Want to link up your data from different sources? Awesome! Just a heads-up, you’ll probably need to do some cleaning first. Let’s dive in and see how our package makes getting your SGIC data ready super easy.
We`ll start by loading trustmebro:
Data
Our key data set trustmebro::sailor_keys is a
longitudinal data set in long format. It is a tibble with 20 rows and 12
columns.
This data should be linked with our survey data
trustmebro::sailor_students, a tibble with 12 rows and 6
columns.
Let us take a quick look at the survey data:
print(trustmebro::sailor_students)
#> # A tibble: 12 × 6
#> sgic school class gender testscore_langauge testscore_calculus
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 "MUC__0308" 54321 "3-B " "Male" 425 394
#> 2 "HÄT 2701" 22345 "2-A" "???" 4596 123
#> 3 "MUK3801" 22345 " 2-B" "Femal… 2456 9485
#> 4 "SAM10" 22345 "3-B" "Femal… 2345 3
#> 5 "T0601" 65432 "1-C" "Femal… 1234 NA
#> 6 " UIT3006 " 12345 "3-3" NA 123 394
#> 7 "@@@@@@" NA "3_2 " "Femal… 56 2938
#> 8 NA 12345 "3@41" " Fe… 986 3948
#> 9 " " unkown NA "Femal… 284 205
#> 10 "MOA2210" 12345 " " "Femal… 105 21
#> 11 "MUK3801" 22345 "2-B" "Femal… 9586 934
#> 12 "T0601" 65432 "1-C" "Femal… NA 764Replace non-alphanumeric characters you don’t want to deal with
Yep, this data needs cleaning. There’s a lot of unnecessary stuff,
like whitespace. You see this all the time with survey data strings. We
can replace all non-alphanumeric characters in string-variables of our
data set trustmebro::sailor_students using
trustmebro::purge_string:
purge_string(sailor_students, replacement = "#")
#> # A tibble: 12 × 6
#> sgic school class gender testscore_langauge testscore_calculus
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 MUC##0308 54321 3#B MALE 425 394
#> 2 H#T2701 22345 2#A ### 4596 123
#> 3 MUK3801 22345 2#B FEMALE 2456 9485
#> 4 SAM10 22345 3#B FEMALE 2345 3
#> 5 T0601 65432 1#C FEMALE 1234 NA
#> 6 UIT3006 12345 3#3 # 123 394
#> 7 ###### # 3#2 FEMALE 56 2938
#> 8 # 12345 3#41 FEMALE 986 3948
#> 9 # UNKOWN # FEMALE 284 205
#> 10 MOA2210 12345 # FEMALE 105 21
#> 11 MUK3801 22345 2#B FEMALE 9586 934
#> 12 T0601 65432 1#C FEMALE NA 764Please note that since we deal with data collected in Germany, umlauts remain unchanged from this.
Recode variables
A few variables need recoding for further analysis. For that, we can provide a recode map:
recode_map <- c(MALE = "M", FEMALE = "F")The recode_map is a named vector where the names represent categories
(in this case, “Male” and “Female”), and the values (“M” and “F”) are
the corresponding codes used for those categories. It is used to map
full category labels to shorter, standardized values. We can pass it to
trustmebro::recode_valinvec, to recode the values
accordingly. A new variable will be added that contains the recoded
values
recode_valinvec(purge_string(sailor_students, replacement = "#"), gender, recode_map, gender_recode)
#> # A tibble: 12 × 7
#> sgic school class gender testscore_langauge testscore_calculus gender_recode
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 MUC#… 54321 3#B MALE 425 394 M
#> 2 H#T2… 22345 2#A ### 4596 123 ###
#> 3 MUK3… 22345 2#B FEMALE 2456 9485 F
#> 4 SAM10 22345 3#B FEMALE 2345 3 F
#> 5 T0601 65432 1#C FEMALE 1234 NA F
#> 6 UIT3… 12345 3#3 # 123 394 #
#> 7 ####… # 3#2 FEMALE 56 2938 F
#> 8 # 12345 3#41 FEMALE 986 3948 F
#> 9 # UNKOWN # FEMALE 284 205 F
#> 10 MOA2… 12345 # FEMALE 105 21 F
#> 11 MUK3… 22345 2#B FEMALE 9586 934 F
#> 12 T0601 65432 1#C FEMALE NA 764 F