R/cleaning_scripts/cleaning_data_22092020_randomforest.R

In gabrielburcea/cvindia: Analysis of symptoms

cleaned_data_22092020_2nd_dataset %>%
  dplyr::group_by(covid_tested) %>%
  tally()

data_rename <- PivotMapper %>%
  dplyr::rename(
    id = ID,
    age = Age,
    gender = Gender,
    location = Location,
    country = Country,
    chills = Chills,
    cough = Cough,
    diarrhoea = Diarrhoea,
    fatigue = Fatigue,
    headache = Headcahe,
    pregnant = Pregnant,
    sputum = Sputum,
    temperature = Temperature,
    sneezing = Sneezing,
    loss_of_smell_and_taste = "Loss of smell and taste",
    muscle_ache = 'Muscle Ache',
    nasal_congestion = "Nasal Congestion",
    nausea_vomiting = "Nausea and Vomiting",
    self_diagnosis = "Self Diagnosis",
    shortness_breath = "Shortness of Breath",
    sore_throat = "Sore Throat",
    loss_appetite = "Loss of Appetite",
    chest_pain = "Chest Pain",
    itchy_eyes = "Itchy Eyes",
    joint_pain = "Joint Pain",
    care_home_worker = 'Care Home Worker',
    health_care_worker = "Healthcare Worker",
    date_completed = 'Date Completed',
    how_unwell = "How Unwell",
    number_days_symptom_showing = "Number Of Days Symptoms Showing",
    reason_for_help = "Reason For Helping",
    language = Language,
    health_condition = "Long Standing Health Issues"
  ) %>%
  dplyr::mutate(id = str_sub(id, -10))



reason_for_help_levels_2 <- c(
  "negative" = "Tested Negative But Have Symptoms,Self-Isolating With No Symptoms",
  "negative"  = "Tested Negative But Have Symptoms,Curious,Self-Isolating With No Symptoms",
  "showing symptoms" =  "Showing Symptoms But Not Tested",
  "positive" = "Tested Positive" ,
  "negative" =  "Tested Negative But Have Symptoms",
  "showing symptoms"  = "Showing Symptoms But Not Tested,Curious",
  "showing symptoms" = "Recovered But Have New Symptoms",
  "showing symptoms" =  "Showing Symptoms But Not Tested,Recovered But Have New Symptoms",
  "showing symptoms"  = "Recovered But Have New Symptoms,Curious",
  "negative"  =  "Tested Negative But Have Symptoms,Curious",
  "positive" =  "Tested Positive,Self-Isolating With No Symptoms",
  "showing symptoms" =  "Tested Negative But Have Symptoms,Showing Symptoms But Not Tested",
  "negative"  = "Tested Negative But Have Symptoms,Live With Someone With Coronavirus",
  "positive" = "Tested Positive,Curious",
  "positive"  =  "Tested Positive,Live With Someone With Coronavirus",
  "positive" =  "Tested Positive,Recovered But Have New Symptoms",
  "showing symptoms" =   "Showing Symptoms But Not Tested,Live With Someone With Coronavirus,Recovered But Have New Symptoms",
  "showing symptoms" =  "Showing Symptoms But Not Tested,Recovered But Have New Symptoms,Curious",
  "showing symptoms" =  "Showing Symptoms But Not Tested,Live With Someone With Coronavirus,Curious",
  "showing symptoms" =  "Showing Symptoms But Not Tested,Live With Someone With Coronavirus",
  "positive"  = "Tested Positive,Recovered But Have New Symptoms,Curious",
  "negative"  =  "Tested Negative But Have Symptoms,Live With Someone With Coronavirus,Recovered But Have New Symptoms"

)
na_strings_reason_for_help_2 <- c(
  "None",
  "Self-Isolating With No Symptoms" ,
  "Curious",
  "Curious,Self-Isolating With No Symptoms",
  "Showing Symptoms But Not Tested,Curious,Self-Isolating With No Symptoms",
  "Live With Someone With Coronavirus",
  "Live With Someone With Coronavirus,Curious",
  "Live With Someone With Coronavirus,Self-Isolating With No Symptoms",
  "Live With Someone With Coronavirus,Curious,Self-Isolating With No Symptoms",
  "Tested Positive,Tested Negative But Have Symptoms,Showing Symptoms But Not Tested",
  "Showing Symptoms But Not Tested,Self-Isolating With No Symptoms",
  "Tested Positive,Tested Negative But Have Symptoms,Showing Symptoms But Not Tested,Recovered But Have New Symptoms,Curious,Self-Isolating With No Symptoms",
  "Tested Positive,Showing Symptoms But Not Tested",
  "Tested Positive,Tested Negative But Have Symptoms,Recovered But Have New Symptoms",
  "Tested Positive,Tested Negative But Have Symptoms,Showing Symptoms But Not Tested,Live With Someone With Coronavirus,Recovered But Have New Symptoms,Curious,Self-Isolating With No Symptoms",
  "Tested Positive,Tested Negative But Have Symptoms,Showing Symptoms But Not Tested,Curious",
  "Tested Positive,Tested Negative But Have Symptoms",
  "Recovered But Have New Symptoms,Self-Isolating With No Symptoms",
  "Recovered But Have New Symptoms,Curious,Self-Isolating With No Symptoms",
  "Tested Negative But Have Symptoms,Showing Symptoms But Not Tested,Recovered But Have New Symptoms,Curious,Self-Isolating With No Symptoms",
  "Tested Negative But Have Symptoms,Showing Symptoms But Not Tested,Curious")

data_cov <- data_rename %>% # here make sure the dataset is ritght - either patients with multiple comorbidities or patients without multitple comorbidties
  dplyr::mutate(covid_tested = forcats::fct_recode(reason_for_help, !!!reason_for_help_levels_2))

data_na <- data_cov %>%
  dplyr::mutate(across(starts_with('covid_tested'),
                       ~ replace(., . %in% na_strings_reason_for_help_2, NA)))
count_cov <- data_na %>%
  dplyr::group_by(covid_tested) %>%
  dplyr::tally()

data_comorb <-  data_na  %>%
  tidyr::separate(
    health_condition,
    c(
      'Comorbidity_one',
      'Comorbidity_two',
      'Comorbidity_three',
      'Comorbidity_four',
      'Comorbidity_five',
      'Comorbidity_six',
      'Comorbidity_seven',
      'Comorbidity_eight',
      'Comorbidity_nine'
    ),
    sep = ","
  )
data_comorb %>% distinct(Comorbidity_one)
data_comorb %>% distinct(Comorbidity_two)
data_comorb %>% distinct(Comorbidity_three)
data_comorb %>% distinct(Comorbidity_four)
data_comorb %>% distinct(Comorbidity_five)
data_comorb %>% distinct(Comorbidity_six)
data_comorb %>% distinct(Comorbidity_seven)
data_comorb %>% distinct(Comorbidity_eight)
data_comorb %>% distinct(Comorbidity_nine)
df_unique <- distinct(data_comorb, id, .keep_all = TRUE)
# ###########################################################
# ### Get patients without multiple comorbidities but also count the number of patients
# ### make sure count on unique number of patients  #########
# ##########################################################
data_c <- data_comorb %>%
  tidyr::pivot_longer(cols = starts_with('Comorbidity'),
                      names_to = 'Comorbidity_count',
                      values_to = 'Comorbidity') %>%
  dplyr::mutate(Comorbidity = if_else(Comorbidity == "None", NA_character_, Comorbidity)) %>%
  distinct() %>%
  dplyr::group_by(id) %>%
  mutate(number_morbidities = sum(!is.na(Comorbidity))) #%>%
#dplyr::filter(Comorbidity_count == "Comorbidity_one") # if running only the for data viz then uncomment this line since it takes out all the other comorbidities
data_na <- data_c %>%
  tidyr::pivot_longer(cols = starts_with('Comorbidity'),
                      names_to = 'Comorbidity_count',
                      values_to = 'Comorbidity') %>%
  tidyr::drop_na('Comorbidity') %>%
  dplyr::select(-Comorbidity_count) %>%
  dplyr::distinct() %>%
  dplyr::mutate(Condition = 'Yes') %>%
  tidyr::pivot_wider(id_cols = -c(Comorbidity, Condition), names_from = Comorbidity, values_from = Condition, values_fill = list(Condition = 'No')) %>%
  dplyr::select(-Comorbidity_one)
# # # # # #################################################
# # # # # ######### Get a numeric dataset #################
# # # # # #################################################

data_na$gender <- as.factor(data_na$gender)
data_na$country <- as.factor(data_na$country)
data_na$chills <- as.factor(data_na$chills)
data_na$cough  <- as.factor(data_na$cough)
data_na$diarrhoea  <- as.factor(data_na$diarrhoea)
data_na$fatigue  <- as.factor(data_na$fatigue)
data_na$headache   <- as.factor(data_na$headache)
data_na$loss_of_smell_and_taste   <- as.factor(data_na$loss_of_smell_and_taste)
data_na$muscle_ache  <- as.factor(data_na$muscle_ache)
data_na$nasal_congestion <- as.factor(data_na$nasal_congestion)
data_na$nausea_vomiting  <- as.factor(data_na$nausea_vomiting)
data_na$self_diagnosis <- as.factor(data_na$self_diagnosis)
data_na$shortness_breath <- as.factor(data_na$shortness_breath)
data_na$sore_throat <- as.factor(data_na$sore_throat)
data_na$sputum <- as.factor(data_na$sputum)
data_na$temperature  <- as.factor(data_na$temperature)
data_na$health_care_worker <- as.factor(data_na$health_care_worker)
data_na$care_home_worker <- as.factor(data_na$care_home_worker)
data_na$asthma   <- as.factor(data_na$`Asthma (managed with an inhaler)`)
data_na$diabetes_type_two <- as.factor(data_na$`Diabetes Type 2`)
data_na$obesity <- as.factor(data_na$Obesity)
data_na$hypertension  <- as.factor(data_na$`High Blood Pressure (hypertension)`)
data_na$heart_disease  <- as.factor(data_na$`Long-Standing Heart Disease`)
data_na$kidney_disease <- as.factor(data_na$`Long-Standing Kidney Disease`)
data_na$lung_condition <- as.factor(data_na$`Long-Standing Lung Condition`)
data_na$liver_disease <- as.factor(data_na$`Long-Standing Liver Disease`)
data_na$diabetes_type_one <- as.factor(data_na$`Diabetes Type 1 (controlled by insulin)`)
data_na$how_unwell <- as.factor(data_na$how_unwell)
data_na$covid_tested <- as.factor(data_na$covid_tested)
data_na$id <- as.character(data_na$id)
data_na$age <- as.numeric(data_na$age)
data_na$gender <- as.factor(data_na$gender)
data_na$pregnant <- as.factor(data_na$pregnant)
data_na$date_completed <- as.Date(data_na$date_completed)
data_na$location <- as.factor(data_na$location)
data_na$loss_appetite <- as.factor(data_na$loss_appetite)
data_na$sneezing <- as.factor(data_na$sneezing)
data_na$chest_pain <- as.factor(data_na$chest_pain)
data_na$itchy_eyes <- as.factor(data_na$itchy_eyes)
data_na$joint_pain <- as.factor(data_na$joint_pain)
# # # #### Refactor the levels ##################################################
data_sel <- data_na %>% # here make sure the dataset is ritght - either patients with multiple comorbidities or patients without multitple comorbidties
  dplyr::select(
    id,
    #covid_tested,
    age,
    gender,
    country,
    location,
    date_completed,
    care_home_worker,
    chills,
    cough,
    diarrhoea,
    fatigue,
    headache,
    health_care_worker,
    how_unwell,
    loss_of_smell_and_taste,
    muscle_ache,
    nasal_congestion,
    nausea_vomiting,
    number_days_symptom_showing,
    pregnant,
    self_diagnosis,
    shortness_breath,
    sore_throat,
    sputum,
    temperature,
    language,
    loss_appetite,
    sneezing,
    chest_pain,
    itchy_eyes,
    joint_pain,
    asthma,
    diabetes_type_one,
    diabetes_type_two,
    obesity,
    hypertension,
    heart_disease,
    lung_condition,
    liver_disease,
    kidney_disease,
    number_morbidities,
    covid_tested,
    number_morbidities,
    reason_for_help
  )
# ## chills
unique(data_sel$'chills')
level_key_chills <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# # Cough #
unique(data_sel$cough)
level_key_cough <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
#  diarrhoea
level_key_diarrhoea <-
  c('Yes' = "Mild",
    'Yes' = "Moderate",
    'Yes' = "Severe")
unique(data_sel$diarrhoea)
# fatigue
unique(data_sel$fatigue)
level_key_fatigue <-
  c(
    'Yes' = "Mild",
    'Yes' = "Moderate",
    'Yes' = "Severe"
  )
### headache
unique(data_sel$headache)
level_key_headache <-
  c(
    'Yes' = "Mild",
    'Yes' = "Moderate",
    'Yes' = "Severe")
# loss of smell and taste
loss_smell_unique <- unique(data_sel$loss_of_smell_and_taste)
level_key_loss_smell_taste <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# Muscle Ache
unique(data_sel$muscle_ache)

level_key_muscle_ache <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# nasal congestion
unique(data_sel$nasal_congestion)
level_key_nasal_congestion <-
  c(
    'Yes' = "Mild",
    'Yes' = "Moderate",
    'Yes' = "Severe")
# nausea and vomiting
unique(data_sel$nausea_vomiting)
level_key_nausea_vomiting <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# self diagnosis
unique(data_sel$self_diagnosis)
level_key_self_diagnosis <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# shortness of breath
unique(data_sel$shortness_breath)
level_key_short_breath <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
#sore_throat
unique(data_sel$sore_throat)
level_key_sore_throat <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# sputum
unique(data_sel$sputum)
level_key_sputum <-
  c( 'Yes' = "Mild",
     'Yes' = "Moderate",
     'Yes' = "Severe")
# care home worker
na_strings_care_home_worker <- c('Care Home Worker', 'Age')
# temperature
unique(data_sel$temperature)
level_key_temperature <-
  c('Yes' = "37.5-38",
    'Yes' = "38.1-39",
    'Yes' = "39.1-41",
    'Yes' = "38.2-39",
    'Yes' = "38.2-39")
# loss_appetite
unique(data_sel$loss_appetite)
# sneezing
unique(data_sel$sneezing)
# chest pain
unique(data_sel$chest_pain)
#itchy_eyes
unique(data_sel$itchy_eyes)
# joint_pain
unique(data_sel$joint_pain)
data_categ_nosev <- data_sel %>%
  dplyr::mutate(chills = forcats::fct_recode(chills, !!!level_key_chills),
                cough = forcats::fct_recode(cough, !!!level_key_cough),
                diarrhoea = forcats::fct_recode(diarrhoea, !!!level_key_diarrhoea),
                fatigue = forcats::fct_recode(fatigue, !!!level_key_fatigue),
                headache = forcats::fct_recode(headache, !!!level_key_headache),
                loss_smell_taste = forcats::fct_recode(loss_of_smell_and_taste, !!!level_key_loss_smell_taste),
                muscle_ache = forcats::fct_recode(muscle_ache, !!!level_key_muscle_ache),
                nasal_congestion = forcats::fct_recode(nasal_congestion, !!!level_key_nasal_congestion),
                nausea_vomiting = forcats::fct_recode(nausea_vomiting, !!!level_key_nausea_vomiting),
                self_diagnosis = forcats::fct_recode(self_diagnosis, !!!level_key_self_diagnosis),
                shortness_breath = forcats::fct_recode(shortness_breath, !!!level_key_short_breath),
                sore_throat = forcats::fct_recode(sore_throat, !!!level_key_sore_throat),
                sputum = forcats::fct_recode(sputum, !!!level_key_sputum),
                temperature = forcats::fct_recode(temperature, !!!level_key_temperature))


csvdata2209202 <- data_categ_nosev %>%
  dplyr::mutate(age_band = dplyr::case_when(
    age == 0 | age <= 19 ~ '0-19',
    age == 20 | age <= 39 ~ '20-39',
    age == 40 | age <= 59 ~ '40-59',
    age >= 60 ~ "60+"))

country_levels <- c("United Kingdom" = "Great Britain",
                    "USA" = "United States of America")


csvdata_22092020 <- csvdata2209202 %>%
  dplyr::mutate(Country = forcats::fct_recode(country, !!!country_levels))


csvdata2209202 %>%
  dplyr::group_by(covid_tested) %>%
  tally()

write.csv(csvdata_22092020, file = "/Users/gabrielburcea/Rprojects/data/your.md/cleaned_data_22092020_2nd_dataset.csv", row.names = FALSE)


country_count <- cleaned_data_22092020 %>%
  dplyr::group_by(Country) %>%
  tally()

# 
# 
# country_levels <- c("United Kingdom" = "Great Britain",
# 
#                     "American Samoa"
#                     "British Overseas Territory" =  "Anguilla",
#                     "Netherlands" = "Aruba",
# 
#                     "British Overseas Territory" = "Bermuda",
# 
#                     "Netherlands" =  "Bonaire",
#                     "British Overseas Territory" = "British Indian Ocean Ter"
#                     "Australia" = "Cocos Island",
# 
#                     "Netherlands" =  "Curaco",
#                     "Denmark" =  "Faroe Islands",
# 
#                     "British Overseas Territory"  = "Isle of Man",
# 
#                     "France" = "Martinique",
#                     "France" = "Mayotte",
#                     "USA" = "Midway Islands",
# 
#                     "British Overseas Territory" = "Montserrat",
# 
#                     "Netherlands" =  "Netherland Antilles",
#                     "Australia" =  "Norfolk Island",
# 
#                     "British Oversease Territory" =  "Pitcairn Island",
#                     "France"  = "French Guiana",
#                     "USA" = "United States of America",
#                     "Congo" = "Zair",
#                     "France" = "Wallis & Futana Is",
#                     "USA"    = "Virgin Islands (USA)",
#                     "British Oversease Territory" = "Virgin Islands (Brit)",
#                     "British Oversease Territory" = "Tuvalu",
#                     "British Overseas Territory" = "Turks & Caicos Is",
#                     "New Zeeland" = "Tokelau",
#                     "Grenada" = "St Vincent & Grenadines"
#                     "Netherlands" "St Maarten",
#                     "British Overseas Territory"= "St Lucia",
#                     "Pitcairn Island" =  "British Overseas Territory"  =  "St Helena",
#                     "Netherlands" = "St Eustatius",
#                     "British Overseas Territory"
#                     "USA" =  "Saipan",
#                     "USA" = "Palau Island"
#                     "Nevis", "Nauru",
#                     "British Overseas Territory" = "Montserrat")
#