Primer práctica de R para Big Data
Tutoría sincrónica 1: 12 de Agosto del 2023
Operaciones básicas con R
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 250.8 500.5 500.5 750.2 1000.0## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 62876 250501 333834 562875 1000000
Data frame
Generación de números aleatorios
## [1] 3.774632 4.587608 1.042575 1.537831 2.643813 4.486999 2.058304 4.050070
## [9] 4.295908 3.260726## [1] 2 3 2 3 2 2 1 3 1 2## [1] 3.57 2.79 3.43 2.47 4.78 1.59 2.78 4.08 1.58 4.07
Carga de bases de datos de la EPH 2022 en r
levantar la base de datos de le Encuesta de Hogares a partir de la página web del INE, www.ine.gog.py
#Descargar y guardado de las bases EPH años 2022 y 2012
#eph2022=read.csv("https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2022/data/REG02_EPHC2022.csv", sep=";")
#write.csv(eph2022,"D:/OneDrive/FACEN_BIGDATA/eph22.csv")
eph2022=read.csv("D:/OneDrive/FACEN_BIGDATA/eph22.csv", sep=",")#eph2012=read.csv("https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2012/data/cfa40r02_eph2012.csv", sep=";")
#write.csv(eph2012,"D:/OneDrive/FACEN_BIGDATA/eph12.csv")
eph2012=read.csv("D:/OneDrive/FACEN_BIGDATA/eph12.csv", sep=",")## [1] "X" "X...UPM" "NVIVI" "NHOGA" "DPTOREP"
## [6] "AREA" "L02" "P02" "P03" "P04"
## [11] "P04A" "P04B" "P05C" "P05P" "P05M"
## [16] "P06" "P08D" "P08M" "P08A" "P09"
## [21] "P10A" "P10AB" "P10Z" "P11A" "P11AB"
## [26] "P11Z" "P12" "P13A" "P13B" "P13C"
## [31] "P14A1" "P14A2" "P14B1" "P14B2" "P14C1"
## [36] "P14C2" "P15" "A01" "A01A" "A02"
## [41] "A03" "A04" "A05" "A07" "A08"
## [46] "A10" "A11A" "A11M" "A11S" "A12"
## [51] "A13REC" "A14REC" "A15" "A16" "A17A"
## [56] "A17M" "A17S" "A18" "B01REC" "B02REC"
## [61] "B03LU" "B03MA" "B03MI" "B03JU" "B03VI"
## [66] "B03SA" "B03DO" "B04" "B05" "B06"
## [71] "B07A" "B07M" "B07S" "B08" "B09A"
## [76] "B09M" "B09S" "B10" "B11" "B12"
## [81] "B13" "B14" "B15" "B16" "B17"
## [86] "B18G" "B18U" "B18D" "B18T" "B19"
## [91] "B20AG" "B20AU" "B20BG" "B20BU" "B21"
## [96] "B22G" "B22U" "B22D" "B22T" "B23"
## [101] "B24" "B25" "B26" "B27" "B28"
## [106] "B291" "B292" "B30" "B31" "B32"
## [111] "B33" "C01REC" "C02REC" "C03" "C04"
## [116] "C05" "C06" "C07" "C08" "C09"
## [121] "C101" "C102" "C11G" "C11U" "C11D"
## [126] "C11T" "C12" "C13AG" "C13AU" "C13BG"
## [131] "C13BU" "C14" "C15" "C16REC" "C17REC"
## [136] "C18" "C19" "D01" "D02" "D03"
## [141] "D04" "D05" "E01A" "E01B" "E01C"
## [146] "E01D" "E01E" "E01F" "E01G" "E01H"
## [151] "E01I" "E01J" "E01K" "E01L" "ED01"
## [156] "ED02" "ED03" "ED0504" "ED06C" "ED07"
## [161] "ED08" "ED09" "ED10" "ED11A" "ED11B"
## [166] "ED11C" "ED11D" "ED11E" "ED11F" "S01A"
## [171] "S01B" "S02" "S03" "S04" "S05"
## [176] "S06" "S07" "S08" "S09" "S10A"
## [181] "S10B" "S10C" "S10D" "S10E" "S10F"
## [186] "S10G" "S10T" "CATE_PEA" "TAMA_PEA" "OCUP_PEA"
## [191] "RAMA_PEA" "HORAB" "HORABC" "HORABCO" "PEAD"
## [196] "PEAA" "TIPOHOGA" "NJEF" "NCON" "NPAD"
## [201] "NMAD" "a..oest" "ra06ya09" "E01AIMDE" "E01BIMDE"
## [206] "E01CIMDE" "E01DDE" "E01EDE" "E01FDE" "E01GDE"
## [211] "E01HDE" "E01IDE" "E01JDE" "E01KDE" "E01LDE"
## [216] "E01KJDE" "E02L1BDE" "E02L2BDE" "fexajustado" "ipcm"
## [221] "pobrezai" "pobnopoi" "quintili" "decili" "quintiai"
## [226] "decilai" "filter_."Exploramos algunas de las variables
Sexo (P06)
##
## 1 6
## 8579 8800##
## Hombres Mujeres
## 8579 8800##
## Hombres Mujeres
## 10610 10541
#### Edad (P02)
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 291 245 285 320 299 345 327 315 300 285 331 326 323 313 274 282 313 318 302 287
## 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
## 290 271 300 285 299 293 284 289 273 262 260 253 244 265 282 221 229 210 235 243
## 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 260 196 239 199 189 177 180 159 177 174 187 170 178 173 171 160 180 180 170 144
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
## 171 156 171 137 130 108 131 116 101 105 103 83 90 60 64 77 66 55 63 42
## 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 100
## 47 30 35 25 33 31 17 23 7 10 11 12 10 4 11 5 1 3 1 1
## 102
## 1
### Agrupaciones
#resultados poblacionales
edadysexo2012=aggregate.data.frame(x = eph2012$fexajustado, by = list(eph2012$P06, eph2012$P02), FUN = "sum")
edadysexo2012$year=2012
colnames(edadysexo2012) <- c("Sexo", "Edad","Cantidad","Year")
edadysexo2012## Sexo Edad Cantidad Year
## 1 Hombres 0 73521 2012
## 2 Mujeres 0 67105 2012
## 3 Hombres 1 73680 2012
## 4 Mujeres 1 64123 2012
## 5 Hombres 2 79031 2012
## 6 Mujeres 2 54732 2012
## 7 Hombres 3 68716 2012
## 8 Mujeres 3 59355 2012
## 9 Hombres 4 56131 2012
## 10 Mujeres 4 72214 2012
## 11 Hombres 5 75015 2012
## 12 Mujeres 5 56983 2012
## 13 Hombres 6 63655 2012
## 14 Mujeres 6 63793 2012
## 15 Hombres 7 75251 2012
## 16 Mujeres 7 67105 2012
## 17 Hombres 8 80053 2012
## 18 Mujeres 8 69504 2012
## 19 Hombres 9 68708 2012
## 20 Mujeres 9 75170 2012
## 21 Hombres 10 71834 2012
## 22 Mujeres 10 61776 2012
## 23 Hombres 11 70408 2012
## 24 Mujeres 11 69640 2012
## 25 Hombres 12 77358 2012
## 26 Mujeres 12 61572 2012
## 27 Hombres 13 65262 2012
## 28 Mujeres 13 67804 2012
## 29 Hombres 14 77360 2012
## 30 Mujeres 14 69235 2012
## 31 Hombres 15 80493 2012
## 32 Mujeres 15 77420 2012
## 33 Hombres 16 90765 2012
## 34 Mujeres 16 71202 2012
## 35 Hombres 17 74570 2012
## 36 Mujeres 17 61613 2012
## 37 Hombres 18 77417 2012
## 38 Mujeres 18 66598 2012
## 39 Hombres 19 55539 2012
## 40 Mujeres 19 70408 2012
## 41 Hombres 20 61122 2012
## 42 Mujeres 20 70213 2012
## 43 Hombres 21 51598 2012
## 44 Mujeres 21 57207 2012
## 45 Hombres 22 60083 2012
## 46 Mujeres 22 71184 2012
## 47 Hombres 23 58435 2012
## 48 Mujeres 23 55026 2012
## 49 Hombres 24 51816 2012
## 50 Mujeres 24 53427 2012
## 51 Hombres 25 49425 2012
## 52 Mujeres 25 51423 2012
## 53 Hombres 26 46067 2012
## 54 Mujeres 26 53297 2012
## 55 Hombres 27 51836 2012
## 56 Mujeres 27 55141 2012
## 57 Hombres 28 49472 2012
## 58 Mujeres 28 56097 2012
## 59 Hombres 29 36512 2012
## 60 Mujeres 29 44066 2012
## 61 Hombres 30 48708 2012
## 62 Mujeres 30 56610 2012
## 63 Hombres 31 40714 2012
## 64 Mujeres 31 46739 2012
## 65 Hombres 32 61396 2012
## 66 Mujeres 32 55902 2012
## 67 Hombres 33 45236 2012
## 68 Mujeres 33 44948 2012
## 69 Hombres 34 30695 2012
## 70 Mujeres 34 37928 2012
## 71 Hombres 35 44795 2012
## 72 Mujeres 35 36967 2012
## 73 Hombres 36 37660 2012
## 74 Mujeres 36 34784 2012
## 75 Hombres 37 31670 2012
## 76 Mujeres 37 38486 2012
## 77 Hombres 38 39917 2012
## 78 Mujeres 38 33600 2012
## 79 Hombres 39 26263 2012
## 80 Mujeres 39 32413 2012
## 81 Hombres 40 42528 2012
## 82 Mujeres 40 44872 2012
## 83 Hombres 41 35453 2012
## 84 Mujeres 41 33664 2012
## 85 Hombres 42 37536 2012
## 86 Mujeres 42 42183 2012
## 87 Hombres 43 34460 2012
## 88 Mujeres 43 32661 2012
## 89 Hombres 44 35434 2012
## 90 Mujeres 44 26234 2012
## 91 Hombres 45 31047 2012
## 92 Mujeres 45 30819 2012
## 93 Hombres 46 30553 2012
## 94 Mujeres 46 30214 2012
## 95 Hombres 47 33934 2012
## 96 Mujeres 47 35258 2012
## 97 Hombres 48 35938 2012
## 98 Mujeres 48 24628 2012
## 99 Hombres 49 29518 2012
## 100 Mujeres 49 32060 2012
## 101 Hombres 50 29074 2012
## 102 Mujeres 50 31851 2012
## 103 Hombres 51 25120 2012
## 104 Mujeres 51 23494 2012
## 105 Hombres 52 29890 2012
## 106 Mujeres 52 31026 2012
## 107 Hombres 53 27210 2012
## 108 Mujeres 53 24871 2012
## 109 Hombres 54 27290 2012
## 110 Mujeres 54 34692 2012
## 111 Hombres 55 22182 2012
## 112 Mujeres 55 20305 2012
## 113 Hombres 56 28677 2012
## 114 Mujeres 56 27385 2012
## 115 Hombres 57 21743 2012
## 116 Mujeres 57 23746 2012
## 117 Hombres 58 24392 2012
## 118 Mujeres 58 21678 2012
## 119 Hombres 59 13053 2012
## 120 Mujeres 59 22131 2012
## 121 Hombres 60 21333 2012
## 122 Mujeres 60 20773 2012
## 123 Hombres 61 15173 2012
## 124 Mujeres 61 10492 2012
## 125 Hombres 62 17528 2012
## 126 Mujeres 62 17468 2012
## 127 Hombres 63 15246 2012
## 128 Mujeres 63 12867 2012
## 129 Hombres 64 9545 2012
## 130 Mujeres 64 13584 2012
## 131 Hombres 65 18968 2012
## 132 Mujeres 65 15293 2012
## 133 Hombres 66 12730 2012
## 134 Mujeres 66 12278 2012
## 135 Hombres 67 10086 2012
## 136 Mujeres 67 11523 2012
## 137 Hombres 68 10763 2012
## 138 Mujeres 68 8764 2012
## 139 Hombres 69 7239 2012
## 140 Mujeres 69 8128 2012
## 141 Hombres 70 11173 2012
## 142 Mujeres 70 10522 2012
## 143 Hombres 71 6969 2012
## 144 Mujeres 71 6686 2012
## 145 Hombres 72 11775 2012
## 146 Mujeres 72 10102 2012
## 147 Hombres 73 8607 2012
## 148 Mujeres 73 9814 2012
## 149 Hombres 74 4861 2012
## 150 Mujeres 74 11709 2012
## 151 Hombres 75 9871 2012
## 152 Mujeres 75 6413 2012
## 153 Hombres 76 5191 2012
## 154 Mujeres 76 5415 2012
## 155 Hombres 77 4503 2012
## 156 Mujeres 77 7150 2012
## 157 Hombres 78 5597 2012
## 158 Mujeres 78 3011 2012
## 159 Hombres 79 6267 2012
## 160 Mujeres 79 5644 2012
## 161 Hombres 80 4329 2012
## 162 Mujeres 80 6469 2012
## 163 Hombres 81 2964 2012
## 164 Mujeres 81 5298 2012
## 165 Hombres 82 4901 2012
## 166 Mujeres 82 7246 2012
## 167 Hombres 83 4123 2012
## 168 Mujeres 83 5270 2012
## 169 Hombres 84 5249 2012
## 170 Mujeres 84 3022 2012
## 171 Hombres 85 3093 2012
## 172 Mujeres 85 3657 2012
## 173 Hombres 86 2287 2012
## 174 Mujeres 86 3997 2012
## 175 Hombres 87 1657 2012
## 176 Mujeres 87 3653 2012
## 177 Hombres 88 1384 2012
## 178 Mujeres 88 609 2012
## 179 Hombres 89 786 2012
## 180 Mujeres 89 1216 2012
## 181 Hombres 90 2450 2012
## 182 Mujeres 90 2589 2012
## 183 Hombres 91 982 2012
## 184 Mujeres 91 1376 2012
## 185 Hombres 92 1723 2012
## 186 Mujeres 92 757 2012
## 187 Hombres 93 126 2012
## 188 Mujeres 93 1993 2012
## 189 Hombres 94 560 2012
## 190 Hombres 95 525 2012
## 191 Mujeres 95 531 2012
## 192 Hombres 96 885 2012
## 193 Mujeres 97 1028 2012
## 194 Mujeres 98 917 2012
## 195 Mujeres 99 383 2012#resultados poblacionales
edadysexo2022=aggregate.data.frame(x = eph2022$FEX, by = list(eph2022$P06, eph2022$P02), FUN = "sum")
edadysexo2022$year=2022
colnames(edadysexo2022) <- c("Sexo", "Edad","Cantidad","Year")
edadysexo2022## Sexo Edad Cantidad Year
## 1 Hombres 0 68641 2022
## 2 Mujeres 0 68554 2022
## 3 Hombres 1 57309 2022
## 4 Mujeres 1 54222 2022
## 5 Hombres 2 68203 2022
## 6 Mujeres 2 71866 2022
## 7 Hombres 3 79717 2022
## 8 Mujeres 3 64557 2022
## 9 Hombres 4 70401 2022
## 10 Mujeres 4 67766 2022
## 11 Hombres 5 76827 2022
## 12 Mujeres 5 80033 2022
## 13 Hombres 6 85832 2022
## 14 Mujeres 6 66435 2022
## 15 Hombres 7 78186 2022
## 16 Mujeres 7 67818 2022
## 17 Hombres 8 75530 2022
## 18 Mujeres 8 59676 2022
## 19 Hombres 9 67865 2022
## 20 Mujeres 9 62468 2022
## 21 Hombres 10 78902 2022
## 22 Mujeres 10 68677 2022
## 23 Hombres 11 73780 2022
## 24 Mujeres 11 65461 2022
## 25 Hombres 12 72510 2022
## 26 Mujeres 12 68400 2022
## 27 Hombres 13 64400 2022
## 28 Mujeres 13 65516 2022
## 29 Hombres 14 64648 2022
## 30 Mujeres 14 49754 2022
## 31 Hombres 15 58970 2022
## 32 Mujeres 15 61821 2022
## 33 Hombres 16 71964 2022
## 34 Mujeres 16 63469 2022
## 35 Hombres 17 62805 2022
## 36 Mujeres 17 72096 2022
## 37 Hombres 18 65633 2022
## 38 Mujeres 18 60649 2022
## 39 Hombres 19 62980 2022
## 40 Mujeres 19 61184 2022
## 41 Hombres 20 65570 2022
## 42 Mujeres 20 66286 2022
## 43 Hombres 21 57442 2022
## 44 Mujeres 21 64425 2022
## 45 Hombres 22 69798 2022
## 46 Mujeres 22 65047 2022
## 47 Hombres 23 64325 2022
## 48 Mujeres 23 60081 2022
## 49 Hombres 24 76616 2022
## 50 Mujeres 24 70617 2022
## 51 Hombres 25 66754 2022
## 52 Mujeres 25 71320 2022
## 53 Hombres 26 61062 2022
## 54 Mujeres 26 72634 2022
## 55 Hombres 27 57594 2022
## 56 Mujeres 27 76776 2022
## 57 Hombres 28 66458 2022
## 58 Mujeres 28 62090 2022
## 59 Hombres 29 69223 2022
## 60 Mujeres 29 58447 2022
## 61 Hombres 30 56856 2022
## 62 Mujeres 30 61209 2022
## 63 Hombres 31 51406 2022
## 64 Mujeres 31 58421 2022
## 65 Hombres 32 56887 2022
## 66 Mujeres 32 47602 2022
## 67 Hombres 33 57053 2022
## 68 Mujeres 33 61813 2022
## 69 Hombres 34 69665 2022
## 70 Mujeres 34 67131 2022
## 71 Hombres 35 48549 2022
## 72 Mujeres 35 52789 2022
## 73 Hombres 36 50844 2022
## 74 Mujeres 36 53179 2022
## 75 Hombres 37 49450 2022
## 76 Mujeres 37 56244 2022
## 77 Hombres 38 50225 2022
## 78 Mujeres 38 48459 2022
## 79 Hombres 39 54505 2022
## 80 Mujeres 39 55594 2022
## 81 Hombres 40 62830 2022
## 82 Mujeres 40 55868 2022
## 83 Hombres 41 43051 2022
## 84 Mujeres 41 47425 2022
## 85 Hombres 42 48179 2022
## 86 Mujeres 42 55218 2022
## 87 Hombres 43 42530 2022
## 88 Mujeres 43 46215 2022
## 89 Hombres 44 40758 2022
## 90 Mujeres 44 42876 2022
## 91 Hombres 45 33710 2022
## 92 Mujeres 45 37289 2022
## 93 Hombres 46 37240 2022
## 94 Mujeres 46 40185 2022
## 95 Hombres 47 33374 2022
## 96 Mujeres 47 29951 2022
## 97 Hombres 48 38441 2022
## 98 Mujeres 48 37757 2022
## 99 Hombres 49 36040 2022
## 100 Mujeres 49 35385 2022
## 101 Hombres 50 32552 2022
## 102 Mujeres 50 33802 2022
## 103 Hombres 51 28699 2022
## 104 Mujeres 51 28225 2022
## 105 Hombres 52 33858 2022
## 106 Mujeres 52 29934 2022
## 107 Hombres 53 29283 2022
## 108 Mujeres 53 32158 2022
## 109 Hombres 54 32194 2022
## 110 Mujeres 54 28411 2022
## 111 Hombres 55 26465 2022
## 112 Mujeres 55 27598 2022
## 113 Hombres 56 29880 2022
## 114 Mujeres 56 33924 2022
## 115 Hombres 57 30775 2022
## 116 Mujeres 57 32848 2022
## 117 Hombres 58 26577 2022
## 118 Mujeres 58 31105 2022
## 119 Hombres 59 24214 2022
## 120 Mujeres 59 25421 2022
## 121 Hombres 60 26333 2022
## 122 Mujeres 60 34100 2022
## 123 Hombres 61 26971 2022
## 124 Mujeres 61 30796 2022
## 125 Hombres 62 25112 2022
## 126 Mujeres 62 31615 2022
## 127 Hombres 63 25681 2022
## 128 Mujeres 63 20645 2022
## 129 Hombres 64 24121 2022
## 130 Mujeres 64 19610 2022
## 131 Hombres 65 22097 2022
## 132 Mujeres 65 18621 2022
## 133 Hombres 66 17584 2022
## 134 Mujeres 66 26911 2022
## 135 Hombres 67 23688 2022
## 136 Mujeres 67 17129 2022
## 137 Hombres 68 18260 2022
## 138 Mujeres 68 13744 2022
## 139 Hombres 69 19158 2022
## 140 Mujeres 69 16766 2022
## 141 Hombres 70 13447 2022
## 142 Mujeres 70 20929 2022
## 143 Hombres 71 15989 2022
## 144 Mujeres 71 12016 2022
## 145 Hombres 72 14884 2022
## 146 Mujeres 72 16377 2022
## 147 Hombres 73 12619 2022
## 148 Mujeres 73 9085 2022
## 149 Hombres 74 11244 2022
## 150 Mujeres 74 9497 2022
## 151 Hombres 75 11222 2022
## 152 Mujeres 75 12359 2022
## 153 Hombres 76 10309 2022
## 154 Mujeres 76 9860 2022
## 155 Hombres 77 9026 2022
## 156 Mujeres 77 9150 2022
## 157 Hombres 78 8143 2022
## 158 Mujeres 78 10196 2022
## 159 Hombres 79 8792 2022
## 160 Mujeres 79 6313 2022
## 161 Hombres 80 8867 2022
## 162 Mujeres 80 6298 2022
## 163 Hombres 81 5434 2022
## 164 Mujeres 81 2972 2022
## 165 Hombres 82 5655 2022
## 166 Mujeres 82 5640 2022
## 167 Hombres 83 4538 2022
## 168 Mujeres 83 3663 2022
## 169 Hombres 84 3477 2022
## 170 Mujeres 84 6284 2022
## 171 Hombres 85 4825 2022
## 172 Mujeres 85 5962 2022
## 173 Hombres 86 2174 2022
## 174 Mujeres 86 3443 2022
## 175 Hombres 87 3095 2022
## 176 Mujeres 87 3438 2022
## 177 Hombres 88 1444 2022
## 178 Mujeres 88 537 2022
## 179 Hombres 89 493 2022
## 180 Mujeres 89 2406 2022
## 181 Hombres 90 1184 2022
## 182 Mujeres 90 1835 2022
## 183 Hombres 91 848 2022
## 184 Mujeres 91 2960 2022
## 185 Hombres 92 968 2022
## 186 Mujeres 92 1681 2022
## 187 Hombres 93 342 2022
## 188 Mujeres 93 801 2022
## 189 Hombres 94 1138 2022
## 190 Mujeres 94 1754 2022
## 191 Hombres 95 561 2022
## 192 Mujeres 95 630 2022
## 193 Mujeres 96 393 2022
## 194 Mujeres 97 569 2022
## 195 Mujeres 98 134 2022
## 196 Mujeres 100 211 2022
## 197 Mujeres 102 273 2022## Sexo Edad Cantidad Year
## 1 Hombres 0 73521 2012
## 2 Mujeres 0 67105 2012
## 3 Hombres 1 73680 2012
## 4 Mujeres 1 64123 2012
## 5 Hombres 2 79031 2012
## 6 Mujeres 2 54732 2012
## 7 Hombres 3 68716 2012
## 8 Mujeres 3 59355 2012
## 9 Hombres 4 56131 2012
## 10 Mujeres 4 72214 2012
## 11 Hombres 5 75015 2012
## 12 Mujeres 5 56983 2012
## 13 Hombres 6 63655 2012
## 14 Mujeres 6 63793 2012
## 15 Hombres 7 75251 2012
## 16 Mujeres 7 67105 2012
## 17 Hombres 8 80053 2012
## 18 Mujeres 8 69504 2012
## 19 Hombres 9 68708 2012
## 20 Mujeres 9 75170 2012
## 21 Hombres 10 71834 2012
## 22 Mujeres 10 61776 2012
## 23 Hombres 11 70408 2012
## 24 Mujeres 11 69640 2012
## 25 Hombres 12 77358 2012
## 26 Mujeres 12 61572 2012
## 27 Hombres 13 65262 2012
## 28 Mujeres 13 67804 2012
## 29 Hombres 14 77360 2012
## 30 Mujeres 14 69235 2012
## 31 Hombres 15 80493 2012
## 32 Mujeres 15 77420 2012
## 33 Hombres 16 90765 2012
## 34 Mujeres 16 71202 2012
## 35 Hombres 17 74570 2012
## 36 Mujeres 17 61613 2012
## 37 Hombres 18 77417 2012
## 38 Mujeres 18 66598 2012
## 39 Hombres 19 55539 2012
## 40 Mujeres 19 70408 2012
## 41 Hombres 20 61122 2012
## 42 Mujeres 20 70213 2012
## 43 Hombres 21 51598 2012
## 44 Mujeres 21 57207 2012
## 45 Hombres 22 60083 2012
## 46 Mujeres 22 71184 2012
## 47 Hombres 23 58435 2012
## 48 Mujeres 23 55026 2012
## 49 Hombres 24 51816 2012
## 50 Mujeres 24 53427 2012
## 51 Hombres 25 49425 2012
## 52 Mujeres 25 51423 2012
## 53 Hombres 26 46067 2012
## 54 Mujeres 26 53297 2012
## 55 Hombres 27 51836 2012
## 56 Mujeres 27 55141 2012
## 57 Hombres 28 49472 2012
## 58 Mujeres 28 56097 2012
## 59 Hombres 29 36512 2012
## 60 Mujeres 29 44066 2012
## 61 Hombres 30 48708 2012
## 62 Mujeres 30 56610 2012
## 63 Hombres 31 40714 2012
## 64 Mujeres 31 46739 2012
## 65 Hombres 32 61396 2012
## 66 Mujeres 32 55902 2012
## 67 Hombres 33 45236 2012
## 68 Mujeres 33 44948 2012
## 69 Hombres 34 30695 2012
## 70 Mujeres 34 37928 2012
## 71 Hombres 35 44795 2012
## 72 Mujeres 35 36967 2012
## 73 Hombres 36 37660 2012
## 74 Mujeres 36 34784 2012
## 75 Hombres 37 31670 2012
## 76 Mujeres 37 38486 2012
## 77 Hombres 38 39917 2012
## 78 Mujeres 38 33600 2012
## 79 Hombres 39 26263 2012
## 80 Mujeres 39 32413 2012
## 81 Hombres 40 42528 2012
## 82 Mujeres 40 44872 2012
## 83 Hombres 41 35453 2012
## 84 Mujeres 41 33664 2012
## 85 Hombres 42 37536 2012
## 86 Mujeres 42 42183 2012
## 87 Hombres 43 34460 2012
## 88 Mujeres 43 32661 2012
## 89 Hombres 44 35434 2012
## 90 Mujeres 44 26234 2012
## 91 Hombres 45 31047 2012
## 92 Mujeres 45 30819 2012
## 93 Hombres 46 30553 2012
## 94 Mujeres 46 30214 2012
## 95 Hombres 47 33934 2012
## 96 Mujeres 47 35258 2012
## 97 Hombres 48 35938 2012
## 98 Mujeres 48 24628 2012
## 99 Hombres 49 29518 2012
## 100 Mujeres 49 32060 2012
## 101 Hombres 50 29074 2012
## 102 Mujeres 50 31851 2012
## 103 Hombres 51 25120 2012
## 104 Mujeres 51 23494 2012
## 105 Hombres 52 29890 2012
## 106 Mujeres 52 31026 2012
## 107 Hombres 53 27210 2012
## 108 Mujeres 53 24871 2012
## 109 Hombres 54 27290 2012
## 110 Mujeres 54 34692 2012
## 111 Hombres 55 22182 2012
## 112 Mujeres 55 20305 2012
## 113 Hombres 56 28677 2012
## 114 Mujeres 56 27385 2012
## 115 Hombres 57 21743 2012
## 116 Mujeres 57 23746 2012
## 117 Hombres 58 24392 2012
## 118 Mujeres 58 21678 2012
## 119 Hombres 59 13053 2012
## 120 Mujeres 59 22131 2012
## 121 Hombres 60 21333 2012
## 122 Mujeres 60 20773 2012
## 123 Hombres 61 15173 2012
## 124 Mujeres 61 10492 2012
## 125 Hombres 62 17528 2012
## 126 Mujeres 62 17468 2012
## 127 Hombres 63 15246 2012
## 128 Mujeres 63 12867 2012
## 129 Hombres 64 9545 2012
## 130 Mujeres 64 13584 2012
## 131 Hombres 65 18968 2012
## 132 Mujeres 65 15293 2012
## 133 Hombres 66 12730 2012
## 134 Mujeres 66 12278 2012
## 135 Hombres 67 10086 2012
## 136 Mujeres 67 11523 2012
## 137 Hombres 68 10763 2012
## 138 Mujeres 68 8764 2012
## 139 Hombres 69 7239 2012
## 140 Mujeres 69 8128 2012
## 141 Hombres 70 11173 2012
## 142 Mujeres 70 10522 2012
## 143 Hombres 71 6969 2012
## 144 Mujeres 71 6686 2012
## 145 Hombres 72 11775 2012
## 146 Mujeres 72 10102 2012
## 147 Hombres 73 8607 2012
## 148 Mujeres 73 9814 2012
## 149 Hombres 74 4861 2012
## 150 Mujeres 74 11709 2012
## 151 Hombres 75 9871 2012
## 152 Mujeres 75 6413 2012
## 153 Hombres 76 5191 2012
## 154 Mujeres 76 5415 2012
## 155 Hombres 77 4503 2012
## 156 Mujeres 77 7150 2012
## 157 Hombres 78 5597 2012
## 158 Mujeres 78 3011 2012
## 159 Hombres 79 6267 2012
## 160 Mujeres 79 5644 2012
## 161 Hombres 80 4329 2012
## 162 Mujeres 80 6469 2012
## 163 Hombres 81 2964 2012
## 164 Mujeres 81 5298 2012
## 165 Hombres 82 4901 2012
## 166 Mujeres 82 7246 2012
## 167 Hombres 83 4123 2012
## 168 Mujeres 83 5270 2012
## 169 Hombres 84 5249 2012
## 170 Mujeres 84 3022 2012
## 171 Hombres 85 3093 2012
## 172 Mujeres 85 3657 2012
## 173 Hombres 86 2287 2012
## 174 Mujeres 86 3997 2012
## 175 Hombres 87 1657 2012
## 176 Mujeres 87 3653 2012
## 177 Hombres 88 1384 2012
## 178 Mujeres 88 609 2012
## 179 Hombres 89 786 2012
## 180 Mujeres 89 1216 2012
## 181 Hombres 90 2450 2012
## 182 Mujeres 90 2589 2012
## 183 Hombres 91 982 2012
## 184 Mujeres 91 1376 2012
## 185 Hombres 92 1723 2012
## 186 Mujeres 92 757 2012
## 187 Hombres 93 126 2012
## 188 Mujeres 93 1993 2012
## 189 Hombres 94 560 2012
## 190 Hombres 95 525 2012
## 191 Mujeres 95 531 2012
## 192 Hombres 96 885 2012
## 193 Mujeres 97 1028 2012
## 194 Mujeres 98 917 2012
## 195 Mujeres 99 383 2012
## 196 Hombres 0 68641 2022
## 197 Mujeres 0 68554 2022
## 198 Hombres 1 57309 2022
## 199 Mujeres 1 54222 2022
## 200 Hombres 2 68203 2022
## 201 Mujeres 2 71866 2022
## 202 Hombres 3 79717 2022
## 203 Mujeres 3 64557 2022
## 204 Hombres 4 70401 2022
## 205 Mujeres 4 67766 2022
## 206 Hombres 5 76827 2022
## 207 Mujeres 5 80033 2022
## 208 Hombres 6 85832 2022
## 209 Mujeres 6 66435 2022
## 210 Hombres 7 78186 2022
## 211 Mujeres 7 67818 2022
## 212 Hombres 8 75530 2022
## 213 Mujeres 8 59676 2022
## 214 Hombres 9 67865 2022
## 215 Mujeres 9 62468 2022
## 216 Hombres 10 78902 2022
## 217 Mujeres 10 68677 2022
## 218 Hombres 11 73780 2022
## 219 Mujeres 11 65461 2022
## 220 Hombres 12 72510 2022
## 221 Mujeres 12 68400 2022
## 222 Hombres 13 64400 2022
## 223 Mujeres 13 65516 2022
## 224 Hombres 14 64648 2022
## 225 Mujeres 14 49754 2022
## 226 Hombres 15 58970 2022
## 227 Mujeres 15 61821 2022
## 228 Hombres 16 71964 2022
## 229 Mujeres 16 63469 2022
## 230 Hombres 17 62805 2022
## 231 Mujeres 17 72096 2022
## 232 Hombres 18 65633 2022
## 233 Mujeres 18 60649 2022
## 234 Hombres 19 62980 2022
## 235 Mujeres 19 61184 2022
## 236 Hombres 20 65570 2022
## 237 Mujeres 20 66286 2022
## 238 Hombres 21 57442 2022
## 239 Mujeres 21 64425 2022
## 240 Hombres 22 69798 2022
## 241 Mujeres 22 65047 2022
## 242 Hombres 23 64325 2022
## 243 Mujeres 23 60081 2022
## 244 Hombres 24 76616 2022
## 245 Mujeres 24 70617 2022
## 246 Hombres 25 66754 2022
## 247 Mujeres 25 71320 2022
## 248 Hombres 26 61062 2022
## 249 Mujeres 26 72634 2022
## 250 Hombres 27 57594 2022
## 251 Mujeres 27 76776 2022
## 252 Hombres 28 66458 2022
## 253 Mujeres 28 62090 2022
## 254 Hombres 29 69223 2022
## 255 Mujeres 29 58447 2022
## 256 Hombres 30 56856 2022
## 257 Mujeres 30 61209 2022
## 258 Hombres 31 51406 2022
## 259 Mujeres 31 58421 2022
## 260 Hombres 32 56887 2022
## 261 Mujeres 32 47602 2022
## 262 Hombres 33 57053 2022
## 263 Mujeres 33 61813 2022
## 264 Hombres 34 69665 2022
## 265 Mujeres 34 67131 2022
## 266 Hombres 35 48549 2022
## 267 Mujeres 35 52789 2022
## 268 Hombres 36 50844 2022
## 269 Mujeres 36 53179 2022
## 270 Hombres 37 49450 2022
## 271 Mujeres 37 56244 2022
## 272 Hombres 38 50225 2022
## 273 Mujeres 38 48459 2022
## 274 Hombres 39 54505 2022
## 275 Mujeres 39 55594 2022
## 276 Hombres 40 62830 2022
## 277 Mujeres 40 55868 2022
## 278 Hombres 41 43051 2022
## 279 Mujeres 41 47425 2022
## 280 Hombres 42 48179 2022
## 281 Mujeres 42 55218 2022
## 282 Hombres 43 42530 2022
## 283 Mujeres 43 46215 2022
## 284 Hombres 44 40758 2022
## 285 Mujeres 44 42876 2022
## 286 Hombres 45 33710 2022
## 287 Mujeres 45 37289 2022
## 288 Hombres 46 37240 2022
## 289 Mujeres 46 40185 2022
## 290 Hombres 47 33374 2022
## 291 Mujeres 47 29951 2022
## 292 Hombres 48 38441 2022
## 293 Mujeres 48 37757 2022
## 294 Hombres 49 36040 2022
## 295 Mujeres 49 35385 2022
## 296 Hombres 50 32552 2022
## 297 Mujeres 50 33802 2022
## 298 Hombres 51 28699 2022
## 299 Mujeres 51 28225 2022
## 300 Hombres 52 33858 2022
## 301 Mujeres 52 29934 2022
## 302 Hombres 53 29283 2022
## 303 Mujeres 53 32158 2022
## 304 Hombres 54 32194 2022
## 305 Mujeres 54 28411 2022
## 306 Hombres 55 26465 2022
## 307 Mujeres 55 27598 2022
## 308 Hombres 56 29880 2022
## 309 Mujeres 56 33924 2022
## 310 Hombres 57 30775 2022
## 311 Mujeres 57 32848 2022
## 312 Hombres 58 26577 2022
## 313 Mujeres 58 31105 2022
## 314 Hombres 59 24214 2022
## 315 Mujeres 59 25421 2022
## 316 Hombres 60 26333 2022
## 317 Mujeres 60 34100 2022
## 318 Hombres 61 26971 2022
## 319 Mujeres 61 30796 2022
## 320 Hombres 62 25112 2022
## 321 Mujeres 62 31615 2022
## 322 Hombres 63 25681 2022
## 323 Mujeres 63 20645 2022
## 324 Hombres 64 24121 2022
## 325 Mujeres 64 19610 2022
## 326 Hombres 65 22097 2022
## 327 Mujeres 65 18621 2022
## 328 Hombres 66 17584 2022
## 329 Mujeres 66 26911 2022
## 330 Hombres 67 23688 2022
## 331 Mujeres 67 17129 2022
## 332 Hombres 68 18260 2022
## 333 Mujeres 68 13744 2022
## 334 Hombres 69 19158 2022
## 335 Mujeres 69 16766 2022
## 336 Hombres 70 13447 2022
## 337 Mujeres 70 20929 2022
## 338 Hombres 71 15989 2022
## 339 Mujeres 71 12016 2022
## 340 Hombres 72 14884 2022
## 341 Mujeres 72 16377 2022
## 342 Hombres 73 12619 2022
## 343 Mujeres 73 9085 2022
## 344 Hombres 74 11244 2022
## 345 Mujeres 74 9497 2022
## 346 Hombres 75 11222 2022
## 347 Mujeres 75 12359 2022
## 348 Hombres 76 10309 2022
## 349 Mujeres 76 9860 2022
## 350 Hombres 77 9026 2022
## 351 Mujeres 77 9150 2022
## 352 Hombres 78 8143 2022
## 353 Mujeres 78 10196 2022
## 354 Hombres 79 8792 2022
## 355 Mujeres 79 6313 2022
## 356 Hombres 80 8867 2022
## 357 Mujeres 80 6298 2022
## 358 Hombres 81 5434 2022
## 359 Mujeres 81 2972 2022
## 360 Hombres 82 5655 2022
## 361 Mujeres 82 5640 2022
## 362 Hombres 83 4538 2022
## 363 Mujeres 83 3663 2022
## 364 Hombres 84 3477 2022
## 365 Mujeres 84 6284 2022
## 366 Hombres 85 4825 2022
## 367 Mujeres 85 5962 2022
## 368 Hombres 86 2174 2022
## 369 Mujeres 86 3443 2022
## 370 Hombres 87 3095 2022
## 371 Mujeres 87 3438 2022
## 372 Hombres 88 1444 2022
## 373 Mujeres 88 537 2022
## 374 Hombres 89 493 2022
## 375 Mujeres 89 2406 2022
## 376 Hombres 90 1184 2022
## 377 Mujeres 90 1835 2022
## 378 Hombres 91 848 2022
## 379 Mujeres 91 2960 2022
## 380 Hombres 92 968 2022
## 381 Mujeres 92 1681 2022
## 382 Hombres 93 342 2022
## 383 Mujeres 93 801 2022
## 384 Hombres 94 1138 2022
## 385 Mujeres 94 1754 2022
## 386 Hombres 95 561 2022
## 387 Mujeres 95 630 2022
## 388 Mujeres 96 393 2022
## 389 Mujeres 97 569 2022
## 390 Mujeres 98 134 2022
## 391 Mujeres 100 211 2022
## 392 Mujeres 102 273 2022library(ggplot2)
ggplot(eph12y22, aes(x = Edad, y = Cantidad, fill = Sexo)) +
geom_bar(stat = "identity") +
scale_fill_hue(labels = c("HOMBRES", "Mujres")) +
facet_grid(. ~ Year)
tablaedades=aggregate.data.frame(x = eph12y22$Cantidad, by = list(eph12y22$Sexo, eph12y22$Year), FUN = "sum")
colnames(tablaedades) <- c("Sexo", "Year","Cantidad")
tablaedades## Sexo Year Cantidad
## 1 Hombres 2012 3224668
## 2 Mujeres 2012 3164229
## 3 Hombres 2022 3705758
## 4 Mujeres 2022 3659784Tutoría sincrónica 2: 19 de Agosto del 2023
Seleccionamos solo algunas de las variables de las bases para los años 2013 al 2022, para armar series de indicadores
Año 2012
** Cargando directamente desde la pagina web del INE
# descargar y guardar las bases en formato csv
#eph2013=read.csv("https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2022/data/REG02_EPHC2022.csv.csv", sep=";")
#eph2014=read.csv("https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2014/data/094b4r02_eph2014.csv", sep=";")
#eph2015=read.csv("https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2015/data/d21c9r02_eph2015.csv", sep=";")
#eph2016=read.csv(, sep=";")
#eph2017=read.csv(, sep=";")
#eph2018=read.csv(, sep=";")
#eph2019=read.csv(, sep=";")
#eph2020=read.csv(, sep=";")
#eph2021=read.csv(, sep=";")
#write.csv(eph2012,"D:/OneDrive/FACEN_BIGDATA/eph12.csv")
#eph2012=read.csv("D:/OneDrive/FACEN_BIGDATA/eph12.csv", sep=",")** Cargando desde un archivo local o desde un repositorio GIT**
## [1] "codigosR" "LICENSE" "R02_EPH2006.sav" "R02_EPH2007.sav"
## [5] "R02_EPH2008.SAV" "R02_EPH2009.SAV" "R02_EPH2010.SAV" "R02_EPH2011.sav"
## [9] "R02_EPH2012.SAV" "R02_EPH2013.SAV" "R02_EPH2014.SAV" "R02_EPH2015.SAV"
## [13] "R02_EPH2016.SAV" "R02_EPH2017.SAV" "R02_EPH2018.sav" "R02_EPH2019.sav"
## [17] "R02_EPH2020.sav" "R02_EPH2021.sav" "R02_EPH2022.SAV" "README.md"
## [21] "reporteEPH"## [1] "UPM" "NVIVI" "NHOGA" "DPTOREP" "AREA"
## [6] "L02" "P02" "P03" "P04" "P04A"
## [11] "P04B" "P05C" "P05P" "P05M" "P06"
## [16] "P08D" "P08M" "P08A" "P09" "P10A"
## [21] "P10AB" "P10Z" "P11A" "P11AB" "P11Z"
## [26] "P12" "P13A" "P13B" "P13C" "P14A1"
## [31] "P14A2" "P14B1" "P14B2" "P14C1" "P14C2"
## [36] "A01" "A01A" "A02" "A03" "A04"
## [41] "A05" "A07" "A08" "A10" "A11A"
## [46] "A11M" "A11S" "A12" "A13REC" "A14REC"
## [51] "A15" "A16" "A17A" "A17M" "A17S"
## [56] "A18" "B01REC" "B02REC" "B03LU" "B03MA"
## [61] "B03MI" "B03JU" "B03VI" "B03SA" "B03DO"
## [66] "B04" "B05" "B06" "B07A" "B07M"
## [71] "B07S" "B08" "B09A" "B09M" "B09S"
## [76] "B10" "B11" "B12" "B13" "B14"
## [81] "B15" "B16G" "B16U" "B16D" "B16T"
## [86] "B17" "B18AG" "B18AU" "B18BG" "B18BU"
## [91] "B19" "B20G" "B20U" "B20D" "B20T"
## [96] "B21" "B22" "B23" "B24" "B25"
## [101] "B26" "B271" "B272" "B28" "B29"
## [106] "B30" "B31" "C01REC" "C02REC" "C03"
## [111] "C04" "C05" "C06" "C07" "C08"
## [116] "C09" "C101" "C102" "C11G" "C11U"
## [121] "C11D" "C11T" "C12" "C13AG" "C13AU"
## [126] "C13BG" "C13BU" "C14" "C15" "C16REC"
## [131] "C17REC" "C18" "C19" "D01" "D02"
## [136] "D03" "D04" "D05" "E01A" "E01B"
## [141] "E01C" "E01D" "E01E" "E01F" "E01G"
## [146] "E01H" "E01I" "E01J" "E01K" "E01L"
## [151] "ED01" "ED02" "ED03" "ED0504" "ED06C"
## [156] "ED07" "ED08" "ED09" "ED10" "ED11A"
## [161] "ED11B" "ED11C" "ED11D" "ED11E" "ED11F"
## [166] "S01A" "S01B" "S02" "S03" "S04"
## [171] "S05" "S06" "S07" "S08" "S09"
## [176] "S10A" "S10B" "S10C" "S10D" "S10E"
## [181] "S10F" "S10G" "S10T" "CATE_PEA" "TAMA_PEA"
## [186] "OCUP_PEA" "RAMA_PEA" "HORAB" "HORABC" "HORABCO"
## [191] "PEAD" "PEAA" "TIPOHOGA" "NJEF" "NCON"
## [196] "NPAD" "NMAD" "añoest" "ra06ya09" "E01AIMDE"
## [201] "E01BIMDE" "E01CIMDE" "E01DDE" "E01EDE" "E01FDE"
## [206] "E01GDE" "E01HDE" "E01IDE" "E01JDE" "E01KDE"
## [211] "E01LDE" "E01KJDE" "E02L1BDE" "E02L2BDE" "fexajustado"
## [216] "ipcm" "pobrezai" "pobnopoi" "quintili" "decili"
## [221] "quintiai" "decilai"## [1] "UPM" "NVIVI" "NHOGA" "DPTOREP" "AREA"
## [6] "L02" "P02" "P03" "P04" "P04A"
## [11] "P04B" "P05C" "P05P" "P05M" "P06"
## [16] "P08D" "P08M" "P08A" "P09" "P10A"
## [21] "P10AB" "P10Z" "P11A" "P11AB" "P11Z"
## [26] "P12" "P13A" "P13B" "P13C" "P14A1"
## [31] "P14A2" "P14B1" "P14B2" "P14C1" "P14C2"
## [36] "A01" "A01A" "A02" "A03" "A04"
## [41] "A04A" "A05" "A07" "A08" "A10"
## [46] "A11A" "A11M" "A11S" "A12" "A13REC"
## [51] "A14REC" "A15" "A16" "A17A" "A17M"
## [56] "A17S" "A18" "B01REC" "B02REC" "B03LU"
## [61] "B03MA" "B03MI" "B03JU" "B03VI" "B03SA"
## [66] "B03DO" "B04" "B05" "B06" "B07A"
## [71] "B07M" "B07S" "B08" "B09A" "B09M"
## [76] "B09S" "B10" "B11" "B12" "B13"
## [81] "B14" "B15" "B16G" "B16U" "B16D"
## [86] "B16T" "B17" "B18AG" "B18AU" "B18BG"
## [91] "B18BU" "B19" "B20G" "B20U" "B20D"
## [96] "B20T" "B21" "B22" "B23" "B24"
## [101] "B25" "B26" "B271" "B272" "B28"
## [106] "B29" "B30" "B31" "C01REC" "C02REC"
## [111] "C03" "C04" "C05" "C06" "C07"
## [116] "C08" "C09" "C101" "C102" "C11G"
## [121] "C11U" "C11D" "C11T" "C12" "C13AG"
## [126] "C13AU" "C13BG" "C13BU" "C14" "C14A"
## [131] "C14B" "C14C" "C15" "C16REC" "C17REC"
## [136] "C18" "C19" "D01" "D02" "D03"
## [141] "D04" "D05" "E01A" "E01B" "E01C"
## [146] "E01D" "E01E" "E01F" "E01G" "E01H"
## [151] "E01I" "E01J" "E01K" "E01L" "E01M"
## [156] "ED01" "ED02" "ED03" "ED0504" "ED06C"
## [161] "ED07" "ED08" "ED09" "ED10" "ED11A"
## [166] "ED11B" "ED11C" "ED11D" "ED11E" "ED11F"
## [171] "ED12" "ED13" "ED14" "ED15" "S01A"
## [176] "S01B" "S02" "S03" "S04" "S05"
## [181] "S06" "S07" "S08" "S09" "S10A"
## [186] "S10B" "S10C" "S10D" "S10E" "S10F"
## [191] "S10G" "S10T" "CATE_PEA" "TAMA_PEA" "OCUP_PEA"
## [196] "RAMA_PEA" "HORAB" "HORABC" "HORABCO" "PEAA"
## [201] "PEAD" "TIPOHOGA" "NJEF" "NCON" "NPAD"
## [206] "NMAD" "añoest" "ra06ya09" "e01aimde" "e01bimde"
## [211] "e01cimde" "e01dde" "e01ede" "e01fde" "e01gde"
## [216] "e01hde" "e01ide" "e01jde" "e01kde" "e01lde"
## [221] "e01mde" "e01kjde" "E02L1BDE" "E02L2BDE" "fexajustado"
## [226] "ipcm" "pobrezai" "pobnopoi" "quintili" "decili"
## [231] "quintiai" "decilai"## [1] "UPM" "NVIVI" "NHOGA" "DPTO" "AREA"
## [6] "L02" "P02" "P03" "P04" "P04A"
## [11] "P04B" "P05C" "P05P" "P05M" "P06"
## [16] "P08D" "P08M" "P08A" "P09" "A01"
## [21] "A01A" "A02" "A03" "A04" "A04A"
## [26] "A05" "A07" "A08" "A10" "A11A"
## [31] "A11M" "A11S" "A12" "A13REC" "A14REC"
## [36] "A15" "A16" "A17A" "A17M" "A17S"
## [41] "A18" "B01REC" "B02REC" "B03LU" "B03MA"
## [46] "B03MI" "B03JU" "B03VI" "B03SA" "B03DO"
## [51] "B04" "B05" "B06" "B07A" "B07M"
## [56] "B07S" "B08" "B09A" "B09M" "B09S"
## [61] "B10" "B11" "B12" "B13" "B14"
## [66] "B15" "B16G" "B16U" "B16D" "B16T"
## [71] "B17" "B18AG" "B18AU" "B18BG" "B18BU"
## [76] "B19" "B20G" "B20U" "B20D" "B20T"
## [81] "B21" "B22" "B23" "B24" "B25"
## [86] "B26" "B271" "B272" "B28" "B29"
## [91] "B30" "B31" "C01REC" "C02REC" "C03"
## [96] "C04" "C05" "C06" "C07" "C08"
## [101] "C09" "C101" "C102" "C11G" "C11U"
## [106] "C11D" "C11T" "C12" "C13AG" "C13AU"
## [111] "C13BG" "C13BU" "C14" "C14A" "C14B"
## [116] "C14C" "C15" "C16REC" "C17REC" "C18"
## [121] "C18A" "C18B" "C19" "D01" "D02"
## [126] "D03" "D04" "D05" "E01A" "E01B"
## [131] "E01C" "E01D" "E01E" "E01F" "E01G"
## [136] "E01H" "E01I" "E01J" "E01K" "E01L"
## [141] "E01M" "ED01" "ED02" "ED03" "ED0504"
## [146] "ED06C" "ED08" "ED09" "ED10" "ED11A"
## [151] "ED11B" "ED11C" "ED11D" "ED11E" "ED11F"
## [156] "ED12" "ED13" "ED14" "ED14A" "ED15"
## [161] "S01A" "S01B" "S02" "S03" "S04"
## [166] "S05" "S06" "S07" "S08" "S09"
## [171] "CATE_PEA" "TAMA_PEA" "OCUP_PEA" "RAMA_PEA" "HORAB"
## [176] "HORABC" "HORABCO" "PEAD" "PEAA" "TIPOHOGA"
## [181] "NJEF" "NCON" "NPAD" "NMAD" "TIC01"
## [186] "TIC02" "TIC03" "TIC0401" "TIC0402" "TIC0403"
## [191] "TIC0404" "TIC0405" "TIC0406" "TIC0407" "TIC0408"
## [196] "TIC0409" "TIC0501" "TIC0502" "TIC0503" "TIC0504"
## [201] "TIC0505" "TIC0506" "TIC0507" "TIC0508" "TIC0509"
## [206] "TIC0510" "TIC0511" "TIC0512" "TIC0513" "TIC06"
## [211] "añoest" "ra06ya09" "e01aimde" "e01bimde" "e01cimde"
## [216] "e01dde" "e01ede" "e01fde" "e01gde" "e01hde"
## [221] "e01ide" "e01jde" "e01kde" "e01lde" "e01mde"
## [226] "e01kjde" "E02L1BDE" "E02L2BDE" "Fexajustado" "ipcm"
## [231] "pobrezai" "pobnopoi" "quintili" "decili" "quintiai"
## [236] "decilai"## [1] "UPM" "NVIVI" "NHOGA" "DPTOREP" "AREA"
## [6] "L02" "P02" "P03" "P04" "P04A"
## [11] "P04B" "P05C" "P05P" "P05M" "P06"
## [16] "P08D" "P08M" "P08A" "P09" "P10A"
## [21] "P10AB" "P10Z" "P11A" "P11AB" "P11Z"
## [26] "P12" "P13A" "P13B" "P13C" "P14A1"
## [31] "P14A2" "P14B1" "P14B2" "P14C1" "P14C2"
## [36] "A01" "A01A" "A02" "A03" "A04"
## [41] "A04A" "A05" "A07" "A08" "A10"
## [46] "A11A" "A11M" "A11S" "A12" "A13REC"
## [51] "A14REC" "A15" "A16" "A17A" "A17M"
## [56] "A17S" "A18" "B01REC" "B02REC" "B03LU"
## [61] "B03MA" "B03MI" "B03JU" "B03VI" "B03SA"
## [66] "B03DO" "B04" "B05" "B06" "B07A"
## [71] "B07M" "B07S" "B08" "B09A" "B09M"
## [76] "B09S" "B10" "B11" "B12" "B13"
## [81] "B14" "B15" "B16G" "B16U" "B16D"
## [86] "B16T" "B17" "B18AG" "B18AU" "B18BG"
## [91] "B18BU" "B19" "B20G" "B20U" "B20D"
## [96] "B20T" "B21" "B22" "B23" "B24"
## [101] "B25" "B26" "B271" "B272" "B28"
## [106] "B29" "B30" "B31" "C01REC" "C02REC"
## [111] "C03" "C04" "C05" "C06" "C07"
## [116] "C08" "C09" "C101" "C102" "C11G"
## [121] "C11U" "C11D" "C11T" "C12" "C13AG"
## [126] "C13AU" "C13BG" "C13BU" "C14" "C14A"
## [131] "C14B" "C14C" "C15" "C16REC" "C17REC"
## [136] "C18" "C19" "D01" "D02" "D03"
## [141] "D04" "D05" "E01A" "E01B" "E01C"
## [146] "E01D" "E01E" "E01F" "E01G" "E01H"
## [151] "E01I" "E01J" "E01K" "E01L" "E01M"
## [156] "ED01" "ED02" "ED03" "ED0504" "ED06C"
## [161] "ED07" "ED08" "ED09" "ED10" "ED11A"
## [166] "ED11B" "ED11C" "ED11D" "ED11E" "ED11F"
## [171] "ED12" "ED13" "ED14" "ED15" "S01A"
## [176] "S01B" "S02" "S03" "S04" "S05"
## [181] "S06" "S07" "S08" "S09" "S10A"
## [186] "S10B" "S10C" "S10D" "S10E" "S10F"
## [191] "S10G" "S10T" "CATE_PEA" "TAMA_PEA" "OCUP_PEA"
## [196] "RAMA_PEA" "HORAB" "HORABC" "HORABCO" "PEAA"
## [201] "PEAD" "TIPOHOGA" "NJEF" "NCON" "NPAD"
## [206] "NMAD" "añoest" "ra06ya09" "e01aimde" "e01bimde"
## [211] "e01cimde" "e01dde" "e01ede" "e01fde" "e01gde"
## [216] "e01hde" "e01ide" "e01jde" "e01kde" "e01lde"
## [221] "e01mde" "e01kjde" "E02L1BDE" "E02L2BDE" "fexajustado"
## [226] "ipcm" "pobrezai" "pobnopoi" "quintili" "decili"
## [231] "quintiai" "decilai"## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(haven)
R02_EPH2013 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2013.SAV",col_select = c("P02","P06","PEAA","PEAD","fexajustado"))R02_EPH2013 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2013.SAV",col_select = c("P02","P06","PEAA","PEAD","fexajustado"))
R02_EPH2014 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2014.SAV",col_select = c("P02","P06","PEAA","PEAD","fexajustado"))
R02_EPH2015 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2015.SAV",col_select = c("P02","P06","PEAA","PEAD","Fexajustado"))
R02_EPH2016 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2016.SAV",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2017 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2017.SAV",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2018 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2018.sav",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2019 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2019.sav",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2020 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2020.sav",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2021 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2021.sav",col_select = c("P02","P06","PEAA","PEAD","FEX"))
R02_EPH2022 <- read_sav("~/GitHub/EPH_PY/ephpy/R02_EPH2022.sav",col_select = c("P02","P06","PEAA","PEAD","FEX"))Agregar el año identificador de cada base
library(dplyr)
R02_EPH2013 <- R02_EPH2013 %>%
rename(FEX = fexajustado)
R02_EPH2014 <- R02_EPH2014 %>%
rename(FEX = fexajustado)
R02_EPH2015 <- R02_EPH2015 %>%
rename(FEX = Fexajustado)R02_EPH2013$year <- 2013
R02_EPH2014$year <- 2014
R02_EPH2015$year <- 2015
R02_EPH2016$year <- 2016
R02_EPH2017$year <- 2017
R02_EPH2018$year <- 2018
R02_EPH2019$year <- 2019
R02_EPH2020$year <- 2020
R02_EPH2021$year <- 2021
R02_EPH2022$year <- 2022## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAA" "PEAD" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## [1] "P02" "P06" "PEAD" "PEAA" "FEX" "year"## tibble [17,379 × 6] (S3: tbl_df/tbl/data.frame)
## $ P02 : dbl+lbl [1:17379] 42, 14, 12, 20, 25, 50, 50, 80, 58, 25, 29, 5, 50, ...
## ..@ label : chr "Edad"
## ..@ format.spss : chr "F3.0"
## ..@ display_width: int 5
## ..@ labels : Named num 999
## .. ..- attr(*, "names")= chr "NR"
## $ P06 : dbl+lbl [1:17379] 6, 1, 1, 6, 1, 1, 6, 1, 6, 6, 1, 1, 6, 1, 6, 1, 6, 1...
## ..@ label : chr "Sexo"
## ..@ format.spss : chr "F1.0"
## ..@ display_width: int 5
## ..@ labels : Named num [1:2] 1 6
## .. ..- attr(*, "names")= chr [1:2] "Hombres" "Mujeres"
## $ PEAD: dbl+lbl [1:17379] 1, 3, 3, 6, 1, 1, 1, 3, 2, 1, 1, NA, 1, ...
## ..@ label : chr "Acividad Económica Desagregada (Horas Habituales B+C)"
## ..@ format.spss : chr "F1.0"
## ..@ display_width: int 6
## ..@ labels : Named num [1:6] 1 2 3 4 6 9
## .. ..- attr(*, "names")= chr [1:6] "Otros ocupados" "Desocupados de 2ª ó más veces" "Inactivos" "Subocup. Visible" ...
## $ PEAA: dbl+lbl [1:17379] 1, 3, 3, 2, 1, 1, 1, 3, 2, 1, 1, NA, 1, ...
## ..@ label : chr "Actividad Económica Agrupada"
## ..@ format.spss : chr "F1.0"
## ..@ display_width: int 6
## ..@ labels : Named num [1:4] 1 2 3 9
## .. ..- attr(*, "names")= chr [1:4] "Ocupados" "Desocupados" "Inactivos" "NR"
## $ FEX : num [1:17379] 370 370 370 370 370 155 155 199 294 294 ...
## ..- attr(*, "label")= chr "Factor de Expansión"
## ..- attr(*, "format.spss")= chr "F5.0"
## ..- attr(*, "display_width")= int 7
## $ year: num [1:17379] 2022 2022 2022 2022 2022 ...juntar todas las bases en una unica
baseeph=rbind(R02_EPH2013,R02_EPH2014,R02_EPH2015,R02_EPH2016,R02_EPH2017,R02_EPH2018,R02_EPH2019,R02_EPH2020,R02_EPH2021,R02_EPH2022)
table(baseeph$year)##
## 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
## 21207 20272 30898 37814 35215 18563 18233 17582 16569 17379## tibble [233,732 × 6] (S3: tbl_df/tbl/data.frame)
## $ P02 : dbl+lbl [1:233732] 78, 33, 8, 2, 0, 61, 59, 55, 26, 22, 54, 40, 2,...
## ..@ label : chr "Edad"
## ..@ format.spss : chr "F3.0"
## ..@ display_width: int 5
## ..@ labels : Named num 999
## .. ..- attr(*, "names")= chr "NR"
## $ P06 : dbl+lbl [1:233732] 6, 6, 1, 6, 6, 6, 1, 6, 1, 6, 1, 6, 1, 1, 1, 6, 1, ...
## ..@ label : chr "Sexo"
## ..@ format.spss : chr "F1.0"
## ..@ display_width: int 5
## ..@ labels : Named num [1:2] 1 6
## .. ..- attr(*, "names")= chr [1:2] "Hombres" "Mujeres"
## $ PEAD: dbl+lbl [1:233732] 3, 1, NA, NA, NA, 3, 3, 3, 1, 1, 1, 1, NA,...
## ..@ label : chr "Acividad Económica Desagregada"
## ..@ format.spss: chr "F1.0"
## ..@ labels : Named num [1:7] 1 2 3 4 5 6 9
## .. ..- attr(*, "names")= chr [1:7] "Ocupados (Excl. Subocupación)" "Desocupados de 2ª o más veces" "Inactivos" "Subocupación visible" ...
## $ PEAA: dbl+lbl [1:233732] 3, 1, NA, NA, NA, 3, 3, 3, 1, 1, 1, 1, NA,...
## ..@ label : chr "Acividad Económica Agrupada"
## ..@ format.spss : chr "F1.0"
## ..@ display_width: int 6
## ..@ labels : Named num [1:4] 1 2 3 9
## .. ..- attr(*, "names")= chr [1:4] "Ocupados" "Desocupados" "Inactivos" "NR"
## $ FEX : num [1:233732] 64 140 140 140 140 140 100 100 100 100 ...
## ..- attr(*, "label")= chr "Factor de expansión ajustado a la Proy Censo 2012"
## ..- attr(*, "format.spss")= chr "F9.2"
## $ year: num [1:233732] 2013 2013 2013 2013 2013 ...**exploramos algunas variables
##
## 1 2 3 9
## 113429 6387 71803 3baseeph$P06<-factor(baseeph$P06, labels=c("Hombres", "Mujeres"))
baseeph$PEAA<-factor(baseeph$PEAA, labels=c("Ocupado","Desocupado","Inactivo","NR"))## Loading required package: grid## year
## P06 2013 2014 2015 2016 2017 2018 2019
## Hombres 3272534 3320448 3368307 3416198 3464451 3512556 3560800
## Mujeres 3212843 3261523 3310424 3359588 3409045 3458673 3508527
## Sum 6485377 6581971 6678731 6775786 6873496 6971229 7069327
## year
## P06 2020 2021 2022 Sum
## Hombres 3608906 3657340 3705758 34887298
## Mujeres 3558610 3609103 3659784 34348120
## Sum 7167516 7266443 7365542 69235418library(dplyr)
library(tidyr)
# Calcular las sumas de fex para cada año
year_totals <- baseeph %>% group_by(year) %>% summarise(Total = sum(FEX))
# Calcular las sumas de fex para cada combinación de year y p06
freq_table <- baseeph %>% group_by(year, P06) %>% summarise(Cantidad = sum(FEX)) %>%
left_join(year_totals, by = "year") %>% mutate(Porcentaje = (Cantidad / Total) * 100)## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.# Convertir a tabla de frecuencias con porcentajes
pivot_freq_table <- freq_table %>% pivot_wider(names_from = P06, values_from = c(Cantidad, Porcentaje))
print(pivot_freq_table)## # A tibble: 10 × 6
## # Groups: year [10]
## year Total Cantidad_Hombres Cantidad_Mujeres Porcentaje_Hombres
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 6485377 3272534 3212843 50.5
## 2 2014 6581971 3320448 3261523 50.4
## 3 2015 6678731 3368307 3310424 50.4
## 4 2016 6775786 3416198 3359588 50.4
## 5 2017 6873496 3464451 3409045 50.4
## 6 2018 6971229 3512556 3458673 50.4
## 7 2019 7069327 3560800 3508527 50.4
## 8 2020 7167516 3608906 3558610 50.4
## 9 2021 7266443 3657340 3609103 50.3
## 10 2022 7365542 3705758 3659784 50.3
## # ℹ 1 more variable: Porcentaje_Mujeres <dbl>##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout# Crear el gráfico interactivo de líneas usando plotly
plot <- ggplot(freq_table, aes(x = year, y = Cantidad, group = P06, color = as.factor(P06))) +
geom_line() +
geom_point() +
labs(title = "Evolución de la cantidad de P06 por Año", x = "Año", y = "Cantidad") +
scale_color_discrete(name = "P06") +
scale_x_continuous(breaks = freq_table$year)
# Convertir el gráfico de ggplot a uno interactivo usando plotly
interactive_plot <- ggplotly(plot)
interactive_plot