How can I bar plot a dataframe with ggplot2?-CodePudding

So I'm trying to create a bar plot to represent two different ammounts, one representing the ammount of transactions made with the coin_type_1 and another to represent the transactions of coin_type_2. As of now, I have this:

  montoTipo1 <- df %>%
  group_by(MONEDA, Monto) %>%
  filter(MONEDA=="1") %>%
  data.frame() %>%
  summarise(Monto_Tipo1 = sum(Monto, na.rm = TRUE))

montoTipo2 <- df %>%
  group_by(MONEDA, Monto) %>%
  filter(MONEDA=="2") %>%
  data.frame() %>%
  summarise(Monto_Tipo2 = sum(Monto, na.rm = TRUE))

nombres.MontosTipo <- c("Tipo de moneda 1","Tipo de moneda 2")
montosTipo <- c(montoTipo1[[1]], montoTipo2[[1]])
df.MontosTipo <- data.frame(row.names = nombres.MontosTipo, montosTipo) %>%
  rename("Montos totales" = montosTipo)

And the output is:

df.MontosTipo
                 Montos totales
Tipo de moneda 1     1617682625
Tipo de moneda 2      248738139

How can I plot so in my x axis the "Tipo de moneda" data appear and the values graphed are the values I have in my dataframe?

CodePudding user response：

As @RuiBarradas has already pointed out, you want to change the rownames into a column, which we can do with tibble::rownames_to_column. Here, I also provide some additional options to further customize the chart. I chose a light hue to fill each bar with. I converted the scientific notation, but if you want the scientific notation along the y-axis, then you can remove the last line here (i.e., scale_y_continuous(labels = comma)).

library(tidyverse)
require(scales)

df.MontosTipo %>% 
  tibble::rownames_to_column("nombres.MontosTipo") %>% 
  ggplot(aes(x = nombres.MontosTipo, y = `Montos totales`, fill = factor(`Montos totales`)))   
  geom_col( )  
  scale_fill_hue(c = 40)  
  theme_bw()  
  theme(legend.position="none")  
  xlab("Nombres")  
  ylab("Montos Totales")  
  scale_y_continuous(labels = comma)

Output

Data

df.MontosTipo <-
  structure(
    list(`Montos totales` = c(1617682625, 248738139)),
    row.names = c("Tipo de moneda 1",
                  "Tipo de moneda 2"),
    class = "data.frame"
  )

CodePudding user response：

You could arrive to the same data.frame in a more simple fashion

df.MontosTipo <- df %>%
  filter(MONEDA=="2" | MONEDA=="1") %>%
  group_by(MONEDA, Monto) %>%
  group_by(MONEDA) %>% 
  summarise('Montos Totales'= sum(Monto, na.rm = TRUE)) %>% 
  rename("Tipo de moneda" = "MONEDA")

Using your current data.frame and as you already have tidyverse then you can run

df.MontosTipo <- 
  df.MontosTipo %>% 
  rownames_to_column("Tipo de moneda")

Then, as the Tipo de moneda is saved in a variable, you can call it inside ggplot to your X-axis using the aesthetics mapping.

ggplot(df.MontosTipo,
       aes(x = `Tipo de moneda`, y = `Montos Totales`, 
           fill = factor(`Tipo de moneda`)))  
  geom_col()

Final thoughts are that is sometimes easier to create variables in the form var_name than var name so you do not have to add tick marks when calling them inside functions. Also, geom_bar() does not accept both X and Y aesthethics so I would rather use geom_bar()