "man", "msle", "mail", "malr", "cis male", "cis man") ~ "Male",
tolower(Gender) %in% c("f", "female", "trans-female", "female (trans)", "female ", "femail", "cis female", "femake", "cis-female/femme", "woman", "female") ~ "Female",
TRUE ~ "Other"
))
# 4. Clean the Age column
# Removing nonsensical ages (keeping only 18 to 100)
Mental_Health_Survey <- Mental_Health_Survey %>%
filter(Age >= 18 & Age <= 100)
# 5. Review the cleaned data
summary(Mental_Health_Survey)## Age Gender Country state
## Min. :18.00 Length:971 Length:971 Length:971
## 1st Qu.:27.00 Class :character Class :character Class :character
## Median :31.00 Mode :character Mode :character Mode :character
## Mean :32.31
## 3rd Qu.:36.00
## Max. :72.00
## self_employed family_history treatment work_interfere
## Length:971 Length:971 Length:971 Length:971
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## remote_work tech_company benefits care_options
## Length:971 Length:971 Length:971 Length:971
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## wellness_program seek_help leave
## Length:971 Length:971 Length:971
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## phys_health_consequence coworkers obs_consequence
## Length:971 Length:971 Length:971
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## comments
## Length:971
## Class :character
## Mode :character
##
##
## head(Mental_Health_Survey)## Age Gender Country state self_employed family_history treatment
## 1 46 Male United States MD Yes Yes No
## 2 29 Male United States NY No Yes Yes
## 3 31 Male United States NC Yes No No
## 4 46 Male United States MA No No Yes
## 5 41 Male United States IA No No Yes
## 6 33 Male United States CA No Yes Yes
## work_interfere remote_work tech_company benefits care_options
## 1 Sometimes Yes Yes Yes Not sure
## 2 Sometimes No Yes Yes Yes
## 3 Never Yes Yes No No
## 4 Often Yes Yes Yes Yes
## 5 Never No No Don't know No
## 6 Rarely No Yes Yes Not sure
## wellness_program seek_help leave phys_health_consequence
## 1 Yes Don't know Very easy No
## 2 No No Somewhat difficult No
## 3 No No Somewhat difficult No
## 4 No No Don't know No
## 5 No Don't know Don't know No
## 6 Don't know Yes Don't know No
## coworkers obs_consequence comments
## 1 Yes Yes <NA>
## 2 Some of them No <NA>
## 3 Some of them No <NA>
## 4 Some of them No <NA>
## 5 No No <NA>
## 6 Yes No Relatively new job. Ask again later#The Mental Health in Tech Survey (2014) provides a snapshot of how mental health was perceived and handled in the technology industry over a decade ago.
Core Findings High Prevalence: Roughly 50% of respondents had sought treatment for a mental health condition, showing it is a widespread reality in tech. The Comfort Gap: Employees are significantly more comfortable discussing physical health issues (like a broken leg) with supervisors than mental health issues (like anxiety). Benefit Awareness: While many companies offer mental health benefits, a large portion of employees are either unaware of them or don’t know how to access them. Anonymity Matters: Respondents cited anonymity as a primary factor in whether they would use employer-provided resources. Why It Matters This dataset is widely used in data science to practice data cleaning (due to messy ‘Gender’ and ‘Age’ fields) and to analyze the stigma that persists in high-pressure work environments. It highlights that providing benefits isn’t enough; a culture of openness is required for those benefits to be effective.