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Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Andromeda
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Ayu Dark
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Ayu Mirage
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Ayu Light
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Cobalt2
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Night Owl
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Night Owl Light
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Azureus
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Bordo
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Hibernus
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Lilac
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Lux
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Minimus
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Obscuro
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Sereno
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Uva
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Noctis Viola
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Palenight
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Pico 8
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Shades of Purple
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Shades of Purple SD
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Synthwave '84
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Tokyo Night
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Tokyo Night Storm
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Tokyo Night Light
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Winter is Coming
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Winter is Coming Light
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales
Winter is Coming Dark
Year.Release <- game$Year.Release
counts <- data.frame(table(Year.Release))
p <- game %>%
select(Year.Release, Global.Sales) %>%
group_by(Year.Release) %>%
summarise(Total.Sales = sum(Global.Sales))
q <- cbind.data.frame(p, counts[2]) # Add counts to data frame
names(q)[3] <- "count"
q$count <- as.numeric(q$count)
ggplot(q, aes(x = Year.Release, y = Total.Sales, label = q$count)) +
geom_col(fill = "green") +
geom_point(y = q$count * 500000, size = 3, shape = 21, fill = "Yellow" ) +
geom_text(y = (q$count + 50) * 500000) + # Position of the text: count of games each year
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = "purple"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
scale_x_discrete("Year.Release", labels = as.character(Year.Release), breaks = Year.Release)
# From https://gexijin.github.io/learnR/the-game-sales-dataset.html#analysis-of-sales