j_ducati
stock data analysis for Ducati and other major automobile manufacturers, 1923-2023
AAA
2023-06-13
1 stock data analysis in R with AAA using tidyquant R pkg
## [1] "D:/AZR_Proj/fiscal_finances"##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors2 current date of data file
#todays date
td<- Sys.Date()
print(td)## [1] "2023-06-13"3 Stock prices function for stock ticker symbols
Shown below are the coding for the names of the various companies along with the stock ticker symbol.
The symbol R environment object is used in the tq_get() function from the tidyquant package. Symbols:
4 create dummy object of a blank data frame.
df_t<-c()
df_t<-data.frame()assign the blank data frame the imported data from the tidy quant library for stock of interest. use tq_get() and tq_get_options() from tidyquant package to import Yahoo Finance stock data.
5 plots and graphs of stock data
5.1 variables of interest
library('rmarkdown')
library('tidyverse')
library('magrittr')
library('Hmisc')
library('ggplot2')
library('plotly')
par(mar=c(1,1,1,1))
#1923
df_t<-data.frame()
df_t <- tq_get(x = c("DMH","BMWYY","MBGYY","TM","GM","F", "FCAU","DODGX"), get = "stock.prices", complete_cases = TRUE, from = "1923-01-01", to = Sys.Date())## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'DMH', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "DMH", env = <environment>, verbose = FALSE, : Unable to import "DMH".
## HTTP error 422.
## Removing DMH.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.df_t$symbol<-as.factor(df_t$symbol)
## HMISC describe()
by(df_t$adjusted, df_t$symbol, FUN = describe) %>% print()## df_t$symbol: BMWYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 3386 0 2949 1 21.27 6.08 10.84 14.48
## .25 .50 .75 .90 .95
## 18.12 21.38 24.66 27.57 30.05
##
## lowest : 7.46128 7.56933 7.62051 7.63189 7.64895
## highest: 38.12 38.15 38.8 38.9 39.48
## ------------------------------------------------------------
## df_t$symbol: DODGX
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 10953 1 9236 1 52.25 59.45 1.916 2.573
## .25 .50 .75 .90 .95
## 6.780 32.585 69.039 143.236 199.214
##
## lowest : 1.44782 1.45481 1.50025 1.54431 1.56753
## highest: 239.773 240.083 240.448 240.617 241.442
## ------------------------------------------------------------
## df_t$symbol: F
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 12868 0 8721 1 5.093 4.866 0.1905 0.2371
## .25 .50 .75 .90 .95
## 0.6466 4.3848 8.3218 11.3123 13.1697
##
## lowest : 0.127007 0.130807 0.131349 0.131892 0.132435
## highest: 21.9901 22.0081 22.0171 22.512 22.6649
## ------------------------------------------------------------
## df_t$symbol: GM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 3161 0 2841 1 31.41 10.65 17.49 19.61
## .25 .50 .75 .90 .95
## 25.35 29.55 35.93 42.77 55.11
##
## lowest : 14.3914 14.5598 14.5828 14.6287 14.6517
## highest: 62.8531 63.3087 63.4473 63.9921 65.1113
## ------------------------------------------------------------
## df_t$symbol: MBGYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 3182 0 2734 1 12.49 3.886 6.853 7.684
## .25 .50 .75 .90 .95
## 10.040 12.430 14.803 17.293 18.682
##
## lowest : 4.72017 4.75289 4.77743 4.94922 5.08829
## highest: 21.6262 21.6603 21.72 21.8054 22.0444
## ------------------------------------------------------------
## df_t$symbol: TM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 10902 0 7138 1 59.01 52.4 3.855 5.107
## .25 .50 .75 .90 .95
## 18.113 47.690 96.652 128.338 141.620
##
## lowest : 2.25945 2.27812 2.2968 2.30613 2.31547
## highest: 202.42 206.61 207.47 210.69 211.37## summary
by(df_t$adjusted, df_t$symbol,FUN = summary) %>% print()## df_t$symbol: BMWYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.461 18.116 21.382 21.273 24.658 39.480
## ------------------------------------------------------------
## df_t$symbol: DODGX
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.448 6.780 32.585 52.248 69.039 241.442 1
## ------------------------------------------------------------
## df_t$symbol: F
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1270 0.6466 4.3848 5.0927 8.3218 22.6649
## ------------------------------------------------------------
## df_t$symbol: GM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 14.39 25.35 29.55 31.41 35.93 65.11
## ------------------------------------------------------------
## df_t$symbol: MBGYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.72 10.04 12.43 12.49 14.80 22.04
## ------------------------------------------------------------
## df_t$symbol: TM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.259 18.113 47.690 59.014 96.652 211.370# 2020
df_t2<-c()
df_t2<-data.frame()
df_t2 <- tq_get(x = c("DMH","BMWYY","MBGYY","TM","GM","F", "FCAU","DODGX"), get = "stock.prices", complete_cases = TRUE, from = "2020-01-01", to = Sys.Date())## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'DMH', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "DMH", env = <environment>, verbose = FALSE, : Unable to import "DMH".
## attempt to set an attribute on NULL
## Removing DMH.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.df_t2$symbol<-as.factor(df_t2$symbol)
## HMISC describe()
by(df_t2$adjusted, df_t2$symbol, FUN = describe) %>% print()## df_t2$symbol: BMWYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 784 1 25.32 6.217 15.15 18.18
## .25 .50 .75 .90 .95
## 21.64 25.06 29.19 32.12 33.93
##
## lowest : 10.6845 11.3139 11.3936 11.7043 11.8557
## highest: 38.12 38.15 38.8 38.9 39.48
## ------------------------------------------------------------
## df_t2$symbol: DODGX
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 855 1 195.6 37.53 129.2 141.5
## .25 .50 .75 .90 .95
## 165.8 211.5 221.6 228.9 232.0
##
## lowest : 99.4447 102.838 104.309 106.409 106.837
## highest: 239.773 240.083 240.448 240.617 241.442
## ------------------------------------------------------------
## df_t2$symbol: F
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 742 1 11.02 4.196 4.795 5.898
## .25 .50 .75 .90 .95
## 7.932 11.540 13.037 15.308 17.777
##
## lowest : 3.58996 3.79586 3.87644 3.90329 3.9391
## highest: 21.9901 22.0081 22.0171 22.512 22.6649
## ------------------------------------------------------------
## df_t2$symbol: GM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 809 1 41.02 13.05 23.60 26.45
## .25 .50 .75 .90 .95
## 33.01 38.57 52.21 57.92 59.47
##
## lowest : 16.6393 17.4317 17.5406 17.8675 17.9665
## highest: 62.8531 63.3087 63.4473 63.9921 65.1113
## ------------------------------------------------------------
## df_t2$symbol: MBGYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 735 1 14.87 4.641 6.864 8.661
## .25 .50 .75 .90 .95
## 11.991 15.172 18.080 19.547 20.489
##
## lowest : 4.72017 4.75289 4.77743 4.94922 5.08829
## highest: 21.6262 21.6603 21.72 21.8054 22.0444
## ------------------------------------------------------------
## df_t2$symbol: TM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 867 0 817 1 152 23.54 123.0 127.4
## .25 .50 .75 .90 .95
## 136.0 147.1 170.3 181.5 185.6
##
## lowest : 108.5 111.1 112.2 115 116.07, highest: 202.42 206.61 207.47 210.69 211.37## summary
by(df_t2$adjusted, df_t2$symbol,FUN = summary) %>% print()## df_t2$symbol: BMWYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.68 21.64 25.06 25.32 29.19 39.48
## ------------------------------------------------------------
## df_t2$symbol: DODGX
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 99.44 165.78 211.46 195.59 221.57 241.44
## ------------------------------------------------------------
## df_t2$symbol: F
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.590 7.932 11.540 11.016 13.037 22.665
## ------------------------------------------------------------
## df_t2$symbol: GM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.64 33.01 38.57 41.02 52.21 65.11
## ------------------------------------------------------------
## df_t2$symbol: MBGYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.72 11.99 15.17 14.87 18.08 22.04
## ------------------------------------------------------------
## df_t2$symbol: TM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 108.5 136.0 147.1 152.0 170.3 211.4# 2016
df_trump<-c()
df_trump<-data.frame()
df_trump <- tq_get(x = c("DMH","BMWYY","MBGYY","TM","GM","F", "FCAU","DODGX"), get = "stock.prices", complete_cases = TRUE, from = "2016-01-01", to = Sys.Date())## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'DMH', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "DMH", env = <environment>, verbose = FALSE, : Unable to import "DMH".
## attempt to set an attribute on NULL
## Removing DMH.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.df_trump$symbol<-as.factor(df_trump$symbol)
## HMISC describe()
by(df_trump$adjusted, df_trump$symbol, FUN = describe) %>% print()## df_trump$symbol: BMWYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1612 1 23.37 5.014 17.56 18.64
## .25 .50 .75 .90 .95
## 20.11 22.70 25.97 29.64 32.01
##
## lowest : 10.6845 11.3139 11.3936 11.7043 11.8557
## highest: 38.12 38.15 38.8 38.9 39.48
## ------------------------------------------------------------
## df_trump$symbol: DODGX
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1841 1 160.5 47.76 98.33 106.23
## .25 .50 .75 .90 .95
## 128.81 146.79 208.94 222.64 228.35
##
## lowest : 84.2222 85.2198 85.4962 85.9529 86.1392
## highest: 239.773 240.083 240.448 240.617 241.442
## ------------------------------------------------------------
## df_trump$symbol: F
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1524 1 9.686 3.041 5.973 6.911
## .25 .50 .75 .90 .95
## 8.019 8.925 11.319 13.473 15.098
##
## lowest : 3.58996 3.79586 3.87644 3.90329 3.9391
## highest: 21.9901 22.0081 22.0171 22.512 22.6649
## ------------------------------------------------------------
## df_trump$symbol: GM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1729 1 36.48 10.09 24.58 26.07
## .25 .50 .75 .90 .95
## 30.66 34.70 39.08 53.73 57.54
##
## lowest : 16.6393 17.4317 17.5406 17.8675 17.9665
## highest: 62.8531 63.3087 63.4473 63.9921 65.1113
## ------------------------------------------------------------
## df_trump$symbol: MBGYY
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1580 1 13.6 3.623 8.826 10.131
## .25 .50 .75 .90 .95
## 11.426 12.963 15.405 18.349 19.341
##
## lowest : 4.72017 4.75289 4.77743 4.94922 5.08829
## highest: 21.6262 21.6603 21.72 21.8054 22.0444
## ------------------------------------------------------------
## df_trump$symbol: TM
## dd[x, ]
## n missing distinct Info Mean Gmd .05 .10
## 1873 0 1681 1 134.9 25.06 104.6 108.9
## .25 .50 .75 .90 .95
## 118.8 130.8 144.3 174.1 180.9
##
## lowest : 96.7983 96.9559 96.9657 97.2612 97.6946
## highest: 202.42 206.61 207.47 210.69 211.37## summary
by(df_trump$adjusted, df_trump$symbol,FUN = summary) %>% print()## df_trump$symbol: BMWYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.68 20.11 22.70 23.37 25.97 39.48
## ------------------------------------------------------------
## df_trump$symbol: DODGX
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 84.22 128.81 146.79 160.51 208.94 241.44
## ------------------------------------------------------------
## df_trump$symbol: F
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.590 8.019 8.925 9.686 11.319 22.665
## ------------------------------------------------------------
## df_trump$symbol: GM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.64 30.66 34.70 36.48 39.08 65.11
## ------------------------------------------------------------
## df_trump$symbol: MBGYY
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.72 11.43 12.96 13.60 15.41 22.04
## ------------------------------------------------------------
## df_trump$symbol: TM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 96.8 118.8 130.8 134.9 144.3 211.4library('rmarkdown')
library('tidyverse')
library('magrittr')
library('Hmisc')
library('ggplot2')
library('plotly')
df_t<- df_t %>% arrange(desc(date)) %>% print()## # A tibble: 44,453 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.
## 7 BMWYY 2023-06-09 38.9 38.9 38.7 38.8 29600 38.8
## 8 MBGYY 2023-06-09 19.6 19.6 19.5 19.5 68200 19.5
## 9 TM 2023-06-09 148 149. 148. 149. 366600 149.
## 10 GM 2023-06-09 37.5 38.2 36.2 36.2 24307400 36.2
## # ℹ 44,443 more rowsdf_t2<- df_t2 %>% arrange(desc(date)) %>% print()## # A tibble: 5,202 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.
## 7 BMWYY 2023-06-09 38.9 38.9 38.7 38.8 29600 38.8
## 8 MBGYY 2023-06-09 19.6 19.6 19.5 19.5 68200 19.5
## 9 TM 2023-06-09 148 149. 148. 149. 366600 149.
## 10 GM 2023-06-09 37.5 38.2 36.2 36.2 24307400 36.2
## # ℹ 5,192 more rowsdf_trump<-df_trump %>% arrange(desc(date)) %>% print()## # A tibble: 11,238 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.
## 7 BMWYY 2023-06-09 38.9 38.9 38.7 38.8 29600 38.8
## 8 MBGYY 2023-06-09 19.6 19.6 19.5 19.5 68200 19.5
## 9 TM 2023-06-09 148 149. 148. 149. 366600 149.
## 10 GM 2023-06-09 37.5 38.2 36.2 36.2 24307400 36.2
## # ℹ 11,228 more rows6 PLOTS of ggplot and plotly
6.1 variables of interest
yr of interest begin_date: 1923,2016,2020
library('rmarkdown')
library('tidyverse')
library('magrittr')
library('Hmisc')
library('ggplot2')
library('plotly')
par(mar=c(1,1,1,1))
#plot1 df_t
df_tgp<-c()
df_tgh<- df_t %>% head() %>% tibble() %>% print()## # A tibble: 6 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.df_tgp <- df_t %>% ggplot(aes(x = date, y = adjusted, fill = symbol))+ geom_smooth(method="loess",se=T)+ geom_line(aes(color= symbol))+ ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/1923-06/13/2023 by AAA")+ facet_wrap(.~symbol, scales="fixed")
# ggplot2
df_tgp## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
#plotly
ggplotly(df_tgp)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).#plot2
df_t2gp<-c()
df_t2gh<- df_t2 %>% head() %>% tibble() %>% print()## # A tibble: 6 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.df_t2gp <- df_t2 %>% ggplot(aes(x = date, y = adjusted, fill = symbol))+ geom_smooth(method="loess",se=T)+ geom_line(aes(color= symbol))+ ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/2020-06/13/2023 by AAA")+ facet_wrap(.~symbol, scales="fixed")
#ggplot2
df_t2gp## `geom_smooth()` using formula = 'y ~ x'
#plotly
ggplotly(df_t2gp)## `geom_smooth()` using formula = 'y ~ x'#plot3
df_t3rump<-c()
df_t3gh<- df_trump %>% head() %>% tibble() %>% print()## # A tibble: 6 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.df_t3rump <- df_trump %>% ggplot(aes(x = date, y = adjusted, fill = symbol))+ geom_smooth(method="loess",se=T)+ geom_line(aes(color= symbol))+ ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/2016-06/13/2023 by AAA")+ facet_wrap(.~symbol, scales="fixed")
#ggplot2
df_t3rump## `geom_smooth()` using formula = 'y ~ x'
#plotly
ggplotly(df_t3rump)## `geom_smooth()` using formula = 'y ~ x'#graph a quick plot of the adjusted value for each symbol by each date.
library('rmarkdown')
library('tidyverse')
library('magrittr')
library('Hmisc')
library('ggplot2')
library('plotly')
#data importing and tidying
df_t %>% ggplot(aes(x=date,y=adjusted,fill=symbol))+geom_smooth(method="loess",se=T)+geom_line(aes(color= symbol))+ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/1923-06/13/2023 by AAA")+facet_grid(symbol~.,scales="free")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
df_t2 %>% ggplot(aes(x=date,y=adjusted,fill=factor(c(symbol))))+geom_smooth(method="loess",se=T)+geom_line(aes(color= factor(c(symbol))))+ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/2020-06/13/2023 by AAA")+facet_grid(symbol~.,scales="free")## `geom_smooth()` using formula = 'y ~ x'
df_trump %>% ggplot(aes(x=date,y=adjusted,fill=factor(c(symbol))))+geom_smooth(method="loess",se=T)+geom_line(aes(color= factor(c(symbol))))+ggtitle(label = "LOESS Model of STOCK Adj Data; DMH, BMWYY, MBGYY, TM, GM, F, FCAU, DODGX, 01/01/2016-06/13/2023 by AAA")+facet_grid(symbol~.,scales="free")## `geom_smooth()` using formula = 'y ~ x'
df_t %>% ggplot(aes(x=date,y=adjusted,fill=factor(c(symbol))))+geom_smooth(method="loess",se=T)+geom_line(aes(color= factor(c(symbol))))+ggtitle(label = "LOESS Model of STOCK Data (TSLA,MSFT,AMZN,GOOGL), 01/01/1923-06/07/2023 by AAA")+facet_grid(.~symbol,scales="fixed")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
df_t %>% arrange(desc(adjusted)) %>% head(30) %>% print()## # A tibble: 30 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DODGX 2022-01-14 258. 258. 258. 258. 0 241.
## 2 DODGX 2022-01-12 257. 257. 257. 257. 0 241.
## 3 DODGX 2022-01-11 256. 256. 256. 256. 0 240.
## 4 DODGX 2022-02-09 256. 256. 256. 256. 0 240.
## 5 DODGX 2022-01-13 256. 256. 256. 256. 0 240.
## 6 DODGX 2022-03-29 252. 252. 252. 252. 0 239.
## 7 DODGX 2022-02-10 254. 254. 254. 254. 0 238.
## 8 DODGX 2022-02-02 253. 253. 253. 253. 0 237.
## 9 DODGX 2022-02-08 253. 253. 253. 253. 0 237.
## 10 DODGX 2022-01-18 253. 253. 253. 253. 0 237.
## # ℹ 20 more rowsdf_t2 %>% arrange(desc(volume)) %>% head(30) %>% print()## # A tibble: 30 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 F 2022-01-04 22.5 24.6 22.4 24.3 311645200 21.9
## 2 F 2021-01-21 11.3 12.1 11.1 11.5 282394100 10.3
## 3 F 2021-05-27 14.4 15.1 14.1 14.9 278939100 13.3
## 4 F 2022-01-05 24.1 25.0 23.5 23.7 253540000 21.3
## 5 F 2023-03-17 11.7 11.7 11.1 11.3 249885100 11.2
## 6 F 2020-03-31 5.03 5.19 4.75 4.83 231800800 4.32
## 7 F 2021-04-29 11.9 11.9 11.1 11.3 230430600 10.1
## 8 F 2021-11-10 19.9 20.5 19.1 19.4 229109600 17.3
## 9 F 2021-05-26 13.2 13.9 13.1 13.9 227538700 12.4
## 10 F 2021-10-28 16.9 17.6 16.7 16.9 215237600 15.1
## # ℹ 20 more rowsdf_t %>% arrange(desc(date)) %>% head(30) %>% print()## # A tibble: 30 × 8
## symbol date open high low close volume adjusted
## <fct> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BMWYY 2023-06-12 39.3 39.5 39.3 39.5 21100 39.5
## 2 MBGYY 2023-06-12 19.7 19.8 19.6 19.8 65800 19.8
## 3 TM 2023-06-12 149. 150. 149. 150. 289800 150.
## 4 GM 2023-06-12 36.3 37.0 36.3 36.7 14901900 36.7
## 5 F 2023-06-12 13.8 13.9 13.6 13.8 44584700 13.8
## 6 DODGX 2023-06-12 223. 223. 223. 223. 0 223.
## 7 BMWYY 2023-06-09 38.9 38.9 38.7 38.8 29600 38.8
## 8 MBGYY 2023-06-09 19.6 19.6 19.5 19.5 68200 19.5
## 9 TM 2023-06-09 148 149. 148. 149. 366600 149.
## 10 GM 2023-06-09 37.5 38.2 36.2 36.2 24307400 36.2
## # ℹ 20 more rowsAlso to get R studio and or R 4.0.3 packages go to: https://cran.r-project.org/web/packages/
R.Version() %>% rbind() %>% print()## platform arch os crt system status major
## . "x86_64-w64-mingw32" "x86_64" "mingw32" "ucrt" "x86_64, mingw32" "" "4"
## minor year month day svn rev language version.string
## . "3.0" "2023" "04" "21" "84292" "R" "R version 4.3.0 (2023-04-21 ucrt)"
## nickname
## . "Already Tomorrow"citation('tidyquant') %>% print()## To cite package 'tidyquant' in publications use:
##
## Dancho M, Vaughan D (2023). _tidyquant: Tidy Quantitative Financial
## Analysis_. R package version 1.0.7,
## <https://CRAN.R-project.org/package=tidyquant>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {tidyquant: Tidy Quantitative Financial Analysis},
## author = {Matt Dancho and Davis Vaughan},
## year = {2023},
## note = {R package version 1.0.7},
## url = {https://CRAN.R-project.org/package=tidyquant},
## }citation('Hmisc') %>% print()## To cite package 'Hmisc' in publications use:
##
## Harrell Jr F (2023). _Hmisc: Harrell Miscellaneous_. R package
## version 5.1-0, <https://CRAN.R-project.org/package=Hmisc>.
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## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {Hmisc: Harrell Miscellaneous},
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## year = {2023},
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## url = {https://CRAN.R-project.org/package=Hmisc},
## }citation('dplyr') %>% print()## To cite package 'dplyr' in publications use:
##
## Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A
## Grammar of Data Manipulation_. R package version 1.1.2,
## <https://CRAN.R-project.org/package=dplyr>.
##
## A BibTeX entry for LaTeX users is
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## @Manual{,
## title = {dplyr: A Grammar of Data Manipulation},
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## url = {https://CRAN.R-project.org/package=dplyr},
## }citation('ggplot2') %>% print()## To cite ggplot2 in publications, please use
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## H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
## Springer-Verlag New York, 2016.
##
## A BibTeX entry for LaTeX users is
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## @Book{,
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## title = {ggplot2: Elegant Graphics for Data Analysis},
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##
## C. Sievert. Interactive Web-Based Data Visualization with R, plotly,
## and shiny. Chapman and Hall/CRC Florida, 2020.
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## A BibTeX entry for LaTeX users is
##
## @Book{,
## author = {Carson Sievert},
## title = {Interactive Web-Based Data Visualization with R, plotly, and shiny},
## publisher = {Chapman and Hall/CRC},
## year = {2020},
## isbn = {9781138331457},
## url = {https://plotly-r.com},
## }citation('data.table') %>% print()## To cite package 'data.table' in publications use:
##
## Dowle M, Srinivasan A (2023). _data.table: Extension of
## `data.frame`_. R package version 1.14.8,
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##
## A BibTeX entry for LaTeX users is
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## url = {https://CRAN.R-project.org/package=data.table},
## }citation('MASS') %>% print()## To cite the MASS package in publications use:
##
## Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with
## S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
##
## A BibTeX entry for LaTeX users is
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## @Book{,
## title = {Modern Applied Statistics with S},
## author = {W. N. Venables and B. D. Ripley},
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## }citation('rmarkdown') %>% print()## To cite package 'rmarkdown' in publications use:
##
## Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K,
## Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2023). _rmarkdown:
## Dynamic Documents for R_. R package version 2.22,
## <https://github.com/rstudio/rmarkdown>.
##
## Xie Y, Allaire J, Grolemund G (2018). _R Markdown: The Definitive
## Guide_. Chapman and Hall/CRC, Boca Raton, Florida. ISBN
## 9781138359338, <https://bookdown.org/yihui/rmarkdown>.
##
## Xie Y, Dervieux C, Riederer E (2020). _R Markdown Cookbook_. Chapman
## and Hall/CRC, Boca Raton, Florida. ISBN 9780367563837,
## <https://bookdown.org/yihui/rmarkdown-cookbook>.
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## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.citation('magrittr') %>% print()## To cite package 'magrittr' in publications use:
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## Bache S, Wickham H (2022). _magrittr: A Forward-Pipe Operator for R_.
## R package version 2.0.3,
## <https://CRAN.R-project.org/package=magrittr>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
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## author = {Stefan Milton Bache and Hadley Wickham},
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## note = {R package version 2.0.3},
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## }citation() %>% print()## To cite R in publications use:
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## }
##
## We have invested a lot of time and effort in creating R, please cite it
## when using it for data analysis. See also 'citation("pkgname")' for
## citing R packages.To be continued…In the famous words of Bob Marley, and the Wailers iirc (if i recall correctly),
“We’ll be forever loving JAH!”
AAA
to be continued….