Hello all:
Very straightforward question as difficult as it is for me to implement:
I have a dataframe: df.
And I have a column called 'admission_date', which are all dates in January 2023 of datatype DATE.
I want to make a bar chart counting how many times each date appears in the dataframe, BUT I want the dates to be sorted chronologically in the x axis.
How would I do this?
Thanks!
R defaults to ordering axes. If I try to generate a reproducible example that looks like what you seem to describe as your data types, it automagically chronologically sorts.
Consider alternatives.
# fake some data
set.seed(42)
accepts <- data.frame(
Day = seq(as.Date("2023/1/1"), as.Date("2023/1/31"), "day"),
Acceptances = sample(1:10,31,replace = TRUE))
accepts
#> Day Acceptances
#> 1 2023-01-01 1
#> 2 2023-01-02 5
#> 3 2023-01-03 1
#> 4 2023-01-04 9
#> 5 2023-01-05 10
#> 6 2023-01-06 4
#> 7 2023-01-07 2
#> 8 2023-01-08 10
#> 9 2023-01-09 1
#> 10 2023-01-10 8
#> 11 2023-01-11 7
#> 12 2023-01-12 4
#> 13 2023-01-13 9
#> 14 2023-01-14 5
#> 15 2023-01-15 4
#> 16 2023-01-16 10
#> 17 2023-01-17 2
#> 18 2023-01-18 3
#> 19 2023-01-19 9
#> 20 2023-01-20 9
#> 21 2023-01-21 4
#> 22 2023-01-22 5
#> 23 2023-01-23 5
#> 24 2023-01-24 4
#> 25 2023-01-25 2
#> 26 2023-01-26 8
#> 27 2023-01-27 3
#> 28 2023-01-28 10
#> 29 2023-01-29 1
#> 30 2023-01-30 10
#> 31 2023-01-31 8
library(fpp3)
#> ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ──
#> ✔ tibble 3.1.8 ✔ tsibble 1.1.3
#> ✔ dplyr 1.0.10 ✔ tsibbledata 0.4.1
#> ✔ tidyr 1.3.0 ✔ feasts 0.3.0
#> ✔ lubridate 1.9.1 ✔ fable 0.3.2
#> ✔ ggplot2 3.4.0
#> ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
#> ✖ lubridate::date() masks base::date()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ tsibble::intersect() masks base::intersect()
#> ✖ tsibble::interval() masks lubridate::interval()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ tsibble::setdiff() masks base::setdiff()
#> ✖ tsibble::union() masks base::union()
library(ggplot2)
library(gt)
library(pander)
accepts |> pander()
| Day | Acceptances |
|---|---|
| 2023-01-01 | 1 |
| 2023-01-02 | 5 |
| 2023-01-03 | 1 |
| 2023-01-04 | 9 |
| 2023-01-05 | 10 |
| 2023-01-06 | 4 |
| 2023-01-07 | 2 |
| 2023-01-08 | 10 |
| 2023-01-09 | 1 |
| 2023-01-10 | 8 |
| 2023-01-11 | 7 |
| 2023-01-12 | 4 |
| 2023-01-13 | 9 |
| 2023-01-14 | 5 |
| 2023-01-15 | 4 |
| 2023-01-16 | 10 |
| 2023-01-17 | 2 |
| 2023-01-18 | 3 |
| 2023-01-19 | 9 |
| 2023-01-20 | 9 |
| 2023-01-21 | 4 |
| 2023-01-22 | 5 |
| 2023-01-23 | 5 |
| 2023-01-24 | 4 |
| 2023-01-25 | 2 |
| 2023-01-26 | 8 |
| 2023-01-27 | 3 |
| 2023-01-28 | 10 |
| 2023-01-29 | 1 |
| 2023-01-30 | 10 |
| 2023-01-31 | 8 |
accepts |> gt()
| Day | Acceptances |
|---|---|
| 2023-01-01 | 1 |
| 2023-01-02 | 5 |
| 2023-01-03 | 1 |
| 2023-01-04 | 9 |
| 2023-01-05 | 10 |
| 2023-01-06 | 4 |
| 2023-01-07 | 2 |
| 2023-01-08 | 10 |
| 2023-01-09 | 1 |
| 2023-01-10 | 8 |
| 2023-01-11 | 7 |
| 2023-01-12 | 4 |
| 2023-01-13 | 9 |
| 2023-01-14 | 5 |
| 2023-01-15 | 4 |
| 2023-01-16 | 10 |
| 2023-01-17 | 2 |
| 2023-01-18 | 3 |
| 2023-01-19 | 9 |
| 2023-01-20 | 9 |
| 2023-01-21 | 4 |
| 2023-01-22 | 5 |
| 2023-01-23 | 5 |
| 2023-01-24 | 4 |
| 2023-01-25 | 2 |
| 2023-01-26 | 8 |
| 2023-01-27 | 3 |
| 2023-01-28 | 10 |
| 2023-01-29 | 1 |
| 2023-01-30 | 10 |
| 2023-01-31 | 8 |
ggplot(accepts, aes(Acceptances,Day)) +
ggtitle("YTD") +
geom_point(size = 2) +
scale_x_continuous(breaks = c(2,4,6,8,10)) +
theme_bw() +
theme(
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(colour = "grey60", linetype = "dashed"))
accepts_tib <- as_tsibble(accepts)
#> Using `Day` as index variable.
autoplot(accepts_tib) +
theme_minimal()
#> Plot variable not specified, automatically selected `.vars = Acceptances`
Created on 2023-01-29 with reprex v2.0.2
Perhaps the issue is the data? I should say that this is why the suggestion of a reproducible example is crucial because I cannot imagine a use-case where this doesn't have a solution but it also leaves the underlying problem less than completely defined.
The key to making the above work is that I have a single value for each relevant date. With something like what you are describing, I suspect it is that a preliminary step may be needed, such as
library(tidyverse)
data %>% group_by(admission_date) %>% summarise(Events = n()) %>% ungroup()
Alternatively, if the issue is that it only plots dates that exist with no white space for the dates in which no entries exist, then you could use seq.Date(from=min(admission_date), to=max(admission_date), by="days") and a join.
library(tidyverse)
Summary.Table <- data %>% group_by(admission_date) %>% summarise(Events = n()) %>% ungroup()
Complete.dates <- seq.Date(from=min(admission_date), to=max(admission_date), by="days")
Plot.Data <- left_join(Complete.dates, Summary.table, by=c("admission_date" = "admission_date"))
Thanks a ton for this! Your point made me realize that I incorrectly converted the date values from string to data, so that they all turned into nulls! However, I solved the problem and then they were arranged chronologically. But when you pointed out it should do that automatically, that's when I realized I needed to give my data a second look!
I am sorry I did not reply more promptly! I will study all this for sure though.
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