Create Barplot from Data.Frame

Create barplot from data.frame

Using base graphics you can do this simply:

mydf <- data.frame( X1=c(A=2, B=4, C=1), X2=c(3,2,NA), X3=c(4,1,NA) )
barplot(t(as.matrix(mydf)), beside=TRUE)

Using additional calls to axis can give the labeling more like in the question.

How can I bar plot a dataframe with ggplot2?

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"
  )

Trying to create bar plot from data frame in R

If we are passing a single parameter, then it can be a named vector

barplot(setNames(myData$Churn, myData$States))

Or otherwise, it can be a formula method

barplot(Churn ~ States, myData)

data

myData <- structure(list(States = c("AK", "AL", "AR", "AZ", "CA", "CO", 
"CT", "DC", "DE", "FL", "GA", "HI", "IA", "ID", "IL", "IN", "KS", 
"KY", "LA", "MA", "MD", "ME", "MI", "MN", "MO", "MS", "MT", "NC", 
"ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", 
"RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA", "WI", "WV", 
"WY"), Churn = c(3L, 8L, 11L, 4L, 9L, 9L, 12L, 5L, 9L, 8L, 8L, 
3L, 3L, 9L, 5L, 9L, 13L, 8L, 4L, 11L, 17L, 13L, 16L, 15L, 7L, 
14L, 14L, 11L, 6L, 5L, 9L, 18L, 6L, 14L, 15L, 10L, 9L, 11L, 8L, 
6L, 14L, 8L, 5L, 18L, 10L, 5L, 8L, 14L, 7L, 10L, 9L)), 
class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", 
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", 
"47", "48", "49", "50", "51"))

Create bar plot in ggplot2 - Place data frame values instead of count

To add to the previous answer, here is how you can get 7 plots (1 for each Clade, which is how I interpreted the question) using facet_wrap():

df <- df %>%
  pivot_longer(-Clades)

ggplot(data = df,
       aes(x = Clades,
           y = value)) +
  geom_bar(aes(fill = Clades),
           stat = 'identity') +
  facet_wrap(~name, scales = 'free_y') +
  theme(axis.text.x = element_blank())

Create a barplot in R by a column value

In base R, you can first aggregate and then use barplot.

barplot(Snowfall~Location, aggregate(Snowfall~Location, df, sum))

How to bar plot each row of a dataframe

• The shape of the required dataframe depends on which plot API is being used to plot.
  
  • pandas and seaborn are both dependent upon matplotlib, but require a different shape to get the same result.

pandas

• Set 'card_name' as the index, and then transpose the dataframe with .T.
• Plot the dataframe directly with pandas.DataFrame.plot and kind='bar'. The index is plotted as the axis.

# set the index and transpose
dft = df.set_index('card_name').T

# display(dft)
card_name  Diamonds  Clovers  HorseShoe
$20          1000.0     10.0        NaN
$25           500.0      NaN     1873.0
$30            25.0    213.0     4657.0
$40             NaN   2199.0        NaN
$50          1500.0      NaN      344.0
$70             NaN     43.0      239.0
$75            30.0      2.0        NaN
$100            1.0      NaN      748.0

# plot
dft.plot(kind='bar', rot=0)

seaborn

• Convert the dataframe from a wide to long format using pandas.DataFrame.melt
• Plot the data with seaborn.barplot, or with seaborn.catplot and kind='bar', then use hue= to specify the column to color by.

# convert the dataframe to long format
dfm = df.melt(id_vars='card_name')

# display(dfm.head())
   card_name variable   value
0   Diamonds      $20  1000.0
1    Clovers      $20    10.0
2  HorseShoe      $20     NaN
3   Diamonds      $25   500.0
4    Clovers      $25     NaN

ax = sns.barplot(data=dfm, x='variable', y='value', hue='card_name')

subplots

pandas

• add the parameter subplots=True

# using the previously transformed dataframe dft
axes = dft.plot(kind='bar', rot=0, subplots=True, figsize=(6, 10))

seaborn

• It's easier to use .catplot to get subplots by specifying the row= and/or col= parameter.

# using the previously transformed dataframe dfm
p = sns.catplot(kind='bar', data=dfm, x='variable', y='value', row='card_name', height=3, aspect=1.5)

How to create barplot from dataframe

Assuming that we have a data.frame

barplot(`colnames<-`(t(df1[-1]), df1[,1]), beside=TRUE, 
    legend.text = TRUE, col = c("red", "green"), 
    args.legend = list(x = "topleft", bty = "n", inset=c(-0.05, 0)))

How to Combine Bar Plots Using Two Different Data Frames Based Off of Column Data in R?

One way could be:
After some data tweaking we could use ggplot2:

library(dplyr)
library(ggplot2)

DF1 %>% 
  mutate(group = 'DF1') %>% 
  bind_rows(DF2) %>% 
  mutate(group = replace_na(group, "DF2"), 
         Change = factor(Change)) %>% 
  ggplot(aes(x=Change, fill=group)) +
  geom_bar(stat = 'count', position=position_dodge()) + 
  theme_classic()

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