Adding New Inputs to a Chart of Monthly Operating Profits

tidyverse
1

I pulled some data from Fama French web site on operating data by month...here is my code that works (below).

I have 7 columns of data, with headers, including Date. I have successfully graphed one column data, but I would like to add several of the other columns of data to my chart with colors, and I'm struggling to add the additional columnar data. Suggestions? Thank you.

Step 1: Set Working Directory

setwd("c:/Users/Dean/OneDrive/Desktop")

Step 2: Call up Working Directory

getwd()

Step 3: Call up packages and libraries

install.packages("tidyverse")
library(tidyverse)
install.packages("rio")
library(rio)
install.packages("dplyr")
library("dplyr")

Step 4: Read FF_OP_Analysis_Since_1963

read.csv("c:/Users/Dean/OneDrive/Desktop/FF_OP_Analysis_Since_1963.csv")
read.csv("FF_OP_Analysis_Since_1963.csv")

Step 5: Create dataframe

df<-read.csv("FF_OP_Analysis_Since_1963.csv")

Install ggplot2 (if not already installed)

install.packages("ggplot2")

Load ggplot2

library(ggplot2)

Example: Calculate Operating Profit by Date

ggplot(data = df, aes(Date, SMALL_LoOP)) +
geom_line()

2

Also, here are the column names as well: Date, SMALL_LoOP, ME1_OP2, ME1_OP9, SMALL_HiOP, BIG_LoOP, BIG_HiOP

3

Try the following. I don't have your data, so I couldn't test the code.

df |> select(Date, SMALL_LoOP, ME1_OP2, ME1_OP9, SMALL_HiOP, BIG_LoOP, BIG_HiOP) |> 
  pivot_longer(cols = -Date, names_to = "Variable", values_to = "Value") |> 
  ggplot(aes(x= Date, y = Value, color = Variable))
4

That looked like it set up the table properly, but you cannot see any lines on the chart...empty.

Data is an Excel CSV file...here is a snippet of the table:

Date SMALL_LoOP ME1_OP2 ME1_OP9 SMALL_HiOP BIG_LoOP BIG_HiOP
196307 -0.9657 -0.8294 -2.2346 2.5642 -1.0687 1.1313
196308 2.4333 2.6256 -0.6244 5.0913 6.2374 5.7975
196309 -2.1246 -0.031 3.6577 -0.7201 -4.9282 -0.5708
196310 1.549 -1.517 4.5372 -0.5986 -2.0294 10.3195
196311 -4.8866 -0.789 -5.7208 -3.999 1.6446 -4.3612
196312 -3.785 -1.3718 -0.1347 -2.1368 3.169 1.6677
196401 7.5529 5.147 2.9066 4.8603 0.2483 3.5718
196402 3.918 4.7447 2.8176 -0.2699 6.1887 2.1659
196403 2.7378 -0.1299 2.3133 3.7433 -1.7251 1.8864
196404 -0.6952 -0.7992 -0.2116 -1.4397 4.1876 1.3738
196405 1.5796 0.7386 2.2588 -1.7503 2.9169 3.6611
196406 2.3038 -3.4242 1.113 3.76 4.4891 0.2734
196407 3.8523 6.0508 1.8796 5.7071 -0.9219 2.3778
196408 -0.3194 2.6002 -2.3171 -1.8086 -5.4859 -1.1284
196409 2.4605 9.7302 5.6853 8.2176 1.8623 2.3715
196410 3.7606 -0.1009 1.3762 2.6976 1.373 -0.6697
196411 -1.4074 -0.9774 -2.6125 1.3333 -6.1009 -1.3031
196412 -1.5714 0.6171 -1.0029 -1.7434 -4.1665 1.2899
196501 8.4073 7.4467 12.068 7.2627 6.9792 6.1292
5

Here is a copy of the output now...

image
image828×696 4.03 KB

6

Oops, I had a copy and paste failure. Try this:

df |> select(Date, SMALL_LoOP, ME1_OP2, ME1_OP9, SMALL_HiOP, BIG_LoOP, BIG_HiOP) |> 
  pivot_longer(cols = -Date, names_to = "Variable", values_to = "Value") |> 
  ggplot(aes(x= Date, y = Value, color = Variable)) + geom_line()
7

FJCC, that is AWESOME!!! Thank you.

Do you know what might be the best way to convert monthly data into annual (average) data? '

Thank you. This is very helpful for a personal research project.

8

Here is code for computing a yearly average for each variable. The dates were coming in as integers, so I extracted the year by truncating the date/100

library(tidyverse)
df <- read.csv("~/R/Play/Dummy.csv")
ByYear <- df |> select(Date, SMALL_LoOP, ME1_OP2, ME1_OP9, SMALL_HiOP, BIG_LoOP, BIG_HiOP) |> 
  pivot_longer(cols = -Date, names_to = "Variable", values_to = "Value") |> 
  mutate(Year = trunc(Date/100)) |> 
  group_by(Year, Variable) |> 
  summarize(Avg = mean(Value, na.rm = TRUE))
#> `summarise()` has grouped output by 'Year'. You can override using the
#> `.groups` argument.
ggplot(ByYear, aes(x= Year, y = Avg, color = Variable)) + geom_line() +
  scale_x_continuous(breaks = 1963:1965)

Created on 2024-01-07 with reprex v2.0.2

9

FJCC, thank you so much…you’ve saved me a ton of frustration and time.