I got an XLSX with data from a questionnaire for my master thesis. The questions and answers for an interviewee are in one row in the second column. The first column contains the date.
The data of the second column comes in a form like this:
"age":"52","height":"170","Gender":"Female",...and so on
I started with:
test12 <- read_xlsx("Testdaten.xlsx")
library(splitstackshape)
test13 <- concat.split(data = test12, split.col= "age", sep =",")
Then I got the questions and the answers as a column divided by a ":".
For e.g. column 1: "age":"52" and column2:"height":"170".
But the data is so messy that sometimes in the column of the age question and answer there is a height question and answer and for some questionnaires questions and answers double.
I would need the questions as variables and the answers as observations. But I have no clue how to get there. I could clean the data in excel first, but with the fact that columns are not constant and there are for e.g. some height questions in the age column I see no chance to do it as I will get new data regularly, formated the same way.
Here is an example of the data:
A tibble: 5 x 2
partner.createdAt partner.wphg.info
<chr> <chr>
1 2019-11-09T12:13:11.099Z "{\"age_years\":\"50\",\"job_des\":\"unemployed\",\"height_cm\":\"170\",\"Gender\":\"female\",\"born_in\":\"Italy\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"5\",\"total_wealth\":\"200000\""
2 2019-11-01T06:43:22.581Z "{\"age_years\":\"34\",\"job_des\":\"self-employed\",\"height_cm\":\"158\",\"Gender\":\"male\",\"born_in\":\"Germany\",\"Alcoholic\":\"true\",\"knowledge_selfass\":\"3\",\"total_wealth\":\"10000\""
3 2019-11-10T07:59:46.136Z "{\"age_years\":\"24\",\"height_cm\":\"187\",\"Gender\":\"male\",\"born_in\":\"England\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"3\",\"total_wealth\":\"150000\""
4 2019-11-11T13:01:48.488Z "{\"age_years\":\"59\",\"job_des\":\"employed\",\"height_cm\":\"167\",\"Gender\":\"female\",\"born_in\":\"United States\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"2\",\"total_wealth\":\"1000000~
5 2019-11-08T14:54:26.654Z "{\"age_years\":\"36\",\"height_cm\":\"180\",\"born_in\":\"Germany\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"5\",\"total_wealth\":\"170000\",\"job_des\":\"employed\",\"Gender\":\"male\""
Thank you so much for your time!
You can loop through each entry, splitting at , as you did. Then you can loop through them all again, splitting at :.
The result will be a bunch of variable/value pairings. This can be all done stacked. Then you just want to pivot back into columns.
Updated the data based on your edit.
data <- tribble(~partner.createdAt, ~partner.wphg.info,
'2019-11-09T12:13:11.099Z', '{\"age_years\":\"50\",\"job_des\":\"unemployed\",\"height_cm\":\"170\",\"Gender\":\"female\",\"born_in\":\"Italy\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"5\",\"total_wealth\":\"200000\"',
'2019-11-01T06:43:22.581Z', '{\"age_years\":\"34\",\"job_des\":\"self-employed\",\"height_cm\":\"158\",\"Gender\":\"male\",\"born_in\":\"Germany\",\"Alcoholic\":\"true\",\"knowledge_selfass\":\"3\",\"total_wealth\":\"10000\"',
'2019-11-10T07:59:46.136Z', '{\"age_years\":\"24\",\"height_cm\":\"187\",\"Gender\":\"male\",\"born_in\":\"England\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"3\",\"total_wealth\":\"150000\"',
'2019-11-11T13:01:48.488Z', '{\"age_years\":\"59\",\"job_des\":\"employed\",\"height_cm\":\"167\",\"Gender\":\"female\",\"born_in\":\"United States\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"2\",\"total_wealth\":\"1000000\"',
'2019-11-08T14:54:26.654Z', '{\"age_years\":\"36\",\"height_cm\":\"180\",\"born_in\":\"Germany\",\"Alcoholic\":\"false\",\"knowledge_selfass\":\"5\",\"total_wealth\":\"170000\",\"job_des\":\"employed\",\"Gender\":\"male\"')
We need a few here. Or you can just call tidyverse.
library(stringr)
library(purrr)
library(dplyr)
library(tibble)
library(tidyr)
This function will create a data frame (or tibble) for each question. The first column is the date, the second is the variable, the third is the value.
clean_record <- function(date, text) {
clean_records <- str_split(text, pattern = ",", simplify = TRUE) %>%
str_remove_all(pattern = "\\\"") %>% # remove double quote
str_remove_all(pattern = "\\{|\\}") %>% # remove curly brackets
str_split(pattern = ":", simplify = TRUE)
tibble(date = as.Date(date), variable = clean_records[,1], value = clean_records[,2])
}
Now we use pmap_dfr from purrr to loop over the rows, outputting each row with an id variable named record.
This will stack the data as described in the function. The mutate() line converts all variable names to lowercase. The distinct() line will filter out rows that are exact duplicates.
What we do then is just pivot on the variable column. Of course, replace data with whatever you name your data frame.
data_clean <- pmap_dfr(data, ~ clean_record(..1, ..2), .id = "record") %>%
mutate(variable = tolower(variable)) %>%
distinct() %>%
pivot_wider(names_from = variable, values_from = value)
The result is something like this. Note how I had reordered some of the columns, but it still works. You are probably not done just yet. All columns are now of type character. You need to figure out the desired type for each and convert.
# A tibble: 5 x 10
record date age_years job_des height_cm gender born_in alcoholic knowledge_selfass total_wealth
<chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1 2019-11-09 50 unemployed 170 female Italy false 5 200000
2 2 2019-11-01 34 self-employed 158 male Germany true 3 10000
3 3 2019-11-10 24 NA 187 male England false 3 150000
4 4 2019-11-11 59 employed 167 female United States false 2 1000000
5 5 2019-11-08 36 employed 180 male Germany false 5 170000
For example, convert age_years to numeric.
data_clean %>%
mutate(age_years = as.numeric(age_years))
I am sure you may run into other things, but this should be a start.