Multiple plots with same x axis

#remove comment sign (#) from output
knitr::opts_chunk$set(echo = TRUE, message = FALSE, comment = "")

Activate all the necessary packages

library(ggplot2)
library(reshape)
library(tidyverse)
library(lubridate)
library(lattice)

Import the data on NA and Mg

Na_Mg =read.csv("Na_Mg.csv")
head(Na_Mg) #shows the first 6 rows

      Date month Na_Egbe Na_Ureje Na_Ero Na_Ref Mg_Egbe Mg_Ureje  Mg_Ero Mg_Ref
1 01-11-17     1   27.40   10.450 44.450  10.55  4.1180   3.6655 2.47200  3.750
2 01-12-17     2   29.25    9.000 40.550   9.75  4.7155   3.9770 2.31425  3.870
3 01-01-18     3   31.10    7.550 36.650  12.35  5.3130   4.2880 2.15650  4.820
4 01-02-18     4   20.40   13.350 43.000  19.65  4.4865   4.1920 2.21150  4.132
5 01-03-18     5   13.55   11.600 39.825  10.60  5.1535   4.6760 2.18400  3.980
6 01-04-18     6   14.00   12.475 36.825  13.74  5.1295   4.4340 2.13500  4.230

The following charts show line plots for behaviours of elements found in various locations at Ado-Ekiti

df1 <- Na_Mg %>% 
  pivot_longer(
    cols = c(-Date, -month),
    names_to = "names",
    values_to = "values"
  ) %>% 
  mutate(Date = dmy(Date))

xyplot(values ~ Date|names,data=df1,type="o",
       scales=list(y=list(relation="free")),
       layout=c(1,8))

Import the data on K and CA

K_Ca =read.csv("K_Ca.csv")
head(K_Ca)

      Date month  K_Egbe K_Ureje K_Ero K_Ref Ca_Egbe Ca_Ureje Ca_Ero Ca_Ref
1 01-11-17     1 12.5500   10.50 28.20 11.00  54.000   22.200 60.450  21.57
2 01-12-17     2 10.5750   11.50 29.65 11.22  49.175   20.225 51.925  20.52
3 01-01-18     3  8.6000   12.50 31.10 11.59  44.350   18.250 43.400  17.28
4 01-02-18     4  9.9000    8.55 35.70  9.05  38.300   25.300 65.200  24.23
5 01-03-18     5 12.6500   14.85 33.40 15.03  27.900   26.950 54.300  27.05
6 01-04-18     6 12.3775   11.70 31.35 11.98  28.100   26.125 43.415  26.25

K_Ca =read.csv("K_Ca.csv")
df2 <- K_Ca %>% 
  pivot_longer(
    cols = c(-Date, -month),
    names_to = "names",
    values_to = "values"
  ) %>% 
  mutate(Date = dmy(Date))

xyplot(values ~ Date|names,data=df2,type="o",
       scales=list(y=list(relation="free")),
       layout=c(1,8))

Mn_Fe =read.csv("Mn_Fe.csv")
df3 <- Mn_Fe %>% 
  pivot_longer(
    cols = c(-Date, -month),
    names_to = "names",
    values_to = "values"
  ) %>% 
  mutate(Date = dmy(Date))

xyplot(values ~ Date|names,data=df3,type="o",
       scales=list(y=list(relation="free")),
       layout=c(1,8))

Cu_Zn =read.csv("Cu_Zn.csv")
df4 <- Cu_Zn %>% 
  pivot_longer(
    cols = c(-Date, -month),
    names_to = "names",
    values_to = "values"
  ) %>% 
  mutate(Date = dmy(Date))

xyplot(values ~ Date|names,data=df4,type="o",
       scales=list(y=list(relation="free")),
       layout=c(1,8))

Cd_Pb =read.csv("Cd_Pb.csv")
df5 <- Cd_Pb %>% 
  pivot_longer(
    cols = c(-Date, -month),
    names_to = "names",
    values_to = "values"
  ) %>% 
  mutate(Date = dmy(Date))

xyplot(values ~ Date|names,data=df5,type="o",
       scales=list(y=list(relation="free")),
       layout=c(1,8))