Investigación local Maullin

Cargo los Paquetes

library("tidyverse")
library("forcats")
library("ggthemes")

Reformateo el data set

df <- read.csv("local_maullin.csv", header = TRUE, sep=",")

Renombro la variable ano como edad

df <- df %>% 
  rename(edad = ano) %>%
  gather(key = "anno", value = "valor", X2017:X2018)

Saco la X de los años

df$anno <- df$anno %>% 
  forcats::fct_recode(., "2017" = "X2017", "2018" = "X2018")

Grafico en linea (solo comparo 1 valor de diferentes años por eso grafico lineal) para cada uno de los topicos

En caso de graficar una variable puedo filtrar por topico, como por ej por fluor

View(df)
df %>%
  filter(Topico == "fluor") %>% 
  ggplot(aes(x = anno, y = valor, color = Topico, group = Topico)) +
  geom_line() +

Error: Incomplete expression:   filter(Topico == "fluor") >
  ggplot(aes(x = anno, y = valor, color = Topico, group = Topico)) +
  geom_line() +
  facet_wrap(~edad)

Esto hace posible ver cada columna en un marco de datos.

glimpse(df)

Observations: 36
Variables: 4
$ edad   <fctr> <1, <1, <1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, <1, <1, <1, 1, 1, 1, 2, ...
$ Topico <fctr> Dieta, Higiene, fluor, Dieta, Higiene, fluor, Dieta, Higiene, fluor, Dieta, Higie...
$ anno   <fctr> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 201...
$ valor  <dbl> 85.7, 28.6, 28.6, 85.7, 28.6, 28.6, 38.9, 55.6, 55.6, 44.4, 66.7, 100.0, 35.0, 75....

Filtro por topico para “Dieta”

Filtro por topico para “Fluor”

Filtro por topico para “Higiene”

Comparo en boxplot años y filtro de edad

Los analisis de ANOVA

anova1 <- aov(valor~anno+edad, data = df)
summary(anova1)

            Df Sum Sq Mean Sq F value Pr(>F)  
anno         1   1982  1981.7   3.731 0.0632 .
edad         5   2257   451.5   0.850 0.5259  
Residuals   29  15404   531.2                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(anova1)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = valor ~ anno + edad, data = df)

$anno
              diff        lwr      upr     p adj
2018-2017 14.83889 -0.8732959 30.55107 0.0632396

$edad
           diff       lwr      upr     p adj
2-1    0.400000 -40.16378 40.96378 1.0000000
3-1   12.216667 -28.34711 52.78044 0.9386410
4-1    8.250000 -32.31378 48.81378 0.9886308
5-1   10.700000 -29.86378 51.26378 0.9644577
<1-1 -10.783333 -51.34711 29.78044 0.9632760
3-2   11.816667 -28.74711 52.38044 0.9463590
4-2    7.850000 -32.71378 48.41378 0.9909293
5-2   10.300000 -30.26378 50.86378 0.9697693
<1-2 -11.183333 -51.74711 29.38044 0.9572315
4-3   -3.966667 -44.53044 36.59711 0.9996478
5-3   -1.516667 -42.08044 39.04711 0.9999970
<1-3 -23.000000 -63.56378 17.56378 0.5250260
5-4    2.450000 -38.11378 43.01378 0.9999672
<1-4 -19.033333 -59.59711 21.53044 0.7088426
<1-5 -21.483333 -62.04711 19.08044 0.5960020

El analisis de ANOVA2

anova2 <- aov(valor~edad+anno+edad:anno, data=df)
summary(anova2)

            Df Sum Sq Mean Sq F value Pr(>F)  
edad         5   2257   451.5   0.745  0.597  
anno         1   1982  1981.7   3.272  0.083 .
edad:anno    5    867   173.4   0.286  0.916  
Residuals   24  14537   605.7                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(anova2)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = valor ~ edad + anno + edad:anno, data = df)

$edad
           diff       lwr      upr     p adj
2-1    0.400000 -43.53412 44.33412 1.0000000
3-1   12.216667 -31.71745 56.15079 0.9524397
4-1    8.250000 -35.68412 52.18412 0.9914121
5-1   10.700000 -33.23412 54.63412 0.9727464
<1-1 -10.783333 -54.71745 33.15079 0.9718243
3-2   11.816667 -32.11745 55.75079 0.9585444
4-2    7.850000 -36.08412 51.78412 0.9931629
5-2   10.300000 -33.63412 54.23412 0.9768809
<1-2 -11.183333 -55.11745 32.75079 0.9670961
4-3   -3.966667 -47.90079 39.96745 0.9997386
5-3   -1.516667 -45.45079 42.41745 0.9999978
<1-3 -23.000000 -66.93412 20.93412 0.5948606
5-4    2.450000 -41.48412 46.38412 0.9999757
<1-4 -19.033333 -62.96745 24.90079 0.7607514
<1-5 -21.483333 -65.41745 22.45079 0.6602912

$anno
              diff       lwr      upr     p adj
2018-2017 14.83889 -2.092782 31.77056 0.0830285

$`edad:anno`
                         diff       lwr       upr     p adj
2:2017-1:2017    2.400000e+00 -70.05497  74.85497 1.0000000
3:2017-1:2017    2.273333e+01 -49.72164  95.18831 0.9896866
4:2017-1:2017    1.736667e+01 -55.08831  89.82164 0.9989268
5:2017-1:2017    2.416667e+01 -48.28831  96.62164 0.9835705
<1:2017-1:2017   2.842171e-14 -72.45497  72.45497 1.0000000
1:2018-1:2017    3.013333e+01 -42.32164 102.58831 0.9266311
2:2018-1:2017    2.853333e+01 -43.92164 100.98831 0.9478344
3:2018-1:2017    3.183333e+01 -40.62164 104.28831 0.8987232
4:2018-1:2017    2.926667e+01 -43.18831 101.72164 0.9387132
5:2018-1:2017    2.736667e+01 -45.08831  99.82164 0.9603492
<1:2018-1:2017   8.566667e+00 -63.88831  81.02164 0.9999990
3:2017-2:2017    2.033333e+01 -52.12164  92.78831 0.9958016
4:2017-2:2017    1.496667e+01 -57.48831  87.42164 0.9997270
5:2017-2:2017    2.176667e+01 -50.68831  94.22164 0.9926798
<1:2017-2:2017  -2.400000e+00 -74.85497  70.05497 1.0000000
1:2018-2:2017    2.773333e+01 -44.72164 100.18831 0.9566725
2:2018-2:2017    2.613333e+01 -46.32164  98.58831 0.9710885
3:2018-2:2017    2.943333e+01 -43.02164 101.88831 0.9365005
4:2018-2:2017    2.686667e+01 -45.58831  99.32164 0.9649984
5:2018-2:2017    2.496667e+01 -47.48831  97.42164 0.9791227
<1:2018-2:2017   6.166667e+00 -66.28831  78.62164 1.0000000
4:2017-3:2017   -5.366667e+00 -77.82164  67.08831 1.0000000
5:2017-3:2017    1.433333e+00 -71.02164  73.88831 1.0000000
<1:2017-3:2017  -2.273333e+01 -95.18831  49.72164 0.9896866
1:2018-3:2017    7.400000e+00 -65.05497  79.85497 0.9999998
2:2018-3:2017    5.800000e+00 -66.65497  78.25497 1.0000000
3:2018-3:2017    9.100000e+00 -63.35497  81.55497 0.9999981
4:2018-3:2017    6.533333e+00 -65.92164  78.98831 0.9999999
5:2018-3:2017    4.633333e+00 -67.82164  77.08831 1.0000000
<1:2018-3:2017  -1.416667e+01 -86.62164  58.28831 0.9998381
5:2017-4:2017    6.800000e+00 -65.65497  79.25497 0.9999999
<1:2017-4:2017  -1.736667e+01 -89.82164  55.08831 0.9989268
1:2018-4:2017    1.276667e+01 -59.68831  85.22164 0.9999410
2:2018-4:2017    1.116667e+01 -61.28831  83.62164 0.9999845
3:2018-4:2017    1.446667e+01 -57.98831  86.92164 0.9998022
4:2018-4:2017    1.190000e+01 -60.55497  84.35497 0.9999706
5:2018-4:2017    1.000000e+01 -62.45497  82.45497 0.9999949
<1:2018-4:2017  -8.800000e+00 -81.25497  63.65497 0.9999987
<1:2017-5:2017  -2.416667e+01 -96.62164  48.28831 0.9835705
1:2018-5:2017    5.966667e+00 -66.48831  78.42164 1.0000000
2:2018-5:2017    4.366667e+00 -68.08831  76.82164 1.0000000
3:2018-5:2017    7.666667e+00 -64.78831  80.12164 0.9999997
4:2018-5:2017    5.100000e+00 -67.35497  77.55497 1.0000000
5:2018-5:2017    3.200000e+00 -69.25497  75.65497 1.0000000
<1:2018-5:2017  -1.560000e+01 -88.05497  56.85497 0.9995974
1:2018-<1:2017   3.013333e+01 -42.32164 102.58831 0.9266311
2:2018-<1:2017   2.853333e+01 -43.92164 100.98831 0.9478344
3:2018-<1:2017   3.183333e+01 -40.62164 104.28831 0.8987232
4:2018-<1:2017   2.926667e+01 -43.18831 101.72164 0.9387132
5:2018-<1:2017   2.736667e+01 -45.08831  99.82164 0.9603492
<1:2018-<1:2017  8.566667e+00 -63.88831  81.02164 0.9999990
2:2018-1:2018   -1.600000e+00 -74.05497  70.85497 1.0000000
3:2018-1:2018    1.700000e+00 -70.75497  74.15497 1.0000000
4:2018-1:2018   -8.666667e-01 -73.32164  71.58831 1.0000000
5:2018-1:2018   -2.766667e+00 -75.22164  69.68831 1.0000000
<1:2018-1:2018  -2.156667e+01 -94.02164  50.88831 0.9932022
3:2018-2:2018    3.300000e+00 -69.15497  75.75497 1.0000000
4:2018-2:2018    7.333333e-01 -71.72164  73.18831 1.0000000
5:2018-2:2018   -1.166667e+00 -73.62164  71.28831 1.0000000
<1:2018-2:2018  -1.996667e+01 -92.42164  52.48831 0.9963941
4:2018-3:2018   -2.566667e+00 -75.02164  69.88831 1.0000000
5:2018-3:2018   -4.466667e+00 -76.92164  67.98831 1.0000000
<1:2018-3:2018  -2.326667e+01 -95.72164  49.18831 0.9876653
5:2018-4:2018   -1.900000e+00 -74.35497  70.55497 1.0000000
<1:2018-4:2018  -2.070000e+01 -93.15497  51.75497 0.9951321
<1:2018-5:2018  -1.880000e+01 -91.25497  53.65497 0.9978422