Load the data and packages

# install.packages("tidyverse","janitor","glue")  
library(tidyverse)
library(lubridate)  
library(janitor)
library(glue)
demo_data <- read_csv("data/yrbss_demo.csv")
qn_data <- read_csv("data/yrbss_qn.csv")

Practice 1

1. Add a column of 1's to qn_data called qn_yes and save the resulting data as qn_data2.

2. Join demo_data and qn_data2 by column record. Keep all rows from demo_data and only rows from qn_data2 that match records in demo_data. Call the resulting data all_data.

3. Create a tabyl() of qn_yes for the data all_data.

4. Create a 2x2 table of qn_yes vs grade.

Note about the data:

• q8 = How often wear bicycle helmet
• q12 = Texted while driving
• q31 = Ever smoked
• qn24 = Bullied past 12 months

qn_data2 <- qn_data %>% add_column(qn_yes = 1)
all_data <- left_join(demo_data, qn_data2)
all_data %>% tabyl(qn_yes)

 qn_yes     n percent valid_percent
      1 10000     0.5             1
     NA 10000     0.5            NA

all_data %>% tabyl(qn_yes,grade)

 qn_yes 10th 11th 12th  9th NA_
      1 2443 2498 2287 2573 199
     NA 2464 2393 2290 2646 207

Practice 2

1. Make DBP_wide into a long dataframe based on the repeated DBP columns and save it as DBP_long.

2. Clean up the visit column of DBP_long so that the values are 1, 2, 3, and save it as DBP_long.

3. Make DBP_long wide with column names visit.1, visit.2, visit.3 for the DBP values, and save it as DBP_wide2.

4. Join DBP_long with BP_long2 so that we have one data frame with columns id, sex, visit, SBP, DBP, and age. Save this as BP_both_long.

Practice 2 Initial Data

Copy and paste the code below into R to create the datasets:

DBP_wide <- tibble(id = letters[1:4],
                  sex = c("F", "M", "M", "F"),
                  v1.DBP = c(88, 84, 102, 70),
                  v2.DBP = c(78, 78, 96, 76),
                  v3.DBP = c(94, 82, 94, 74),
                  age=c(23, 56, 41, 38)
                  )
BP_wide <- tibble(id = letters[1:4],
                     sex = c("F", "M", "M", "F"),
                     SBP_v1 = c(130, 120, 130, 119),
                     SBP_v2 = c(110, 116, 136, 106),
                     SBP_v3 = c(112, 122, 138, 118))
BP_long <- BP_wide %>% 
  gather(key = "visit", value = "SBP", SBP_v1:SBP_v3)
BP_long2 <- BP_long %>% 
  mutate(visit = str_replace(visit,"SBP_v",""))

Practice 2 solutions (1/2)

DBP_long <- DBP_wide %>%
  gather(key = "visit", value = "DBP", 
         v1.DBP, v2.DBP, v3.DBP) %>%
  mutate(visit = str_replace(
    visit, c("v"), "")) %>%
  mutate(visit = str_replace(
    visit, ".DBP", ""))  
DBP_long

# A tibble: 12 x 5
   id    sex     age visit   DBP
   <chr> <chr> <dbl> <chr> <dbl>
 1 a     F        23 1        88
 2 b     M        56 1        84
 3 c     M        41 1       102
 4 d     F        38 1        70
 5 a     F        23 2        78
 6 b     M        56 2        78
 7 c     M        41 2        96
 8 d     F        38 2        76
 9 a     F        23 3        94
10 b     M        56 3        82
11 c     M        41 3        94
12 d     F        38 3        74

DBP_wide2 <- DBP_long %>% 
  spread(
    key = "visit", value = "DBP",
    sep=".") # specify separating character
DBP_wide2

# A tibble: 4 x 6
  id    sex     age visit.1 visit.2 visit.3
  <chr> <chr> <dbl>   <dbl>   <dbl>   <dbl>
1 a     F        23      88      78      94
2 b     M        56      84      78      82
3 c     M        41     102      96      94
4 d     F        38      70      76      74

Practice 2 solutions (2/2)

BP_both_long <- left_join(BP_long2, DBP_long, by = c("id", "sex", "visit"))
BP_both_long

# A tibble: 12 x 6
   id    sex   visit   SBP   age   DBP
   <chr> <chr> <chr> <dbl> <dbl> <dbl>
 1 a     F     1       130    23    88
 2 b     M     1       120    56    84
 3 c     M     1       130    41   102
 4 d     F     1       119    38    70
 5 a     F     2       110    23    78
 6 b     M     2       116    56    78
 7 c     M     2       136    41    96
 8 d     F     2       106    38    76
 9 a     F     3       112    23    94
10 b     M     3       122    56    82
11 c     M     3       138    41    94
12 d     F     3       118    38    74

Practice 3

messy_data <- tibble(NAME = c("J N","A C","D E"), 
                     `months follow up` = c("", 10, 11), 
                     `Date of visit` = c("July 31, 2003", "Nov 12, 2005", "Aug 3, 2007"))

1. Clean column names with clean_names().
2. Replace missing ("") data in months_follow_up with NA.
3. Convert months_follow_up to a numeric variable.
4. Convert date_of_visit to a date.
5. Create a column called date_last_visit that is the date of visit plus months of follow up.
6. Remove rows (cases) with missing data in months_follow_up.
7. Remove the spaces in name.

messy_data

# A tibble: 3 x 3
  NAME  `months follow up` `Date of visit`
  <chr> <chr>              <chr>          
1 J N   ""                 July 31, 2003  
2 A C   10                 Nov 12, 2005   
3 D E   11                 Aug 3, 2007

Practice solutions 3 (1/2)

clean_data <- messy_data %>% 
  clean_names() %>%
  mutate(
    months_follow_up = replace_na(months_follow_up,""),
    months_follow_up = as.numeric(months_follow_up),
    date_of_visit = mdy(date_of_visit),
    date_last_visit = date_of_visit + months(months_follow_up))
clean_data

# A tibble: 3 x 4
  name  months_follow_up date_of_visit date_last_visit
  <chr>            <dbl> <date>        <date>         
1 J N                 NA 2003-07-31    NA             
2 A C                 10 2005-11-12    2006-09-12     
3 D E                 11 2007-08-03    2008-07-03

Practice solutions 3 (2/2)

clean_data <- clean_data %>%
  drop_na(months_follow_up) %>%
  mutate(name = str_replace_all(name," ",""))
clean_data

# A tibble: 2 x 4
  name  months_follow_up date_of_visit date_last_visit
  <chr>            <dbl> <date>        <date>         
1 AC                  10 2005-11-12    2006-09-12     
2 DE                  11 2007-08-03    2008-07-03