Introduction
In this section, we show a few R functions for exploring MCS data; there’s a lot of data in the MCS, so finding a specific variable can be challenging. Variables do not generally have names that are descriptive and there can be some slight changes in naming conventions across sweeps. (The variable for height in centimeters is ECHTCMA0 in Sweep 5 but [A-G]CHTCM00 in other sweeps, for example.) In what follows, we will use the R functions to find variables for cohort members’ SDQ, which has been collected in many of the sweeps.
The packages we will use are:
# Load Packages
library(tidyverse) # For data manipulation
library(haven) # For importing .dta files
library(labelled) # For searching imported datasets
library(codebookr) # For creating .docx codebooks
labelled::lookfor()
The labelled package contains functionality for attaching and examining metadata in dataframes (for instance, adding labels to variables [columns]). Beyond this, it also contains the lookfor() function, which replicates similar functionality in Stata. lookfor() also one to search for variables in a dataframe by keyword (regular expression); the function searches variable names as well as associated metadata. It returns an object containing matching variables, their labels, and their types, etc.. Below, we read in the MCS 17-year sweep (Sweep 7) CM-level derived data which contains derived variables (17y/mcs7_cm_derived.dta) and use lookfor() to search for variables related to the "SDQ" (Strengths and Difficulties Questionnaire).
mcs_17y <- read_dta("17y/mcs7_cm_derived.dta")
lookfor(mcs_17y, "sdq")
pos variable label col_type missing values
12 GEMOTION_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
13 GCONDUCT_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
14 GHYPER_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
15 GPEER_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
16 GPROSOC_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
17 GEBDTOT_C S7 DV Self-reported CM s~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
18 GEMOTION S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
19 GCONDUCT S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
20 GHYPER S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
21 GPEER S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
22 GPROSOC S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
23 GEBDTOT S7 DV Parent-reported CM~ dbl+lbl 0 [-9] Refusal
[-8] Don't know
[-1] Not applicable
Users may consider it easier to create a tibble of the lookfor() output, which can be searched and filtered using dplyr functions. Below, we create a tibble (a type of data.frame with good printing defaults) of the lookfor() output and use filter() to find variables with "sdq" in their labels. Note, we convert both the variable names and labels to lower case to make the search case insensitive.
mcs_17y_lookfor <- lookfor(mcs_17y) %>%
as_tibble() %>%
mutate(variable_low = str_to_lower(variable),
label_low = str_to_lower(label))
mcs_17y_lookfor %>%
filter(str_detect(label_low, "sdq"))
# A tibble: 12 × 9
pos variable label col_type missing levels value_labels variable_low
<int> <chr> <chr> <chr> <int> <name> <named list> <chr>
1 12 GEMOTION_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> gemotion_c
2 13 GCONDUCT_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> gconduct_c
3 14 GHYPER_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> ghyper_c
4 15 GPEER_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> gpeer_c
5 16 GPROSOC_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> gprosoc_c
6 17 GEBDTOT_C S7 DV Sel… dbl+lbl 0 <NULL> <dbl [3]> gebdtot_c
7 18 GEMOTION S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> gemotion
8 19 GCONDUCT S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> gconduct
9 20 GHYPER S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> ghyper
10 21 GPEER S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> gpeer
11 22 GPROSOC S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> gprosoc
12 23 GEBDTOT S7 DV Par… dbl+lbl 0 <NULL> <dbl [3]> gebdtot
# ℹ 1 more variable: label_low <chr>
codebookr::codebook()
The MCS datasets that are downloadable from the UK Data Service come bundled with data dictionaries within the mrdoc subfolder. However, these are limited in some ways. The codebookr package enables the creation of data dictionaries that are more customisable, and in our opinion, easy to read. Below we create a codebook for the MCS 17-year sweep derived variable dataset. These codebooks are intended to be saved and viewed in Microsoft Word.
cdb <- codebook(mcs_17y)
print(cdb, "mcs_17y_codebook.docx") # Saves as .docx (Word) file
A screenshot of the codebook is shown below.
Create a Lookup Table Across All Datasets
Creating the lookfor() and codebook() one dataset at a time does not allow one to get a quick overview of the variables available in the MCS, including the sweeps repeatedly measured characteristics are available in. Below we create a tibble, df_lookfor, that contains lookfor() results for all the .dta files in the MCS folder.
To do this, we create a function, create_lookfor(), that takes a file path to a .dta file, reads in the first row of the dataset (faster than reading the full dataset), and applies lookfor() to it. We call this function with a mutate() function call to create a set of lookups for every .dta file we can find in the MCS folder. map() loops over every value in the file_path column, creating a corresponding lookup table for that file, stored as a list-column. unnest() expands the results out, so rather than have one row per file_path, we have one row per variable.
create_lookfor <- function(file_path){
read_dta(file_path, n_max = 1) %>%
lookfor() %>%
as_tibble()
}
df_lookfor <- tibble(file_path = list.files(pattern = "\\.dta$", recursive = TRUE)) %>%
filter(!str_detect(file_path, "^UKDS")) %>%
mutate(lookfor = map(file_path, create_lookfor)) %>%
unnest(lookfor) %>%
mutate(variable_low = str_to_lower(variable),
label_low = str_to_lower(label)) %>%
separate(file_path,
into = c("sweep", "file"),
sep = "/",
remove = FALSE) %>%
relocate(file_path, pos, .after = last_col())
We can use the resulting object to search for variables with "sdq" in their labels.
df_lookfor %>%
filter(str_detect(label_low, "sdq")) %>%
select(file, variable, label)
# A tibble: 73 × 3
file variable label
<chr> <chr> <chr>
1 mcs5_cm_teacher_survey.dta EEMOTI_T S5 DV TEACHER SDQ Emotional Symptoms
2 mcs5_cm_teacher_survey.dta ECOND_T S5 DV TEACHER SDQ Conduct Problems
3 mcs5_cm_teacher_survey.dta EHYPER_T S5 DV TEACHER SDQ Hyperactivity/Inattent…
4 mcs5_cm_teacher_survey.dta EPEER_T S5 DV TEACHER SDQ Peer Problems
5 mcs5_cm_teacher_survey.dta EPROSO_T S5 DV TEACHER SDQ Prosocial
6 mcs5_cm_teacher_survey.dta EEBDTO_T S5 DV TEACHER SDQ Total Difficulties
7 mcs5_cm_teacher_survey.dta EEBDIF_T S5 DV TEACHER SDQ CM has Difficulties in…
8 mcs6_cm_derived.dta FEMOTION S6 DV Parent-reported CM SDQ Emotional S…
9 mcs6_cm_derived.dta FCONDUCT S6 DV Parent-reported CM SDQ Conduct Pro…
10 mcs6_cm_derived.dta FHYPER S6 DV Parent-reported CM SDQ Hyperactivi…
# ℹ 63 more rows