Hi ,I want to draw the time series plot. However, the time sequence is wrong.
How can i sole it? thanks
this is my code.
library(TSA)
mdata <- read.csv("C:/Users/user/Desktop/5.12 excel file 1.csv")
mdata[1:10,]
price = mdata[,6]
date = mdata[,1]
plot(date,price,type="l",ylab="price",xlab = "time")
Ideally, you should provide some data (either a file or a copy-paste-safe table) so everybody can reproduce you problem. I suspect your variable date is of type "character", which would expect why for instance "2019/10/14" goes before "2019/2/4" (character vectors are ordered alphabetically). Maybe transforming this variable to class "date" using the as_date function from the lubridate package could solve the issue:
library(TSA)
library(lubridate)
mdata <- read.csv("C:/Users/user/Desktop/5.12 excel file 1.csv")
mdata[1:10,]
price = mdata[,6]
date = as_date(mdata[,1])
plot(date,price,type="l",ylab="price",xlab = "time")
a1.pdf (78.8 KB)
this is my file.
there is another question.
i want to draw every day in the plot, but the plot just record for some days.
thank you
If you need specific help, please provide a proper REPRoducible EXample (reprex) illustrating your issue.
We can't copy from a screenshot, please post formated code instead, here is how to do it.
sq <- tibble::tribble(
~Date, ~Adj.Close,
"2019/1/2", 2510.030029,
"2019/1/3", 2447.889893,
"2019/1/4", 2531.939941,
"2019/1/7", 2549.689941,
"2019/1/8", 2574.409912,
"2019/1/9", 2584.959961,
"2019/1/10", 2596.639893,
"2019/1/11", 2596.26001,
"2019/1/14", 2582.610107,
"2019/1/15", 2610.300049
)
head(sq)rmatted text`
Sorry but I don't quite understand your issue, can you explain what your problem is in the context of this example?
library(lubridate)
#mdata <- read.csv("C:/Users/user/Desktop/5.12 excel file 1.csv")
mdata <- tibble::tribble(
~Date, ~Adj.Close,
"2019/1/2", 2510.030029,
"2019/1/3", 2447.889893,
"2019/1/4", 2531.939941,
"2019/1/7", 2549.689941,
"2019/1/8", 2574.409912,
"2019/1/9", 2584.959961,
"2019/1/10", 2596.639893,
"2019/1/11", 2596.26001,
"2019/1/14", 2582.610107,
"2019/1/15", 2610.300049)
price = mdata$Adj.Close
date = ymd(mdata$Date)
plot(x = date, y = price, type="l", ylab="price", xlab = "time")
Created on 2020-10-24 by the reprex package (v0.3.0.9001)
As you can see, I want to plot the time series plot. There are hundreds of data in my excel file. There are 10 columns in it. In fact, I only need date and Adj.Close these two columns. Thus, I need to put date in x axis and Adj.Close in y axis.
When I used the way that you provided, the date in x axis was disappear. The plot exchanged the date into each months in the x axis. But, I want to record the date in x axis. (just like the question part that I post)
Each date need to be corresponded to each price. This is my new plot.
thank you
If I understand correctly, your concern is that the x-axis in the new plot seems to have been converted to months, as opposed to dates (yyyy-mm-dd). Correct me if I'm wrong. In that case, this is just due to the data.frame in the reprex example having fewer rows than the data.frame you are using. All of the rows of the reprex data.frame belong to the same year. That's why only months are displayed in the x-axis (years are redundant). Using the data you provided in the a1.pdf file, I put together this code, which should do the job, if I have understoof you correctly. By using ggplot2 to plot the data you have more control over the labels of the x-axis.
library(dplyr)
library(lubridate)
library(ggplot2)
d <- tibble::tribble(
~Date.Adj.Close,
"2019/1/2 2510.03",
"2019/1/3 2447.89",
"2019/1/4 2531.94",
"2019/1/7 2549.69",
"2019/1/8 2574.41",
"2019/1/9 2584.96",
"2019/1/10 2596.64",
"2019/1/11 2596.26",
"2019/1/14 2582.61",
"2019/1/15 2610.3",
"2019/1/16 2616.1",
"2019/1/17 2635.96",
"2019/1/18 2670.71",
"2019/1/22 2632.9",
"2019/1/23 2638.7",
"2019/1/24 2642.33",
"2019/1/25 2664.76",
"2019/1/28 2643.85",
"2019/1/29 2640",
"2019/1/30 2681.05",
"2019/1/31 2704.1",
"2019/2/1 2706.53",
"2019/2/4 2724.87",
"2019/2/5 2737.7",
"2019/2/6 2731.61",
"2019/2/7 2706.05",
"2019/2/8 2707.88",
"2019/2/11 2709.8",
"2019/2/12 2744.73",
"2019/2/13 2753.03",
"2019/2/14 2745.73",
"2019/2/15 2775.6",
"2019/2/19 2779.76",
"2019/2/20 2784.7",
"2019/2/21 2774.88",
"2019/2/22 2792.67",
"2019/2/25 2796.11",
"2019/2/26 2793.9",
"2019/2/27 2792.38",
"2019/2/28 2784.49",
"2019/3/1 2803.69",
"2019/3/4 2792.81",
"2019/3/5 2789.65",
"2019/3/6 2771.45",
"2019/3/7 2748.93",
"2019/3/8 2743.07",
"2019/3/11 2783.3",
"2019/3/12 2791.52",
"2019/3/13 2810.92",
"2019/3/14 2808.48",
"2019/3/15 2822.48",
"2019/3/18 2832.94",
"2019/3/19 2832.57",
"2019/3/20 2824.23",
"2019/3/21 2854.88",
"2019/3/22 2800.71",
"2019/3/25 2798.36",
"2019/3/26 2818.46",
"2019/3/27 2805.37",
"2019/3/28 2815.44",
"2019/3/29 2834.4",
"2019/4/1 2867.19",
"2019/4/2 2867.24",
"2019/4/3 2873.4",
"2019/4/4 2879.39",
"2019/4/5 2892.74",
"2019/4/8 2895.77",
"2019/4/9 2878.2",
"2019/4/10 2888.21",
"2019/4/11 2888.32",
"2019/4/12 2907.41",
"2019/4/15 2905.58",
"2019/4/16 2907.06",
"2019/4/17 2900.45",
"2019/4/18 2905.03",
"2019/4/22 2907.97",
"2019/4/23 2933.68",
"2019/4/24 2927.25",
"2019/4/25 2926.17",
"2019/4/26 2939.88",
"2019/4/29 2943.03",
"2019/4/30 2945.83",
"2019/5/1 2923.73",
"2019/5/2 2917.52",
"2019/5/3 2945.64",
"2019/5/6 2932.47",
"2019/5/7 2884.05",
"2019/5/8 2879.42",
"2019/5/9 2870.72",
"2019/5/10 2881.4",
"2019/5/13 2811.87",
"2019/5/14 2834.41",
"2019/5/15 2850.96",
"2019/5/16 2876.32",
"2019/5/17 2859.53",
"2019/5/20 2840.23",
"2019/5/21 2864.36",
"2019/5/22 2856.27",
"2019/5/23 2822.24",
"2019/5/24 2826.06",
"2019/5/28 2802.39",
"2019/5/29 2783.02",
"2019/5/30 2788.86",
"2019/5/31 2752.06",
"2019/6/3 2744.45",
"2019/6/4 2803.27",
"2019/6/5 2826.15",
"2019/6/6 2843.49",
"2019/6/7 2873.34",
"2019/6/10 2886.73",
"2019/6/11 2885.72",
"2019/6/12 2879.84",
"2019/6/13 2891.64",
"2019/6/14 2886.98",
"2019/6/17 2889.67",
"2019/6/18 2917.75",
"2019/6/19 2926.46",
"2019/6/20 2954.18",
"2019/6/21 2950.46",
"2019/6/24 2945.35",
"2019/6/25 2917.38",
"2019/6/26 2913.78",
"2019/6/27 2924.92",
"2019/6/28 2941.76",
"2019/7/1 2964.33",
"2019/7/2 2973.01",
"2019/7/3 2995.82",
"2019/7/5 2990.41",
"2019/7/8 2975.95",
"2019/7/9 2979.63",
"2019/7/10 2993.07",
"2019/7/11 2999.91",
"2019/7/12 3013.77",
"2019/7/15 3014.3",
"2019/7/16 3004.04",
"2019/7/17 2984.42",
"2019/7/18 2995.11",
"2019/7/19 2976.61",
"2019/7/22 2985.03",
"2019/7/23 3005.47",
"2019/7/24 3019.56",
"2019/7/25 3003.67",
"2019/7/26 3025.86",
"2019/7/29 3020.97",
"2019/7/30 3013.18",
"2019/7/31 2980.38",
"2019/8/1 2953.56",
"2019/8/2 2932.05",
"2019/8/5 2844.74",
"2019/8/6 2881.77",
"2019/8/7 2883.98",
"2019/8/8 2938.09",
"2019/8/9 2918.65",
"2019/8/12 2882.7",
"2019/8/13 2926.32",
"2019/8/14 2840.6",
"2019/8/15 2847.6",
"2019/8/16 2888.68",
"2019/8/19 2923.65",
"2019/8/20 2900.51",
"2019/8/21 2924.43",
"2019/8/22 2922.95",
"2019/8/23 2847.11",
"2019/8/26 2878.38",
"2019/8/27 2869.16",
"2019/8/28 2887.94",
"2019/8/29 2924.58",
"2019/8/30 2926.46",
"2019/9/3 2906.27",
"2019/9/4 2937.78",
"2019/9/5 2976",
"2019/9/6 2978.71",
"2019/9/9 2978.43",
"2019/9/10 2979.39",
"2019/9/11 3000.93",
"2019/9/12 3009.57",
"2019/9/13 3007.39",
"2019/9/16 2997.96",
"2019/9/17 3005.7",
"2019/9/18 3006.73",
"2019/9/19 3006.79",
"2019/9/20 2992.07",
"2019/9/23 2991.78",
"2019/9/24 2966.6",
"2019/9/25 2984.87",
"2019/9/26 2977.62",
"2019/9/27 2961.79",
"2019/9/30 2976.74",
"2019/10/1 2940.25",
"2019/10/2 2887.61",
"2019/10/3 2910.63",
"2019/10/4 2952.01",
"2019/10/7 2938.79",
"2019/10/8 2893.06",
"2019/10/9 2919.4",
"2019/10/10 2938.13",
"2019/10/11 2970.27",
"2019/10/14 2966.15",
"2019/10/15 2995.68",
"2019/10/16 2989.69",
"2019/10/17 2997.95",
"2019/10/18 2986.2",
"2019/10/21 3006.72",
"2019/10/22 2995.99",
"2019/10/23 3004.52",
"2019/10/24 3010.29",
"2019/10/25 3022.55",
"2019/10/28 3039.42",
"2019/10/29 3036.89",
"2019/10/30 3046.77",
"2019/10/31 3037.56",
"2019/11/1 3066.91",
"2019/11/4 3078.27",
"2019/11/5 3074.62",
"2019/11/6 3076.78",
"2019/11/7 3085.18",
"2019/11/8 3093.08",
"2019/11/11 3087.01",
"2019/11/12 3091.84",
"2019/11/13 3094.04",
"2019/11/14 3096.63",
"2019/11/15 3120.46",
"2019/11/18 3122.03",
"2019/11/19 3120.18",
"2019/11/20 3108.46",
"2019/11/21 3103.54",
"2019/11/22 3110.29",
"2019/11/25 3133.64",
"2019/11/26 3140.52",
"2019/11/27 3153.63",
"2019/11/29 3140.98",
"2019/12/2 3113.87",
"2019/12/3 3093.2",
"2019/12/4 3112.76",
"2019/12/5 3117.43",
"2019/12/6 3145.91",
"2019/12/9 3135.96",
"2019/12/10 3132.52",
"2019/12/11 3141.63",
"2019/12/12 3168.57",
"2019/12/13 3168.8",
"2019/12/16 3191.45",
"2019/12/17 3192.52",
"2019/12/18 3191.14",
"2019/12/19 3205.37",
"2019/12/20 3221.22",
"2019/12/23 3224.01",
"2019/12/24 3223.38",
"2019/12/26 3239.91",
"2019/12/27 3240.02",
"2019/12/30 3221.29"
) %>%
separate(Date.Adj.Close, c("date", "price"), sep = " ") %>%
mutate(date = ymd(date),
price = as.numeric(price))
ggplot(d, aes(x = date, y = price)) +
geom_line()
the problem here is that if you want dates to look like dates, they need to be R Dates and not pieces of text. You can use lubridate as others have suggested, or, since your text dates are year-month-day, you can use as.Date:
This adds a new column called date to your dataframe (note lowercase d):
sq %>% mutate(date = as.Date(Date)) -> sq
sq
and then you can plot like this:
ggplot(sq, aes(x = date, y = Adj.Close)) + geom_point() + geom_line()
ggplot will format the dates on the x-axis as appropriate (for me, it marked Jan 7 and Jan 14 as tick marks on the axis). If you have more dates, the look might be different, but it will be clear where each date belongs on the axis.
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