Ce zici de asta:
library (dplyr) library (stringr) library (tidyr) date%>% collect (cheie, valoare, varsta_5, varsta_6, varsta_7, inaltime_5, inaltime_6, inaltime_7)%>% separat (cheie, c (“cheie”, “obs_time”) ), “_”)%>% spread (cheie, valoare)%>% muta (integral_age = etaj (varsta))%>% group_by (sex, integral_age)%>% muta (htz = scala (inaltime), sex_init = str_to_lower (str_extract (sex, “^.”)), sa = paste0 (“htz”, sex_init, integral_age))%>% ungroup ()%>% spread (sa, htz)
Mai intai am dori sa punem datele intr-un format corect.
Pentru aceasta, mai intai adunam toate coloanele de varsta si inaltime in doar doua coloane: cheie si valoare. apoi tasta preia numele variabilei originale ca valori, valoarea preia valoarea sub variabila corespunzatoare, iar alte variabile sunt copiate asa cum este. Datele arata acum astfel:
# A tibble: 6.000 x 4 valoare_ cheie sexuala a copilului <int> <fct> <chr> <dbl> 1 1 Varsta masculina_5 5.67 2 1 Varsta masculina_6 7.02 3 1 Varsta masculina_7 8.86 4 1 Inaltime masculina_5 79.2 5 1 Inaltime masculina_6 95.8 6 1 Inaltime masculina_7 85,0 7 2 Varsta masculina_5 3,38 8 2 Varsta masculina_6 5,06 9 2 Varsta masculina_7 5,47 10 2 Inaltime masculina_5 79,2 # … cu 5.990 de randuri
In al doilea rand, separa coloana cheie in doua coloane: cheie si obs_time folosind „_” ca delimitator. Datele arata acum:
# A tibble: 6.000 x 5 cheie sexuala copil_id valoare obs_time <int> <fct> <chr> <chr> <dbl> 1 1 Masculin varsta 5 5.67 2 1 Varsta masculina 6 7.02 3 1 Varsta masculina 7 8.86 4 1 Inaltimea masculina 5 79,2 5 1 Inaltime masculina 6 95,8 6 1 Inaltime masculina 7 85,0 7 2 Varsta masculina 5 3,38 8 2 Varsta masculina 6 5,06 9 2 Varsta masculina 7 5,47 10 2 Inaltimea masculina 5 79,2 # … cu 5.990 de randuri
In al treilea rand, distribuim valorile in doua variabile: varsta si inaltime. Datele arata acum:
# A tibble: 3.000 x 5 sex copil_id sex obs_time inaltime de varsta <int> <fct> <chr> <dbl> <dbl> 1 1 Barbat 5 5.67 79.2 2 1 Barbat 6 7.02 95.8 3 1 Barbat 7 8.86 85.0 4 2 Barbat 5 3.38 79,2 5 2 Masculin 6 5,06 81,8 6 2 Barbat 7 5,47 102. 7 3 Barbat 5,04 80,4 8 3 Barbat 6 6,37 95,3 9 3 Barbat 7 7,01 97,4 10 4 Barbat 5 6,25 90,8 # … cu 2.990 de randuri
In al patrulea rand, pana la a saptea, mutam categoria de varsta integrala si apoi grupam dupa sex si intreaga, astfel incat atunci cand vom face scala, aceasta va fi aplicata separat pentru fiecare dintre aceste grupuri. Apoi facem scalarea in fiecare grup, extragem prima initiala a sexului si construim nume variabile corespunzatoare valorilor proaspete scalate toate intr-o coloana numita sa. Putem apoi elimina gruparea. Datele arata acum:
# A tibble: 3.000 x 9 copil_id sex obs_time varsta inaltime intreg_age htz sex_init sa <int> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> 1 1 Barbat 5 5.67 79.2 5 -0.967 m htzm5 2 1 Mascul 6 7,02 95,8 7 0,345 m htzm7 3 1 Mascul 7 8,86 85,0 8 -1,20 m htzm8 4 2 Masculin 5 3,38 79,2 3 -0,580 m htzm3 5 2 Masculi 6,06 8,8,8 5-0,01 m 2 htzm5 6 2 Masculin 7 5,47 102. 5 1,55 m htzm5 7 3 Masculin 5 5,04 80,4 5 -0,829 m htzm5 8 3 Masculi 6 6,37 95,3 6 0,455 m htzm6 9 3 Masculi 7 7,01 97,4 7 0,529 m htzm7 10 4 Masculin 6 6,25 90,8 6-0,0366 m htzm6 # … cu inca 2.990 de randuri
In sfarsit, putem raspandi datele in variabilele pe care le-ati solicitat. Si acum avem:
# A tibble: 3.
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000 x 32 sex child_id obs_time varsta inaltime integral_age sex_init htzf0 htzf1 htzf10 htzf11 htzf2 htzf3 <int> <fct> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 1 Masculin 5 5.67 79.2 5 m NA NA NA NA NA NA 2 1 Masculin 6 7.02 95.8 7 m NA NA NA NA NA NA 3 1 Masculin 8 8.86 85,0 8 m NA NA NA NA NA NA NA 4 2 Masculin 5 3,38 79,2 3 m NA NA NA NA NA NA 5 2 Masculin 6 5.06 81.8 5 m NA NA NA NA NA NA NA 6 2 Masculin 7 5.47 102. 5 m NA NA NA NA NA NA NA 7 3 Masculin 5 5.04 80.4 5 m NA NA NA NA NA NA 8 3 Masculin 6 6,37 95,3 6 m NA NA NA NA NA NA 9 9 Masculin 7 7,01 97,4 7 m NA NA NA NA NA NA NA 10 4 Masculin 5 6,25 90,8 6 m NA NA NA NA NA NA NA # … cu inca 2.990 de randuri si inca 19 variabile: htzf4 <dbl>, htzf5 <dbl>, htzf6 <dbl>, # htzf7 <dbl>, htzf8 <dbl>, htzf9 <dbl>, htzm0 <dbl>, htzm1 <dbl>, htzm10 <dbl>, htzm11 <dbl>, # htzm12 <dbl>, htzm2 <dbl>, htzm3 <dbl>, htzm4 <dbl>, htzm5 <dbl>, htzm5 <dbl> <dbl>, htzm7 <dbl>, # htzm8 <dbl>, htzm9 <dbl>
