Taller 2

Desarrollo taller

Cargamos la base de datos

library(tidyverse)

Warning: package 'dplyr' was built under R version 4.5.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.1     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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 errors

# 1.1 Descripcion de la base de datos


DASS <- read_delim(
  "../Input/DASS.csv",
  delim = "\t"
)

Rows: 39775 Columns: 172
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr   (2): country, major
dbl (170): Q1A, Q1I, Q1E, Q2A, Q2I, Q2E, Q3A, Q3I, Q3E, Q4A, Q4I, Q4E, Q5A, ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(DASS)

# A tibble: 6 × 172
    Q1A   Q1I   Q1E   Q2A   Q2I   Q2E   Q3A   Q3I   Q3E   Q4A   Q4I   Q4E   Q5A
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     4    28  3890     4    25  2122     2    16  1944     4     8  2044     4
2     4     2  8118     1    36  2890     2    35  4777     3    28  3090     4
3     3     7  5784     1    33  4373     4    41  3242     1    13  6470     4
4     2    23  5081     3    11  6837     2    37  5521     1    27  4556     3
5     2    36  3215     2    13  7731     3     5  4156     4    10  2802     4
6     1    18  6116     1    28  3193     2     2 12542     1     8  6150     3
# ℹ 159 more variables: Q5I <dbl>, Q5E <dbl>, Q6A <dbl>, Q6I <dbl>, Q6E <dbl>,
#   Q7A <dbl>, Q7I <dbl>, Q7E <dbl>, Q8A <dbl>, Q8I <dbl>, Q8E <dbl>,
#   Q9A <dbl>, Q9I <dbl>, Q9E <dbl>, Q10A <dbl>, Q10I <dbl>, Q10E <dbl>,
#   Q11A <dbl>, Q11I <dbl>, Q11E <dbl>, Q12A <dbl>, Q12I <dbl>, Q12E <dbl>,
#   Q13A <dbl>, Q13I <dbl>, Q13E <dbl>, Q14A <dbl>, Q14I <dbl>, Q14E <dbl>,
#   Q15A <dbl>, Q15I <dbl>, Q15E <dbl>, Q16A <dbl>, Q16I <dbl>, Q16E <dbl>,
#   Q17A <dbl>, Q17I <dbl>, Q17E <dbl>, Q18A <dbl>, Q18I <dbl>, Q18E <dbl>, …

glimpse(DASS)

Rows: 39,775
Columns: 172
$ Q1A                   <dbl> 4, 4, 3, 2, 2, 1, 1, 1, 4, 3, 3, 3, 1, 1, 1, 3, …
$ Q1I                   <dbl> 28, 2, 7, 23, 36, 18, 20, 34, 4, 38, 38, 37, 35,…
$ Q1E                   <dbl> 3890, 8118, 5784, 5081, 3215, 6116, 4325, 4796, …
$ Q2A                   <dbl> 4, 1, 1, 3, 2, 1, 1, 1, 4, 2, 1, 3, 1, 4, 1, 1, …
$ Q2I                   <dbl> 25, 36, 33, 11, 13, 28, 34, 9, 14, 28, 16, 35, 2…
$ Q2E                   <dbl> 2122, 2890, 4373, 6837, 7731, 3193, 4009, 2618, …
$ Q3A                   <dbl> 2, 2, 4, 2, 3, 2, 2, 1, 3, 4, 2, 2, 1, 1, 1, 2, …
$ Q3I                   <dbl> 16, 35, 41, 37, 5, 2, 38, 39, 1, 9, 28, 18, 30, …
$ Q3E                   <dbl> 1944, 4777, 3242, 5521, 4156, 12542, 3604, 5823,…
$ Q4A                   <dbl> 4, 3, 1, 1, 4, 1, 3, 1, 4, 1, 1, 2, 1, 2, 1, 2, …
$ Q4I                   <dbl> 8, 28, 13, 27, 10, 8, 40, 12, 20, 7, 34, 10, 15,…
$ Q4E                   <dbl> 2044, 3090, 6470, 4556, 2802, 6150, 4826, 6596, …
$ Q5A                   <dbl> 4, 4, 4, 3, 4, 3, 4, 3, 3, 4, 3, 4, 1, 3, 1, 2, …
$ Q5I                   <dbl> 34, 10, 11, 28, 2, 40, 22, 4, 3, 41, 41, 23, 17,…
$ Q5E                   <dbl> 2153, 5078, 3927, 3269, 5628, 6428, 2842, 7635, …
$ Q6A                   <dbl> 4, 4, 3, 3, 2, 1, 1, 2, 4, 4, 3, 3, 2, 1, 1, 1, …
$ Q6I                   <dbl> 33, 40, 9, 26, 9, 4, 42, 31, 7, 22, 23, 20, 6, 1…
$ Q6E                   <dbl> 2416, 2790, 3704, 3231, 6522, 17001, 2342, 7384,…
$ Q7A                   <dbl> 4, 3, 1, 4, 4, 1, 3, 2, 4, 3, 1, 1, 1, 1, 1, 1, …
$ Q7I                   <dbl> 10, 18, 17, 2, 34, 33, 6, 24, 17, 21, 29, 30, 8,…
$ Q7E                   <dbl> 2818, 3408, 4550, 7138, 2374, 2944, 9018, 11570,…
$ Q8A                   <dbl> 4, 4, 3, 2, 4, 3, 3, 1, 4, 4, 3, 3, 3, 3, 1, 1, …
$ Q8I                   <dbl> 13, 1, 5, 19, 11, 7, 31, 33, 29, 11, 12, 5, 11, …
$ Q8E                   <dbl> 2259, 8342, 3021, 3079, 3054, 8626, 3717, 2958, …
$ Q9A                   <dbl> 2, 3, 2, 3, 4, 3, 3, 1, 4, 4, 1, 4, 1, 2, 1, 2, …
$ Q9I                   <dbl> 21, 37, 32, 31, 7, 14, 39, 15, 31, 26, 31, 6, 9,…
$ Q9E                   <dbl> 5541, 916, 5864, 9650, 2975, 9639, 7023, 12300, …
$ Q10A                  <dbl> 1, 2, 4, 3, 3, 2, 4, 1, 3, 4, 1, 3, 3, 1, 1, 2, …
$ Q10I                  <dbl> 38, 32, 21, 17, 14, 20, 35, 5, 21, 12, 5, 24, 39…
$ Q10E                  <dbl> 4441, 1537, 3722, 4179, 3524, 6175, 3312, 3605, …
$ Q11A                  <dbl> 4, 2, 2, 2, 2, 1, 1, 2, 4, 4, 3, 3, 3, 2, 1, 2, …
$ Q11I                  <dbl> 31, 21, 10, 5, 33, 34, 28, 10, 16, 4, 11, 21, 3,…
$ Q11E                  <dbl> 2451, 3926, 3424, 5928, 3033, 6008, 3930, 5338, …
$ Q12A                  <dbl> 4, 2, 1, 1, 4, 2, 2, 1, 4, 4, 2, 3, 2, 1, 1, 1, …
$ Q12I                  <dbl> 24, 25, 36, 21, 23, 21, 41, 40, 11, 35, 2, 28, 7…
$ Q12E                  <dbl> 3325, 3691, 3236, 2838, 2132, 9267, 4558, 4842, …
$ Q13A                  <dbl> 4, 4, 4, 1, 4, 1, 2, 1, 4, 4, 2, 3, 3, 4, 1, 2, …
$ Q13I                  <dbl> 14, 26, 23, 20, 17, 41, 5, 27, 35, 33, 37, 17, 1…
$ Q13E                  <dbl> 1416, 2004, 2489, 2560, 1314, 5290, 2883, 1422, …
$ Q14A                  <dbl> 4, 4, 1, 4, 4, 3, 2, 2, 3, 4, 4, 3, 4, 1, 1, 3, …
$ Q14I                  <dbl> 37, 4, 34, 29, 16, 1, 19, 11, 30, 13, 10, 29, 2,…
$ Q14E                  <dbl> 5021, 8888, 7290, 5139, 3181, 25694, 8984, 10166…
$ Q15A                  <dbl> 4, 3, 4, 2, 4, 2, 3, 2, 4, 2, 2, 1, 1, 1, 1, 1, …
$ Q15I                  <dbl> 27, 27, 12, 22, 26, 9, 13, 30, 32, 31, 6, 14, 32…
$ Q15E                  <dbl> 2342, 4109, 6587, 3597, 2249, 7634, 41618, 4058,…
$ Q16A                  <dbl> 4, 3, 4, 2, 3, 4, 4, 1, 4, 4, 3, 1, 3, 3, 1, 2, …
$ Q16I                  <dbl> 39, 19, 22, 35, 19, 37, 10, 7, 6, 32, 27, 11, 33…
$ Q16E                  <dbl> 2480, 4058, 3627, 3336, 2623, 8513, 17311, 7770,…
$ Q17A                  <dbl> 3, 4, 4, 3, 4, 2, 2, 1, 3, 4, 2, 3, 3, 4, 1, 3, …
$ Q17I                  <dbl> 6, 12, 38, 10, 35, 25, 37, 42, 2, 3, 17, 4, 27, …
$ Q17E                  <dbl> 2476, 3692, 2905, 4506, 3093, 9078, 4514, 1513, …
$ Q18A                  <dbl> 4, 2, 2, 1, 4, 1, 2, 2, 4, 1, 3, 2, 2, 3, 1, 3, …
$ Q18I                  <dbl> 35, 6, 18, 14, 38, 15, 2, 38, 25, 40, 8, 7, 4, 1…
$ Q18E                  <dbl> 1627, 3373, 2998, 2695, 7098, 4381, 43266690, 32…
$ Q19A                  <dbl> 3, 1, 2, 1, 4, 1, 1, 1, 4, 4, 1, 4, 4, 1, 1, 1, …
$ Q19I                  <dbl> 17, 23, 8, 25, 37, 23, 3, 8, 28, 16, 30, 8, 41, …
$ Q19E                  <dbl> 9050, 6015, 10233, 8128, 1938, 6647, 22234, 9377…
$ Q20A                  <dbl> 3, 1, 1, 2, 4, 2, 3, 2, 4, 4, 1, 3, 2, 1, 1, 1, …
$ Q20I                  <dbl> 30, 16, 16, 15, 15, 36, 7, 1, 42, 1, 39, 13, 36,…
$ Q20E                  <dbl> 7001, 3023, 4258, 3125, 3502, 6250, 5111, 10548,…
$ Q21A                  <dbl> 1, 2, 4, 1, 3, 1, 4, 1, 3, 4, 1, 2, 3, 1, 1, 2, …
$ Q21I                  <dbl> 11, 22, 28, 6, 32, 39, 15, 26, 13, 30, 24, 33, 2…
$ Q21E                  <dbl> 4719, 2670, 2888, 4061, 4776, 3842, 2831, 1798, …
$ Q22A                  <dbl> 4, 3, 3, 1, 3, 1, 1, 1, 4, 3, 1, 4, 2, 3, 1, 2, …
$ Q22I                  <dbl> 20, 3, 4, 40, 18, 16, 30, 36, 39, 17, 18, 22, 23…
$ Q22E                  <dbl> 2984, 5727, 59592, 4272, 4463, 7876, 103530, 408…
$ Q23A                  <dbl> 4, 1, 2, 1, 4, 1, 3, 1, 4, 1, 1, 1, 1, 2, 1, 2, …
$ Q23I                  <dbl> 36, 39, 3, 12, 4, 27, 14, 25, 19, 5, 14, 15, 13,…
$ Q23E                  <dbl> 1313, 3641, 11732, 4029, 2436, 3124, 3398, 2053,…
$ Q24A                  <dbl> 4, 2, 4, 1, 2, 2, 3, 1, 3, 4, 3, 2, 2, 3, 1, 2, …
$ Q24I                  <dbl> 42, 33, 2, 9, 40, 12, 29, 19, 36, 14, 13, 41, 5,…
$ Q24E                  <dbl> 2444, 2670, 8834, 5630, 4047, 6836, 4551, 6303, …
$ Q25A                  <dbl> 4, 2, 2, 1, 4, 1, 2, 2, 2, 3, 1, 3, 1, 1, 1, 1, …
$ Q25I                  <dbl> 1, 7, 29, 18, 31, 31, 17, 14, 12, 2, 25, 9, 14, …
$ Q25E                  <dbl> 9880, 7649, 7358, 30631, 3787, 12063, 7096, 7299…
$ Q26A                  <dbl> 4, 3, 1, 2, 4, 1, 2, 2, 4, 4, 1, 3, 3, 1, 1, 3, …
$ Q26I                  <dbl> 2, 11, 30, 24, 42, 3, 27, 41, 15, 10, 40, 2, 38,…
$ Q26E                  <dbl> 4695, 2537, 4928, 9870, 2102, 9264, 2908, 3395, …
$ Q27A                  <dbl> 4, 3, 2, 4, 2, 1, 1, 2, 2, 4, 3, 2, 3, 2, 1, 3, …
$ Q27I                  <dbl> 5, 5, 15, 4, 1, 35, 8, 20, 22, 8, 26, 42, 34, 11…
$ Q27E                  <dbl> 1677, 2907, 3036, 2411, 12351, 3957, 3189, 2520,…
$ Q28A                  <dbl> 3, 4, 1, 1, 4, 1, 2, 1, 4, 4, 1, 2, 1, 3, 1, 1, …
$ Q28I                  <dbl> 4, 9, 19, 16, 3, 42, 36, 35, 37, 36, 42, 16, 42,…
$ Q28E                  <dbl> 6723, 1685, 4127, 9478, 2410, 2537, 2409, 5961, …
$ Q29A                  <dbl> 4, 3, 2, 3, 2, 3, 2, 1, 4, 4, 3, 3, 3, 3, 1, 1, …
$ Q29I                  <dbl> 3, 41, 37, 1, 22, 17, 1, 28, 8, 42, 20, 38, 25, …
$ Q29E                  <dbl> 5953, 4726, 3934, 7618, 5056, 10880, 1672595, 33…
$ Q30A                  <dbl> 2, 3, 2, 3, 4, 2, 3, 1, 3, 3, 2, 3, 3, 3, 1, 1, …
$ Q30I                  <dbl> 26, 17, 26, 32, 39, 5, 4, 13, 24, 37, 32, 36, 22…
$ Q30E                  <dbl> 8062, 6063, 10782, 12639, 3343, 8462, 9032, 1053…
$ Q31A                  <dbl> 4, 2, 4, 3, 3, 2, 4, 1, 4, 4, 3, 2, 1, 2, 1, 2, …
$ Q31I                  <dbl> 12, 20, 1, 34, 27, 32, 32, 22, 40, 19, 7, 39, 12…
$ Q31E                  <dbl> 5560, 3307, 8273, 5378, 3012, 5615, 5133, 5667, …
$ Q32A                  <dbl> 4, 3, 3, 1, 4, 1, 2, 1, 2, 2, 3, 2, 1, 2, 1, 2, …
$ Q32I                  <dbl> 7, 14, 39, 41, 20, 30, 16, 37, 9, 6, 35, 12, 19,…
$ Q32E                  <dbl> 3032, 4995, 3501, 8923, 3520, 11412, 5469, 6062,…
$ Q33A                  <dbl> 2, 3, 1, 2, 4, 4, 4, 1, 4, 4, 1, 4, 1, 2, 1, 1, …
$ Q33I                  <dbl> 29, 38, 27, 38, 8, 6, 23, 21, 38, 25, 22, 40, 20…
$ Q33E                  <dbl> 3316, 2505, 3824, 2977, 1868, 5112, 2690, 1892, …
$ Q34A                  <dbl> 3, 2, 4, 4, 4, 1, 3, 1, 4, 4, 2, 3, 2, 1, 1, 2, …
$ Q34I                  <dbl> 40, 34, 25, 3, 25, 29, 9, 32, 27, 15, 33, 31, 10…
$ Q34E                  <dbl> 3563, 2540, 2141, 5620, 2536, 3070, 7122, 1405, …
$ Q35A                  <dbl> 4, 2, 3, 1, 3, 3, 2, 1, 2, 3, 3, 2, 1, 1, 1, 3, …
$ Q35I                  <dbl> 23, 31, 6, 7, 24, 10, 18, 29, 23, 29, 36, 26, 29…
$ Q35E                  <dbl> 5594, 4359, 17461, 16760, 3725, 13377, 8044, 101…
$ Q36A                  <dbl> 4, 3, 4, 1, 4, 2, 2, 1, 4, 4, 1, 3, 2, 4, 1, 1, …
$ Q36I                  <dbl> 41, 15, 24, 8, 30, 38, 21, 23, 10, 39, 4, 3, 37,…
$ Q36E                  <dbl> 1477, 3925, 1557, 6427, 2130, 4506, 2242, 3675, …
$ Q37A                  <dbl> 1, 4, 4, 2, 3, 2, 4, 1, 4, 4, 1, 1, 4, 1, 1, 2, …
$ Q37I                  <dbl> 18, 13, 40, 39, 29, 24, 11, 16, 26, 23, 15, 32, …
$ Q37E                  <dbl> 3885, 4609, 4446, 3760, 3952, 17227, 3951, 5432,…
$ Q38A                  <dbl> 2, 2, 4, 1, 3, 2, 4, 1, 3, 4, 2, 2, 2, 1, 1, 1, …
$ Q38I                  <dbl> 9, 30, 42, 13, 21, 13, 24, 3, 5, 24, 3, 19, 1, 2…
$ Q38E                  <dbl> 5265, 3755, 1883, 4112, 10694, 7844, 2272, 2897,…
$ Q39A                  <dbl> 4, 2, 2, 3, 3, 1, 4, 2, 2, 4, 3, 4, 4, 2, 1, 3, …
$ Q39I                  <dbl> 19, 42, 35, 42, 41, 26, 33, 6, 33, 27, 21, 1, 18…
$ Q39E                  <dbl> 1892, 2323, 5790, 2769, 3231, 20253, 3398, 2732,…
$ Q40A                  <dbl> 3, 1, 2, 4, 4, 1, 2, 1, 4, 4, 1, 3, 2, 4, 1, 3, …
$ Q40I                  <dbl> 22, 24, 14, 33, 12, 22, 12, 2, 41, 18, 9, 34, 40…
$ Q40E                  <dbl> 4228, 5713, 4432, 4432, 3604, 8528, 5101, 9251, …
$ Q41A                  <dbl> 4, 2, 1, 4, 4, 1, 2, 1, 4, 3, 1, 1, 3, 2, 1, 1, …
$ Q41I                  <dbl> 32, 8, 20, 30, 28, 11, 25, 17, 34, 20, 19, 25, 2…
$ Q41E                  <dbl> 1574, 1334, 2203, 3643, 1950, 4370, 93656, 2954,…
$ Q42A                  <dbl> 4, 2, 4, 2, 3, 2, 3, 2, 4, 4, 2, 2, 2, 3, 1, 3, …
$ Q42I                  <dbl> 15, 29, 31, 36, 6, 19, 26, 18, 18, 34, 1, 27, 26…
$ Q42E                  <dbl> 2969, 5562, 5768, 3698, 6265, 10310, 84607, 8665…
$ country               <chr> "IN", "US", "PL", "US", "MY", "US", "MX", "GB", …
$ source                <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, …
$ introelapse           <dbl> 19, 1, 5, 3, 1766, 4, 1143, 234, 17, 2, 3, 515, …
$ testelapse            <dbl> 167, 193, 271, 261, 164, 349, 45459, 232, 195, 1…
$ surveyelapse          <dbl> 166, 186, 122, 336, 157, 213, 170, 152, 242, 140…
$ TIPI1                 <dbl> 1, 6, 2, 1, 2, 2, 2, 7, 1, 1, 5, 6, 5, 5, 3, 3, …
$ TIPI2                 <dbl> 5, 5, 5, 1, 5, 1, 5, 6, 4, 7, 3, 5, 1, 2, 5, 5, …
$ TIPI3                 <dbl> 7, 4, 2, 7, 3, 6, 6, 4, 5, 5, 6, 6, 4, 5, 6, 4, …
$ TIPI4                 <dbl> 7, 7, 2, 4, 6, 1, 5, 5, 7, 7, 6, 6, 6, 5, 1, 5, …
$ TIPI5                 <dbl> 7, 5, 5, 6, 5, 7, 3, 3, 5, 5, 3, 6, 5, 2, 6, 6, …
$ TIPI6                 <dbl> 7, 4, 6, 4, 5, 7, 2, 2, 7, 7, 4, 2, 5, 6, 5, 5, …
$ TIPI7                 <dbl> 7, 7, 5, 6, 5, 7, 6, 6, 6, 1, 4, 5, 7, 7, 3, 4, …
$ TIPI8                 <dbl> 5, 7, 5, 1, 6, 2, 3, 3, 7, 2, 7, 3, 6, 6, 2, 3, …
$ TIPI9                 <dbl> 1, 1, 3, 6, 3, 6, 5, 5, 1, 1, 5, 3, 2, 6, 7, 4, …
$ TIPI10                <dbl> 1, 5, 2, 1, 3, 7, 5, 2, 4, 7, 7, 3, 1, 2, 2, 4, …
$ VCL1                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ VCL2                  <dbl> 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, …
$ VCL3                  <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
$ VCL4                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, …
$ VCL5                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ VCL6                  <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
$ VCL7                  <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
$ VCL8                  <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, …
$ VCL9                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
$ VCL10                 <dbl> 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, …
$ VCL11                 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, …
$ VCL12                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
$ VCL13                 <dbl> 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, …
$ VCL14                 <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ VCL15                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, …
$ VCL16                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, …
$ education             <dbl> 2, 2, 2, 1, 3, 2, 2, 4, 2, 1, 1, 2, 3, 4, 3, 2, …
$ urban                 <dbl> 3, 3, 3, 3, 2, 3, 3, 2, 3, 1, 2, 1, 0, 2, 2, 2, …
$ gender                <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, …
$ engnat                <dbl> 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 1, …
$ age                   <dbl> 16, 16, 17, 13, 19, 20, 17, 29, 16, 18, 15, 18, …
$ screensize            <dbl> 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, …
$ uniquenetworklocation <dbl> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ hand                  <dbl> 1, 2, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ religion              <dbl> 12, 7, 4, 4, 10, 4, 7, 2, 12, 2, 6, 6, 1, 12, 1,…
$ orientation           <dbl> 1, 0, 3, 5, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, …
$ race                  <dbl> 10, 70, 60, 70, 10, 70, 60, 60, 70, 60, 60, 60, …
$ voted                 <dbl> 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, …
$ married               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, …
$ familysize            <dbl> 2, 4, 3, 5, 4, 4, 4, 2, 4, 3, 1, 2, 2, 5, 2, 1, …
$ major                 <chr> NA, NA, NA, "biology", "Psychology", NA, "Mechat…

names(DASS)

  [1] "Q1A"                   "Q1I"                   "Q1E"                  
  [4] "Q2A"                   "Q2I"                   "Q2E"                  
  [7] "Q3A"                   "Q3I"                   "Q3E"                  
 [10] "Q4A"                   "Q4I"                   "Q4E"                  
 [13] "Q5A"                   "Q5I"                   "Q5E"                  
 [16] "Q6A"                   "Q6I"                   "Q6E"                  
 [19] "Q7A"                   "Q7I"                   "Q7E"                  
 [22] "Q8A"                   "Q8I"                   "Q8E"                  
 [25] "Q9A"                   "Q9I"                   "Q9E"                  
 [28] "Q10A"                  "Q10I"                  "Q10E"                 
 [31] "Q11A"                  "Q11I"                  "Q11E"                 
 [34] "Q12A"                  "Q12I"                  "Q12E"                 
 [37] "Q13A"                  "Q13I"                  "Q13E"                 
 [40] "Q14A"                  "Q14I"                  "Q14E"                 
 [43] "Q15A"                  "Q15I"                  "Q15E"                 
 [46] "Q16A"                  "Q16I"                  "Q16E"                 
 [49] "Q17A"                  "Q17I"                  "Q17E"                 
 [52] "Q18A"                  "Q18I"                  "Q18E"                 
 [55] "Q19A"                  "Q19I"                  "Q19E"                 
 [58] "Q20A"                  "Q20I"                  "Q20E"                 
 [61] "Q21A"                  "Q21I"                  "Q21E"                 
 [64] "Q22A"                  "Q22I"                  "Q22E"                 
 [67] "Q23A"                  "Q23I"                  "Q23E"                 
 [70] "Q24A"                  "Q24I"                  "Q24E"                 
 [73] "Q25A"                  "Q25I"                  "Q25E"                 
 [76] "Q26A"                  "Q26I"                  "Q26E"                 
 [79] "Q27A"                  "Q27I"                  "Q27E"                 
 [82] "Q28A"                  "Q28I"                  "Q28E"                 
 [85] "Q29A"                  "Q29I"                  "Q29E"                 
 [88] "Q30A"                  "Q30I"                  "Q30E"                 
 [91] "Q31A"                  "Q31I"                  "Q31E"                 
 [94] "Q32A"                  "Q32I"                  "Q32E"                 
 [97] "Q33A"                  "Q33I"                  "Q33E"                 
[100] "Q34A"                  "Q34I"                  "Q34E"                 
[103] "Q35A"                  "Q35I"                  "Q35E"                 
[106] "Q36A"                  "Q36I"                  "Q36E"                 
[109] "Q37A"                  "Q37I"                  "Q37E"                 
[112] "Q38A"                  "Q38I"                  "Q38E"                 
[115] "Q39A"                  "Q39I"                  "Q39E"                 
[118] "Q40A"                  "Q40I"                  "Q40E"                 
[121] "Q41A"                  "Q41I"                  "Q41E"                 
[124] "Q42A"                  "Q42I"                  "Q42E"                 
[127] "country"               "source"                "introelapse"          
[130] "testelapse"            "surveyelapse"          "TIPI1"                
[133] "TIPI2"                 "TIPI3"                 "TIPI4"                
[136] "TIPI5"                 "TIPI6"                 "TIPI7"                
[139] "TIPI8"                 "TIPI9"                 "TIPI10"               
[142] "VCL1"                  "VCL2"                  "VCL3"                 
[145] "VCL4"                  "VCL5"                  "VCL6"                 
[148] "VCL7"                  "VCL8"                  "VCL9"                 
[151] "VCL10"                 "VCL11"                 "VCL12"                
[154] "VCL13"                 "VCL14"                 "VCL15"                
[157] "VCL16"                 "education"             "urban"                
[160] "gender"                "engnat"                "age"                  
[163] "screensize"            "uniquenetworklocation" "hand"                 
[166] "religion"              "orientation"           "race"                 
[169] "voted"                 "married"               "familysize"           
[172] "major"

library(dplyr)

#Creamos una base aparte solo con las variables que nos interesan

DASS_A <- DASS %>%
  select(
    starts_with("Q") & ends_with("A"),
    starts_with("TIPI"),
    country,
    education,
    urban,
    gender,
    engnat,
    age,
    screensize,
    uniquenetworklocation,
    hand,
    religion,
    orientation,
    race,
    voted,
    married,
    familysize,
    major
  )
#Creamos los totales de las subescalas de la prueba


DASS_A <- DASS_A %>%
  mutate(
    estres = rowSums(select(., Q1A, Q6A, Q8A, Q11A, Q12A, Q14A,
                            Q18A, Q22A, Q27A, Q29A, Q32A, Q33A,
                            Q35A, Q39A), na.rm = TRUE),

    ansiedad = rowSums(select(., Q2A, Q4A, Q7A, Q9A, Q15A, Q19A,
                              Q20A, Q23A, Q25A, Q28A, Q30A, Q36A,
                              Q40A, Q41A), na.rm = TRUE),

    depresion = rowSums(select(., Q3A, Q5A, Q10A, Q13A, Q16A, Q17A,
                               Q21A, Q24A, Q26A, Q31A, Q34A, Q37A,
                               Q38A, Q42A), na.rm = TRUE)
  )
DASS_A <- DASS_A %>%
  mutate(consecutivo = row_number())
# 2 Estadísticas descriptivas: Elegimos variable estrato_vivienda
summary(DASS_A)

      Q1A             Q2A             Q3A             Q4A            Q5A       
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.00   1st Qu.:2.000  
 Median :3.000   Median :2.000   Median :2.000   Median :2.00   Median :2.000  
 Mean   :2.619   Mean   :2.172   Mean   :2.226   Mean   :1.95   Mean   :2.521  
 3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.00   3rd Qu.:3.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.00   Max.   :4.000  
      Q6A            Q7A             Q8A            Q9A            Q10A      
 Min.   :1.00   Min.   :1.000   Min.   :1.00   Min.   :1.00   Min.   :1.000  
 1st Qu.:2.00   1st Qu.:1.000   1st Qu.:2.00   1st Qu.:2.00   1st Qu.:1.000  
 Median :2.00   Median :2.000   Median :2.00   Median :3.00   Median :2.000  
 Mean   :2.54   Mean   :1.925   Mean   :2.48   Mean   :2.67   Mean   :2.447  
 3rd Qu.:3.00   3rd Qu.:3.000   3rd Qu.:3.00   3rd Qu.:4.00   3rd Qu.:4.000  
 Max.   :4.00   Max.   :4.000   Max.   :4.00   Max.   :4.00   Max.   :4.000  
      Q11A            Q12A            Q13A            Q14A           Q15A      
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.00   1st Qu.:1.000  
 Median :3.000   Median :2.000   Median :3.000   Median :2.00   Median :2.000  
 Mean   :2.803   Mean   :2.426   Mean   :2.785   Mean   :2.58   Mean   :1.827  
 3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:4.000   3rd Qu.:4.00   3rd Qu.:2.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.00   Max.   :4.000  
      Q16A           Q17A            Q18A            Q19A            Q20A      
 Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.00   1st Qu.:2.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000  
 Median :2.00   Median :3.000   Median :2.000   Median :2.000   Median :2.000  
 Mean   :2.52   Mean   :2.659   Mean   :2.478   Mean   :1.946   Mean   :2.323  
 3rd Qu.:4.00   3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000  
 Max.   :4.00   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
      Q21A           Q22A            Q23A            Q24A            Q25A      
 Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:1.00   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.000   1st Qu.:1.000  
 Median :2.00   Median :2.000   Median :1.000   Median :2.000   Median :2.000  
 Mean   :2.35   Mean   :2.344   Mean   :1.562   Mean   :2.437   Mean   :2.184  
 3rd Qu.:3.00   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.000  
 Max.   :4.00   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
      Q26A            Q27A            Q28A            Q29A      
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.000  
 Median :3.000   Median :3.000   Median :2.000   Median :3.000  
 Mean   :2.659   Mean   :2.612   Mean   :2.217   Mean   :2.653  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:4.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
      Q30A            Q31A            Q32A            Q33A      
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:1.000   1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.000  
 Median :2.000   Median :2.000   Median :2.000   Median :2.000  
 Mean   :2.392   Mean   :2.377   Mean   :2.446   Mean   :2.414  
 3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
      Q34A            Q35A            Q36A            Q37A      
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000  
 Median :3.000   Median :2.000   Median :2.000   Median :2.000  
 Mean   :2.634   Mean   :2.303   Mean   :2.268   Mean   :2.374  
 3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
      Q38A            Q39A            Q40A            Q41A            Q42A     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:1.000   1st Qu.:2.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.00  
 Median :2.000   Median :2.000   Median :3.000   Median :2.000   Median :3.00  
 Mean   :2.393   Mean   :2.454   Mean   :2.651   Mean   :1.966   Mean   :2.68  
 3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:4.00  
 Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.00  
     TIPI1           TIPI2           TIPI3           TIPI4      
 Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.:2.000   1st Qu.:3.000   1st Qu.:4.000   1st Qu.:4.000  
 Median :4.000   Median :5.000   Median :5.000   Median :6.000  
 Mean   :3.786   Mean   :4.193   Mean   :4.742   Mean   :5.173  
 3rd Qu.:5.000   3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:7.000  
 Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
     TIPI5           TIPI6           TIPI7           TIPI8           TIPI9     
 Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.00  
 1st Qu.:4.000   1st Qu.:4.000   1st Qu.:5.000   1st Qu.:3.000   1st Qu.:2.00  
 Median :5.000   Median :5.000   Median :6.000   Median :5.000   Median :4.00  
 Mean   :4.934   Mean   :4.852   Mean   :5.274   Mean   :4.281   Mean   :3.65  
 3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:7.000   3rd Qu.:6.000   3rd Qu.:5.00  
 Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.00  
     TIPI10        country            education         urban     
 Min.   :0.000   Length:39775       Min.   :0.000   Min.   :0.00  
 1st Qu.:2.000   Class :character   1st Qu.:2.000   1st Qu.:2.00  
 Median :4.000   Mode  :character   Median :3.000   Median :2.00  
 Mean   :3.731                      Mean   :2.504   Mean   :2.22  
 3rd Qu.:5.000                      3rd Qu.:3.000   3rd Qu.:3.00  
 Max.   :7.000                      Max.   :4.000   Max.   :3.00  
     gender         engnat           age            screensize   
 Min.   :0.00   Min.   :0.000   Min.   :  13.00   Min.   :1.000  
 1st Qu.:2.00   1st Qu.:1.000   1st Qu.:  18.00   1st Qu.:1.000  
 Median :2.00   Median :2.000   Median :  21.00   Median :1.000  
 Mean   :1.79   Mean   :1.636   Mean   :  23.61   Mean   :1.275  
 3rd Qu.:2.00   3rd Qu.:2.000   3rd Qu.:  25.00   3rd Qu.:2.000  
 Max.   :3.00   Max.   :2.000   Max.   :1998.00   Max.   :2.000  
 uniquenetworklocation      hand          religion       orientation   
 Min.   :1.0           Min.   :0.000   Min.   : 0.000   Min.   :0.000  
 1st Qu.:1.0           1st Qu.:1.000   1st Qu.: 4.000   1st Qu.:1.000  
 Median :1.0           Median :1.000   Median :10.000   Median :1.000  
 Mean   :1.2           Mean   :1.135   Mean   : 7.556   Mean   :1.643  
 3rd Qu.:1.0           3rd Qu.:1.000   3rd Qu.:10.000   3rd Qu.:2.000  
 Max.   :2.0           Max.   :3.000   Max.   :12.000   Max.   :5.000  
      race           voted          married       familysize    
 Min.   :10.00   Min.   :0.000   Min.   :0.00   Min.   :  0.00  
 1st Qu.:10.00   1st Qu.:1.000   1st Qu.:1.00   1st Qu.:  2.00  
 Median :10.00   Median :2.000   Median :1.00   Median :  3.00  
 Mean   :31.31   Mean   :1.706   Mean   :1.16   Mean   :  3.51  
 3rd Qu.:60.00   3rd Qu.:2.000   3rd Qu.:1.00   3rd Qu.:  4.00  
 Max.   :70.00   Max.   :2.000   Max.   :3.00   Max.   :133.00  
    major               estres         ansiedad       depresion    
 Length:39775       Min.   :14.00   Min.   :14.00   Min.   :14.00  
 Class :character   1st Qu.:27.00   1st Qu.:22.00   1st Qu.:25.00  
 Mode  :character   Median :35.00   Median :29.00   Median :35.00  
                    Mean   :35.15   Mean   :30.05   Mean   :35.06  
                    3rd Qu.:43.00   3rd Qu.:37.00   3rd Qu.:46.00  
                    Max.   :56.00   Max.   :56.00   Max.   :56.00  
  consecutivo   
 Min.   :    1  
 1st Qu.: 9944  
 Median :19888  
 Mean   :19888  
 3rd Qu.:29832  
 Max.   :39775

summary(DASS_A $ estres)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  14.00   27.00   35.00   35.15   43.00   56.00

mean(DASS_A $ estres, na.rm = TRUE)

[1] 35.15389

sd(DASS_A $ estres, na.rm = TRUE)#?siempre se debe poner los datos faltantes? NA.RM

[1] 10.52329

Definición del modelo de regresión lineal

Para el modelo de regresión lineal buscaremos predecir:

y = estrés

a través de las variables explicativas

x = tamaño de la familia + edad+ ansiedad

En este caso, creemos que los puntajes de estrés pueden ser predecidos por el sexo, edad y puntajes de ansiedad de los participantes. Con respecto a la edad, nuestra hipótesis es que las personas mas jovenes tendrán mayores puntajes de estrés, debido a las demandas académicas y laborales. Así mismo, con respecto al tamaño de la familia, se espera encontrar que las personas con menos miembros en su familia presenten mayores puntajes de estrés, debido a una red de apoyo mas pequeña. Por último, con respecto a la ansiedad, se espera que las personas con mayor puntajes de ansiedad, presenten mayores puntajes de estrés.

modelo <- lm(estres ~ age + ansiedad + familysize , data = DASS_A, na.action = na.exclude)
summary(modelo)


Call:
lm(formula = estres ~ age + ansiedad + familysize, data = DASS_A, 
    na.action = na.exclude)

Residuals:
     Min       1Q   Median       3Q      Max 
-25.9957  -4.3756  -0.4037   3.8759  30.4568 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 10.754328   0.117959  91.170  < 2e-16 ***
age          0.006680   0.001464   4.562 5.07e-06 ***
ansiedad     0.825333   0.003082 267.778  < 2e-16 ***
familysize  -0.160483   0.014684 -10.929  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.27 on 39771 degrees of freedom
Multiple R-squared:  0.645, Adjusted R-squared:  0.645 
F-statistic: 2.409e+04 on 3 and 39771 DF,  p-value: < 2.2e-16

Intrerpretación

Según el modelo de regresión, la edad es un predictor significativo (b = 0.0065; p <0.001) siendo que por cada año adicional, aumenta el puntaje de estrés en un 0,0065 puntos. Con respecto a la ansiedad, resultó ser un predictor significativo (b = 0.822, p <0.001) siendo que por cada aumento de un punto en la ansiedad, aumenta en 0.822 el puntaje de estrés. Por último, la variable tamaño de familia, predice significativamente el puntaje de estrés (b =-0.160, p<0.001) siendo que a menor cantidad de personas en la familia, aumenta en 0.16 puntos el estrés. El modelo explica el 64% (R2 = 0.645) de la varianza del estrés.
Diagnóstico de supuestos
1. Linealidad: Según el gráfico de linealidad, no se observa un patrón lineal en los residuos del modelo.

par(mfrow=c(1,2))   
plot(modelo, 1, caption = "Modelo")

Normalidad: Según la prueba de Shapiro-Wilk, el modelo no tiene normalidad de residuos (p<0.001). Se corre la prueba con 5000 observaciones aleatorias ya que la base tiene 39775 observaciones. Sin embargo al hacer la gráfica de residuos, se observa un posible comportamiento normal. Es posible que la prueba de Shapiro-Wilk sea significativa debido a que es suceptible a las muestras grandes.

set.seed(123)
residuos_muestra <- sample(residuals(modelo), 5000)
shapiro.test(residuos_muestra)


    Shapiro-Wilk normality test

data:  residuos_muestra
W = 0.99272, p-value = 2.572e-15

par(mfrow=c(2,2))   
plot(modelo, 2, caption = "Modelo")   
plot(density(modelo$residuals))

Homocedasticidad: Se realiza la gráfica de homocedasticidad y se ajusta para ver mejor la distribución debido a la cantidad de observaciones. En la gráfica se observa una distribución heterocedastica.

par(mfrow=c(1,2))   
plot(modelo, 3, caption = "Modelo")

DASS_A %>%
  mutate(residuos = residuals(modelo), ajustados = fitted(modelo)) %>%
  ggplot(aes(x = ajustados, y = residuos)) +
  geom_point(alpha = 0.03) +
  geom_hline(yintercept = 0, color = "red", linetype = "dashed") +
  geom_smooth(se = FALSE, color = "blue") +
  labs(x = "Valores ajustados", y = "Residuos", 
       title = "Gráfico de homocedasticidad") +
  theme_minimal()

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Multicolinealidad: Se realiza el diagnóstico de multicolinealidad por medio de una matriz de correlación entre las variables independientes del modelo. Según la matriz, no existe multicolinealidad debido a que las correlaciones entre las variables son muy bajas (<0.60)

variables.cor <- cor(DASS_A[, c("age", "ansiedad", "familysize")], 
                      method = "pearson")
round(variables.cor, digits = 2)

             age ansiedad familysize
age         1.00     -0.1       0.02
ansiedad   -0.10      1.0       0.00
familysize  0.02      0.0       1.00